# US 2020/0401905 A1 · Latent computing property preference discovery and computing environment migration plan recommendation > Agents: this data is available as JSON from the patentgrep API and MCP server — see https://patentgrep.com/llms.txt - Number: US 2020/0401905 A1 - Document ID: US20200401905A1 - Status: Published application - Kind: A1 - Application number: 16/447,166 - Filed: 2019-06-20 - Published: 2020-12-24 - Earliest claimed: 2019-06-20 - Assignees: International Business Machines Corporation - Applicants: International Business Machines Corporation - Inventors: Jinho Hwang; Maja Vukovic; John Rofrano; Anup Kalia; Ya Bin Dang; Jie Ma; Lijun Mei - Web page: https://patentgrep.com/patent/US20200401905A1 ## CPC - G06N 5/02: Knowledge representation; Symbolic representation - G06N 20/10: using kernel methods, e.g. support vector machines [SVM] - G06F 9/45558: {Hypervisor-specific management and integration aspects} - G06F 2009/4557: {Distribution of virtual machine instances; Migration and load balancing} - G06F 2009/45591: {Monitoring or debugging support} - G06N 3/08: Learning methods - G06N 5/01: Dynamic search techniques; Heuristics; Dynamic trees; Branch-and-bound - G06N 7/01: Probabilistic graphical models, e.g. probabilistic networks - G06N: COMPUTING ARRANGEMENTS BASED ON SPECIFIC COMPUTATIONAL MODELS - G06F: ELECTRIC DIGITAL DATA PROCESSING (computer systems based on specific computational models G06N) ## Abstract Systems, computer-implemented methods, and computer program products that can facilitate computing environment migration plan recommendation based on one or more latent entity computing property preferences are provided. According to an embodiment, a system can comprise a memory that stores computer executable components and a processor that executes the computer executable components stored in the memory. The computer executable components can comprise an analysis component that employs a model to discover a latent computing property preference of an entity operating in a first computing environment. The computer executable components can further comprise a recommendation component that recommends a computing environment migration plan to a second computing environment based on the latent computing property preference of the entity. In some embodiments, the recommendation component recommends discovered latent computing property preferences of the entity to construct the computing environment migration plan. ## Claims ### Claim 1 A system, comprising: a memory that stores computer executable components; and a processor that executes the computer executable components stored in the memory, wherein the computer executable components comprise: an analysis component that employs a model to discover a latent computing property preference of an entity operating in a first computing environment; and a recommendation component that recommends a computing environment migration plan to a second computing environment based on the latent computing property preference of the entity. ### Claim 2 (depends on 1) The system of claim 1, wherein the model comprises at least one of an artificial intelligence model, a topic model, or a latent Dirichlet allocation model. ### Claim 3 (depends on 1) The system of claim 1, wherein the analysis component discovers the latent computing property preference of the entity based on data corresponding to the entity comprising at least one of entity interview data, entity source data, entity application configuration data, entity middleware data, entity network data, entity storage device data, entity location data, or entity documentation data. ### Claim 4 (depends on 1) The system of claim 1, wherein the analysis component employs the model to map one or more hierarchical topics indicative of one or more latent computing property preferences of the entity to one or more classes indicative of one or more computing properties of the second computing environment using one or more bipartite graphs. ### Claim 5 (depends on 1) The system of claim 1, wherein the recommendation component recommends the computing environment migration plan based on at least one of: a similarity measure between one or more latent computing property preferences of the entity and one or more computing properties of the second computing environment; or a maximum support measure comprising a ratio of a number of the one or more latent computing property preferences of the entity that correspond to a computing property of the second computing environment and a total number of the one or more latent computing property preferences of the entity. ### Claim 6 (depends on 1) The system of claim 1, wherein the analysis component employs the model to: discover one or more latent computing property preferences of the entity based on feedback data from the entity corresponding to the latent computing property preference of the entity; or modify the latent computing property preference of the entity based on the feedback data. ### Claim 7 (depends on 1) The system of claim 1, wherein the recommendation component recommends the computing environment migration plan based on feedback data from the entity corresponding to the latent computing property preference of the entity, thereby facilitating at least one of improved implementation time, accuracy, effectiveness, or customization of the computing environment migration plan. ### Claim 8 (depends on 1) The system of claim 1, wherein the computer executable components further comprise at least one of: a data collection component that collects at least one of data corresponding to the entity or one or more computing properties of the second computing environment; or an interface component that presents to the entity the latent computing property preference of the entity and receives feedback data from the entity corresponding to the latent computing property preference of the entity. ### Claim 9 A computer-implemented method, comprising: employing, by a system operatively coupled to a processor, a model to discover a latent computing property preference of an entity operating in a first computing environment; and recommending, by the system, a computing environment migration plan to a second computing environment based on the latent computing property preference of the entity. ### Claim 10 (depends on 9) The computer-implemented method of claim 9, wherein the employing comprises: employing, by the system, at least one of an artificial intelligence model, a topic model, or a latent Dirichlet allocation model to discover the latent computing property preference of the entity. ### Claim 11 (depends on 9) The computer-implemented method of claim 9, wherein the employing comprises: employing, by the system, the model to discover the latent computing property preference of the entity based on data corresponding to the entity comprising at least one of entity interview data, entity source data, entity application configuration data, entity middleware data, entity network data, entity storage device data, entity location data, or entity documentation data. ### Claim 12 (depends on 9) The computer-implemented method of claim 9, further comprising: employing, by the system, the model to map one or more hierarchical topics indicative of one or more latent computing property preferences of the entity to one or more classes indicative of one or more computing properties of the second computing environment using one or more bipartite graphs. ### Claim 13 (depends on 9) The computer-implemented method of claim 9, wherein the recommending comprises: recommending, by the system, the computing environment migration plan based on at least one of: a similarity measure between one or more latent computing property preferences of the entity and one or more computing properties of the second computing environment; or a maximum support measure comprising a ratio of a number of the one or more latent computing property preferences of the entity that correspond to a computing property of the second computing environment and a total number of the one or more latent computing property preferences of the entity. ### Claim 14 (depends on 9) The computer-implemented method of claim 9, further comprising: employing, by the system, the model to discover one or more latent computing property preferences of the entity based on feedback data from the entity corresponding to the latent computing property preference of the entity or to modify the latent computing property preference of the entity based on the feedback data. ### Claim 15 (depends on 9) The computer-implemented method of claim 9, further comprising: recommending, by the system, the computing environment migration plan based on feedback data from the entity corresponding to the latent computing property preference of the entity, thereby facilitating at least one of improved implementation time, accuracy, effectiveness, or customization of the computing environment migration plan. ### Claim 16 A computer program product facilitating computing environment migration plan recommendation based on one or more latent entity computing property preferences, the computer program product comprising a computer readable storage medium having program instructions embodied therewith, the program instructions executable by a processor to cause the processor to: employ, by the processor, a model to discover a latent computing property preference of an entity operating in a first computing environment; and recommend, by the processor, a computing environment migration plan to a second computing environment based on the latent computing property preference of the entity. ### Claim 17 (depends on 16) The computer program product of claim 16, wherein the program instructions are further executable by the processor to cause the processor to: employ, by the processor, at least one of an artificial intelligence model, a topic model, or a latent Dirichlet allocation model to discover the latent computing property preference of the entity based on data corresponding to the entity comprising at least one of entity interview data, entity source data, entity application configuration data, entity middleware data, entity network data, entity storage device data, entity location data, or entity documentation data. ### Claim 18 (depends on 16) The computer program product of claim 16, wherein the program instructions are further executable by the processor to cause the processor to: employ, by the processor, the model to map one or more hierarchical topics indicative of one or more latent computing property preferences of the entity to one or more classes indicative of one or more computing properties of the second computing environment using one or more bipartite graphs. ### Claim 19 (depends on 16) The computer program product of claim 16, wherein the program instructions are further executable by the processor to cause the processor to: recommend, by the processor, the computing environment migration plan based on at least one of: a similarity measure between one or more latent computing property preferences of the entity and one or more computing properties of the second computing environment; or a maximum support measure comprising a ratio of a number of the one or more latent computing property preferences of the entity that correspond to a computing property of the second computing environment and a total number of the one or more latent computing property preferences of the entity. ### Claim 20 (depends on 16) The computer program product of claim 16, wherein the program instructions are further executable by the processor to cause the processor to: employ, by the processor, the model to discover one or more latent computing property preferences of the entity based on feedback data from the entity corresponding to the latent computing property preference of the entity or to modify the latent computing property preference of the entity based on the feedback data. ### Claim 21 (depends on 16) The computer program product of claim 16, wherein the program instructions are further executable by the processor to cause the processor to: recommend, by the processor, the computing environment migration plan based on feedback data from the entity corresponding to the latent computing property preference of the entity. ### Claim 22 A system, comprising: a memory that stores computer executable components; and a processor that executes the computer executable components stored in the memory, wherein the computer executable components comprise: a learner component that learns one or more computing property preference patterns of an entity based on feedback data from the entity corresponding to one or more computing property preferences of the entity; and an analysis component that employs an artificial intelligence model to discover a latent computing property preference of the entity based on the one or more computing property preference patterns of the entity. ### Claim 23 (depends on 22) The system of claim 22, wherein the computer executable components further comprise: a recommendation component that recommends a computing environment migration plan from a first computing environment to a second computing environment based on the latent computing property preference of the entity. ### Claim 24 A computer-implemented method, comprising: learning, by a system operatively coupled to a processor, one or more computing property preference patterns of an entity based on feedback data from the entity corresponding to one or more computing property preferences of the entity; and employing, by the system, an artificial intelligence model to discover a latent computing property preference of the entity based on the one or more computing property preference patterns of the entity. ### Claim 25 (depends on 24) The computer-implemented method of claim 24, further comprising: recommending, by the system, a computing environment migration plan from a first computing environment to a second computing environment based on the latent computing property preference of the entity.