# US 11,443,241 B2 · Method and system for automating repetitive task on user interface > Agents: this data is available as JSON from the patentgrep API and MCP server — see https://patentgrep.com/llms.txt - Number: US 11,443,241 B2 - Document ID: US11443241B2 - Status: Granted - Kind: B2 - Application number: 16/835,038 - Filed: 2020-03-30 - Published: 2021-09-30 - Granted: 2022-09-13 - Earliest claimed: 2020-03-26 - Assignees: Wipro Limited - Applicants: Wipro Limited - Inventors: Sourav Mudi; Rishav Das - Web page: https://patentgrep.com/patent/US11443241B2 ## CPC - G06N 3/084: Backpropagation, e.g. using gradient descent - G06N 20/00: Machine learning - G06F 8/427: {Parsing} - G06F 9/451: Execution arrangements for user interfaces - G06F 16/00: Information retrieval; Database structures therefor; File system structures therefor - G06N 3/008: based on physical entities controlled by simulated intelligence so as to replicate intelligent life forms, e.g. based on robots replicating pets or humans in their appearance or behaviour - G06N 3/044: Recurrent networks, e.g. Hopfield networks - G06N 3/0442: characterised by memory or gating, e.g. long short-term memory [LSTM] or gated recurrent units [GRU] - G06N 3/09: Supervised learning - G06N: COMPUTING ARRANGEMENTS BASED ON SPECIFIC COMPUTATIONAL MODELS - G06F: ELECTRIC DIGITAL DATA PROCESSING (computer systems based on specific computational models G06N) ## Abstract A method and system for automating repetitive task on a user interface is disclosed. The method includes identifying a plurality of Document Object Model (DOM) elements from a repetitive pattern. The method further includes identifying a set of dynamic DOM elements from the plurality of DOM elements. The method further includes determining a path and a path position within the application code of the repetitive pattern for each of the set of dynamic DOM elements. The method further includes training an Artificial Intelligence (AI) model to identify the repetitive pattern and to identify the corresponding path and the path position within the application code component, for each of the set of dynamic DOM elements. The method further includes automatically executing a code for each of the set of dynamic DOM elements based on the associated path and path position. ## Claims ### Claim 1 A method for automating repetitive task on a user interface, the method comprising: identifying, by a Robotic Process Automation (RPA) device, a plurality of Document Object Model (DOM) elements from a repetitive pattern, wherein the plurality of DOM elements are identified based on parsing of an application code component of the repetitive pattern; identifying, by the RPA device, a set of dynamic DOM elements from the plurality of DOM elements, wherein frequency of change of state of each of the set of dynamic DOM elements is above a first predefined threshold, and wherein execution of the set of dynamic DOM elements is based on a sequence within the repetitive pattern; determining, by the RPA device, a path and a path position within the application code component of the repetitive pattern for each of the set of dynamic DOM elements; training, by the RPA device, an Artificial Intelligence (AI) model to: identify the repetitive pattern; and identify, for each of the set of dynamic DOM elements, the corresponding path and the path position within the application code component; and automatically executing, by the RPA device using the AI model, a code for each of the set of dynamic DOM elements based on the associated path and path position, wherein the code for each of the set of dynamic DOM elements are executed in conformance with the sequence within the repetitive pattern. ### Claim 2 (depends on 1) The method of claim 1, further comprising identifying a plurality of repetitive patterns within the user interface, wherein frequency of repetition for each of the plurality of repetitive patterns is above a second predefined threshold. ### Claim 3 (depends on 2) The method of claim 2, wherein the plurality of repetitive patterns are identified from a set of data comprising details of an application code associated with the user interface. ### Claim 4 (depends on 2) The method of claim 2, further comprising: determining similarity between the set of dynamic DOM elements within each of the plurality of repetitive patterns; and classifying each of the plurality of repetitive patterns into a plurality of pattern categories based on the determined similarity, wherein two or more repetitive patterns from the plurality of repetitive patterns are classified in a pattern category from the plurality of pattern categories, when similarity between the two or more repetitive patterns is above a similarity threshold. ### Claim 5 (depends on 4) The method of claim 4, further comprising training the AI model to uniquely identify a pattern category associated with each of the plurality of repetitive patterns. ### Claim 6 (depends on 5) The method of claim 5, further comprising: determining misidentification of a pattern category associated with the repetitive pattern by the AI model; and performing incremental learning of the AI model based on the determined misidentification. ### Claim 7 (depends on 1) The method of claim 1, wherein identifying the set of dynamic DOM elements from the plurality of DOM elements comprises: determining an element value associated with an attribute of each of the plurality of DOM elements; determining a frequency of change of the element value corresponding to the attribute of each of the plurality of DOM elements, based on a comparison with corresponding historic element values, wherein change of an element value associated with an attribute of a DOM element corresponds to change of state of the DOM element; and establishing each of the set of dynamic DOM elements as dynamic, wherein the frequency of change of the element value corresponding to the attribute of each of the set of dynamic DOM elements is above the first predefined threshold. ### Claim 8 (depends on 1) The method of claim 1, further comprising identifying a set of static DOM elements from the plurality of DOM elements, wherein frequency of change of state of each of the set of static DOM elements is equal to or less than the first predefined threshold. ### Claim 9 (depends on 1) The method of claim 1, further comprises identifying error in automatic execution of the code for each of the set of dynamic DOM elements, by the AI model, in conformance with the sequence within the repetitive pattern. ### Claim 10 (depends on 9) The method of claim 9, further comprising performing incremental learning of the AI model based on the identified error to improve accuracy of the AI model, wherein accuracy of the AI model is improved to execute the code for each of the set of dynamic DOM elements in conformance with the sequence within the repetitive pattern. ### Claim 11 A system for automating repetitive task on a user interface, the system comprising: a processor; and a memory communicatively coupled to the processor, wherein the memory stores processor instructions, which, on execution, causes the processor to: identify a plurality of Document Object Model (DOM) elements from a repetitive pattern, wherein the plurality of DOM elements are identified based on parsing of an application code component of the repetitive pattern; identify a set of dynamic DOM elements from the plurality of DOM elements, wherein frequency of change of state of each of the set of dynamic DOM elements is above a first predefined threshold, and wherein execution of the set of dynamic DOM elements is based on a sequence within the repetitive pattern; determine a path and a path position within the application code component of the repetitive pattern for each of the set of dynamic DOM elements; train an Artificial Intelligence (AI) model to: identify the repetitive pattern; and identify, for each of the set of dynamic DOM elements, the corresponding path and the path position within the application code component; and automatically execute a code for each of the set of dynamic DOM elements based on the associated path and path position, wherein the code for each of the set of dynamic DOM elements are executed in conformance with the sequence within the repetitive pattern. ### Claim 12 (depends on 11) The system of claim 11, wherein the processor instructions further cause the processor to identify a plurality of repetitive patterns within the user interface, wherein frequency of repetition for each of the plurality of repetitive patterns is above a second predefined threshold. ### Claim 13 (depends on 12) The system of claim 12, wherein the plurality of repetitive patterns are identified from a set of data comprising details of an application code associated with the user interface. ### Claim 14 (depends on 12) The system of claim 12, wherein the processor instructions further cause the processor to: determine similarity between the set of dynamic D©M elements within each of the plurality of repetitive patterns; and classify each of the plurality of repetitive patterns into a plurality of pattern categories based on the determined similarity, wherein two or more repetitive patterns from the plurality of repetitive patterns are classified in a pattern category from the plurality of pattern categories, when similarity between the two or more repetitive patterns is above a similarity threshold. ### Claim 15 (depends on 14) The system of claim 14, wherein the processor instructions further cause the processor to train the AI model to uniquely identify a pattern category associated with each of the plurality of repetitive patterns. ### Claim 16 (depends on 15) The system of claim 15, wherein the processor instructions further cause the processor to: determine misidentification of a pattern category associated with the repetitive pattern by the AI model; and perform incremental learning of the AI model based on the determined misidentification. ### Claim 17 (depends on 11) The system of claim 11, wherein the processor instructions further cause the processor to: determine an element value associated with an attribute of each of the plurality of DOM elements; determine a frequency of change of the element value corresponding to the attribute of each of the plurality of DOM elements, based on a comparison with corresponding historic element values, wherein change of an element value associated with an attribute of a DOM element corresponds to change of state of the DOM element; and establish each of the set of dynamic DOM elements as dynamic, wherein the frequency of change of the element value corresponding to the attribute of each of the set of dynamic DOM elements is above the first predefined threshold. ### Claim 18 (depends on 11) The system of claim 11, wherein the processor instructions further cause the processor to identify a set of static DOM elements from the plurality of DOM elements, wherein frequency of change of state of each of the set of static DOM elements is equal to or less than the first predefined threshold. ### Claim 19 (depends on 11) The system of claim 11, the processor instructions further cause the processor to identify error in automatic execution of the code for each of the set of dynamic DOM elements, by the AI model, in conformance with the sequence within the repetitive pattern. ### Claim 20 (depends on 19) The system of claim 19, the processor instructions further cause the processor to perform incremental learning of the AI model based on the identified error to improve accuracy of the AI model, wherein accuracy of the AI model is improved to execute the code for each of the set of dynamic DOM elements in conformance with the sequence within the repetitive pattern. ### Claim 21 A non-transitory computer-readable storage medium for automating repetitive task on a user interface, having stored thereon, a set of computer-executable instructions causing a computer comprising one or more processors to perform steps comprising: identify a plurality of Document Object Model (DOM) elements from a repetitive pattern, wherein the plurality of DOM elements are identified based on parsing of an application code component of the repetitive pattern; identify a set of dynamic DOM elements from the plurality of DOM elements, wherein frequency of change of state of each of the set of dynamic DOM elements is above a first predefined threshold, and wherein execution of the set of dynamic DOM elements is based on a sequence within the repetitive pattern; determine a path and a path position within the application code component of the repetitive pattern for each of the set of dynamic DOM elements; train an Artificial Intelligence (AI) model to: identify the repetitive pattern; and identify, for each of the set of dynamic DOM elements, the corresponding path and the path position within the application code component; and automatically execute a code for each of the set of dynamic DOM elements based on the associated path and path position, wherein the code for each of the set of dynamic DOM elements are executed in conformance with the sequence within the repetitive pattern.