# US 11,308,667 B1 · Chart erratum and refactoring > Agents: this data is available as JSON from the patentgrep API and MCP server — see https://patentgrep.com/llms.txt - Number: US 11,308,667 B1 - Document ID: US11308667B1 - Status: Granted - Kind: B1 - Application number: 17/120,360 - Filed: 2020-12-14 - Granted: 2022-04-19 - Earliest claimed: 2020-12-14 - Assignees: International Business Machines Corporation - Applicants: International Business Machines Corporation - Inventors: Zachary A. Silverstein; Trudy L. Hewitt; Saswati Dana; Mayoore Selvarasa Jaiswal; Jonathan D. Dunne - Web page: https://patentgrep.com/patent/US11308667B1 ## CPC - G06T 11/26: Drawing of charts or graphs - G06V 30/413: Classification of content, e.g. text, photographs or tables - G06V 30/422: Technical drawings; Geographical maps - G06F 16/9024: {Graphs; Linked lists (G06F16/9027 takes precedence)} - G06N 20/00: Machine learning - G06T: IMAGE DATA PROCESSING OR GENERATION, IN GENERAL - G06V: IMAGE OR VIDEO RECOGNITION OR UNDERSTANDING - G06F: ELECTRIC DIGITAL DATA PROCESSING (computer systems based on specific computational models G06N) - G06N: COMPUTING ARRANGEMENTS BASED ON SPECIFIC COMPUTATIONAL MODELS ## Abstract A computer-implemented method is provided. The embodiments include evaluating, by one or more processors, a specimen chart relative to a chart erratum model that has features mapped to an optimum state for a first chart type. The method also includes generating a first risk score for a first sample feature of the specimen chart. The first risk score may include a delta from the optimum state. The method also includes refactoring the specimen chart to mitigate the first risk score of the first sample feature. ## Claims ### Claim 1 A computer-implemented method, comprising: evaluating, by one or more processors, a specimen chart relative to a chart erratum model comprising features mapped to an optimum state for a first chart type; generating a first risk score for a first sample feature of the specimen chart, wherein the first risk score comprises a delta from the optimum state; and refactoring the specimen chart to mitigate the first risk score of the first sample feature. ### Claim 2 (depends on 1) The method of claim 1, comprising analyzing a plurality of charts to extract a pattern, wherein the pattern comprises a plurality of chart types defined by features. ### Claim 3 (depends on 2) The method of claim 2, comprising mapping the features to optimum states for each chart type. ### Claim 4 (depends on 2) The method of claim 2, wherein analyzing the plurality of charts comprises training a selection from the group consisting of a convolutional neural network and a support-vector machine. ### Claim 5 (depends on 1) The method of claim 1, wherein the first chart type is selected from the group consisting of a bar chart, a line graph, a table, and a pie chart. ### Claim 6 (depends on 1) The method of claim 1, wherein refactoring the specimen chart comprises labeling the first sample feature with a numerated risk score. ### Claim 7 (depends on 1) The method of claim 1, wherein refactoring the specimen chart comprises a selection from the group consisting of highlighting the first sample feature and changing an appearance of the first sample feature. ### Claim 8 (depends on 1) The method of claim 1, comprising: generating a second risk score for a second sample feature; and generating an overall risk score for the specimen chart based on the first risk score and the second risk score. ### Claim 9 A computer program product comprising: one or more computer readable storage media, and program instructions collectively stored on the one or more computer readable storage media, the program instructions comprising: evaluating, by one or more processors, a specimen chart relative to a chart erratum model comprising features mapped to an optimum state for a first chart type; generating a first risk score for a first sample feature of the specimen chart, wherein the first risk score comprises a delta from the optimum state; and refactoring the specimen chart to mitigate the first risk score of the first sample feature. ### Claim 10 (depends on 9) The computer program product of claim 9, wherein the program instructions comprise analyzing a plurality of charts to extract a pattern, wherein the pattern comprises a plurality of chart types defined by features. ### Claim 11 (depends on 10) The computer program product of claim 10, wherein the program instructions comprise mapping the features to optimum states for each chart type. ### Claim 12 (depends on 10) The computer program product of claim 10, wherein analyzing the plurality of charts comprises training a selection from the group consisting of a convolutional neural network and a support-vector machine. ### Claim 13 (depends on 9) The computer program product of claim 9, wherein the first chart type is selected from the group consisting of a bar chart, a line graph, a table, and a pie chart. ### Claim 14 (depends on 9) The computer program product of claim 9, wherein refactoring the specimen chart comprises labeling the first sample feature with a numerated risk score. ### Claim 15 (depends on 9) The computer program product of claim 9, wherein refactoring the specimen chart comprises a selection from the group consisting of highlighting the first sample feature and changing an appearance of the first sample feature. ### Claim 16 (depends on 9) The computer program product of claim 9, wherein the program instructions comprise: generating a second risk score for a second sample feature; and generating an overall risk score for the specimen chart based on the first risk score and the second risk score. ### Claim 17 A computer system comprising: one or more computer processors, one or more computer readable storage media, and program instructions collectively stored on the one or more computer readable storage media for execution by at least one of the one or more computer processors, the program instructions comprising: evaluating, by one or more processors, a specimen chart relative to a chart erratum model comprising features mapped to an optimum state for a first chart type; generating a first risk score for a first sample feature of the specimen chart, wherein the first risk score comprises a delta from the optimum state; and refactoring the specimen chart to mitigate the first risk score of the first sample feature. ### Claim 18 (depends on 17) The computer system of claim 17, wherein the program instructions comprise: analyzing a plurality of charts to extract a pattern, wherein the pattern comprises a plurality of chart types defined by features; and mapping the features to optimum states for each chart type. ### Claim 19 (depends on 17) The computer system of claim 17, wherein refactoring the specimen chart comprises a selection from the group consisting of labeling the first sample feature with a numerated risk score, highlighting the first sample feature, and changing an appearance of the first sample feature. ### Claim 20 (depends on 17) The computer system of claim 17, wherein the program instructions comprise: generating a second risk score for a second sample feature; and generating an overall risk score for the specimen chart based on the first risk score and the second risk score.