“Insight-Led Activity Reporting And Digital Health Management” in Patent Application Approval Process (USPTO 20220398181): Patent Application

2023 JAN 02 (NewsRx) -- By a News Reporter-Staff News Editor at Insurance Daily News -- A patent application by the inventors FANG, Richard (Belevue, WA, US); WAGLE, Justin James (Pacifica, CA, US); WU, Chang-Ling (Seattle, WA, US), filed on June 15, 2021, was made available online on December 15, 2022, according to news reporting originating from Washington, D.C., by NewsRx correspondents.

This patent application has not been assigned to a company or institution.

The following quote was obtained by the news editors from the background information supplied by the inventors: “Parents who have children generally want to develop healthy digital habits for their children when using electronic devices. For example, parents may want to monitor screen time usage, potentially harmful behavior, and/or potentially harmful content for children. The status-quo today requires parents to draw from their own intuition for monitoring device usage by children or personal experience which is rather unreliable. Alternatively, parents may spend lots of time researching for themselves statistics about screen time for children but still require keen data-scientist eyes to spot what is relevant to their situation.”

In addition to the background information obtained for this patent application, NewsRx journalists also obtained the inventors’ summary information for this patent application: “This Summary is provided to introduce a selection of concepts in a simplified form that are further described below in the Detailed Description. This Summary is not intended to identify key features or essential features of the claimed subject matter, nor is it intended to be used as an aid in determining the scope of the claimed subject matter.

“One example implementation relates to a method. The method may include determining a device usage behavior of a user of the user device by using a machine learning model that receives device data and determines the device usage behavior based on the device data, wherein the device usage behavior indicates device usage habits or device usage patterns of the user. The method may include generating, by the machine learning model, at least one insight for the device usage behavior that indicates a healthy device usage habit of the user or an unhealthy device usage habit of the user. The method may include providing one or more recommendations for the at least one insight to promote healthy device usage behavior.

“Another example implementation relates to a method. The method may include determining a device usage behavior of a user of the user device by using a machine learning model that receives device data and determines the device usage behavior based on the device data, wherein the device usage behavior indicates device usage habits or device usage patterns of the user. The method may include generating, by the machine learning model, at least one insight for the device usage behavior that indicates an abnormality in the device usage behavior of the user relating to educational usage of the user device. The method may include providing one or more recommendations for the at least one insight, wherein the one or more recommendations include an action based on the at least one insight.

“Another example implementation relates to a system. The system may include one or more processors; memory in electronic communication with the one or more processors; and instructions stored in the memory, the instructions executable by the one or more processors to: train, at a server, a device model using aggregated device data received from a plurality of user devices in the system; and provide the device model to the plurality of user devices, wherein each device model operating on a user device of the plurality of user devices is operable to: determine a device usage behavior of a user of the user device based on the device data of the user device, wherein the device usage behavior indicates device usage habits or device usage patterns of the user; generate at least one insights for the device usage behaviors; and provide one or more recommendations for the at least one insight.

“Additional features and advantages will be set forth in the description that follows. Features and advantages of the disclosure may be realized and obtained by means of the systems and methods that are particularly pointed out in the appended claims. Features of the present disclosure will become more fully apparent from the following description and appended claims, or may be learned by the practice of the disclosed subject matter as set forth hereinafter.”

The claims supplied by the inventors are:

“1. A method implemented by a user device, comprising: determining a device usage behavior of a user of the user device by using a machine learning model that receives device data and determines the device usage behavior based on the device data, wherein the device usage behavior indicates device usage habits or device usage patterns of the user; generating, by the machine learning model, at least one insight for the device usage behavior that indicates a healthy device usage habit of the user or an unhealthy device usage habit of the user; and providing one or more recommendations for the at least one insight to promote healthy device usage behavior.

“2. The method of claim 1, wherein the one or more recommendations further include access to a curated set of information providing advice or suggestions related to the device usage behavior.

“3. The method of claim 2, wherein the curated set of information includes one or more articles automatically identified by the machine learning model for improving the device usage behavior based on similarities identified in the one or more articles to the device usage behavior.

“4. The method of claim 2, wherein the healthy device usage habit of the user or the unhealthy device usage habit of the user is determined by comparing the device usage behavior to a threshold level.

“5. The method of claim 4, wherein the threshold level is determined based on a geographic region for the user.

“6. The method of claim 4, wherein the threshold level is determined based on comparing the device usage behavior to device usage of other users with a similar demographic to the user.

“7. The method of claim 2, wherein the user is a child and the unhealthy device usage habit is one or more of regular device usage late at night, playing games for a long period of time, or screen time usage above a threshold level, and wherein the one or more recommendations include an action to pause access to the user device for a determined time period or prevent access to the user device.

“8. The method of claim 1, wherein the machine learning model is trained by a server in communication with the user device and provided to the user device from the server to run on the user device.

“9. A method implemented by a user device, comprising: determining a device usage behavior of a user of the user device by using a machine learning model that receives device data and determines the device usage behavior based on the device data, wherein the device usage behavior indicates device usage habits or device usage patterns of the user; generating, by the machine learning model, at least one insight for the device usage behavior that indicates an abnormality in the device usage behavior of the user relating to educational usage of the user device; and providing one or more recommendations for the at least one insight, wherein the one or more recommendations include an action based on the at least one insight.

“10. The method of claim 9, wherein the action includes viewing a summary of educational assignments assigned to the user including past due assignments or upcoming assignments.

“11. The method of claim 9, wherein the machine learning model handles seasonality exceptions or location-based exceptions of the user device in determining whether the abnormality in the device usage behavior occurred.

“12. The method of claim 9, wherein the at least one insight for the device usage behavior indicates detected usage swapping by the user from the education usage of the user device to entertainment usage of the user device and provides a reason for the usage swapping.

“13. The method of claim 12, wherein the reason is distraction or disengagement of a class or class assignment.

“14. The method of claim 9, wherein the machine learning model is trained by a server in communication with the user device and provided to the user device from the server to run on the user device.

“15. A system, comprising: one or more processors; memory in electronic communication with the one or more processors; and instructions stored in the memory, the instructions executable by the one or more processors to: train, at a server, a device model using aggregated device data received from a plurality of user devices in the system; and provide the device model to the plurality of user devices, wherein each device model operating on a user device of the plurality of user devices is operable to: determine a device usage behavior of a user of the user device based on the device data of the user device, wherein the device usage behavior indicates device usage habits or device usage patterns of the user; generate at least one insights for the device usage behaviors; and provide one or more recommendations for the at least one insight.

“16. The system of claim 15, wherein the device usage behavior is an amount of screen time for the user and the at least one insight includes a comparison of the screen time to an average or threshold level.

“17. The system of claim 15, wherein the at least one insight includes a detected abnormality in the device usage behavior, a healthy device usage habit, or an unhealthy device usage habit.

“18. The system of claim 15, wherein the at least one insight includes regularly using a social media application during a mealtime and the one or more recommendations include automatically pausing access to the user device during the mealtime.

“19. The system of claim 15, wherein the at least one insight includes a recommended application or content based on the device usage behavior and the one or more recommendations include a direct link to download the application or content to the user device.

“20. The system of claim 15, wherein the at least on insight includes identifying a life stage change for the user based on the device usage behavior and the one or more recommendations include access to information for how to discuss the life stage change.”

URL and more information on this patent application, see: FANG, Richard; WAGLE, Justin James; WU, Chang-Ling. Insight-Led Activity Reporting And Digital Health Management. U.S. Patent Application Number 20220398181, filed June 15, 2021 and posted December 15, 2022. Patent URL (for desktop use only): https://ppubs.uspto.gov/pubwebapp/external.html?q=(20220398181)&db=US-PGPUB&type=ids

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