{"schema_version":"1.0","canonical_url":"https://patentable.app/patents/US-11475313","patent":{"patent_number":"US-11475313","title":"Unsupervised, semi-supervised, and supervised learning using deep learning based probabilistic generative models","assignee":null,"inventors":[],"filing_date":"2020-02-13T00:00:00.000Z","publication_date":"2022-10-18T00:00:00.000Z","cpc_codes":["G06N","G06N","G06N","G06V","G06V"],"num_claims":20,"abstract":"Embodiments of the present systems and methods may provide techniques to discover features such as object categories that provide improved accuracy and performance. For example, in an embodiment, a method may comprise extracting, at the computer system, features from a dataset comprising a plurality of data samples using a backbone neural network to form a features vector for each data sample, training, at the computer system, using the features vectors for at least some of the plurality of data samples, an unsupervised generative probabilistic model to perform clustering of extracted features of the at least some of the plurality of data samples by minimizing a negative Log-Likelihood function, wherein clusters of extracted features form categories, and categorizing, at the computer system, at least some different data samples of the plurality of data samples, into the formed categories."},"analysis":{"summary":null,"layman_explanation":null,"technical_analysis":null,"business_analysis":null,"faqs":null,"topics":[],"tech_cluster":null},"seo":{"title":"Unsupervised, semi-supervised, and supervised learning using deep learning based probabilistic generative models","description":"Embodiments of the present systems and methods may provide techniques to discover features such as object categories that provide improved accuracy and performance. For example, in an embodiment, a me","keywords":[]},"attribution":{"source":"Patentable","source_url":"https://patentable.app","canonical_url":"https://patentable.app/patents/US-11475313","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-11475313","citation_suggestion":"Patentable. \"Unsupervised, semi-supervised, and supervised learning using deep learning based probabilistic generative models\" (US-11475313). https://patentable.app/patents/US-11475313","copyright_holder":"Nomic Interactive Technology LLC"},"links":{"html":"https://patentable.app/patents/US-11475313","json":"https://patentable.app/api/llm-context/US-11475313","site":"https://patentable.app","llms_txt":"https://patentable.app/llms.txt"},"generated_at":"2026-05-30T19:08:08.066Z"}