# US 2023/0410322 A1 · Inference apparatus and learning apparatus > Agents: this data is available as JSON from the patentgrep API and MCP server — see https://patentgrep.com/llms.txt - Number: US 2023/0410322 A1 - Document ID: US20230410322A1 - Status: Published application - Kind: A1 - Application number: 18/239,328 - Filed: 2023-08-29 - Published: 2023-12-21 - Earliest claimed: 2021-04-13 - Assignees: Mitsubishi Electric Corporation - Applicants: Mitsubishi Electric Corporation - Inventors: Akito ARAI; Katsuhiro KUSANO; Shogo SHIMIZU; Seiji OKUMURA - Web page: https://patentgrep.com/patent/US20230410322A1 ## CPC - G06F 18/25: Fusion techniques - G06T 7/20: Analysis of motion (motion estimation for coding, decoding, compressing or decompressing digital video signals H04N19/43, H04N19/51) - G06T 7/50: Depth or shape recovery - G06T 7/70: Determining position or orientation of objects or cameras (camera calibration G06T7/80) - G06T 11/26: Drawing of charts or graphs - G06V 10/62: relating to a temporal dimension, e.g. time-based feature extraction; Pattern tracking - G06V 10/761: {Proximity, similarity or dissimilarity measures} - G06V 10/80: Fusion, i.e. combining data from various sources at the sensor level, preprocessing level, feature extraction level or classification level (multimodal speaker identification or verification G10L17/10) - G06V 20/52: Surveillance or monitoring of activities, e.g. for recognising suspicious objects (recognising microscopic objects G06V20/69) - G06V 40/28: {Recognition of hand or arm movements, e.g. recognition of deaf sign language (static hand signs G06V40/113)} - G06T 2207/20081: Training; Learning - G06T 2207/30241: Trajectory - G06F: ELECTRIC DIGITAL DATA PROCESSING (computer systems based on specific computational models G06N) - G06T: IMAGE DATA PROCESSING OR GENERATION, IN GENERAL - G06V: IMAGE OR VIDEO RECOGNITION OR UNDERSTANDING ## Abstract A shape similarity computation unit (2021) computes, as a shape similarity, a similarity in shape between a learning movement path curve which is a movement path curve obtained through learning and an observation movement path curve which is a movement path curve obtained through observation. A position similarity computation unit (2022) computes, as a position similarity, a similarity in position between the learning movement path curve and the observation movement path curve when the learning movement path curve and the observation movement path curve are placed in a same coordinate space. A conformity computation unit (203) computes a conformity between the learning movement path curve and the observation movement path curve, using the shape similarity and the position similarity. ## Claims ### Claim 1 An inference apparatus comprising: processing circuitry to compute, as a shape similarity, a similarity in shape between a learning movement path curve which is a movement path curve obtained through learning and an observation movement path curve which is a movement path curve obtained through observation; to compute, as a position similarity, a similarity in position between the learning movement path curve and the observation movement path curve when the learning movement path curve and the observation movement path curve are placed in a same coordinate space; and to compute a conformity between the learning movement path curve and the observation movement path curve, using the shape similarity and the position similarity, wherein the learning movement path curve is divided into a plurality of curve components constituting the learning movement path curve, the observation movement path curve is divided into a plurality of curve components corresponding to the plurality of curve components of the learning movement path curve, and the processing circuitry computes the shape similarity for each pair of corresponding curve components between the learning movement path curve and the observation movement path curve, computes the position similarity for each pair of corresponding curve components between the learning movement path curve and the observation movement path curve, and sets a significance level for each curve component of the learning movement path curve based on an attribute of each curve component, and computes the conformity, using the shape similarity, the position similarity, and the significance level of each curve component. ### Claim 2 (depends on 1) The inference apparatus according to claim 1, wherein the learning movement path curve is a movement path curve in which a movement path of a moving object is represented, and the processing circuitry sets the significance level for each curve component based on a movement speed of the object for each curve component, the moving speed being the attribute of each curve component of the learning movement path curve. ### Claim 3 (depends on 2) The inference apparatus according to claim 2, wherein the processing circuitry sets a higher significance level for a curve component with a lower movement speed of the object. ### Claim 4 (depends on 1) The inference apparatus according to claim 1, wherein the learning movement path curve is a movement path curve generated by aggregating a plurality of basic movement path curves, and the processing circuitry sets the significance level for each curve component based on a distribution of the plurality of basic movement path curves for each curve component that would arise when the learning movement path curve and the plurality of basic movement path curves are superimposed in the same coordinate space, the distribution being the attribute of each curve component of the learning movement path curve. ### Claim 5 (depends on 4) The inference apparatus according to claim 4, wherein the processing circuitry sets a higher significance level for a curve component at which a likelihood that is calculated from the distribution of the plurality of basic movement path curves is higher. ### Claim 6 (depends on 1) The inference apparatus according to claim 1, wherein the processing circuitry corrects at least either of the shape similarity and the position similarity for each curve component, using the significance level of the same curve component, and after making correction using the significance level for each curve component, computes the conformity between the learning movement path curve and the observation movement path curve, using the shape similarity and the position similarity for each curve component. ### Claim 7 (depends on 1) The inference apparatus according to claim 1, wherein the processing circuitry computes the shape similarity between each of a plurality of learning movement path curves and the observation movement path curve, computes the position similarity between each of the plurality of learning movement path curves and the observation movement path curve, and computes the conformity between each of the plurality of learning movement path curves and the observation movement path curve, using the shape similarity and the position similarity computed between each of the plurality of learning movement path curves and the observation movement path curve, and selects a learning movement path curve from among the plurality of learning movement path curves based on a result of computation of the conformity. ### Claim 8 (depends on 4) The inference apparatus according to claim 4, wherein the processing circuitry sets the significance level for a curve component of the learning movement path curve based on a multimodal distribution of the plurality of basic movement path curves at the curve component. ### Claim 9 (depends on 4) The inference apparatus according to claim 4, wherein the processing circuitry sets the significance level for a curve component of the learning movement path curve based on a distribution of the plurality of basic movement path curves for the curve component and on a heading direction of at least any basic movement path curve of the plurality of basic movement path curves for the curve component. ### Claim 10 (depends on 7) The inference apparatus according to claim 7, wherein the processing circuitry computes the shape similarity between the plurality of learning movement path curves, and computes the position similarity between each of the plurality of learning movement path curves and the observation movement path curve if the shape similarity between the plurality of learning movement path curves is greater than or equal to a threshold value. ### Claim 11 (depends on 1) The inference apparatus according to claim 1, wherein the processing circuitry obtains additional data that can be used in computation of the conformity between the learning movement path curve and the observation movement path curve in addition to the shape similarity and the position similarity, and uses the additional data to compute the conformity between the learning movement path curve and the observation movement path curve. ### Claim 12 A learning apparatus comprising: processing circuitry to generate a learning movement path curve through learning, the learning movement path curve being a movement path curve to be divided into a plurality of curve components; and to set a coefficient value for each curve component of the learning movement path curve based on an attribute of each curve component, wherein the processing circuitry generates the learning movement path curve by aggregating a plurality of basic movement path curves, and sets the coefficient value for each curve component based on a distribution of the plurality of basic movement path curves for each curve component that would arise when the learning movement path curve and the plurality of basic movement path curves are superimposed in a same coordinate space, the distribution being the attribute of each curve component of the learning movement path curve. ### Claim 13 (depends on 12) The learning apparatus according to claim 12, wherein the processing circuitry generates a movement path curve in which a movement path of a moving object is represented as the learning movement path curve, and sets the coefficient value for each curve component based on a movement speed of the object for each curve component, the moving speed being the attribute of each curve component of the learning movement path curve. ### Claim 14 (depends on 13) The learning apparatus according to claim 13, wherein the processing circuitry sets a higher coefficient value for a curve component with a lower movement speed of the object. ### Claim 15 (depends on 14) The learning apparatus according to claim 14, wherein the processing circuitry sets a higher coefficient value for a curve component at which a likelihood that is calculated from the distribution of the plurality of basic movement path curves is higher. ### Claim 16 (depends on 14) The learning apparatus according to claim 14, wherein the processing circuitry sets the coefficient value for a curve component of the learning movement path curve based on a multimodal distribution of the plurality of basic movement path curves at the curve component.