# US 11,443,186 B2 · Method and system for executing processes in operating systems > Agents: this data is available as JSON from the patentgrep API and MCP server — see https://patentgrep.com/llms.txt - Number: US 11,443,186 B2 - Document ID: US11443186B2 - Status: Granted - Kind: B2 - Application number: 16/701,419 - Filed: 2019-12-03 - Published: 2021-04-08 - Granted: 2022-09-13 - Earliest claimed: 2019-10-03 - Assignees: Wipro Limited - Applicants: Wipro Limited - Inventors: Rishav Das - Web page: https://patentgrep.com/patent/US11443186B2 ## CPC - G06F 9/4843: {by program, e.g. task dispatcher, supervisor, operating system} - G06F 9/3891: {organised in groups of units sharing resources, e.g. clusters} - G06F 9/4881: {Scheduling strategies for dispatcher, e.g. round robin, multi-level priority queues} - G06F 16/9027: {Trees} - G06F 16/9035: Filtering based on additional data, e.g. user or group profiles - G06F 18/23: Clustering techniques - G06F 18/2413: based on distances to training or reference patterns - 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 3/08: Learning methods - G06N 3/088: Non-supervised learning, e.g. competitive learning - G06N 3/09: Supervised learning - G06F: ELECTRIC DIGITAL DATA PROCESSING (computer systems based on specific computational models G06N) - G06N: COMPUTING ARRANGEMENTS BASED ON SPECIFIC COMPUTATIONAL MODELS ## Abstract A method and system for executing processes in an operating system is disclosed. The method may include assigning a tag Identifier (ID) and a first tree ID to each of a plurality of processes based on associated at least one attribute. The method may further include determining content patterns associated with each of the plurality of processes using a deep learning network. The method may include assigning a second tree ID to each of at least one process from the plurality of processes based on the identified content pattern. The method may further include generating a set of clusters for the plurality of processes based on the second tree ID assigned to each of the at least one process. The method may include executing each process within a cluster from the set of clusters based on execution of a single process within the cluster. ## Claims ### Claim 1 A method for executing processes in an operating system, the method comprising: assigning, by a scheduling device, a tag Identifier (ID) and a first tree ID to each of a plurality of processes based on associated at least one attribute; determining, by the scheduling device, content pattern associated with each of the plurality of processes using a deep learning network; assigning, by the scheduling device, a second tree ID to each of at least one process from the plurality of processes based on the identified content pattern; generating, by the scheduling device, a set of clusters for the plurality of processes based on the second tree ID assigned to each of the at least one process; and executing, by the scheduling device, each process within a cluster from the set of clusters based on execution of a single process within the cluster. ### Claim 2 (depends on 1) The method of claim 1, further comprising: converting a plurality of user actions into the plurality of processes; and storing the plurality of processes in response to the converting, wherein each of the plurality of processes is assigned a process ID. ### Claim 3 (depends on 2) The method of claim 2, wherein the tree ID is same as the tag ID for a process from the plurality of processes, wherein the process forms a node in a tree represented by the tree ID. ### Claim 4 (depends on 1) The method of claim 1, wherein the at least one attribute associated with a process from the plurality of processes comprises at least one of an ID of the process, a type associated with the process, or an application associated with the process. ### Claim 5 (depends on 1) The method of claim 1, wherein the deep learning network is a Long Term Short Memory (LSTM) Network, and wherein the LSTM network uses a tan-h activation function. ### Claim 6 (depends on 1) The method of claim 1, wherein each of the plurality of processes comprises a plurality of tokens, and each of the plurality of tokens comprises at least one literal. ### Claim 7 (depends on 6) The method of claim 6, wherein determining the content pattern associated with a process from the plurality of processes comprises identifying the plurality of tokens and the at least one literal within each of the plurality of tokens by the deep learning network. ### Claim 8 (depends on 7) The method of claim 7, further comprising identifying the content pattern within the process as an existing content pattern, based on identification of the plurality of tokens and the at least one literal within each of the plurality of tokens. ### Claim 9 (depends on 7) The method of claim 7, further comprising: identifying the content pattern within the process as a new content pattern, based on identification of the plurality of tokens and the at least one literal within each of the plurality of tokens; and training the deep learning network for the new content pattern. ### Claim 10 (depends on 1) The method of claim 1 further comprising reusing output of the process executed from the cluster to execute each of remaining processes within the cluster. ### Claim 11 A system for executing processes in an operating system, 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: assign a tag Identifier (ID) and a first tree ID to each of a plurality of processes based on associated at least one attribute; determine content pattern associated with each of the plurality of processes using a deep learning network; assign a second tree ID to each of at least one process from the plurality of processes based on the identified content pattern; generate a set of clusters for the plurality of processes based on the second tree ID assigned to each of the at least one process; and execute each process within a cluster from the set of clusters based on execution of a single process within the cluster. ### Claim 12 (depends on 11) The system of claim 11, wherein the processor instructions further cause the processor to: convert plurality of user actions into the plurality of processes; and store the plurality of processes in response to the converting, wherein each of the plurality of processes is assigned a process ID. ### Claim 13 (depends on 11) The system of claim 11, wherein the at least one attribute associated with a process from the plurality of processes comprises at least one of an ID of the process, a type associated with the process, or an application associated with the process. ### Claim 14 (depends on 11) The system of claim 11, wherein the deep learning network is a Long Term Short Memory (LSTM) Network, and wherein the LSTM network uses a tan-h activation function. ### Claim 15 (depends on 11) The system of claim 11, wherein each of the plurality of processes comprises a plurality of tokens, and each of the plurality of tokens comprises at least one literal. ### Claim 16 (depends on 15) The system of claim 15, wherein to determine the content pattern associated with a process from the plurality of processes, the processor instructions further cause the processor to identify the plurality of tokens and the at least one literal within each of the plurality of tokens by the deep learning network. ### Claim 17 (depends on 16) The system of claim 16, wherein the processor instructions further cause the processor to identify the content pattern within the process as an existing content pattern, based on identification of the plurality of tokens and the at least one literal within each of the plurality of tokens. ### Claim 18 (depends on 17) The system of claim 17, wherein the processor instructions further cause the processor to: identify the content pattern within the process as a new content pattern, based on identification of the plurality of tokens and the at least one literal within each of the plurality of tokens; and train the deep learning network for the new content pattern. ### Claim 19 (depends on 11) The system of claim 11, wherein the processor instructions further cause the processor to reuse output of the process executed from the cluster to execute each of remaining processes within the cluster. ### Claim 20 A non-transitory computer-readable storage medium having stored thereon, a set of computer-executable instructions causing a computer comprising one or more processors to perform steps comprising: assigning a tag Identifier (ID) and a first tree ID to each of a plurality of processes based on associated at least one attribute; determining content pattern associated with each of the plurality of processes using a deep learning network; assigning a second tree ID to each of at least one process from the plurality of processes based on the identified content pattern; generating a set of clusters for the plurality of processes based on the second tree ID assigned to each of the at least one process; and executing each process within a cluster from the set of clusters based on execution of a single process within the cluster.