Abstract: A method, computer program product, and computing system for processing target content generated by processing source content using a generative artificial intelligence (AI) model, where the generative AI model performs a task using the source content to generate the target content. An ontological concept is extracted from the source content using a natural language processing (NLP) engine. An ontological concept is extracted from the target content using the NLP engine. An ontological concept comparison score is generated by comparing the ontological concept from the source content and the ontological concept from the target content based upon, at least in part, the task performed using the source content to generate the target content. An issue is identified in the target content based upon, the ontological concept comparison score and the task performed using the source content to generate the target content.

Abstract: A method, computer program product, and computing system for identifying a token associated with data previously processed by a data processing service. In response to identifying the token, a subset of processing functionality not performed by the data processing service previously is determined. The data is processed with the subset of processing functionality not performed by the data processing service previously.

Abstract: A method, computer program product, and computing system for processing a raw content portion to identify personal information, thus defining identified personal information; confirming the efficacy of the identified personal information; replacing the identified personal information within the raw content portion with one or more surrogates, thus defining a deidentified content portion; and performing a federated learning operation on the deidentified content portion to generate training data for a machine learning model.

Abstract: Technology is disclosed for improving user privacy and providing user control over user-activity data collected from personal computing devices (i.e., user devices). User devices may be configured to operate in a private mode that enables a user to control, for example, which aspects of user-activity data are provided to applications and services running on their user device; to obscure or modify aspects of user-activity data so that certain applications and services, which may require this information to operate, may still function, but that the obscured information provided to these applications and services preserves user privacy or no longer may be used to identify the user; or to remove evidence of user-activity data created, monitored, reported, or otherwise collected by or on the user device while the user is operating their user device in the private mode setting.

Abstract: Technology is disclosed for improving user privacy and providing user control over user-activity data collected from personal computing devices (i.e., user devices). User devices may be configured to operate in a private mode that enables a user to control, for example, which aspects of user-activity data are provided to applications and services running on their user device; to obscure or modify aspects of user-activity data so that certain applications and services, which may require this information to operate, may still function, but that the obscured information provided to these applications and services preserves user privacy or no longer may be used to identify the user; or to remove evidence of user-activity data created, monitored, reported, or otherwise collected by or on the user device while the user is operating their user device in the private mode setting.

Abstract: An online service may maintain or create data for a user, and a user may be allowed to exert control over how the data are used. In one example, there may be several categories of data, and the user may be able to specify who may use the data, and the purpose for which the data may be used. Additionally, a user may be able to see how many of his “friends” (or other contacts) have extended trust to a particular entity, which may aid the user in making a decision about whether to extend trust to that entity. User interfaces may be provided to allow users to specify how their data are to be used.

Abstract: Technology is disclosed for improving user privacy and providing user control over user-activity data collected from personal computing devices (i.e., user devices). User devices may be configured to operate in a private mode that enables a user to control, for example, which aspects of user-activity data are provided to applications and services running on their user device; to obscure or modify aspects of user-activity data so that certain applications and services, which may require this information to operate, may still function, but that the obscured information provided to these applications and services preserves user privacy or no longer may be used to identify the user; or to remove evidence of user-activity data created, monitored, reported, or otherwise collected by or on the user device while the user is operating their user device in the private mode setting.

Abstract: An online service may maintain or create data for a user, and a user may be allowed to exert control over how the data are used. In one example, there may be several categories of data, and the user may be able to specify who may use the data, and the purpose for which the data may be used. Additionally, a user may be able to see how many of his “friends” (or other contacts) have extended trust to a particular entity, which may aid the user in making a decision about whether to extend trust to that entity. User interfaces may be provided to allow users to specify how their data are to be used.

Abstract: A computing system for calculating a total calorie count is provided. The computing system comprising an electronic personal assistant application program executed on a client computing device, and including a purchase tracking module configured to receive notifications of purchases made for individual food items, a calorie tracking module configured to identify calorie counts associated with each purchased food item and to calculate a calorie count statistic based thereon, a calorie apportionment module configured to apportion the calorie counts of the food items to a user so that the calorie count statistic is based upon an apportioned total of the calorie counts of the food items that have been apportioned to the user; and a graphical user interface module configured to display a graphical user interface including an indicator of the calorie count statistic on a display associated with the client computing device.

Abstract: A client computing device is disclosed that comprises a processor and an electronic personal assistant application program. The personal assistant application program may be configured to capture user data associated with user activities across a plurality of computer programs. The user data may be sent to a personal assistant user data interpretation engine. A health-related suggestion based on at least a subset of the user data and anonymized statistics of a user population retrieved from an aggregated knowledge base may be received. The health-related suggestion may be displayed on a display associated with the client computing device.

Abstract: A computing system comprises an electronic personal assistant application program executed on a client computing device. Included are a health insurance retriever configured to retrieve health insurance information of a user, a history combiner configured to combined aggregated histories of a plurality of users into an anonymized combined time and location-based data, and a health recommender configured to identify at least one health condition of the user, determine a health recommendation at least on the identified health condition, and output the health recommendation including the recommended health care service, providers, and an indication of insurance coverage of the recommended service at each of the providers.

Abstract: In implementations, a divergence from a pattern of detected instances of an event is identified, where the event is of a routine of a user. The identified divergence corresponds to an out of routine event. Contextual information is generated corresponding to the identified out of routine event. Furthermore, one or more recommended actions for presenting content to the user are selected based on the contextual information. An indication of the identified out of routine event, the contextual information, and the one or more recommended actions are provided to a service, which may be an application on a user device. The service can present content to the user based on the identified out of routine event, the generated contextual information corresponding to the identified out of routine event, and the selected one or more recommended actions. The service may follow or disregard any of the selected one or more recommended actions.

Abstract: A computing system is provided. The computing system includes an electronic personal assistant application server configured to determine a target location for a selected user and time, determine a group of other registered users who are or will be physically present at the target location at or around the predetermined time, determine group health data representing a health condition of the group of other registered users, the group health data being determined based on anonymous medical records statistics for the group of other registered users, the anonymous medical records statistics including biometric data sensed by at least one biometric sensor associated with at least one user computing device of one of the other registered users, identify a health risk level for the target location using the group health data, and generate an activity warning for the target location based on the health risk level.

Abstract: A method that includes accessing, from a database, a correlation between a first variable and a second variable; retrieving a list of suggested user behavior modifications for the correlation; selecting a user behavior modification from the list; presenting the selected user behavior modification to a computer device; receiving adherence data indicating a level of adherence by the user to the user behavior modification; and reevaluating the correlation between the first variable and the second variable based on the adherence data. The method may improve the determination of causality, versus merely correlation, between the variables. This determination may permit for improved recommendations to individuals to achieve a desired user outcome.

Abstract: Technology is disclosed for improving user privacy and providing user control over user-activity data collected from personal computing devices (i.e., user devices). User devices may be configured to operate in a private mode that enables a user to control, for example, which aspects of user-activity data are provided to applications and services running on their user device; to obscure or modify aspects of user-activity data so that certain applications and services, which may require this information to operate, may still function, but that the obscured information provided to these applications and services preserves user privacy or no longer may be used to identify the user; or to remove evidence of user-activity data created, monitored, reported, or otherwise collected by or on the user device while the user is operating their user device in the private mode setting.

Abstract: Examples are disclosed that relate to providing healthcare-related information. One example provides a computing device comprising a logic machine and a storage machine holding instructions executable by the logic machine to receive an input of information regarding a health state of a user, obtain, based upon the information regarding the health state of the user, an inference of a possible health condition of the user, output a notification of the inference, the notification comprising a first representation of the inference, receive data representing a mechanism for authorizing a healthcare practitioner to access a second representation of the inference, and output a user-selectable control for triggering the mechanism. The instructions may be further executable to receive an input via the user-selectable control triggering the mechanism, and, in response, send authorization to provide the healthcare practitioner with access to the second representation of the inference.

Abstract: Examples are provided for a health monitoring computing system, including a data interface configured to receive appointment data and health tracking data, the health tracking data including biometric data measured by one or more biometric sensors worn by a user, health-scheduling logic configured to determine that the user is to see a healthcare professional within a threshold period of time based at least on computer analysis of the appointment data, and appointment-optimization logic configured to generate a list of diagnosis-relevant health information based at least on computer analysis of the health tracking data. The example health monitoring computing system also includes an output device configured to present the list to the user for editing prior to an appointment with the healthcare professional.

Abstract: Examples are disclosed herein that relate to modifying an analysis of personal behavior based on determining a subset of personal data to be non-routine. One example provides a computing device configured to receive personal data relating to personal behavior of a user, receive contextual data regarding the personal data, determine a subset of the personal data to be non-routine based upon the contextual data, and modify an analysis of personal behavior based upon the subset of the personal data determined to be non-routine.

Abstract: Examples are disclosed herein that relate to integrating health data and calendar data of one or more users and providing insights for a selected user to help the user accomplish an outcome of interest. The insights may be identified based on a group of cohorts determined to be similar to the selected user and/or used to predict a likelihood that the selected user will achieve an outcome of interest. Additional insights may be provided by monitoring an effect that following a recommendation has on the selected user achieving the outcome of interest. Recommendations and/or updates to recommendations may be provided based on the insights.

Abstract: A system for providing care to a ward that alerts a caregiver of the caregiver's capacity to deal competently with the ward's needs.