# US 2020/0401885 A1 · Collaborative real-time solution efficacy > Agents: this data is available as JSON from the patentgrep API and MCP server — see https://patentgrep.com/llms.txt - Number: US 2020/0401885 A1 - Document ID: US20200401885A1 - Status: Published application - Kind: A1 - Application number: 16/521,755 - Filed: 2019-07-25 - Published: 2020-12-24 - Earliest claimed: 2019-06-19 - Assignees: International Business Machines Corporation - Applicants: International Business Machines Corporation - Inventors: Trudy L. Hewitt; Kelley Anders; Jonathan D. Dunne; Lisa Ann Cassidy; Jeremy R. Fox; Pauric Grant - Web page: https://patentgrep.com/patent/US20200401885A1 ## CPC - G06N 3/08: Learning methods - G06F 11/3006: {where the computing system is distributed, e.g. networked systems, clusters, multiprocessor systems (multiprogramming arrangements G06F9/46; allocation of resources G06F9/50)} - G06F 11/302: {where the computing system component is a software system} - G06F 11/3409: {for performance assessment} - 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 7/01: Probabilistic graphical models, e.g. probabilistic networks - G06N 20/00: Machine learning - G06F 11/3447: {Performance evaluation by modeling} - G06N: COMPUTING ARRANGEMENTS BASED ON SPECIFIC COMPUTATIONAL MODELS - G06F: ELECTRIC DIGITAL DATA PROCESSING (computer systems based on specific computational models G06N) ## Abstract In an approach to determining the effectiveness of a proposed solution, one or more computer processors monitor real-time communications. The one or more computer processors identify or more topics associated with the monitored real-time communications. The one or more computer processors feed the identified one or more topics and associated real-time communications into a solution efficacy model. The one or more computer processors generate based on one or more calculations by the solution efficacy model, an efficacy rating for the identified real-time communications. The one or more computer processors generate a prioritization of the identified real-time communications based on the generated efficacy rating. ## Claims ### Claim 1 A computer-implemented method comprising: monitoring, by one or more computer processors, real-time communications; identifying, by one or more computer processors, one or more topics associated with the monitored real-time communications; feeding, by one or more computer processors, the identified one or more topics and associated real-time communications into a solution efficacy model; generating, by one or more computer processors, based on one or more calculations by the solution efficacy model, an efficacy rating for the identified real-time communications; and generating, by one or more computer processors, a prioritization of the identified real-time communications based on the generated efficacy rating. ### Claim 2 (depends on 1) The method of claim 1, further comprises: retrieving, by one or more computer processors, historical communications; determining, by one or more computer processors, one or more topics associated with the retrieved historical communications; determining, by one or more computer processors, one or more solutions associated with the determined topics; and training, by one or more computer processors, the solution efficacy model based, at least in part, on the determined solutions and determined topics. ### Claim 3 (depends on 2) The method of claim 2, further comprises: feeding, by one or more computer processors, the identified one or more topics associated with the monitored real-time communications into the trained solution efficacy model; and generating, by one or more computer processors, based on one or more calculations by the trained solution efficacy model, an efficacy rating for the fed real-time communications. ### Claim 4 (depends on 2) The method of claim 2, wherein the one or more topics associated with historical communications are limited by a plurality of features selected from the group consisting of: identified one or more authors, related problem sets, related solutions, related programmatic conventions, and temporal events. ### Claim 5 (depends on 1) The method of claim 1, wherein generating the prioritization of the identified real-time communications based on the generated efficacy rating, further comprises: presenting, by one or more computer processors, the identified real-time communications distinguishably from prior communications based on the generated efficacy rating. ### Claim 6 (depends on 5) The method of claim 5, wherein presenting the identified real-time communications, further comprises: transforming, by one or more computer processors, one or more stylistic elements of the real-time communications based on the generated efficacy rating, wherein stylistic elements are selected from the group consisting of: font, font size, character style, font color, background color, capitalizations, general transparency, and relative transparency; and presenting, by one or more computer processors, the transformed one or more stylistic elements. ### Claim 7 (depends on 1) The method of claim 1, wherein the solution efficacy model is a recurrent neural network. ### Claim 8 (depends on 1) The method of claim 1, wherein the solution efficacy model is a latent Dirichlet allocation. ### Claim 9 (depends on 1) The method of claim 1, further comprises: logging, by one or more computer processors, real-time communications, associated topics, associated solutions, and generated efficacy ratings into a linguistic corpus; and retraining, by one or more computer processors, the solution efficacy model utilizing the linguistic corpus.