System

Abstract

An object of a system according to an embodiment is to integrate data from a plurality of services and propose an optimal restaurant on the basis of a condition of a user.SOLUTION: A system includes a data collection part, a condition input part, and a proposal part. The data collection unit collects and aggregates data from a plurality of services. The condition input unit inputs a condition of a user. The proposal unit proposes an optimum restaurant on the basis of the data aggregated by the data collection unit and the condition input by the condition input unit.SELECTED DRAWING: Figure 1

Description

[Technical Field]

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

[0002] Patent document 1 discloses a persona chatbot control method performed by at least one processor, the method including the steps of receiving a user utterance, adding the user utterance to a prompt including an instruction sentence related to a description of the 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] Japanese Patent Publication No. 2022-180282 Summary of the Invention [Problem to be solved by the invention]

[0004] Conventional technologies have had the problem of not being able to fully integrate data from multiple services and suggest optimal restaurants based on the user's criteria.

[0005] The system according to the embodiment aims to integrate data from multiple services and propose optimal restaurants based on the user's requirements. [Means for solving the problem]

[0006] The system according to the embodiment includes a data collection unit, a condition input unit, and a suggestion unit. The data collection unit collects and aggregates data from multiple services. The condition input unit inputs user conditions. The suggestion unit suggests optimal restaurants based on the data aggregated by the data collection unit and the conditions input by the condition input unit. [Effects of the Invention]

[0007] The system according to the embodiment can integrate data from multiple services and suggest optimal restaurants based on the user's criteria. [Brief explanation of the drawings]

[0008] [Figure 1] 1 is a conceptual diagram showing an example of the configuration of a data processing system according to a first embodiment. [Figure 2] 1 is a conceptual diagram showing an example of main functions of a data processing device and a smart device according to a first embodiment. [Figure 3] FIG. 10 is a conceptual diagram showing an example of the configuration of a data processing system according to a second embodiment. [Figure 4] FIG. 10 is a conceptual diagram showing an example of main functions of a data processing device and smart glasses according to a second embodiment. [Figure 5] FIG. 10 is a conceptual diagram showing an example of the configuration of a data processing system according to a third embodiment. [Figure 6] FIG. 11 is a conceptual diagram showing an example of main functions of a data processing device and a headset-type terminal according to a third embodiment. [Figure 7] FIG. 10 is a conceptual diagram showing an example of the configuration of a data processing system according to a fourth embodiment. [Figure 8] FIG. 10 is a conceptual diagram showing an example of main functions of a data processing device and a robot according to a fourth embodiment. [Figure 9] 1 shows an emotion map onto which multiple emotions are mapped. [Figure 10] 1 shows an emotion map onto which multiple emotions are mapped. DETAILED DESCRIPTION OF THE INVENTION

[0009] An example of an embodiment of a system according to the technology of the present disclosure will be described below with reference to the accompanying drawings.

[0010] First, the terms used in the following description will be explained.

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

[0012] In the following embodiments, a coded RAM (Random Access Memory) is a memory in which information is temporarily stored and is used as a working memory by a processor.

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

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

[0015] In the following embodiments, "A and / or B" is synonymous with "at least one of A and B." In other words, "A and / or B" means that it may be only A, only B, or a combination of A and B. Furthermore, in this specification, the same concept as "A and / or B" is also applied when three or more things are expressed connected by "and / or."

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

[0017] 1, a 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, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also 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 includes a computer 36, a reception device 38, an output device 40, a camera 42, and a communication I / F 44. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The reception device 38, the output device 40, and the camera 42 are also connected to the bus 52.

[0020] The reception device 38 includes a touch panel 38A and a microphone 38B, and receives user input. The touch panel 38A detects contact with a pointer (for example, a pen or a finger) to receive user input by the touch of the pointer. The microphone 38B detects the user's voice to receive user input by voice. The control unit 46A transmits data indicating the user input received by the touch panel 38A and the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 (see FIG. 2) acquires the data indicating the user input.

[0021] Output device 40 includes a display 40A and a speaker 40B, and presents data to a user by outputting the data in a form of expression that the user can perceive (e.g., audio and / or text). Display 40A displays visible information such as text and images in accordance with instructions from processor 46. Speaker 40B outputs audio in accordance with instructions from processor 46. Camera 42 is a compact digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor.

[0022] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 control the exchange of various information between the processor 46 and the processor 28 via the network 54.

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

[0024] 2, in the data processing device 12, a specific process 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" according to the technology of the present 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 process is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0025] The storage 32 stores a data generation model 58 and an 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 a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0026] In the smart device 14, the specific processing is performed by the processor 46. The storage 50 stores a specific processing program 60. The specific processing program 60 is used together 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 the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. Note that the smart device 14 has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can also perform processing similar to that of the specific processing unit 290 using these models.

[0027] Note that a device other than the data processing device 12 may 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 a processing result (prediction result, etc.) using the data generation model 58 by communicating with the server device having the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device owned by a user (e.g., a mobile phone, a robot, a home appliance, etc.). Next, an example of processing by the data processing system 10 according to the first embodiment will be described.

[0028] (Example 1) The restaurant recommendation system according to an embodiment of the present invention aggregates data from multiple services and uses a generation AI to suggest restaurants that meet the user's requirements. This allows the restaurant recommendation system to easily find restaurants that best suit the user's preferences and requirements.

[0029] A restaurant recommendation system according to an embodiment includes a data collection unit, a condition input unit, and a proposal unit. The data collection unit collects and aggregates data from multiple services. For example, the data is collected from restaurant review sites, reservation sites, social networking sites, and the like. The data collection unit collects data using, for example, an API and stores the data in a database. The condition input unit inputs user conditions. For example, a user may input conditions such as "I want to eat Japanese food," "My budget is under 3,000 yen," "It's within a five-minute walk from the station," and "It has a vegetarian menu." The condition input unit analyzes the conditions input by the user and understands the user's needs. The proposal unit proposes optimal restaurants based on the data collected by the data collection unit and the conditions input by the condition input unit. For example, the proposal unit lists restaurants that meet the user's conditions and proposes them to the user. The proposal unit also provides information such as restaurant ratings, menu details, location, and business hours. This allows the restaurant recommendation system according to an embodiment to propose optimal restaurants based on the user's conditions.

[0030] The data collection unit can analyze images of restaurant menus and automatically generate ratings based on the appearance and color of the dishes. For example, the data collection unit collects images of restaurant menus and uses a generation AI to analyze the appearance and color of the dishes. For example, it evaluates the color of the dishes and the beauty of their presentation, and quantifies their visual appeal. The data collection unit also analyzes the menu images and automatically generates ratings based on the appearance of the dishes. For example, it sets the ratings so that vivid colors and beautiful presentations receive high ratings. The data collection unit also uses a generation AI to analyze the appearance and color of the dishes from the menu images and automatically generate ratings. For example, it assigns a high rating if the dish looks beautiful. This makes it possible to automatically generate ratings based on the appearance and color of the dishes.

[0031] The data collection unit can collect the restaurant's past sales data and analyze sales trends to identify popular menu items and peak time periods. The data collection unit, for example, collects the restaurant's past sales data and uses the generation AI to analyze sales trends. For example, it identifies the time of day and day of the week when specific menu items sell well. The data collection unit also analyzes the sales data to identify popular menu items and peak time periods. For example, it finds a tendency for specific menu items to sell well during specific time periods. The data collection unit also uses the generation AI to analyze the restaurant's sales data to identify popular menu items and peak time periods. For example, it lists menu items and time periods with high sales. This makes it possible to analyze sales trends and identify popular menu items and peak time periods.

[0032] The data collection unit collects the ambient sounds of a restaurant and can suggest quiet or lively restaurants that match the user's preferences. The data collection unit, for example, collects the ambient sounds of a restaurant and analyzes them using a generation AI. For example, it measures the music and noise levels inside the restaurant and identifies quiet or lively restaurants. The data collection unit also analyzes the ambient sounds and suggests restaurants that match the user's preferences. For example, it suggests restaurants with low noise levels to a user who prefers quiet restaurants. The data collection unit also uses a generation AI to analyze the ambient sounds of a restaurant and make suggestions that match the user's preferences. For example, it suggests restaurants that play music to a user who prefers lively restaurants. This makes it possible to suggest quiet or lively restaurants that match the user's preferences.

[0033] The data collection unit collects 360-degree images of the restaurant interior to analyze the restaurant's interior and atmosphere, and can make suggestions that take visual elements into consideration. For example, the data collection unit collects 360-degree images of the restaurant and uses a generation AI to analyze the interior and atmosphere. For example, it evaluates the design and decoration of the restaurant. The data collection unit also analyzes the 360-degree images and makes suggestions that take visual elements into consideration. For example, it suggests restaurants with modern designs to a user. The data collection unit also uses a generation AI to analyze 360-degree images of the restaurant and makes suggestions that take visual elements into consideration. For example, it suggests restaurants with a calm atmosphere to a user. This makes it possible to make suggestions that take visual elements into consideration.

[0034] The condition input unit can analyze the user's past eating and drinking history and automatically learn preference trends, simplifying condition input. The condition input unit, for example, collects the user's past eating and drinking history and analyzes it using a generation AI. For example, it identifies preference trends based on restaurants the user has visited in the past and menu items they have ordered. The condition input unit also analyzes the eating and drinking history and automatically learns the user's preference trends. For example, it finds a tendency to prefer specific dishes or price ranges. The condition input unit also uses a generation AI to analyze the user's past eating and drinking history and automatically learn preference trends. For example, it identifies the types of dishes and seasonings that the user prefers. This makes it possible to analyze the user's past eating and drinking history and automatically learn preference trends, simplifying condition input.

[0035] The condition input unit can input the user's health condition and allergy information and suggest safe menus based on that. The condition input unit, for example, inputs the user's health condition and allergy information and uses generation AI to suggest safe menus. For example, it lists menus that do not contain allergenic ingredients. The condition input unit also suggests safe menus based on the health condition and allergy information. For example, it suggests low-calorie and low-salt menus. The condition input unit also uses generation AI to analyze the user's health condition and allergy information and suggest safe menus. For example, it prioritizes suggesting menus that do not contain specific ingredients. This makes it possible to suggest safe menus based on the user's health condition and allergy information.

[0036] The condition input unit also takes into account the preferences of the user's friends and family and can suggest restaurants that are ideal for group use. The condition input unit, for example, collects the preferences of the user's friends and family and analyzes them using a generation AI. For example, it suggests restaurants that will satisfy everyone in the group. The condition input unit also takes into account the preferences of the friends and family and suggests restaurants that are ideal for group use. For example, it lists restaurants that offer dishes and price ranges that everyone likes. The condition input unit also uses a generation AI to analyze the preferences of the user's friends and family and suggests restaurants that are ideal for group use. For example, it suggests restaurants that offer menus that will satisfy everyone. This makes it possible to take into account the preferences of the user's friends and family and suggest restaurants that are ideal for group use.

[0037] The condition input unit can use the user's current location information to suggest restaurants that take travel time and transportation means into consideration. The condition input unit, for example, collects the user's current location information and analyzes it using a generation AI. For example, it suggests restaurants that take travel time and transportation means into consideration. The condition input unit also suggests restaurants that take travel time and transportation means into consideration based on the current location information. For example, it lists restaurants that are within walking distance or that are easily accessible by public transportation. The condition input unit also uses a generation AI to analyze the user's current location information to suggest restaurants that take travel time and transportation means into consideration. For example, it suggests restaurants that are closest to the user's current location. In this way, it is possible to use the user's current location information to suggest restaurants that take travel time and transportation means into consideration.

[0038] The suggestion unit can perform a detailed analysis of other users' impressions and ratings of the restaurant menu to be suggested, and provide a specific reason for the recommendation. For example, the suggestion unit collects other users' impressions and ratings of the restaurant menu to be suggested, and analyzes them using a generation AI. For example, it provides a specific reason for the recommendation. The suggestion unit also analyzes the impressions and ratings of the menu in detail, and provides a specific reason for the recommendation. For example, it explains why a particular menu is rated as delicious. The suggestion unit also uses a generation AI to analyze other users' impressions and ratings, and provides a specific reason for the recommendation. For example, it explains why a particular menu is popular. This makes it possible to perform a detailed analysis of other users' impressions and ratings, and provide a specific reason for the recommendation.

[0039] The suggestion unit can include special menus and campaign information tailored to the season or event when suggesting restaurants. For example, the suggestion unit collects special menus and campaign information tailored to the season or event, and includes it in the proposal using the generation AI. For example, it introduces seasonal menus and event benefits. The suggestion unit also includes special menus and campaign information in the proposal. For example, it proposes special menus tailored to Christmas or Valentine's Day. The suggestion unit also uses the generation AI to include special menus and campaign information tailored to the season or event in the proposal. For example, it introduces recommended menus for each season. This makes it possible to include special menus and campaign information tailored to the season or event.

[0040] The suggestion unit can check the reservation status of the restaurants to be suggested in real time and prioritize suggesting restaurants that allow immediate reservations. For example, the suggestion unit can check the reservation status of the restaurants to be suggested in real time and prioritize suggesting restaurants that allow immediate reservations. For example, it can list restaurants with available seats. The suggestion unit can also analyze the reservation status in real time and suggest restaurants that allow immediate reservations. For example, it can prioritize suggesting restaurants that are easy to make reservations for. The suggestion unit can also use generation AI to check the reservation status of restaurants in real time and suggest restaurants that allow immediate reservations. For example, it can list restaurants that allow reservations to be confirmed. This allows the suggestion unit to check the reservation status of the restaurants to be suggested in real time and prioritize suggesting restaurants that allow immediate reservations.

[0041] When suggesting restaurants, the suggestion unit takes into account the user's past feedback and can make suggestions that will result in higher satisfaction. For example, the suggestion unit collects the user's past feedback and analyzes it using a generation AI. For example, it prioritizes suggestions of restaurants that have received high ratings in the past. The suggestion unit also takes into account the past feedback and makes suggestions that will result in higher satisfaction. For example, it lists restaurants that offer the user's favorite dishes and services. The suggestion unit also uses a generation AI to analyze the user's past feedback and make suggestions that will result in higher satisfaction. For example, it adjusts the content of the suggestions based on the past ratings. This allows the suggestion unit to take into account the user's past feedback and make suggestions that will result in higher satisfaction.

[0042] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0043] The restaurant recommendation system can also analyze a user's past dining history and automatically learn their preferences, simplifying the input of search criteria. For example, preferences can be identified based on the restaurants a user has visited in the past and the menu items they have ordered. It can also identify preferences for specific dishes and price ranges. This allows the system to analyze a user's past dining history, automatically learn their preferences, and simplify the input of search criteria.

[0044] The data collection unit can collect ambient sounds from restaurants and suggest quiet or lively restaurants that match the user's preferences. For example, the data collection unit can measure the music and noise levels inside the restaurant and identify quiet or lively restaurants. Furthermore, it can suggest restaurants with low noise levels to a user who prefers quiet restaurants, and restaurants with music playing to a user who prefers lively restaurants. This makes it possible to suggest quiet or lively restaurants that match the user's preferences.

[0045] The data collection unit can collect 360-degree images of the restaurant interior to analyze the restaurant's interior and atmosphere, and can also make suggestions that take visual elements into account. For example, the design and decoration of the restaurant can be evaluated, and suggestions can be made to users who prefer restaurants with a modern design. Similarly, suggestions can be made to users who prefer restaurants with a calm atmosphere. This allows suggestions to be made that take visual elements into account.

[0046] The condition input unit can also input the user's health condition and allergy information and suggest safe menus based on that. For example, it can list menus that do not contain allergens and suggest low-calorie and low-salt menus. This allows safe menus to be suggested based on the user's health condition and allergy information.

[0047] The suggestion unit can also perform a detailed analysis of other users' impressions and ratings of the restaurant menu to be suggested and provide specific reasons for the recommendation. For example, it can explain why a particular menu item is rated as delicious and explain why a particular menu item is popular. This allows the impressions and ratings of other users to be analyzed in detail and specific reasons for the recommendation to be provided.

[0048] When proposing restaurants, the suggestion unit can also include information on special menus and campaigns that match the season or event. For example, the suggestion unit can introduce seasonal menus and event benefits and suggest special menus for Christmas or Valentine's Day. This makes it possible to include information on special menus and campaigns that match the season or event.

[0049] The suggestion unit can also check the reservation status of the restaurants it proposes in real time and prioritize suggesting restaurants that allow immediate reservations. For example, it can list restaurants with available seats and prioritize suggesting restaurants that are easy to reserve. This allows it to check the reservation status of the restaurants it proposes in real time and prioritize suggesting restaurants that allow immediate reservations.

[0050] The processing flow of the first embodiment will be briefly explained below.

[0051] Step 1: The data collection unit collects and aggregates data from multiple services. For example, data is collected from restaurant review sites, reservation sites, social media, etc. The data collection unit uses APIs to collect the data and stores it in a database. Step 2: The condition input unit inputs the user's conditions. For example, the user may input conditions such as "I want to eat Japanese food," "My budget is under 3,000 yen," "Within a five-minute walk from the station," and "Has a vegetarian menu." The condition input unit analyzes the conditions entered by the user and understands the user's needs. Step 3: The suggestion unit suggests optimal restaurants based on the data collected by the data collection unit and the conditions entered by the condition input unit. For example, the suggestion unit lists restaurants that meet the user's conditions and suggests them to the user. The suggestion unit also provides information such as restaurant ratings, menu details, locations, and business hours.

[0052] (Example 2) The restaurant recommendation system according to an embodiment of the present invention aggregates data from multiple services and uses a generation AI to suggest restaurants that meet the user's requirements. This allows the restaurant recommendation system to easily find restaurants that best suit the user's preferences and requirements.

[0053] A restaurant recommendation system according to an embodiment includes a data collection unit, a condition input unit, and a proposal unit. The data collection unit collects and aggregates data from multiple services. For example, the data is collected from restaurant review sites, reservation sites, social networking sites, and the like. The data collection unit collects data using, for example, an API and stores the data in a database. The condition input unit inputs user conditions. For example, a user may input conditions such as "I want to eat Japanese food," "My budget is under 3,000 yen," "It's within a five-minute walk from the station," and "It has a vegetarian menu." The condition input unit analyzes the conditions input by the user and understands the user's needs. The proposal unit proposes optimal restaurants based on the data collected by the data collection unit and the conditions input by the condition input unit. For example, the proposal unit lists restaurants that meet the user's conditions and proposes them to the user. The proposal unit also provides information such as restaurant ratings, menu details, location, and business hours. This allows the restaurant recommendation system according to an embodiment to propose optimal restaurants based on the user's conditions.

[0054] The data collection unit can analyze images of restaurant menus and automatically generate ratings based on the appearance and color of the dishes. For example, the data collection unit collects images of restaurant menus and uses a generation AI to analyze the appearance and color of the dishes. For example, it evaluates the color of the dishes and the beauty of their presentation, and quantifies their visual appeal. The data collection unit also analyzes the menu images and automatically generates ratings based on the appearance of the dishes. For example, it sets the ratings so that vivid colors and beautiful presentations receive high ratings. The data collection unit also uses a generation AI to analyze the appearance and color of the dishes from the menu images and automatically generate ratings. For example, it assigns a high rating if the dish looks beautiful. This makes it possible to automatically generate ratings based on the appearance and color of the dishes.

[0055] The data collection unit can collect the restaurant's past sales data and analyze sales trends to identify popular menu items and peak time periods. The data collection unit, for example, collects the restaurant's past sales data and uses the generation AI to analyze sales trends. For example, it identifies the time of day and day of the week when specific menu items sell well. The data collection unit also analyzes the sales data to identify popular menu items and peak time periods. For example, it finds a tendency for specific menu items to sell well during specific time periods. The data collection unit also uses the generation AI to analyze the restaurant's sales data to identify popular menu items and peak time periods. For example, it lists menu items and time periods with high sales. This makes it possible to analyze sales trends and identify popular menu items and peak time periods.

[0056] The data collection unit can use the emotion estimation function to analyze user emotions from posts on the SNS and prioritize tally up restaurants with a high percentage of positive emotions. The data collection unit, for example, collects posts on the SNS and analyzes user emotions using the emotion estimation function. For example, it identifies restaurants with a high percentage of positive emotions. The data collection unit also uses the emotion estimation function to analyze user emotions from posts on the SNS and prioritize tally up restaurants with a high percentage of positive emotions. For example, it lists restaurants with a high number of positive comments. The data collection unit also analyzes posts on the SNS and prioritizes tally up restaurants with a high percentage of positive emotions. For example, it evaluates restaurants based on the user's emotion score. This allows it to prioritize tally up restaurants with a high percentage of positive emotions.

[0057] The data collection unit collects the ambient sounds of a restaurant and can suggest quiet or lively restaurants that match the user's preferences. The data collection unit, for example, collects the ambient sounds of a restaurant and analyzes them using a generation AI. For example, it measures the music and noise levels inside the restaurant and identifies quiet or lively restaurants. The data collection unit also analyzes the ambient sounds and suggests restaurants that match the user's preferences. For example, it suggests restaurants with low noise levels to a user who prefers quiet restaurants. The data collection unit also uses a generation AI to analyze the ambient sounds of a restaurant and make suggestions that match the user's preferences. For example, it suggests restaurants that play music to a user who prefers lively restaurants. This makes it possible to suggest quiet or lively restaurants that match the user's preferences.

[0058] The data collection unit collects 360-degree images of the restaurant interior to analyze the restaurant's interior and atmosphere, and can make suggestions that take visual elements into consideration. For example, the data collection unit collects 360-degree images of the restaurant and uses a generation AI to analyze the interior and atmosphere. For example, it evaluates the design and decoration of the restaurant. The data collection unit also analyzes the 360-degree images and makes suggestions that take visual elements into consideration. For example, it suggests restaurants with modern designs to a user. The data collection unit also uses a generation AI to analyze 360-degree images of the restaurant and makes suggestions that take visual elements into consideration. For example, it suggests restaurants with a calm atmosphere to a user. This makes it possible to make suggestions that take visual elements into consideration.

[0059] The data collection unit can use the emotion estimation function to analyze emotions contained in restaurant reviews and preferentially suggest restaurants with fewer negative emotions. The data collection unit, for example, collects restaurant reviews and analyzes emotions using the emotion estimation function. For example, it identifies restaurants with reviews with fewer negative emotions. The data collection unit also uses the emotion estimation function to analyze emotions contained in the reviews and preferentially suggest restaurants with fewer negative emotions. For example, it lists restaurants with many positive reviews. The data collection unit also analyzes the reviews and preferentially suggests restaurants with fewer negative emotions. For example, it evaluates restaurants based on the user's emotion score. This allows it to preferentially suggest restaurants with fewer negative emotions.

[0060] The condition input unit can analyze the user's past eating and drinking history and automatically learn preference trends, simplifying condition input. The condition input unit, for example, collects the user's past eating and drinking history and analyzes it using a generation AI. For example, it identifies preference trends based on restaurants the user has visited in the past and menu items they have ordered. The condition input unit also analyzes the eating and drinking history and automatically learns the user's preference trends. For example, it finds a tendency to prefer specific dishes or price ranges. The condition input unit also uses a generation AI to analyze the user's past eating and drinking history and automatically learn preference trends. For example, it identifies the types of dishes and seasonings that the user prefers. This makes it possible to analyze the user's past eating and drinking history and automatically learn preference trends, simplifying condition input.

[0061] The condition input unit can input the user's health condition and allergy information and suggest safe menus based on that. The condition input unit, for example, inputs the user's health condition and allergy information and uses generation AI to suggest safe menus. For example, it lists menus that do not contain allergenic ingredients. The condition input unit also suggests safe menus based on the health condition and allergy information. For example, it suggests low-calorie and low-salt menus. The condition input unit also uses generation AI to analyze the user's health condition and allergy information and suggest safe menus. For example, it prioritizes suggesting menus that do not contain specific ingredients. This makes it possible to suggest safe menus based on the user's health condition and allergy information.

[0062] The condition input unit can use an emotion estimation function to detect stress or frustration felt by the user while entering conditions in real time and improve the input process. The condition input unit, for example, uses the emotion estimation function to analyze the emotions felt by the user while entering conditions in real time. For example, it detects stress or frustration and improves the input process. The condition input unit also monitors the user's emotions in real time and improves the input process if the user feels stressed or frustrated. For example, it simplifies the input items. The condition input unit also uses a generative AI to analyze the user's emotions and improve the input process. For example, it provides an input assistance function if the user feels stressed. This makes it possible to detect stress or frustration felt by the user while entering conditions in real time and improve the input process.

[0063] The condition input unit also takes into account the preferences of the user's friends and family and can suggest restaurants that are ideal for group use. The condition input unit, for example, collects the preferences of the user's friends and family and analyzes them using a generation AI. For example, it suggests restaurants that will satisfy everyone in the group. The condition input unit also takes into account the preferences of the friends and family and suggests restaurants that are ideal for group use. For example, it lists restaurants that offer dishes and price ranges that everyone likes. The condition input unit also uses a generation AI to analyze the preferences of the user's friends and family and suggests restaurants that are ideal for group use. For example, it suggests restaurants that offer menus that will satisfy everyone. This makes it possible to take into account the preferences of the user's friends and family and suggest restaurants that are ideal for group use.

[0064] The condition input unit can use the user's current location information to suggest restaurants that take travel time and transportation means into consideration. The condition input unit, for example, collects the user's current location information and analyzes it using a generation AI. For example, it suggests restaurants that take travel time and transportation means into consideration. The condition input unit also suggests restaurants that take travel time and transportation means into consideration based on the current location information. For example, it lists restaurants that are within walking distance or that are easily accessible by public transportation. The condition input unit also uses a generation AI to analyze the user's current location information to suggest restaurants that take travel time and transportation means into consideration. For example, it suggests restaurants that are closest to the user's current location. In this way, it is possible to use the user's current location information to suggest restaurants that take travel time and transportation means into consideration.

[0065] The condition input unit can use the emotion estimation function to analyze the emotional response to the conditions entered by the user and make suggestions that elicit positive emotions. The condition input unit, for example, uses the emotion estimation function to analyze the emotional response to the conditions entered by the user. For example, it makes suggestions that elicit positive emotions. The condition input unit also analyzes the user's emotional response and makes suggestions that elicit positive emotions. For example, it preferentially suggests restaurants that match the conditions preferred by the user. The condition input unit also uses a generation AI to analyze the user's emotional response and make suggestions that elicit positive emotions. For example, it suggests restaurants that match the conditions that satisfy the user. In this way, it is possible to analyze the emotional response to the conditions entered by the user and make suggestions that elicit positive emotions.

[0066] The suggestion unit can perform a detailed analysis of other users' impressions and ratings of the restaurant menu to be suggested, and provide a specific reason for the recommendation. For example, the suggestion unit collects other users' impressions and ratings of the restaurant menu to be suggested, and analyzes them using a generation AI. For example, it provides a specific reason for the recommendation. The suggestion unit also analyzes the impressions and ratings of the menu in detail, and provides a specific reason for the recommendation. For example, it explains why a particular menu is rated as delicious. The suggestion unit also uses a generation AI to analyze other users' impressions and ratings, and provides a specific reason for the recommendation. For example, it explains why a particular menu is popular. This makes it possible to perform a detailed analysis of other users' impressions and ratings, and provide a specific reason for the recommendation.

[0067] The suggestion unit can include special menus and campaign information tailored to the season or event when suggesting restaurants. For example, the suggestion unit collects special menus and campaign information tailored to the season or event, and includes it in the proposal using the generation AI. For example, it introduces seasonal menus and event benefits. The suggestion unit also includes special menus and campaign information in the proposal. For example, it proposes special menus tailored to Christmas or Valentine's Day. The suggestion unit also uses the generation AI to include special menus and campaign information tailored to the season or event in the proposal. For example, it introduces recommended menus for each season. This makes it possible to include special menus and campaign information tailored to the season or event.

[0068] The suggestion unit uses the emotion estimation function to monitor the user's emotional response to the proposed restaurant in real time, and can continuously make optimal suggestions. The suggestion unit, for example, uses the emotion estimation function to monitor the user's emotional response to the proposed restaurant in real time. For example, it prioritizes suggestions that evoke strong positive emotions. The suggestion unit also analyzes the user's emotional response in real time and continuously makes optimal suggestions. For example, it repeatedly makes suggestions that satisfy the user. The suggestion unit also uses a generation AI to monitor the user's emotional response to the proposed restaurant and continuously makes optimal suggestions. For example, it adjusts the content of the suggestion based on the user's emotion score. This allows the user's emotional response to the proposed restaurant to be monitored in real time, and can continuously make optimal suggestions.

[0069] The suggestion unit can check the reservation status of the restaurants to be suggested in real time and prioritize suggesting restaurants that allow immediate reservations. For example, the suggestion unit can check the reservation status of the restaurants to be suggested in real time and prioritize suggesting restaurants that allow immediate reservations. For example, it can list restaurants with available seats. The suggestion unit can also analyze the reservation status in real time and suggest restaurants that allow immediate reservations. For example, it can prioritize suggesting restaurants that are easy to make reservations for. The suggestion unit can also use generation AI to check the reservation status of restaurants in real time and suggest restaurants that allow immediate reservations. For example, it can list restaurants that allow reservations to be confirmed. This allows the suggestion unit to check the reservation status of the restaurants to be suggested in real time and prioritize suggesting restaurants that allow immediate reservations.

[0070] When suggesting restaurants, the suggestion unit takes into account the user's past feedback and can make suggestions that will result in higher satisfaction. For example, the suggestion unit collects the user's past feedback and analyzes it using a generation AI. For example, it prioritizes suggestions of restaurants that have received high ratings in the past. The suggestion unit also takes into account the past feedback and makes suggestions that will result in higher satisfaction. For example, it lists restaurants that offer the user's favorite dishes and services. The suggestion unit also uses a generation AI to analyze the user's past feedback and make suggestions that will result in higher satisfaction. For example, it adjusts the content of the suggestions based on the past ratings. This allows the suggestion unit to take into account the user's past feedback and make suggestions that will result in higher satisfaction.

[0071] The suggestion unit uses the emotion estimation function to analyze other users' emotional reactions to the proposed restaurant, and can prioritize suggesting restaurants that are likely to resonate with the user emotionally. The suggestion unit, for example, uses the emotion estimation function to analyze other users' emotional reactions to the proposed restaurant. For example, it prioritizes suggesting restaurants that have a lot of positive emotions. The suggestion unit also analyzes other users' emotional reactions to suggest restaurants that are likely to resonate with the user emotionally. For example, it lists restaurants with high emotion scores. The suggestion unit also uses a generation AI to analyze other users' emotional reactions to suggest restaurants that are likely to resonate with the user emotionally. For example, it prioritizes suggesting restaurants that have a lot of positive emotions. This makes it possible to analyze other users' emotional reactions to the proposed restaurant, and prioritize suggesting restaurants that are likely to resonate with the user emotionally.

[0072] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0073] The restaurant recommendation system can also analyze a user's past dining history and automatically learn their preferences, simplifying the input of search criteria. For example, preferences can be identified based on the restaurants a user has visited in the past and the menu items they have ordered. It can also identify preferences for specific dishes and price ranges. This allows the system to analyze a user's past dining history, automatically learn their preferences, and simplify the input of search criteria.

[0074] The data collection unit can collect ambient sounds from restaurants and suggest quiet or lively restaurants that match the user's preferences. For example, the data collection unit can measure the music and noise levels inside the restaurant and identify quiet or lively restaurants. Furthermore, it can suggest restaurants with low noise levels to a user who prefers quiet restaurants, and restaurants with music playing to a user who prefers lively restaurants. This makes it possible to suggest quiet or lively restaurants that match the user's preferences.

[0075] The data collection unit can collect 360-degree images of the restaurant interior to analyze the restaurant's interior and atmosphere, and can also make suggestions that take visual elements into account. For example, the design and decoration of the restaurant can be evaluated, and suggestions can be made to users who prefer restaurants with a modern design. Similarly, suggestions can be made to users who prefer restaurants with a calm atmosphere. This allows suggestions to be made that take visual elements into account.

[0076] The data collection unit can also use the emotion estimation function to analyze user emotions from posts on social media and prioritize restaurants with a high number of positive emotions in the tally. For example, it can list restaurants with a high number of positive comments and rate them based on the user's emotion score. This allows restaurants with a high number of positive emotions to be prioritized in the tally.

[0077] The condition input unit can also input the user's health condition and allergy information and suggest safe menus based on that. For example, it can list menus that do not contain allergens and suggest low-calorie and low-salt menus. This allows safe menus to be suggested based on the user's health condition and allergy information.

[0078] The condition input unit can also use the emotion estimation function to detect stress or frustration felt by the user while inputting data in real time and improve the input process. For example, it can detect stress or frustration and simplify input items. It can also provide an input assistance function when stress is felt. This allows the stress or frustration felt by the user while inputting data to be detected in real time and the input process to be improved.

[0079] The suggestion unit can also perform a detailed analysis of other users' impressions and ratings of the restaurant menu to be suggested and provide specific reasons for the recommendation. For example, it can explain why a particular menu item is rated as delicious and explain why a particular menu item is popular. This allows the impressions and ratings of other users to be analyzed in detail and specific reasons for the recommendation to be provided.

[0080] When proposing restaurants, the suggestion unit can also include information on special menus and campaigns that match the season or event. For example, the suggestion unit can introduce seasonal menus and event benefits and suggest special menus for Christmas or Valentine's Day. This makes it possible to include information on special menus and campaigns that match the season or event.

[0081] The suggestion unit can also use the emotion estimation function to monitor the user's emotional response to the suggested restaurants in real time and continuously make optimal suggestions. For example, it can prioritize suggestions that evoke strong positive emotions and repeatedly make suggestions that satisfy the user. This allows the user's emotional response to the suggested restaurants to be monitored in real time and continuously make optimal suggestions.

[0082] The suggestion unit can also check the reservation status of the restaurants it proposes in real time and prioritize suggesting restaurants that allow immediate reservations. For example, it can list restaurants with available seats and prioritize suggesting restaurants that are easy to reserve. This allows it to check the reservation status of the restaurants it proposes in real time and prioritize suggesting restaurants that allow immediate reservations.

[0083] The processing flow of the second embodiment will be briefly explained below.

[0084] Step 1: The data collection unit collects and aggregates data from multiple services. For example, data is collected from restaurant review sites, reservation sites, social media, etc. The data collection unit uses APIs to collect the data and stores it in a database. Step 2: The condition input unit inputs the user's conditions. For example, the user may input conditions such as "I want to eat Japanese food," "My budget is under 3,000 yen," "Within a five-minute walk from the station," and "Has a vegetarian menu." The condition input unit analyzes the conditions entered by the user and understands the user's needs. Step 3: The suggestion unit suggests optimal restaurants based on the data collected by the data collection unit and the conditions entered by the condition input unit. For example, the suggestion unit lists restaurants that meet the user's conditions and suggests them to the user. The suggestion unit also provides information such as restaurant ratings, menu details, locations, and business hours.

[0085] 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 a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the 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.

[0086] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (Internet Search<URL: https: / / openai.com / blog / chatgpt> Examples of generative AIs include the data generation model 58, such as a neural network model (e.g., a neural network model), and a neural network model (e.g., a neural network model). The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating speech, text data indicating text, and image data indicating an image is also input to the data generation model 58. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specification processing unit 290 performs the above-mentioned specification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0087] Furthermore, the processing by the data processing system 10 described above is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart device 14, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart device 14. Furthermore, 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.

[0088] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0089] 3, the data processing system 210 includes the data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0090] 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, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0091] The smart glasses 214 include a computer 36, a microphone 238, a speaker 240, a camera 42, and a communication I / F 44. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, and the camera 42 are also connected to the bus 52.

[0092] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0093] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0094] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0095] Fig. 4 shows an example of the main functions of the data processing device 12 and the smart glasses 214. As shown in Fig. 4, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0096] 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 in accordance with the specific processing program 56 executed on the RAM 30.

[0097] The storage 32 stores a data generation model 58 and an 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 a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0098] In the smart glasses 214, the specific processing is performed by the processor 46. A specific processing program 60 is stored in the storage 50. 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 the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. The smart glasses 214 also have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0099] Note that a device other than the data processing device 12 may 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 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0100] 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 a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0101] The data generation model 58 is a so-called generative AI. An example of the 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 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0102] 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 executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart glasses 214, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart glasses 214. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the smart glasses 214 or an external device, etc., and the smart glasses 214 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0103] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0104] 5, the data processing system 310 includes the data processing device 12 and a headset type terminal 314. An example of the data processing device 12 is a server.

[0105] 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, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0106] The headset type terminal 314 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication I / F 44, and a display 343. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, the camera 42, and the display 343 are also connected to the bus 52.

[0107] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0108] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0109] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0110] Fig. 6 shows an example of the main functions of the data processing device 12 and the headset type terminal 314. As shown in Fig. 6, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0111] 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 in accordance with the specific processing program 56 executed on the RAM 30.

[0112] The storage 32 stores a data generation model 58 and an 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 a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0113] In the headset type terminal 314, the specific processing is performed by the processor 46. A specific processing program 60 is stored in the storage 50. 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 the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. Note that the headset type terminal 314 has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can also perform processing similar to that of the specific processing unit 290 using these models.

[0114] Note that a device other than the data processing device 12 may 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 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0115] The specific processing unit 290 transmits the result of the specific processing to the headset type terminal 314. In the headset type terminal 314, the control unit 46A causes the speaker 240 and the display 343 to output the result of the specific processing. The microphone 238 acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0116] The data generation model 58 is a so-called generative AI. An example of the 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 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0117] 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 executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the headset type terminal 314, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the headset type terminal 314. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the headset type terminal 314 or an external device, etc., and the headset type terminal 314 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0118] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0119] 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.

[0120] 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, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0121] The robot 414 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication I / F 44, and a control target 443. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, the camera 42, and the control target 443 are also connected to the bus 52.

[0122] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0123] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS image sensor or a CCD image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0124] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0125] The control 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 emotions of the robot 414 can be expressed by controlling these motors. In addition, the facial expressions of the robot 414 can also be expressed by controlling the light emission state of the LEDs in the eyes of the robot 414.

[0126] Fig. 8 shows an example of the main functions of the data processing device 12 and the robot 414. As shown in Fig. 8, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0127] 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 in accordance with the specific processing program 56 executed on the RAM 30.

[0128] The storage 32 stores a data generation model 58 and an 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 a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0129] In the robot 414, the specific processing is performed by the processor 46. A specific processing program 60 is stored in the storage 50. 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 the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. The robot 414 has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can also perform processing similar to that of the specific processing unit 290 using these models.

[0130] Note that a device other than the data processing device 12 may 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 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0131] 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 control target 443 to output the result of the specific processing. The microphone 238 acquires voice indicating a user input regarding the result of the specific processing. The control unit 46A transmits voice data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the voice data.

[0132] The data generation model 58 is a so-called generative AI. An example of the 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 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0133] 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 executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the robot 414, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the robot 414. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the robot 414 or an external device, etc., and the robot 414 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0134] The emotion identification model 59 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 an emotion map (see FIG. 9), which is a specific mapping. Similarly, the emotion identification model 59 may determine the robot's emotion, and the identification processing unit 290 may perform identification processing using the robot's emotion.

[0135] FIG. 9 illustrates an emotion map 400 on which multiple emotions are mapped. In the emotion map 400, emotions are arranged in concentric circles radiating from the center. Emotions closer to the center of the concentric circles are more primitive. Emotions representing states and behaviors arising from a state of mind are arranged on the outer edges of the concentric circles. The concept of emotion encompasses both emotions and mental states. Emotions generally generated from reactions occurring in the brain are arranged on the left side of the concentric circles. Emotions generally induced by situational judgment are arranged on the right side of the concentric circles. Emotions generally generated from reactions occurring in the brain and induced by situational judgment are arranged on the upper and lower sides of the concentric circles. Furthermore, the emotion of "pleasure" is arranged on the upper side of the concentric circles, and the emotion of "discomfort" is arranged on the lower side. In this way, in the emotion map 400, multiple emotions are mapped based on the structure by which emotions are generated, and emotions that tend to occur simultaneously are mapped close to each other.

[0136] These emotions are distributed in the 3 o'clock direction on emotion map 400, and typically fluctuate between relief and anxiety. In the right half of emotion map 400, situational awareness dominates over internal sensations, resulting in a sense of calm.

[0137] The inside of emotion map 400 represents what is going on in the mind, and the outside of emotion map 400 represents behavior, so the further you go outside emotion map 400, the more visible the emotions become (the more they are expressed in behavior).

[0138] Human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, a state of discomfort is expressed, and when they approach the ideal, a state of pleasure is expressed. Emotions can also be created for robots, cars, and motorcycles, based on various balances, such as posture and remaining battery life. When these balances deviate from the ideal, a state of discomfort is expressed, and when they approach the ideal, a state of pleasure is expressed. An emotion map can be generated, for example, based on Dr. Mitsuyoshi's emotion map (Research on speech emotion recognition and brain physiological signal analysis systems for emotions, Tokushima University, doctoral dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map lists emotions belonging to the area called "reaction," where sensation is dominant. The right half of the emotion map lists emotions belonging to the area called "situation," where situational awareness is dominant.

[0139] The emotion map defines two emotions that promote learning. One is a negative emotion on the situation side, around the middle of "repentance" or "reflection." In other words, this occurs 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 a positive emotion on the response side, around "desire." In other words, this occurs when the robot experiences positive feelings such as "I want more" or "I want to know more."

[0140] The emotion identification model 59 inputs user input into a pre-trained neural network, obtains emotion values ​​indicating each emotion shown in the emotion map 400, and determines the user's emotion. This neural network is pre-trained based on multiple pieces of training data that are combinations of user input and emotion values ​​indicating each emotion shown in the emotion map 400. Furthermore, this neural network is trained so that emotions that are located close to each other have similar values, as in the emotion map 900 shown in FIG. 10. FIG. 10 shows an example in which multiple emotions, "relieved," "calm," and "reassuring," have similar emotion values.

[0141] In the above embodiment, an example was given in which a specific process is performed by one computer 22, but the technology disclosed herein is not limited to this, and distributed processing of the specific process may be performed by multiple computers including computer 22.

[0142] In the above embodiment, an example in which the specific processing program 56 is stored in the storage 32 has been described, but the technology of the present disclosure is not limited to this. For example, the specific processing program 56 may be stored in a portable, computer-readable, non-transitory storage medium such as a USB (Universal Serial Bus) memory. The specific processing program 56 stored in the non-transitory storage medium is installed in the computer 22 of the data processing device 12. The processor 28 executes the specific processing in accordance with the specific processing program 56.

[0143] 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.

[0144] It is not necessary to store all 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 all of the specific processing program 56 in the storage 32; only a portion of the specific processing program 56 may be stored.

[0145] The hardware resource for executing a specific process can be any of the following types of processors: A processor, for example, is a CPU, which is a general-purpose processor that functions as a hardware resource for executing a specific process by executing software, i.e., a program. A processor also includes a dedicated electrical circuit, such as an FPGA (Field-Programmable Gate Array), a PLD (Programmable Logic Device), or an ASIC (Application Specific Integrated Circuit), which is a processor with a circuit configuration designed specifically for executing a specific process. Each processor has built-in or connected memory, and each processor uses the memory to execute the specific process.

[0146] The hardware resource that executes the specific process may be configured with one of these various processors, or may be configured with 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). Also, the hardware resource that executes the specific process may be a single processor.

[0147] As an example of a system configured with a single processor, first, one processor is configured by combining one or more CPUs and software, and this processor functions as a hardware resource that executes a specific process. Second, there is a system that uses a processor that realizes the functions of an entire system including multiple hardware resources that execute a specific process on a single IC chip, as typified by SoC (System-on-a-chip). In this way, a specific process is realized using one or more of the above-mentioned various processors as hardware resources.

[0148] Furthermore, the hardware structure of these various processors can be, more specifically, an electric circuit that combines circuit elements such as semiconductor devices. The specific processing described above is merely an example. Therefore, it goes without saying that unnecessary steps may be deleted, new steps may be added, or the processing order may be rearranged, without departing from the spirit of the invention.

[0149] In the above example, the first to fourth embodiments have been described separately, but some or all of these embodiments may be combined. The smart device 14, smart glasses 214, headset terminal 314, and robot 414 are merely examples, and they may be combined, or other devices may be used. In the above example, the first and second embodiments have been described separately, but they may be combined.

[0150] The above-described description and illustrations are a detailed explanation of the parts related to the technology of the present disclosure and are merely an example of the technology of the present disclosure. For example, the above description of the configuration, functions, actions, and effects is an explanation of an example of the configuration, functions, actions, and effects of the parts related to the technology of the present disclosure. Therefore, it goes without saying that unnecessary parts may be deleted, new elements may be added, or replacements may be made to the above-described description and illustrations within the scope of the gist of the technology of the present disclosure. Furthermore, to avoid confusion and facilitate understanding of the parts related to the technology of the present disclosure, the above-described description and illustrations omit explanations of common technical knowledge that do not require particular explanation to enable the implementation of the technology of the present disclosure.

[0151] All publications, patent applications, and technical standards mentioned in this specification are herein incorporated by reference to the same extent as if each individual publication, patent application, or technical standard was specifically and individually indicated to be incorporated by reference. [Explanation of symbols]

[0152] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Device 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robot

Claims

1. a data collection unit that collects and aggregates data from multiple services; a condition input section for inputting user conditions; a suggestion unit that suggests an optimal restaurant based on the data collected by the data collection unit and the conditions input by the condition input unit. A system characterized by:

2. The data collection unit Analyzes restaurant menu images and automatically generates ratings based on the appearance and color of the food.

2. The system of claim 1.

3. The data collection unit Collecting historical sales data from restaurants and analyzing sales trends to identify popular menu items and peak hours 2. The system of claim 1.

4. The data collection unit Analyzing the user's emotions from the posts on the SNS and prioritizing the counting of restaurants with a large number of positive emotions 2. The system of claim 1.

5. The data collection unit Collecting ambient sounds from restaurants and suggesting quiet or lively restaurants that match the user's preferences 2. The system of claim 1.

Images