# US 10,503,569 B2 · Feature-based application programming interface cognitive comparative benchmarking > Agents: this data is available as JSON from the patentgrep API and MCP server — see https://patentgrep.com/llms.txt - Number: US 10,503,569 B2 - Document ID: US10503569B2 - Status: Granted - Kind: B2 - Application number: 15/954,951 - Filed: 2018-04-17 - Published: 2019-10-17 - Granted: 2019-12-10 - Earliest claimed: 2018-04-17 - Assignees: International Business Machines Corporation - Applicants: International Business Machines Corporation - Inventors: Harish Bharti; Amol Dhondse; Abhay Patra; Anand Pikle; Rakesh Shinde - Web page: https://patentgrep.com/patent/US10503569B2 ## CPC - G06F 9/543: {User-generated data transfer, e.g. clipboards, dynamic data exchange [DDE], object linking and embedding [OLE]} - G06F 9/541: {via adapters, e.g. between incompatible applications} - G06F 16/285: {Clustering or classification} - G06F 21/00: Security arrangements for protecting computers, components thereof, programs or data against unauthorised activity - G06N 20/00: Machine learning - G06Q 30/0283: Price estimation or determination - G06F 16/2246: {Trees, e.g. B+trees} - G06N 5/022: Knowledge engineering; Knowledge acquisition - G06F: ELECTRIC DIGITAL DATA PROCESSING (computer systems based on specific computational models G06N) - G06N: COMPUTING ARRANGEMENTS BASED ON SPECIFIC COMPUTATIONAL MODELS - G06Q: INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR ## Abstract Attribute-based application programming interface (API) comparative benchmarking is provided. In response to determining that a target API maps to an existing API classification based on attributes of the target API, a weighted average of benchmark confidence scores of other APIs in a same class as the target API is determined. A benchmark confidence score is determined for the target API based on feedback, reviews, and ratings. The benchmark confidence score of the target API is compared with the weighted average of benchmark scores. An attribute-based API classification mapping is updated based on the comparison. Pricing for the target API is determined based on a weighted average of API pricing across the other APIs in the same class as the target API. ## Claims ### Claim 1 A computer-implemented method for attribute-based application programming interface (API) comparative benchmarking, the computer-implemented method comprising: responsive to a computer determining that a target API maps to an existing API classification based on attributes of the target API, determining, by the computer, a weighted average of benchmark confidence scores of other APIs in a same class as the target API; determining, by the computer, a benchmark confidence score for the target API based on feedback, reviews, and ratings; comparing, by the computer, the benchmark confidence score of the target API with the weighted average of benchmark scores; updating, by the computer, an attribute-based API classification mapping based on the comparing; and determining, by the computer, pricing for the target API based on a weighted average of API pricing across the other APIs in the same class as the target API. ### Claim 2 (depends on 1) The computer-implemented method of claim 1 further comprising: responsive to the computer determining that the target API does not map to an existing API classification based on the attributes of the target API, generating, by the computer, a new class for the target API based on a baseline API taxonomy; adding, by the computer, the new class to the baseline API taxonomy; and determining, by the computer, the pricing for the target API based on a depth weighted average of API pricing across parent API nodes in an API portfolio hierarchy. ### Claim 3 (depends on 1) The computer-implemented method of claim 1 further comprising: retrieving, by the computer, attribute information corresponding to a set of target APIs from a plurality of data sources; and generating, by the computer, a baseline API taxonomy for the set of target APIs based on the retrieved attribute information corresponding to the set of target APIs from the plurality of data sources. ### Claim 4 (depends on 1) The computer-implemented method of claim 1 further comprising: performing, by the computer, cognitive attribute-based API classification of all available APIs using natural language processing on a baseline API taxonomy; and generating, by the computer, the attribute-based API classification mapping based on supervised machine learning and the cognitive attribute-based API classification of all available APIs. ### Claim 5 (depends on 1) The computer-implemented method of claim 1 further comprising: determining, by the computer, reference pricing using machine learning, wherein the machine learning determines API classification using natural language processing based on API documentation for price benchmarking of the target API in relation to the other APIs in the same class having similar function across standards and providers. ### Claim 6 (depends on 1) The computer-implemented method of claim 1 further comprising: dynamically adjusting, by the computer, pricing parameters based on quality and relevance of a reference API determined to be in the same class as the target API and based on competitive evaluation of the quality and relevance of the reference API across one or more API providers using supervised machine learning on at least one of an atomic level and an aggregated level. ### Claim 7 (depends on 1) The computer-implemented method of claim 1 further comprising: deriving, by the computer, target pricing of APIs based on respective API benchmark confidence scores, wherein the respective API benchmark confidence scores are based on closeness of API nodes to other API nodes at one or more of a same level and a distance from parent API nodes in an API portfolio hierarchy within a same API class. ### Claim 8 (depends on 1) The computer-implemented method of claim 1 further comprising: automatically adjusting, by the computer, API pricing thresholds via continuous discovery of similar APIs across an organization and third-party providers given a business context that is based on organizational or domain feedback. ### Claim 9 (depends on 1) The computer-implemented method of claim 1 further comprising: iteratively refining, by the computer, API pricing using benchmark confidence scores and re-pricing closest matching APIs to the target API based on dynamically varying weights, wherein the computer dynamically varies the weights based on input from a user. ### Claim 10 (depends on 1) The computer-implemented method of claim 1 further comprising: determining, by the computer, API price based on comparative benchmark confidence scoring with respect to APIs with similar functional and non-functional features. ### Claim 11 (depends on 1) The computer-implemented method of claim 1 further comprising: generating, by the computer, an interactive graphical user interface that makes it easier for a user to study impact of different API features on cognitive feature-based comparative API benchmarking. ### Claim 12 A computer system for attribute-based application programming interface (API) comparative benchmarking, the computer system comprising: a bus system; a storage device connected to the bus system, wherein the storage device stores program instructions; and a processor connected to the bus system, wherein the processor executes the program instructions to: determine a weighted average of benchmark confidence scores of other APIs in a same class as a target API in response to determining that the target API maps to an existing API classification based on attributes of the target API; determine a benchmark confidence score for the target API based on feedback, reviews, and ratings; compare the benchmark confidence score of the target API with the weighted average of benchmark scores; update an attribute-based API classification mapping based on comparing; and determine pricing for the target API based on a weighted average of API pricing across the other APIs in the same class as the target API. ### Claim 13 (depends on 12) The computer system of claim 12, wherein the processor further executes the program instructions to: generate a new class for the target API based on a baseline API taxonomy in response to determining that the target API does not map to an existing API classification based on the attributes of the target API; add the new class to the baseline API taxonomy; and determine the pricing for the target API based on a depth weighted average of API pricing across parent API nodes in an API portfolio hierarchy. ### Claim 14 A computer program product for attribute-based application programming interface (API) comparative benchmarking, the computer program product comprising a computer readable storage medium having program instructions embodied therewith, the program instructions executable by a computer to cause the computer to perform a method comprising: responsive to the computer determining that a target API maps to an existing API classification based on attributes of the target API, determining, by the computer, a weighted average of benchmark confidence scores of other APIs in a same class as the target API; determining, by the computer, a benchmark confidence score for the target API based on feedback, reviews, and ratings; comparing, by the computer, the benchmark confidence score of the target API with the weighted average of benchmark scores; updating, by the computer, an attribute-based API classification mapping based on the comparing; and determining, by the computer, pricing for the target API based on a weighted average of API pricing across the other APIs in the same class as the target API. ### Claim 15 (depends on 14) The computer program product of claim 14 further comprising: responsive to the computer determining that the target API does not map to an existing API classification based on the attributes of the target API, generating, by the computer, a new class for the target API based on a baseline API taxonomy; adding, by the computer, the new class to the baseline API taxonomy; and determining, by the computer, the pricing for the target API based on a depth weighted average of API pricing across parent API nodes in an API portfolio hierarchy. ### Claim 16 (depends on 14) The computer program product of claim 14 further comprising: retrieving, by the computer, attribute information corresponding to a set of target APIs from a plurality of data sources; and generating, by the computer, a baseline API taxonomy for the set of target APIs based on the retrieved attribute information corresponding to the set of target APIs from the plurality of data sources. ### Claim 17 (depends on 14) The computer program product of claim 14 further comprising: performing, by the computer, cognitive attribute-based API classification of all available APIs using natural language processing on a baseline API taxonomy; and generating, by the computer, the attribute-based API classification mapping based on supervised machine learning and the cognitive attribute-based API classification of all available APIs. ### Claim 18 (depends on 14) The computer program product of claim 14 further comprising: determining, by the computer, reference pricing using machine learning, wherein the machine learning determines API classification using natural language processing based on API documentation for price benchmarking of the target API in relation to the other APIs in the same class having similar function across standards and providers. ### Claim 19 (depends on 14) The computer program product of claim 14 further comprising: dynamically adjusting, by the computer, pricing parameters based on quality and relevance of a reference API determined to be in the same class as the target API and based on competitive evaluation of the quality and relevance of the reference API across one or more API providers using supervised machine learning on at least one of an atomic level and an aggregated level. ### Claim 20 (depends on 14) The computer program product of claim 14 further comprising: deriving, by the computer, target pricing of APIs based on respective API benchmark confidence scores, wherein the respective API benchmark confidence scores are based on closeness of API nodes to other API nodes at one or more of a same level and a distance from parent API nodes in an API portfolio hierarchy within a same API class.