{"schema_version":"1.0","canonical_url":"https://patentable.app/patents/US-10540611","patent":{"patent_number":"US-10540611","title":"Scalable complex event processing with probabilistic machine learning models to predict subsequent geolocations","assignee":null,"inventors":[],"filing_date":"2016-05-05T00:00:00.000Z","publication_date":"2020-01-21T00:00:00.000Z","cpc_codes":["G06N","G06F","G06N","G06N"],"num_claims":21,"abstract":"Provided is a process, including: obtaining a set of historical geolocations; segmenting the historical geolocations into a plurality of temporal bins; determining pairwise transition probabilities between a set of geographic places based on the historical geolocations; configuring a compute cluster by assigning subsets of the transition probabilities to computing devices in the compute cluster; receiving a geolocation stream indicative of current geolocations of individuals; selecting a computing device in the compute cluster in response to determining that the computing device contain transition probabilities for the received respective geolocation; selecting transition probabilities applicable to the received respective geolocation from among the subset of transition probabilities assigned to the selected computing device; predicting a subsequent geographic place based on the selected transition probabilities."},"analysis":{"summary":null,"layman_explanation":null,"technical_analysis":null,"business_analysis":null,"faqs":null,"topics":[],"tech_cluster":null},"seo":{"title":"Scalable complex event processing with probabilistic machine learning models to predict subsequent geolocations","description":"Provided is a process, including: obtaining a set of historical geolocations; segmenting the historical geolocations into a plurality of temporal bins; determining pairwise transition probabilities be","keywords":[]},"attribution":{"source":"Patentable","source_url":"https://patentable.app","canonical_url":"https://patentable.app/patents/US-10540611","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-10540611","citation_suggestion":"Patentable. \"Scalable complex event processing with probabilistic machine learning models to predict subsequent geolocations\" (US-10540611). https://patentable.app/patents/US-10540611","copyright_holder":"Nomic Interactive Technology LLC"},"links":{"html":"https://patentable.app/patents/US-10540611","json":"https://patentable.app/api/llm-context/US-10540611","site":"https://patentable.app","llms_txt":"https://patentable.app/llms.txt"},"generated_at":"2026-05-30T22:36:33.198Z"}