Sare neuronalak

SYSTEM AND METHOD FOR PREDICTING FORMATION IN SPORTS

Resumen de: US2025336208A1

A system and method of predicting a team's formation on a playing surface are disclosed herein. A computing system retrieves one or more sets of event data for a plurality of events. Each set of event data corresponds to a segment of the event. A deep neural network, such as a mixture density network, learns to predict an optimal permutation of players in each segment of the event based on the one or more sets of event data. The deep neural network learns a distribution of players for each segment based on the corresponding event data and optimal permutation of players. The computing system generates a fully trained prediction model based on the learning. The computing system receives target event data corresponding to a target event. The computing system generates, via the trained prediction model, an expected position of each player based on the target event data.

SYSTEMS AND METHODS FOR COMPUTING FEATURING SYNTHETIC COMPUTING OPERATORS AND COLLABORATION

Resumen de: WO2025226986A1

One embodiment is directed to a synthetic engagement system for process-based problem solving, comprising: a computing system comprising one or more operatively coupled computing resources; and a user interface operated by the computing system and configured to engage a human operator in accordance with a predetermined process configuration toward an established requirement based at least in part upon one or more specific facts; wherein the user interface is configured to allow the human operator to select and interactively engage one or more synthetic operators operated by the computing system to proceed through the predetermined process configuration, and to return result to the human operator selected to at least partially satisfy the established requirement; and wherein each of the one or more synthetic operators is informed by a convolutional neural network informed at least in part by historical actions of a particular actual human operator and a synthetic operator background configuration.

CODING OF AN EVENT IN AN ANALOG DATA FLOW WITH A FIRST EVENT DETECTION SPIKE AND A SECOND DELAYED SPIKE

Resumen de: US2025335754A1

A set of spike pattern data is stored on a set of storage neurons of a neural network. The storing includes storing first parameters indicative of a presence of spikes on respective neurons of the set of storage neurons, and storing, on the set of storage neurons of the neural network, second parameters indicative of a timing of spikes on the respective neurons of the set of storage neurons.

ARTIFICIAL INTELLIGENCE MODELING TO PREDICT ELECTRONIC ACCOUNT DATA

Resumen de: US2025335967A1

Disclosed methods and system describe a server that uses AI modeling to predict negative cash flow at a user level. The server periodically retrieves data associated with the user, the data comprising monetary attributes associated with one or more accounts of the user; executes a deep neural network model trained based upon historical data associated with at least a subset of the users configured to predict a negative cash flow in one or more accounts of the user, a depth of the negative cash flow, and a duration of the negative cash flow; transmits, to a second server, the predicted values, whereby when the second server determines that a likelihood of account needs satisfies a threshold, the second server establishes an electronic communication session with an electronic device of the user; trains the deep neural network when the second server establishes the electronic communication session.

Semantic Segmentation to Identify and Treat Plants in a Field and Verify the Plant Treatments

Resumen de: US2025328760A1

A farming machine including a number of treatment mechanisms treats plants according to a treatment plan as the farming machine moves through the field. The control system of the farming machine executes a plant identification model configured to identify plants in the field for treatment. The control system generates a treatment map identifying which treatment mechanisms to actuate to treat the plants in the field. To generate a treatment map, the farming machine captures an image of plants, processes the image to identify plants, and generates a treatment map. The plant identification model can be a convolutional neural network having an input layer, an identification layer, and an output layer. The input layer has the dimensionality of the image, the identification layer has a greatly reduced dimensionality, and the output layer has the dimensionality of the treatment mechanisms.

DYNAMIC DETERMINATION OF INFERENCE-TIME PARAMETERS

Resumen de: EP4636650A1

Some embodiments are directed to a method for dynamic determination of inference-time parameters to control the stochastic generation process of a generative neural network. The method may include dynamically determining for an inference request, at least from operational context information, at least one of the inference-time parameters.

SYSTEM AND METHOD FOR VARIATIONAL ANNEALING TO SOLVE FINANCIAL OPTIMIZATION PROBLEMS

Resumen de: AU2024204682A1

A system and method for variational annealing to solve financial optimization problems is provided. The financial optimization problem is encoded as objective function represented in terms of an energy function. An autoregressive neural network is trained to minimize the cost function via variational emulation of classical or quantum annealing. Optimal solutions to the financial optimization problem are obtained after a stopping criterion is set. An optimal solution may be selected according to user defined metrics, and optionally applied to a real-world system associated with the financial optimization problem. A system and method for variational annealing to solve financial optimization problems is provided. The financial optimization problem is encoded as objective function represented in terms of an energy function. An autoregressive neural network is trained to minimize the cost function via variational emulation of classical or quantum annealing. Optimal solutions to the financial optimization problem are obtained after a stopping criterion is set. An optimal solution may be selected according to user defined metrics, and optionally applied to a real-world system associated with the financial optimization problem. ul u l s y s t e m a n d m e t h o d f o r v a r i a t i o n a l a n n e a l i n g t o s o l v e f i n a n c i a l o p t i m i z a t i o n p r o b l e m s i s p r o v i d e d h e f i n a n c i a l o p t i m i z a t i o n p r o b l e m i s e n c o d e d a s o b

NEURAL NETWORK CLASSIFIERS FOR BLOCK CHAIN DATA STRUCTURES

Resumen de: US2025323782A1

Disclosed is a neural network enabled interface server and blockchain interface establishing a blockchain network implementing event detection, tracking and management for rule based compliance, with significant implications for anomaly detection, resolution and safety and compliance reporting.

AUDIO NOISE DETERMINATION USING ONE OR MORE NEURAL NETWORKS

Resumen de: US2025324199A1

Apparatuses, systems, and techniques are presented to reduce noise in audio. In at least one embodiment, one or more neural networks are used to determine a noise signal in one or more speech signals.

AUGMENTING MACHINE LEARNING LANGUAGE MODELS USING SEARCH ENGINE RESULTS

Resumen de: US2025322236A1

Methods, systems, and apparatus, including computer programs encoded on computer storage media, for augmenting machine learning language models using search engine results. One of the methods includes obtaining question data representing a question; generating, from the question data, a search engine query for a search engine; obtaining a plurality of documents identified by the search engine in response to processing the search engine query; generating, from the plurality of documents, a plurality of conditioning inputs each representing at least a portion of one or more of the obtained documents; for each of a plurality of the generated conditioning inputs, processing a network input generated from (i) the question data and (ii) the conditioning input using a neural network to generate a network output representing a candidate answer to the question; and generating, from the network outputs representing respective candidate answers, answer data representing a final answer to the question.

DYNAMIC DETERMINATION OF INFERENCE-TIME PARAMETERS

Resumen de: US2025322212A1

A method for dynamic determination of inference-time parameters to control the stochastic generation process of a generative neural network. The method may include dynamically determining for an inference request, at least from operational context information, at least one of the inference-time parameters.

CODE UNIT GENERATOR FOR A MACHINE LEARNING BASED QUESTION AND ANSWER (Q&A) ASSISTANT

Resumen de: US2025322271A1

A multimodal content management system having a block-based data structure can include an artificial intelligence (AI)-based code unit generator that can generate code units executable against the block-based data structure to provide information requested by users. For example, the code units can be generated in response to natural language prompts received via a question and answer Q&A assistant engine. A neural network can be trained on block types, block dependencies, block content values, block content types, and/or block format. The neural network can receive a set of tokens generated based on a natural language prompt and generate one or more query strings to be included in a particular code unit. The tokens can be indicative of block properties, content, or other items in the block-based data structure. The code unit can be structured to execute more than one query against the block-based data structure such that a particular result set can include content items of different modalities.

TOKEN SELECTION IN TRANSFORMER NEURAL NETWORKS FOR EFFICIENT INFERENCING

Resumen de: US2025322275A1

Certain aspects of the present disclosure provide techniques and apparatus for processing data using a transformer neural network. The method generally includes generating, via a first attention layer of a machine learning model, a first attention map based on an input into the machine learning model; identifying, using a token prediction model, a first subset of tokens in the first attention map more relevant to a second attention layer of the machine learning model and a second subset of tokens in the first attention map less relevant to the second attention layer of the machine learning model; generating, via the second attention layer of the machine learning model, a second attention map based on the first subset of tokens in the first attention map; and generating an inference based on the second attention map and the second subset of tokens in the first attention map.

REAL TIME CONTEXT DEPENDENT DEEP LEARNING

Resumen de: US2025322233A1

In an example, an apparatus comprises a plurality of execution units comprising and logic, at least partially including hardware logic, to receive a plurality of data inputs for training a neural network, wherein the data inputs comprise training data and weights inputs; represent the data inputs in a first form; and represent the weight inputs in a second form. Other embodiments are also disclosed and claimed.

SYSTEMS AND METHODS FOR A TRANSFORMER NEURAL NETWORK FOR PREDICTIONS IN POSITION-BASED SPORTING EVENTS

Resumen de: US2025315695A1

A method of generating a set of predictions associated with position-based sporting events using an axial transformer neural network, the method including: receiving an input tuple, including a set of tensors representing game context, team strength, player strength, live team features, live player features, game events, and a super feature; inputting the input tuple into an axial transformer neural network by inputting each tensor from the set of tensors within a corresponding initial embedding layer; concatenating the initial embedding layers to form a single tensor; applying self-attention to the single tensor through axial transformer layers of the axial transformer neural network; mapping output embeddings from the axial transformer layers to target layers; and generating a set of target metric predictions for each racer, team, and overall for the position-based sporting events, based on the output embeddings from the target layers.

SYSTEMS AND METHODS FOR A TRANSFORMER NEURAL NETWORK FOR PREDICTIONS IN STRIKING-BASED SPORTING EVENTS

Resumen de: WO2025212906A1

A method of generating a set of predictions associated with a striking-based sporting event using an axial transformer neural network, the method including: receiving an input tuple, including a set of tensors representing game context, team strength, player strength, live team features, live player features, game events, and a super feature; inputting the input tuple into an axial transformer neural network by inputting each tensor from the set of tensors within a corresponding initial embedding layer; concatenating the initial embedding layers to form a single tensor; applying self-attention to the single tensor through axial transformer layers of the axial transformer neural network; mapping output embeddings from the axial transformer layers to target layers; and generating a set of target metric predictions for each of a set of players, one or more teams, and a match, based on the output embeddings from the target layers.

SYSTEMS AND METHODS FOR A TRANSFORMER NEURAL NETWORK FOR PREDICTIONS IN POSSESSION-BASED SPORTING EVENTS

Nº publicación: US2025315643A1 09/10/2025

Solicitante:

STATS LLC [US]
STATS LLC

Resumen de: US2025315643A1

A method of generating a set of predictions associated with a possession-based sporting event using an axial transformer neural network, the method including: receiving an input tuple, including a set of tensors representing game context, team strength, player strength, live team features, live player features, game events, and a super feature; inputting the input tuple into an axial transformer neural network by inputting each tensor from the set of tensors within a corresponding initial embedding layer; concatenating the initial embedding layers to form a single tensor; applying self-attention to the single tensor; mapping output embeddings from the axial transformer layers to target layers, each of the output embeddings being of a dimension of a target metric; and generating a set of target metric predictions for each of a set of players, one or more teams, and a match, based on the output embeddings from the target layers.