system

Abstract

The system according to this embodiment aims to efficiently analyze vast amounts of word-of-mouth data and provide users with useful information. [Solution] The system according to the embodiment comprises a collection unit, an analysis unit, a provision unit, a question construction unit, and a personalization unit. The collection unit collects word-of-mouth data. The analysis unit analyzes the word-of-mouth data collected by the collection unit and creates a summary. The provision unit provides the summary created by the analysis unit to each user attribute. The question construction unit creates word-of-mouth data when the user answers questions in a chat format after visiting the store. The personalization unit utilizes the word-of-mouth data created by the question construction unit for personalization functions.

Description

Technical Field

[0001] The technology of the present disclosure relates to a system.

Background Art

[0002] Patent Document 1 discloses a persona chatbot control method performed by at least one processor, including steps of receiving a user utterance, adding the user utterance to a prompt including an instruction sentence related to an explanation of a chatbot character, encoding the prompt, and inputting the encoded prompt into a language model to generate a chatbot utterance in response to the user utterance.

Prior Art Documents

Patent Documents

[0003]

Patent Document 1

Summary of the Invention

Problems to be Solved by the Invention

[0004] In the conventional technology, there is a problem that it is difficult to efficiently extract useful information from a huge amount of review data and provide it to users.

[0005] The system according to the embodiment aims to efficiently analyze a huge amount of review data and provide useful information to users.

Means for Solving the Problems

[0006] The system according to this embodiment comprises a collection unit, an analysis unit, a provision unit, a question construction unit, and a personalization unit. The collection unit collects word-of-mouth data. The analysis unit analyzes the word-of-mouth data collected by the collection unit and creates a summary. The provision unit provides the summaries created by the analysis unit to users according to their attributes. The question construction unit creates word-of-mouth data when users answer questions in a chat format after visiting a store. The personalization unit utilizes the word-of-mouth data created by the question construction unit for personalization functions. [Effects of the Invention]

[0007] The system according to this embodiment can efficiently analyze a vast amount of word-of-mouth data and provide users with useful information. [Brief explanation of the drawing]

[0008] [Figure 1] This is a conceptual diagram showing an example of the configuration of a data processing system according to the first embodiment. [Figure 2] This is a conceptual diagram showing an example of the essential functions of a data processing device and a smart device according to the first embodiment. [Figure 3] This is a conceptual diagram showing an example of the configuration of a data processing system according to the second embodiment. [Figure 4] This is a conceptual diagram showing an example of the main functions of a data processing device and smart glasses according to the second embodiment. [Figure 5] This is a conceptual diagram showing an example of the configuration of a data processing system according to the third embodiment. [Figure 6] This is a conceptual diagram showing an example of the main functions of a data processing device and a headset-type terminal according to the third embodiment. [Figure 7] This is a conceptual diagram showing an example of the configuration of a data processing system according to the fourth embodiment. [Figure 8] This is a conceptual diagram showing an example of the main functions of a data processing device and a robot according to the fourth embodiment. [Figure 9] This shows an emotion map where multiple emotions are mapped. [Figure 10]This shows an emotion map where multiple emotions are mapped. [Modes for carrying out the invention]

[0009] Hereinafter, an example of an embodiment of the system relating to the technology of this disclosure will be described with reference to the attached drawings.

[0010] First, let's explain the terminology used in the following explanation.

[0011] In the following embodiments, the signed processor (hereinafter simply referred to as "processor") may be a single arithmetic unit or a combination of multiple arithmetic units. Furthermore, the processor may be a single type of arithmetic unit or a combination of multiple types of arithmetic units. Examples of arithmetic units include CPU (Central Processing Unit), GPU (Graphics Processing Unit), GPGPU (General-Purpose computing on Graphics Processing Units), APU (Accelerated Processing Unit), or TPU (Tensor Processing Unit).

[0012] In the following embodiments, signed RAM (Random Access Memory) is a memory that temporarily stores information and is used as work memory by the processor.

[0013] In the following embodiments, the signed storage is one or more non-volatile storage devices that store various programs and various parameters. Examples of non-volatile storage devices include flash memory (SSD (Solid State Drive)), magnetic disks (e.g., hard disks), or magnetic tapes.

[0014] In the following embodiments, the numbered communication I / F (Interface) is an interface including a communication processor, an antenna, and the like. The communication I / F manages communication between multiple computers. Examples of communication standards applied to the communication I / F include wireless communication standards such as 5G (5th Generation Mobile Communication System), Wi-Fi (registered trademark), or Bluetooth (registered trademark).

[0015] In the following embodiments, "A and / or B" is synonymous with "at least one of A and B". That is, "A and / or B" means that it may be only A, only B, or a combination of A and B. Also, in this specification, when expressing three or more matters connected by "and / or", the same concept as "A and / or B" is applied.

[0016] [First Embodiment] FIG. 1 shows an example of the configuration of a data processing system 10 according to the first embodiment.

[0017] As shown in FIG. 1, the data processing system 10 includes a data processing device 12 and a smart device 14. An example of the data processing device 12 is a server.

[0018] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, the RAM 30, and the storage 32 are connected to a bus 34. Also, the database 24 and the communication I / F 26 are connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[0019] The smart device 14 comprises a computer 36, a receiving device 38, an output device 40, a camera 42, and a communication interface 44. The computer 36 comprises a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The receiving device 38, output device 40, and camera 42 are also connected to the bus 52.

[0020] The reception device 38 is equipped with a touch panel 38A and a microphone 38B, and accepts user input. The touch panel 38A accepts user input via touch by detecting contact with an object (e.g., a pen or finger). The microphone 38B accepts user input via voice by detecting the user's voice. The control unit 46A transmits data indicating the user input received by the touch panel 38A and microphone 38B to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 (see Figure 2) acquires the data indicating the user input.

[0021] The output device 40 includes a display 40A and a speaker 40B, and presents data to the user by outputting the data in a form perceptible to the user (e.g., audio and / or text). The display 40A displays visible information such as text and images according to instructions from the processor 46. The speaker 40B outputs audio according to instructions from the processor 46. The camera 42 is a small digital camera equipped with an optical system such as a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor.

[0022] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various types of information between processor 46 and processor 28 via network 54.

[0023] Figure 2 shows an example of the main functions of the data processing device 12 and the smart device 14.

[0024] As shown in Figure 2, in the data processing device 12, a specific processing is performed by the processor 28. A specific processing program 56 is stored in the storage 32. The specific processing program 56 is an example of a "program" related to the technology of this disclosure. The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0025] Storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0026] In the smart device 14, specific processing is performed by the processor 46. The storage 50 stores a specific processing program 60. The specific processing program 60 is used in conjunction with the specific processing program 56 by the data processing system 10. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as a control unit 46A according to the specific processing program 60 executed on the RAM 48. The smart device 14 also has a data generation model 58 and an emotion identification model 59, similar to the data generation model and emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0027] Furthermore, other devices besides the data processing device 12 may also have the data generation model 58. For example, a server device (e.g., a generation server) may have the data generation model 58. In this case, the data processing device 12 obtains processing results (such as prediction results) using the data generation model 58 by communicating with the server device having the data generation model 58. The data processing device 12 may also be a server device or a terminal device owned by a user (e.g., a mobile phone, robot, home appliance, etc.). Next, an example of processing by the data processing system 10 according to the first embodiment will be described.

[0028] (Example of form 1) The restaurant reservation support system according to an embodiment of the present invention is a system that utilizes generative AI to provide more convenient support for restaurant reservations. This restaurant reservation support system collects review data, and the generative AI analyzes it to create summaries, allowing users to easily grasp the atmosphere of a restaurant. Furthermore, summaries are created for each user attribute, making it easy to find users who are likely to be reliable references for choosing a restaurant. In addition, users can easily create reviews after visiting a restaurant by simply answering questions in a chat format. This review data is used for personalization functions and is also used to reward users, such as receiving coupons, depending on how helpful their reviews are to other users. For example, the restaurant reservation support system collects a vast amount of review data posted by other users. For example, it collects data from restaurant review sites and social media. Next, the restaurant reservation support system analyzes the collected review data using generative AI and creates summaries. For example, it analyzes reviews about a specific restaurant and summarizes the atmosphere and characteristics of that restaurant. Furthermore, the restaurant reservation support system creates summaries for each user attribute. For example, it creates summaries tailored to usage scenarios and user attributes, such as summaries for women in their 30s or summaries for families. This makes it possible to easily grasp the atmosphere of a restaurant. Furthermore, the restaurant reservation support system can analyze reviews not only at the restaurant level but also at the user level. For example, it can analyze reviews posted by specific users to determine whether those users are trustworthy. This makes it easy to find users who could be a reliable source of information when choosing a restaurant. In addition, the restaurant reservation support system allows users to easily create reviews after visiting a restaurant simply by answering questions in a chat format. For example, after visiting a restaurant, simply answering questions such as "How was the food?" or "How was the atmosphere of the restaurant?" will automatically generate a review. Because the restaurant reservation support system constructs questions each time based on the restaurant's menu and the user's preferences, the reviews become more helpful to other users. For example, questions about specific menu items or questions based on the user's past preferences are constructed.This review data will be used for personalization features and also for rewarding users, such as awarding coupons, based on how helpful the reviews were to other users. For example, coupons will be issued for reviews that other users have rated as "helpful." This will allow the restaurant reservation support system to make restaurant reservations more convenient for users.

[0029] The restaurant reservation support system according to this embodiment comprises a collection unit, an analysis unit, a provision unit, a question construction unit, and a personalization unit. The collection unit collects review data. The collection unit collects review data from, for example, restaurant review sites and social media. The collection unit can collect text data from, for example, restaurant review sites. The collection unit can also collect image data from social media. Furthermore, the collection unit can also collect audio data. For example, the collection unit collects text data from restaurant review sites and inputs it into a generation AI. The collection unit can also collect image data from social media and input it into a generation AI. The collection unit can also collect audio data and input it into a generation AI. The analysis unit analyzes the review data collected by the collection unit and creates a summary. For example, the analysis unit analyzes the collected review data and creates a summary about a specific restaurant. The analysis unit can analyze the review data using, for example, text mining technology. Furthermore, the analysis unit can analyze the review data using sentiment analysis technology. Furthermore, the analysis unit can analyze the review data using natural language processing technology. For example, the analysis department analyzes review data using text mining technology and creates summaries about specific restaurants. The analysis department can also analyze review data using sentiment analysis technology and create summaries about specific restaurants. The analysis department can also analyze review data using natural language processing technology and create summaries about specific restaurants. The service department provides the summaries created by the analysis department according to user attributes. For example, the service department creates and provides summaries according to user attributes. For example, the service department can create summaries based on attributes such as age, gender, and interests. The service department can also create summaries according to usage scenarios. Furthermore, the service department can create summaries based on the user's past behavior history. For example, the service department can create and provide summaries according to age. The service department can also create and provide summaries according to gender. The service department can also create and provide summaries according to interests. The question building department creates reviews when users answer questions in a chat format after visiting the restaurant.The question building unit creates questions that users will answer after visiting the store. For example, the question building unit can create questions about the taste of the food or the atmosphere of the store. It can also create questions about specific menu items. Furthermore, the question building unit can create questions based on the user's past preferences. For example, the question building unit can create questions about the taste of the food. For example, the question building unit can create questions about the atmosphere of the store. For example, the question building unit can create questions about specific menu items. The personalization unit utilizes the review data created by the question building unit for personalization features. For example, the personalization unit can issue coupons, etc., based on ratings from other users. For example, the personalization unit can issue coupons for reviews that other users have rated as "helpful". Furthermore, the personalization unit can award points based on ratings from other users. Furthermore, the personalization unit can offer benefits based on ratings from other users. For example, the personalization unit can issue coupons for reviews that other users have rated as "helpful". The personalization section can also offer benefits based on ratings from other users. This allows the restaurant reservation support system according to this embodiment to efficiently collect, analyze, provide, construct questions for, and personalize review data.

[0030] The data collection unit collects word-of-mouth data. For example, the data collection unit collects word-of-mouth data from restaurant review sites and social media. Specifically, it can collect text data from restaurant review sites. This includes reviews, ratings, and comments posted by users. The data collection unit periodically crawls this text data to obtain the latest word-of-mouth information. The data collection unit can also collect image data from social media. For example, it can collect photos of food and images of the restaurant's atmosphere posted by users. This allows for the collection of word-of-mouth data that also includes visual information. Furthermore, the data collection unit can also collect audio data. For example, it can collect reviews and comments posted by users via audio and convert them into text data. The data collection unit inputs this data into a generation AI. The generation AI analyzes the collected text data, image data, and audio data to generate integrated word-of-mouth data. This allows the data collection unit to efficiently collect word-of-mouth data in various formats and enrich the overall system database. The data collection unit can flexibly set the data collection frequency and the types of data to be collected, and can also collect data tailored to specific campaigns and events. This allows the data collection unit to constantly gather the latest and most diverse word-of-mouth data, improving the overall performance of the system.

[0031] The analysis department analyzes the review data collected by the collection department and creates summaries. For example, the analysis department can analyze the collected review data and create summaries about specific restaurants. Specifically, it can analyze review data using text mining techniques. Text mining techniques extract frequently occurring keywords and phrases from review data, clarifying the characteristics and evaluation points of restaurants. The analysis department can also analyze review data using sentiment analysis techniques. Sentiment analysis techniques identify positive and negative evaluations from the review data and grasp the overall evaluation trend. Furthermore, the analysis department can also analyze review data using natural language processing techniques. Natural language processing techniques can understand the context of the review data and create more detailed summaries. For example, the analysis department can analyze review data using text mining techniques and create summaries about specific restaurants. The analysis department can also analyze review data using sentiment analysis techniques and create summaries about specific restaurants. The analysis department can also analyze review data using natural language processing techniques and create summaries about specific restaurants. This allows the analysis department to analyze the collected review data from multiple perspectives and provide useful information to users. Furthermore, the analytics department can also perform long-term evaluations and forecasts by considering past data and trends. This allows the analytics department to contribute not only to real-time situational awareness but also to future predictions and strategic decision-making.

[0032] The service provider provides summaries created by the analysis department, tailored to each user's attributes. For example, the service provider can create and provide summaries based on user attributes. Specifically, summaries can be created based on attributes such as age, gender, and interests. For instance, summaries on trendy dishes and Instagrammable menus can be provided for younger users, while summaries on health-conscious menus and restaurants with a relaxed atmosphere can be provided for older users. The service provider can also create summaries based on usage scenarios. For example, summaries on restaurants suitable for dates or restaurants suitable for families can be provided. Furthermore, the service provider can create summaries based on the user's past behavior history. For example, summaries tailored to the user's preferences can be provided based on restaurants visited in the past and menus rated. The service provider can also create and provide summaries based on age, gender, and interests. This allows the service provider to provide personalized information tailored to user attributes and usage scenarios, thereby improving user satisfaction. In addition, the service provider can collect user feedback and continuously improve the accuracy and content of the summaries provided. This allows the service provider to always provide users with the latest and most optimal information, improving the reliability and usability of the entire system.

[0033] The question building unit creates reviews based on questions users answer in a chat format after visiting a restaurant. Specifically, it can create questions about the taste of the food and the atmosphere of the restaurant. For example, it can create questions like, "How was the taste of the food?" or "How was the atmosphere of the restaurant?" The question building unit can also create questions about specific menu items. For example, it can create questions like, "What do you recommend?" or "How was the dessert?" Furthermore, the question building unit can create questions based on the user's past preferences. For example, for a user who previously enjoyed spicy food, it could create a question like, "How was the spiciness level?" The question building unit can create questions about the taste of the food. The question building unit can also create questions about the atmosphere of the restaurant. The question building unit can also create questions about specific menu items. This allows the question building unit to collect detailed reviews based on user experiences and enrich the overall system database. Furthermore, the question building unit can collect user responses in real time and quickly reflect the information in collaboration with the analysis and delivery units. This allows the question building unit to collect high-quality word-of-mouth data based on user experiences and improve the overall system performance.

[0034] The Personalization Unit utilizes the review data created by the Question Construction Unit for personalization features. For example, the Personalization Unit issues coupons based on ratings from other users. Specifically, it can issue coupons for reviews that have been rated as "helpful" by other users. For instance, if a review of a particular restaurant receives high ratings from many users, the Personalization Unit will issue a coupon to the user who posted that review. The Personalization Unit can also award points based on ratings from other users. For example, if a review receives high ratings from many users, the Personalization Unit will award points to the user who posted that review. Furthermore, the Personalization Unit can offer benefits based on ratings from other users. For example, users who post highly-rated reviews can receive benefits that can be used for their next reservation. The Personalization Unit issues coupons for reviews that have been rated as "helpful" by other users. The Personalization Unit can also award points based on ratings from other users. The Personalization Unit can also offer benefits based on ratings from other users. This allows the Personalization Unit to encourage user review submissions and improve the quality and quantity of review data across the entire system. Furthermore, the personalization unit can provide individually optimized benefits and coupons based on the user's preferences and behavioral history. This allows the personalization unit to improve user satisfaction and promote overall system usage.

[0035] The data collection unit can collect review data from restaurant review websites or social media. For example, the data collection unit can collect text data from restaurant review websites. For example, the data collection unit can collect text data from restaurant review websites and input it into a generating AI. For example, the data collection unit can collect image data from social media. For example, the data collection unit can collect image data from social media and input it into a generating AI. For example, the data collection unit can collect audio data. For example, the data collection unit can collect audio data and input it into a generating AI. In this way, the data collection unit can obtain a wide range of data by collecting review data from restaurant review websites and social media. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input text data collected from restaurant review websites into a generating AI and have the generating AI perform analysis of the text data.

[0036] The analysis department can analyze collected review data and create summaries about specific restaurants. For example, the analysis department can analyze collected review data and create summaries about specific restaurants. The analysis department can analyze review data using text mining techniques, for example. The analysis department can analyze review data using text mining techniques and create summaries about specific restaurants. The analysis department can analyze review data using sentiment analysis techniques, for example. The analysis department can analyze review data using sentiment analysis techniques and create summaries about specific restaurants. The analysis department can analyze review data using natural language processing techniques, for example. The analysis department can analyze review data using natural language processing techniques and create summaries about specific restaurants. This allows the analysis department to create summaries about specific restaurants, making it easy for users to grasp the atmosphere of the establishment. Some or all of the above-described processes in the analysis department may be performed using, for example, generative AI, or without generative AI. For example, the analysis department can input collected review data into a generative AI and have the generative AI create summaries.

[0037] The service provider can create and provide summaries tailored to user attributes. For example, the service provider can create and provide summaries tailored to user attributes. For example, the service provider can create summaries based on attributes such as age, gender, and interests. For example, the service provider can create and provide summaries tailored to age. For example, the service provider can create and provide summaries tailored to gender. For example, the service provider can create and provide summaries tailored to interests. In this way, by providing summaries tailored to user attributes, the service provider can provide information that is appropriate for the usage scenario. Some or all of the above processing in the service provider may be performed using AI, for example, or without AI. For example, the service provider can input user attribute information into a generating AI and have the generating AI create a summary.

[0038] The question building unit can build questions that users will answer after visiting the store. The question building unit can, for example, build questions that users will answer after visiting the store. The question building unit can, for example, build questions about the taste of the food or the atmosphere of the store. The question building unit can, for example, build questions about the taste of the food. The question building unit can, for example, build questions about the atmosphere of the store. The question building unit can, for example, build questions about a specific menu item. The question building unit can, for example, build questions about a specific menu item. In this way, the question building unit can easily create reviews by building questions that users will answer after visiting the store. Some or all of the above processing in the question building unit may be performed using AI, for example, or not using AI. For example, the question building unit can input user response data into a generating AI and have the generating AI build the questions.

[0039] The personalization unit can issue coupons, etc., based on ratings from other users. For example, the personalization unit can issue coupons, etc., based on ratings from other users. For example, the personalization unit can issue coupons for reviews that have been rated as "helpful" by other users. For example, the personalization unit can award points based on ratings from other users. For example, the personalization unit can offer benefits based on ratings from other users. In this way, the personalization unit encourages users to post reviews by issuing coupons, etc., based on ratings from other users. Some or all of the above processing in the personalization unit may be performed using AI, for example, or without AI. For example, the personalization unit can input rating data from other users into a generating AI and have the generating AI issue coupons.

[0040] The data collection unit can analyze a user's past review posting history and select the optimal data collection method. For example, the data collection unit can collect review data according to the time slots when the user frequently posted in the past. For example, the data collection unit can collect review data from platforms that the user has preferred to use in the past. For example, the data collection unit can analyze trends in the content the user has posted in the past and prioritize the collection of relevant data. In this way, the data collection unit can select the optimal data collection method by analyzing a user's past review posting history. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input the user's past posting history data into a generating AI and have the generating AI select the optimal data collection method.

[0041] The data collection unit can filter the collected review data based on the user's current areas of interest. For example, the data collection unit can prioritize collecting review data for restaurants that the user is currently interested in. For example, the data collection unit can collect review data related to a specific cuisine genre that the user has shown interest in. For example, the data collection unit can collect review data for restaurants in areas the user has recently visited. In this way, the data collection unit can collect highly relevant data by filtering based on the user's current areas of interest. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input the user's areas of interest data into a generating AI and have the generating AI perform the filtering.

[0042] The data collection unit can prioritize the collection of highly relevant data by considering the user's geographical location when collecting review data. For example, the data collection unit can prioritize the collection of review data about restaurants in the area where the user is currently located. For example, the data collection unit can collect review data about restaurants in areas the user has visited in the past. For example, the data collection unit can collect review data about restaurants in areas the user plans to visit in the future. In this way, the data collection unit can provide information appropriate to the region by prioritizing the collection of highly relevant data by considering the user's geographical location. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input the user's geographical location information into a generating AI and have the generating AI perform the collection of highly relevant data.

[0043] The data collection unit can analyze users' social media activity and collect relevant data when collecting word-of-mouth data. For example, the data collection unit can collect word-of-mouth data about restaurants shared by users on social media. For example, the data collection unit can collect word-of-mouth data related to posts by influencers that users follow. For example, the data collection unit can collect word-of-mouth data about restaurants that are being discussed in groups or communities that users participate in. This allows the data collection unit to efficiently collect relevant data by analyzing users' social media activity. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input user social media activity data into a generating AI and have the generating AI perform the collection of related data.

[0044] The analysis unit can adjust the level of detail in summaries based on specific keywords when analyzing review data. For example, the analysis unit can summarize review data containing a specific dish name in detail. For example, the analysis unit can summarize review data containing keywords related to a specific service in detail. For example, the analysis unit can summarize review data containing keywords related to a specific event or campaign in detail. This allows the analysis unit to provide important information in detail by adjusting the level of detail in summaries based on specific keywords. Some or all of the above processing in the analysis unit may be performed using, for example, a generative AI, or not using a generative AI. For example, the analysis unit can input review data into a generative AI and have the generative AI perform the adjustment of the level of detail in the summaries.

[0045] The analysis unit can apply different summarization algorithms depending on the category when analyzing review data. For example, the analysis unit can apply a summarization algorithm related to the taste of the food and the service to restaurant review data. For example, the analysis unit can apply a summarization algorithm related to the atmosphere and the quality of the drinks to cafe review data. For example, the analysis unit can apply a summarization algorithm related to the types of drinks and the nighttime atmosphere to bar review data. In this way, the analysis unit can provide summaries appropriate to each category by applying different summarization algorithms depending on the category. Some or all of the above processing in the analysis unit may be performed using, for example, a generative AI, or not using a generative AI. For example, the analysis unit can input review data into a generative AI and have the generative AI apply different summarization algorithms.

[0046] The analysis unit can determine the priority of summaries based on the posting date when analyzing review data. For example, the analysis unit may prioritize summarizing recently posted review data. For example, the analysis unit may prioritize summarizing review data posted during a specific event period. For example, the analysis unit may determine the priority of summaries based on seasonal trends. This allows the analysis unit to prioritize providing the latest information by determining the priority of summaries based on the posting date. Some or all of the above processing in the analysis unit may be performed using, for example, a generative AI, or not using a generative AI. For example, the analysis unit may input review data into a generative AI and have the generative AI perform the determination of the summary priority.

[0047] The analysis unit can improve the accuracy of its summaries by referring to other relevant data sources when analyzing review data. For example, the analysis unit can improve the accuracy of its summaries by referring to information on a restaurant's official website. For example, the analysis unit can improve the accuracy of its summaries by referring to social media posts. For example, the analysis unit can improve the accuracy of its summaries by referring to data from other review sites. In this way, the analysis unit improves the accuracy of its summaries by referring to other relevant data sources. Some or all of the above processing in the analysis unit may be performed using, for example, a generative AI, or not using a generative AI. For example, the analysis unit can input other data sources into a generative AI and have the generative AI perform the task of improving the accuracy of its summaries.

[0048] The service provider can select the optimal service method by referring to the user's past browsing history when providing summaries. For example, the service provider may prioritize providing summaries of restaurants the user has previously viewed. For example, the service provider may provide summaries of cuisine genres the user has previously viewed. For example, the service provider may provide summaries of restaurants in a region the user has previously viewed. This allows the service provider to select the optimal service method by referring to the user's past browsing history. Some or all of the above processing in the service provider may be performed using AI, for example, or without AI. For example, the service provider may input the user's past browsing history data into a generating AI and have the generating AI select the optimal service method.

[0049] The service provider can customize the content provided based on the user's attribute information when providing summaries. For example, the service provider can provide summaries tailored to the user's age. For example, the service provider can provide summaries tailored to the user's gender. For example, the service provider can provide summaries tailored to the user's family structure. In this way, the service provider can provide individually optimized information by customizing the content based on the user's attribute information. Some or all of the above processing in the service provider may be performed using AI, for example, or without AI. For example, the service provider can input the user's attribute information into a generating AI and have the generating AI perform the customization of the content provided.

[0050] The service provider can select the optimal display method when providing a summary, taking into account the user's device information. For example, if the user is using a smartphone, the service provider will provide a display method that matches the screen size. For example, if the user is using a tablet, the service provider will provide a display method optimized for a larger screen. For example, if the user is using a personal computer, the service provider will provide a display method that includes detailed information. In this way, by selecting the optimal display method considering the user's device information, the service provider can provide the optimal display according to the device. Some or all of the above processing in the service provider may be performed using AI, for example, or without AI. For example, the service provider can input the user's device information into a generating AI and have the generating AI select the optimal display method.

[0051] The service provider can analyze the user's social media activity and adjust the content provided when delivering summaries. For example, the service provider can provide summaries about restaurants that the user has shared on social media. For example, the service provider can provide summaries related to posts by influencers that the user follows. For example, the service provider can provide summaries about restaurants that are being discussed in groups or communities that the user participates in. In this way, the service provider can provide highly relevant information by analyzing the user's social media activity. Some or all of the above processing in the service provider may be performed using AI, for example, or not using AI. For example, the service provider can input the user's social media activity data into a generating AI and have the generating AI adjust the content provided.

[0052] The question building unit can select the most suitable question by referring to the user's past answer history when building a question. For example, the question building unit can analyze trends in questions the user has answered in the past and build relevant questions. For example, the question building unit can build questions based on topics the user has preferred to answer in the past. For example, the question building unit can consider topics the user has avoided in the past and build appropriate questions. In this way, the question building unit can select the most suitable question by referring to the user's past answer history. Some or all of the above processes in the question building unit may be performed using AI, for example, or without AI. For example, the question building unit can input the user's past answer history data into a generating AI and have the generating AI select the most suitable question.

[0053] The question building unit can customize the question content based on the user's attribute information when building a question. For example, the question building unit can build a question based on the user's age. For example, the question building unit can build a question based on the user's gender. For example, the question building unit can build a question based on the user's family structure. In this way, the question building unit can provide individually optimized questions by customizing the question content based on the user's attribute information. Some or all of the above processing in the question building unit may be performed using AI, for example, or without AI. For example, the question building unit can input the user's attribute information into a generating AI and have the generating AI perform the customization of the question content.

[0054] The question building unit can select the optimal question format by considering the user's device information when building a question. For example, if the user is using a smartphone, the question building unit will build a short text-based question. For example, if the user is using a tablet, the question building unit will build a detailed question. For example, if the user is using a personal computer, the question building unit will build a question with multiple choices. In this way, the question building unit can provide the most appropriate question for the device by selecting the optimal question format by considering the user's device information. Some or all of the above processing in the question building unit may be performed using AI, for example, or without AI. For example, the question building unit can input the user's device information into a generating AI and have the generating AI select the optimal question format.

[0055] The question building unit can analyze the user's social media activity and adjust the question content when building a question. For example, the question building unit can build a question based on what the user has shared on social media. For example, the question building unit can build a question related to posts by influencers the user follows. For example, the question building unit can build a question based on topics that are being discussed in groups or communities the user participates in. In this way, the question building unit can provide highly relevant questions by analyzing the user's social media activity. Some or all of the above processing in the question building unit may be performed using AI, for example, or not using AI. For example, the question building unit can input the user's social media activity data into a generating AI and have the generating AI adjust the question content.

[0056] The personalization unit can select the optimal personalization method by referring to the user's past behavior history during the personalization process. For example, the personalization unit may personalize based on information about restaurants the user has previously viewed. For example, the personalization unit may personalize based on the content of reviews the user has previously posted. For example, the personalization unit may personalize based on the user's past coupon usage history. In this way, the personalization unit can select the optimal personalization method by referring to the user's past behavior history. Some or all of the above processing in the personalization unit may be performed using AI, for example, or without AI. For example, the personalization unit can input the user's past behavior history data into a generating AI and have the generating AI select the optimal personalization method.

[0057] The personalization unit can customize content based on user attribute information during the personalization process. For example, the personalization unit can provide personalized content tailored to the user's age. For example, the personalization unit can provide personalized content tailored to the user's gender. For example, the personalization unit can provide personalized content tailored to the user's family structure. In this way, the personalization unit can provide individually optimized information by customizing content based on the user's attribute information. Some or all of the above-described processes in the personalization unit may be performed using AI, for example, or without AI. For example, the personalization unit can input user attribute information into a generating AI and have the generating AI perform content customization.

[0058] The personalization unit can provide optimal content by considering the user's geographical location information during the personalization process. For example, the personalization unit may prioritize providing restaurant information in the user's current location. For example, the personalization unit may provide restaurant information in areas the user has visited in the past. For example, the personalization unit may provide restaurant information in areas the user plans to visit in the future. In this way, the personalization unit can provide information appropriate to the region by considering the user's geographical location information and providing optimal content. Some or all of the above processing in the personalization unit may be performed using AI, for example, or without AI. For example, the personalization unit can input the user's geographical location information into a generating AI and have the generating AI provide optimal content.

[0059] The personalization unit can analyze the user's social media activity and adjust the content during the personalization process. For example, the personalization unit can personalize based on restaurant information shared by the user on social media. For example, the personalization unit can provide content related to posts by influencers the user follows. For example, the personalization unit can provide restaurant information that is being discussed in groups or communities the user participates in. In this way, the personalization unit can provide highly relevant information by analyzing the user's social media activity. Some or all of the above processing in the personalization unit may be performed using AI, for example, or without AI. For example, the personalization unit can input the user's social media activity data into a generating AI and have the generating AI perform content adjustments.

[0060] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0061] The restaurant reservation support system can analyze a user's past reservation history and make optimal reservation suggestions. For example, it can analyze the trends of restaurants a user has visited in the past and suggest restaurants that offer a similar atmosphere and cuisine. Based on a user's past coupon usage history, it can suggest restaurants where coupons can be used. Considering the time slots a user has previously reserved, it can suggest restaurants that are available for the same time slot. This enables optimal reservation suggestions based on the user's past behavior history. Some or all of the above processing in the suggestion unit may be performed using AI or not. For example, the suggestion unit can input the user's past reservation history data into a generating AI and have the generating AI execute reservation suggestions.

[0062] The restaurant reservation support system can make optimal reservation suggestions by taking into account the user's geographical location. For example, it can prioritize suggesting restaurants in the area where the user is currently located. It can also suggest restaurants in areas the user has visited in the past. It can also suggest restaurants in areas the user plans to visit in the future. This enables optimal reservation suggestions that take into account the user's geographical location. Some or all of the above processing in the suggestion unit may be performed using AI or not. For example, the suggestion unit can input the user's geographical location information into a generating AI and have the generating AI execute reservation suggestions.

[0063] The restaurant reservation support system can analyze a user's social media activity and make relevant reservation suggestions. For example, it can make reservation suggestions based on restaurants the user has shared on social media. It can suggest restaurants related to posts by influencers the user follows. It can suggest restaurants that are trending in groups or communities the user belongs to. This enables optimal reservation suggestions based on the user's social media activity. Some or all of the above processing in the suggestion section may be performed using AI or not. For example, the suggestion section can input the user's social media activity data into a generating AI and have the generating AI execute reservation suggestions.

[0064] The restaurant reservation support system can analyze a user's past review posting history and make optimal reservation suggestions. For example, it can analyze the content of reviews posted by a user in the past and suggest restaurants that offer a similar atmosphere and cuisine. Based on the trends of restaurants that the user has given high ratings to in the past, it can suggest similar restaurants. Based on the trends of restaurants that the user has given low ratings to in the past, it can suggest restaurants to avoid. This makes it possible to make optimal reservation suggestions based on the user's past review posting history. Some or all of the above processing in the suggestion section may be performed using AI or not. For example, the suggestion section can input the user's past review posting history data into a generating AI and have the generating AI execute reservation suggestions.

[0065] The restaurant reservation support system can make optimal reservation suggestions by referring to the user's past browsing history. For example, it can make reservation suggestions based on information about restaurants the user has previously viewed. It can make reservation suggestions based on the type of cuisine the user has previously viewed. It can make reservation suggestions based on restaurants in the area the user has previously viewed. This makes it possible to make optimal reservation suggestions based on the user's past browsing history. Some or all of the above processing in the suggestion unit may be performed using AI or not. For example, the suggestion unit can input the user's past browsing history data into a generating AI and have the generating AI execute reservation suggestions.

[0066] The restaurant reservation support system can customize reservation suggestions based on user attribute information. For example, it can make reservation suggestions based on the user's age, gender, or family structure. This enables optimal reservation suggestions based on the user's attribute information. Some or all of the above processing in the suggestion unit may be performed using AI or not. For example, the suggestion unit can input user attribute information into a generating AI and have the generating AI execute reservation suggestions.

[0067] The following briefly describes the processing flow for example form 1.

[0068] Step 1: The data collection unit collects review data. The data collection unit collects review data from sources such as restaurant review websites and social media. The data collection unit can collect text data from, for example, restaurant review websites. The data collection unit can also collect image data from social media. Furthermore, the data collection unit can also collect audio data. For example, the data collection unit collects text data from restaurant review websites and inputs it into the generation AI. The data collection unit can also collect image data from social media and input it into the generation AI. The data collection unit can also collect audio data and input it into the generation AI. Step 2: The analysis department analyzes the review data collected by the collection department and creates a summary. For example, the analysis department analyzes the collected review data and creates a summary about a specific restaurant. For example, the analysis department can analyze the review data using text mining techniques. The analysis department can also analyze the review data using sentiment analysis techniques. Furthermore, the analysis department can analyze the review data using natural language processing techniques. For example, the analysis department can analyze the review data using text mining techniques and create a summary about a specific restaurant. The analysis department can also analyze the review data using sentiment analysis techniques and create a summary about a specific restaurant. The analysis department can also analyze the review data using natural language processing techniques and create a summary about a specific restaurant. Step 3: The service provider provides summaries created by the analysis provider to each user attribute. For example, the service provider creates and provides summaries based on user attributes. For example, the service provider can create summaries based on attributes such as age, gender, and interests. The service provider can also create summaries based on usage scenarios. Furthermore, the service provider can create summaries based on the user's past behavior history. For example, the service provider can create and provide summaries based on age. The service provider can also create and provide summaries based on gender. The service provider can also create and provide summaries based on interests. Step 4: The Question Building Unit creates reviews by having users answer questions in a chat format after their visit. The Question Building Unit constructs questions that users will answer after their visit. For example, the Question Building Unit can construct questions about the taste of the food or the atmosphere of the restaurant. It can also construct questions about specific menu items. Furthermore, the Question Building Unit can construct questions based on the user's past preferences. For example, the Question Building Unit can construct questions about the taste of the food. The Question Building Unit can also construct questions about the atmosphere of the restaurant. The Question Building Unit can also construct questions about specific menu items. Step 5: The Personalization Unit utilizes the review data created by the Question Construction Unit for personalization features. For example, the Personalization Unit issues coupons based on ratings from other users. For example, the Personalization Unit can issue coupons for reviews that have been rated as "helpful" by other users. The Personalization Unit can also award points based on ratings from other users. Furthermore, the Personalization Unit can offer benefits based on ratings from other users. For example, the Personalization Unit issues coupons for reviews that have been rated as "helpful" by other users. The Personalization Unit can also award points based on ratings from other users. The Personalization Unit can also offer benefits based on ratings from other users.

[0069] (Example of form 2)The restaurant reservation support system according to an embodiment of the present invention is a system that utilizes generative AI to provide more convenient support for restaurant reservations. This restaurant reservation support system collects review data, and the generative AI analyzes it to create summaries, allowing users to easily grasp the atmosphere of a restaurant. Furthermore, summaries are created for each user attribute, making it easy to find users who are likely to be reliable references for choosing a restaurant. In addition, users can easily create reviews after visiting a restaurant by simply answering questions in a chat format. This review data is used for personalization functions and is also used to reward users, such as receiving coupons, depending on how helpful their reviews are to other users. For example, the restaurant reservation support system collects a vast amount of review data posted by other users. For example, it collects data from restaurant review sites and social media. Next, the restaurant reservation support system analyzes the collected review data using generative AI and creates summaries. For example, it analyzes reviews about a specific restaurant and summarizes the atmosphere and characteristics of that restaurant. Furthermore, the restaurant reservation support system creates summaries for each user attribute. For example, it creates summaries tailored to usage scenarios and user attributes, such as summaries for women in their 30s or summaries for families. This makes it possible to easily grasp the atmosphere of a restaurant. Furthermore, the restaurant reservation support system can analyze reviews not only at the restaurant level but also at the user level. For example, it can analyze reviews posted by specific users to determine whether those users are trustworthy. This makes it easy to find users who could be a reliable source of information when choosing a restaurant. In addition, the restaurant reservation support system allows users to easily create reviews after visiting a restaurant simply by answering questions in a chat format. For example, after visiting a restaurant, simply answering questions such as "How was the food?" or "How was the atmosphere of the restaurant?" will automatically generate a review. Because the restaurant reservation support system constructs questions each time based on the restaurant's menu and the user's preferences, the reviews become more helpful to other users. For example, questions about specific menu items or questions based on the user's past preferences are constructed.This review data will be used for personalization features and also for rewarding users, such as awarding coupons, based on how helpful the reviews were to other users. For example, coupons will be issued for reviews that other users have rated as "helpful." This will allow the restaurant reservation support system to make restaurant reservations more convenient for users.

[0070] The restaurant reservation support system according to this embodiment comprises a collection unit, an analysis unit, a provision unit, a question construction unit, and a personalization unit. The collection unit collects review data. The collection unit collects review data from, for example, restaurant review sites and social media. The collection unit can collect text data from, for example, restaurant review sites. The collection unit can also collect image data from social media. Furthermore, the collection unit can also collect audio data. For example, the collection unit collects text data from restaurant review sites and inputs it into a generation AI. The collection unit can also collect image data from social media and input it into a generation AI. The collection unit can also collect audio data and input it into a generation AI. The analysis unit analyzes the review data collected by the collection unit and creates a summary. For example, the analysis unit analyzes the collected review data and creates a summary about a specific restaurant. The analysis unit can analyze the review data using, for example, text mining technology. Furthermore, the analysis unit can analyze the review data using sentiment analysis technology. Furthermore, the analysis unit can analyze the review data using natural language processing technology. For example, the analysis department analyzes review data using text mining technology and creates summaries about specific restaurants. The analysis department can also analyze review data using sentiment analysis technology and create summaries about specific restaurants. The analysis department can also analyze review data using natural language processing technology and create summaries about specific restaurants. The service department provides the summaries created by the analysis department according to user attributes. For example, the service department creates and provides summaries according to user attributes. For example, the service department can create summaries based on attributes such as age, gender, and interests. The service department can also create summaries according to usage scenarios. Furthermore, the service department can create summaries based on the user's past behavior history. For example, the service department can create and provide summaries according to age. The service department can also create and provide summaries according to gender. The service department can also create and provide summaries according to interests. The question building department creates reviews when users answer questions in a chat format after visiting the restaurant.The question building unit creates questions that users will answer after visiting the store. For example, the question building unit can create questions about the taste of the food or the atmosphere of the store. It can also create questions about specific menu items. Furthermore, the question building unit can create questions based on the user's past preferences. For example, the question building unit can create questions about the taste of the food. For example, the question building unit can create questions about the atmosphere of the store. For example, the question building unit can create questions about specific menu items. The personalization unit utilizes the review data created by the question building unit for personalization features. For example, the personalization unit can issue coupons, etc., based on ratings from other users. For example, the personalization unit can issue coupons for reviews that other users have rated as "helpful". Furthermore, the personalization unit can award points based on ratings from other users. Furthermore, the personalization unit can offer benefits based on ratings from other users. For example, the personalization unit can issue coupons for reviews that other users have rated as "helpful". The personalization section can also offer benefits based on ratings from other users. This allows the restaurant reservation support system according to this embodiment to efficiently collect, analyze, provide, construct questions for, and personalize review data.

[0071] The data collection unit collects word-of-mouth data. For example, the data collection unit collects word-of-mouth data from restaurant review sites and social media. Specifically, it can collect text data from restaurant review sites. This includes reviews, ratings, and comments posted by users. The data collection unit periodically crawls this text data to obtain the latest word-of-mouth information. The data collection unit can also collect image data from social media. For example, it can collect photos of food and images of the restaurant's atmosphere posted by users. This allows for the collection of word-of-mouth data that also includes visual information. Furthermore, the data collection unit can also collect audio data. For example, it can collect reviews and comments posted by users via audio and convert them into text data. The data collection unit inputs this data into a generation AI. The generation AI analyzes the collected text data, image data, and audio data to generate integrated word-of-mouth data. This allows the data collection unit to efficiently collect word-of-mouth data in various formats and enrich the overall system database. The data collection unit can flexibly set the data collection frequency and the types of data to be collected, and can also collect data tailored to specific campaigns and events. This allows the data collection unit to constantly gather the latest and most diverse word-of-mouth data, improving the overall performance of the system.

[0072] The analysis department analyzes the review data collected by the collection department and creates summaries. For example, the analysis department can analyze the collected review data and create summaries about specific restaurants. Specifically, it can analyze review data using text mining techniques. Text mining techniques extract frequently occurring keywords and phrases from review data, clarifying the characteristics and evaluation points of restaurants. The analysis department can also analyze review data using sentiment analysis techniques. Sentiment analysis techniques identify positive and negative evaluations from the review data and grasp the overall evaluation trend. Furthermore, the analysis department can also analyze review data using natural language processing techniques. Natural language processing techniques can understand the context of the review data and create more detailed summaries. For example, the analysis department can analyze review data using text mining techniques and create summaries about specific restaurants. The analysis department can also analyze review data using sentiment analysis techniques and create summaries about specific restaurants. The analysis department can also analyze review data using natural language processing techniques and create summaries about specific restaurants. This allows the analysis department to analyze the collected review data from multiple perspectives and provide useful information to users. Furthermore, the analytics department can also perform long-term evaluations and forecasts by considering past data and trends. This allows the analytics department to contribute not only to real-time situational awareness but also to future predictions and strategic decision-making.

[0073] The service provider provides summaries created by the analysis department, tailored to each user's attributes. For example, the service provider can create and provide summaries based on user attributes. Specifically, summaries can be created based on attributes such as age, gender, and interests. For instance, summaries on trendy dishes and Instagrammable menus can be provided for younger users, while summaries on health-conscious menus and restaurants with a relaxed atmosphere can be provided for older users. The service provider can also create summaries based on usage scenarios. For example, summaries on restaurants suitable for dates or restaurants suitable for families can be provided. Furthermore, the service provider can create summaries based on the user's past behavior history. For example, summaries tailored to the user's preferences can be provided based on restaurants visited in the past and menus rated. The service provider can also create and provide summaries based on age, gender, and interests. This allows the service provider to provide personalized information tailored to user attributes and usage scenarios, thereby improving user satisfaction. In addition, the service provider can collect user feedback and continuously improve the accuracy and content of the summaries provided. This allows the service provider to always provide users with the latest and most optimal information, improving the reliability and usability of the entire system.

[0074] The question building unit creates reviews based on questions users answer in a chat format after visiting a restaurant. Specifically, it can create questions about the taste of the food and the atmosphere of the restaurant. For example, it can create questions like, "How was the taste of the food?" or "How was the atmosphere of the restaurant?" The question building unit can also create questions about specific menu items. For example, it can create questions like, "What do you recommend?" or "How was the dessert?" Furthermore, the question building unit can create questions based on the user's past preferences. For example, for a user who previously enjoyed spicy food, it could create a question like, "How was the spiciness level?" The question building unit can create questions about the taste of the food. The question building unit can also create questions about the atmosphere of the restaurant. The question building unit can also create questions about specific menu items. This allows the question building unit to collect detailed reviews based on user experiences and enrich the overall system database. Furthermore, the question building unit can collect user responses in real time and quickly reflect the information in collaboration with the analysis and delivery units. This allows the question building unit to collect high-quality word-of-mouth data based on user experiences and improve the overall system performance.

[0075] The Personalization Unit utilizes the review data created by the Question Construction Unit for personalization features. For example, the Personalization Unit issues coupons based on ratings from other users. Specifically, it can issue coupons for reviews that have been rated as "helpful" by other users. For instance, if a review of a particular restaurant receives high ratings from many users, the Personalization Unit will issue a coupon to the user who posted that review. The Personalization Unit can also award points based on ratings from other users. For example, if a review receives high ratings from many users, the Personalization Unit will award points to the user who posted that review. Furthermore, the Personalization Unit can offer benefits based on ratings from other users. For example, users who post highly-rated reviews can receive benefits that can be used for their next reservation. The Personalization Unit issues coupons for reviews that have been rated as "helpful" by other users. The Personalization Unit can also award points based on ratings from other users. The Personalization Unit can also offer benefits based on ratings from other users. This allows the Personalization Unit to encourage user review submissions and improve the quality and quantity of review data across the entire system. Furthermore, the personalization unit can provide individually optimized benefits and coupons based on the user's preferences and behavioral history. This allows the personalization unit to improve user satisfaction and promote overall system usage.

[0076] The data collection unit can collect review data from restaurant review websites or social media. For example, the data collection unit can collect text data from restaurant review websites. For example, the data collection unit can collect text data from restaurant review websites and input it into a generating AI. For example, the data collection unit can collect image data from social media. For example, the data collection unit can collect image data from social media and input it into a generating AI. For example, the data collection unit can collect audio data. For example, the data collection unit can collect audio data and input it into a generating AI. In this way, the data collection unit can obtain a wide range of data by collecting review data from restaurant review websites and social media. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input text data collected from restaurant review websites into a generating AI and have the generating AI perform analysis of the text data.

[0077] The analysis department can analyze collected review data and create summaries about specific restaurants. For example, the analysis department can analyze collected review data and create summaries about specific restaurants. The analysis department can analyze review data using text mining techniques, for example. The analysis department can analyze review data using text mining techniques and create summaries about specific restaurants. The analysis department can analyze review data using sentiment analysis techniques, for example. The analysis department can analyze review data using sentiment analysis techniques and create summaries about specific restaurants. The analysis department can analyze review data using natural language processing techniques, for example. The analysis department can analyze review data using natural language processing techniques and create summaries about specific restaurants. This allows the analysis department to create summaries about specific restaurants, making it easy for users to grasp the atmosphere of the establishment. Some or all of the above-described processes in the analysis department may be performed using, for example, generative AI, or without generative AI. For example, the analysis department can input collected review data into a generative AI and have the generative AI create summaries.

[0078] The service provider can create and provide summaries tailored to user attributes. For example, the service provider can create and provide summaries tailored to user attributes. For example, the service provider can create summaries based on attributes such as age, gender, and interests. For example, the service provider can create and provide summaries tailored to age. For example, the service provider can create and provide summaries tailored to gender. For example, the service provider can create and provide summaries tailored to interests. In this way, by providing summaries tailored to user attributes, the service provider can provide information that is appropriate for the usage scenario. Some or all of the above processing in the service provider may be performed using AI, for example, or without AI. For example, the service provider can input user attribute information into a generating AI and have the generating AI create a summary.

[0079] The question building unit can build questions that users will answer after visiting the store. The question building unit can, for example, build questions that users will answer after visiting the store. The question building unit can, for example, build questions about the taste of the food or the atmosphere of the store. The question building unit can, for example, build questions about the taste of the food. The question building unit can, for example, build questions about the atmosphere of the store. The question building unit can, for example, build questions about a specific menu item. The question building unit can, for example, build questions about a specific menu item. In this way, the question building unit can easily create reviews by building questions that users will answer after visiting the store. Some or all of the above processing in the question building unit may be performed using AI, for example, or not using AI. For example, the question building unit can input user response data into a generating AI and have the generating AI build the questions.

[0080] The personalization unit can issue coupons, etc., based on ratings from other users. For example, the personalization unit can issue coupons, etc., based on ratings from other users. For example, the personalization unit can issue coupons for reviews that have been rated as "helpful" by other users. For example, the personalization unit can award points based on ratings from other users. For example, the personalization unit can offer benefits based on ratings from other users. In this way, the personalization unit encourages users to post reviews by issuing coupons, etc., based on ratings from other users. Some or all of the above processing in the personalization unit may be performed using AI, for example, or without AI. For example, the personalization unit can input rating data from other users into a generating AI and have the generating AI issue coupons.

[0081] The data collection unit can estimate the user's emotions and adjust the timing of collecting review data based on the estimated user emotions. For example, if the user has positive emotions, the data collection unit will collect review data immediately. If the user has negative emotions, the data collection unit will collect review data after a certain period of time. If the user has neutral emotions, the data collection unit will collect review data at an appropriate time. In this way, the data collection unit can collect data at the appropriate time by adjusting the timing of review data collection based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input user emotion data into a generative AI and have the generative AI adjust the collection timing.

[0082] The data collection unit can analyze a user's past review posting history and select the optimal data collection method. For example, the data collection unit can collect review data according to the time slots when the user frequently posted in the past. For example, the data collection unit can collect review data from platforms that the user has preferred to use in the past. For example, the data collection unit can analyze trends in the content the user has posted in the past and prioritize the collection of relevant data. In this way, the data collection unit can select the optimal data collection method by analyzing a user's past review posting history. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input the user's past posting history data into a generating AI and have the generating AI select the optimal data collection method.

[0083] The data collection unit can filter the collected review data based on the user's current areas of interest. For example, the data collection unit can prioritize collecting review data for restaurants that the user is currently interested in. For example, the data collection unit can collect review data related to a specific cuisine genre that the user has shown interest in. For example, the data collection unit can collect review data for restaurants in areas the user has recently visited. In this way, the data collection unit can collect highly relevant data by filtering based on the user's current areas of interest. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input the user's areas of interest data into a generating AI and have the generating AI perform the filtering.

[0084] The data collection unit can estimate the user's emotions and determine the priority of the review data to collect based on the estimated user emotions. For example, if the user has positive emotions, the data collection unit will prioritize collecting positive review data. For example, if the user has negative emotions, the data collection unit will prioritize collecting negative review data. For example, if the user has neutral emotions, the data collection unit will collect balanced review data. In this way, the data collection unit can prioritize collecting appropriate data by determining the priority of the review data to collect based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input user emotion data into a generative AI and have the generative AI perform the determination of the priority of the data to collect.

[0085] The data collection unit can prioritize the collection of highly relevant data by considering the user's geographical location when collecting review data. For example, the data collection unit can prioritize the collection of review data about restaurants in the area where the user is currently located. For example, the data collection unit can collect review data about restaurants in areas the user has visited in the past. For example, the data collection unit can collect review data about restaurants in areas the user plans to visit in the future. In this way, the data collection unit can provide information appropriate to the region by prioritizing the collection of highly relevant data by considering the user's geographical location. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input the user's geographical location information into a generating AI and have the generating AI perform the collection of highly relevant data.

[0086] The data collection unit can analyze users' social media activity and collect relevant data when collecting word-of-mouth data. For example, the data collection unit can collect word-of-mouth data about restaurants shared by users on social media. For example, the data collection unit can collect word-of-mouth data related to posts by influencers that users follow. For example, the data collection unit can collect word-of-mouth data about restaurants that are being discussed in groups or communities that users participate in. This allows the data collection unit to efficiently collect relevant data by analyzing users' social media activity. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input user social media activity data into a generating AI and have the generating AI perform the collection of related data.

[0087] The analysis unit can estimate the user's emotions and adjust the way the summary is expressed based on the estimated emotions. For example, if the user has positive emotions, the analysis unit will create a summary using bright and positive language. If the user has negative emotions, the analysis unit will create a summary using calm and objective language. If the user has neutral emotions, the analysis unit will create a summary using balanced language. In this way, the analysis unit can provide a summary with appropriate language by adjusting the way the summary is expressed based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the analysis unit may be performed using a generative AI, or not using a generative AI. For example, the analysis unit can input user emotion data into a generative AI and have the generative AI adjust the way the summary is expressed.

[0088] The analysis unit can adjust the level of detail in summaries based on specific keywords when analyzing review data. For example, the analysis unit can summarize review data containing a specific dish name in detail. For example, the analysis unit can summarize review data containing keywords related to a specific service in detail. For example, the analysis unit can summarize review data containing keywords related to a specific event or campaign in detail. This allows the analysis unit to provide important information in detail by adjusting the level of detail in summaries based on specific keywords. Some or all of the above processing in the analysis unit may be performed using, for example, a generative AI, or not using a generative AI. For example, the analysis unit can input review data into a generative AI and have the generative AI perform the adjustment of the level of detail in the summaries.

[0089] The analysis unit can apply different summarization algorithms depending on the category when analyzing review data. For example, the analysis unit can apply a summarization algorithm related to the taste of the food and the service to restaurant review data. For example, the analysis unit can apply a summarization algorithm related to the atmosphere and the quality of the drinks to cafe review data. For example, the analysis unit can apply a summarization algorithm related to the types of drinks and the nighttime atmosphere to bar review data. In this way, the analysis unit can provide summaries appropriate to each category by applying different summarization algorithms depending on the category. Some or all of the above processing in the analysis unit may be performed using, for example, a generative AI, or not using a generative AI. For example, the analysis unit can input review data into a generative AI and have the generative AI apply different summarization algorithms.

[0090] The analysis unit can estimate the user's emotions and adjust the length of the summary based on the estimated emotions. For example, if the user has positive emotions, the analysis unit provides a detailed summary. For example, if the user has negative emotions, the analysis unit provides a concise summary. For example, if the user has neutral emotions, the analysis unit provides a summary of standard length. Thus, the analysis unit can provide a summary of appropriate length by adjusting the length of the summary based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the analysis unit may be performed using a generative AI, or not using a generative AI. For example, the analysis unit can input user emotion data into a generative AI and have the generative AI adjust the length of the summary.

[0091] The analysis unit can determine the priority of summaries based on the posting date when analyzing review data. For example, the analysis unit may prioritize summarizing recently posted review data. For example, the analysis unit may prioritize summarizing review data posted during a specific event period. For example, the analysis unit may determine the priority of summaries based on seasonal trends. This allows the analysis unit to prioritize providing the latest information by determining the priority of summaries based on the posting date. Some or all of the above processing in the analysis unit may be performed using, for example, a generative AI, or not using a generative AI. For example, the analysis unit may input review data into a generative AI and have the generative AI perform the determination of the summary priority.

[0092] The analysis unit can improve the accuracy of its summaries by referring to other relevant data sources when analyzing review data. For example, the analysis unit can improve the accuracy of its summaries by referring to information on a restaurant's official website. For example, the analysis unit can improve the accuracy of its summaries by referring to social media posts. For example, the analysis unit can improve the accuracy of its summaries by referring to data from other review sites. In this way, the analysis unit improves the accuracy of its summaries by referring to other relevant data sources. Some or all of the above processing in the analysis unit may be performed using, for example, a generative AI, or not using a generative AI. For example, the analysis unit can input other data sources into a generative AI and have the generative AI perform the task of improving the accuracy of its summaries.

[0093] The service provider can estimate the user's emotions and adjust the way the summary is presented based on the estimated emotions. For example, if the user has positive emotions, the service provider will present the summary in a bright and positive tone. If the user has negative emotions, the service provider will present the summary in a calm and objective tone. If the user has neutral emotions, the service provider will present the summary in a balanced tone. In this way, the service provider can provide a summary in an appropriate manner by adjusting the way the summary is presented based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the service provider may be performed using AI, for example, or without AI. For example, the service provider can input user emotion data into a generative AI and have the generative AI adjust the way the summary is presented.

[0094] The service provider can select the optimal service method by referring to the user's past browsing history when providing summaries. For example, the service provider may prioritize providing summaries of restaurants the user has previously viewed. For example, the service provider may provide summaries of cuisine genres the user has previously viewed. For example, the service provider may provide summaries of restaurants in a region the user has previously viewed. This allows the service provider to select the optimal service method by referring to the user's past browsing history. Some or all of the above processing in the service provider may be performed using AI, for example, or without AI. For example, the service provider may input the user's past browsing history data into a generating AI and have the generating AI select the optimal service method.

[0095] The service provider can customize the content provided based on the user's attribute information when providing summaries. For example, the service provider can provide summaries tailored to the user's age. For example, the service provider can provide summaries tailored to the user's gender. For example, the service provider can provide summaries tailored to the user's family structure. In this way, the service provider can provide individually optimized information by customizing the content based on the user's attribute information. Some or all of the above processing in the service provider may be performed using AI, for example, or without AI. For example, the service provider can input the user's attribute information into a generating AI and have the generating AI perform the customization of the content provided.

[0096] The service provider can estimate the user's emotions and adjust the display order of summaries based on the estimated emotions. For example, if the user has positive emotions, the service provider will prioritize displaying positive summaries. For example, if the user has negative emotions, the service provider will prioritize displaying negative summaries. For example, if the user has neutral emotions, the service provider will display balanced summaries. In this way, by adjusting the display order of summaries based on the user's emotions, the service provider can provide information to the user in the most optimal order. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the service provider may be performed using AI, for example, or without AI. For example, the service provider can input user emotion data into a generative AI and have the generative AI adjust the display order of summaries.

[0097] The service provider can select the optimal display method when providing a summary, taking into account the user's device information. For example, if the user is using a smartphone, the service provider will provide a display method that matches the screen size. For example, if the user is using a tablet, the service provider will provide a display method optimized for a larger screen. For example, if the user is using a personal computer, the service provider will provide a display method that includes detailed information. In this way, by selecting the optimal display method considering the user's device information, the service provider can provide the optimal display according to the device. Some or all of the above processing in the service provider may be performed using AI, for example, or without AI. For example, the service provider can input the user's device information into a generating AI and have the generating AI select the optimal display method.

[0098] The service provider can analyze the user's social media activity and adjust the content provided when delivering summaries. For example, the service provider can provide summaries about restaurants that the user has shared on social media. For example, the service provider can provide summaries related to posts by influencers that the user follows. For example, the service provider can provide summaries about restaurants that are being discussed in groups or communities that the user participates in. In this way, the service provider can provide highly relevant information by analyzing the user's social media activity. Some or all of the above processing in the service provider may be performed using AI, for example, or not using AI. For example, the service provider can input the user's social media activity data into a generating AI and have the generating AI adjust the content provided.

[0099] The question construction unit can estimate the user's emotions and adjust the content of the questions based on the estimated emotions. For example, if the user has positive emotions, the question construction unit will prioritize constructing positive questions. For example, if the user has negative emotions, the question construction unit will avoid negative questions and construct neutral questions. For example, if the user has neutral emotions, the question construction unit will construct balanced questions. In this way, the question construction unit can provide appropriate questions by adjusting the content of the questions based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the question construction unit may be performed using AI, for example, or not using AI. For example, the question construction unit can input user emotion data into a generative AI and have the generative AI adjust the content of the questions.

[0100] The question building unit can select the most suitable question by referring to the user's past answer history when building a question. For example, the question building unit can analyze trends in questions the user has answered in the past and build relevant questions. For example, the question building unit can build questions based on topics the user has preferred to answer in the past. For example, the question building unit can consider topics the user has avoided in the past and build appropriate questions. In this way, the question building unit can select the most suitable question by referring to the user's past answer history. Some or all of the above processes in the question building unit may be performed using AI, for example, or without AI. For example, the question building unit can input the user's past answer history data into a generating AI and have the generating AI select the most suitable question.

[0101] The question building unit can customize the question content based on the user's attribute information when building a question. For example, the question building unit can build a question based on the user's age. For example, the question building unit can build a question based on the user's gender. For example, the question building unit can build a question based on the user's family structure. In this way, the question building unit can provide individually optimized questions by customizing the question content based on the user's attribute information. Some or all of the above processing in the question building unit may be performed using AI, for example, or without AI. For example, the question building unit can input the user's attribute information into a generating AI and have the generating AI perform the customization of the question content.

[0102] The question construction unit can estimate the user's emotions and adjust the order of questions based on the estimated emotions. For example, if the user has positive emotions, the question construction unit will construct positive questions first. For example, if the user has negative emotions, the question construction unit will postpone negative questions. For example, if the user has neutral emotions, the question construction unit will construct questions in a balanced order. In this way, the question construction unit can provide questions in an appropriate order by adjusting the order of questions based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the question construction unit may be performed using AI or not using AI. For example, the question construction unit can input user emotion data into a generative AI and have the generative AI adjust the order of questions.

[0103] The question building unit can select the optimal question format by considering the user's device information when building a question. For example, if the user is using a smartphone, the question building unit will build a short text-based question. For example, if the user is using a tablet, the question building unit will build a detailed question. For example, if the user is using a personal computer, the question building unit will build a question with multiple choices. In this way, the question building unit can provide the most appropriate question for the device by selecting the optimal question format by considering the user's device information. Some or all of the above processing in the question building unit may be performed using AI, for example, or without AI. For example, the question building unit can input the user's device information into a generating AI and have the generating AI select the optimal question format.

[0104] The question building unit can analyze the user's social media activity and adjust the question content when building a question. For example, the question building unit can build a question based on what the user has shared on social media. For example, the question building unit can build a question related to posts by influencers the user follows. For example, the question building unit can build a question based on topics that are being discussed in groups or communities the user participates in. In this way, the question building unit can provide highly relevant questions by analyzing the user's social media activity. Some or all of the above processing in the question building unit may be performed using AI, for example, or not using AI. For example, the question building unit can input the user's social media activity data into a generating AI and have the generating AI adjust the question content.

[0105] The personalization unit can estimate the user's emotions and adjust the personalized content based on the estimated emotions. For example, if the user has positive emotions, the personalization unit will prioritize providing positive content. For example, if the user has negative emotions, the personalization unit will avoid negative content. For example, if the user has neutral emotions, the personalization unit will provide balanced content. In this way, the personalization unit can provide appropriate content by adjusting the personalized content based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the personalization unit may be performed using AI, for example, or without AI. For example, the personalization unit can input user emotion data into a generative AI and have the generative AI perform adjustments to the personalized content.

[0106] The personalization unit can select the optimal personalization method by referring to the user's past behavior history during the personalization process. For example, the personalization unit may personalize based on information about restaurants the user has previously viewed. For example, the personalization unit may personalize based on the content of reviews the user has previously posted. For example, the personalization unit may personalize based on the user's past coupon usage history. In this way, the personalization unit can select the optimal personalization method by referring to the user's past behavior history. Some or all of the above processing in the personalization unit may be performed using AI, for example, or without AI. For example, the personalization unit can input the user's past behavior history data into a generating AI and have the generating AI select the optimal personalization method.

[0107] The personalization unit can customize content based on user attribute information during the personalization process. For example, the personalization unit can provide personalized content tailored to the user's age. For example, the personalization unit can provide personalized content tailored to the user's gender. For example, the personalization unit can provide personalized content tailored to the user's family structure. In this way, the personalization unit can provide individually optimized information by customizing content based on the user's attribute information. Some or all of the above-described processes in the personalization unit may be performed using AI, for example, or without AI. For example, the personalization unit can input user attribute information into a generating AI and have the generating AI perform content customization.

[0108] The personalization unit can estimate the user's emotions and determine personalization priorities based on the estimated emotions. For example, if the user has positive emotions, the personalization unit will prioritize providing positive content. For example, if the user has negative emotions, the personalization unit will avoid negative content. For example, if the user has neutral emotions, the personalization unit will provide balanced content. In this way, the personalization unit can provide information in the appropriate order by determining personalization priorities based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the personalization unit may be performed using AI or not using AI. For example, the personalization unit can input user emotion data into a generative AI and have the generative AI perform the determination of personalization priorities.

[0109] The personalization unit can provide optimal content by considering the user's geographical location information during the personalization process. For example, the personalization unit may prioritize providing restaurant information in the user's current location. For example, the personalization unit may provide restaurant information in areas the user has visited in the past. For example, the personalization unit may provide restaurant information in areas the user plans to visit in the future. In this way, the personalization unit can provide information appropriate to the region by considering the user's geographical location information and providing optimal content. Some or all of the above processing in the personalization unit may be performed using AI, for example, or without AI. For example, the personalization unit can input the user's geographical location information into a generating AI and have the generating AI provide optimal content.

[0110] The personalization unit can analyze the user's social media activity and adjust the content during the personalization process. For example, the personalization unit can personalize based on restaurant information shared by the user on social media. For example, the personalization unit can provide content related to posts by influencers the user follows. For example, the personalization unit can provide restaurant information that is being discussed in groups or communities the user participates in. In this way, the personalization unit can provide highly relevant information by analyzing the user's social media activity. Some or all of the above processing in the personalization unit may be performed using AI, for example, or without AI. For example, the personalization unit can input the user's social media activity data into a generating AI and have the generating AI perform content adjustments.

[0111] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0112] The restaurant reservation support system can estimate the user's emotions and make reservation suggestions based on those emotions. For example, if the user has positive emotions, it can suggest special events or promotions. If the user has negative emotions, it can suggest restaurants with a relaxing atmosphere. If the user has neutral emotions, it can suggest generally popular restaurants. This enables optimal reservation suggestions tailored to the user's emotions. Emotion estimation is achieved using an emotion engine or generative AI. Generative AI includes, but is not limited to, text generation AI or multimodal generation AI. Some or all of the processing described above in the suggestion unit may be performed using AI or not. For example, the suggestion unit can input user emotion data into the generative AI and have the generative AI execute reservation suggestions.

[0113] The restaurant reservation support system can analyze a user's past reservation history and make optimal reservation suggestions. For example, it can analyze the trends of restaurants a user has visited in the past and suggest restaurants that offer a similar atmosphere and cuisine. Based on a user's past coupon usage history, it can suggest restaurants where coupons can be used. Considering the time slots a user has previously reserved, it can suggest restaurants that are available for the same time slot. This enables optimal reservation suggestions based on the user's past behavior history. Some or all of the above processing in the suggestion unit may be performed using AI or not. For example, the suggestion unit can input the user's past reservation history data into a generating AI and have the generating AI execute reservation suggestions.

[0114] The restaurant reservation support system can make optimal reservation suggestions by taking into account the user's geographical location. For example, it can prioritize suggesting restaurants in the area where the user is currently located. It can also suggest restaurants in areas the user has visited in the past. It can also suggest restaurants in areas the user plans to visit in the future. This enables optimal reservation suggestions that take into account the user's geographical location. Some or all of the above processing in the suggestion unit may be performed using AI or not. For example, the suggestion unit can input the user's geographical location information into a generating AI and have the generating AI execute reservation suggestions.

[0115] The restaurant reservation support system can analyze a user's social media activity and make relevant reservation suggestions. For example, it can make reservation suggestions based on restaurants the user has shared on social media. It can suggest restaurants related to posts by influencers the user follows. It can suggest restaurants that are trending in groups or communities the user belongs to. This enables optimal reservation suggestions based on the user's social media activity. Some or all of the above processing in the suggestion section may be performed using AI or not. For example, the suggestion section can input the user's social media activity data into a generating AI and have the generating AI execute reservation suggestions.

[0116] The restaurant reservation support system can estimate the user's emotions and suggest canceling or changing the reservation based on those emotions. For example, if the user has negative emotions, it can suggest canceling the reservation. If the user has positive emotions, it can suggest changing the reservation. If the user has neutral emotions, it can suggest keeping the reservation. This makes it possible to suggest the most appropriate reservation cancellation or change based on the user's emotions. Emotion estimation is achieved using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI or multimodal generation AI. Some or all of the processing described above in the suggestion unit may be performed using AI or not. For example, the suggestion unit can input user emotion data into the generative AI and have the generative AI execute a suggestion to cancel or change the reservation.

[0117] The restaurant reservation support system can analyze a user's past review posting history and make optimal reservation suggestions. For example, it can analyze the content of reviews posted by a user in the past and suggest restaurants that offer a similar atmosphere and cuisine. Based on the trends of restaurants that the user has given high ratings to in the past, it can suggest similar restaurants. Based on the trends of restaurants that the user has given low ratings to in the past, it can suggest restaurants to avoid. This makes it possible to make optimal reservation suggestions based on the user's past review posting history. Some or all of the above processing in the suggestion section may be performed using AI or not. For example, the suggestion section can input the user's past review posting history data into a generating AI and have the generating AI execute reservation suggestions.

[0118] The restaurant reservation support system can estimate the user's emotions and send reservation reminders based on those emotions. For example, if the user has positive emotions, the reminder can be sent earlier. If the user has negative emotions, the reminder can be sent later. If the user has neutral emotions, the reminder can be sent at the normal time. This enables the sending of optimal reminders according to the user's emotions. Emotion estimation is achieved using an emotion engine or generative AI. Generative AI may be, but is not limited to, text generation AI or multimodal generation AI. Some or all of the above processing in the reminder unit may be performed using AI or not. For example, the reminder unit can input user emotion data into the generative AI and have the generative AI execute the reminder sending.

[0119] The restaurant reservation support system can make optimal reservation suggestions by referring to the user's past browsing history. For example, it can make reservation suggestions based on information about restaurants the user has previously viewed. It can make reservation suggestions based on the type of cuisine the user has previously viewed. It can make reservation suggestions based on restaurants in the area the user has previously viewed. This makes it possible to make optimal reservation suggestions based on the user's past browsing history. Some or all of the above processing in the suggestion unit may be performed using AI or not. For example, the suggestion unit can input the user's past browsing history data into a generating AI and have the generating AI execute reservation suggestions.

[0120] The restaurant reservation support system can estimate the user's emotions and send a reservation confirmation message based on those emotions. For example, if the user has positive emotions, the system can send a confirmation message using cheerful and positive language. If the user has negative emotions, the system can send a confirmation message using calm and objective language. If the user has neutral emotions, the system can send a confirmation message using balanced language. This enables the sending of the most appropriate confirmation message according to the user's emotions. Emotion estimation is achieved using an emotion engine or generative AI. Generative AI may be, but is not limited to, text-generating AI or multimodal-generating AI. Some or all of the above processing in the confirmation message section may be performed using AI or not. For example, the confirmation message section can input the user's emotion data into the generative AI and have the generative AI execute the sending of the confirmation message.

[0121] The restaurant reservation support system can customize reservation suggestions based on user attribute information. For example, it can make reservation suggestions based on the user's age, gender, or family structure. This enables optimal reservation suggestions based on the user's attribute information. Some or all of the above processing in the suggestion unit may be performed using AI or not. For example, the suggestion unit can input user attribute information into a generating AI and have the generating AI execute reservation suggestions.

[0122] The following briefly describes the processing flow for example form 2.

[0123] Step 1: The data collection unit collects review data. The data collection unit collects review data from sources such as restaurant review websites and social media. The data collection unit can collect text data from, for example, restaurant review websites. The data collection unit can also collect image data from social media. Furthermore, the data collection unit can also collect audio data. For example, the data collection unit collects text data from restaurant review websites and inputs it into the generation AI. The data collection unit can also collect image data from social media and input it into the generation AI. The data collection unit can also collect audio data and input it into the generation AI. Step 2: The analysis department analyzes the review data collected by the collection department and creates a summary. For example, the analysis department analyzes the collected review data and creates a summary about a specific restaurant. For example, the analysis department can analyze the review data using text mining techniques. The analysis department can also analyze the review data using sentiment analysis techniques. Furthermore, the analysis department can analyze the review data using natural language processing techniques. For example, the analysis department can analyze the review data using text mining techniques and create a summary about a specific restaurant. The analysis department can also analyze the review data using sentiment analysis techniques and create a summary about a specific restaurant. The analysis department can also analyze the review data using natural language processing techniques and create a summary about a specific restaurant. Step 3: The service provider provides summaries created by the analysis provider to each user attribute. For example, the service provider creates and provides summaries based on user attributes. For example, the service provider can create summaries based on attributes such as age, gender, and interests. The service provider can also create summaries based on usage scenarios. Furthermore, the service provider can create summaries based on the user's past behavior history. For example, the service provider can create and provide summaries based on age. The service provider can also create and provide summaries based on gender. The service provider can also create and provide summaries based on interests. Step 4: The Question Building Unit creates reviews by having users answer questions in a chat format after their visit. The Question Building Unit constructs questions that users will answer after their visit. For example, the Question Building Unit can construct questions about the taste of the food or the atmosphere of the restaurant. It can also construct questions about specific menu items. Furthermore, the Question Building Unit can construct questions based on the user's past preferences. For example, the Question Building Unit can construct questions about the taste of the food. The Question Building Unit can also construct questions about the atmosphere of the restaurant. The Question Building Unit can also construct questions about specific menu items. Step 5: The Personalization Unit utilizes the review data created by the Question Construction Unit for personalization features. For example, the Personalization Unit issues coupons based on ratings from other users. For example, the Personalization Unit can issue coupons for reviews that have been rated as "helpful" by other users. The Personalization Unit can also award points based on ratings from other users. Furthermore, the Personalization Unit can offer benefits based on ratings from other users. For example, the Personalization Unit issues coupons for reviews that have been rated as "helpful" by other users. The Personalization Unit can also award points based on ratings from other users. The Personalization Unit can also offer benefits based on ratings from other users.

[0124] The specific processing unit 290 transmits the result of the specific processing to the smart device 14. In the smart device 14, the control unit 46A causes the output device 40 to output the result of the specific processing. The microphone 38B acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0125] Data generation model 58 is a form of so-called generative AI (Artificial Intelligence). An example of data generation model 58 is ChatGPT (registered trademark) (Internet search).<URL: https: / / openai.com / blog / chatgpt> Examples of generative AI include text generation AI, image generation AI, and multimodal generation AI. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and with inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats from audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVMs), k-means clustering, convolutional neural networks (CNNs), recurrent neural networks (RNNs), generative adversarial networks (GANs), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each of the above parts is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example.Furthermore, processing performed by AI, including generative AI, may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by AI, including generative AI.

[0126] Furthermore, the processing performed by the data processing system 10 described above is carried out by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart device 14, but it may also be carried out by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart device 14. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the smart device 14 or an external device, and the smart device 14 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0127] Each of the multiple elements described above, including the collection unit, analysis unit, provision unit, question construction unit, and personalization unit, is implemented in at least one of the smart device 14 and the data processing unit 12. For example, the collection unit collects word-of-mouth data using the camera 42 and microphone 38B of the smart device 14 and analyzes it using the specific processing unit 290 of the data processing unit 12. The analysis unit is implemented in the specific processing unit 290 of the data processing unit 12 and analyzes the collected word-of-mouth data to create a summary. The provision unit is implemented in the control unit 46A of the smart device 14 and provides a summary according to the user's attributes. The question construction unit is implemented in the control unit 46A of the smart device 14 and constructs questions for the user to answer after visiting the store. The personalization unit is implemented in the specific processing unit 290 of the data processing unit 12 and issues coupons, etc., based on evaluations from other users. The correspondence between each unit and the device or control unit is not limited to the example described above and can be changed in various ways.

[0128] [Second Embodiment] Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment.

[0129] As shown in Figure 3, the data processing system 210 includes a data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0130] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN and / or LAN.

[0131] The smart glasses 214 include a computer 36, a microphone 238, a speaker 240, a camera 42, and a communication interface 44. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, and camera 42 are also connected to the bus 52.

[0132] The microphone 238 receives voice signals from the user and accepts instructions from the user. The microphone 238 captures the voice signals from the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0133] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, which captures images of the area around the user (for example, an imaging range defined by a field of view equivalent to the field of vision of a typical healthy person).

[0134] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0135] Figure 4 shows an example of the main functions of the data processing device 12 and the smart glasses 214. As shown in Figure 4, the data processing device 12 performs specific processing by the processor 28. The storage 32 stores the specific processing program 56.

[0136] The processor 28 reads a specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 acting as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0137] Storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0138] In the smart glasses 214, specific processing is performed by the processor 46. The storage 50 stores a specific processing program 60. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 acting as a control unit 46A according to the specific processing program 60 executed on the RAM 48. The smart glasses 214 also have a data generation model 58 and an emotion identification model 59, similar to the data generation model and emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0139] Furthermore, other devices besides the data processing device 12 may also have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 obtains processing results (such as prediction results) using the data generation model 58 by communicating with the server device that has the data generation model 58. Also, the data processing device 12 may be a server device or a terminal device owned by the user (for example, a mobile phone, robot, home appliance, etc.).

[0140] The specific processing unit 290 transmits the result of the specific processing to the smart glasses 214. In the smart glasses 214, the control unit 46A causes the speaker 240 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0141] The data generation model 58 is a so-called generative AI. An example of a data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats such as audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVM), k-means clustering, convolutional neural networks (CNN), recurrent neural networks (RNN), generative adversarial networks (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each part described above is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example. Also, processing performed by an AI including a generative AI may be replaced by rule-based processing, and rule-based processing may be replaced by processing performed by an AI including a generative AI.

[0142] The data processing system 210 according to the second embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 210 is performed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart glasses 214, but it may also be performed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart glasses 214. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the smart glasses 214 or an external device, and the smart glasses 214 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0143] Each of the multiple elements described above, including the collection unit, analysis unit, provision unit, question construction unit, and personalization unit, is implemented in at least one of the smart glasses 214 and the data processing unit 12. For example, the collection unit collects word-of-mouth data using the camera 42 and microphone 238 of the smart glasses 214 and analyzes it using the identification processing unit 290 of the data processing unit 12. The analysis unit is implemented in the identification processing unit 290 of the data processing unit 12 and analyzes the collected word-of-mouth data to create a summary. The provision unit is implemented in the control unit 46A of the smart glasses 214 and provides a summary according to the user's attributes. The question construction unit is implemented in the control unit 46A of the smart glasses 214 and constructs questions for the user to answer after visiting the store. The personalization unit is implemented in the identification processing unit 290 of the data processing unit 12 and issues coupons, etc., based on evaluations from other users. The correspondence between each unit and the device or control unit is not limited to the example described above and can be changed in various ways.

[0144] [Third Embodiment] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[0145] As shown in Figure 5, the data processing system 310 includes a data processing device 12 and a headset terminal 314. An example of the data processing device 12 is a server.

[0146] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN and / or LAN.

[0147] The headset terminal 314 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication interface 44, and a display 343. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, camera 42, and display 343 are also connected to the bus 52.

[0148] The microphone 238 receives voice signals from the user and accepts instructions from the user. The microphone 238 captures the voice signals from the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0149] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, which captures images of the area around the user (for example, an imaging range defined by a field of view equivalent to the field of vision of a typical healthy person).

[0150] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0151] Figure 6 shows an example of the main functions of the data processing device 12 and the headset terminal 314. As shown in Figure 6, the data processing device 12 performs specific processing using the processor 28. The storage 32 stores the specific processing program 56.

[0152] The processor 28 reads a specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 acting as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0153] Storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0154] In the headset terminal 314, specific processing is performed by the processor 46. The storage 50 stores a specific program 60. The processor 46 reads the specific program 60 from the storage 50 and executes the read specific program 60 on the RAM 48. The specific processing is realized by the processor 46 acting as a control unit 46A according to the specific program 60 executed on the RAM 48. The headset terminal 314 also has a data generation model 58 and an emotion identification model 59, similar to the data generation model and emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0155] Furthermore, other devices besides the data processing device 12 may also have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 obtains processing results (such as prediction results) using the data generation model 58 by communicating with the server device that has the data generation model 58. Also, the data processing device 12 may be a server device or a terminal device owned by the user (for example, a mobile phone, robot, home appliance, etc.).

[0156] The specific processing unit 290 transmits the result of the specific processing to the headset terminal 314. In the headset terminal 314, the control unit 46A causes the speaker 240 and display 343 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0157] The data generation model 58 is a so-called generative AI. An example of a data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats such as audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVM), k-means clustering, convolutional neural networks (CNN), recurrent neural networks (RNN), generative adversarial networks (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each part described above is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example. Also, processing performed by an AI including a generative AI may be replaced by rule-based processing, and rule-based processing may be replaced by processing performed by an AI including a generative AI.

[0158] The data processing system 310 according to the third embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 310 is performed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the headset terminal 314, but may also be performed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the headset terminal 314. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the headset terminal 314 or an external device, and the headset terminal 314 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0159] Each of the multiple elements described above, including the collection unit, analysis unit, provision unit, question construction unit, and personalization unit, is implemented in at least one of the headset terminal 314 and the data processing unit 12. For example, the collection unit collects word-of-mouth data using the camera 42 and microphone 238 of the headset terminal 314 and analyzes it using the specific processing unit 290 of the data processing unit 12. The analysis unit is implemented in the specific processing unit 290 of the data processing unit 12 and analyzes the collected word-of-mouth data to create a summary. The provision unit is implemented in the control unit 46A of the headset terminal 314 and provides a summary according to the user's attributes. The question construction unit is implemented in the control unit 46A of the headset terminal 314 and constructs questions for the user to answer after visiting the store. The personalization unit is implemented in the specific processing unit 290 of the data processing unit 12 and issues coupons, etc., based on evaluations from other users. The correspondence between each unit and the device or control unit is not limited to the example described above and can be changed in various ways.

[0160] [Fourth Embodiment] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[0161] As shown in Figure 7, the data processing system 410 includes a data processing device 12 and a robot 414. An example of the data processing device 12 is a server.

[0162] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN and / or LAN.

[0163] The robot 414 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication interface 44, and a controlled object 443. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, camera 42, and controlled object 443 are also connected to the bus 52.

[0164] The microphone 238 receives voice signals from the user and accepts instructions from the user. The microphone 238 captures the voice signals from the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0165] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS image sensor or CCD image sensor, which captures images of the area around the user (for example, an imaging range defined by a field of view equivalent to the field of vision of a typical healthy person).

[0166] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0167] The controlled object 443 includes a display device, LEDs in the eyes, and motors that drive the arms, hands, and feet. The posture and gestures of the robot 414 are controlled by controlling the motors of the arms, hands, and feet. Some of the robot 414's emotions can be expressed by controlling these motors. The robot 414's facial expressions can also be expressed by controlling the illumination state of the LEDs in its eyes.

[0168] Figure 8 shows an example of the main functions of the data processing device 12 and the robot 414. As shown in Figure 8, the data processing device 12 performs specific processing using the processor 28. The storage 32 stores the specific processing program 56.

[0169] The processor 28 reads a specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 acting as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0170] Storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0171] In robot 414, specific processing is performed by processor 46. A specific program 60 is stored in storage 50. Processor 46 reads the specific program 60 from storage 50 and executes it on RAM 48. The specific processing is achieved by processor 46 acting as a control unit 46A according to the specific program 60 executed on RAM 48. Robot 414 also has data generation model 58 and emotion identification model 59, similar to those of the robot, and can perform processing similar to that of the specific processing unit 290 using these models.

[0172] Furthermore, other devices besides the data processing device 12 may also have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 obtains processing results (such as prediction results) using the data generation model 58 by communicating with the server device that has the data generation model 58. Also, the data processing device 12 may be a server device or a terminal device owned by the user (for example, a mobile phone, robot, home appliance, etc.).

[0173] The specific processing unit 290 transmits the result of the specific processing to the robot 414. In the robot 414, the control unit 46A causes the speaker 240 and the controlled object 443 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0174] The data generation model 58 is a so-called generative AI. An example of a data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats such as audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVM), k-means clustering, convolutional neural networks (CNN), recurrent neural networks (RNN), generative adversarial networks (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each part described above is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example. Also, processing performed by an AI including a generative AI may be replaced by rule-based processing, and rule-based processing may be replaced by processing performed by an AI including a generative AI.

[0175] The data processing system 410 according to the fourth embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 410 is performed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the robot 414, but it may also be performed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the robot 414. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the robot 414 or an external device, and the robot 414 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0176] Each of the multiple elements described above, including the collection unit, analysis unit, provision unit, question construction unit, and personalization unit, is implemented by, for example, at least one of the robot 414 and the data processing unit 12. For example, the collection unit collects word-of-mouth data using the camera 42 and microphone 238 of the robot 414 and analyzes it using the specific processing unit 290 of the data processing unit 12. The analysis unit is implemented by, for example, the specific processing unit 290 of the data processing unit 12, which analyzes the collected word-of-mouth data and creates a summary. The provision unit is implemented by, for example, the control unit 46A of the robot 414, which provides a summary according to the user's attributes. The question construction unit is implemented by, for example, the control unit 46A of the robot 414, which constructs questions for the user to answer after visiting the store. The personalization unit is implemented by, for example, the specific processing unit 290 of the data processing unit 12, which issues coupons, etc., based on evaluations from other users. The correspondence between each unit and the device or control unit is not limited to the example described above, and various changes are possible.

[0177] Furthermore, the emotion identification model 59, acting as an emotion engine, may determine the user's emotion according to a specific mapping. Specifically, the emotion identification model 59 may determine the user's emotion according to a specific mapping, which is an emotion map (see Figure 9). Similarly, the emotion identification model 59 may also determine the robot's emotion, and the identification processing unit 290 may perform identification processing using the robot's emotion.

[0178] Figure 9 shows the emotion map 400, in which multiple emotions are mapped. In the emotion map 400, emotions are arranged in concentric circles radiating from the center. The closer to the center of the concentric circles, the more primitive the emotions are located. Further out of the concentric circles, emotions representing states and actions arising from mental states are located. Emotion is a concept that includes feelings and mental states. On the left side of the concentric circles, emotions that are generally generated from reactions occurring in the brain are located. On the right side of the concentric circles, emotions that are generally induced by situational judgment are located. Above and below the concentric circles, emotions that are generally generated from reactions occurring in the brain and induced by situational judgment are located. In addition, the emotion of "pleasure" is located on the upper side of the concentric circles, and the emotion of "displeasure" is located on the lower side. Thus, in the emotion map 400, multiple emotions are mapped based on the structure in which emotions arise, and emotions that are likely to occur simultaneously are mapped close together.

[0179] These emotions are distributed at the 3 o'clock position on the Emotion Map 400, and usually fluctuate between feelings of security and anxiety. In the right half of the Emotion Map 400, situational awareness takes precedence over internal feelings, resulting in a calm impression.

[0180] The inside of the Emotion Map 400 represents inner thoughts, while the outside represents actions. Therefore, the further you go from the outside of the Emotion Map 400, the more visible (expressed in actions) your emotions become.

[0181] Here, human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, it results in discomfort, and when they approach the ideal, it results in pleasure. Similarly, in robots, cars, and motorcycles, emotions can be created based on various balances, such as posture and battery level. When these balances deviate from the ideal, it results in discomfort, and when they approach the ideal, it results in pleasure. The emotion map can be generated based, for example, on Dr. Mitsuyoshi's emotion map (Research on a system for analyzing brain physiological signals of speech emotion recognition and emotion, Tokushima University, doctoral dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map contains emotions belonging to a region called "response," where sensation is dominant. The right half of the emotion map contains emotions belonging to a region called "situation," where situational awareness is dominant.

[0182] The emotion map defines two emotions that promote learning. One is the emotion around the middle of the negative "repentance" and "reflection" on the situation side. In other words, it is when the robot experiences negative emotions such as "I never want to feel this way again" or "I don't want to be scolded again." The other is the emotion around the positive "desire" on the reaction side. In other words, it is when the robot has positive feelings such as "I want more" or "I want to know more."

[0183] The emotion identification model 59 inputs user input into a pre-trained neural network, obtains emotion values ​​representing each emotion shown in the emotion map 400, and determines the user's emotion. This neural network is pre-trained based on multiple training data sets, which are combinations of user input and emotion values ​​representing each emotion shown in the emotion map 400. Furthermore, this neural network is trained so that emotions located close together have similar values, as shown in the emotion map 900 in Figure 10. Figure 10 shows an example where multiple emotions such as "reassured," "calm," and "confident" have similar emotion values.

[0184] In the above embodiment, an example was given in which a specific process is performed by a single computer 22. However, the technology of this disclosure is not limited thereto, and a distributed processing method for the specific process may be used, which includes computer 22 and multiple other computers.

[0185] In the above embodiment, an example was given in which the specific processing program 56 is stored in the storage 32, but the technology of this disclosure is not limited thereto. For example, the specific processing program 56 may be stored in a portable, computer-readable, non-temporary storage medium such as a USB (Universal Serial Bus) memory. The specific processing program 56 stored in the non-temporary storage medium is installed in the computer 22 of the data processing device 12. The processor 28 executes specific processing according to the specific processing program 56.

[0186] Alternatively, the specific processing program 56 may be stored in a storage device such as a server connected to the data processing device 12 via the network 54, and the specific processing program 56 may be downloaded and installed on the computer 22 in response to a request from the data processing device 12.

[0187] Furthermore, it is not necessary to store the entirety of the specific processing program 56 in a storage device such as a server connected to the data processing device 12 via the network 54, or to store the entirety of the specific processing program 56 in the storage 32; it is acceptable to store only a portion of the specific processing program 56.

[0188] The following types of processors can be used as hardware resources to perform specific processing. Examples of processors include a CPU, a general-purpose processor that functions as a hardware resource to perform specific processing by executing software, i.e., a program. Other examples of processors include dedicated electrical circuits, such as FPGAs (Field-Programmable Gate Arrays), PLDs (Programmable Logic Devices), or ASICs (Application Specific Integrated Circuits), which have circuit configurations specifically designed to perform specific processing. All of these processors have built-in or connected memory, and all of them perform specific processing by using memory.

[0189] The hardware resource that performs a specific process may consist of one of these various processors, or it may consist of a combination of two or more processors of the same or different types (for example, a combination of multiple FPGAs, or a combination of a CPU and an FPGA). Alternatively, the hardware resource that performs a specific process may consist of a single processor.

[0190] Examples of configurations using a single processor include, firstly, a configuration in which one or more CPUs and software are combined to form a single processor, and this processor functions as a hardware resource that performs a specific process. Secondly, there is a configuration using a processor that realizes the functions of the entire system, including multiple hardware resources that perform a specific process, on a single IC chip, as exemplified by SoCs (System-on-a-chip). In this way, a specific process is realized using one or more of the above types of processors as hardware resources.

[0191] Furthermore, the hardware structure of these various processors can more specifically utilize electrical circuits that combine circuit elements such as semiconductor devices. Also, the specific processing described above is merely an example. Therefore, it goes without saying that unnecessary steps can be deleted, new steps added, or the processing order rearranged, as long as it does not deviate from the main purpose.

[0192] Furthermore, although the above-described examples were divided into four embodiments, some or all of these embodiments may be combined. Also, the smart device 14, smart glasses 214, headset terminal 314, and robot 414 are just examples, and they may be combined, or other devices may be used. Also, although the above-described examples were divided into two embodiments, Embodiment 1 and Embodiment 2, these may be combined.

[0193] The descriptions and illustrations presented above are detailed explanations of the technical aspects of this disclosure and are merely examples of the technical aspects. For example, the above descriptions of the structure, function, operation, and effect are examples of the structure, function, operation, and effect of the technical aspects of this disclosure. Therefore, it goes without saying that you may delete unnecessary parts, add new elements, or replace elements in the descriptions and illustrations presented above, as long as you do not deviate from the essence of the technical aspects of this disclosure. Furthermore, in order to avoid confusion and facilitate understanding of the technical aspects of this disclosure, explanations of common technical knowledge and other things that do not require special explanation to enable the implementation of the technical aspects of this disclosure have been omitted from the descriptions and illustrations presented above.

[0194] All documents, patent applications, and technical standards described herein are incorporated by reference to the same extent as if each individual document, patent application, and technical standard were specifically and individually noted to be incorporated by reference.

[0195] (Note 1) The collection department collects word-of-mouth data, The analysis unit analyzes the word-of-mouth data collected by the aforementioned collection unit and creates a summary, A provisioning unit provides summaries created by the aforementioned analysis unit for each user attribute, The question-building department creates reviews by having users answer questions in a chat format after visiting the store, The system includes a personalization unit that utilizes the word-of-mouth data created by the question building unit for personalization purposes. A system characterized by the following features. (Note 2) The aforementioned collection unit is Collect review data from restaurant review websites or social media. The system described in Appendix 1, characterized by the features described herein. (Note 3) The aforementioned analysis unit is We analyze collected customer review data and create summaries about specific restaurants. The system described in Appendix 1, characterized by the features described herein. (Note 4) The aforementioned supply unit is, Create and provide summaries tailored to user attributes. The system described in Appendix 1, characterized by the features described herein. (Note 5) The aforementioned question construction unit, Create questions that users will answer after visiting the store. The system described in Appendix 1, characterized by the features described herein. (Note 6) The personalization unit described above is We issue coupons and other benefits based on ratings from other users. The system described in Appendix 1, characterized by the features described herein. (Note 7) The aforementioned collection unit is We estimate user sentiment and adjust the timing of collecting word-of-mouth data based on the estimated user sentiment. The system described in Appendix 1, characterized by the features described herein. (Note 8) The aforementioned collection unit is We analyze users' past review posting history and select the optimal collection method. The system described in Appendix 1, characterized by the features described herein. (Note 9) The aforementioned collection unit is When collecting user review data, filtering is performed based on the user's current areas of interest. The system described in Appendix 1, characterized by the features described herein. (Note 10) The aforementioned collection unit is It estimates user sentiment and determines the priority of the word-of-mouth data to collect based on the estimated user sentiment. The system described in Appendix 1, characterized by the features described herein. (Note 11) The aforementioned collection unit is When collecting user review data, we prioritize collecting data that is highly relevant based on the user's geographical location. The system described in Appendix 1, characterized by the features described herein. (Note 12) The aforementioned collection unit is When collecting word-of-mouth data, relevant data is collected based on the user's social media activity. The system described in Appendix 1, characterized by the features described herein. (Note 13) The aforementioned analysis unit is It estimates the user's emotions and adjusts the way the summary is presented based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 14) The aforementioned analysis unit is When analyzing review data, adjust the level of detail in the summary based on specific keywords. The system described in Appendix 1, characterized by the features described herein. (Note 15) The aforementioned analysis unit is When analyzing review data, different summarization algorithms are applied depending on the category. The system described in Appendix 1, characterized by the features described herein. (Note 16) The aforementioned analysis unit is It estimates the user's sentiment and adjusts the length of the summary based on the estimated user sentiment. The system described in Appendix 1, characterized by the features described herein. (Note 17) The aforementioned analysis unit is When analyzing user review data, prioritize summaries based on the posting date. The system described in Appendix 1, characterized by the features described herein. (Note 18) The aforementioned analysis unit is When analyzing word-of-mouth data, improve the accuracy of summaries based on other relevant data sources. The system described in Appendix 1, characterized by the features described herein. (Note 19) The aforementioned supply unit is, We estimate the user's emotions and adjust how summaries are delivered based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 20) The aforementioned supply unit is, When providing summaries, the optimal delivery method is selected based on the user's past browsing history. The system described in Appendix 1, characterized by the features described herein. (Note 21) The aforementioned supply unit is, When providing summaries, customize the content based on the user's attribute information. The system described in Appendix 1, characterized by the features described herein. (Note 22) The aforementioned supply unit is, It estimates the user's sentiment and adjusts the display order of summaries based on the estimated user sentiment. The system described in Appendix 1, characterized by the features described herein. (Note 23) The aforementioned supply unit is, When providing a summary, the optimal display method is selected based on the user's device information. The system described in Appendix 1, characterized by the features described herein. (Note 24) The aforementioned supply unit is, When providing summaries, we adjust the content based on the user's social media activity. The system described in Appendix 1, characterized by the features described herein. (Note 25) The aforementioned question construction unit, The system estimates the user's emotions and adjusts the content of the questions based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 26) The aforementioned question construction unit, When constructing questions, the most suitable questions are selected based on the user's past answer history. The system described in Appendix 1, characterized by the features described herein. (Note 27) The aforementioned question construction unit, When constructing a question, customize the question content based on the user's attribute information. The system described in Appendix 1, characterized by the features described herein. (Note 28) The aforementioned question construction unit, The system estimates the user's emotions and adjusts the order of questions based on those emotions. The system described in Appendix 1, characterized by the features described herein. (Note 29) The aforementioned question construction unit, When constructing questions, the optimal question format is selected based on the user's device information. The system described in Appendix 1, characterized by the features described herein. (Note 30) The aforementioned question construction unit, When constructing questions, adjust the question content based on the user's social media activity. The system described in Appendix 1, characterized by the features described herein. (Note 31) The personalization unit described above is It estimates the user's emotions and adjusts the personalization content based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 32) The personalization unit described above is During personalization, the optimal personalization method is selected based on the user's past behavior history. The system described in Appendix 1, characterized by the features described herein. (Note 33) The personalization unit described above is When personalizing, the content is customized based on the user's attribute information. The system described in Appendix 1, characterized by the features described herein. (Note 34) The personalization unit described above is It estimates the user's emotions and determines personalization priorities based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 35) The personalization unit described above is When personalizing content, the system provides the most relevant information based on the user's geographical location. The system described in Appendix 1, characterized by the features described herein. (Note 36) The personalization unit described above is When personalizing content, adjust the content based on the user's social media activity. The system described in Appendix 1, characterized by the features described herein. [Explanation of Symbols]

[0196] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Devices 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robots

Claims

1. The data collection department collects word-of-mouth data, The analysis unit analyzes the word-of-mouth data collected by the aforementioned collection unit and creates a summary, A provisioning unit provides summaries created by the aforementioned analysis unit for each user attribute, The question-building department creates reviews by having users answer questions in a chat format after visiting the store, The system includes a personalization unit that utilizes the word-of-mouth data created by the question building unit for personalization purposes. A system characterized by the following features.

2. The aforementioned collection unit is Collect review data from restaurant review websites or social media. The system according to feature 1.

3. The aforementioned analysis unit is We analyze collected customer review data and create summaries about specific restaurants. The system according to feature 1.

4. The aforementioned supply unit is, Create and provide summaries tailored to user attributes. The system according to feature 1.

5. The aforementioned question construction unit, Create questions that users will answer after visiting the store. The system according to feature 1.

6. The personalization unit described above is We issue coupons and other benefits based on ratings from other users. The system according to feature 1.

7. The aforementioned collection unit is We estimate user sentiment and adjust the timing of collecting word-of-mouth data based on the estimated user sentiment. The system according to feature 1.

8. The aforementioned collection unit is We analyze users' past review posting history and select the optimal collection method. The system according to feature 1.

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