SENSOR-BASED LEADING INDICATORS IN A PERSONAL AREA NETWORK; SYSTEMS, METHODS, AND APPARATUS

    公开(公告)号:US20240055133A1

    公开(公告)日:2024-02-15

    申请号:US17888456

    申请日:2022-08-15

    CPC classification number: G16H50/30 G16H40/67 G16H50/70 A61B5/0024

    Abstract: A sensor-based leading indicator management ecosystem is described. Sensor data associated with the individual, possibly within a Personal Area Network (PAN) and related to the healthcare of the individual, is compiled and converted to a one or more sets of leading indicators with respect to one or more possible future healthcare actions. Leading indicators may then be compiled into one or more condition state vectors that represent encoded inputs into one or more trained action prediction agents. The trained action prediction agents then generate, possibly in real-time or based on time-series data, predicted actions that may be required at a predicted point-of-care or a moment-of-care. Further, action prediction agents may be context or domain specific.

    Managing digital blockchains via digital tokens, systems, methods, and apparatus

    公开(公告)号:US11880824B1

    公开(公告)日:2024-01-23

    申请号:US18131809

    申请日:2023-04-06

    CPC classification number: G06Q20/3672 G06Q20/10 G06Q2220/00

    Abstract: A computer-based digital notarized ledger management system comprises at least one computer-readable memory storing a primary ledger and software instructions. The system further comprises at least one processor coupled with the memory, the processor performs operations upon software instruction execution. The at least one processor performs instructions to receive a ledger request comprising target ledger parameters, generate in the memory, at least one digital token representing a target ledger based on target ledger parameters, mint according to at least one primary ledger smart contract, the at least one digital token as a set of non-fungible tokens (NFTs) having at least some of the target ledger parameters and representing the target ledger on the primary ledger, instruct at least one computing node to instantiate the target ledger in at least one node memory according to the target ledger parameters, and instruct the at least one node to enable target ledger transactions.

    DISTRIBUTED LEDGER TRACKING OF EVENT DATA
    94.
    发明公开

    公开(公告)号:US20230211236A1

    公开(公告)日:2023-07-06

    申请号:US18118080

    申请日:2023-03-06

    CPC classification number: A63F13/69 A63F13/355 H04L9/50 H04L9/3213

    Abstract: Techniques including data collection, organization and usage are provided, including in connection with computer-based gaming. Methods and systems are provided for establishing a set of data chronicling at least a portion of a duration of a computer-based gaming event that includes at least one user engaging in gaming using a computer or a computer-based device. Event data is obtained for chronicling chronologically ordered in-game events. Hardware and software related data is obtained that relates to the computer or the computer-based device and is relevant to the chronicling of the portion of the gaming event. Utilizing a distributed ledger technology or blockchain, the event data and the hardware and software related data are recorded in establishing the set of data chronicling at least a portion of the duration of the gaming event.

    Distributed machine learning systems, apparatus, and methods

    公开(公告)号:US11461690B2

    公开(公告)日:2022-10-04

    申请号:US15651345

    申请日:2017-07-17

    Abstract: A distributed, online machine learning system is presented. Contemplated systems include many private data servers, each having local private data. Researchers can request that relevant private data servers train implementations of machine learning algorithms on their local private data without requiring de-identification of the private data or without exposing the private data to unauthorized computing systems. The private data servers also generate synthetic or proxy data according to the data distributions of the actual data. The servers then use the proxy data to train proxy models. When the proxy models are sufficiently similar to the trained actual models, the proxy data, proxy model parameters, or other learned knowledge can be transmitted to one or more non-private computing devices. The learned knowledge from many private data servers can then be aggregated into one or more trained global models without exposing private data.

    Latency management in an event driven gaming network

    公开(公告)号:US11433301B2

    公开(公告)日:2022-09-06

    申请号:US17653153

    申请日:2022-03-02

    Abstract: One exemplary aspect relates to normalizing latency in a networking environment to reduce the chances of creating an unfair advantage. While an exemplary aspect will be discussed in relation to a gaming environment, it is to be appreciated that the techniques disclosed herein can be applied to other environments where latency normalization or the ability to maintain latency between various endpoints is desired. For example, other environments include eSporting, on-line betting, fantasy esports, streaming services, etc. Some more specific examples include World of Warcraft®, Overwatch®, H1Z1®, PUBG®, Fortnite®, Realm Royale®, Planet Side 2®, real-time strategy games, slot machines, electronic poker tournaments, etc.

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