Abstract: The present disclosure includes gathering a tagged document of a type, collecting a repository of tags pertaining to the predetermined type of document, providing the tagged document and the repository of tags to train a first pre-trained LLM, identifying a first tag in the gathered document, pairing one text with the first tag, identifying one value associated with the paired first tag and the text, formatting the paired first tag and the text and the associated value to form a training message to train a second pre-trained LLM, providing an unseen document of the type to the first trained LLM, generating, via executing the first trained LLM, a second tag from the unseen document, providing the second tag and the unseen document to the second trained LLM, and identifying, via executing the second trained LLM, an unseen text paired with the second tag and an associated value.

Abstract: In order to facilitate artificial intelligence-based anomaly detection in electronic records, systems and methods include establishing a computer-implemented clustering process based on record attributes (e.g., using a k-means algorithm) such that records are grouped into clusters and pairs of records within each cluster are subsequently analyzed; generating feature vectors by normalizing and concatenating selected attributes and filtering out vectors exhibiting low variance based on first predetermined parameters; applying an ensemble of at least three anomaly detection models (e.g.

Abstract: The present disclosure includes gathering a tagged document of a type, collecting a repository of tags pertaining to the predetermined type of document, providing the tagged document and the repository of tags to train a first pre-trained LLM, identifying a first tag in the gathered document, pairing one text with the first tag, identifying one value associated with the paired first tag and the text, formatting the paired first tag and the text and the associated value to form a training message to train a second pre-trained LLM, providing an unseen document of the type to the first trained LLM, generating, via executing the first trained LLM, a second tag from the unseen document, providing the second tag and the unseen document to the second trained LLM, and identifying, via executing the second trained LLM, an unseen text paired with the second tag and an associated value.

Abstract: Systems and methods of the present disclosure may receive, from a user computing device, a user-provided data record query including a natural language request for information associated with one or more data sources. User persona attributes of the user may be determined, such as a user role or security parameters or both. Based on the user persona attributes a context query may be generated to obtain context attributes associated with the user-provided query. The natural language request and the context attributes are input into the model orchestration large language model (LLM) to output instructions to machine learning (ML) agents based on the context attributes. The ML agents output responses associated with the user-provided data record query based on the instructions, and the responses are input into the model orchestration LLM to output to the user computing device a natural language response based on the context attributes.

Abstract: Systems and methods of the present disclosure may receive, from a user computing device, a user-provided data record query including a natural language request for information associated with one or more data sources. User persona attributes of the user may be determined, such as a user role or security parameters or both. Based on the user persona attributes a context query may be generated to obtain context attributes associated with the user-provided query. The natural language request and the context attributes are input into the model orchestration large language model (LLM) to output instructions to machine learning (ML) agents based on the context attributes. The ML agents output responses associated with the user-provided data record query based on the instructions, and the responses are input into the model orchestration LLM to output to the user computing device a natural language response based on the context attributes.

Abstract: A machine-learning based method includes receiving an instruction to model an engagement predicting score for a user. User-specific, activity-specific data is obtained from digital resources that include a user-specific activity performance data regarding performance of at least one activity by the user, an object data for an object that allows the user to perform the at least one activity, and user-specific personal data of the user. A user-specific activity engagement labeling data for the at least one activity is predicted by utilizing a first-type data pipeline on the at least one user-specific activity performance data. User-specific, activity-specific data features are predicted by utilizing a second-type data pipeline on the user-specific, activity-specific data. The engagement predicting score is predicted from the user-specific, activity-specific data features and the user-specific activity engagement labeling data.

Abstract: Systems and methods of the present disclosure may receive, from a user computing device, a user-provided data record query including a natural language request for information associated with one or more data sources. User persona attributes of the user may be determined, such as a user role or security parameters or both. Based on the user persona attributes a context query may be generated to obtain context attributes associated with the user-provided query. The natural language request and the context attributes are input into the model orchestration large language model (LLM) to output instructions to machine learning (ML) agents based on the context attributes. The ML agents output responses associated with the user-provided data record query based on the instructions, and the responses are input into the model orchestration LLM to output to the user computing device a natural language response based on the context attributes.

Abstract: A machine-learning based method includes receiving an instruction to model an engagement predicting score for a user. User-specific, activity-specific data is obtained from digital resources that include a user-specific activity performance data regarding performance of at least one activity by the user, an object data for an object that allows the user to perform the at least one activity, and user-specific personal data of the user. A user-specific activity engagement labeling data for the at least one activity is predicted by utilizing a first-type data pipeline on the at least one user-specific activity performance data. User-specific, activity-specific data features are predicted by utilizing a second-type data pipeline on the user-specific, activity-specific data. The engagement predicting score is predicted from the user-specific, activity-specific data features and the user-specific activity engagement labeling data.

Abstract: A method of object detection in paired imaging includes detecting areas of interest for each image of a set of multi-view images, each detected area of interest having a corresponding initial probability of being an area of interest; determining a matching probability for each detected area of interest across the set of multi-view images such that detected areas of interest from one image of the set of multi-view images are assigned matching probabilities with respect to detected areas of interest of other images of the set of multi-view images; generating a modified probability for each detected area of interest according to one or more object-specific weighting factors and one or more of the matching probabilities for that detected area of interest; adjusting the initial probability of each detected area of interest using the modified probability to generate a refined probability for each detected area of interest; and identifying the detected areas of interest in each image that have refined probabilities

Abstract: A machine-learning based method includes receiving an instruction to model an engagement predicting score for a user. User-specific, activity-specific data is obtained from digital resources that include a user-specific activity performance data regarding performance of at least one activity by the user, an object data for an object that allows the user to perform the at least one activity, and user-specific personal data of the user. A user-specific activity engagement labeling data for the at least one activity is predicted by utilizing a first-type data pipeline on the at least one user-specific activity performance data. User-specific, activity-specific data features are predicted by utilizing a second-type data pipeline on the user-specific, activity-specific data. The engagement predicting score is predicted from the user-specific, activity-specific data features and the user-specific activity engagement labeling data.

Abstract: A machine-learning based method includes receiving an instruction to model an engagement predicting score for a user. User-specific, activity-specific data is obtained from digital resources that include a user-specific activity performance data regarding performance of at least one activity by the user, an object data for an object that allows the user to perform the at least one activity, and user-specific personal data of the user. A user-specific activity engagement labeling data for the at least one activity is predicted by utilizing a first-type data pipeline on the at least one user-specific activity performance data. User-specific, activity-specific data features are predicted by utilizing a second-type data pipeline on the user-specific, activity-specific data. The engagement predicting score is predicted from the user-specific, activity-specific data features and the user-specific activity engagement labeling data.