WO2015186023A1

WO2015186023A1 - Method and system of determining an user attitude profile for a mobile device user

Info

Publication number: WO2015186023A1
Application number: PCT/IB2015/053961
Authority: WO
Inventors: Pranav KUMAR JHA; Ashwin RAMASWAMY; Raghvendra VARMA
Original assignee: MUBBLE NETWORKS PRIVATE Ltd
Current assignee: MUBBLE NETWORKS PRIVATE Ltd
Priority date: 2014-06-04
Filing date: 2015-05-27
Publication date: 2015-12-10
Anticipated expiration: 2016-12-04

Abstract

A method of determining an user attitude profile for a mobile device user is disclosed. The method comprises of collecting usage data for multiple usage parameters on the mobile device of a user, analyzing the usage data to identify a usage pattern; such that each usage parameter is given a priority rank and a usage pattern is identified by giving weightage to each usage parameter based on it's priority rank, the usage pattern describing usage behavior of the user. The method further comprises of matching the usage pattern with a set of predefined usage patterns, each predefined usage pattern having linked user attitude profile, and generating a matching score for the usage pattern with one or more of the predefined usage patterns; the matching score indicating the probability of the user matching one or more user attitude profile.

Description

METHOD AND SYSTEM OF DETERMINING AN USER ATTITUDE PROFILE FOR A MOBILE DEVICE USER

The present invention discloses a method and system of determining a user attitude profile for a mobile device user.

With the advent of smartphones in the market and subsequent increase in mobile device internet usage, marketing via mobile devices has become an integral part of marketing strategies of companies. Marketing via mobile devices benefits consumers, mobile service providers, publishers and marketers and drives higher revenues by targeting a specific set of users. This target set of mobile device users are identified to be the most likely consumers of the company’s services and products. Such target set of mobile device users are identified based on the analysis of their related mobile usage behaviour. The mobile usage behaviour is determined majorly from the volume of mobile data generated on the mobile devices. The mobile usage behaviour analytics is a powerful tool used by the marketers to find their target consumers. Although, various means of analysis of mobile usage data is known in the art, the publishers and marketers are continuously challenged to identify the appropriate group of users i.e. the most likely users of the advertised service or product.

It is also known in the art to segment the mobile device users on the basis of the mobile data generated on the mobile devices. Such systems inherently compromise the privacy of users by transmitting the user interaction data of the users outside the mobile devices to the marketers for analysis and segmentation.

United States Patent Application Publication Number 2014/0040017 A1 to Bafna et al, describes a method of mobile analytics for selling mobile applications in particular, over the mobile marketplace. The method includes monitoring the usage of mobile applications on the user’s mobile device and focuses on grouping users based on the usage of the mobile applications on the user’s mobile devices. Thereafter, the usage data related to the mobile applications is provided to the analytics group of marketing campaigns. As such the user information and the user interaction data is transmitted to the marketers. Further, the present document focus is limited to tracking only the mobile applications for determining usage behaviour of mobile users.

The above disclosed methods provide an aggregated profile of the customers and in some cases the mobile application usage of the customers to a group of mobile marketing companies. In view of the above, a method and system of determining the target mobile device users is required which assists specifying the target users of the marketer’s services or products, without compromising the user’s privacy over his mobile device usage data.

The following is a brief description of the embodiments as illustrated in the accompanying drawings. It is to be understood that the features illustrated in and described with reference to the drawings are not to be construed as limiting of the scope of the present disclosure. In the accompanying drawings:

Figure 1 illustrates an embodiment of the system for determining an user attitude profile for a mobile device user in accordance with the present disclosure.

A method of determining an user attitude profile for a mobile device user is disclosed. The method comprises of collecting usage data for multiple usage parameters on the mobile device of a user, analyzing the usage data to identify a usage pattern; such that each usage parameter is given a priority rank and a usage pattern is identified by giving weightage to each usage parameter based on it’s priority rank, the usage pattern describing usage behavior of the user. The method further comprises of matching the usage pattern with a set of predefined usage patterns, each predefined usage pattern having linked user attitude profile, and generating a matching score for the usage pattern with one or more of the predefined usage patterns; the matching score indicating the probability of the user matching one or more user attitude profile.

A method for determining an user attitude profile for a mobile device user is disclosed. Such method comprising of transmitting to a mobile device of a user a set of predefined usage patterns, each predefined usage pattern having linked user attitude profile, the usage pattern describing usage behavior of a user. Further, the mobile device is configured to collect usage data for multiple usage parameters on the mobile device of the user, analyze the usage data to identify a usage pattern; such that each usage parameter is given a priority rank and a usage pattern is identified by giving weightage to each usage parameter based on its priority rank. Further, the mobile device is configured to match the usage pattern with the set of predefined usage patterns; and generate a matching score for the usage pattern with one or more of the predefined usage patterns; the matching score indicating the probability of the user matching one or more user attitude profile. The method further comprises of receiving from the mobile device the matching scores.

A system for determining the user attitude profile for a mobile device user is disclosed. Such system comprising of comprising of a database of predefined usage patterns, each predefined usage pattern having linked user attitude profile, the usage pattern describing usage behavior of the user. The system further comprises of a mobile analytics module configured to collect usage data for multiple usage parameters on the mobile device of the user, analyze the usage data to identify a usage pattern; such that each usage parameter is given a priority rank and a usage pattern is identified by giving weightage to each usage parameter based on it’s priority rank. The mobile analytics module is further configured to match the usage pattern with the predefined usage patterns, and generate a matching score for the usage pattern with one or more of the predefined usage patterns; the matching score indicating the probability of the user matching one or more user attitude profile.

A system for determining the user attitude profile for a mobile device user is disclosed. The system comprises of a database of predefined usage patterns, each predefined usage pattern having linked user attitude profile, the usage pattern describing usage behavior of the user. Further, the system comprises of a transmission module for transmitting to a mobile device of a user the predefined usage patterns. The mobile device is configured to collect usage data for multiple usage parameters on the mobile device of the user, analyze the usage data to identify a usage pattern; such that each usage parameter is given a priority rank and a usage pattern is identified by giving weightage to each usage parameter based on its priority rank. Further, the mobile device is configured to match the usage pattern with the set of predefined usage patterns, and generate a matching score for the usage pattern with one or more of the predefined usage patterns; the matching score indicating the probability of the user matching one or more user attitude profile. The system further comprises of a receiving module configured to receive from the mobile device the matching scores.

It will be understood by those skilled in the art that the foregoing objects and the following description of the nature of invention are exemplary and explanatory of the invention and are not intended to be restrictive thereof.

For the purpose of promoting an understanding of the principles of the invention, reference will now be made to various alternative embodiments and specific language will be used to describe the same. It will nevertheless be understood that no limitation of the scope of the invention is thereby intended, such alterations and further modifications in the illustrated method and system, and such further applications of the principles of the invention as illustrated therein being contemplated as would normally occur to one skilled in the art to which the invention relates.

It will be understood by those skilled in the art that the following description is exemplary and explanatory of the invention and are not intended to be restrictive thereof.

Many of the functional units described in this specification have been labeled as modules, in order to more particularly emphasize their implementation independence. For example, a module may be implemented as a hardware circuit comprising custom very large scale integration circuits or gate arrays, off-the-shelf semiconductors such as logic, chips, transistors, or the other discrete components. A module may also be implemented in programmable hardware devices such as field programmable gate arrays, programmable array logic, programmable logic devices or the like.

Modules may also be implemented in software for execution by various types of processors or microprocessors. An identified module of executable code may, for instance, comprise one or more physical or logical blocks of computer instructions which may, for instance, be organized as an object, procedure, or function. Nevertheless, the executable of an identified module need not be physically located together, but may comprise disparate instructions stored in different locations which, when joined together, comprise the module and achieve the stated purpose for the module.

Indeed, a module of executable code could be a single instruction, or many instructions, and may even be distributed over several different code segments, among different programs, and across several memory devices. Similarly, operational data may be identified and illustrated herein within modules, and may be embodied in any suitable form and organized within any suitable type of data structure. The operational data maybe collected as a single data set, or may be distributed over different locations including over different member disks, and may exist, at least partially, merely as electronic signals on a system or network.

Reference throughout this specification to “one embodiment” “an embodiment” or similar language means that a particular feature, structure, or characteristic described in connection with the embodiment is included in at least one embodiment of the present invention. Thus, appearances of the phrase “in one embodiment”, “in an embodiment” and similar language throughout this specification may, but do not necessarily, all refer to the same embodiment.

A method and a system of determining a user attitude profile for a mobile device user is disclosed. The user attitude profile is a set of data points indicative of user’s attitude or his likeness as consumers of certain services or products. Such user attitude data could be collected from various sources such as personal surveys, social media surveys and various other known behavioral collection methods known in the art. Further, the attitude data may include all forms of psychographic data, such as personality, values, lifestyle etc. which marketers capture to understand their target audience. Such attitude data for each user is collected, analyzed and stored as a set of data points comprising the user attitude profiles. Multiple such user attitude profiles are identified by marketers. Accordingly, one or more user attitude profiles for a given mobile device user can be determined. Once the user attitude profile of a mobile device user is identified, they can targeted by marketers by sending out the relevant marketing messages for the mobile device user. The mobile device may be a smartphone, tablet, or any other known device capable of using features such as mobile applications, and browsing internet websites.

A method of determining a user attitude profile for a mobile device user comprises of collecting usage data for multiple usage parameters on the mobile device of the user. The usage data, collected on the mobile device of the user, is indicative of the usage behaviour data based on the mobile device usage. The usage data collected on the mobile device user may be indicative of various types of locations visited (for example, mall, gym, ATM, school etc.), mobile applications, website categories, or include any other representative keyword determining different forms of usage data for a mobile device users. In accordance with an embodiment, the usage data is collected in the form of a set of data points. For example, the set of data points may include but not be limited to:
1. Data points representing the possible locations of the mobile device user, such as location values (i.e. latitudinal and longitudinal values) of various places visited by the mobile device user. Such data points may be stored in the form of a table referred herein to as a ‘points of interest’ table. Each entry in this table contains the type of “point of interest” (ex: ATM, school, gym, mall, multiplex) and its location (latitudinal/longitudinal values).
2. Data points representing the possible mobile applications used by the mobile device user on his device. Such data points may be stored in the form of a table referred herein to as ‘a table of top 100 Apps’ to use for analytics. Each entry in this table contains mobile application name, the type of mobile application, and a rank indicating its popularity in the region.
3. Data points representing the possible websites browsed by the mobile device user on his device. Such data points may be stored in the form of a table referred herein to as ‘a table of top 100 websites’ URL’ to use for analytics. Each entry in this table contains the type of website, and a rank indicating its popularity in the region.
4. Data points representing special names used in the banking and transportation and other service areas. Such data points may be stored in the form of a table including 200-300 special names that are used by Banks, Airlines, E-tailers, Telecommunications companies and others to send notifications text messages (SMS) to their customers.

In accordance with an embodiment, the usage data is collected on the mobile device for a period of two to three weeks. The usage data is constantly refreshed after a fixed interval. By way of an example, the usage data collected is refreshed after an interval of three months on the mobile device of the user.

The collected usage data or the set of data points may be stored either in the internal memory of the mobile device or the storage of the mobile device. In accordance with a specific embodiment, the internal memory or the storage of the mobile device includes one or more SDK (software development kits). The SDK contains a set of development tools that allow for the creation of various applications to execute collection and storage of the usage data on the mobile device in accordance with the teachings of the present application.

Further, the method of determining a user attitude profile for a mobile device user comprises of analyzing the usage data to identify a usage pattern, such that each usage parameter is given a priority rank and a usage pattern is identified by giving weightage to each usage parameter based on its priority rank. In accordance with an embodiment, the priority ranks are pre-assigned by default to the usage parameters. An example of such default priority rank assignment, from high to low, is as follows:
(i) Location data
(ii) Mobile Application usage data
(iii) Website/Browser usage data
(iv) Phone calls usage data
(v) SMS/Texting usage data

Based on the priority ranks assigned to each of the usage parameters, the collected usage data is analyzed to identify usage pattern of the mobile device user by giving weightage to each usage parameter based on its priority rank. The priority ranks determine the weightage of each data point. The usage patterns describe the usage behaviour of the mobile device user. The usage behavior is a specific usage behavior exhibited on the mobile device by the user while using his mobile device. In accordance with an example, such specific usage behavior may include the following:
(i) Locations visited (for example: ATM, school, gym, mall multiplex, etc.)
(ii)A type of mobile application used the most by the user (such as news reading mobile application, a social media mobile application etc.)
(iii) A type of website visited the most by the user, etc.

Further, the method of determining a user attitude profile for a mobile device comprises of matching the usage pattern with a set of predefined usage patterns, each predefined usage pattern having linked user attitude profile. In accordance with an embodiment, the predefined usage patterns are common usage behaviour identified from various users or a group of mobile device users. Applicant’s co-pending application titled: “Method and System of Identifying a Target Set of Mobile Device Users”, discloses identifying such usage patterns from a group of mobile device users and linking the usage patterns to the user attitude data collected from each user from the group of users. The usage patterns are linked to the user attitude data on identifying a co-relation between the usage patterns and that user attitude data. The user attitude data is collected from each user from the group of users and thus forms the user attitude profile of the user. Accordingly, these identified usage patterns having a linked user attitude profile are the predefined for the present application.

In accordance with an embodiment, the usage patterns identified for a mobile device user on the mobile device is thereafter matched with a set of predefined usage patterns. In accordance with an embodiment, the internal memory or the storage of the mobile device includes one or more SDK (software development kits) which contain a set of development tools that allow creation of various applications for the storing of the predefined usage patterns in the internal memory or the storage of the mobile device. Further, the SDK contains one or more mobile applications for executing the matching of the usage patterns with a set of predefined usage patterns.

In accordance with an embodiment, the matching score indicates the probability of the user matching one or more user attitude profiles. Each of the predefined usage patterns has one or more user attitude profiles linked to it. Accordingly, the matching score indicates the likelihood of a similarity exhibited between the usage pattern, identified for a mobile device of a user, and the set of predefined usage patterns. In accordance with an embodiment, the result of the assessment of the matching score is saved as a percentage ranging between 0 and 100. The matching score is generated for each usage pattern or may be for each data point from the set of data points.

Further, in accordance with further embodiments, the matching score is periodically regenerated for identifying the user as a target user for one or more user attitude profiles based on the matching score. For example, a pre-configured time period of three months is set for regenerating the matching scores or if any matching score is older than a preconfigured time period of three months.

In accordance with an embodiment, the method of determining a user attitude profile for a mobile device user further comprises of transmitting to a central server the matching scores. In accordance with a further embodiment, the internal memory or the storage of the mobile device includes one or more SDK (software development kits) which contain a set of development tools that allow creation of various applications for transmitting the matching scores of the usage patterns identified for a mobile device of the user to a central server, the mobile device being capable of communication with the central server. Such communication could be through wireless IP networks or any other known means in the art. Further, in accordance with an embodiment, only the matching scores are transmitted to the central server from the mobile device. The matching score transmitted to the central server enable the advertisers to identify the mobile device user as a target user.

In accordance with an embodiment, the method of determining a user attitude profile for a mobile device user further comprises of identifying the user as a target user for one or more user attitude profiles based on the matching score. The internal memory or the storage of the mobile device includes one or more SDK (software development kits) which contain a set of development tools that allow creation of various applications for identifying the user as a target user for one or more user attitude profiles based on the matching score.

In accordance with another embodiment, the method of determining a user attitude profile for a mobile device user further comprises of transmitting to a central server the user attitude profile for which the user has been identified as a target user. The internal memory or the storage of the mobile device includes one or more SDK (software development kits) which contain a set of development tools that allow creation of various applications for transmitting such user attitude profiles to the central server.

A method of determining a user attitude profile for a mobile device is disclosed wherein the method comprises of transmitting to a mobile device of a user a set of predefined usage patterns, each predefined usage pattern having linked user attitude profile, the usage pattern describing usage behavior of a user. Wherein, the mobile device is configured to collect usage data for multiple usage parameters on the mobile device of the user, to analyze the usage data to identify a usage pattern, such that each usage parameter is given a priority rank and a usage pattern is identified by giving weightage to each usage parameter based on its priority rank. Further, the mobile device is configured to match the usage pattern with the set of predefined usage patterns, and to generate a matching score for the usage pattern with one or more of the predefined usage patterns, the matching score indicating the probability of the user matching one or more user attitude profile. The method determining a user attitude profile for a mobile device further comprises of receiving from the mobile device the matching scores.

The set of predefined usage patterns transmitted to the mobile device of the user are downloaded from a central server, the mobile device being in communication with the central server through known means in the art such as through an IP network or any other known means in the art. In accordance with an embodiment, the set of predefined usage patterns are the saved usage patterns, having linked a user attitude profile, as disclosed in the applicant’s co-pending application titled: “Method and System of Identifying a Target Set of Mobile Device Users”.

In accordance with a further embodiment, along with the set of predefined usage patterns are a set of reference data points stored on the central server may also be transmitted to the mobile device. The reference data points includes values representing one or more location types (mall, gym, ATM, etc.), mobile applications, website categories, or include any other representative keyword determining different levels and forms of usage data for a mobile device user.

In accordance with an embodiment, the set of reference data points may be used to determine the user attitude profile for a mobile device user. The set of reference data points stored on the central server may be pre-analyzed to determine one or more user attitude profile linked to the reference data points. The collected usage data on the mobile device of the user may be matched with the set of reference data points to generate a matching score, the matching score indicating the probability of the user matching one or more user attitude profiles, linked to the reference data points from the set of reference data points.

The central server referred to above, is controlled by the administrators of marketers or advertisers marketing different services and products. Such advertisers wish to determine the segment of the mobile device users who are the most likely consumers of the marketed services or products. The segment herein refers to the user attitude profile determined for a mobile device user in accordance with the teachings of the present application.

The predefined usage patterns and/ or the set of reference data points may be installed to be stored in the internal memory or storage of the mobile device either through one or more SDK (software development kits) which contain a set of development tools that allow creation of applications for storing of the predefined usage patterns and/or set of reference data points on the mobile device of the user, or may be downloaded through mobile applications by including the usage patterns and/or the set of reference data points in one or more such mobile applications. Such mobile applications belonging to various publishers are may be identified as being frequently or likely to be used or downloaded by the user of the mobile device. Further, various other known means in the art may be employed for such transmission of predefined usage patterns and/or set of reference data points to the mobile device of the user.

Further, in accordance to the embodiments of the present disclosure, the method determining a user attitude profile for a mobile device comprises of receiving from the mobile device the matching scores. The matching scores are generated from the matching between the usage patterns, identified on the mobile device of the user, with the predefined usage patterns, or may be generated from the matching between the usage data collected on the mobile device and the set of reference data points.

In accordance with an embodiment, the internal memory or storage of the mobile device includes an SDK (software development kits) which contain a set of development tools that allow creation of applications through which the set of predefined usage patterns are installed and stored in the internal memory or storage of the mobile device, and through which collection and storage of the usage data on the mobile device; matching of the usage patterns with the set of predefined usage patterns; and transmission of the matching score of the usage patterns identified for a mobile device of the user, are executed. Further, through the applications created, transmission of user attitude profiles, for which the user has been identified as a target user, to the central server, may also be executed.

A system for determining the user attitude profile for a mobile device user is disclosed. The disclosed system comprises of a database of predefined usage patterns, each predefined usage pattern having linked user attitude profile, the usage pattern describing usage behavior of the user, and a mobile analytics module. Further, the mobile analytics module is configured to collect usage data for multiple usage parameters on the mobile device of the user, analyze the usage data to identify a usage pattern, such that each usage parameter is given a priority rank and a usage pattern is identified by giving weightage to each usage parameter based on its priority rank. Further, the mobile analytics group is configured to match the usage pattern with the predefined usage patterns, and to generate a matching score for the usage pattern with one or more of the predefined usage patterns; the matching score indicating the probability of the user matching one or more user attitude profile.

The database may also be referred to as a reference data store and contains the predefined usage patterns, the predefined usage patterns stored in the database being the usage patterns identified in accordance with the teachings of the applicant’s co-pending application titled: “Method and System of Identifying a Target Set of Mobile Device Users”, and having linked a user attitude profile. Further, in another embodiment, the reference data store along with the predefined usage patterns may also include the set of reference data points, disclosed above in the present application. The mobile analytics module may further comprise of appropriate usage recorder module to collect and store the usage data for multiple usage parameters on the mobile device of the user. Further, the analytics module may comprise of one or more processors to analyze the usage data stored in the usage recorder module to identify a usage pattern for the mobile device user, to match the usage patterns with the predefined usage patterns stored in the database and to generate a matching score according to teachings of the present application.

In accordance with an embodiment of the present disclosure, the internal memory or the storage of the mobile device includes one or more SDK (software development kits) which contains a set of development tools that allow for the creation of various applications to execute the functions of the analytics module and store the database containing the predefined usage patterns and/or set of reference data points in the internal memory or storage of the mobile device. Also, in accordance with further embodiments, other known means in the art may also be employed to execute the functions of the analytics module, as disclosed in the present application, and storing the database in the mobile device of the user.

In accordance with further embodiment, the mobile analytics module is further configured to identify the user as a target user for one or more user attitude profiles based on the matching score.

In accordance with an embodiment, the above disclosed system may further comprise of a transmission module for transmitting to a central server the matching scores. In accordance with an alternate embodiment, the transmission module is further configured to transmit to a central server, the user attitude profile for which the user has been identified as a target user.

In accordance with a further embodiment, the mobile analytics module is further configured to regenerate the matching scores and identifying the user as a target user for one or more user attitude profiles based on the matching score.

A system for determining the user attitude profile for a mobile device user is disclosed wherein the system comprises of a database of predefined usage patterns, each predefined usage pattern having linked user attitude profile, the usage pattern describing usage behavior of the user, a transmission module for transmitting to a mobile device of a user the predefined usage patterns, and a receiving module configured to receive from the mobile device the matching scores. Further, the mobile device configured to collect usage data for multiple usage parameters on the mobile device of the user, to analyze the usage data to identify a usage pattern; such that each usage parameter is given a priority rank and a usage pattern is identified by giving weightage to each usage parameter based on its priority rank. Further, the mobile device is configured to match the usage pattern with the set of predefined usage patterns; and to generate a matching score for the usage pattern with one or more of the predefined usage patterns, the matching score indicating the probability of the user matching one or more user attitude profile.

In accordance with an embodiment of the present disclosure, the database, the transmission module and the receiving module reside on a central server wherein the central server is in communication with the mobile device. The transmission module transmits predefined usage patterns and/or the set of reference data points, as disclosed in the present application, to the mobile device. Further, the mobile device after generating the matching scores, according to the teachings of the present application, further transmits the generated matching scores to the central server. The receiving module on the central server is configured to receive the matching scores from the mobile device. The transmission module and the receiving module can be implemented either as a hardware circuit, a hardware programmable device or as software logic, or in the form of programmable microprocessors or by such other various means known in the art configured to perform the functions as taught in the present disclosure. The database can be a memory device or storage implemented as hardware circuit or software logic, and is configured to store the data as received from the receiving module

In accordance with a further embodiment, the above disclosed system further comprises of an analytics module configured to identifying the user as a target user for one or more user attitude profiles based on the matching score.

In accordance with a further embodiment, the mobile device is further configured to identify the user as a target user for one or more user attitude profiles based on the matching score, and the receiving module is further configured to receive the user attitude profile for which the user has been identified as a target user.

In accordance with a further embodiment, the transmission module is further configured to transmit to the mobile device a set of usage parameters for which usage data is to be collected. Further, the transmission module is further configured to transmit to the mobile device advertisements for attitude profiles of the target user of which the user has been identified. The advertisements of the products and services relevant to the user attitude profile determined for a mobile device user may be transmitted to the mobile device by including the advertisements in the mobile applications used or downloaded by the user. Such mobile applications through which advertisements are transmitted belong to various publishers who agree or wish to monetize their mobile applications through such advertisements. Such interaction between the marketers who want to send relevant advertisements and publishers occur through Demand Side Platform (DSP) and Supply Side Platform (SSP), the means of employing the same are known in the teachings of the art. In accordance with an embodiment, only the user attitude profile or the matching scores may be shared between the marketers and publishers and no data related to the usage data of the mobile device of the user is shared in such interactions.

The transmission module can be implemented either as a hardware circuit, a hardware programmable device or as software logic, or in the form of programmable microprocessors or by such other various means known in the art configured to perform the functions as taught in the present disclosure.

It may also be possible that the above said database and transmission, receiving and analytics module may reside on separate servers and are configured to remotely access data from each others through various communications means known in the art, the database, the transmission module and the receiving module being in communication with the mobile device.

In accordance with an example, Figure 1 depicts the systems as disclosed above in the present application. Figure 1 depicts a central server (100) comprising a database (101), a transmission module (102) in communication with the database (101), a receiving module (103) and an analytics module (104). The central server (100) is in communication with the mobile device (200). The mobile device (200) comprises, within the internal storage or storage of the mobile device (200), a database (201), referred to as the ‘mobile database’ in the figure, an analytics module (202), referred to as ‘mobile analytics module’ in the figure, and a transmission module (203), referred to as the ‘mobile transmission module’.

SPECIFIC EMBODIMENTS

Such method(s) further comprising of transmitting to a central server the matching scores.

Such method(s) further comprising of identifying the user as a target user for one or more user attitude profiles based on the matching score.

Such method(s) further comprising of transmitting to a central server the user attitude profile for which the user has been identified as a target user.

Such method(s) further comprising of periodically regenerating the matching scores and identifying the user as a target user for one or more user attitude profiles based on the matching score.

A method for determining an user attitude profile for a mobile device user is disclosed. Such method comprising of transmitting to a mobile device of a user a set of predefined usage patterns, each predefined usage pattern having linked user attitude profile, the usage pattern describing usage behavior of a user. Further, the mobile device is configured to collect usage data for multiple usage parameters on the mobile device of the user, analyze the usage data to identify a usage pattern; such that each usage parameter is given a priority rank and a usage pattern is identified by giving weightage to each usage parameter based on its priority rank. Further, the mobile device is configured to match the usage pattern with the set of predefined usage patterns; and generate a matching score for the usage pattern with one or more of the predefined usage patterns; the matching score indicating the probability of the user matching one or more user attitude profile. The method further comprising of receiving from the mobile device the matching scores.

Such method(s) wherein the mobile device is further configured to identify the user as a target user for one or more user attitude profiles based on the matching score, the method further comprising receiving the user attitude profile for which the user has been identified as a target user.

Such method(s) further comprising of transmitting to the mobile device a set of usage parameters for which usage data is to be collected.

Such system(s) further comprising of a transmission module for transmitting to a central server the matching scores.

Such system(s) wherein the mobile analytics module is further configured to identify the user as a target user for one or more user attitude profiles based on the matching score.

Such system(s) wherein the transmission module is further configured to transmit to a central server the user attitude profile for which the user has been identified as a target user.

Such system(s) wherein the mobile analytics module is further configured to regenerate the matching scores and identifying the user as a target user for one or more user attitude profiles based on the matching score.

Such system(s) further comprising a receiving module configured to receive from a central server a set of predefined usage patterns.

Such system(s) wherein the receiving module is further configured to receive a set of usage parameters for which usage data is to be collected.

Such system(s) further comprising an analytics module configured to identifying the user as a target user for one or more user attitude profiles based on the matching score.

Such system(s) wherein the mobile device is further configured to identify the user as a target user for one or more user attitude profiles based on the matching score, the receiving module further configured to receive the user attitude profile for which the user has been identified as a target user.

Such system(s) wherein the transmission module is further configured to transmit to the mobile device a set of usage parameters for which usage data is to be collected.

Such system(s) wherein the transmission module is further configured to transmit to the mobile device advertisements for attitude profiles of the target user of which the user has been identified.

The disclosed method and system introduces a practical way to use smartphones to determine the segments or user attitude profile of the mobile device users. The disclosed method and systems enables identifying the relevant users for advertisement purposes by the marketers and publishers. However, no internal usage data of the mobile device is shared externally with the marketers or publishers. As such the privacy of an individual user’s mobile device data is always ensured.

Claims

A method of determining an user attitude profile for a mobile device user comprising:
- collecting usage data for multiple usage parameters on the mobile device of a user;
- analyzing the usage data to identify a usage pattern; such that each usage parameter is given a priority rank and a usage pattern is identified by giving weightage to each usage parameter based on it’s priority rank, the usage pattern describing usage behavior of the user;
- matching the usage pattern with a set of predefined usage patterns, each predefined usage pattern having linked user attitude profile; and
- generating a matching score for the usage pattern with one or more of the predefined usage patterns; the matching score indicating the probability of the user matching one or more user attitude profile.
A method as claimed in claim 1 further comprising transmitting to a central server the matching scores.
A method as claimed in claim 1 further comprising identifying the user as a target user for one or more user attitude profiles based on the matching score.
A method as claimed in claim 2 further comprising transmitting to a central server the user attitude profile for which the user has been identified as a target user.
A method as claimed in claim 1 further comprising periodically regenerating the matching scores and identifying the user as a target user for one or more user attitude profiles based on the matching score.
A method for determining an user attitude profile for a mobile device user comprising:
- transmitting to a mobile device of a user a set of predefined usage patterns, each predefined usage pattern having linked user attitude profile, the usage pattern describing usage behavior of a user;
the mobile device configured to:
--collect usage data for multiple usage parameters on the mobile device of the user;
--analyze the usage data to identify a usage pattern; such that each usage parameter is given a priority rank and a usage pattern is identified by giving weightage to each usage parameter based on it’s priority rank
--match the usage pattern with the set of predefined usage patterns; and
--generate a matching score for the usage pattern with one or more of the predefined usage patterns; the matching score indicating the probability of the user matching one or more user attitude profile;
- receiving from the mobile device the matching scores.
A method as claimed in claim 6 further comprising identifying the user as a target user for one or more user attitude profiles based on the matching score.
A method as claimed in claim 6 wherein the mobile device is further configured to identify the user as a target user for one or more user attitude profiles based on the matching score, the method further comprising receiving the user attitude profile for which the user has been identified as a target user.
A method as claimed in claim 7 further comprising transmitting to the mobile device a set of usage parameters for which usage data is to be collected.
A system for determining the user attitude profile for a mobile device user comprising:
- a database of predefined usage patterns, each predefined usage pattern having linked user attitude profile, the usage pattern describing usage behavior of the user; and
- a mobile analytics module configured to:
-- collect usage data for multiple usage parameters on the mobile device of the user;
-- analyze the usage data to identify a usage pattern; such that each usage parameter is given a priority rank and a usage pattern is identified by giving weightage to each usage parameter based on it’s priority rank;
--match the usage pattern with the predefined usage patterns; and
--generate a matching score for the usage pattern with one or more of the predefined usage patterns; the matching score indicating the probability of the user matching one or more user attitude profile.
A system as claimed in claim 10 further comprising a transmission module for transmitting to a central server the matching scores.
A system as claimed in claim 11 wherein the mobile analytics module further configured to identify the user as a target user for one or more user attitude profiles based on the matching score.
A system as claimed in claim 12 wherein the transmission module further configured to transmit to a central server the user attitude profile for which the user has been identified as a target user.
A system as claimed in claim 10 wherein the mobile analytics module further configured to regenerate the matching scores and identifying the user as a target user for one or more user attitude profiles based on the matching score.
A system as claimed in claim 10 further comprising a receiving module configured to receive from a central server a set of predefined usage patterns.
A system as claimed in claim 10 wherein the receiving module further configured to receive a set of usage parameters for which usage data is to be collected.
A system for determining the user attitude profile for a mobile device user comprising:
- a database of predefined usage patterns, each predefined usage pattern having linked user attitude profile, the usage pattern describing usage behavior of the user;
- a transmission module for transmitting to a mobile device of a user the predefined usage patterns;
- the mobile device configured to:
-- collect usage data for multiple usage parameters on the mobile device of the user;
-- analyze the usage data to identify a usage pattern; such that each usage parameter is given a priority rank and a usage pattern is identified by giving weightage to each usage parameter based on it’s priority rank;
-- match the usage pattern with the set of predefined usage patterns; and
-- generate a matching score for the usage pattern with one or more of the predefined usage patterns; the matching score indicating the probability of the user matching one or more user attitude profile;
- a receiving module configured to receive from the mobile device the matching scores.
A system as claimed in claim 17 further comprising an analytics module configured to identifying the user as a target user for one or more user attitude profiles based on the matching score.
A system as claimed in claim 17 wherein the mobile device is further configured to identify the user as a target user for one or more user attitude profiles based on the matching score, the receiving module further configured to receive the user attitude profile for which the user has been identified as a target user.
A system as claimed in claim 17 wherein the transmission module further configured to transmit to the mobile device a set of usage parameters for which usage data is to be collected.
A system as claimed in claim 18 or 19 wherein the transmission module further configured to transmit to the mobile device advertisements for attitude profiles of the target user of which the user has been identified.