IDENTIFYING AND TARGETING COMMUNICATION NETWORK EVENTS VIA UNIFORM RESOURCE LOCATOR SETS

    公开(公告)号:US20230171269A1

    公开(公告)日:2023-06-01

    申请号:US17539187

    申请日:2021-11-30

    CPC classification number: H04L63/1416

    Abstract: A processing system may track accessing of a plurality of uniform resource locators among users of a communication network, determine an occurrence of an event of an event type based upon an increased accessing of the plurality of uniform resource locators for at least one time period, identify a first sub-group of the users associated with the event, apply a first action in the communication network to a test group including at least a first portion of the first sub-group, the first action addressing a demand associated with the event of the event type, track at least a first success rate of the first action for the first portion of the first sub-group, and apply the first action to at least a second portion of the first sub-group in response to determining that the first success rate for the first portion of the first sub-group exceeds a threshold success rate.

    SYSTEM AND METHOD FOR EVALUATING GENERATIVE LARGE LANGUAGE MODELS

    公开(公告)号:US20250111237A1

    公开(公告)日:2025-04-03

    申请号:US18476632

    申请日:2023-09-28

    Abstract: Aspects of the subject disclosure may include, for example, a device that facilitates obtaining a plurality of prompts from a selected subject matter domain of a database configured to measure an effectiveness of a generative large language model (LLM) to distinguish variances between each prompt of the plurality of prompts; supplying the plurality of prompts to the LLM; receiving respective responses to each of the prompts from the LLM; transforming each of the prompts and respective responses to each of the prompts into an embedding space; determining, by applying domain-based metrics to the embedding space, a quality measurement of each respective response to produce a plurality of quality measurements; and generating, according to the plurality of quality measurements, a performance of the LLM. Other embodiments are disclosed.

    COMPRESSION OF UNIFORM RESOURCE LOCATOR SEQUENCES FOR MACHINE LEARNING-BASED DETECTION OF TARGET CATEGORY EXAMPLES

    公开(公告)号:US20220303306A1

    公开(公告)日:2022-09-22

    申请号:US17203099

    申请日:2021-03-16

    Abstract: A processing system may identify a plurality of uniform resource locators associated with a target category of a plurality users of a communication network, identify a plurality of sequences of URLs, each sequence comprising URLs from among the plurality of URLs, each sequence associated with a user known to be of the target category, and train a machine learning model with the plurality of sequences to detect additional sequences that are indicative of the target category. The processing system may next obtain a set of URLs associated with an additional user, identify a sequence comprising URLs, from among the plurality of URLs, that are contained within the set of URLs, apply the sequence as an input to the machine learning model that has been trained, and obtain an output of the machine learning model quantifying a measure of which the sequence is indicative of the target category.

    Identifying and targeting communication network events via uniform resource locator sets

    公开(公告)号:US12255901B2

    公开(公告)日:2025-03-18

    申请号:US17539187

    申请日:2021-11-30

    Abstract: A processing system may track accessing of a plurality of uniform resource locators among users of a communication network, determine an occurrence of an event of an event type based upon an increased accessing of the plurality of uniform resource locators for at least one time period, identify a first sub-group of the users associated with the event, apply a first action in the communication network to a test group including at least a first portion of the first sub-group, the first action addressing a demand associated with the event of the event type, track at least a first success rate of the first action for the first portion of the first sub-group, and apply the first action to at least a second portion of the first sub-group in response to determining that the first success rate for the first portion of the first sub-group exceeds a threshold success rate.

    COMPRESSION OF USER INTERACTION DATA FOR MACHINE LEARNING-BASED DETECTION OF TARGET CATEGORY EXAMPLES

    公开(公告)号:US20220385689A1

    公开(公告)日:2022-12-01

    申请号:US17330029

    申请日:2021-05-25

    Abstract: A processing system may identify a plurality of user interaction data associated with a target category of a plurality of users, identify a relevant subset of user interaction data, compress the plurality of user interaction data to the relevant subset of user interaction data, train a machine learning model with the relevant subset of user interaction data, obtain additional user interaction data associated with an additional user, identify a relevant subset of the additional user interaction data, apply the relevant subset of the additional user interaction data as an input to the machine learning model, obtain an output of the machine learning model quantifying a measure of which the relevant subset of the additional user interaction data is indicative of the target category, and perform at least one action responsive to the measure of which the relevant subset of the additional user interaction data is indicative of the target category.

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