# US 2023/0056637 A1 · Hardware and software configuration management and deployment > Agents: this data is available as JSON from the patentgrep API and MCP server — see https://patentgrep.com/llms.txt - Number: US 2023/0056637 A1 - Document ID: US20230056637A1 - Status: Published application - Kind: A1 - Application number: 17/445,338 - Filed: 2021-08-18 - Published: 2023-02-23 - Earliest claimed: 2021-08-18 - Assignees: Kyndryl, Inc. - Applicants: Kyndryl, Inc. - Inventors: Joseph Reyes; Andrew S. Kronstadt; Rennad Murugan; Marci Devorah Formato; Jennifer L. Szkatulski; Bernhard Julius Klingenberg - Web page: https://patentgrep.com/patent/US20230056637A1 ## CPC - G06F 11/3442: {for planning or managing the needed capacity} - G06N 3/0442: characterised by memory or gating, e.g. long short-term memory [LSTM] or gated recurrent units [GRU] - G06F 9/5005: {to service a request} - G06F 9/505: {considering the load} - G06F 9/5055: {considering software capabilities, i.e. software resources associated or available to the machine} - G06N 3/045: Combinations of networks - G06F 2209/5019: Workload prediction - G06F: ELECTRIC DIGITAL DATA PROCESSING (computer systems based on specific computational models G06N) - G06N: COMPUTING ARRANGEMENTS BASED ON SPECIFIC COMPUTATIONAL MODELS ## Abstract A system, method, and computer program product for implementing configuration solution service management is provided. The method includes ingesting from managed hardware and software service components associated with a first entity, data and associated metadata associated with establishing baseline analysis code. The data and associated metadata are analyzed via execution of code associated with a recurrent neural network and long short-term memory configured to determine if a current managed hardware or software solution service design is associated with client requirements and industry trends. Recommendations, statistical analysis, predictions, a proposed new client profile, and sizing attributes of the additional managed hardware and software service components are generated. In response, additional managed hardware and software service components are configured with respect to operationally functionality for the first entity. ## Claims ### Claim 1 A hardware device comprising a processor coupled to a computer-readable memory unit, said memory unit comprising instructions that when executed by the processor implements a configuration solution service management method comprising: ingesting, by said processor from managed hardware and software service components associated with a first entity, data and associated metadata associated with establishing baseline analysis code; analyzing, by said processor, said data and associated metadata via execution of code associated with a recurrent neural network (RNN) and long short-term memory (LSTM) configured to determine if a current managed hardware or software solution service design is associated with client requirements and industry trends; first generating, by said processor based on results of said analyzing, recommendations, statistical analysis, and predictions associated with implemented managed solution services with respect to said managed hardware and software service components; second generating, by said processor, a proposed new client profile configured to predict future managed solution service offerings associated with additional managed hardware and software service components for said first entity, a sizing of said additional managed hardware and software service components, and current trends within an industry and geography associated with said first entity; and configuring, by said processor based on said proposed new client profile, said additional managed hardware and software service components with respect to operationally functionality for said first entity. ### Claim 2 (depends on 1) The hardware device of claim 1, wherein said establishing said baseline analysis code comprises feeding said data and associated metadata into a centralized managed service database analytics system configured to associate a client system with industry and geography specific trending analysis data and code. ### Claim 3 (depends on 1) The hardware device of claim 1, wherein associated RNN model sequences of said data and associated metadata are executed such that each sample of said data and associated metadata is determined to be dependent from previous samples associated with said data and associated metadata. ### Claim 4 (depends on 3) The hardware device of claim 3, wherein said LSTM is associated with previously generated data within a memory structure. ### Claim 5 (depends on 3) The hardware device of claim 3, wherein said LSTM is configured to be executed to classify, process, predict, and train a model generated as a result of said analyzing. ### Claim 6 (depends on 1) The hardware device of claim 1, wherein said first generating is executed based on: client requirements of said entity with respect to client requirements of additional entities within the same industry of said entity, geographical and custom configurations hardware and software service components of said additional entities, and industry trends associated with said additional entities and determined via execution of said code associated with said RNN and said LSTM. ### Claim 7 (depends on 1) The hardware device of claim 1, wherein said method further comprises: deploying, by said processor with respect to results of said configuring, said additional managed hardware and software service components with respect to a facility of said first entity. ### Claim 8 (depends on 1) The hardware device of claim 1, wherein said data and associated metadata comprise entity related data selected from the group consisting of industry related data, geographical related data, and hardware and software related data. ### Claim 9 A configuration solution service management method comprising: ingesting, by a processor of a hardware device from managed hardware and software service components associated with a first entity, data and associated metadata associated with establishing baseline analysis code; analyzing, by said processor, said data and associated metadata via execution of code associated with a recurrent neural network (RNN) and long short-term memory (LSTM) configured to determine if a current managed hardware or software solution service design is associated with client requirements and industry trends; first generating, by said processor based on results of said analyzing, recommendations, statistical analysis, and predictions associated with implemented managed solution services with respect to said managed hardware and software service components; second generating, by said processor, a proposed new client profile configured to predict future managed solution service offerings associated with additional managed hardware and software service components for said first entity, a sizing of said additional managed hardware and software service components, and current trends within an industry and geography associated with said first entity; and configuring, by said processor based on said proposed new client profile, said additional managed hardware and software service components with respect to operationally functionality for said first entity. ### Claim 10 (depends on 9) The method of claim 9, wherein said establishing said baseline analysis code comprises feeding said data and associated metadata into a centralized managed service database analytics system configured to associate a client system with industry and geography specific trending analysis data and code. ### Claim 11 (depends on 9) The method of claim 9, wherein associated RNN model sequences of said data and associated metadata are executed such that each sample of said data and associated metadata is determined to be dependent from previous samples associated with said data and associated metadata. ### Claim 12 (depends on 11) The method of claim 11, wherein said LSTM is associated with previously generated data within a memory structure. ### Claim 13 (depends on 11) The method of claim 11, wherein said LSTM is configured to be executed to classify, process, predict, and train a model generated as a result of said analyzing. ### Claim 14 (depends on 11) The method of claim 11, wherein said first generating is executed based on: client requirements of said entity with respect to client requirements of additional entities within the same industry of said entity, geographical and custom configurations hardware and software service components of said additional entities, and industry trends associated with said additional entities and determined via execution of said code associated with said RNN and said LSTM. ### Claim 15 (depends on 9) The method of claim 9, further comprising: deploying, by said processor with respect to results of said configuring, said additional managed hardware and software service components with respect to a facility of said first entity. ### Claim 16 (depends on 9) The method of claim 9, wherein said data and associated metadata comprise entity related data selected from the group consisting of industry related data, geographical related data, and hardware and software related data. ### Claim 17 (depends on 9) The method of claim 9, further comprising: providing at least one support service for at least one of creating, integrating, hosting, maintaining, and deploying computer-readable code in the hardware device, said code being executed by the processor to implement: said ingesting, said analyzing, said first generating, said second generating, and said configuring. ### Claim 18 A computer program product, comprising a computer readable hardware storage device storing a computer readable program code, said computer readable program code comprising an algorithm that when executed by a processor of a hardware device implements a configuration solution service management method, said method comprising: ingesting, by said processor from managed hardware and software service components associated with a first entity, data and associated metadata associated with establishing baseline analysis code; analyzing, by said processor, said data and associated metadata via execution of code associated with a recurrent neural network (RNN) and long short-term memory (LSTM) configured to determine if a current managed hardware or software solution service design is associated with client requirements and industry trends; first generating, by said processor based on results of said analyzing, recommendations, statistical analysis, and predictions associated with implemented managed solution services with respect to said managed hardware and software service components; second generating, by said processor, a proposed new client profile configured to predict future managed solution service offerings associated with additional managed hardware and software service components for said first entity, a sizing of said additional managed hardware and software service components, and current trends within an industry and geography associated with said first entity; and configuring, by said processor based on said proposed new client profile, said additional managed hardware and software service components with respect to operationally functionality for said first entity. ### Claim 19 (depends on 18) The computer program product of claim 18, wherein said establishing said baseline analysis code comprises feeding said data and associated metadata into a centralized managed service database analytics system configured to associate a client system with industry and geography specific trending analysis data and code. ### Claim 20 (depends on 18) The computer program product of claim 18, wherein associated RNN model sequences of said data and associated metadata are executed such that each sample of said data and associated metadata is determined to be dependent from previous samples associated with said data and associated metadata.