5 results about "Product reviews" patented technology

There are provided systems and methods for intelligent detection and acquisition of authentic product reviews for cross-platform availability. A service provider may provide computing services to merchants and users for processing various interactions, such as purchasing items electronically. When items or other products are purchased, users may leave reviews. To determine an authenticity of the reviews, the service provider may utilize an intelligent system that may include an LLM or other generative AI. The system may automatically generate questions for users that may be designed by the LLM to elicit responses that verify whether a review is authentic and / or relevant. This may include receiving responses and scoring those responses to verify the user is providing an authentic review. If so, the review may be written to a blockchain, which may allow the review to be pushed to users and / or automatically injected to product pages and checkout flows.

An object of a system according to an embodiment is to accurately and quickly analyze review information of a product, extract important information, and generate and update a summary.SOLUTION: A system includes a collection part, an analysis part, a summarization part, and an update part. The collection unit collects data of product reviews. The analysis unit analyzes the review information collected by the collection unit using a natural language processing technique. The summarizer extracts important information based on the analysis result obtained by the analyzer, and generates a summary. The updating unit updates the summary generated by the summarizing unit in real time.SELECTED DRAWING: Figure 1

Disclosed are a method, a device, and a system for evaluating a product review. The method for evaluating a product review may: display, by using a server, a search box, through which the product review can be searched, on a buyer terminal; input a keyword on the basis of the search box; extract at least one piece of product review information as a search result corresponding to the input keyword; display the extracted product review information on the buyer terminal; and when a purchase decision is made in a shopping mall introduced on the basis of the displayed product review information, input, from the buyer terminal, a review evaluation corresponding to the product review. Therefore, it is possible to associate the review evaluation of the product review with the product review.

A system is provided.SOLUTION: A system comprising: means for collecting merchandise reviews based on a query received from a user; means for analyzing the collected merchandise reviews using natural-language processing techniques to classify the reviews and extract key phrases; means for evaluating the reliability of the reviews and filtering out stealth marketing and false reviews; means for generating an easy-to-read summary from the analyzed reviews; means for generating a personalized review based on the user's browsing and purchasing histories; and means for displaying the generated summary on the user's device.SELECTED DRAWING: Figure 1

The present disclosure relates to the technical field of artificial intelligence, and particularly relates to a product review aspect sentiment analysis method and device, electronic equipment, storage medium and program product. The product review aspect sentiment analysis method comprises: obtaining target product review data; inputting the target product review data and a prompt instruction into a fine-tuning model to obtain an aspect category and a sentiment tendency corresponding to the target product review data; wherein the fine-tuning model is obtained by performing LoRA fine-tuning on a base model based on sample product review data and a preset prompt instruction Prompt template. The present disclosure uses a fine-tuning model obtained by fine-tuning a base model in a LoRA lightweight manner, and realizes product review fine-grained sentiment analysis in combination with a prompt instruction, without relying on a static sentiment dictionary and a syntax analyzer, can effectively adapt to network new words, dynamic semantics and non-standard comments in spoken language, and can accurately identify multi-dimensional product aspects and corresponding sentiment tendencies in complex comments.