system

Abstract

The system according to the embodiment aims to suggest restaurants based on the user's preferences and provide rewards based on the user's ratings. [Solution] A system according to an embodiment includes a suggestion unit, a management unit, and a reward unit. The suggestion unit suggests restaurants based on a user's preferences. The management unit manages information about the restaurants suggested by the suggestion unit. The reward unit provides rewards based on ratings stored by the management unit.

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 do not adequately provide a system that effectively suggests restaurants based on a user's preferences and provides rewards based on ratings, and there is room for improvement.

[0005] The system according to the embodiment aims to suggest restaurants based on the user's preferences and provide rewards based on the user's ratings. [Means for solving the problem]

[0006] The system according to the embodiment includes a suggestion unit, a management unit, and a reward unit. The suggestion unit suggests restaurants based on a user's preferences. The management unit manages information about the restaurants suggested by the suggestion unit. The reward unit provides rewards based on ratings stored by the management unit. [Effects of the Invention]

[0007] The system according to the embodiment can suggest restaurants based on the user's preferences and provide rewards based on the user's ratings. [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) A restaurant suggestion system according to an embodiment of the present invention is a system that suggests restaurants based on a user's preferences and manages the user's past visit history, impressions, and ratings. This system provides a mechanism whereby a user can receive points in an electronic payment system or a coupon for their next visit when they input their impressions and ratings of a restaurant. First, the user inputs information about the restaurant, such as the type of cuisine, price range, and location. This information is stored in the system. Next, the system suggests restaurants based on the user's preferences. For example, the system suggests similar restaurants based on the information and ratings of restaurants the user has visited in the past. Furthermore, the system manages the user's history of restaurants visited, impressions, and ratings. The user can input their impressions and ratings of the restaurants they have visited. This information is stored in the system and used for next time suggestions. Furthermore, when a user inputs their impressions and ratings, they can receive points in an electronic payment system or a coupon for their next visit. For example, when a user inputs a restaurant rating, they are awarded points in the electronic payment system. These points can be used the next time they visit the restaurant. This mechanism allows users to easily find restaurants that suit their preferences and receive points and coupons by inputting restaurant ratings. This allows the restaurant suggestion system to suggest restaurants that match the user's preferences and provide rewards based on the user's evaluation.

[0029] The restaurant suggestion system according to the embodiment includes a suggestion unit, a management unit, and a reward unit. The suggestion unit suggests restaurants based on a user's preferences. For example, the suggestion unit suggests similar restaurants based on information about and ratings of restaurants the user has visited in the past. The suggestion unit can also suggest restaurants based on the user's current mood and physical condition. For example, if the user is feeling stressed, restaurants with a relaxing atmosphere can be preferentially suggested. The management unit manages information about the restaurants suggested by the suggestion unit. For example, the management unit stores impressions and ratings of restaurants entered by the user. The management unit can also analyze the user's past rating history and select an optimal management method. The reward unit provides rewards based on the ratings stored by the management unit. For example, the reward unit provides points or coupons based on the ratings entered by the user. The reward unit can also provide points for an electronic payment system. As a result, the restaurant suggestion system according to the embodiment can suggest restaurants that match the user's preferences and provide rewards based on the ratings.

[0030] The restaurant suggestion system includes an input unit for inputting restaurant information from a user. The input unit allows the user to input restaurant information. For example, the input unit provides an interface for the user to input information such as the type of cuisine, price range, and location. The input unit not only allows the user to input information manually, but also allows the user to input information using voice input or image recognition technology. For example, the user can take a photo of a restaurant menu using a smartphone camera and automatically input the menu information using image recognition technology. Furthermore, the input unit can analyze the user's past input history and select the optimal input method. For example, the input unit can automatically display restaurant information that the user has frequently input in the past as candidates. This allows the user to easily input restaurant information, and the system can suggest restaurants based on the user's preferences.

[0031] The suggestion unit can suggest restaurants based on the user's past visit history or ratings. The suggestion unit can suggest restaurants based on the user's past visit history, for example. For example, the suggestion unit can analyze information about restaurants the user has visited in the past and suggest similar restaurants. The suggestion unit can also suggest restaurants based on the user's ratings. For example, the suggestion unit can suggest restaurants in the same genre as a restaurant that the user has given a high rating to. Furthermore, the suggestion unit can suggest restaurants based on the user's current mood or physical condition. For example, if the user wants to relax, the suggestion unit can suggest a restaurant with a quiet and calm atmosphere. This allows the suggestion unit to suggest the optimal restaurant based on the user's past visit history and ratings.

[0032] The management unit can store restaurant reviews and ratings entered by the user. The management unit, for example, stores restaurant reviews and ratings entered by the user. For example, the management unit can store reviews and ratings entered by the user using text input or voice input in a database. The management unit can also analyze the user's past rating history and select the optimal management method. For example, the management unit can prioritize managing information about restaurants that the user has given high ratings to in the past. Furthermore, the management unit can filter reviews and ratings based on the user's current living situation and areas of interest. For example, if the user is health-conscious, the management unit can prioritize managing health-related ratings. In this way, the management unit can save the reviews and ratings entered by the user and use them for the next suggestion.

[0033] The reward unit can provide points or coupons based on the rating entered by the user. The reward unit, for example, provides points or coupons based on the rating entered by the user. For example, the reward unit can award points in an electronic payment system when the user enters a rating for a restaurant. These points can be used the next time the user visits the restaurant. The reward unit can also provide coupons for the next visit based on the rating entered by the user. For example, the reward unit can provide a discount coupon that can be used the next time the user visits a restaurant that the user has given a high rating to. Furthermore, the reward unit can analyze the user's past rating history and select the optimal reward method. For example, the reward unit can provide a special reward to restaurants that the user has given a high rating to in the past. This allows the reward unit to provide rewards based on the rating entered by the user.

[0034] The reward unit can provide points for the electronic payment system. For example, when a user inputs a rating for a restaurant, the reward unit can award points for the electronic payment system. These points can be used the next time the user visits the restaurant. For example, the reward unit can provide special points for restaurants that the user has given a high rating. The reward unit can also analyze the user's past rating history and select the optimal point awarding method. For example, the reward unit can provide special points for restaurants that the user has given a high rating in the past. In this way, the reward unit can provide points for the electronic payment system.

[0035] The reward unit can provide a coupon for the next visit. For example, when a user inputs a rating for a restaurant, the reward unit can provide a coupon that can be used the next time the user visits the restaurant. For example, the reward unit can provide a discount coupon that can be used the next time the user visits a restaurant that the user has given a high rating to. The reward unit can also analyze the user's past rating history and select the optimal coupon provision method. For example, the reward unit can provide a special coupon for a restaurant that the user has given a high rating to in the past. This allows the reward unit to provide a coupon for the next visit.

[0036] When making suggestions, the suggestion unit can suggest restaurants based not only on the user's past visit history but also on the user's current mood and physical condition. The suggestion unit, for example, can suggest restaurants by combining the user's past visit history with the user's current mood and physical condition. For example, the suggestion unit can suggest optimal restaurants by combining the user's ratings of restaurants visited in the past with the user's current mood. Furthermore, if the user is not feeling well, the suggestion unit can also suggest restaurants that offer health-oriented menus. Furthermore, if the user is in the mood to eat a specific dish, the suggestion unit can also suggest restaurants that offer that dish. This allows the suggestion unit to suggest optimal restaurants based on the user's current mood and physical condition.

[0037] When making suggestions, the suggestion unit can also consider the ratings of the user's friends or family to suggest restaurants. For example, the suggestion unit can prioritize suggesting restaurants that the user's friends have given high ratings. For example, the suggestion unit can prioritize suggesting restaurants that the user's friends have given high ratings. The suggestion unit can also suggest restaurants that the user's family frequently visits. Furthermore, the suggestion unit can also suggest restaurants that the user has not yet visited based on the ratings of the user's friends and family. This allows the suggestion unit to suggest optimal restaurants taking into account the ratings of the user's friends and family.

[0038] When making suggestions, the suggestion unit can suggest restaurants taking into consideration the user's current location and travel route. The suggestion unit can suggest restaurants taking into consideration, for example, the user's current location and travel route. For example, the suggestion unit can suggest restaurants closest to the user's current location. The suggestion unit can also suggest restaurants on the user's travel route. Furthermore, the suggestion unit can also suggest restaurants close to the user's destination. This allows the suggestion unit to suggest optimal restaurants taking into consideration the user's current location and travel route.

[0039] When making suggestions, the suggestion unit can analyze the user's social media activity and suggest related restaurants. The suggestion unit, for example, analyzes the user's social media activity and suggests related restaurants. For example, the suggestion unit can suggest restaurants that the user has "liked" on social media. The suggestion unit can also suggest restaurants that influencers the user follows have visited. Furthermore, the suggestion unit can suggest restaurants that the user has shared on social media. This allows the suggestion unit to analyze the user's social media activity and suggest related restaurants.

[0040] During management, the management unit can analyze the user's past rating history and select the optimal management method. The management unit, for example, analyzes the user's past rating history and selects the optimal management method. For example, the management unit can prioritize managing information about restaurants that the user has previously given high ratings to. The management unit can also filter information about restaurants that the user has previously given low ratings to. Furthermore, the management unit can analyze the user's rating history and adjust the management method based on rating trends. This allows the management unit to analyze the user's past rating history and select the optimal management method.

[0041] During management, the management unit can filter impressions and ratings based on the user's current living situation and areas of interest. The management unit filters impressions and ratings based on, for example, the user's current living situation and areas of interest. For example, if the user is health-conscious, the management unit can prioritize managing health-related ratings. Also, if the user is interested in a particular dish, the management unit can prioritize managing ratings related to that dish. Furthermore, the management unit can filter highly relevant ratings based on the user's living situation. This allows the management unit to filter impressions and ratings based on the user's current living situation and areas of interest.

[0042] During management, the management unit can prioritize managing highly relevant impressions and reviews by taking into account the user's geographical location information. The management unit, for example, prioritizes managing highly relevant impressions and reviews by taking into account the user's geographical location information. For example, the management unit can prioritize managing impressions and reviews of restaurants close to the user's current location. The management unit can also manage highly relevant impressions and reviews based on the user's past visit history. Furthermore, the management unit can filter the most appropriate impressions and reviews based on the user's geographical location information. This allows the management unit to prioritize managing highly relevant impressions and reviews by taking into account the user's geographical location information.

[0043] During management, the management unit can analyze the user's social media activity and manage related comments and ratings. The management unit, for example, analyzes the user's social media activity and manages related comments and ratings. For example, the management unit can prioritize managing comments and ratings shared by the user on social media. The management unit can also manage comments and ratings of influencers followed by the user. Furthermore, the management unit can analyze the user's social media activity and filter out highly relevant comments and ratings. This allows the management unit to analyze the user's social media activity and manage related comments and ratings.

[0044] When providing a reward, the reward unit can analyze the user's past rating history and select the optimal reward method. The reward unit, for example, analyzes the user's past rating history and selects the optimal reward method. For example, the reward unit can provide a special reward to restaurants that the user has previously given high ratings to. The reward unit can also provide feedback for improvement to restaurants that the user has previously given low ratings to. Furthermore, the reward unit can analyze the user's rating history and adjust the reward method based on rating trends. This allows the reward unit to analyze the user's past rating history and select the optimal reward method.

[0045] When providing a reward, the reward unit can customize the reward based on the user's current living situation and areas of interest. The reward unit customizes the reward based on the user's current living situation and areas of interest, for example. For example, if the user is health-conscious, the reward unit can provide a health-related reward. Also, if the user is interested in a particular dish, the reward unit can provide a reward related to that dish. Furthermore, the reward unit can provide a highly relevant reward based on the user's living situation. This allows the reward unit to customize the reward based on the user's current living situation and areas of interest.

[0046] When providing a reward, the reward unit can select the optimal reward method taking into account the user's geographical location information. The reward unit selects the optimal reward method taking into account, for example, the user's geographical location information. For example, the reward unit can provide a coupon that can be used at a restaurant close to the user's current location. The reward unit can also provide a highly relevant reward based on the user's past visit history. Furthermore, the reward unit can also provide the optimal reward based on the user's geographical location information. This allows the reward unit to select the optimal reward method taking into account the user's geographical location information.

[0047] When providing a reward, the reward unit can analyze the user's social media activity and provide a related reward. The reward unit, for example, analyzes the user's social media activity and provides a related reward. For example, the reward unit can provide a special reward for a restaurant that the user shared on social media. The reward unit can also provide a special reward for a restaurant that an influencer the user follows has visited. Furthermore, the reward unit can analyze the user's social media activity and provide a highly relevant reward. This allows the reward unit to analyze the user's social media activity and provide a related reward.

[0048] The input unit can analyze the user's past input history and select the optimal input method when inputting information. The input unit, for example, analyzes the user's past input history and selects the optimal input method. For example, the input unit can automatically display restaurant information that the user has frequently input in the past as candidates. The input unit can also preferentially suggest input methods (voice, text, etc.) that the user has used in the past. Furthermore, the input unit can predict and suggest restaurant information to be used during a specific time period from the user's past input history. This allows the input unit to analyze the user's past input history and select the optimal input method.

[0049] The input unit can filter the input content based on the user's current living situation and areas of interest when inputting the information. The input unit filters the input content based on the user's current living situation and areas of interest, for example. For example, if the user is health-conscious, the input unit can preferentially input health-related restaurant information. Also, if the user is interested in a particular dish, the input unit can preferentially input restaurant information related to that dish. Furthermore, the input unit can filter highly relevant restaurant information based on the user's living situation. This allows the input unit to filter the input content based on the user's current living situation and areas of interest.

[0050] The input unit can, at the time of input, preferentially input highly relevant information in consideration of the user's geographical location information. The input unit, for example, preferentially inputs highly relevant information in consideration of the user's geographical location information. For example, the input unit can preferentially input information about restaurants close to the user's current location. The input unit can also preferentially input information about restaurants on the user's travel route. Furthermore, the input unit can also preferentially input information about restaurants close to the user's destination. This allows the input unit to preferentially input information about highly relevant information in consideration of the user's geographical location information.

[0051] The input unit can analyze the user's social media activity and input related information at the time of input. The input unit can, for example, analyze the user's social media activity and input related information. For example, the input unit can input information about restaurants that the user has "liked" on social media. The input unit can also input information about restaurants visited by influencers that the user follows. Furthermore, the input unit can input information about restaurants that the user has shared on social media. This allows the input unit to analyze the user's social media activity and input related information.

[0052] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0053] The suggestion unit not only suggests restaurants based on the user's preferences, but can also suggest restaurants suited to specific events or seasons based on the user's past visit history and ratings. For example, the suggestion unit can analyze information about restaurants the user has visited in the past and suggest restaurants suited to special events such as Christmas or Valentine's Day. The suggestion unit can also suggest restaurants that offer seasonal special menus. Furthermore, the suggestion unit can suggest restaurants suited to special occasions such as the user's birthday or anniversary. In this way, the suggestion unit can suggest optimal restaurants suited to specific events or seasons based on the user's preferences and past visit history.

[0054] The input unit can also provide a function that allows a user to refer to the ratings and impressions of other users when entering restaurant information. For example, the input unit can display the ratings and impressions of other users about the restaurant that the user is about to enter. The input unit can also preferentially display restaurant information that other users have given high ratings to. Furthermore, the input unit can display the ratings and impressions of other users who have entered similar information based on the information entered by the user. This allows the input unit to refer to the ratings and impressions of other users when entering restaurant information, allowing the user to enter more accurate information.

[0055] The suggestion unit not only suggests restaurants based on the user's past visit history and ratings, but can also suggest restaurants taking into consideration the user's dietary restrictions and allergy information. For example, the suggestion unit can analyze information about restaurants the user has visited in the past, and if the user is allergic to a particular ingredient, suggest restaurants that do not use that ingredient. Furthermore, if the user has dietary restrictions such as being vegetarian or vegan, the suggestion unit can suggest restaurants that accommodate those restrictions. Furthermore, if the user is health-conscious, the suggestion unit can suggest restaurants that offer low-calorie or low-sugar menus. This allows the suggestion unit to suggest optimal restaurants taking into consideration the user's dietary restrictions and allergy information.

[0056] The management unit can not only store the impressions and ratings of restaurants entered by the user, but also store photos and videos of restaurants visited by the user. For example, the management unit can store photos and videos taken by the user at restaurants in a database. The management unit can also provide a function that allows other users to comment on and rate photos and videos taken by the user. Furthermore, the management unit can evaluate the atmosphere of the restaurant and the appearance of the food based on the photos and videos taken by the user. In this way, the management unit can store the photos and videos as well as the impressions and ratings entered by the user, and use them for next time suggestions.

[0057] The reward unit can provide points or coupons based on the rating entered by the user, as well as provide special rewards when the user completes a specific mission. For example, the reward unit can provide special points when the user visits a specific number of restaurants within a certain period of time. The reward unit can also provide special coupons when the user visits restaurants of a specific genre. Furthermore, the reward unit can also provide special rewards when the user invites other users to visit restaurants. In this way, the reward unit can provide rewards based on the rating entered by the user, as well as provide special rewards when the user completes a specific mission.

[0058] The processing flow of the first embodiment will be briefly explained below.

[0059] Step 1: The suggestion unit suggests restaurants based on the user's preferences. For example, the suggestion unit may suggest similar restaurants based on information and ratings of restaurants the user has visited in the past. The suggestion unit may also suggest restaurants based on the user's current mood and physical condition. For example, if the user is feeling stressed, the suggestion unit may preferentially suggest restaurants with a relaxing atmosphere. Step 2: The management unit manages the information about the restaurants suggested by the suggestion unit. For example, the management unit stores the impressions and ratings of restaurants entered by the user. The management unit can also analyze the user's past rating history and select the optimal management method. Step 3: The reward unit provides rewards based on the evaluations stored by the management unit. For example, the reward unit provides points or coupons based on the evaluations entered by the user. The reward unit can also provide points for an electronic payment system.

[0060] (Example 2) A restaurant suggestion system according to an embodiment of the present invention is a system that suggests restaurants based on a user's preferences and manages the user's past visit history, impressions, and ratings. This system provides a mechanism whereby a user can receive points in an electronic payment system or a coupon for their next visit when they input their impressions and ratings of a restaurant. First, the user inputs information about the restaurant, such as the type of cuisine, price range, and location. This information is stored in the system. Next, the system suggests restaurants based on the user's preferences. For example, the system suggests similar restaurants based on the information and ratings of restaurants the user has visited in the past. Furthermore, the system manages the user's history of restaurants visited, impressions, and ratings. The user can input their impressions and ratings of the restaurants they have visited. This information is stored in the system and used for next time suggestions. Furthermore, when a user inputs their impressions and ratings, they can receive points in an electronic payment system or a coupon for their next visit. For example, when a user inputs a restaurant rating, they are awarded points in the electronic payment system. These points can be used the next time they visit the restaurant. This mechanism allows users to easily find restaurants that suit their preferences and receive points and coupons by inputting restaurant ratings. This allows the restaurant suggestion system to suggest restaurants that match the user's preferences and provide rewards based on the user's evaluation.

[0061] The restaurant suggestion system according to the embodiment includes a suggestion unit, a management unit, and a reward unit. The suggestion unit suggests restaurants based on a user's preferences. For example, the suggestion unit suggests similar restaurants based on information about and ratings of restaurants the user has visited in the past. The suggestion unit can also suggest restaurants based on the user's current mood and physical condition. For example, if the user is feeling stressed, restaurants with a relaxing atmosphere can be preferentially suggested. The management unit manages information about the restaurants suggested by the suggestion unit. For example, the management unit stores impressions and ratings of restaurants entered by the user. The management unit can also analyze the user's past rating history and select an optimal management method. The reward unit provides rewards based on the ratings stored by the management unit. For example, the reward unit provides points or coupons based on the ratings entered by the user. The reward unit can also provide points for an electronic payment system. As a result, the restaurant suggestion system according to the embodiment can suggest restaurants that match the user's preferences and provide rewards based on the ratings.

[0062] The restaurant suggestion system includes an input unit for inputting restaurant information from a user. The input unit allows the user to input restaurant information. For example, the input unit provides an interface for the user to input information such as the type of cuisine, price range, and location. The input unit not only allows the user to input information manually, but also allows the user to input information using voice input or image recognition technology. For example, the user can take a photo of a restaurant menu using a smartphone camera and automatically input the menu information using image recognition technology. Furthermore, the input unit can analyze the user's past input history and select the optimal input method. For example, the input unit can automatically display restaurant information that the user has frequently input in the past as candidates. This allows the user to easily input restaurant information, and the system can suggest restaurants based on the user's preferences.

[0063] The suggestion unit can suggest restaurants based on the user's past visit history or ratings. The suggestion unit can suggest restaurants based on the user's past visit history, for example. For example, the suggestion unit can analyze information about restaurants the user has visited in the past and suggest similar restaurants. The suggestion unit can also suggest restaurants based on the user's ratings. For example, the suggestion unit can suggest restaurants in the same genre as a restaurant that the user has given a high rating to. Furthermore, the suggestion unit can suggest restaurants based on the user's current mood or physical condition. For example, if the user wants to relax, the suggestion unit can suggest a restaurant with a quiet and calm atmosphere. This allows the suggestion unit to suggest the optimal restaurant based on the user's past visit history and ratings.

[0064] The management unit can store restaurant reviews and ratings entered by the user. The management unit, for example, stores restaurant reviews and ratings entered by the user. For example, the management unit can store reviews and ratings entered by the user using text input or voice input in a database. The management unit can also analyze the user's past rating history and select the optimal management method. For example, the management unit can prioritize managing information about restaurants that the user has given high ratings to in the past. Furthermore, the management unit can filter reviews and ratings based on the user's current living situation and areas of interest. For example, if the user is health-conscious, the management unit can prioritize managing health-related ratings. In this way, the management unit can save the reviews and ratings entered by the user and use them for the next suggestion.

[0065] The reward unit can provide points or coupons based on the rating entered by the user. The reward unit, for example, provides points or coupons based on the rating entered by the user. For example, the reward unit can award points in an electronic payment system when the user enters a rating for a restaurant. These points can be used the next time the user visits the restaurant. The reward unit can also provide coupons for the next visit based on the rating entered by the user. For example, the reward unit can provide a discount coupon that can be used the next time the user visits a restaurant that the user has given a high rating to. Furthermore, the reward unit can analyze the user's past rating history and select the optimal reward method. For example, the reward unit can provide a special reward to restaurants that the user has given a high rating to in the past. This allows the reward unit to provide rewards based on the rating entered by the user.

[0066] The reward unit can provide points for the electronic payment system. For example, when a user inputs a rating for a restaurant, the reward unit can award points for the electronic payment system. These points can be used the next time the user visits the restaurant. For example, the reward unit can provide special points for restaurants that the user has given a high rating. The reward unit can also analyze the user's past rating history and select the optimal point awarding method. For example, the reward unit can provide special points for restaurants that the user has given a high rating in the past. In this way, the reward unit can provide points for the electronic payment system.

[0067] The reward unit can provide a coupon for the next visit. For example, when a user inputs a rating for a restaurant, the reward unit can provide a coupon that can be used the next time the user visits the restaurant. For example, the reward unit can provide a discount coupon that can be used the next time the user visits a restaurant that the user has given a high rating to. The reward unit can also analyze the user's past rating history and select the optimal coupon provision method. For example, the reward unit can provide a special coupon for a restaurant that the user has given a high rating to in the past. This allows the reward unit to provide a coupon for the next visit.

[0068] The suggestion unit can estimate the user's emotions and adjust the restaurant suggestion method based on the estimated user's emotions. The suggestion unit, for example, estimates the user's emotions and adjusts the restaurant suggestion method based on the estimated user's emotions. For example, if the user is feeling stressed, the suggestion unit can preferentially suggest restaurants with a relaxing atmosphere. Furthermore, if the user is having fun, the suggestion unit can also suggest restaurants with a high level of entertainment. Furthermore, if the user is tired, the suggestion unit can also suggest quiet and relaxing restaurants. In this way, the suggestion unit can suggest the optimal restaurant based on the user's emotions. Emotion estimation is realized using an emotion estimation function, for example, using an emotion engine or a generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI.

[0069] When making suggestions, the suggestion unit can suggest restaurants based not only on the user's past visit history but also on the user's current mood and physical condition. The suggestion unit, for example, can suggest restaurants by combining the user's past visit history with the user's current mood and physical condition. For example, the suggestion unit can suggest optimal restaurants by combining the user's ratings of restaurants visited in the past with the user's current mood. Furthermore, if the user is not feeling well, the suggestion unit can also suggest restaurants that offer health-oriented menus. Furthermore, if the user is in the mood to eat a specific dish, the suggestion unit can also suggest restaurants that offer that dish. This allows the suggestion unit to suggest optimal restaurants based on the user's current mood and physical condition.

[0070] When making suggestions, the suggestion unit can also consider the ratings of the user's friends or family to suggest restaurants. For example, the suggestion unit can prioritize suggesting restaurants that the user's friends have given high ratings. For example, the suggestion unit can prioritize suggesting restaurants that the user's friends have given high ratings. The suggestion unit can also suggest restaurants that the user's family frequently visits. Furthermore, the suggestion unit can also suggest restaurants that the user has not yet visited based on the ratings of the user's friends and family. This allows the suggestion unit to suggest optimal restaurants taking into account the ratings of the user's friends and family.

[0071] The suggestion unit can estimate the user's emotions and determine the priority of restaurants to suggest based on the estimated user's emotions. The suggestion unit can, for example, estimate the user's emotions and determine the priority of restaurants to suggest based on the estimated user's emotions. For example, if the user is feeling stressed, the suggestion unit can preferentially suggest restaurants that are relaxing. Furthermore, if the user is having fun, the suggestion unit can preferentially suggest restaurants that are highly entertaining. Furthermore, if the user is tired, the suggestion unit can preferentially suggest restaurants that are quiet and relaxing. In this way, the suggestion unit can determine the priority of restaurants based on the user's emotions. Emotion estimation is realized using an emotion estimation function, for example, using an emotion engine or a generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI.

[0072] When making suggestions, the suggestion unit can suggest restaurants taking into consideration the user's current location and travel route. The suggestion unit can suggest restaurants taking into consideration, for example, the user's current location and travel route. For example, the suggestion unit can suggest restaurants closest to the user's current location. The suggestion unit can also suggest restaurants on the user's travel route. Furthermore, the suggestion unit can also suggest restaurants close to the user's destination. This allows the suggestion unit to suggest optimal restaurants taking into consideration the user's current location and travel route.

[0073] When making suggestions, the suggestion unit can analyze the user's social media activity and suggest related restaurants. The suggestion unit, for example, analyzes the user's social media activity and suggests related restaurants. For example, the suggestion unit can suggest restaurants that the user has "liked" on social media. The suggestion unit can also suggest restaurants that influencers the user follows have visited. Furthermore, the suggestion unit can suggest restaurants that the user has shared on social media. This allows the suggestion unit to analyze the user's social media activity and suggest related restaurants.

[0074] The management unit can estimate the user's emotions and adjust the input method of impressions and evaluations based on the estimated user emotions. For example, the management unit can estimate the user's emotions and adjust the input method of impressions and evaluations based on the estimated user emotions. For example, the management unit can provide a simple input interface when the user is stressed. The management unit can also provide detailed input options when the user is relaxed. Furthermore, the management unit can prioritize voice input when the user is in a hurry. This allows the management unit to adjust the input method of impressions and evaluations based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI.

[0075] During management, the management unit can analyze the user's past rating history and select the optimal management method. The management unit, for example, analyzes the user's past rating history and selects the optimal management method. For example, the management unit can prioritize managing information about restaurants that the user has previously given high ratings to. The management unit can also filter information about restaurants that the user has previously given low ratings to. Furthermore, the management unit can analyze the user's rating history and adjust the management method based on rating trends. This allows the management unit to analyze the user's past rating history and select the optimal management method.

[0076] During management, the management unit can filter impressions and ratings based on the user's current living situation and areas of interest. The management unit filters impressions and ratings based on, for example, the user's current living situation and areas of interest. For example, if the user is health-conscious, the management unit can prioritize managing health-related ratings. Also, if the user is interested in a particular dish, the management unit can prioritize managing ratings related to that dish. Furthermore, the management unit can filter highly relevant ratings based on the user's living situation. This allows the management unit to filter impressions and ratings based on the user's current living situation and areas of interest.

[0077] The management unit can estimate the user's emotions and adjust the display method of the impressions and evaluations based on the estimated user emotions. For example, the management unit can estimate the user's emotions and adjust the display method of the impressions and evaluations based on the estimated user emotions. For example, if the user is nervous, the management unit can provide a simple, highly visible display method. If the user is relaxed, the management unit can provide a display method including detailed information. Furthermore, if the user is in a hurry, the management unit can provide a display method that focuses on the main points. This allows the management unit to adjust the display method of the impressions and evaluations based on the user's emotions. Emotion estimation is realized using an emotion estimation function, for example, using an emotion engine or generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI.

[0078] During management, the management unit can prioritize managing highly relevant impressions and reviews by taking into account the user's geographical location information. The management unit, for example, prioritizes managing highly relevant impressions and reviews by taking into account the user's geographical location information. For example, the management unit can prioritize managing impressions and reviews of restaurants close to the user's current location. The management unit can also manage highly relevant impressions and reviews based on the user's past visit history. Furthermore, the management unit can filter the most appropriate impressions and reviews based on the user's geographical location information. This allows the management unit to prioritize managing highly relevant impressions and reviews by taking into account the user's geographical location information.

[0079] During management, the management unit can analyze the user's social media activity and manage related comments and ratings. The management unit, for example, analyzes the user's social media activity and manages related comments and ratings. For example, the management unit can prioritize managing comments and ratings shared by the user on social media. The management unit can also manage comments and ratings of influencers followed by the user. Furthermore, the management unit can analyze the user's social media activity and filter out highly relevant comments and ratings. This allows the management unit to analyze the user's social media activity and manage related comments and ratings.

[0080] The reward unit can estimate the user's emotion and adjust the reward provision method based on the estimated user's emotion. For example, the reward unit can estimate the user's emotion and adjust the reward provision method based on the estimated user's emotion. For example, if the user is relaxed, the reward unit can provide a reward provision method that includes detailed explanations. If the user is in a hurry, the reward unit can also provide a quick and concise reward provision method. Furthermore, if the user is enjoying themselves, the reward unit can also provide a highly entertaining reward provision method. This allows the reward unit to adjust the reward provision method based on the user's emotion. Emotion estimation is realized using an emotion estimation function, for example, using an emotion engine or generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI.

[0081] When providing a reward, the reward unit can analyze the user's past rating history and select the optimal reward method. The reward unit, for example, analyzes the user's past rating history and selects the optimal reward method. For example, the reward unit can provide a special reward to restaurants that the user has previously given high ratings to. The reward unit can also provide feedback for improvement to restaurants that the user has previously given low ratings to. Furthermore, the reward unit can analyze the user's rating history and adjust the reward method based on rating trends. This allows the reward unit to analyze the user's past rating history and select the optimal reward method.

[0082] When providing a reward, the reward unit can customize the reward based on the user's current living situation and areas of interest. The reward unit customizes the reward based on the user's current living situation and areas of interest, for example. For example, if the user is health-conscious, the reward unit can provide a health-related reward. Also, if the user is interested in a particular dish, the reward unit can provide a reward related to that dish. Furthermore, the reward unit can provide a highly relevant reward based on the user's living situation. This allows the reward unit to customize the reward based on the user's current living situation and areas of interest.

[0083] The reward unit can estimate the user's emotions and determine the priority of rewards based on the estimated user emotions. For example, the reward unit can estimate the user's emotions and determine the priority of rewards based on the estimated user emotions. For example, if the user is relaxed, the reward unit can prioritize providing rewards that include detailed explanations. Also, if the user is in a hurry, the reward unit can prioritize providing quick and concise rewards. Furthermore, if the user is having fun, the reward unit can prioritize providing rewards that are highly entertaining. In this way, the reward unit can determine the priority of rewards based on the user's emotions. Emotion estimation is realized using an emotion estimation function, for example, using an emotion engine or a generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI.

[0084] When providing a reward, the reward unit can select the optimal reward method taking into account the user's geographical location information. The reward unit selects the optimal reward method taking into account, for example, the user's geographical location information. For example, the reward unit can provide a coupon that can be used at a restaurant close to the user's current location. The reward unit can also provide a highly relevant reward based on the user's past visit history. Furthermore, the reward unit can also provide the optimal reward based on the user's geographical location information. This allows the reward unit to select the optimal reward method taking into account the user's geographical location information.

[0085] When providing a reward, the reward unit can analyze the user's social media activity and provide a related reward. The reward unit, for example, analyzes the user's social media activity and provides a related reward. For example, the reward unit can provide a special reward for a restaurant that the user shared on social media. The reward unit can also provide a special reward for a restaurant that an influencer the user follows has visited. Furthermore, the reward unit can analyze the user's social media activity and provide a highly relevant reward. This allows the reward unit to analyze the user's social media activity and provide a related reward.

[0086] The input unit can estimate the user's emotions and adjust the input method for restaurant information based on the estimated user emotions. The input unit, for example, estimates the user's emotions and adjusts the input method for restaurant information based on the estimated user emotions. For example, the input unit can provide a simple input interface when the user is stressed. The input unit can also provide detailed input options when the user is relaxed. Furthermore, the input unit can prioritize voice input when the user is in a hurry. This allows the input unit to adjust the input method for restaurant information based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. The generative AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI.

[0087] The input unit can analyze the user's past input history and select the optimal input method when inputting information. The input unit, for example, analyzes the user's past input history and selects the optimal input method. For example, the input unit can automatically display restaurant information that the user has frequently input in the past as candidates. The input unit can also preferentially suggest input methods (voice, text, etc.) that the user has used in the past. Furthermore, the input unit can predict and suggest restaurant information to be used during a specific time period from the user's past input history. This allows the input unit to analyze the user's past input history and select the optimal input method.

[0088] The input unit can filter the input content based on the user's current living situation and areas of interest when inputting the information. The input unit filters the input content based on the user's current living situation and areas of interest, for example. For example, if the user is health-conscious, the input unit can preferentially input health-related restaurant information. Also, if the user is interested in a particular dish, the input unit can preferentially input restaurant information related to that dish. Furthermore, the input unit can filter highly relevant restaurant information based on the user's living situation. This allows the input unit to filter the input content based on the user's current living situation and areas of interest.

[0089] The input unit can estimate the user's emotions and determine the priority of restaurant information to be input based on the estimated user emotions. The input unit, for example, estimates the user's emotions and determines the priority of restaurant information to be input based on the estimated user emotions. For example, if the user is feeling stressed, the input unit can prioritize input of restaurant information that offers relaxation. Furthermore, if the user is enjoying themselves, the input unit can prioritize input of restaurant information that is highly entertaining. Furthermore, if the user is tired, the input unit can prioritize input of restaurant information that is quiet and relaxing. In this way, the input unit can prioritize input of restaurant information based on the user's emotions. Emotion estimation is realized using an emotion estimation function, for example, using an emotion engine or a generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI.

[0090] The input unit can, at the time of input, preferentially input highly relevant information in consideration of the user's geographical location information. The input unit, for example, preferentially inputs highly relevant information in consideration of the user's geographical location information. For example, the input unit can preferentially input information about restaurants close to the user's current location. The input unit can also preferentially input information about restaurants on the user's travel route. Furthermore, the input unit can also preferentially input information about restaurants close to the user's destination. This allows the input unit to preferentially input information about highly relevant information in consideration of the user's geographical location information.

[0091] The input unit can analyze the user's social media activity and input related information at the time of input. The input unit can, for example, analyze the user's social media activity and input related information. For example, the input unit can input information about restaurants that the user has "liked" on social media. The input unit can also input information about restaurants visited by influencers that the user follows. Furthermore, the input unit can input information about restaurants that the user has shared on social media. This allows the input unit to analyze the user's social media activity and input related information. === Hard Collateral 1-1 === Each of the multiple elements including the suggestion unit, management unit, reward unit, and input unit described above is realized, for example, by at least one of the smart device 14 and the data processing device 12. For example, the suggestion unit is realized by the control unit 46A of the smart device 14 and suggests restaurants based on the user's preferences. The management unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and manages the user's ratings and history. The reward unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and provides points and coupons based on the ratings. The input unit is realized, for example, by the reception device 38 of the smart device 14 and provides an interface for the user to input restaurant information. === Hard Collateral 1-2 === Each of the multiple elements including the above-described suggestion unit, management unit, reward unit, and input unit is realized, for example, by at least one of the smart glasses 214 and the data processing device 12. For example, the suggestion unit is realized by the control unit 46A of the smart glasses 214 and suggests restaurants based on the user's preferences. The management unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and manages the user's ratings and history. The reward unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and provides points and coupons based on the ratings. The input unit is realized, for example, by the microphone 238 of the smart glasses 214 and provides an interface for the user to input restaurant information. === Hard Collateral 1-3 === Each of the multiple elements including the above-mentioned suggestion unit, management unit, reward unit, and input unit is realized, for example, by at least one of the headset terminal 314 and the data processing device 12. For example, the suggestion unit is realized by the control unit 46A of the headset terminal 314 and suggests restaurants based on the user's preferences. The management unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and manages the user's ratings and history. The reward unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and provides points and coupons based on the ratings. The input unit is realized, for example, by the microphone 238 of the headset terminal 314 and provides an interface for the user to input restaurant information. === Hard Collateral 1-4 === Each of the multiple elements including the suggestion unit, management unit, reward unit, and input unit described above is realized, for example, by at least one of the robot 414 and the data processing device 12. For example, the suggestion unit is realized by the control unit 46A of the robot 414 and suggests restaurants based on the user's preferences. The management unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and manages the user's ratings and history. The reward unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and provides points and coupons based on the ratings. The input unit is realized, for example, by the microphone 238 of the robot 414 and provides an interface for the user to input restaurant information.

[0092] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0093] The suggestion unit not only suggests restaurants based on the user's preferences, but can also suggest restaurants suited to specific events or seasons based on the user's past visit history and ratings. For example, the suggestion unit can analyze information about restaurants the user has visited in the past and suggest restaurants suited to special events such as Christmas or Valentine's Day. The suggestion unit can also suggest restaurants that offer seasonal special menus. Furthermore, the suggestion unit can suggest restaurants suited to special occasions such as the user's birthday or anniversary. In this way, the suggestion unit can suggest optimal restaurants suited to specific events or seasons based on the user's preferences and past visit history.

[0094] The input unit can also provide a function that allows a user to refer to the ratings and impressions of other users when entering restaurant information. For example, the input unit can display the ratings and impressions of other users about the restaurant that the user is about to enter. The input unit can also preferentially display restaurant information that other users have given high ratings to. Furthermore, the input unit can display the ratings and impressions of other users who have entered similar information based on the information entered by the user. This allows the input unit to refer to the ratings and impressions of other users when entering restaurant information, allowing the user to enter more accurate information.

[0095] The suggestion unit not only suggests restaurants based on the user's past visit history and ratings, but can also suggest restaurants taking into consideration the user's dietary restrictions and allergy information. For example, the suggestion unit can analyze information about restaurants the user has visited in the past, and if the user is allergic to a particular ingredient, suggest restaurants that do not use that ingredient. Furthermore, if the user has dietary restrictions such as being vegetarian or vegan, the suggestion unit can suggest restaurants that accommodate those restrictions. Furthermore, if the user is health-conscious, the suggestion unit can suggest restaurants that offer low-calorie or low-sugar menus. This allows the suggestion unit to suggest optimal restaurants taking into consideration the user's dietary restrictions and allergy information.

[0096] The management unit can not only store the impressions and ratings of restaurants entered by the user, but also store photos and videos of restaurants visited by the user. For example, the management unit can store photos and videos taken by the user at restaurants in a database. The management unit can also provide a function that allows other users to comment on and rate photos and videos taken by the user. Furthermore, the management unit can evaluate the atmosphere of the restaurant and the appearance of the food based on the photos and videos taken by the user. In this way, the management unit can store the photos and videos as well as the impressions and ratings entered by the user, and use them for next time suggestions.

[0097] The reward unit can provide points or coupons based on the rating entered by the user, as well as provide special rewards when the user completes a specific mission. For example, the reward unit can provide special points when the user visits a specific number of restaurants within a certain period of time. The reward unit can also provide special coupons when the user visits restaurants of a specific genre. Furthermore, the reward unit can also provide special rewards when the user invites other users to visit restaurants. In this way, the reward unit can provide rewards based on the rating entered by the user, as well as provide special rewards when the user completes a specific mission.

[0098] The suggestion unit not only estimates the user's emotions and adjusts the restaurant suggestion method based on the estimated user's emotions, but also customizes the restaurant's menu and services based on the user's emotions. For example, if the user is feeling stressed, the suggestion unit can suggest restaurants that offer relaxing menus and services. Also, if the user is having fun, the suggestion unit can suggest restaurants that offer entertaining menus and services. Furthermore, if the user is tired, the suggestion unit can suggest restaurants with a quiet and relaxing atmosphere. In this way, the suggestion unit can customize the restaurant's menu and services based on the user's emotions.

[0099] The management unit not only estimates the user's emotions and adjusts the input method for impressions and ratings based on the estimated user emotions, but also customizes the display method for impressions and ratings based on the user's emotions. For example, if the user is feeling stressed, the management unit can provide a simple, highly visible display method. If the user is relaxed, the management unit can provide a display method including detailed information. Furthermore, if the user is in a hurry, the management unit can provide a display method that focuses on the main points. In this way, the management unit can customize the display method for impressions and ratings based on the user's emotions.

[0100] The reward unit not only estimates the user's emotion and adjusts the method of providing a reward based on the estimated user's emotion, but also customizes the content of the reward based on the user's emotion. For example, if the user is relaxed, the reward unit can provide a relaxing reward. Also, if the user is having fun, the reward unit can provide a highly entertaining reward. Furthermore, if the user is tired, the reward unit can provide a refreshing reward. In this way, the reward unit can customize the content of the reward based on the user's emotion.

[0101] The input unit not only estimates the user's emotions and adjusts the input method for restaurant information based on the estimated user's emotions, but also customizes the input content based on the user's emotions. For example, the input unit can provide simple input content when the user is stressed. Also, the input unit can provide detailed input content when the user is relaxed. Furthermore, the input unit can prioritize voice input when the user is in a hurry. This allows the input unit to customize the input content based on the user's emotions.

[0102] The suggestion unit not only estimates the user's emotions and determines the priority of restaurants to suggest based on the estimated user's emotions, but also customizes the types of restaurants to suggest based on the user's emotions. For example, if the user is feeling stressed, the suggestion unit can preferentially suggest restaurants that are relaxing. Also, if the user is having fun, the suggestion unit can preferentially suggest restaurants that are highly entertaining. Furthermore, if the user is tired, the suggestion unit can preferentially suggest quiet and relaxing restaurants. This allows the suggestion unit to customize the types of restaurants to suggest based on the user's emotions.

[0103] The processing flow of the second embodiment will be briefly explained below.

[0104] Step 1: The suggestion unit suggests restaurants based on the user's preferences. For example, the suggestion unit may suggest similar restaurants based on information and ratings of restaurants the user has visited in the past. The suggestion unit may also suggest restaurants based on the user's current mood and physical condition. For example, if the user is feeling stressed, the suggestion unit may preferentially suggest restaurants with a relaxing atmosphere. Step 2: The management unit manages the information about the restaurants suggested by the suggestion unit. For example, the management unit stores the impressions and ratings of restaurants entered by the user. The management unit can also analyze the user's past rating history and select the optimal management method. Step 3: The reward unit provides rewards based on the evaluations stored by the management unit. For example, the reward unit provides points or coupons based on the evaluations entered by the user. The reward unit can also provide points for an electronic payment system.

[0105] 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.

[0106] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (registered trademark) (Internet search engine).<URL: https: / / openai.com / blog / chatgpt> Examples of the generative AI include a neural network (NN) and a neural network (NN). 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 (e.g., still image data or video data). 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 one or more data formats of voice data, text data, image data, etc. 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 may perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processing of each of the above-mentioned parts is performed by an AI, the processing may be performed in part or entirely by the AI, but is not limited to these examples. The processing performed by an AI including the generative AI may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by an AI including the generative AI.

[0107] 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.

[0108] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0109] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0110] 3, the data processing system 210 includes a data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0111] 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.

[0112] 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.

[0113] 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.

[0114] 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).

[0115] 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.

[0116] 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.

[0117] 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.

[0118] 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.

[0119] 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.

[0120] 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.

[0121] 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.

[0122] 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 including an instruction, as well as inference data such as audio data indicating speech, text data indicating text, and image data indicating an image (e.g., still image data or video data). 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 one or more data formats, such as audio data, text data, and image 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 models 58 include AIs other than the generation AI. Examples of AIs other than the generation AI include, but are not limited to, linear regression, logistic regression, decision trees, random forests, support vector machines (SVMs), k-means clustering, convolutional neural networks (CNNs), recurrent neural networks (RNNs), generative adversarial networks (GANs), and naive Bayes. These AIs can perform various types of processing, but are not limited to these examples. The AI ​​may also be an AI agent. When the processing of each of the above-described parts is performed by an AI, the processing may be performed in part or entirely by the AI, but is not limited to these examples. Processing performed by an AI, including the generation AI, may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by an AI, including the generation AI.

[0123] 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.

[0124] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0125] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0126] 5, the data processing system 310 includes the data processing device 12 and a headset terminal 314. An example of the data processing device 12 is a server.

[0127] 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.

[0128] 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.

[0129] 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.

[0130] 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).

[0131] 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.

[0132] 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.

[0133] 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.

[0134] 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.

[0135] In the headset type terminal 314, the identification process is performed by the processor 46. A identification program 60 is stored in the storage 50. The processor 46 reads the identification program 60 from the storage 50 and executes the read identification program 60 on the RAM 48. The identification process is realized by the processor 46 operating as a control unit 46A in accordance with the identification 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 identification processing unit 290 using these models.

[0136] 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.

[0137] 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.

[0138] 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 including an instruction, as well as inference data such as audio data indicating speech, text data indicating text, and image data indicating an image (e.g., still image data or video data). 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 one or more data formats, such as audio data, text data, and image 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 models 58 include AIs other than the generation AI. Examples of AIs other than the generation AI include, but are not limited to, linear regression, logistic regression, decision trees, random forests, support vector machines (SVMs), k-means clustering, convolutional neural networks (CNNs), recurrent neural networks (RNNs), generative adversarial networks (GANs), and naive Bayes. These AIs can perform various types of processing, but are not limited to these examples. The AI ​​may also be an AI agent. When the processing of each of the above-described parts is performed by an AI, the processing may be performed in part or entirely by the AI, but is not limited to these examples. Processing performed by an AI, including the generation AI, may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by an AI, including the generation AI.

[0139] 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.

[0140] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0141] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0142] 7, a 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.

[0143] 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.

[0144] 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.

[0145] 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.

[0146] 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).

[0147] 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.

[0148] 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.

[0149] 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.

[0150] 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.

[0151] 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.

[0152] In the robot 414, the processor 46 performs the identification process. The storage 50 stores the identification program 60. The processor 46 reads the identification program 60 from the storage 50 and executes the read identification program 60 on the RAM 48. The identification process is realized by the processor 46 operating as the control unit 46A in accordance with the identification program 60 executed on the RAM 48. The robot 414 also 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 perform the same process as the identification processing unit 290 using these models.

[0153] 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.

[0154] 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.

[0155] 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 including an instruction, as well as inference data such as audio data indicating speech, text data indicating text, and image data indicating an image (e.g., still image data or video data). 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 one or more data formats, such as audio data, text data, and image 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 models 58 include AIs other than the generation AI. Examples of AIs other than the generation AI include, but are not limited to, linear regression, logistic regression, decision trees, random forests, support vector machines (SVMs), k-means clustering, convolutional neural networks (CNNs), recurrent neural networks (RNNs), generative adversarial networks (GANs), and naive Bayes. These AIs can perform various types of processing, but are not limited to these examples. The AI ​​may also be an AI agent. When the processing of each of the above-described parts is performed by an AI, the processing may be performed in part or entirely by the AI, but is not limited to these examples. Processing performed by an AI, including the generation AI, may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by an AI, including the generation AI.

[0156] 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.

[0157] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0158] 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.

[0159] 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.

[0160] 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.

[0161] 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).

[0162] 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.

[0163] 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."

[0164] 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.

[0165] 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.

[0166] 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.

[0167] 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.

[0168] 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.

[0169] 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.

[0170] 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.

[0171] 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.

[0172] 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.

[0173] 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.

[0174] 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.

[0175] 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.

[0176] [Explanation of symbols]

[0177] 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 suggestion unit that suggests restaurants based on the user's preferences; a management unit that manages information on restaurants suggested by the suggestion unit; a reward unit that provides rewards based on the evaluations stored by the management unit. A system characterized by:

2. Equipped with an input unit for inputting restaurant information by the user The system of claim 1 .

3. The suggestion unit Suggest restaurants based on the user's past visit history or ratings The system of claim 1 .

4. The management unit Save restaurant reviews and ratings entered by users The system of claim 1 .

5. The reward unit: Offer points or coupons based on user-entered ratings The system of claim 1 .

6. The reward unit: Providing points for electronic payment systems The system of claim 1 .

7. The reward unit: Offer a coupon for your next visit The system of claim 1 .

8. The suggestion unit Estimate the user's emotions and adjust the restaurant suggestion method based on the estimated user emotions The system of claim 1 .

9. The suggestion unit When suggesting restaurants, the system will not only suggest restaurants based on the user's past visit history, but also their current mood and physical condition. The system of claim 1 .

Images