WO2024137388A1 - Methods and apparatus for automated synchronized allocation using dynamic presentation - Google Patents

Abstract

A non-transitory, processor-readable medium stores instructions that when executed by a processor, cause the processor to receive an input from a user that includes a request. The processor is caused to execute a machine-learning model to extract predetermined natural-language identifier combinations based on the request. The processor is caused to query a database to retrieve appointments based on the predetermined natural-language identifier combinations and to be sent to the user. In response to receiving a signal representing a selection of an appointment, the processor is caused to send a signal to a server associated with the appointment. The processor is caused to receive payment information from the user to secure the appointment. The processor is caused to send a signal representing the payment information to the server for verification. In response to verification, the processor is caused to transmit a notification to the user indicating a confirmation of the appointment.

Description

METHODS AND APPARATUS FOR AUTOMATED SYNCHRONIZED

ALLOCATION USING DYNAMIC PRESENTATION

CROSS-REFERENCE TO RELATED APPLICATIONS

This application claims the benefit of priority to: U.S. Provisional Patent Application Serial No. 63/433,617, filed December 19, 2022; U.S. Provisional Patent Application Serial No. 63/493,193, filed March 30, 2023; and U.S. Provisional Patent Application Serial No. 63/600,173, filed November 17. 2023. The entire content of each of these applications is hereby incorporated by reference herein.

BACKGROUND Online booking and reservation system offer convenient methods for users to leam more about availability of appointments and to schedule appointments at their convenience. However, some current technologies lack seamless integration with various platforms, resulting in conflicts in scheduling appointments, compatibility issues, limited flexibilities with offering appointments from a large catalog of businesses, or loss of revenue for businesses. For example, open tee times, incomplete tee times, or incomplete golf groups can lead to lost revenue opportunities for golf courses as well as dissatisfaction for golfers.

SUMMARY OF THE INVENTION

One aspect of the invention provides a computer-implemented method for booking tee times. The computer-implemented method includes: determining if a vacancy at a tee time exists within a group of golfers based on a booking database, the booking database including one or more selected from the group consisting of: a database of a golf course and a database created from a plurality of user accounts, wherein the determining of the vacancy defines at least one available tee time; electronically providing the at least one available tee time to a user through an electronic communication system, the electronic communication system being one or more selected from the group consisting of: a mobile application, a social media channel, and an SMS messaging system; receiving an input from the user to book the at least one of the available tee time through the electronic communication system via a chatbot or social media engine; and reserving the at least one of the available tee times such that the vacancy within the group is terminated based on the input.

This aspect of the invention can have a variety of embodiments. The electronically providing can be through a push notification. The computer-implemented method can further include providing details of a golf course to the user.

The electronically providing can include tailoring the at least one available tee time based on information about the user and information about the group. The tailoring can be based on one or more selected from the group consisting of: an experience level of the user, a difficulty rating of the course, and an experience level of the group.

A bot can be used to look up and book the at least one available tee time based at least in part on a plurality of user preferences.

The electronically providing can include providing a visual representation of the vacancy within the group of golfers. The visual representation can be a digital card. The electronic communication system can be the social media channel, wherein the electronically providing includes using a bot to provide the visual representation within the social media channel. The bot can use artificial intelligence to book tee times. The bot can be integrated with an e-commerce platform.

The receiving the input can include a user reaction to subscribe to an e-commerce- embedded platform. The user reaction can be a "like” to a social media post.

Another aspect of the invention provides a computer-implemented method for booking tee times. The computer-implemented method includes: accessing a database including information defining a plurality of tee times, a plurality of groups, and a plurality of vacancies of each group, the database including one or more selected from the group consisting of: a database of a golf course and a database created from a plurality of user accounts; collecting historical data on purchase history, including average pricing and quantity for a given time; determining whether to purchase at least one of the vacancies based at least in part on the historical data; and reserving a plurality of vacancies based at least in part on the historical data.

This aspect of the invention can have a variety' of embodiments. The computer- implemented method can further include providing an available tee time to a user through a mobile application.

The given time can be selected from the group consisting of: a particular hour, a particular date, a particular weekday, a particular week, a particular month, and a particular season.

The determining can include using an Al tool. The determining can include using weather forecasts to provide predicted weather conditions. The determining can include evaluating risks associated with canceled tee times in view of the predicted weather conditions.

The reserving can be done automatically.

Another aspect of the invention provides a system configured to implement the methods as described herein. The system includes: a server configured to access a database the defining plurality of vacancies with a respective group at a respective tee time; and a mobile device configured to implement a mobile application, the mobile device being configured to provide input to the server.

Another aspect of the invention provides a non-transitory, processor-readable medium storing instructions that, when executed by a processor, cause the processor to: receive an input from a user that includes a request; execute a machine-learning model to extract a plurality of predetermined natural -language identifier combinations based on the request; uery a database to retrieve a plurality of appointments based on the plurality' of predetermined natural-language identifier combinations and to be sent to the user; in response to receiving a signal representing a selection of an appointment from the plurality of appointments, send a signal to a server associated with the appointment; receive payment information from the user to secure the appointment; send a signal that represents the payment information to the server for verification of the payment information; and in response to the verification, transmit a notification to the user indicating a confirmation of the appointment.

This aspect of the invention can have a variety’ of embodiments. The input can include a combination of voice inputs and natural-language inputs.

The machine-learning model can be at least one of: a natural-language model, a supervised machine-learning model, and an unsupervised machine-learning model. The instructions can further cause the processor to train the machine-learning model using a training set that includes a natural-language identifiers correlated to combinations of naturallanguage identifiers describing reserving an appointment.

The instructions can further cause the processor to present information including the plurality of appointments and the notification on a chat-based display of a user device operated by the user. The instructions can further cause the processor generate a signal to be sent to the user that represent invitations to the appointment that the user can share to other users via social media Short Message Service (SMS). The instructions can further cause the processor to transmit an audio output to enable the plurality of appointments and the confirmation of the appointment to be read out loud on a user device operated by the user. The notification can include a push notification.

DEFINITIONS

The instant invention is most clearly understood with reference to the following definitions.

As used herein, the singular form "a," "an," and "the" include plural references unless the context clearly dictates otherwise.

As used in the specification and claims, the terms "comprises." "comprising," "containing," "having," and the like can have the meaning ascribed to them in U.S. patent law and can mean "includes," "including," and the like.

Unless specifically stated or obvious from context, the term "or," as used herein, is understood to be inclusive.

Ranges provided herein are understood to be shorthand for all of the values within the range. For example, a range of 1 to 50 is understood to include any number, combination of numbers, or sub-range from the group consisting 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28. 29. 30, 31, 32, 33, 34, 35, 36, 37, 38, 39, 40, 41. 42, 43, 44, 45, 46, 47, 48, 49, or 50 (as well as fractions thereof unless the context clearly dictates otherwise).

BRIEF DESCRIPTION OF THE DRAWINGS

For a fuller understanding of the nature and desired objects of the present invention, reference is made to the following detailed description taken in conjunction with the accompanying drawing figures wherein like reference characters denote corresponding parts throughout the several views.

The invention is best understood from the following detailed description when read in connection with the accompanying drawings. It is emphasized that, according to common practice, the various features of the drawing are not to scale. On the contrary, the dimensions of the various features are arbitrarily deformed or reduced for clarity. Included in the drawings are the following figures:

FIG. 1 is a block diagram of a system for automated synchronized reservation using dynamic presentation, according to some embodiment.

FIG. 2 is an example flow diagram of a booking system for golf tee times, according to some embodiment. FIG. 3 is an example flow diagram of a booking system for setting prices for purchase by users, according to some embodiment.

FIG. 4 is an example screenshot of a dynamic presentation for displaying available tee times, according to some embodiment. FIG. 5 is an example screenshot of a dynamic presentation for requesting payment information to user, according to some embodiment.

FIG. 6 is an example screenshot of a dynamic presentation for sharing a purchased tee time to other users, according to some embodiment.

FIG. 7A illustrates a visual representation of a tee time, according to some embodiment.

FIG. 7B illustrates an application implementing a visual representation of a tee time, according to some embodiment.

FIG. 8 is a flow diagram of a method for automated synchronized reservation using dynamic presentation, according to some embodiment. FIG. 9 is a flow diagram of a method for determining vacancies and reserving tee times, according to some embodiment.

FIG. 10 is flow diagram of a method for collecting historical data to reserve vacancies, according to some embodiment.

DETAILED DESCRIPTION

The present disclosure provides a computer-implemented method for booking tee times, and systems implementing the same. In certain embodiments, a computer- implemented method for booking tee times includes: determining if a vacancy at a tee time exists within a group of golfers based on a booking database, the booking database including one or more selected from the group consisting of: a database of a golf course and a database created from a plurality of user accounts, wherein the determining of the vacancy defines at least one available tee time; electronically providing the at least one available tee time to a user through an electronic communication system, the electronic communication system being one or more selected from the group consisting of: a mobile application, a social media channel, and an SMS messaging system; receiving input from the user to book the at least one of the available tee time through the electronic communication system via a chatbot or social media engine; and reserving the at least one of the available tee time such that the vacancy within the group is terminated based on the user-provided input. In certain embodiments, a computer-implemented method for booking tee times includes: accessing a database including information defining a plurality of tee times, a plurality of groups, and a plurality of vacancies of each group, the database including one or more selected from the group consisting of: a database of a golf course and a database created from a plurality of user accounts; collecting historical data on purchase history, including average pricing and quantity for a given time; determining whether to purchase at least one of the vacancies based at least in part on the historical data; and reserving a plurality of vacancies based at least in part on the historical data.

In certain embodiments, a system configured to implement the methods described herein can include a server configured to access a database defining plurality of vacancies with a respective group at a respective tee time. The system can also include a mobile device configured to implement a mobile application, the mobile device being configured to provide input to the server.

In certain embodiments, a non-transitory, processor-readable medium stores instructions that when executed by a processor, cause the processor to receive an input from a user that includes a request. The processor is further caused to execute a machine-learning model to extract a plurality of predetermined natural-language identifier combinations based on the request. The processor is further caused to query a database to retrieve a plurality of appointments based on the plurality of predetermined natural-language identifier combinations and to be sent to the user. The processor is further caused to, in response to receiving a signal representing a selection of an appointment from the plurality of appointments, send a signal to a server associated with the appointment. The processor is further caused to receive payment information from the user to secure the appointment. The processor is further caused to send a signal that represents the payment information to the server for verification of the payment information. The processor is further caused to, in response to the verification, transmit a notification to the user indicating a confirmation of the appointment.

In some embodiments, a processor of the present disclosure can receive inputs from a user operating a mobile device such as, for example, a smartphone, a table, a laptop, and/or the like. A mobile application of the present disclosure can be installed on the mobile device operated by the user such that the processor can receive the inputs and information from the user. The user can request availability of a specific type of appointment in which the processor can query a database and provide to the user a list of available times and locations for the specific type of appointment to the user in a user-friendly interface such as, for example, a dynamic presentation, chat-based display, adaptive content display, interactive chat log, and/or the like. In some cases, the dynamic presentation can be a single view that is embedded on a display of a mobile device.

For instance, a user may want to determine availability of an appointment such as, for example, a golf tee time. Some challenges that users can face include quickly finding available appointments and at unspecified locations that offer those appointments. For example, the user may spend too much time searching multiple businesses and contacting each business to determine availability. Such challenges are further exacerbated when the user is in a rush or spontaneously is in need of an appointment. In some cases, the present disclosure discloses a booking system and/or automated booking system that provides that enables an interactive dialogue with the user using text-to-speech and/or speech-to-text technology to transcribe inputs provided by the user (e.g., voice inputs, text inputs, etc.). In some cases, the present disclosure can query a database storing information about various businesses that provide services (e.g., golf tee times) to specific requests from the user and quickly presents a list of available appointments for the services requested by the user in a presentable fashion that is user-friendly. In some implementations, the list of available appointments can be synced in real-time to avoid booking conflicts with other users seeking the same services and/or appointments to enable fast, quick, and non-conflicting allocation of services to users. In other words, the booking system can quickly process requests from multiple users operating multiple mobile devices by identifying available appointments from locations and/or businesses specified by the users, and secures appointments without booking conflicts.

In some cases, the problem of processing inputs arises when a user provides different types of inputs. For instance, the user may prefer using a voice input to request available tee times for a golf course at a specific location. The booking system can execute machinelearning models such as, for example, natural-language models and/or algorithms to extract keywords and/or phrases to provide details to complete the request. The user may then also prefer providing text inputs by typing on a keyboard. The booking system can process natural-language inputs to extract keywords and/or phrases to provide details to complete the request without disruption. In other words, the present disclosure can include a chatbot to interact with the user and complete the request.

In some cases, a user may face the problem of quickly processing payment for a service from a business that was presented by a third party that is different from the business. For example, some Al chatbots can provide information of a service from a business that the user requests, but the user is required to reach out to the business to book an appointment for that service and provide payment information to secure the appointment. In some cases, the present disclosure can include a platform that the user can interact with and can receive payment information from the user (directly or indirectly), secure the payment information, await verification of the payment information from the business associated with the service, and enable transfer of funds to book the service in the platform without requiring the user to access a platform specific to the business itself.

In some cases, a business may face the problem of difficulties of finding users to book appointments for sendees. For instance, a business such as, for example, a golf course, may seek to quickly fill multiple vacant tee times. In some cases, the present disclosure can quickly provide available tee times to users and can quickly secure an appointment for an available tee time and facilitate payment of the appointment for both the users and the golf course. In some implementations, the present disclosure can enable users to share the vacant tee times of the golf course from the information provided to other users, resulting in quicker booking of other available tee times. It is important to note that the present disclosure can be compatible with any type of business offering any type service subject to booking.

Chatbot

A chatbot of the present disclosure (e.g., a “Quatro Fi chatbot”) can play a crucial role in filling golf tee times by streamlining the reservation process, promoting available tee times, and providing personalized suggestions to golfers. A chatbot can leverage artificial intelligence and natural-language processing (NLP) capabilities and can engage users, drive participation, and enhance customer experiences at golf courses. For example, the chatbot can utilize speech recognition technology', automatic speech recognition (ASR) technology, and/or the like, to convert spoken words (e.g., voice inputs) into text (and/or vice versa). In some cases, the chatbot can leverage NLP algorithms that analyze transcribed text from spoken words to understand a user's intent and extract relevant information. For example, a booking system of the present disclosure can identify keywords and phrases related to golf tee times, such as "book a tee time," "available tee times," etc. Once the user's intent is determined, the booking system can query a database or an API to retrieve information about available tee times at nearby golf courses. In some cases, the NLP capabilities of the booking system can generate a structured request to obtain the information.

A chatbot of the present disclosure (e.g., a “Quatro Fi chatbot”) can simplify the process of booking tee times. Golfers can communicate directly with the Quatro Fi chatbot via text or voice inputs on their preferred platform, such as a golf course's website or a mobile application. In one embodiment, the chatbot can access the golf course's scheduling system, present available tee times to users, and reserve slots on users’ behalf. A quick and seamless process encourages golfers to secure their reservations without the need for human intervention, saving time and effort for both golfers and staff. The booking system can book a tee time and the transcribed text from the user to create a booking request. The booking system can then interact with a tee time reservation system or a third-party booking service to secure the tee time.

A chatbot of the present disclosure can proactively promote available tee times to potential customers. By analyzing user preferences and past reservations, the chatbot can send personalized notifications to golfers when suitable tee times become available. A targeted approach can increase the likelihood of filling open slots, as golfers receive timely, relevant information tailored to their preferences. Additionally, the chatbot can share special offers and promotions, such as discounted rates or package deals, further incentivizing golfers to book. For instance, the booking system can convert retrieved tee time data into a user- friendly format, such as a list or a calendar view. Text-to-speech (TTS) technology can be used to read available tee times aloud to the user.

A chatbot of the present disclosure can provide personalized recommendations based on golfers' skill levels, interests, and availability⁷. By gathering information from user profiles, the Quatro Fi chatbot can suggest specific tee times or match golfers with other players of similar skill levels, fostering a more enjoyable and competitive experience. This customization not only adds value to the golfing experience but also encourages golfers to return to the course more frequently.

A chatbot of the present disclosure can serve as a valuable source of information for golfers, providing course details, weather updates, and local amenities. This ensures that golfers are well-informed and prepared for their tee times, leading to a more positive overall experience.

A chatbot of the present disclosure can greatly assist in filling golf tee times bystreamlining the reservation process, offering personalized suggestions, and promoting available slots. By utilizing advanced Al technology', a chatbot of the present disclosure can enhance customer experiences and increase golf course utilization, ultimately benefiting both golfers and course operators. In some cases, if a user wishes to share a tee time with friends, the booking system can generate and send invitations via messaging apps or email. The booking system can include social-media integration to share tee time information with a broader audience. In some cases, sharing can be performed using voice input and/or via SMS or URL.

A chatbot of the present disclosure can save payment information such as, for example bank information, credit card information, and/or the like of users and payment methods of businesses. The chatbot can execute automatic transition from speech-to-text and/or text-to-speech when interacting with a user to execute booking of. for example, a tee time, and payment of securing the reservation. For tee times that require payment, the booking system can facilitate online payments through integrated payment gateways. User information, including payment details, can be securely processed to complete online payments.

A chatbot of the present disclosure can merge with various notification and networking features and confirm booking of an appointment via a bot response and/or a notification indicating payment completion for booking the appointment, which can be recorded and stored in a database. The chatbot can provide a notification that includes confirmation details of booking the appointment to a user via text and/or text-to-speech. In some cases, the chatbot can also send notifications that remind the user of the appointment.

A chatbot of the present disclosure can interact with a user to handle errors or misunderstanding in inputs from the user. The chatbot can offer clarification and/or alternative suggestions of unrecognized inputs and/or unavailable tee times. The chatbot can also be integrated with a feedback mechanisms that can collect user input to continually improve the speech recognition and NLP technology of the booking system to be more accurate and user-friendly. In some cases, the booking system can be configured to ensure compliance with data protection regulations (e.g., GDPR) and adhere to industry -specific legal requirements (e.g., payment card industry standards for payment processing).

Social Media Engine

A social media messaging engine (e.g., a Quatro Fi social media messaging engine) can play an influential role in driving impulse purchases of available golf tee times by capitalizing on the ubiquity and convenience of social media platforms. By utilizing targeted marketing strategies, personalized messaging, and seamless payment options, golf courses can encourage potential customers to make on-the-spot bookings, ultimately maximizing course utilization and revenue.

Targeted marketing can be harnessed through social media algorithms, which are designed to serve users with content relevant to their interests. By leveraging such algorithms, golf courses can ensure that their available tee times are presented to users who have expressed an interest in golf or related activities. Such a targeted approach can increase the likelihood of catching the attention of potential golfers and stimulating impulse purchases.

Personalized messaging can be employed to create a sense of urgency and exclusivity. The messaging engine can generate customized promotions based on users' interests, preferences, and previous interactions with the golf course. For example, limited-time offers, flash sales, or last-minute discounts can be communicated to a user through push notifications, enticing golfers to seize the opportunity and book a tee time immediately.

Seamless payment and reservation options embedded within social media platforms can further facilitate impulse purchases. By integrating booking and payment systems directly into the messaging engine, golf courses can offer users a frictionless booking experience. This eliminates the need to navigate away from the platform, reducing the likelihood of users abandoning their bookings due to inconvenience or frustration. In turn, this streamlines the purchasing process and increases the chances of securing impulse bookings.

The messaging engine can foster a sense of community among golfers by enabling them to share their booked tee times with friends and followers. Such a social aspect not only encourages others to join in but also increases exposure and awareness of available tee times, further stimulating impulse purchases.

Lastly, the messaging engine can provide quick and easy access to relevant information such as course details, weather updates, and directions, making it even more convenient for users to decide on an impulse booking.

In conclusion, a social media messaging engine can be a powerful tool in driving impulse purchases of available golf tee times by leveraging targeted marketing, personalized messaging, and integrated payment systems. By tapping into the immense reach and convenience of social media platforms, golf courses can effectively stimulate impulse bookings, maximizing both course utilization and revenue.

Social Media / Network

In certain embodiments, a social media platform or network can be exclusively focused towards golfers. Certain embodiments can connect users with other golfers (e g., members/users) in the geographic area of the user. Such an aspect is particularly useful when a user is traveling and is interested in finding new games and partners to golf with. A user can join a tee time/group, that has already been booked (e.g., through an application or outside of the application). Golfers can have a profile that will reference if they are members at a private golf club; accordingly, certain aspects can provide non-member users the ability to connect and play with members of clubs they otherwise would not have access to. In one specific example, if one is traveling to Chicago, and wanted to try and play Olympia Fields (a private club), one would search for members in the area to see if they would be open to one joining as a guest. The guest would pay the club the guest golfing fees. In certain embodiments, the interactions can be focused through an app of the present disclosure (e.g., a Quatro Fi app). In other embodiments, the interactions can be integrated with Social Media platforms (e.g.. Facebook, Instagram, Linkedln, Twitter, etc.) to share posts, notifications, announcements, and the like.

Notification Engine

In certain embodiments, users (e.g., Quatro Fi members), can receive an app or phone notifications with available tee times or open spots to join, based on the user defined criteria/settings. For example, a notification can display: ‘'Tee time available to join a threesome tomorrow at 2PM. In certain embodiments, potential discounts can be provided to golfers for joining incomplete tee times (less than 4 golfers). This can help fill up all tee time slots for golf courses. Tee times are a max 4 golfers/slots. and many tee times only have 1, 2 or 3 golfers, which can be a missed revenue opportunity for golf courses and/or an increased cost to golfers. Embodiments of the present disclosure can assist courses in filling those vacancies or slots. Tee time booking can thus be merged together with a notification engine, social medial platforms, and/or networking features.

Commoditization of Tee Times

Various Al analytical tools and automation can be used in connection with purchasing and/or reserving tee times in advance. Purchasing of tee times can be based on historical tee time booking data, to determine best days, months, times, courses to acquire tee times. In certain embodiments, weather predictions and forecasts can be incorporate in the determination of which tee times to purchase (e.g., to limit losses/financial exposure to canceled tee times due to weather conditions). In certain embodiments, a system can purchase tee times wholesale, and later sell said tee times to users (e.g.. Quatro Fi members) at higher, lower, or retail prices.

FIG. 1 is a block diagram of a system 100 for automated synchronized reservation using dynamic presentation, according to some embodiment. The system 100 can include a compute device 101, a user compute device 121, a client compute device 131, a payment service 140, and/or a network 114. The compute device 101 can include a processor 102, a memory 103, database 105, a network interface 106, I/O interfaces 107, and/or an authenticator 108 that communicate with each other, and with other components, via a bus 104. The bus 104 can include any of several types of bus structures including, but not limited to, a memory bus, a memory controller, a peripheral bus, a local bus, and any combinations thereof, using any of a variety⁷ of bus architectures. The compute device 101 can be or include, for example, a computer workstation, a terminal computer, a server computer, a handheld device (e.g.. a tablet computer, a smartphone, etc.), a web appliance, a network router, a network switch, a network bridge, any machine capable of executing a sequence of instructions that specify an action to be taken by that machine, and any combinations thereof. The compute device 101 can also include multiple compute devices that can be used to implement a specially configured set of instructions for causing one or more of the compute devices to perform any one or more of the aspects and/or methodologies described herein.

The network interface 106, can be utilized for connecting the compute device 101 to one or more of a variety of networks (e.g., network 114) and one or more remote devices connected thereto. The network 114 can include, for example, a private network, a Virtual Private Network (VPN), a Multiprotocol Label Switching (MPLS) circuit, the Internet, an intranet, a local area network (LAN), a wide area network (WAN), a metropolitan area network (MAN), a worldwide interoperability for microwave access network (WiMAX®), an optical fiber (or fiber optic)-based network, a Bluetooth® network, a virtual network, and/or any combination thereof. In some instances, the network can be a wireless network such as. for example, a Wi-Fi or wireless local area network (“WLAN”), a wireless wide area network (“WWAN”), and/or a cellular network. In other instances, the network can be a w ired network such as, for example, an Ethernet network, a digital subscription line (“DSL”) network, a broadband network, and/or a fiber-optic network. In some instances, the compute device 101 can use Application Programming Interfaces (APIs) and/or data interchange formats (e.g., Representational State Transfer (REST), JavaScript Object Notation (JSON), Extensible Markup Language (XML), Simple Object Access Protocol (SOAP), and/or Java Message Service (JMS)). The communications sent via the network 114 can be encrypted or unencrypted. In some instances, the network 114 can include multiple networks or subnetworks operatively coupled to one another by, for example, network bridges, routers, switches, gateways and/or the like.

The processor 102 can be or include, for example, a hardw are-based integrated circuit (IC), or any other suitable processing device configured to run and/or execute a set of instructions or code. For example, the processor 102 can be a general-purpose processor, a central processing unit (CPU), an accelerated processing unit (APU), an application specific integrated circuit (ASIC), a field programmable gate array (FPGA), a programmable logic array (PLA), a complex programmable logic device (CPLD), a programmable logic controller (PLC) and/or the like. In some implementations, the processor 102 can be configured to run any of the methods and/or portions of methods discussed herein.

The memory 103 can be or include, for example, a random-access memory (RAM), a memory buffer, a hard drive, a read-only memory (ROM), an erasable programmable readonly memory (EPROM), and/or the like. In some instances, the memory can store, for example, one or more software programs and/or code that can include instructions to cause the processor 102 to perform one or more processes, functions, and/or the like. In some implementations, the memory 103 can include extendable storage units that can be added and used incrementally. In some implementations, the memory 103 can be a portable memory (e.g., a flash drive, a portable hard disk, and/or the like) that can be operatively coupled to the processor 102. In some instances, the memory 103 can be remotely operatively coupled with a compute device (not shown); for example, a remote database device can serve as a memory and be operatively coupled to the compute device. The memory 103 can include various components (e.g., machine-readable media) including, but not limited to, a random-access memory component, a read-only component, and any combinations thereof. In one example, a basic input/output system (BIOS), including basic routines that help to transfer information between components within the compute device 101, such as during start-up. can be stored in memory 103. The memory 103 can further include any number of program modules including, for example, an operating system, one or more application programs, other program modules, program data, and any combinations thereof. In some cases, the memory 103 can include a chatbot that can communicate with the user operating the user compute device 121 in an interactive fashion.

In some implementations, the memory 103 can store information such as, for example, historical data 111 and a machine-learning model 113. The historical data 111 can include purchase history of a user such as. for example, average pricing of purchase, quantity’ of purchase, and/or the like for a given time. In some cases, the historical data 111 can include information of voice inputs and natural-language inputs (e g., text) inputted by various users. In some cases, the memory' 103 can store predetermined combination of inputs (e.g., voice inputs, transcribed voice inputs, natural-language inputs, letters, words, phrases, etc.) to identify the predetermined combinations to quickly process requests from users. For instance, the predetermined combination of inputs predetermined natural-language identifier combinations such as, for example, “book,” “schedule,” “appointment,” “tee time,” and/or the like. The processor 102 can be caused to query’ the database 105 to retrieve available appointments for tee times from golf courses in response to identifying keywords predetermined combinations of inputs such as, for example, “book a tee time.”

The machine-learning model 113 can include a set of model parameters such as weights, biases, or activation functions that can be executed to annotate and/or classify voice inputs (or transcribed voice inputs), natural-language inputs (e.g.. combinations of naturallanguage identifiers), and/or the like. The machine-learning model 113 can be executed during a training phase and/or an execution phase.

In the training phase, the machine-learning model 113 can receive training data and optimizes (or improves) the set of model parameters of the machine-learning model 113. The set of model parameters can be optimized (or improved) such that the voice inputs (or transcribed voice inputs), natural-language inputs (e.g., combinations of natural-language identifiers), and/or the like in the training data can be annotated and/or classified correctly with a certain likelihood of correctness (e.g., a pre-set likelihood of correctness).

In some instances, the training data can be divided into batches of data based on a memory size, a memory type, a processor type, and/or the like. In some instances, the voice inputs (or transcribed voice inputs), natural-language inputs (e.g., combinations of naturallanguage identifiers), and/or the like, can be divided into batches of data based on a type of the processor 102 (e.g., CPU, GPU, and/or the like), number of cores of the processor 102. and/or other characteristic of the memory 103 or the processor 102.

In some instances, the training data can be divided into a training set, a test set, and/or a validation set. For example, the training data can be randomly divided so that 60% of the training data is in the training set, 40% of the training data is in the test set. and 20% of the training data is in the validation set. The machine-learning model 1 13 can be iteratively optimized (or improved) based on the training set while being tested on the test set to avoid overfitting and/or underfitting of the training set. Once the machine-learning model 113 is trained based on the training set and the test set, a performance of the machine-learning model 113 can be further verified based on the validation set.

In the execution phase, the machine-learning model 113 (that is trained in the training phase) receives at least an input (e.g., voice inputs (or transcribed voice inputs), naturallanguage inputs (e.g., combinations of natural-language identifiers) , etc.) not among the set of inputs used in the training phase) to be annotated and/or classified. Because the execution phase is performed using the set model parameters that were already optimized during the training phase, the execution phase can be computationally quick.

In some cases, the machine-learning model 113 can be or include at least one of a deep neural network model (DNN), an artificial neural network (ANN) model, a fully connected neural network, a convolutional neural network (CNN), a residual network model, a region proposal network (RPN) model, a feature pyramid network (FPN) model, a generative adversarial network (GAN), a K-Nearest Neighbors (KNN) model, a Support Vector Machine (SVM), a decision tree, a random forest, an analysis of variation (ANOVA), boosting, a Naive Bayes classifier, and/or the like.

The I/O interface(s) 107 of the compute device 101 can enable the compute device 101 to communicate with other devices via the network 114. I/O interface(s) 107 can be any suitable component(s) that enable communication between internal components of the compute device 101 and external devices, such as, for example, the user compute device 121, the client compute device 131, and/or the payment service 140.

The authenticator 108 can be a hardware and/or software component conjured to maintain security and privacy and can be integrated with various authentication methods for verifying users for booking and/or purchasing appointments and/or services. In some implementations, the authenticator 108 can utilize Secure Sockets Layer (SSL) encry ption and secure sensitive information (e.g., user information, user payment info, etc.)

The database 105 can be a data storage system that stores information about various services and businesses. For instance, the database 105 can include multiple tables of available tee times for multiple golf courses. In some cases, the database 105 can include multiple tables of available appointments for haircuts for multiple hair salons. It is important to note that the database 105 can store information about available appointments and/or booked appointments of various types of businesses and services. In some cases, the database 105 can be continuously updated with booking reservation such that any appointment in a table in the database 105 is automatically and/or updated in real-time when a user books and/or pays for an appointment, cancels an appointment, and/or modifies an appointment.

The compute device 101 can receive inputs from the user compute device 121 operated by the user. The user compute device can be structurally similar to the compute device 101. The user compute device 121 can include a memory 123 and a processor 122. In some cases, the memory 123 of the user compute device can be structurally similar to the memory' 103 of the compute device. The processor 122 of the user compute device 121 can be structurally similar to the processor 102 of the compute device.

The user compute device 121 can receive information that is presented an a user- friendly format on a display 125 of the user compute device 121. The display 125 can be or include a chat-based display. In some implementations, the display 125 can present a dynamic presentation 127 of information from the compute device 101. For instance, the dynamic presentation 127 can include information that is dynamically presented or updated in response to user inputs, creating a fluid and responsive user-friendly and/or chat-based interface. The dynamic presentation 127 can enable an interactive dialogue between the user and a chatbot that presents questions and/or information to the user on the display 125. The dynamic presentation 127 can also enable real-time updates reflecting changes or inputs from the user as they occur.

The client compute device 131 can be structurally similar to the compute device 101. The client compute device 131 can include a processor 132 and a memory' 133. The processor 132 of the client compute device 131 can be structurally similar to the processor 102 of the compute device 101. The memory 133 of the client compute device 131 can be structurally similar to the memory 103 of the compute device 101. The compute device 101 can send a signal to the client compute device 131 to retrieve a copy of a database from the client compute device 131 that includes details such as, for example, a list of appointments including available appointments and/or booked appointments. For instance, the client compute device 131 can be, for example, server for a golf course in which the compute device 101 can request and/or obtain a list of tee times to present to the user. For example, the request from the user can be a request for available tee times for a golf course in a specific city at a specific time. The processor 102 of the compute device 101 can retrieve available tee times from the list of tee times provided by the server of the golf course to present to the user via the dynamic presentation 127 and/or the display 125 of the user compute device 121.

The payment service 140 can be or include a payment platform that is integrated with the compute device 101 and/or the client compute device 131 that the compute device 101 can use to secure payment information from the user. In some cases, the payment service 140 can complete transfer of funds between the user and the client compute device 131 to secure appointments booked by the user.

FIG. 2 is an example flow diagram 200 of a booking system for golf tee times, according to some embodiment. The flow diagram can include a user 201 providing inputs such as voice inputs for a processor of a compute device to analyze and transcribe. The processor and the compute device can be consistent with the processor 102 and the compute device 101 of FIG. 1. The processor can be, for example, a speech-to-text processor to receive and transcribe voice commands to text and/or natural -language identifiers. In some cases, the processor can be or include a machine-learning model such as, for example, a natural-language model. Once the voice inputs are transcribed to text inputs, a tee times manager 215 can receive the text inputs to extract keywords and/or phrases. For instance, the text inputs can be, for example, ‘'available tee times.” In some cases, the tee times manager 215 can also be the processor 102 of FIG. 1 and request tee times from a client compute device and/or a server of the client compute device (not shown in FIG. 2) to retrieve available tee times 213. In some cases, the tee times manager 215 can query a database of the client compute device to retrieve the available tee times 213 to be presented to the user 201. The user 201 can select an available tee time and book the selected available tee time via a booking system 209 of the compute device. By booking the selected available tee time, the user can also initiate payment to secure the booking via a payment system 203. In some cases, the payment system 203 can be consistent with the payment service 140 of FIG. 1. Once the booking is successful, the booking system 209 can update the available tee times 213 and remove the booked tee time from the available tee times 213. The user 201 can further share tee time details of the booked tee time via a sharing service 205. The sharing service 205 can or include the network interface 106 of FIG. 1. In some cases, the sharing service 205 can be or include a social media engine, SMS system, and/or the like, that enables the user 202 to send the tee time details to friends 211.

FIG. 3 is an example flow diagram 300 of a booking system for setting prices for purchase by users, according to some embodiment. As shown in FIG. 3, a user 301 can cause execution, via a compute device, of an API to determine open tee times 305 for a golf course 305. The compute device can query a database 307 to identify available tee times. In some cases, the database 307 can be accessed to retrieve information that defines a plurality⁷ of tee times, a plurality of groups, and a plurality of vacancies of each group. The database 307 can include one or more selected from the group consisting of: a database of a golf course and a database created from a plurality of user accounts.

The compute device can import available tee times 309. If there are available tee times, the compute device can determine a resale probability⁷ score 311 from historical data that was collected on purchase history including average pricing and quantity of tee times for a given time. Based on the resale probability⁷ score, the compute device can set prices to the user 313. The user can purchase 315 a tee time and the compute device can notify users of the tee time 317 that was purchased.

FIG. 4 is an example screenshot of a dynamic presentation for displaying available tee times, according to some embodiment. As shown in FIG. 4, a processor present the dynamic presentation as a part of a chat-based display of a mobile application for a booking system. The processor can receive a voice input and transcribe the voice input via a machine-learning model (e.g., natural-language model) and present the transcnbed voice input. "‘Show me tea times in Atlanta today,” onto the display as shown in FIG. 4. The processor can control a chatbot that enables interactive dialog with a user that can be shown on the display. The processor can query a database of a golf course in Atlanta and present onto the display available tee times for the user to select. In some implementations, the user can have the option to respond to questions from the chatbot using voice inputs and/or natural -language inputs by typing on a keyboard. In some implementations, a user can create an account for the booking system and login the account to interact with a chatbot at a chat-based display (e.g., dynamic presentation) to request tee times using speech-to-text. In response to processing the request, the processor can display available tee times with prices in the same view of the chat-based display or a new view.

FIG. 5 is an example screenshot of a dynamic presentation for requesting payment information to user, according to some embodiment. As shown in FIG. 5, a processor present the dynamic presentation as a part of a chat-based display of a mobile application for a booking system and in response to selecting an available tee time, the processor can present a form on the display of the user to input payment information that can be sent to an integrated payment service to enable verification of the payment information by the golf course to complete a transfer of funds to secure the tee time. Following selection of the available tee time, the user can be automatically directed to a check-out screen to pay for the selection. In some cases, a payment method and/or payment information can be already saved in an account associated with the user.

FIG. 6 is an example screenshot of a dynamic presentation for sharing a purchased tee time to other users, according to some embodiment. As shown in FIG. 6. a processor can present the dynamic presentation as a part of a chat-based display of a mobile application for a booking system. In some cases, a chatbot that interacts with the user via the chat-based display can provide a visual representation of a confirmation of the payment of the selection in the form of a notification. Following a purchase of a tee time, a user can share and/or send the purchased tee time to other users. For instance, a tee time can require four golfers such that the user can send invitations to the purchased tee times to three other users via social media and/or SMS. The processor can generate a dynamic card that can be sharable to other users via URL.

FIG. 7A illustrates a visual representation of a tee time, according to some embodiment. FIG. 7B illustrates an application implementing a visual representation of a tee time, according to some embodiment. In certain embodiments, a system of the present disclosure can electronically provide visual representation of a vacancy within the group of golfers. For example, the visual representation can be a digital card. Such a visual representation is illustrated in FIGS. 7A-7B, Referring specifically to FIG. 7A, a visual representation 702 (e.g., a digital card) of a group is illustrated. Visual representation 702 illustrates the number of players, the date of the tee time, the time of the tee time, and the preferences of the group. Referring now to FIG. 7B, an application 204 (e.g., an '‘app’’) is illustrated on an electronic display (e.g., a smart phone screen). A visual representation 706 (e.g., a digital card) is illustrated including a vacancy 708. A user interested in joining the group can interact with visual representation 706 to book a tee time. For example, a user can click on the icon illustrating vacancy 708 and be brought to an e-commerce platform and/or automatically reserve the spot. In certain embodiments, the card can be deleted from social media posts (e.g., automatically).

FIG. 8 is a flow diagram of a method 800 for automated synchronized reservation using dynamic presentation, according to some embodiment. In some cases, the method 800 can be performed by a processor of a compute device. At 805, the method 800 includes receiving an input from a user that includes a request. The input can be received from a user compute device operated by the user. In some implementations, the input can include a combination of voice inputs and natural-language inputs (e.g., transcribed voice inputs, natural-language identifiers, combinations of natural-language identifiers, etc.).

At 810, the method 800 includes executing a machine-learning model to extract a plurality⁷ of predetermined natural-language identifier combinations based on the request. In some cases, the machine-learning model can be at least one of: a natural-language model, a supervised machine-learning model, and an unsupervised machine-learning model. In some implementations, prior to executing the machine-learning model, the method 800 can include training the machine-learning model using a training set that includes a natural -language i den ti Tiers correlated to combinations of natural-language identifiers describing reserving an appointment. At 815, the method 800 includes querying a database to retrieve a plurality of appointments based on the plurality of predetermined natural-language identifier combinations and to be sent to the user. In some cases, a predetermined natural-language identifier combination can be or include keywords and/or phrases that the processor can recognize and query the database based on the predetermined natural-language identifier combinations.

At 820. the method 800 includes, in response to receiving a signal representing a selection of an appointment from the plurality of appointments, sending a signal to a server associated with the appointment. In some cases, the server can be a client compute device as described in FIG. 1. In some implementations, prior to receiving the signal, the method 800 can include presenting the plurality of appointments on a display of the user compute device operated by the user in the form of a dynamic presentation. For instance, the dynamic presentation can include an interactive chat log.

At 825, the method 800 includes receiving payment information from the user to secure the appointment. In some implementations, prior to receiving the payment information, the method 800 can include presenting a form as a dynamic presentation on the display of the user compute device.

At 830, the method 800 includes sending a signal that represents the payment information to the server for verification of the payment information. The payment information can include bank account information, credit card information, and/or the like.

At 835, the method 800 includes, in response to verification, transmitting a notification to the user indicating a confirmation of the appointment. In some cases, the notification can be presented as a dynamic presentation on the display of the user compute device. In some cases, an audio output device of the user compute device can produce an audio reading of the notification to the user. In some implementations, the plurality of appointments and the notification are rendered on a chat-based display of the user compute device operated by the user. In some cases, the method 800 can include generating a signal to be sent to the user that represents invitations to the appointment that the user can share to other users via social media and/or Short Message Service (SMS). In some cases, the notification can be a push notification.

FIG. 9 is a flow diagram of a method 900 for determining vacancies and reserving tee times, according to some embodiment. In some implementations, the method 900 can be performed by a processor of a compute device and/or automatically. At 905, the method 900 includes determining if a vacancy at a tee time exists within a group of golfers based on a booking database. The booking database can include one or more selected from the group consisting of: a database of a golf course and a database created from a plurality of user accounts. Determining of a vacancy can define at least one available tee time.

At 910, the method 900 includes electronically providing the at least one available tee time to a user through an electronic communication system, the electronic communication system being one or more selected from the group consisting of: a mobile application, a social media channel, and an SMS messaging system. In some cases, the method 900 can include electronically providing the at least one available tee time via a push notification. The user can receive the at least one available tee time on a user compute device (e.g., mobile device) operated by the user. In some cases, the method 900 can include electronically providing additional details of a golf course (or multiple golf courses) associated with the at least one available tee time such as, for example, location, prices, operating hours, contact information, and/or the like.

In some cases, electronically providing can tailor the available tee time based on information about the user and information about the group. In some cases, the tailoring is based on an experience level of the user, a difficulty rating of the course, an experience level of the group. In some implementations the processor of the compute device can execute (or train) a machine-learning model to perform the tailoring.

At 915, the method 900 includes receiving an input from the user to book the at least one of the available tee time through the electronic communication system via a chatbot or social media engine. In some implementations, the processor the compute device can use a hot to look up and book available tee times based at least in part on a plurality⁷ of user preferences (e.g.. ty pe of golf course, location, price range, etc.). In some cases, the bot can use artificial intelligence to book tee times. In some cases, the bot can be integrated with an e-commerce platform. The method 900 can include receiving the input that includes a user reaction (e.g., a “like” to a social media post) to subscribe to an e-commerce-embedded platform.

At 920, the method 900 includes reserving the at least one of the available tee time such that the vacancy within the group is terminated based on the input. In some instances, the method 900 can include providing a visual representation of the vacancy within the group of golfers. In some cases, the visual representation can be, for example, a digital card. In some cases, the electronic communication system can be the social-media channel, wherein the electronically providing includes using a bot to provide the visual representation within the social-media channel. In some implementations, the compute device can be a server configured to access a database defining the plurality of vacancies with a respective group at a respective tee time. In some cases, the user compute device (e.g., a mobile device), can be configured to implement a mobile application, the mobile device being configured to provide input to the server.

FIG. 10 is flow diagram of a method 1000 for collecting historical data to reserve vacancies, according to some embodiment. In some implementations, the method 1000 can be performed by a processor of a compute device and/or automatically. At 1005, the method includes accessing a database including information defining a plurality of tee times, a plurality of groups, and a plurality of vacancies of each group, the database including one or more selected from the group consisting of: a database of a golf course and a database created from a plurality of user accounts. In some implementations, the method 1000 can include providing an available tee time to a user through a mobile application that is installed on a user compute device operated by a user.

At 1010, the method 1000 includes collecting historical data on purchase history, including average pricing and quantity for a given time. In some cases, the given time can be selected from the group consisting of: a particular hour, a particular date, a particular weekday, a particular week, a particular month, and a particular season.

At 1015, the method 1000 includes determining whether to purchase at least one of the vacancies based at least in part on the historical data. In some cases, determining whether to purchase can include using an Al tool, using weather forecasts to provide predicted weather conditions, evaluating risks associated with canceled tee times in view of the predicted weather conditions, and/or the like.

At 1020, the method 1000 includes reserving a plurality of vacancies based at least in part on the historical data. In some cases, the method 1000 can include reserving the plurality of vacancies automatically.

In some implementations, the compute device can be a server configured to access a database defining the plurality of vacancies with a respective group at a respective tee time. In some cases, the user compute device (e.g., a mobile device), can be configured to implement a mobile application, the mobile device being configured to provide input to the server. EQUIVALENTS

Although preferred embodiments of the invention have been described using specific terms, such description is for illustrative purposes only, and it is to be understood that changes and variations may be made without departing from the spirit or scope of the following claims.

INCORPORATION BY REFERENCE

The entire contents of all patents, published patent applications, and other references cited herein are hereby expressly incorporated herein in their entireties by reference.

Claims

1. A computer-implemented method for booking tee times, comprising: determining if a vacancy at a tee time exists within a group of golfers based on a booking database, the booking database including one or more selected from the group consisting of: a database of a golf course and a database created from a plurality⁷ of user accounts, wherein the determining of the vacancy defines at least one available tee time; electronically providing the at least one available tee time to a user through an electronic communication system, the electronic communication system being one or more selected from the group consisting of: a mobile application, a social media channel, and an SMS messaging system; receiving an input from the user to book the at least one of the available tee time through the electronic communication system via a chatbot or social media engine; and reserving the at least one of the available tee times such that the vacancy within the group is terminated based on the input.

2. The computer-implemented method of claim 1, wherein the electronically providing is through a push notification.

3. The computer-implemented method of claim 1, further comprising: providing details of a golf course to the user.

4. The computer-implemented method of claim 1, wherein the electronically providing includes tailoring the at least one available tee time based on information about the user and information about the group.

5. The computer-implemented method of claim 4, wherein the tailoring is based on one or more selected from the group consisting of: an experience level of the user, a difficulty rating of the course, and an experience level of the group.

6. The computer-implemented method of claim 1, wherein a bot is used to look up and book the at least one available tee time based at least in part on a plurality of user preferences.

7. The computer-implemented method of claim 1, wherein the electronically providing includes providing a visual representation of the vacancy within the group of golfers.

8. The computer-implemented method of claim 7, wherein the visual representation is a digital card.

9. The computer-implemented method of claim 7, wherein the electronic communication system is the social media channel, wherein the electronically providing includes using a hot to provide the visual representation within the social media channel.

10. The computer-implemented method of claim 9, wherein the hot uses artificial intelligence to book tee times.

11. The computer-implemented method of claim 9, wherein the hot is integrated with an e-commerce platform.

12. The computer-implemented method of claim 1, wherein the receiving the input includes a user reaction to subscribe to an e-commerce-embedded platform.

13. The computer-implemented method of claim 12, wherein the user reaction is a ’‘like” to a social media post.

14. A computer-implemented method for booking tee times, comprising: accessing a database including information defining a plurality of tee times, a plurality of groups, and a plurality of vacancies of each group, the database including one or more selected from the group consisting of: a database of a golf course and a database created from a plurality of user accounts; collecting historical data on purchase history, including average pricing and quantity for a given time; determining whether to purchase at least one of the vacancies based at least in part on the historical data; and reserving a plurality of vacancies based at least in part on the historical data.

15. The computer-implemented method of claim 14, further comprising: providing an available tee time to a user through a mobile application.

16. The computer-implemented method of claim 14, wherein the given time is selected from the group consisting of: a particular hour, a particular date, a particular weekday, a particular week, a particular month, and a particular season.

17. The computer-implemented method of claim 14, wherein the determining includes using an Al tool.

18. The computer-implemented method of claim 14, wherein the determining includes using weather forecasts to provide predicted weather conditions.

19. The computer-implemented method of claim 18, wherein the determining includes evaluating risks associated with canceled tee times in view of the predicted weather conditions.

20. The computer-implemented method of claim 14, wherein the reserving is done automatically.

21. A system configured to implement the method of claims 1 and 14, comprising: a server configured to access a database the defining plurality of vacancies with a respective group at a respective tee time; and a mobile device configured to implement a mobile application, the mobile device being configured to provide input to the server.

22. A non-transitory, processor-readable medium storing instructions that, when executed by a processor, cause the processor to: receive an input from a user that includes a request; execute a machine-learning model to extract a plurality of predetermined naturallanguage identifier combinations based on the request; query a database to retrieve a plurality of appointments based on the plurality of predetermined natural-language identifier combinations and to be sent to the user; in response to receiving a signal representing a selection of an appointment from the plurality of appointments, send a signal to a server associated with the appointment; receive payment information from the user to secure the appointment; send a signal that represents the payment information to the serv er for verification of the payment information; and in response to the verification, transmit a notification to the user indicating a confirmation of the appointment.

23. The non-transitory. processor-readable medium of claim 22, wherein the input includes a combination of voice inputs and natural-language inputs.

24. The non-transitory, processor-readable medium of claim 22, wherein the machinelearning model is at least one of: a natural-language model, a supervised machine-learning model, and an unsupervised machine-learning model.

25. The non-transitory, processor-readable medium of claim 22, wherein the instructions further cause the processor to train the machine-learning model using a training set that includes a natural-language identifiers correlated to combinations of natural-language identifiers describing reserving an appointment.

26. The non-transitory, processor-readable medium of claim 22, wherein the instructions further cause the processor to present information including the plurality of appointments and the notification on a chat-based display of a user device operated by the user.

27. The non-transitory, processor-readable medium of claim 22, wherein the instructions further cause the processor generate a signal to be sent to the user that represent invitations to the appointment that the user can share to other users via social media Short Message Service (SMS).

28. The non-transitory, processor-readable medium of claim 22, wherein the instructions further cause the processor to transmit an audio output to enable the plurality of appointments and the confirmation of the appointment to be read out loud on a user device operated by the user.

29. The non-transitory. processor-readable medium of claim 22, wherein the notification includes a push notification.