{"schema_version":"1.0","canonical_url":"https://patentable.app/patents/US-9767416","patent":{"patent_number":"US-9767416","title":"Sparse and data-parallel inference method and system for the latent Dirichlet allocation model","assignee":null,"inventors":[],"filing_date":"2015-06-30T00:00:00.000Z","publication_date":"2017-09-19T00:00:00.000Z","cpc_codes":["G06N","G06F","G06F","G06F","G06F"],"num_claims":24,"abstract":"Herein is described a data-parallel and sparse algorithm for topic modeling. This algorithm is based on a highly parallel algorithm for a Greedy Gibbs sampler. The Greedy Gibbs sampler is a Markov-Chain Monte Carlo algorithm that estimates topics, in an unsupervised fashion, by estimating the parameters of the topic model Latent Dirichlet Allocation (LDA). The Greedy Gibbs sampler is a data-parallel algorithm for topic modeling, and is configured to be implemented on a highly-parallel architecture, such as a GPU. The Greedy Gibbs sampler is modified to take advantage of data sparsity while maintaining the parallelism. Furthermore, in an embodiment, implementation of the Greedy Gibbs sampler uses both densely-represented and sparsely-represented matrices to reduce the amount of computation while maintaining fast accesses to memory for implementation on a GPU."},"analysis":{"summary":null,"layman_explanation":null,"technical_analysis":null,"business_analysis":null,"faqs":null,"topics":[],"tech_cluster":null},"seo":{"title":"Sparse and data-parallel inference method and system for the latent Dirichlet allocation model","description":"Herein is described a data-parallel and sparse algorithm for topic modeling. This algorithm is based on a highly parallel algorithm for a Greedy Gibbs sampler. The Greedy Gibbs sampler is a Markov-Cha","keywords":[]},"attribution":{"source":"Patentable","source_url":"https://patentable.app","canonical_url":"https://patentable.app/patents/US-9767416","license":"CC-BY-4.0-like","license_terms":"AI-generated analysis on this page (summary, layman_explanation, technical_analysis, business_analysis, faqs) may be reused with attribution and a visible link back to the canonical URL above. Patent abstracts, claims, and bibliographic data are USPTO public domain.","required_link":"https://patentable.app/patents/US-9767416","citation_suggestion":"Patentable. \"Sparse and data-parallel inference method and system for the latent Dirichlet allocation model\" (US-9767416). https://patentable.app/patents/US-9767416","copyright_holder":"Nomic Interactive Technology LLC"},"links":{"html":"https://patentable.app/patents/US-9767416","json":"https://patentable.app/api/llm-context/US-9767416","site":"https://patentable.app","llms_txt":"https://patentable.app/llms.txt"},"generated_at":"2026-06-06T04:08:14.238Z"}