Patent Issued for Computing system implementing morbidity prediction using a correlative health assertion library (USPTO 11568364): Hi.Q Inc.

2023 FEB 20 (NewsRx) -- By a News Reporter-Staff News Editor at Insurance Daily News -- From Alexandria, Virginia, NewsRx journalists report that a patent by the inventors Fan, Cathy Ye (San Francisco, CA, US), Hinchey, Ryan (Mountain View, CA, US), Jiao, Shuo (Sunnyvale, CA, US), Shah, Munjal (Los Altos, CA, US), Singh, Ardaman (Union City, CA, US), filed on December 29, 2020, was published online on January 31, 2023.

The patent’s assignee for patent number 11568364 is Hi.Q Inc. (Plano, Texas, United States).

News editors obtained the following quote from the background information supplied by the inventors: “Online services exist which provide interactive gaming and social environments for users. These services generally exist for amusement only.

“There also exists a questionnaire, termed the Patient Activation Measure (“PAM”), provided by Insignia Health under license from the State of Oregon, which includes a static set of questions that are knowledge-based and deemed correlative to health.”

As a supplement to the background information on this patent, NewsRx correspondents also obtained the inventors’ summary information for this patent: “Some embodiments include a system and method for predicting a health outcome of a user based on a determination of knowledge the user possesses regarding issues of physiological or mental health.

“Still further, in some embodiments, a system and method is provided for providing a health service benefit to a user based on their predicted health, as determined from the user’s knowledge of human health.

“In one embodiment, a collection of assertions are stored in which each assertion pertains to human health. For each individual in a control population of persons, a value of a predetermined health parameter is determined which is indicative of that person’s health. For each assertion of the collection, a correlative health parameter is determined which is indicative of an association between those individuals in the control population that have independent knowledge of the assertion and the value of the predetermined health parameter for persons of the control population. The collection of assertions can be stored by associating each assertion with the determined correlative health parameter for that assertion. An interface is provided for a user to indicate the user’s independent knowledge of each assertion in at least a subset of assertions from the collection. A health outcome is predicted for the user based at least in part on the correlative health parameter of individual assertions in the subset of assertions.

“In still another embodiment, a health outcome of a user is predicted based on a knowledge profile determination of the user. In one embodiment, a knowledge profile is determined for the user which reflects the user’s independent knowledge of individual assertions in a collection of assertions. A correlation is determined as between a set of facets of the user’s knowledge profile and a corresponding set of facets of multiple individual person’s knowledge profile. The knowledge profile can be determined for at least a set of assertions from the collection of assertions. A health outcome is determined for each of the multiple individual persons. The health outcome of the user can then be predicted based in part on the correlation and the health outcome of each of the multiple individuals.

“In still another embodiment, a knowledge profile is determined for the user to reflect the user’s independent knowledge of individual assertions in a collection of assertions. Each assertion in the collection can be non-specific to the user or to any person of the population, but otherwise known to be correlative to human health. A determination is made as to a first correlation value as between the knowledge profile of the user and a knowledge profile of a control group of persons for whom one or more health outcomes are known. A first health outcome is predicted for the user based on the first correlation value. A health service benefit is provided to the user based at least in part on the predicted health outcome.”

The claims supplied by the inventors are:

“1. A computing system implementing morbidity prediction for a health service, the computing system comprising: a network communication interface to communicate, over one or more wireless networks, with computing devices of users of the health service; one or more processors; and a memory storing instructions that, when executed by the one or more processors, cause the computing system to: execute a correlation model to determine a correlation value for each respective health assertion in a collection of health assertions based on (i) answers to the respective health assertion provided by individuals in a control group, and (ii) known health outcomes of each individual in the control group, wherein the correlation value for each respective health assertion in the collection corresponds to a set of health correlations between knowledge associated with the respective health assertion and the known health outcomes of the individuals in the control group, wherein the collection of health assertions are configured to test general health knowledge of the user of the health service and not query user-specific information of the users; generate, over the one or more wireless networks, a health trivia session to be presented on a computing device of a user, the health trivia session comprising a set of health assertions from the collection of health assertions; receive, over the one or more wireless networks, a corresponding set of responses to the set of health assertions from the computing device of the user; for each response in the corresponding set of responses, determine a correctness for the response, the correctness indicating whether the user answered a corresponding health assertion correctly or incorrectly; based on (i) the correctness of each response in the corresponding set of responses, and (ii) the correlation value of each health assertion in the set of health assertions provided during the trivia session, generate a morbidity profile for the user, the morbidity profile corresponding to one or more health risks of the user; based on the morbidity profile of the user, determine an underwriting class for a health service product for the user; based on the underwriting class for the health service product for the user, determine a specified price for the health service product; and generate, over the one or more wireless networks, a service customer interface to be displayed on the computing device of the user, the service customer interface enabling the user to purchase the health service product in the specified underwriting class and at the specified price.

“2. The computing system of claim 1, wherein the executed instructions further cause the computing system to: access, over the one or more wireless networks, social media content of the user; and based on the social media content, classify the user into one or more lifestyle categories.

“3. The computing system of claim 2, wherein the executed instructions cause the computing system to further generate the morbidity profile for the user based on the one or more lifestyle categories of the user as determined from the social media content.

“4. The computing system of claim 1, wherein the executed instructions further cause the computing system to: access, over the one or more wireless networks, social media data associated with one or more individuals in the control group; wherein the executed instructions cause the computing system to determine the known health outcomes of the one or more individuals in the control group based, at least in part, on the social media data.

“5. The computing system of claim 4, wherein the known health outcomes of the one or more individuals in the control group comprise morbidity outcomes.

“6. The computing system of claim 1, wherein the underwriting class corresponds to one of a premium or a discount for the health service product.

“7. The computing system of claim 1, wherein the health service product comprises a life insurance or a health insurance product.

“8. The computing system of claim 1, wherein the set of health assertions for the health trivia session comprise multiple choice health assertions or questions.

“9. A non-transitory computer readable medium storing instructions that, when executed by one or more processors of a computing system, cause the computing system to: communicate, over one or more wireless networks, with computing devices of users of a health service; execute a correlation model to determine a correlation value for each respective health assertion in a collection of health assertions based on (i) answers to the respective health assertion provided by individuals in a control group, and (ii) known health outcomes of each individual in the control group, wherein the correlation value for each respective health assertion in the collection corresponds to a set of health correlations between knowledge associated with the respective health assertion and the known health outcomes of the individuals in the control group, wherein the collection of health assertions are configured to test general health knowledge of the user of the health service and not query user-specific information of the users; generate, over the one or more wireless networks, a health trivia session to be presented on a computing device of a user, the health trivia session comprising a set of health assertions from the collection of health assertions; receive, over the one or more wireless networks, a corresponding set of responses to the set of health assertions from the computing device of the user; for each response in the corresponding set of responses, determine a correctness for the response, the correctness indicating whether the user answered a corresponding health assertion correctly or incorrectly; based on (i) the correctness of each response in the corresponding set of responses, and (ii) the correlation value of each health assertion in the set of health assertions provided during the trivia session, generate a morbidity profile for the user, the morbidity profile corresponding to one or more health risks of the user; based on the morbidity profile of the user, determine an underwriting class for a health service product for the user; based on the underwriting class for the health service product for the user, determine a specified price for the health service product; and generate, over the one or more wireless networks, a service customer interface to be displayed on the computing device of the user, the service customer interface enabling the user to purchase the health service product in the specified underwriting class and at the specified price.

“10. The non-transitory computer readable medium of claim 9, wherein the executed instructions further cause the computing system to: access, over the one or more wireless networks, social media content of the user; and based on the social media content, classify the user into one or more lifestyle categories.

“11. The non-transitory computer readable medium of claim 10, wherein the executed instructions cause the computing system to further generate the morbidity profile for the user based on the one or more lifestyle categories of the user as determined from the social media content.

“12. The non-transitory computer readable medium of claim 9, wherein the executed instructions further cause the computing system to: access, over the one or more wireless networks, social media data associated with one or more individuals in the control group; wherein the executed instructions cause the computing system to determine the known health outcomes of the one or more individuals in the control group based, at least in part, on the social media data.

“13. The non-transitory computer readable medium of claim 12, wherein the known health outcomes of the one or more individuals in the control group comprise morbidity outcomes.

“14. The non-transitory computer readable medium of claim 9, wherein the underwriting class corresponds to one of a premium or a discount for the health service product.

“15. The non-transitory computer readable medium of claim 9, wherein the health service product comprises a life insurance or a health insurance product.

“16. The non-transitory computer readable medium of claim 9, wherein the set of health assertions for the health trivia session comprise multiple choice health assertions or questions.”

There are additional claims. Please visit full patent to read further.

For additional information on this patent, see: Fan, Cathy Ye. Computing system implementing morbidity prediction using a correlative health assertion library. U.S. Patent Number 11568364, filed December 29, 2020, and published online on January 31, 2023. Patent URL (for desktop use only): https://ppubs.uspto.gov/pubwebapp/external.html?q=(11568364)&db=USPAT&type=ids

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