# US 2021/0240741 A1 · Data processing method, data processing device, and storage medium > Agents: this data is available as JSON from the patentgrep API and MCP server — see https://patentgrep.com/llms.txt - Number: US 2021/0240741 A1 - Document ID: US20210240741A1 - Status: Published application - Kind: A1 - Application number: 17/138,922 - Filed: 2020-12-31 - Published: 2021-08-05 - Earliest claimed: 2020-01-30 - Assignees: SCREEN Holdings Co., Ltd. - Applicants: SCREEN Holdings Co., Ltd. - Inventors: Hideji NAOHARA; Takashi IKEUCHI - Web page: https://patentgrep.com/patent/US20210240741A1 ## CPC - G06F 16/285: {Clustering or classification} - G06F 16/24568: {Data stream processing; Continuous queries} - G06N 5/04: Inference or reasoning models - G06N 20/00: Machine learning - G06F: ELECTRIC DIGITAL DATA PROCESSING (computer systems based on specific computational models G06N) - G06N: COMPUTING ARRANGEMENTS BASED ON SPECIFIC COMPUTATIONAL MODELS ## Abstract A data processing method includes: acquiring time series data; acquiring evaluation values; performing classification; and performing extraction. In the acquiring of the time series data, a plurality of time series data acquired by a substrate processing device are acquired. In the acquiring of the evaluation values, the evaluation values of the plurality of time series data are acquired. In the performing of classification, each of the plurality of time series data is classified into one of a plurality of classifications based on the evaluation value. In the performing of extraction, the time series data corresponding to one of the plurality of classifications is extracted as extracted time series data. ## Claims ### Claim 1 A data processing method comprising: acquiring a plurality of time series data acquired by a substrate processing device; acquiring evaluation values of the plurality of time series data; classifying each of the plurality of time series data into one of a plurality of classifications based on the evaluation value; and extracting the time series data corresponding to one of the plurality of classifications as extracted time series data. ### Claim 2 (depends on 1) The data processing method according to claim 1, wherein, in the acquiring of the evaluation values, the evaluation values of the plurality of time series data are acquired through comparison between the plurality of time series data and reference data. ### Claim 3 (depends on 2) The data processing method according to claim 2, wherein, in the acquiring of the evaluation values, the evaluation value in a case in which a difference between values of the time series data and the reference data is large is larger than the evaluation value in a case in which a difference between values of the time series data and the reference data is small. ### Claim 4 (depends on 1) The data processing method according to claim 1, further comprising displaying an evaluation value graph representing changes of the evaluation value of the extracted time series data and the extracted time series data in a switchable manner. ### Claim 5 (depends on 1) The data processing method according to claim 1, further comprising storing the extracted time series data. ### Claim 6 (depends on 1) The data processing method according to claim 1, further comprising classifying the extracted time series data into one of a plurality of clusters through clustering processing. ### Claim 7 (depends on 6) The data processing method according to claim 6, further comprising displaying an evaluation value graph representing the evaluation value of the extracted time series data classified into one of the plurality of clusters and the extracted time series data in a switchable manner. ### Claim 8 (depends on 6) The data processing method according to claim 6, further comprising generating a learning database in which cause and countermeasure information for the substrate processing device that corresponds to the time series data is assigned to a cluster corresponding to the extracted time series data. ### Claim 9 A data processing method comprising: acquiring a plurality of time series data acquired by a substrate processing device; acquiring evaluation values of the plurality of time series data; classifying each of the plurality of time series data into one of a plurality of classifications based on the evaluation value; and matching target time series data corresponding to one of the plurality of classifications and time series data included in a learning database. ### Claim 10 (depends on 9) The data processing method according to claim 9, wherein cause and countermeasure information corresponding to the time series data is stored in the learning database, and wherein, in the matching of the target time series data, reading the cause and countermeasure information corresponding to the time series data is further included in a case in which a matching rate between the target time series data and at least one piece of the time series data included in the learning database is higher than a threshold. ### Claim 11 (depends on 9) The data processing method according to claim 9, wherein, in the acquiring of the evaluation values, the evaluation values of the plurality of time series data are acquired through comparison between the plurality of time series data and reference data. ### Claim 12 (depends on 11) The data processing method according to claim 11, wherein, in the acquiring of the evaluation values, the evaluation value in a case in which a difference between values of the time series data and the reference data is large is larger than the evaluation value in a case in which a difference between values of the time series data and the reference data is small. ### Claim 13 (depends on 9) The data processing method according to claim 9, further comprising storing the target time series data. ### Claim 14 A data processing device comprising: a data acquisition part configured to acquire a plurality of time series data acquired by a substrate processing device; an evaluation value acquisition part configured to acquire evaluation values of the plurality of time series data; a classification part configured to classify each of the plurality of time series data into one of a plurality of classifications based on the evaluation value; and an extraction part configured to extract the time series data corresponding to one of the plurality of classifications as extracted time series data. ### Claim 15 A data processing device comprising: a data acquisition part configured to acquire a plurality of time series data acquired by a substrate processing device; an evaluation value acquisition part configured to acquire evaluation values of the plurality of time series data; a classification part configured to classify each of the plurality of time series data into one of a plurality of classifications based on the evaluation value; and a matching part configured to match target time series data corresponding to one of the plurality of classifications and time series data included in a learning database. ### Claim 16 A non-transitory computer-readable storage medium storing a program causing a computer to execute: acquiring a plurality of time series data acquired by a substrate processing device; acquiring evaluation values of the plurality of time series data; classifying each of the plurality of time series data into one of a plurality of classifications based on the evaluation value; and extracting the time series data corresponding to one of the plurality of classifications as extracted time series data. ### Claim 17 A non-transitory computer-readable storage medium storing a program causing a computer to execute: acquiring a plurality of time series data acquired by a substrate processing device; acquiring evaluation values of the plurality of time series data; classifying each of the plurality of time series data into one of a plurality of classifications based on the evaluation value; and matching target time series data corresponding to one of the plurality of classifications and time series data included in a learning database.