US 12,437,159 B2
Methods and systems for enhanced searching of conversation data and related analytics in a contact center
Lev Haikin, Tel-Aviv (IL); Avraham Faizakof, Tel-Aviv (IL); Nelly David, Tel-Aviv (IL); Eyal Orbach, Tel-Aviv (IL); and Rotem Maoz, Tel-Aviv (IL)
Filed by GENESYS CLOUD SERVICES, INC., Menlo Park, CA (US)
Filed on Nov. 2, 2023, as Appl. No. 18/386,335.
Prior Publication US 2025/0165720 A1, May 22, 2025
Int. Cl. G06F 40/40 (2020.01); G06F 16/951 (2019.01); G06F 40/279 (2020.01); G06F 40/35 (2020.01); G06Q 30/015 (2023.01)
CPC G06F 40/40 (2020.01) [G06F 16/951 (2019.01); G06F 40/279 (2020.01); G06F 40/35 (2020.01); G06Q 30/015 (2023.01)] 20 Claims
OG exemplary drawing
 
1. A computer-implemented method for facilitating contact center analytics related to abstractive search, wherein the method includes an offline indexing process for generating insights from conversation data derived from interactions of the contact center and storing the generated insights in an index that enables abstractive search, wherein the conversation data for a given interaction comprising text of a natural language conversation occurring between an agent of the contact center and a customer during the given interaction, and wherein, when described in relation to an exemplary first interaction of the interactions from which a first insight of the insights is generated, the offline indexing process comprises the steps of:
receiving the conversation data for the first interaction;
determining an insight type for generating as the first insight;
based on the insight type, determining inputs, the inputs including:
a question prompt;
an answer prefix; and
a relevant portion of the conversation data;
inputting the determined inputs into a large language model (LLM), wherein the LLM is configured to receive the inputs and generate output text answering a question contained in the question prompt pursuant to an answer form suggested by the answer prefix given content contained in the relevant portion of the conversation data of the first interaction;
generating the output text via operation of the LLM, the generated output text comprising the first insight;
transforming the output text of the first insight via a sentence transformer, wherein the sentence transformer comprises an embeddings language-model configured to transform the output text by computing a vector embedding representative of a semantic meaning of the output text; and
storing the computed vector embedding of the first insight in the index.