公开(公告)号：US20250165678A1

公开(公告)日：2025-05-22

申请号：US18511873

申请日：2023-11-16

Applicant: Roku, Inc.

Inventor： Gak Wee Low ,  Sunil Ramesh ,  Michael Patrick Cutter ,  Andrew Peter Fogg ,  Michael Richard Sanders

IPC: G06F30/27

Abstract: Conducting user research with human users is time consuming and expensive. Provided with quality training data that represents human users having different personae, models can be trained to offer responses that can emulate different human users. The models may offer synthetic users that can extract information from and respond to inputs to the models. The inputs can include prompts and responses can include answers to the prompts. In some cases, data about human users can be used to build different synthetic user memories, which may be used to generate different prompt chains corresponding to different users. The prompt chain can be used to prompt a model to respond based on the contextual information in the synthetic user memory.

公开(公告)号：US20250166026A1

公开(公告)日：2025-05-22

申请号：US18788469

申请日：2024-07-30

Applicant: Roku, Inc.

Inventor： Arthur Man Lok ,  Michael Richard Sanders ,  Ritwick Babbar ,  Ronica Jethwa ,  Sunil Ramesh

IPC: G06Q30/0282

Abstract: There is significant manual work in analyzing product reviews. Even if there are sufficient resources, human reviews can be inconsistent and subjective. A product review analysis platform leveraging engineered prompts and a large language model can address some of these issues. The platform includes a pipeline to summarize reviews, produce sentiment scores to rating categories, detect negative sentiment, extract main categories tags, extract sub-categories tags within a main categories tag, and produce weekly summaries. A dashboard can be included to visualize the enriched reviews. In some cases, synthetic users may fill in data gaps. An action recommendation engine can be included to determine appropriate resolutions. In some cases, the feature vectors generated by the large language model in response to receiving an engineered prompt can be stored in a vector database along with appropriate resolutions, such that incoming reviews can be routed appropriately using the vector database.