Predicting customer interaction using deep learning model
US12079826B1 · kind B1 · utility
Assignee
Inventors
Key dates
| Filing date | Jun 25, 2021 |
| Grant date | Sep 3, 2024 |
| Priority date | — |
| Expiry date | Jun 25, 2041 |
Classification
- Technology area (CPC G)Physics
- CPC primaryG06N20/00
- WIPO fieldComputer technology
- WIPO sectorElectrical engineering
Abstract
Techniques are described for personalizing customer interactions using one or more machine learning models for customer communications. For example, a computing system includes a memory and one or more processors in communication with the memory. The one or more processors are configured to: retrieve, from a database in memory, one or more sets of emotion factor values for communication data associated with a customer over time; classify, using an emotion propensity model running on the one or more processors, the customer into an emotional profile according to the customer's typical emotional response during customer communications based on the one or more sets of emotion factor values for the communication data associated with the customer over time; and determine a probability that the customer will respond positively to a particular type of customer engagement based on the emotional profile for the customer.
Source: USPTO / EPO open patent data. Objective bibliographic and citation counts.