Patent Issued for Data processing system with machine learning engine to provide output generation functions (USPTO 11348134): Allstate Insurance Company

2022 JUN 20 (NewsRx) -- By a News Reporter-Staff News Editor at Insurance Daily News -- Allstate Insurance Company (Northbrook, Illinois, United States) has been issued patent number 11348134, according to news reporting originating out of Alexandria, Virginia, by NewsRx editors.

The patent’s inventors are Chintakindi, Sunil (Menlo Park, CA, US), Gibson, Timothy W. (Barrington, IL, US), Hayes, Howard (Glencoe, IL, US), Kodali, Anuradha (Fremont, CA, US), Likhterman, Aleksandr (Wheeling, IL, US), Rosanwo, Soton Ayodele (Chicago, IL, US).

This patent was filed on September 25, 2019 and was published online on May 31, 2022.

From the background information supplied by the inventors, news correspondents obtained the following quote: “Nearly everyone today uses some sort of personal mobile device on a regular basis. For instance, people use smartphones, cell phones, wearable devices such as smart watches and fitness monitors, tablets, laptops, and the like. These personal mobile devices are regularly carried by users throughout the day and can capture a variety of information regarding the user’s usage of their personal mobile device, locations visited, and the like.

“In addition, user data is collected and stored by a variety of different sources. For instance, user data that is collected by a mobile device, wearable device, or other similar device of a user may be stored via one or more third party systems (e.g., cloud-based environments) or entity systems with the permission of the user (e.g., upon downloading an application, executing an application, or the like). This data may be transmitted to an entity for evaluation and may be useful in evaluating user behaviors and identifying insights that may be helpful to users. In some examples, this data may be useful to other entities to evaluate users and generate offers. However, it is difficult for users to view and/or retrieve this data from the third party or entity. Accordingly, it may be advantageous to retrieve data from the third party and analyze the data to generate and provide outputs to a user.

“Further, some conventional processes require human interaction, evaluation, and the like. Given the volume of data captured by the mobile device, stored by one or more entities, and the like, it would be advantageous to perform these processes based on sensor data collected rather than requiring human evaluation.”

Supplementing the background information on this patent, NewsRx reporters also obtained the inventors’ summary information for this patent: “The following presents a simplified summary in order to provide a basic understanding of some aspects of the disclosure. The summary is not an extensive overview of the disclosure. It is neither intended to identify key or critical elements of the disclosure nor to delineate the scope of the disclosure. The following summary merely presents some concepts of the disclosure in a simplified form as a prelude to the description below.

“Aspects of the disclosure relate to methods, computer-readable media, systems, and apparatuses for evaluating device usage, capturing user data, and generating one or more outputs based on the device usage and/or user data. For instance, data from one or more sensors within a mobile device can be received and processed to determine movement associated with the device. In addition, an amount of usage (e.g., hours, minutes, etc.) associated with the device can be received. In some examples, application usage and/or types of applications used can also be received. This data can be processed to determine travel patterns for the user of the device and those travel patterns can be utilized to build a risk profile for the user. The risk profile can be utilized for a variety of purposes, including determining rate information and confirming information provided by the user, as well as for generating other insights.

“Additional aspects of the disclosure relate to methods, computer-readable media, systems, and apparatuses for retrieving user data collected by, for example, a mobile device of a user, from a third party system for use in evaluating user behaviors, generating outputs and insights, and the like. In some examples, machine learning may be used to evaluate user data and/or generate one or more outputs.

“In some arrangements, methods, computer-readable media, systems, and/or apparatuses are provided for receiving a request to generate an offer. For instance, user input may be received requesting generation of an offer. In response to receiving the request, in some examples, an application may be transmitted to a device, such as a mobile device of a user. In some examples, the transmission of the application and request for an offer may be performed in advance of other processes described herein (e.g., just before, days or hours before, or the like). In some examples, the application may be executed by the device and may facilitate establishing a communication session with a third party system, identifying and extracting data from the third party system, and transmitting the extracted data to an entity for evaluation. In some examples, evaluation by the entity may include generating one or more insights, outputs and the like. In some arrangements, the evaluation may be performed using machine learning and, in some examples, may be performed in real-time or near real-time.

“These and other features and advantages of the disclosure will be apparent from the additional description provided herein.”

The claims supplied by the inventors are:

“1. A computing platform, comprising: a processing unit comprising a processor; and a memory unit storing computer-executable instructions, which when executed by the processing unit, cause the computing platform to: establish a wireless connection with a mobile device of a user; initiate a communication session with the mobile device; receive, from the mobile device and during the communication session, a request to generate an offer; transmit, during the communication session, an application to a mobile device of a user; execute the application on the mobile device of the user to identify and extract data from a third party system, the extracted data being received by the third party system from a plurality of devices of the user, the plurality of devices including the mobile device and at least one other device of a different device type, and the extracted data including a first type of data and a second type of data; receive, from the third party system, via the mobile device of the user and during the communication session, the data; filter, using time series relationships, the data to favor data from the mobile device and remove at least some data from the at least one other device of a different device type; analyze, using a first type of machine learning algorithm, the first type of data to evaluate the user; analyze, using a second type of machine learning algorithm different from the first type of machine learning algorithm, the second type of data to evaluate the user; and generate an output based on the analyzed first type of data and second type of data.

“2. The computing platform of claim 1, wherein the first type of data received from the third party system is location data corresponding to locations of the mobile device at a plurality of days and times.

“3. The computing platform of claim 2, wherein the first type of data is captured by a global positioning system of the mobile device and stored by the third party system.

“4. The computing platform of claim 3, wherein the first type of data is captured and stored prior to receiving the request to generate the offer.

“5. The computing platform of claim 2, wherein the first type of data includes a plurality of location entries corresponding to each location of the mobile device at a particular day and time.

“6. The computing platform of claim 5, wherein each location entry includes longitude and latitude coordinates of the location and a time and data date stamp.

“7. The computing platform of claim 1, further including instructions that, when executed, cause the computing platform to generate one or more insights related to the user including at least one of: frequently visited locations, time spent driving within predefined distance of a home location, and distances travelled.

“8. A computing device, comprising: a processing unit comprising a processor; and a memory unit storing computer-executable instructions, which when executed by the processing unit, cause the computing device to: generate, based on user input from a user, a request for an offer; establish a first wireless connection with a computing platform; initiate a first communication session with the computing platform; transmit, during the first communication session, the generated request to the computing platform; responsive to transmitting the request, receiving, from the computing platform and via the wireless connection, an application for execution on the computing device; executing the application on the computing device to: establish a second wireless connection with a third party computing system; initiate a second communication session with the third party computing system; extract user data associated with the user and stored on the third party computing system, the extracted data being received by the third party system from a plurality of devices of the user, the plurality of devices including the computing device and at least one other device of a different device type, and the extracted data including a first type of data and a second type of data; receive, from the third party computing system and during the second communication session, the extracted user data; transmit the received, extracted user data to the computing platform; and receive, from the computing platform, a generated output based on the extracted user data, wherein the output is generated based on data filtered using time series relationships to favor data from the computing device and remove at least some data from the at least one other device of a different device type, and based on machine learning analysis of the first type of data using a first machine learning algorithm and machine learning analysis of the second type of data using a second, different machine learning algorithm.

“9. The computing device of claim 8, wherein the first type of data is location data corresponding to locations of the computing device at a plurality of days and times.

“10. The computing device of claim 9, wherein the first type of data is captured by a global positioning system of the computing device and stored by the third party computing system.

“11. The computing device of claim 10, wherein the first type of data is captured and stored prior to generating the request to generate the offer.

“12. The computing device of claim 10, wherein the first type of data includes a plurality of location entries corresponding to each location of the computing device at a particular day and time.

“13. The computing device of claim 12, wherein each location entry includes longitude and latitude coordinates of the location and a time and data date stamp.

“14. One or more non-transitory computer-readable media storing computer-executable instructions that, when executed by a computing device, cause the computing device to: generate, based on user input from a user, a request for an offer; establish a first wireless connection with a computing platform; initiate a first communication session with the computing platform; transmit, during the first communication session, the generated request to a computing platform; responsive to transmitting the request, receiving, from the computing platform and via the wireless connection, an application for execution on the computing device; executing the application on the computing device, including: establish a second wireless connection with a third party computing system; initiate a second communication session with the third party computing system; extract user data associated with the user and stored on the third party computing system, the extracted data being received by the third party system from a plurality of devices of the user, the plurality of devices including the computing device and at least one other device of a different device type, and the extracted data including a first type of data and a second type of data; receive, from the third party computing system and during the second communication session, the extracted user data; transmit the received, extracted user data to the computing platform; and receive, from the computing platform, a generated output based on data filtered using time series relationships to favor data from the computing device and remove at least some data from the at least one other device of a different device type, and based on the extracted user data, wherein the output is generated based on machine learning analysis of the first type of data using a first machine learning algorithm and machine learning analysis of the second type of data using a second, different machine learning algorithm.

“15. The one or more non-transitory computer-readable media of claim 14, wherein the first type of data is location data corresponding to locations of the computing device at a plurality of days and times.

“16. The one or more non-transitory computer-readable media of claim 15, wherein the first type of data is captured by a global positioning system of the computing device and stored by the third party computing system.

“17. The one or more non-transitory computer-readable media of claim 16, wherein the first type of data is captured and stored prior to generating the request to generate the offer.

“18. The one or more non-transitory computer-readable media of claim 16, wherein the first type of data includes a plurality of location entries corresponding to each location of the computing device at a particular day and time.

“19. The one or more non-transitory computer-readable media of claim 18, wherein each location entry includes longitude and latitude coordinates of the location and a time and date stamp.”

For the URL and additional information on this patent, see: Chintakindi, Sunil. Data processing system with machine learning engine to provide output generation functions. U.S. Patent Number 11348134, filed September 25, 2019, and published online on May 31, 2022. Patent URL: http://patft.uspto.gov/netacgi/nph-Parser?Sect1=PTO1&Sect2=HITOFF&d=PALL&p=1&u=%2Fnetahtml%2FPTO%2Fsrchnum.htm&r=1&f=G&l=50&s1=11348134.PN.&OS=PN/11348134RS=PN/11348134

(Our reports deliver fact-based news of research and discoveries from around the world.)