{"schema_version":"1.0","canonical_url":"https://patentable.app/patents/US-11501137","patent":{"patent_number":"US-11501137","title":"Feature engineering in neural networks optimization","assignee":null,"inventors":[],"filing_date":"2019-06-28T00:00:00.000Z","publication_date":"2022-11-15T00:00:00.000Z","cpc_codes":["G06F","G06N","G06F","G06F","G06F","G06F","G06N","G06N","G06N","G06N"],"num_claims":20,"abstract":"A transitive closure data structure is constructed for a pair of features represented in a vector space corresponding to an input dataset. The data structure includes a set of entries corresponding to a set of all possible paths between a first feature in the pair and a second feature in the pair in a graph of the vector space. The data structure is reduced by removing a subset of the set of entries such that only a single entry corresponding to a single path remains in the transitive closure data structure. A feature cross is formed from a cluster of features remaining in a reduced ontology graph resulting from the reducing the transitive closure data structure. A layer is configured in a neural network to represent the feature cross, which causes the neural network to produce a prediction that is within a defined accuracy relative to the dataset."},"analysis":{"summary":null,"layman_explanation":null,"technical_analysis":null,"business_analysis":null,"faqs":null,"topics":[],"tech_cluster":null},"seo":{"title":"Feature engineering in neural networks optimization","description":"A transitive closure data structure is constructed for a pair of features represented in a vector space corresponding to an input dataset. The data structure includes a set of entries corresponding to","keywords":[]},"attribution":{"source":"Patentable","source_url":"https://patentable.app","canonical_url":"https://patentable.app/patents/US-11501137","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-11501137","citation_suggestion":"Patentable. \"Feature engineering in neural networks optimization\" (US-11501137). https://patentable.app/patents/US-11501137","copyright_holder":"Nomic Interactive Technology LLC"},"links":{"html":"https://patentable.app/patents/US-11501137","json":"https://patentable.app/api/llm-context/US-11501137","site":"https://patentable.app","llms_txt":"https://patentable.app/llms.txt"},"generated_at":"2026-05-30T14:37:55.276Z"}