US20260127652
2026-05-07
Physics
G06Q30/0631
Generative artificial intelligence (GenAI) is leveraged to enhance user research through the use of synthetic personas. This approach addresses the challenges of traditional user research, such as the high cost and difficulty in assembling representative human focus groups. By utilizing GenAI, synthetic personas can simulate human users, allowing for more flexible and scalable user research.
The method involves collecting data across various categories, including individual user profiles, product features, company metadata, and historical data. This data is processed using a neural network to evaluate and score the data in each category. These scores are then aggregated to produce an overall fit score, guiding the recommendation of areas that require evaluation related to the product or service.
Based on the collected user profile data, synthetic personas are generated. These personas are GenAI-generated profiles that simulate users and can be used to evaluate the identified areas of focus. Synthetic personas are created using models trained on extensive human data, allowing them to mimic various user characteristics and behaviors.
Synthetic personas engage in interactions to generate and test ideas, optimizing products and services for market success. These interactions include conversations where personas contribute feedback, which can imagine new product characteristics beyond current offerings. Multiple versions of each persona can be created to explore different thinking and learning modalities.
The approach provides actionable recommendations by running scalable user research scenarios with synthetic personas. A unique feature allows personas to "forget" previous experiments, eliminating bias and enabling precise variable testing. Feedback from synthetic personas can simulate users from different geo-locations or cultural backgrounds, offering tailored product recommendations for specific markets.