# US 11,451,842 B2 · Method and system for self-correcting match states > Agents: this data is available as JSON from the patentgrep API and MCP server — see https://patentgrep.com/llms.txt - Number: US 11,451,842 B2 - Document ID: US11451842B2 - Status: Granted - Kind: B2 - Application number: 17/308,279 - Filed: 2021-05-05 - Published: 2022-06-02 - Granted: 2022-09-20 - Earliest claimed: 2020-12-02 - Assignees: Simplebet, Inc. - Applicants: SimpleBet, Inc. - Inventors: Simon Gershey; Kyle Engel; Dave Lucia; Zachary Schumacher - Web page: https://patentgrep.com/patent/US11451842B2 ## CPC - H04N 21/251: {Learning process for intelligent management, e.g. learning user preferences for recommending movies (details of learning user preferences for the retrieval of video data in a video database G06F16/739; computer systems using learning methods G06N3/08)} - G06N 5/01: Dynamic search techniques; Heuristics; Dynamic trees; Branch-and-bound - G06N 20/00: Machine learning - G06V 10/98: Detection or correction of errors, e.g. by rescanning the pattern or by human intervention; Evaluation of the quality of the acquired patterns - H04N 21/2187: Live feed - H04N 21/25891: {being end-user preferences (retrieval of video data in a video database based on user preferences G06F16/739; arrangements for recognizing users' preferences H04H60/46; user profiles in network data switching protocols H04L67/306; processing of user preferences or user profiles in wireless networks H04W8/18)} - H04N 21/26291: {for providing content or additional data updates, e.g. updating software modules, stored at the client (deployment, distribution, installation, update of software G06F8/65; error detection or correction during software upgrading G06F11/1433; arrangements for updating broadcast information or broadcast-related information H04H60/25)} - H04N: PICTORIAL COMMUNICATION, e.g. TELEVISION - G06N: COMPUTING ARRANGEMENTS BASED ON SPECIFIC COMPUTATIONAL MODELS - G06V: IMAGE OR VIDEO RECOGNITION OR UNDERSTANDING ## Abstract A system and method arranged to predict a match state event by a machine learning platform. The system and method including monitoring, by the machine learning platform, multimedia content on a live data feed, receiving, by the machine learning platform, current match state data corresponding to the multimedia content, and predicting, by the machine learning platform, an anomaly in the multimedia content based on an analysis of received multimedia content and current match state data. The system and method can include generating, by the machine learning platform, a confidence score regarding a likelihood of the anomaly in the multimedia content, and resolving, by the machine learning platform, the anomaly based on the confidence score. Resolving the anomaly includes extracting a portion of the multimedia content containing the anomaly, ignoring the anomaly, or correcting the anomaly. ## Claims ### Claim 1 A computer-implemented method for predicting a match state event by a machine learning platform, the method comprising: receiving, by a machine learning platform, a live data feed; monitoring, by the machine learning platform, multimedia content on the live data feed; receiving, by the machine learning platform, current match state data corresponding to the multimedia content, wherein the current match state data is based on a current state of a live match; predicting, by the machine learning platform, an anomaly in the multimedia content based on an analysis of received multimedia content and current match state data; generating, by the machine learning platform, a confidence score regarding a likelihood of the anomaly in the multimedia content; resolving, by the machine learning platform, the anomaly in the multimedia content based on a determination that the confidence score is within a reference score range; and predicting, by the machine learning platform, a match state event based on the current match state data and the multimedia content, including the resolved anomaly, wherein resolving the anomaly includes extracting a portion of the multimedia content containing the anomaly, ignoring the anomaly, or correcting the anomaly, and wherein the match state event comprises an outcome of a sporting match. ### Claim 2 (depends on 1) The computer-implemented method in claim 1, further comprising: forwarding the extracted portion of the multimedia content to an intelligent technology platform to analyze and predict a future event state based, at least in part, on the multimedia content. ### Claim 3 (depends on 1) The computer-implemented method in claim 1, further comprising: receiving, by the machine learning platform, historical event data comprising past live data feed updates. ### Claim 4 (depends on 1) The computer-implemented method in claim 1, further comprising: receiving, by the machine learning platform, historical event data comprising past match states. ### Claim 5 (depends on 1) The computer-implemented method in claim 1, further comprising: receiving, by the machine learning platform, historical event data comprising past live data feed updates and past match states. ### Claim 6 (depends on 5) The computer-implemented method in claim 5, further comprising: receiving, by the machine learning platform, historical event data comprising past live data feed updates and past match states, wherein the predicting, by the machine learning platform, the anomaly in the multimedia content includes analyzing the past live data feed updates and the past match states. ### Claim 7 (depends on 1) The computer-implemented method in claim 1, wherein resolving the anomaly comprises: determining processor computing time or memory capacity required to correct the anomaly. ### Claim 8 (depends on 1) The computer-implemented method in claim 1, wherein resolving the anomaly comprises: predicting, by the machine learning platform, correctability of the anomaly. ### Claim 9 (depends on 8) The computer-implemented method in claim 8, wherein correctability of the anomaly comprises waiting for an update of the multimedia content that resolves the anomaly. ### Claim 10 A system for predicting a match state event by a machine learning platform, the system comprising: a communication unit arranged to receive a live data feed; a match state collector arranged to store match state data; an anomaly predictor arranged to monitor multimedia content on the live data feed, receive match state data from the match state collector, predict, by a machine learning platform, accuracy of the multimedia content based on the multimedia content and corresponding match state data received from the match state collector, and, generate, by the machine learning platform, a confidence score for each element of the multimedia content; an error corrector arranged to resolve an anomaly in the multimedia content based on a determination that the confidence score is within a reference score range; and a match state predictor arranged to predict a match state event based on the current match state data and the multimedia content, including the resolved anomaly, wherein the current match state data is based on a current state of a live match, wherein the error corrector resolves the anomaly by extracting a portion of the multimedia content containing the anomaly, ignoring the anomaly, or correcting the anomaly, and wherein the match state event comprises an outcome of a sporting match. ### Claim 11 (depends on 10) The system in claim 10, wherein the match state predictor unit is arranged to receive the multimedia content and predict a future event state based, at least in part, on the multimedia content. ### Claim 12 (depends on 10) The system in claim 10, wherein the anomaly predictor is further arranged to receive historical event data comprising past live data feed updates. ### Claim 13 (depends on 10) The system in claim 10, wherein the anomaly predictor is further arranged to receive historical event data comprising past match states. ### Claim 14 (depends on 10) The system in claim 10, wherein the anomaly predictor is further arranged to receive historical event data comprising past live data feed updates and past match states. ### Claim 15 (depends on 10) The system in claim 10, wherein the anomaly predictor comprises a machine learning system. ### Claim 16 (depends on 15) The system in claim 15, wherein the machine learning system is trained to: receive historical event data comprising past live data feed updates and past match states; and predict the anomaly in the multimedia content based, at least in part, on the past live data feed updates and the past match states. ### Claim 17 (depends on 15) The system in claim 15, wherein the machine learning system is trained to: determine processor computing time or memory capacity required to correct the anomaly. ### Claim 18 (depends on 15) The system in claim 15, wherein the machine learning system is trained to: predict correctability of the anomaly. ### Claim 19 (depends on 18) The system in claim 18, wherein correctability of the anomaly comprises waiting for an update of the multimedia content that resolves the anomaly. ### Claim 20 A non-transitory computer-readable storage medium containing computer executable instructions that, when executed by a computing device containing a machine learning system, cause the computing device to perform operations comprising: receiving, by machine learning system, a live data feed; monitoring, by the machine learning system, multimedia content on the live data feed; receiving, by the machine learning system, current match state data corresponding to the multimedia content, wherein the current match state data is based on a current state of a live match; predicting, by the machine learning system, an anomaly in the multimedia content based on an analysis of received multimedia content and current match state data; generating, by the machine learning system, a confidence score regarding a likelihood of the anomaly in the multimedia content; resolving, by the machine learning system, the anomaly in the multimedia content based on a determination that the confidence score is within a reference score range; and predicting, by the machine learning platform, a match state event based on the current match state data and the multimedia content, including the resolved anomaly, wherein resolving the anomaly includes extracting a portion of the multimedia content containing the anomaly, ignoring the anomaly, or correcting the anomaly, and wherein the match state event comprises an outcome of a sporting match.