Enhanced data collection and analysis facility

US10389828B2 · US · B2

Patent metadata
FieldValue
Publication numberUS-10389828-B2
Application numberUS-201715581556-A
CountryUS
Kind codeB2
Filing dateApr 28, 2017
Priority dateApr 28, 2017
Publication dateAug 20, 2019
Grant dateAug 20, 2019

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  1. Title

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  2. Abstract

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  3. Assignees and inventors

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  4. Key dates

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  5. First independent claim

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  6. CPC / IPC classifications

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  7. Citations and related patents

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Abstract

Official abstract text for this publication.

In one general aspect, a system and method are described for generating a classification model to determine predictive user behavior. The method may include obtaining data from a mobile network provider. The data including a plurality of utilization metrics pertaining to a plurality of mobile devices carrying out a plurality of network interactions, the plurality of mobile devices being associated with a plurality of users. The method may also include categorizing the data into a plurality of Internet domains associated with the data and determining a plurality of patterns in the data. The method may further include determining an additional pattern in the data, and generating a plurality of rules based on the plurality of patterns and the additional pattern.

First claim

Opening claim text (preview).

What is claimed is: 1. A computer-implemented method for generating a classification model to determine predictive user behavior, the method comprising: obtaining data from a mobile network provider, the data including a plurality of utilization metrics pertaining to a plurality of mobile devices carrying out a plurality of network interactions, the plurality of mobile devices being associated with a plurality of users; categorizing the data into a plurality of Internet domains comprising Internet Protocol (IP) resources associated with the data, wherein the data is categorized according to particular categories of the IP resources in which the data pertains, and wherein the IP resources include two or more of a web-site or a web-based service; determining a plurality of patterns in the data, the plurality of patterns being used to correlate a first category represented in the data with at least a portion of the plurality of Internet domains; determining an additional pattern in the data, the additional pattern correlating a second category represented in the data with at least a portion of the plurality of Internet domains; and generating a plurality of rules based on the plurality of patterns and the additional pattern. 2. The method of claim 1 , further comprising: obtaining additional data from one or more mobile network providers; applying the plurality of rules to the additional data to classify the data according to one or more of the plurality of patterns; generating a plurality of age bands, each of which correlate to at least one of the plurality of patterns represented in the data; generating at least two gender groups, one of which correlates to the additional pattern represented in the data; recognizing, for presentation in a graphical user interface, a plurality of graphical reports indicating behavior for mobile device users represented in the additional data, the behavior indicated in the plurality of patterns and graphed according to age band and gender; and in response to receiving a request to view analysis of the additional data, presenting, in the graphical user interface, at least one of the plurality of graphical reports. 3. The method of claim 1 , further comprising: grouping the plurality of Internet domains into a plurality of content topics representing the data; determining browsing patterns in the data according to the plurality of content topics, the behavior being identified and processed according to a plurality of predefined age bands and gender groups; and generating a plurality of updated rules based on the determined browsing patterns. 4. The method of claim 1 , further comprising: determining that a portion of the plurality of utilization metrics include automated mobile device network activities; and before categorizing the data into the plurality of Internet domains, filtering the portion from the data, the filtering being based at least in part on a plurality of mobile call rules. 5. The method of claim 4 , wherein the mobile call rules pertain to call time, call duration, gap duration consistency, devices called, and device location. 6. The method of claim 1 , wherein the plurality of Internet domains define a browsing profile associated with one or more of the plurality of mobile devices. 7. The method of claim 1 , wherein the plurality of utilization metrics are associated with one or more voice transaction, short message service transaction, HTTP access transaction, and location transaction. 8. The method of claim 1 , further comprising filtering the data by selecting and removing a portion of the Internet domains from the data in response to determining that the data represents less than a predefined threshold time for visiting the Internet domains. 9. A computer program product for generating a classification model to determine predictive user behavior, the computer program product being tangibly embodied on a non-transitory computer-readable storage medium and comprising instructions that, when executed by at least one computing device, are configured to cause the at least one computing device to: obtain data from a mobile network provider, the data including a plurality of utilization metrics pertaining to a plurality of mobile devices carrying out a plurality of network interactions, the plurality of mobile devices being associated with a plurality of users; categorize the data into a plurality of Internet domains comprising Internet Protocol (IP) resources associated with the data, wherein the data is categorized according to particular categories of the IP resources in which the data pertains, and wherein the IP resources include two or more of a web-site or a web-based service; determine a plurality of patterns in the data, the plurality of patterns being used to correlate a first category represented in the data with at least a portion of the plurality of Internet domains; determine an additional pattern in the data, the additional pattern correlating a second category represented in the data with at least a portion of the plurality of Internet domains; and generate a plurality of rules based on the plurality of patterns and the additional pattern. 10. The computer program product of claim 9 , wherein the instructions are further configured to cause the at least one computing device to: obtain additional data from one or more mobile network providers; apply the plurality of rules to the additional data to classify the data according to one or more of the plurality of patterns; generate a plurality of age bands, each of which correlate to at least one of the plurality of patterns represented in the data; generate at least two gender groups, one of which correlates to the additional pattern represented in the data; recognize, for presentation in a graphical user interface, a plurality of graphical reports indicating behavior for mobile device users represented in the additional data, the behavior indicated in the plurality of patterns and graphed according to age band and gender; and in response to receiving a request to view analysis of the additional data, present, in the graphical user interface, at least one of the plurality of graphical reports. 11. The computer program product of claim 9 , wherein the instructions are further configured to cause the at least one computing device to: group the plurality of Internet domains into a plurality of content topics representing the data; determine browsing patterns in the data according to the plurality of content topics, the behavior being identified and processed according to a plurality of predefined age bands and gender groups; and generate a plurality of updated rules based on the determined browsing patterns. 12. The computer program product of claim 9 , wherein the instructions are further configured to cause the at least one computing device to: determine that a portion of the plurality of utilization metrics include automated mobile device network activities; and before categorizing the data into the plurality of Internet domains, filter the portion from the data, the filtering being based at least in part on a plurality of mobile call rules. 13. The computer program product of claim 9 , wherein the plurality of Internet domains define a browsing profile associated with one or more of the plurality of mobile devices. 14. The computer program product of claim 9 , wherein the plurality of utilization metrics are associated with one or more voice transaction, short message service transaction, HTTP access transaction, and location transaction. 15. The computer program produc

Assignees

Inventors

Classifications

  • Office automation; Time management · CPC title

  • Replication or mirroring of data, e.g. scheduling or transport for data synchronisation between network nodes · CPC title

  • Performance of employee with respect to a job function · CPC title

  • Marketing; Price estimation or determination; Fundraising · CPC title

  • Collaborative creation, e.g. joint development of products or services · CPC title

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Frequently asked questions

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What does patent US10389828B2 cover?
In one general aspect, a system and method are described for generating a classification model to determine predictive user behavior. The method may include obtaining data from a mobile network provider. The data including a plurality of utilization metrics pertaining to a plurality of mobile devices carrying out a plurality of network interactions, the plurality of mobile devices being associa…
Who is the assignee on this patent?
Sap Se
What technology area does this patent fall under?
Primary CPC classification G06Q10/0633. Mapped technology areas include Physics.
When was this patent published?
Publication date Tue Aug 20 2019 00:00:00 GMT+0000 (Coordinated Universal Time) (B2). Legal status and post-grant events are not shown on this page.
What related patents are in patentsdb?
We list 8 related publications on this page (citations in our corpus or others sharing the same primary CPC).