AGENT-BASED CARBON EMISSION REDUCTION SYSTEM

    公开(公告)号:US20250148431A1

    公开(公告)日:2025-05-08

    申请号:US18938823

    申请日:2024-11-06

    Abstract: Systems and methods for an agent-based carbon emission reduction system. A carbon product of a supply chain system can be limited below a carbon product threshold by performing a corrective action to monitored entities based on a calculated carbon emission. The carbon emission can be calculated based on carbon-relevant data and a calculation route by utilizing an agent-based simulation model that simulates a learned relationship between a supply chain system and the carbon-relevant data. The calculation route can be determined based on the carbon-relevant data based on a relevance of a carbon product contribution of monitored entities to a goal of the monitored entities. Carbon-relevant data can be extracted from the monitored entities.

    INFORMATION EXTRACTION WITH LARGE LANGUAGE MODELS

    公开(公告)号:US20240379200A1

    公开(公告)日:2024-11-14

    申请号:US18649145

    申请日:2024-04-29

    Abstract: Methods and systems for information extraction include configuring a language model with an information extraction instruction prompt and at least one labeled example prompt. Configuration of the language model is validated using at least one validation prompt. Errors made by the language model in response to the at least one validation prompt are corrected using a correction prompt. Information extraction is performed on an unlabeled sentence using the language model to identify a relation from the unlabeled sentence. An action is performed responsive to the identified relation.

    CORRELATION-AWARE EXPLAINABLE ONLINE CHANGE POINT DETECTION

    公开(公告)号:US20250062953A1

    公开(公告)日:2025-02-20

    申请号:US18800726

    申请日:2024-08-12

    Abstract: Systems and methods for correlation-aware explainable online change point detection. Collected data metrics from the cloud system can be transformed to correlation matrices. Correlation shifts from the correlation matrices can be captured as differences of correlation between batches of collected data metrics through determined statistics of the batches of collected data metrics across timesteps. Change points in the cloud system can be detected based on the correlation shifts to obtain detected change points. System maintenance can be performed autonomously based on the detected change points from identified system entities to optimize the cloud system with an updated configuration.

    ENSEMBLE LEARNING ENHANCED PROMPTING FOR OPEN RELATION EXTRACTION

    公开(公告)号:US20240378447A1

    公开(公告)日:2024-11-14

    申请号:US18650289

    申请日:2024-04-30

    Abstract: Systems and methods are provided for extracting relations from text data, including collecting labeled text data from diverse sources, including digital archives and online repositories, each source including sentences annotated with detailed grammatical structures. Initial relational data is generated from the grammatical structures by applying advanced parsing and machine learning techniques using a sophisticated rule-based algorithm. Training sets are generated for enhancing the diversity and complexity of a relation dataset by applying data augmentation techniques to the initial relational data. A neural network model is trained using an array of semantically equivalent but syntactically varied prompt templates designed to test and refine linguistic capabilities of a model. A final relation extraction output is determined by implementing a vote-based decision system integrating statistical analysis and utilizing a weighted voting mechanism to optimize extraction accuracy and reliability.

    EVIDENCE-BASED OUT-OF-DISTRIBUTION DETECTION ON MULTI-LABEL GRAPHS

    公开(公告)号:US20240136063A1

    公开(公告)日:2024-04-25

    申请号:US18481383

    申请日:2023-10-05

    CPC classification number: G16H50/20 G16H50/70

    Abstract: Systems and methods for out-of-distribution detection of nodes in a graph includes collecting evidence to quantify predictive uncertainty of diverse labels of nodes in a graph of nodes and edges using positive evidence from labels of training nodes of a multi-label evidential graph neural network. Multi-label opinions are generated including belief and disbelief for the diverse labels. The opinions are combined into a joint belief by employing a comultiplication operation of binomial opinions. The joint belief is classified to detect out-of-distribution nodes of the graph. A corrective action is performed responsive to a detection of an out-of-distribution node. The systems and methods can employ evidential deep learning.

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