US-12620009-B2 - Method and system for artificial intelligence-based generation of travel and dining reviews

US12620009B2US 12620009 B2US12620009 B2US 12620009B2US-12620009-B2

Abstract

Systems and methods for using an artificial intelligence-based technique for automatic generation of travel and dining reviews are provided. The method includes: receiving a request for a review of an entity that provides a service to a user; applying a first artificial intelligence (AI) algorithm to the received request in order to generate the review of the entity; and outputting the review of the entity. The entity provides either or both of a travel-related service and a dining-related service, and as such, the entity may include a restaurant or a hotel. The AI algorithm may use a large language model and/or may be trained such that the review has a style and a tone of a Zagat review.

Inventors

Christopher Stang
Kevin BICHOUPAN
Allison Beer
Jessica Staddon
Jonathan LALIMA
Hillary REINSBERG
Janko BAZHDAVELA
Ricardo MELA
Vineeth RAVI
Simran LAMBA
Katie HAINSEY

Assignees

JPMORGAN CHASE BANK, N.A.

Dates

Publication Date: 20260505
Application Date: 20240126

Claims (18)

1 . A method for generating a review, the method being implemented by at least one processor, the method comprising: receiving, by the at least one processor, a request for a review of an entity that provides a service to a user; soliciting a user device, by the at least one processor and in response to the receiving of the request for the review, at least user submission data that relates to an aspect of service provided by the entity, wherein the soliciting is performed by automated transmission of at least one message to the user device in order to prompt the user device to provide the at least user submission data; applying, by the at least one processor, a first artificial intelligence (AI) algorithm to the received request and the at least user submission data in order to generate the review of the entity; automatically generating, via the first AI algorithm, the review of the entity, wherein the automatically generating includes determining of an intent in the at least user submission data utilized by the first AI algorithm and creating synthetic embeddings verbatims for the determined intent; checking, by the at least one processor, the automatically generated review of the entity to detect presence of a hallucination by validating the automatically generated review of the entity using one or more transaction data related to the entity; modifying the automatically generated review of the entity by removing the detected hallucination for improved accuracy of the automatically generated review of the entity; and outputting the modified review of the entity that is absent of the detected hallucination, wherein the entity provides at least one from among a travel-related service and a dining-related service.
2 . The method of claim 1 , wherein the entity includes at least one from among a restaurant and a hotel.
3 . The method of claim 1 , wherein the first AI algorithm is configured to use at least one from among a sentiment analysis technique, a parts-of-speech (POS) tagging technique, and an extractive summarization and ranking technique to generate the review.
4 . The method of claim 1 , wherein the first AI algorithm is trained to generate the review conforming to a target style and a target tone.
5 . The method of claim 4 , wherein the first AI algorithm is trained by using historical data that includes previously published reviews conforming to the target style and the target tone.
6 . The method of claim 4 , wherein the first AI algorithm is configured to use a large language model (LLM) to generate the review.
7 . The method of claim 1 , wherein the automated transmission includes: transmitting at least one message to the user device in order to prompt the user to provide a first submission that relates to a first aspect of the at least one from among the travel-related service and the dining-related service.
8 . The method of claim 7 , wherein the at least one message comprises at least one from among a first message that relates to providing a name and a location of the at least one from among the travel-related service and the dining-related service, a second message that relates to providing at least one stylistic constraint, and a third message that relates to providing at least one example of a review of a different entity to be used as a model.
9 . A computing apparatus for generating a review, the computing apparatus comprising: a processor; a memory; and a communication interface coupled to each of the processor and the memory, wherein the processor is configured to: receive, via the communication interface, a request for a review of an entity that provides a service to a user; solicit a user device, in response to the request for the review received, at least user submission data that relates to an aspect of service provided by the entity, wherein the user device is solicited by automated transmission of at least one message to the user device in order to prompt the user device to provide the at least user submission data; apply a first artificial intelligence (AI) algorithm to the received request and the at least user submission data in order to generate the review of the entity; automatically generate, via the first AI algorithm, the review of the entity, wherein the review of the entity is automatically generated by determining of an intent in the at least user submission data utilized by the first AI algorithm and creating synthetic embeddings verbatims for the determined intent; check the automatically generated review of the entity to detect presence of a hallucination by validating the automatically generated review of the entity using one or more transaction data related to the entity; modify the automatically generated review of the entity by removing the detected hallucination for improved accuracy of the automatically generated review of the entity; and output the modified review of the entity that is absent of the detected hallucination, wherein the entity provides at least one from among a travel-related service and a dining-related service.
10 . The computing apparatus of claim 9 , wherein the entity includes at least one from among a restaurant and a hotel.
11 . The computing apparatus of claim 9 , wherein the first AI algorithm is configured to use at least one from among a sentiment analysis technique, a parts-of-speech (POS) tagging technique, and an extractive summarization and ranking technique to generate the review.
12 . The computing apparatus of claim 9 , wherein the first AI algorithm is trained to generate the review conforming to a target style and a target tone.
13 . The computing apparatus of claim 12 , wherein the first AI algorithm is trained by using historical data that includes previously published reviews conforming to the target style and the target tone.
14 . The computing apparatus of claim 9 , wherein the first AI algorithm is configured to use a large language model (LLM) to generate the review.
15 . The computing apparatus of claim 9 , wherein the automated transmission includes: transmitting, via the communication interface, at least one message to the user device in order to prompt the user to provide a first submission that relates to a first aspect of the at least one from among the travel-related service and the dining-related service.
16 . The computing apparatus of claim 15 , wherein the at least one message comprises at least one from among a first message that relates to providing a name and a location of the at least one from among the travel-related service and the dining-related service, a second message that relates to providing at least one stylistic constraint, and a third message that relates to providing at least one example of a review of a different entity to be used as a model.
17 . A non-transitory computer readable storage medium storing instructions for generating a review, the storage medium comprising executable code which, when executed by a processor, causes the processor to: receive a request for a review of an entity that provides a service to a user; solicit a user device, in response to the request for the review received, at least user submission data that relates to an aspect of service provided by the entity, wherein the user device is solicited by automated transmission of at least one message to the user device in order to prompt the user device to provide the at least user submission data; apply a first artificial intelligence (AI) algorithm to the received request and the at least user submission data in order to generate the review of the entity; automatically generate, via the first AI algorithm, the review of the entity, wherein the review of the entity is automatically generated by determining of an intent in the at least user submission data utilized by the first AI algorithm and creating synthetic embeddings verbatims for the determined intent; check the automatically generated review of the entity to detect presence of a hallucination by validating the automatically generated review of the entity using one or more transaction data related to the entity; and modify the automatically generated review of the entity by removing the detected hallucination for improved accuracy of the automatically generated review of the entity; and output the modified review of the entity that is absent of the detected hallucination, wherein the entity provides at least one from among a travel-related service and a dining-related service.
18 . The storage medium of claim 17 , wherein the entity includes at least one from among a restaurant and a hotel.

Description

CROSS-REFERENCE TO RELATED APPLICATIONS This application claims the benefit of U.S. Provisional Patent Application Ser. No. 63/457,950, filed Apr. 7, 2023, which is hereby incorporated by reference in its entirety. BACKGROUND 1. Field of the Disclosure This technology generally relates to systems for and methods of using an artificial intelligence-based technique for generating travel and dining reviews. 2. Background Information Consumers rely on reviews of a wide variety of products and services to assist them in assessing whether a particular product or service is worthy of purchase, and also in assessing the value of the product or service with respect to cost. In the case of consumer services relating to travel and dining, such as, for example, restaurants, hotels, and various types of tourist attractions, such reviews have traditionally been produced by obtaining submissions from individual persons that have had an experience that relates to the travel or dining service, and then relying on editorial personnel to develop a review that is based on a collective judgment that corresponds to the submissions. However, this process is manually intensive and relatively cumbersome, and therefore, may also suffer from various shortcomings, such as being susceptible to quickly becoming outdated. In addition, given the ever-increasing numbers of restaurants and hotels and other travel-related entities, the scale and capacity that is required for generating such reviews with accuracy and fidelity is not satisfied with the traditional approach. In view of the above, there is an unmet need for systems and methods of using an artificial intelligence-based technique for automatic generation of travel and dining reviews. SUMMARY The present disclosure, through one or more of its various aspects, embodiments, and/or specific features or sub-components, provides, inter alia, various systems, servers, devices, methods, media, programs, and platforms for using an artificial intelligence (AI)-based technique for automatic generation of travel and dining reviews. The various aspects, embodiments, features, and/or sub-components provide optimized processes of using an artificial intelligence-based technique for automatic generation of travel and dining reviews. According to an aspect of the present disclosure, a method for generating a review is provided. The method is implemented by at least one processor. The method includes: receiving, by the at least one processor, a request for a review of an entity that provides a service to a user; applying, by the at least one processor, a first artificial intelligence (AI) algorithm to the received request in order to generate the review of the entity; and outputting, by the at least one processor, the review of the entity. The entity provides at least one from among a travel-related service and a dining-related service. The entity may include at least one from among a restaurant and a hotel. The first AI algorithm may be configured to use at least one from among a sentiment analysis technique, a parts-of-speech (POS) tagging technique, and an extractive summarization and ranking technique to generate the review. The first AI algorithm may be trained to generate the review such that the review has a style and a tone that is imitative of a Zagat review. The first AI algorithm may be trained by using historical data that includes previously published Zagat reviews. The first AI algorithm may be configured to use a large language model (LLM) to generate the review. The method may further include: transmitting at least one message to a user in order to prompt the user to provide a first submission that relates to a first aspect of the at least one from among the travel-related service and the dining-related service; receiving a response to the at least one message; and using the response as an input to the first AI algorithm. The at least one message may include at least one from among a first message that relates to providing a name and a location of the at least one from among the travel-related service and the dining-related service, a second message that relates to providing at least one stylistic constraint, and a third message that relates to providing at least one example of a review of a different entity to be used as a model. The method may further include checking the review of the entity to determine whether a hallucination that relates to the first AI algorithm has been included, and when a determination is made that the review of the entity includes a hallucination, modifying the review of the entity to remove the hallucination. According to another exemplary embodiment, a computing apparatus for generating a review is provided. The computing apparatus includes a processor; a memory; and a communication interface coupled to each of the processor and the memory. The processor is configured to: receive, via the communication interface, a request for a review of an entity that