Meta Platforms multi-task neural network patent
Summary
USPTO granted Meta Platforms Patent US12591775B1 for a multi-task neural network training process using collaborative data and matrix factorization. The patent covers systems and methods for generating interaction matrices from user-item data and training neural networks through latent vector comparisons. Patent granted March 31, 2026 to Meta Platforms, Inc.
What changed
USPTO granted Patent US12591775B1 to Meta Platforms, Inc. covering a multi-task neural network training process that generates interaction matrices from user-item collaboration data, applies matrix factorization to produce user and item vectors, and trains neural networks by comparing latent item vectors against matrix-derived item vectors. The patent names inventors Oren Sar Shalom and Yaakov Aviv and contains 17 claims under CPC classification G06N 3/08.
Technology companies and AI developers working with recommendation systems, collaborative filtering, or multi-task learning architectures should review this patent to assess potential licensing needs or freedom-to-operate implications for their own systems. Patent grants do not impose compliance deadlines but establish intellectual property rights that may affect product development strategies.
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Mar 31, 2026GovPing captured this document from the original source. If the source has since changed or been removed, this is the text as it existed at that time.
Multi-task training process based on collaborative data
Grant US12591775B1 Kind: B1 Mar 31, 2026
Assignee
Meta Platforms, Inc.
Inventors
Oren Sar Shalom, Yaakov Bibas
Abstract
Systems, apparatuses and methods provide technology that generates an interaction matrix from collaboration data, where the collaboration data represents user interactions with a plurality of items. The technology applies a matrix factorization process to the interaction matrix to generate user vectors and matrix item vectors, and executes a multi-task training process with a neural network. The multi-task training process includes processing the collaboration data with the neural network to generate latent item vectors, and training the neural network based on a comparison of the latent item vectors to the matrix item vectors.
CPC Classifications
G06N 3/08
Filing Date
2022-07-29
Application No.
17816036
Claims
17
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