# US 2019/0318295 A1 · Automated ticket resolution > Agents: this data is available as JSON from the patentgrep API and MCP server — see https://patentgrep.com/llms.txt - Number: US 2019/0318295 A1 - Document ID: US20190318295A1 - Status: Published application - Kind: A1 - Application number: 16/456,479 - Filed: 2019-06-28 - Published: 2019-10-17 - Earliest claimed: 2017-03-13 - Assignees: Accenture Global Solutions Limited - Applicants: Accenture Global Solutions Limited - Inventors: Krupa SRIVASTAVA; Vijayaraghavan Koushik; Rajendra T. Prasad; VijayChandar Natesan; Rajesh Pappu; Chetan Kumar Kaikady; David Golding; Luke Higgins; Leon Whine; Raanan Zion; Sunder Sankararaman; Krishankumar Dorai Rangarajan; Radhu Selvaraj; Lakshmi Paramasivam; Udayakumar Nagarajan; Parvatha Narayanan; Chandrasskhar Athinarayanan; Senthil Kumar Venugopal; Srinivasan Rangaswamy - Web page: https://patentgrep.com/patent/US20190318295A1 ## CPC - G06Q 10/063118: {Staff planning in a project environment} - G06N 5/04: Inference or reasoning models - G06N 20/00: Machine learning - G06Q 10/06313: {Resource planning in a project environment} - G06Q 10/103: {Workflow collaboration or project management} - H04L 41/16: using machine learning or artificial intelligence - H04L 41/40: using virtualisation of network functions or resources, e.g. SDN or NFV entities - H04L 41/5074: Handling of user complaints or trouble tickets - H04L 41/5096: {wherein the managed service relates to distributed or central networked applications} - H04L 67/1097: for distributed storage of data in networks, e.g. transport arrangements for network file system [NFS], storage area networks [SAN] or network attached storage [NAS] - 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 - G06N: COMPUTING ARRANGEMENTS BASED ON SPECIFIC COMPUTATIONAL MODELS - H04L: TRANSMISSION OF DIGITAL INFORMATION, e.g. TELEGRAPHIC COMMUNICATION (arrangements common to telegraphic and telephonic communication H04M) ## Abstract A device may communicate with a server to obtain historical ticket data. The device may generate a data model, based on the historical ticket data. The device may communicate with a client device to obtain ticket data relating to an issue associated with a project. The device may classify, using the data model, the ticket data into a ticket type. The device may generate, using the data model and based on the ticket type, a set of recommended resolutions for resolving the issue associated with the project. The device may select, from the set of recommended resolutions, a particular resolution based on a set of selection criteria. The device may automatically perform one or more actions to implement the particular resolution to resolve the issue associated with the project. ## Claims ### Claim at position 1 1-20. (canceled) ### Claim 21 A method comprising: classifying, by a device and using a data model to apply a natural language processing technique, ticket data relating to an issue into a first ticket type; modifying, by the device and based on receiving an indication to modify a classification of the ticket data, the classification of the ticket data from the first ticket type to a second ticket type; processing, by the device, using the data model, and based on modifying the classification of the ticket data, historical ticket data; generating, by the device, using the data model, and based on processing the historical ticket data, a set of recommended resolutions for resolving the issue; applying, by the device, a machine learning technique to one or more of the ticket data or the historical ticket data, the machine learning technique being applied to predict another issue; generating, by the device, additional ticket data based on predicting the other issue, the additional ticket data being different than the ticket data; and generating, by the device and based on processing the additional ticket data using the data model, another set of recommended resolutions for resolving the other issue. ### Claim 22 (depends on 21) The method of claim 21, further comprising: crawling an internet for the historical ticket data; and wherein processing the historical ticket data comprises: processing the historical ticket data based on crawling the internet for the historical ticket data. ### Claim 23 (depends on 21) The method of claim 21, further comprising: generating the data model based on obtaining the historical ticket data. ### Claim 24 (depends on 21) The method of claim 21, wherein processing the historical ticket data comprises: processing a subset of the historical ticket data having the second ticket type. ### Claim 25 (depends on 21) The method of claim 21, further comprising: selecting a particular resolution, of the set of recommended resolutions, by applying a machine learning technique or a natural language processing technique; and implementing the particular resolution. ### Claim 26 (depends on 25) The method of claim 25, further comprising: updating a ticket data archive based on selecting the particular resolution. ### Claim 27 (depends on 25) The method of claim 25, further comprising: monitoring a progress of an implementation of the particular resolution; and providing a status update based on monitoring the progress. ### Claim 28 A device, comprising: one or more memories; and one or more processors communicatively coupled to the one or more memories, configured to: classify, using a data model to apply a natural language processing technique, ticket data relating to an issue into a first ticket type; modify, based on receiving an indication to modify a classification of the ticket data, the classification of the ticket data from the first ticket type to a second ticket type; process using the data model, and based on modifying the classification of the ticket data, historical ticket data; generate using the data model, and based on processing the historical ticket data, a set of recommended resolutions for resolving the issue; apply a machine learning technique to one or more of the ticket data or the historical ticket data, the machine learning technique being applied to predict another issue; generate additional ticket data based on predicting the other issue, the additional ticket data being different than the ticket data; and generate, based on processing the additional ticket data using the data model, another set of recommended resolutions for resolving the other issue. ### Claim 29 (depends on 28) The device of claim 28, wherein the one or more processors are further configured to: crawl an internet for the historical ticket data; and wherein the one or more processors, when processing the historical ticket data, are configured to: process the historical ticket data based on crawling the internet for the historical ticket data. ### Claim 30 (depends on 28) The device of claim 28, wherein the one or more processors are further configured to: generate the data model based on obtaining the historical ticket data. ### Claim 31 (depends on 28) The device of claim 28, wherein the one or more processors, when processing the historical ticket data, are configured to: process a subset of the historical ticket data having the second ticket type. ### Claim 32 (depends on 28) The device of claim 28, wherein the one or more processors are further configured to: select a particular resolution, of the set of recommended resolutions, by applying a machine learning technique or a natural language processing technique; and implement the particular resolution. ### Claim 33 (depends on 32) The device of claim 32, wherein the one or more processors are further configured to: update a ticket data archive based on selecting the particular resolution. ### Claim 34 (depends on 32) The device of claim 32, wherein the one or more processors are further configured to: monitor a progress of an implementation of the particular resolution; and provide a status update based on monitoring the progress. ### Claim 35 A non-transitory computer-readable medium storing instructions, the instructions comprising: one or more instructions that, when executed by one or more processors, cause the one or more processors to: classify, using a data model to apply a natural language processing technique, ticket data relating to an issue into a first ticket type; modify, based on receiving an indication to modify a classification of the ticket data, the classification of the ticket data from the first ticket type to a second ticket type; process using the data model, and based on modifying the classification of the ticket data, historical ticket data; generate using the data model, and based on processing the historical ticket data, a set of recommended resolutions for resolving the issue; apply a machine learning technique to one or more of the ticket data or the historical ticket data, the machine learning technique being applied to predict another issue; generate additional ticket data based on predicting the other issue, the additional ticket data being different than the ticket data; and generate, based on processing the additional ticket data using the data model, another set of recommended resolutions for resolving the other issue. ### Claim 36 (depends on 35) The non-transitory computer-readable medium of claim 35, wherein the one or more instructions, when executed by the one or more processors, further cause the one or more processors to: crawl an internet for the historical ticket data; and wherein the one or more instructions, that cause the one or more processors to process the historical ticket data, cause the one or more processors to: process the historical ticket data based on crawling the internet for the historical ticket data. ### Claim 37 (depends on 35) The non-transitory computer-readable medium of claim 35, wherein the one or more instructions, when executed by the one or more processors, further cause the one or more processors to: generate the data model based on obtaining the historical ticket data. ### Claim 38 (depends on 35) The non-transitory computer-readable medium of claim 35, wherein the one or more instructions, that cause the one or more processors to process the historical ticket data, cause the one or more processors to: process a subset of the historical ticket data having the second ticket type. ### Claim 39 (depends on 35) The non-transitory computer-readable medium of claim 35, wherein the one or more instructions, when executed by the one or more processors, further cause the one or more processors to: select a particular resolution, of the set of recommended resolutions, by applying a machine learning technique or a natural language processing technique; and implement the particular resolution. ### Claim 40 (depends on 39) The non-transitory computer-readable medium of claim 39, wherein the one or more instructions, when executed by the one or more processors, further cause the one or more processors to: monitor a progress of an implementation of the particular resolution; and provide a status update based on monitoring the progress.