Alerta

AGENT PROMPT GENERATION USING TOOL DESCRIPTION EMBEDDINGS

Absstract of: WO2025221523A1

Methods, systems, and apparatuses include receiving, via a conversational interface, user input from a user of an online system. A user input embedding is generated for the user input. A vector store is retrieved including tool description embeddings. A similarity search is performed using the user input embedding and the tool description embeddings. A set of tool descriptions is determined using results of the similarity search. A prompt is generated using the set of tool descriptions and the user input. Machine learning agents are applied to the prompt to cause the machine learning agents to use tools associated with the set of tool descriptions. A response to the prompt is received, from the machine learning agents, in response to the machine learning agents using the tools. An output to the user input based on the response is sent, via the conversational interface, to the user of the online system.

SYSTEMS AND METHODS FOR BATTERY PERFORMANCE PREDICTION

Absstract of: US2025328780A1

Methods, systems, and apparatus, including computer programs encoded on computer storage media, for battery performance prediction. One of the methods includes actions of receiving battery test data of a battery cell. The battery test data includes data of at least one battery cell property of at least two battery tests. Each battery test includes applying pulses on the battery cell during a battery cycle. The battery test data is provided as input to a machine learning system to predict battery cell performance. The machine learning system includes a machine learning model that has been trained using training data includes test data of battery cells that reached respective end of life (EOL) cycles. In response, a prediction result for the battery cell is automatically generated by the machine learning model. The prediction result indicates an EOL cycle of the battery cell. An action is taken based on the prediction result.

BANDWIDTH PREDICTION USING MACHINE LEARNING

Absstract of: WO2025221398A1

Systems, methods, and apparatus, including computer-readable media, for bandwidth prediction using machine learning. In some implementations, a device detects a series of requests for streaming media content. The device generates a set of feature values based on times that the requests for the streaming media content were issued. The device provides the set of feature values as input to a machine learning model that has been trained to predict a time that a future request for media content will be issued. The device receives output of the machine learning model that indicates a predicted time of a subsequent request for the streaming media content or a predicted time to request bandwidth allocation for the subsequent request. Based on the output generated by the machine learning model, the device sends a bandwidth allocation request to allocate bandwidth to transmit data in a wireless network.

MULTISTAGE FEED RANKING SYSTEM WITH METHODOLOGY PROVIDING SCALABLE MULTI-OBJECTIVE MODEL APPROXIMATION

Absstract of: US2025328821A1

Approximating a more complex multi-objective feed item scoring model using a less complex single objective feed item scoring model in a multistage feed ranking system of an online service. The disclosed techniques can facilitate multi-objective optimization for personalizing and ranking feeds including balancing personalizing a feed for viewer experience, downstream professional or social network effects, and upstream effects on content creators. The techniques can approximate the multi-objective model-that uses a rich set of machine learning features for scoring feed items at a second pass ranker in the ranking system-with the more lightweight, single objective model-that uses fewer machine learning features at a first pass ranker in the ranking system. The single objective model can more efficiently score a large set of feed items while maintaining much of the multi-objective model's richness and complexity and with high recall at the second pass ranking stage.

MACHINE LEARNING MODEL ADMINISTRATION AND OPTIMIZATION

Absstract of: US2025190475A1

Systems and methods are configured to generate a set of potential responses to a prompt using one or more data models with data from at least a plurality of data domains of an enterprise information environment that includes access controls. A deterministic response is selected from the set of potential responses based on scoring of the validation data and restricting based on access controls in view profile information associated with the prompt. These enterprise generative AI systems and methods support granular enterprise access controls, privacy, and security requirements. enterprise generative AI providing traceable references and links to source information underlying the generative AI insights. These systems and methods enable dramatically increased utility for enterprise users to information, analyses, and predictive analytics associated with and derived from a combination of enterprise and external information systems.

METHOD, SYSTEM, AND COMPUTER PROGRAM PRODUCT FOR GLOBAL PERSONALIZED RECOMMENDATION

Absstract of: WO2025217351A1

Methods, systems, and computer program products for providing global personalized recommendations are provided. An example method may include generating embeddings for a first plurality of entities based on a first dataset, determining first identifiers of the first plurality of entities included in the first dataset that corresponds to second identifiers of a second plurality of entities included in a second dataset to provide a matched set of entities, wherein the second dataset includes attribute data associated with each entity of the second plurality of entities, generating a graph representation of the second plurality of entities, and wherein the graph includes nodes and each node represents an entity of the second plurality of entities, determining one or more first nodes that lacks data associated with a node embedding, and generating data associated with the node embedding for the one or more first nodes using a graph neural network (GNN) machine learning model.

SENSOR READING CORRECTION

Absstract of: AU2024243389A1

Disclosed are systems, methods, and devices for correcting or otherwise cleaning sensor data. Sensor readings and metadata or other information about the sensor readings can be collected, and one or more detection rules (e.g., machine learning models or other detection rules) can be automatically generated for modifying subsequent sensor data. Sensor readings can be refined or supplemented by applying applicable detection rules.

ITEM WEIGHT PREDICTION WITH MACHINE LEARNING

Absstract of: WO2025216929A1

A smart shopping cart includes a load sensor to measure the weight of items added to the cart. To avoid waiting for the load sensor to converge, a detection system predicts the weight of items added to the storage area of a smart shopping cart based on the shape of a load curve output by the load sensor when an item is added to the cart. The detection system receives load data from the load sensor, detects that an item was added to the storage area of the shopping cart during a time period and identifies a set of load measurements captured by the load sensor during the time period. The set of load measurements comprise a load curve, to which the detection system applies a weight prediction model to generate a predicted weight of the added item.

SYSTEMS AND METHODS FOR LEAKAGE DETECTION, PREVENTION, AND MITIGATION

Absstract of: WO2025215513A2

Disclosed embodiments relate to systems and methods for acoustically detecting leakage of a fluid using one or more acoustic sensors. Techniques include receiving a signal from the one or more acoustic sensors; performing pre-processing on the signal; inputting the pre-processed signal to a machine learning algorithm; receiving, based on the pre-processed signal and the machine learning algorithm, a classification of the pre- processed signal, the classification being associated with an acoustic profile of leakage of a fluid; and providing a prompt associated with the classification to a user device.

METHOD FOR DISPLACING DEFECT CENTERS IN A SUBSTRATE FOR QUANTUM APPLICATIONS IN A DEFINED DIRECTION

Absstract of: WO2025215207A1

Method of automated spatial patterning of defect centers (6) in a substrate, particularly a diamond crystal lattice (50), comprising the following steps: - providing a defect center (6) distribution to a machine-learning model, the machine-learning model being particularly trained to determine an output for displacement of at least one defect center (6) based on the provided defect center (6) distribution, the machine learning model providing an output for displacement of individual defect centers (6), - particularly providing a substrate, particularly diamond, comprising defect centers (6) in a bulk structure of the substrate, particularly a bulk diamond crystal lattice (50), - detecting the position of at least one defect center (6) in the bulk structure of the substrate, particularly the bulk diamond crystal lattice (50), and - displacing (61) the at least one defect center (6) in the bulk structure of the substrate, particularly the bulk diamond crystal lattice (50), particularly site-specific, in a defined direction.

SYSTEMS AND METHODS TO GENERATE SUGGESTED RESPONSES TO CUSTOMER INQUIRIES FOR CUSTOMER RELATIONSHIP MANAGEMENT

Absstract of: WO2025217449A1

The present disclosure relates to generating suggested responses to customer requests using machine learning models. In one example, a method includes: receiving, from a customer, a customer request via a communication channel; displaying in a customer support user interface the customer request; processing the customer request with a machine learning model to determine a suggested response to the customer request; and displaying in an agent assistance user interface element in the customer support user interface: the suggested response to the customer request; a first user interface element configured to implement the suggested response; and a second user interface element configured to dismiss or modify the suggested response.

USING MACHINE LEARNING TO PREDICT CELL THERAPY CHARACTERISTICS

Absstract of: WO2025217397A1

Disclosed are systems and methods for improving processes for developing cell therapies by applying machine learning to data including manufacturing process data and clinical measurements (e.g., patient response and treatment data) to determine parameters and settings for a manufacturing process for engineering cells for use in cell therapy. Parameters and settings for a manufacturing process for genetically engineered T-cells including, but not limited to, Chimeric Antigen Receptor (CAR) T cells can be determined. A method can include receiving a set of process parameters of a cell engineering process, predicting a clinical response associated with an output of the cell engineering process by applying a machine learning model on the received set of process parameters, where the machine learning model is trained on process parameter data and clinical response data, and generating a visualization for use in a graphical user interface of the predicted clinical response.

SYSTEMS FOR CHEMISTRY TASKS BASED ON ARTIFICIAL INTELLIGENCE METHODS WITH IMPROVED REASONING USING CHEMISTRY FEEDBACK

Absstract of: WO2025215117A1

Methods and systems are disclosed in which the trustworthiness of predictive models, e.g., machine learning models, is enhanced by incorporating feedback regarding training set reactions is used to train the model so that the model is adapted so that subsequent predictions align with or account for the feedback. The feedback may include, e.g., process level reasoning, mechanism level reasoning, outlines of mechanistic reasoning, suggestions of reference reactions, or estimates of a probability of success of a given reaction. The feedback may itself be generated or proposed by a machine learning model. The model may direct an automated laboratory to perform reactions from which feedback is extracted and used to train the model.

METHOD OF AUTOMATED SPATIAL PATTERNING OF DEFECT CENTERS IN A SUBSTRATE

Absstract of: WO2025215209A1

Method of automated spatial patterning of defect centers (6) in a substrate, particularly a diamond crystal lattice (50), comprising the following steps: - providing a defect center (6) distribution to a machine-learning model, the machine- learning model being particularly trained to determine an output for displacement of at least one defect center (6) based on the provided defect center (6) distribution, the machine learning model providing an output for displacement of individual defect centers (6), - particularly providing a substrate, particularly diamond, comprising defect centers (6) in a bulk structure of the substrate, particularly a bulk diamond crystal lattice (50), - detecting the position of at least one defect center (6) in the bulk structure of the substrate, particularly the bulk diamond crystal lattice (50), and - displacing (61) the at least one defect center (6) in the bulk structure of the substrate, particularly the bulk diamond crystal lattice (50), particularly site-specific.

REDUCED POWER MACHINE LEARNING SYSTEM FOR ARRHYTHMIA DETECTION

Absstract of: US2025322958A1

Techniques are disclosed for using feature delineation to reduce the impact of machine learning cardiac arrhythmia detection on power consumption of medical devices. In one example, a medical device performs feature-based delineation of cardiac electrogram data sensed from a patient to obtain cardiac features indicative of an episode of arrhythmia in the patient. The medical device determines whether the cardiac features satisfy threshold criteria for application of a machine learning model for verifying the feature-based delineation of the cardiac electrogram data. In response to determining that the cardiac features satisfy the threshold criteria, the medical device applies the machine learning model to the sensed cardiac electrogram data to verify that the episode of arrhythmia has occurred or determine a classification of the episode of arrhythmia.

MACHINE LEARNING BASED QUESTION AND ANSWER (Q&A) ASSISTANT

Absstract of: WO2025216752A1

A multimodal content management system having a block-based data structure can include an artificial intelligence (AI)-based embeddings generator and indexer. After receiving an item update instruction that includes an object (e.g., a block content, a block property, or a block schema) identifier and an update payload, the system can transform the update payload—for example, by generating a chunk to capture at least a portion of the update payload. The chunk can correspond to a particular content modality included in the update payload. The system can generate and retrievably store a vector comprising a set of embeddings corresponding to the chunk, where the embeddings represent a vectorized portion of block content, block property, or block schema.

SYSTEM FOR OPTIMAL DECISION-MAKING AND METHODS THEREOF

Absstract of: WO2025215419A1

The present disclosure provides a system and method for optimal decision-making in multi-criteria decision-making (MCDM) problems. The invention addresses limitations of conventional approaches, which rely heavily on subjective expert inputs and biased preprocessing techniques, by introducing a statistically driven framework based on distribution normalization and data-driven weight assignment. The system comprises modules for preprocessing, evaluation, assessment, and output generation, wherein input data is normalized, criteria constraints inverted where necessary, and statistical weights optimally assigned. Decision alternatives are then computed, evaluated, and ranked to derive one or more optimal decisions. This framework ensures unbiased, efficient, and replicable outcomes across applications including Geographic Information Systems (GIS), Data Analysis, Artificial Intelligence, and Machine Learning.

TECHNIQUES FOR ADJUSTING MIXED REALITY GRAPHICAL ENVIRONMENTS USING MACHINE LEARNING

Absstract of: US2025322297A1

Techniques are described for training a machine learning model on parameters calculated from usage parameters of a plurality of training instances of a mixed reality graphical environment (MRGE) to determine usage scenarios using a supervisory signal and then using the trained machine learning model to ascertain usage scenarios for non-training instances of the MRGE to determine usage scenarios. The ascertained usage scenarios may then be used to dynamically adjust features of the non-training instances of an MRGE.

SYSTEMS AND METHODS FOR MACHINE LEARNING-BASED SITE-SPECIFIC THREAT MODELING AND THREAT DETECTION

Absstract of: US2025322269A1

Systems and methods for implementing a threat model that classifies contextual events as threats. The method can include: accessing a threat model; identifying a set of contextual events, wherein each contextual event comprises a set of semantic primitives predicted from a plurality of sensor streams; and determining a threat level for each contextual event based on threat probabilities.

TECHNIQUES FOR GENERATING SYNTHETIC DATA

Absstract of: US2025322262A1

A system and method include generating synthetic data by generating a first set of hyperparameters for a first trained machine learning model and a second set of hyperparameters for a second trained machine learning model, generating a plurality of synthetic data vectors using the first and second trained machine learning models, computing an error function for the first and second set of hyperparameters using a third machine learning model, computing an objective function value, responsive to determining that the objective function value is not an optimal value, updating the first set of hyperparameters and the second set of hyperparameters or responsive to determining that the objective function value is an optimal value outputting the plurality of synthetic data vectors as a set of synthetic data.

TECHNIQUES FOR GENERATING SYNTHETIC DATA

Absstract of: US2025322260A1

A system and method include generating synthetic data by generating a first set of hyperparameters for a first trained machine learning model and a second set of hyperparameters for a second trained machine learning model, generating a plurality of synthetic data vectors using the first and second trained machine learning models, computing an error function for the first and second set of hyperparameters using a third machine learning model, computing an objective function value, responsive to determining that the objective function value is not an optimal value, updating the first set of hyperparameters and the second set of hyperparameters or responsive to determining that the objective function value is an optimal value outputting the plurality of synthetic data vectors as a set of synthetic data.

RESULT SET RANKING ENGINE FOR A MACHINE LEARNING BASED QUESTION AND ANSWER (Q&A) ASSISTANT

Absstract of: US2025322272A1

A multimodal content management system having a block-based data structure can include a question and answer (Q&A) assistant (e.g., a chatbot). The system can receive a natural language prompt and generate a result set. The result set can include blocks (e.g., blocks that include responsive content, including content in different modalities). The system can apply a set of authority signals to items in the result set to generate a ranked result set. The authority signals can be generated using aspects of the block-based data structure, such as block properties. The system can cause the Q&A assistant to return a set of hyperlinks to the ranked result set items. The hyperlinks can be operable to enable navigation to block content without closing the Q&A assistant.

Item Weight Prediction with Machine Learning

Absstract of: US2025322289A1

A smart shopping cart includes a load sensor to measure the weight of items added to the cart. To avoid waiting for the load sensor to converge, a detection system predicts the weight of items added to the storage area of a smart shopping cart based on the shape of a load curve output by the load sensor when an item is added to the cart. The detection system receives load data from the load sensor, detects that an item was added to the storage area of the shopping cart during a time period and identifies a set of load measurements captured by the load sensor during the time period. The set of load measurements comprise a load curve, to which the detection system applies a weight prediction model to generate a predicted weight of the added item.

TECHNIQUES FOR OPTIMIZING PROJECT DATA STORAGE

Absstract of: US2025321930A1

Techniques for optimizing project data storage are disclosed. An example system includes processors and memories communicatively coupled with the processors storing a trained machine learning (ML) model, a data inbox, a project database associated with a project, and instructions that cause the processors to: receive, at the data inbox, an input including data corresponding to the project, wherein the data is formatted in accordance with a non-standardized format; execute the trained ML model to: extract the data from the input, and analyze the data to output (i) a predicted classification and (ii) a predicted impact associated with the project; convert the data to a standardized format based on the predicted classification; store (i) the data and (ii) the predicted impact in the project database; and generate an indication of the data and the predicted impact for display to a user as part of the data inbox.

SYSTEMS AND METHODS TO EXTRACT SEMANTIC INFORMATION FROM DOCUMENTS

Nº publicación: US2025322167A1 16/10/2025

Applicant:

INSTABASE INC [US]
Instabase, Inc

Absstract of: US2025322167A1

Systems and methods to use one or more machine learning models to summarize a set of one or more documents are disclosed. Exemplary implementations may obtain one or more documents including divisions and organized into individual hierarchies; identify the divisions using at least one of the one or more machine learning models, wherein individual sets of sections and sets of subsections are identified; create sets of semantic vectors characterizing semantic meaning of individual divisions organized at the bottom level of individual hierarchies using at least one of the one or more machine learning models, wherein semantic vectors for individual subsections are created; and recursively generate summary vectors summarizing semantic meaning of individual divisions using at least one of the one or more machine learning models, wherein summary vectors are generated for subsections based on the semantic vectors, sections based on subsection summary vectors, and documents based on section summary vectors.