Agblox, Inc.

Clearag, Inc.

Performance Livestock Analytics, Inc.

Thomas N Blair

Alex A Kurzhanskiy

Spyros J Lazaris

Tony Chiyung Lei

Craig I Forman

Leo Richard Jolicoeur

John J Mewes

Dustin M Salentiny

Dustin C Balsley

Dane T Kuper

Michael G Mcerlean

Michael G McErlean

Joerg Rings

Alexey Goder

Joshua Peter Francis Yoder

Gregory Burlet

A data modeling and analytics platform augments and annotates content captured from a user's online interactions and other documents. The data modeling and analytics platform is performed within a machine learning and artificial intelligence-based processing environment that enables observability, explainability, and data analytics for dynamic information discovery over time within a user library that includes files representing the online interactions and documents containing information of user interest.

Data analytics platform for stateful, temporally-augmented observability, explainability and augmentation in web-based interactions and other user media

A data analytics platform is provided for forecasting future states of commodities and other assets, based on processing of both textual and numerical data sources. The platform includes a multi-layer machine learning-based model that extracts sentiment from textual data in a natural language processing engine, evaluates numerical data in a time-series analysis, and generates an initial forecast for the commodity or asset being analyzed. The platform includes multiple applications of neural networks to develop augmented forecasts from further analysis of relevant information as it is collected. These include commodity-specific neural networks designed to continually develop taxonomies used to process commodity sentiment, and applications of reinforcement learning, symbolic networks, and unsupervised meta learning to improve overall performance and accuracy of the forecasts generated.

Generative artificial intelligence-based agents using customized neural networks

Sentiment and rules-based equity analysis using customized neural networks in multi-layer, machine learning-based model

Curated sentiment analysis in multi-layer, machine learning-based forecasting model using customized, commodity-specific neural networks

A method and system is provided in an adaptive framework for modeling livestock growth. The adaptive framework processes input data relative to livestock growth in an ensemble of one or more models and an artificial intelligence layer configured to select the most appropriate or primary model to optimize, predict, and recommend livestock feed operations based upon environmental, physiological, location and time variables within such input data. The adaptive framework also optimizes workflow by pen and by producer, based upon historical performance, gender and breed and the management practices of the producer.

Adaptive livestock growth modeling using machine learning approaches to predict growth and recommend livestock management operations and activities

Detection and identification a field's boundaries is performed in a workflow based on processing images of the field captured at different times, relative to a defined seed point. Images are clipped to align with the seed point and a bounding box around the seed point, and a mask is built by extracting edges of the field from the images. The workflow floods an area around the seed point that has pixels of a similar color, using the mask as an initial boundary. The flooded area is compared to threshold parameter values, which are tuned to refine the identified boundary. Flooded areas in multiple images are combined, and a boundary is built based on the combined flooded set. Manual, interactive tuning of floodfill areas allows for a separate boundary detection and identification workflow or for refinement of the automatic boundary detection workflow.

Image-based field boundary detection and identification

A method and system is provided in a framework and workflow for assisting in livestock feeding operations by tracking and weighing ingredients from an electronic scale associated with feed mixing equipment. The framework and workflow captures, transfers, stores and processes parameters for cattle feeder ration weight data collection. The method and system include initializing a collection of weight data related to components of a feed ration for a mixing of feed for livestock, capturing a scale identifier from a scale interface on mixing equipment, and capturing weight information from the scale interface as the components of a feed ration are loaded into the mixing equipment. Such information is broadcasted over a wireless radio communication connection, such as via a Bluetooth&#xae; device, to a mobile application, and displayed during feed mixing operation.

Asynchronous capture, processing, and adaptability of real-time feeder livestock ration weight information and transfer over wireless connection for mobile device, machine-to-machine supply chain control, and application processing

Growing community of inventors

San Francisco, CA, United States of America

Thomas N Blair