VIRTUAL ASSISTANTS FOR PREEMPTIVE MEDICAL DATA ANALYSIS AND TREATMENT

    公开(公告)号:US20230402158A1

    公开(公告)日:2023-12-14

    申请号:US17806197

    申请日:2022-06-09

    CPC classification number: G16H40/20 G16H10/60 G16H70/40 G06F9/453

    Abstract: Virtual assistants (VAs) can be managed in interactions with users, other VAs, and devices, including in connection with medical care associated with users. A VA management component (VAMC) can analyze medical information and other information relating to users, including information obtained by a VA(s) from a user(s). Based on the analysis, VAMC can determine a model relating to medical care associated with users. VAMC can determine a proposed action relating to the health or medical care associated with a user based on the model. A VA can present action information relating to the proposed action, or other information relating to health or medical care associated with the user, to the user or a device associated with the user. VAMC can determine drug-drug or drug-food interactions or side effects, and the VA can notify the user regarding the drug-drug or drug-food interactions or side effects.

    SIMILARITY-BASED CATEGORIZATION FOR COLLECTION TREATMENT INCORPORATING FUTURE CUSTOMER RELATIONSHIP MANAGEMENT (CRM) CHANNELS

    公开(公告)号:US20230362070A1

    公开(公告)日:2023-11-09

    申请号:US17738624

    申请日:2022-05-06

    CPC classification number: H04L41/5064 H04L41/5067

    Abstract: Aspects of the subject disclosure may include, for example, obtaining a list of a plurality of communication channels, each communication channel being associated with at least one respective feature of a plurality of features; correlating each communication channel in the list with at least one respective user response of a plurality of user responses; receiving an identification of a new communication channel that does not exist in the list, the new communication channel being associated with at least one feature of the plurality of features; determining, based upon the at least one feature that is associated with the new communication channel, with which one or more of the plurality of communication channels in the list the new communication channel is similar, resulting in a determination; and assigning, based upon the determination, at least one of the plurality of user responses to the new communication channel. Other embodiments are disclosed.

    SIMULATING TRAINING DATA TO MITIGATE BIASES IN MACHINE LEARNING MODELS

    公开(公告)号:US20230350977A1

    公开(公告)日:2023-11-02

    申请号:US17661026

    申请日:2022-04-27

    CPC classification number: G06K9/6257 G06K9/6289 G06N20/00

    Abstract: A method performed by a processing system including at least one processor includes identifying an insufficiency in a representation of a subpopulation in training data for a machine learning model, generating simulated data to mitigate the insufficiency in the representation, and training the machine learning model using an enhanced training data set that includes the training data and the simulated data to produce a trained machine learning model. In some examples, the generating and the training may be repeated in response to determining that an output of the trained machine learning model still reflects the insufficiency in the representation of the subpopulation or reflects an insufficiency in a representation of another subpopulation. In other examples, the simulated data may be stored for future reuse.

    REINFORCEMENT LEARNING FOR AUTOMATED INDIVIDUALIZED NEGOTIATION AND INTERACTION

    公开(公告)号:US20230325845A1

    公开(公告)日:2023-10-12

    申请号:US17717802

    申请日:2022-04-11

    CPC classification number: G06Q30/016 G06F9/455 G06N20/00

    Abstract: Aspects of the subject disclosure may include, for example, a method in which a processing system analyzes data including a user profile and historical data relating to previous interactions between an automated agent and equipment of the user. The method also includes determining a desirable outcome of an interaction between the automated agent and the user equipment; constructing a model for generating an expected outcome of a step of the interaction; using the model to perform a simulation of a next step of the interaction by generating an expected outcome for each of a plurality of possible actions, resulting in a plurality of expected outcomes; and selecting a next action for the next step of the interaction. If the desirable outcome is not obtained, the system can refine the plurality of possible actions to perform a simulation of a subsequent step of the interaction. Other embodiments are disclosed.

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