{"schema_version":"1.0","canonical_url":"https://patentable.app/patents/US-11983171","patent":{"patent_number":"US-11983171","title":"Using multiple trained models to reduce data labeling efforts","assignee":null,"inventors":[],"filing_date":"2023-07-07T00:00:00.000Z","publication_date":"2024-05-14T00:00:00.000Z","cpc_codes":["G06F","G06F","G06F","G06F","G06F","G06N","G06N","G06N"],"num_claims":20,"abstract":"A method of labeling a dataset includes inputting a testing set comprising a plurality of input data samples into a plurality of pre-trained machine learning models to generate a set of embeddings output by the plurality of pre-trained machine learning models. The method further includes performing an iterative cluster labeling algorithm that includes generating a plurality of clusterings from the set of embeddings, analyzing the plurality of clusterings to identify a target embedding with a highest duster quality, analyzing the target embedding to determine a compactness for each of the plurality of clusterings of the target embedding, and identifying a target cluster among the plurality of clusterings of the target embedding based on the compactness. The method further includes assigning pseudo-labels to the subset of the plurality of input data samples that are members of the target duster."},"analysis":{"summary":null,"layman_explanation":null,"technical_analysis":null,"business_analysis":null,"faqs":null,"topics":[],"tech_cluster":null},"seo":{"title":"Using multiple trained models to reduce data labeling efforts","description":"A method of labeling a dataset includes inputting a testing set comprising a plurality of input data samples into a plurality of pre-trained machine learning models to generate a set of embeddings out","keywords":[]},"attribution":{"source":"Patentable","source_url":"https://patentable.app","canonical_url":"https://patentable.app/patents/US-11983171","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-11983171","citation_suggestion":"Patentable. \"Using multiple trained models to reduce data labeling efforts\" (US-11983171). https://patentable.app/patents/US-11983171","copyright_holder":"Nomic Interactive Technology LLC"},"links":{"html":"https://patentable.app/patents/US-11983171","json":"https://patentable.app/api/llm-context/US-11983171","site":"https://patentable.app","llms_txt":"https://patentable.app/llms.txt"},"generated_at":"2026-05-31T05:13:45.010Z"}