{"schema_version":"1.0","canonical_url":"https://patentable.app/patents/US-11954176","patent":{"patent_number":"US-11954176","title":"Generation of virtual training sets for neural net applications","assignee":null,"inventors":[],"filing_date":"2023-04-27T00:00:00.000Z","publication_date":"2024-04-09T00:00:00.000Z","cpc_codes":["G06F","G06V","G06N","G06N","G06N","G06N","G06N","G06N","G06N"],"num_claims":16,"abstract":"One embodiment of the present invention provides a computer implemented method for generating a training set to train a convolutional neural network comprising the steps of providing prediction space data to a General Logic Gate Module (GLGM). Prediction space expert judgement is also provided to the GLGM and to a sensitivity and importance module. The GLGM determines or outputs state possibilities. The state possibilities are provided to the sensitivity and importance module and to the feature extraction module. Feature extraction algorithms are applied to the state possibilities within the feature extraction module to produce a training possibility set that is a virtual training possibility set. The training possibility set is provided to a state inferential module and to a final training set. From the state inferential module a possibility ranking is generated that is independent of the convolutional neural network and further the output from the state inferential module is provided to a sensitivity and importance module for analysis. A sensitivity parameter and an importance parameter is determined from the output from the sensitivity and importance module. The state possibility ranking is provided to the final training set. The sensitivity parameter and importance parameter are provided to a final training set and a training set structure metric. A convolutional neural network input layer is generated from the final training set informed by one or more of the state possibility ranking, the sensitivity parameter, the importance parameter and the training possibility set. A convolutional neural network layer design is generated from the training set structure metric."},"analysis":{"summary":null,"layman_explanation":null,"technical_analysis":null,"business_analysis":null,"faqs":null,"topics":[],"tech_cluster":null},"seo":{"title":"Generation of virtual training sets for neural net applications","description":"One embodiment of the present invention provides a computer implemented method for generating a training set to train a convolutional neural network comprising the steps of providing prediction space ","keywords":[]},"attribution":{"source":"Patentable","source_url":"https://patentable.app","canonical_url":"https://patentable.app/patents/US-11954176","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-11954176","citation_suggestion":"Patentable. \"Generation of virtual training sets for neural net applications\" (US-11954176). https://patentable.app/patents/US-11954176","copyright_holder":"Nomic Interactive Technology LLC"},"links":{"html":"https://patentable.app/patents/US-11954176","json":"https://patentable.app/api/llm-context/US-11954176","site":"https://patentable.app","llms_txt":"https://patentable.app/llms.txt"},"generated_at":"2026-05-31T12:23:31.466Z"}