Patent Issued for System, method, and program product for generating and providing simulated user absorption information (USPTO 11791033): Aimcast IP LLC

2023 NOV 08 (NewsRx) -- By a News Reporter-Staff News Editor at Insurance Daily News -- From Alexandria, Virginia, NewsRx journalists report that a patent by the inventors Arazi, Matan (Santa Monica, CA, US), filed on September 13, 2022, was published online on October 17, 2023.

The patent’s assignee for patent number 11791033 is Aimcast IP LLC (Santa Monica, California, United States).

News editors obtained the following quote from the background information supplied by the inventors: “Content recommendation systems have been an established industry in which extensive technology has been developed by a variety of companies such as Netflix® and Amazon® to provide users with content tailored to their specific interests. For example, a Netflix® subscriber, upon logging into their account may have categories of video on demand content such as “Because You Watched” or “You May Also Like” displayed to them, including specific content selections generated by a content recommendation system. These conventional content recommendation systems rely on extremely large datasets collected from a large number of users and/or subscribers, including content viewing data, content rating data, navigation data related to user navigation through content menus, and other impressions made on the respective website or digital content application interface.

“However, these conventional systems have an inherent technical flaw in that they cannot reliably recommend content that is tailored to a specific user’s interest when there is only a relatively small set of data available for a limited amount of users of the system. For example, an enterprise content recommendation system does not have access to the vast data sets used in conventional data recommendation systems such that conventional systems are unable to provide accurate recommendations targeted and tailored to its users in real-time. This lack of data poses technical challenges in generating content tailored specifically to a single user among a relatively small number of users which provide limited data on which to generate recommendations.

“Lifestyle and health management has been an established industry with extensive content in periodicals, books, membership services, and the like. A long felt need in this industry has been to address a fundamental issue with respect to any health management scheme, which is effective assistance in self-monitoring and health and lifestyle management. Certain programs have addressed this issue by introducing oversight and/or peer support. For example, services such as Weight Watchers® include periodic meetings for members of the program to offer guidance, support, and a certain degree of oversight. With technological advancements in personal devices, certain software applications (“apps”) provide for recording user activity, journaling consumption, monitoring health parameters (e.g., heartrate), etc., which may be tied to traditional health management services like Weight Watchers® and other more modern technological solutions like MyFitnessPal® provided by Under Armor® and “Diabetes Prevention Program” provided by Lark™ (http://www.lark.com/dpp-diabetes-prevention-program/) to name a few.

“Current technology relies largely upon self-reporting (e.g., meal and consumption journaling) with retrospective oversight. Such retrospective analysis, which may include some form of reward and punishment scheme, incentivizes inaccurate reporting (either on purpose or by accident) and fails to provide users with effective support at the moments when support is most needed. Indeed, applications today are not technologically capable of providing a real-time notification for an accurate and timely stimulus. Furthermore, the retrospective reporting gives rise to opportunities for users to cheat the system by inaccurate reportage after the fact. Current systems, methods, and program products are unable to evaluate user activity, user preference to provide precisely timed and situationally targeted prompts and/or stimuli for encouraging lifestyle choices and reinforcing health habits at or before a decision point is being made by a user. Moreover, as noted above, current systems, methods, and program products that do not have access to vast data sets, are unable to generate accurate content recommendations targeted and tailored to its users in real-time.”

As a supplement to the background information on this patent, NewsRx correspondents also obtained the inventors’ summary information for this patent: “In view of the above, it is an object of the present disclosure to provide a technological solution to address the long felt need and technological challenges faced in conventional content recommendation systems in which limited data is available to provide targeted and tailored content to individual users in real time. In embodiments, the present disclosure relates to systems, methods, and program products that overcome this technical problem by generating and providing simulated user absorption data pertaining to users and based on target profiles and targeted situations that may be used to situationally target content recommendations where large data sets are not available.

“It is also an object of the present disclosure to provide a technological solution to address the long felt need and technological challenges faced in health management services of procuring precisely timed health management directives, such as prompts, rewards, recommendations, challenges or other stimuli to users, such that positive choices are encouraged (and potentially, rewarded) at moments of decision, in contrast with conventional systems that are based on after-the-fact analysis, reward, and punishment. The present disclosure provides for an automated health care system using machine learning and/or heuristic systems that encourages individuals to make everyday choices by detecting situations in real time at or in advance of a decision point in being made by a user in which stimuli are most likely to be the most successful. Another advantage of the present disclosure is that by providing pre-emptive identification of user decision points and real time data capture, both the ability and inclination of users to provide inaccurate reporting is diminished. Collectively, these advantages work in favor of the users themselves as well because it makes it harder to effectively fool the system, method and program product described herein, thereby increasing user compliance and benefits from using the present disclosure.

“In embodiments, a method may include: (a) generating, by a situation simulation module of a content optimization system, a first simulation including a first target profile and a first target situation; (b) obtaining, by a simulated content module, a first content selection from amongst a plurality of content selections, wherein the first content selection and first content selection information associated with the first content selection are stored in a content database; © generating, by a training set module of the content optimization system, a first simulated content training set, wherein the first simulated content training set is based on: (i) first lifestyle information associated with the first simulation from a lifestyle database; (ii) first absorption information associated with one or more previously viewed content selections associated with the first simulation from an absorption database; and (iii) first content information associated with the one or more previously viewed content selections from the content database; (d) generating, using a first neural network, first simulated absorption information as an output based on the first content selection as an input and the first simulated content training set; and (e) transmitting, by an output module of the content optimization system, the first simulated absorption information for display on a first user device via a user interface.

“In embodiments, the generating step a) may further include generating, by a target profile module of the content optimization system, the first target profile based on first target profile information, wherein the first target profile information may include a first plurality of specified parameters and a first plurality of tags associated with one or more features, and wherein generating the first target profile is performed by the steps of: (1) displaying, by the output module, a target profile definition interface of the content optimization system on the first user device associated with a first user, wherein the target profile definition interface includes the one or more features; (2) obtaining, by the target profile definition interface from the first user device: (i) a first selection of the one or more features associated with the first target profile; (ii) the first plurality of specified parameters associated with the one or more features; and (iii) the first plurality of tags associated with the one or more features; (3) generating, by the target profile module, the first target profile by selecting from a user profile database a first subset of user profile information based on the first selection of the one or more features, the first plurality of specified parameters associated with the one or more features, and the first plurality of tags associated with the one or more features; (4) storing, by the target profile module, the first target profile in the user profile database; and (5) sending, by the target profile module, the first target profile to the training set module.

“In embodiments, the target profile definition interface is generated by obtaining a first list of the one or more features and a first record count associated with each respective feature of the one or more features from a definitions database.”

The claims supplied by the inventors are:

“1. A content optimization system providing simulated absorption information comprising: a) a processor: b) memory operably connected to the processor, wherein the memory includes processor executable code that when executed by the processor performs the steps of: i. generating, by a situation simulation module implemented via the processor executable code, a first simulation comprising a first target profile and a first target situation; ii. obtaining, by a simulated content module implemented via the processor executable code, a first content selection from amongst a plurality of content selections, wherein the first content selection and first content selection information associated with the first content selection are stored in a content database; iii. generating, by a training set module implemented via the processor executable code, a first simulated content training set, wherein the first simulated content training set is based on: 1. first lifestyle information associated with the first simulation from a lifestyle database; 2. first absorption information associated with one or more previously viewed content selections associated with the first simulation from an absorption database; and 3. first content information associated with the one or more previously viewed content selections from the content database; iv. generating, using a first neural network, first simulated absorption information as an output based on the first content selection as an input and the first simulated content training set; and v. transmitting, by an output module implemented via the processor executable code, the first simulated absorption information for display on a first user device via a user interface.

“2. The content optimization system of claim 1, wherein the generating step i. further comprises generating, by a target profile module implemented via the processor executable code, the first target profile based on first target profile information, wherein the first target profile information comprises a first plurality of specified parameters and a first plurality of tags associated with one or more features, and wherein generating the first target profile is performed by the steps of: 1. Displaying, by the output module, a target profile definition interface of the content optimization system on a first user device associated with a first user, wherein the target profile definition interface includes the one or more features; 2. Obtaining, by the target profile definition interface from the first user device: i. a first selection of the one or more features associated with the first target profile; ii. the first plurality of specified parameters associated with the one or more features; and iii. the first plurality of tags associated with the one or more features; 3. Selecting, from a user profile database, a first subset of user profile information based on the first selection of the one or more features, the first plurality of specified parameters associated with the one or more features, and the first plurality of tags associated with the one or more features; 4. Storing, by the target profile module, the first target profile in the user profile database; and 5. Sending, by the target profile module, the first target profile to the training set module.

“3. The content optimization system of claim 2, wherein the target profile definition interface is generated by obtaining a first list of the one or more features and a first record count associated with each respective feature of the one or more features from a definitions database.

“4. The content optimization system of claim 2, wherein the one or more features comprise age, gender, lifetime system spending, and social media usage information.

“5. The content optimization system of claim 2, wherein the first plurality of specified parameters comprises one or more of a first plurality of relational operators and a first plurality of logical operators.

“6. The content optimization system of claim 1, wherein the generating step i. further comprises generating, by the target profile module, the first target situation based on first target situation information, wherein the first target situation information comprises a second plurality of specified parameters and a second plurality of tags associated with one or more situations, and wherein generating the first target situation is performed by the steps of: 1. Displaying, by the output module, a target situation definition interface of the content optimization system on a first user device, wherein the target situation definition interface includes the one or more situations; 2. Obtaining, by the target situation definition interface from the first user device: (i) a second selection of the one or more situations associated with the first target situation; (ii) the second plurality of specified parameters associated with the one or more situations; and (iii) the second plurality of tags associated with the one or more situations; 3. Selecting from the lifestyle database a first subset of lifestyle information based on the second selection of the one or more situations, the second plurality of specified parameters associated with the one or more situations, and the second plurality of tags associated with the one or more situations; 4. Storing, by the target profile module, the first target situation in the lifestyle database; and 5. sending, by the target profile module, the first target situation to the training set module.

“7. The content optimization system of claim 6, wherein the target profile definition interface is generated by obtaining a second list of the one or more situations and a second record count associated with each respective situation of the one or more situations from the definitions database.

“8. The content optimization system of claim 6, wherein the one or more situations comprise a work situation.

“9. The content optimization system of claim 6, wherein the one or more situations comprise a commute situation.

“10. The content optimization system of claim 6, wherein the one or more situations comprise a home situation.

“11. The content optimization system of claim 6, wherein the second plurality of specified parameters comprises one or more of a second plurality of relational operators and a second plurality of logical operators.

“12. The content optimization system of claim 1, wherein the first content selection is obtained by selection by a first user of the first user device via the user interface.

“13. The content optimization system of claim 1, wherein the first content selection is obtained by generating, by the simulated content module, the first content selection based on the first simulation.

“14. The content optimization system of claim 1, wherein the first simulation is generated by the steps of: 1. Displaying, by the output module, a simulation definition interface of the content optimization system to the first user device, wherein the target situation definition interface includes the first target profile and the first target situation; 2. Obtaining, by the simulation definition interface from the first user device: i. a third selection of first target profile and the first target situation; and ii. a third plurality of tags associated with the first target profile and the first target situation; 3. Generating, by the situation simulation module, the first simulation based on the third selection of the first target profile, the first target situation, and the third plurality of tags; 4. Storing, by the situation simulation module, the first simulation in the lifestyle database; and 5. sending, from the situation simulation module to a simulation module of the content optimization system, the first simulation.

“15. The content optimization system of claim 1, wherein the first neural network implements a machine learning algorithm.

“16. The system of claim 1, wherein the first neural network is a deep neural network.

“17. The system of claim 1, wherein the first user device is a content provider device.

“18. The system of claim 1, wherein the user interface is a content provider user interface.”

For additional information on this patent, see: Arazi, Matan. System, method, and program product for generating and providing simulated user absorption information. U.S. Patent Number 11791033, filed September 13, 2022, and published online on October 17, 2023. Patent URL (for desktop use only): https://ppubs.uspto.gov/pubwebapp/external.html?q=(11791033)&db=USPAT&type=ids

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