System

Abstract

An object of a system according to an embodiment is to propose the best meal for the day near the user's home.SOLUTION: A system includes an information collection unit, an analysis unit, and a proposal unit. The information collection unit collects information on menus, prices, coupons, reviews, and the like of restaurants on the Internet. The analysis unit analyzes the information collected by the information collection unit and calculates the most profitable meal of the day near the home of the user. The suggestion unit suggests the optimal meal plan calculated by the analysis unit to the user.SELECTED DRAWING: Figure 1

Description

[Technical Field]

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

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

[0003] [Patent Document 1] Japanese Patent Publication No. 2022-180282 Summary of the Invention [Problem to be solved by the invention]

[0004] Conventional technology has faced the challenge of efficiently collecting and analyzing the vast amount of restaurant information available on the Internet and proposing optimal meal plans to users.

[0005] The system according to the embodiment aims to suggest the best value meals of the day near the user's home. [Means for solving the problem]

[0006] The system according to the embodiment includes an information collection unit, an analysis unit, and a suggestion unit. The information collection unit collects information such as restaurant menus, prices, coupons, and reviews on the Internet. The analysis unit analyzes the information collected by the information collection unit and calculates the best value meal plan for that day near the user's home. The suggestion unit suggests the optimal meal plan calculated by the analysis unit to the user. [Effects of the Invention]

[0007] The system according to the embodiment can suggest the best value meals of the day near the user's home. [Brief explanation of the drawings]

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

[0028] (Example 1) The meal recommendation system according to an embodiment of the present invention is a system that collects and analyzes information on the Internet and recommends the best value meal of the day near the user's home, thereby enabling the user to easily find the best value meal of the day near their home.

[0029] A meal suggestion system according to an embodiment includes an information collection unit, an analysis unit, and a suggestion unit. The information collection unit collects information such as restaurant menus, prices, coupons, and reviews on the Internet. For example, the information collection unit acquires menu information from the restaurant's official website. The information collection unit can also collect discount information from coupon sites. The information collection unit can also collect user ratings from review sites. For example, the information collection unit acquires the latest menu information from the restaurant's official website, collects discount coupons from coupon sites, and collects user ratings from review sites. The analysis unit analyzes the information collected by the information collection unit and calculates the best meal deal for that day near the user's home. For example, the analysis unit comprehensively considers menu prices, coupon discount rates, and review ratings to suggest the most cost-effective meal. The analysis unit can also calculate a meal plan taking into account the user's preferences and conditions. The analysis unit calculates an optimal meal plan based on the collected information. For example, the analysis unit comprehensively considers menu prices, coupon discount rates, and review ratings to calculate an optimal meal plan taking into account the user's preferences and conditions. The suggestion unit suggests the optimal meal plan calculated by the analysis unit to the user. For example, the suggestion unit notifies the user of the suggestion through a smartphone app. The suggestion unit can also notify the user of the suggestion through a website. The suggestion unit can also automatically update the suggestion content. For example, the suggestion unit notifies the user of the suggestion through a smartphone app, notifies the user of the suggestion through a website, and automatically updates the suggestion content. In this way, the meal suggestion system according to the embodiment allows the user to easily find the best value meal of the day near their home. For example, the user can receive suggestions through a smartphone app and a website, and the suggestion content is automatically updated, allowing the user to always find the optimal meal plan based on the latest information.

[0030] The information collection unit can also collect social media posts from restaurants or real-time user feedback. For example, the generation AI collects posts from official social media accounts of restaurants to obtain information about menu changes and special events. For example, it analyzes posts on Facebook and Instagram to collect the latest menus and discount information. The information collection unit also analyzes posts on review sites and forums to collect real-time user feedback. For example, it collects the latest reviews on Yelp and TripAdvisor to reflect restaurant ratings and user opinions. The information collection unit also analyzes hashtags on social media to collect trending information about specific restaurants and menus. For example, it analyzes posts with hashtags like #lunch and #dinner to identify popular menus and restaurants. This allows the system to recommend optimal meal plans based on the latest information.

[0031] The information collection unit can compare the data with the restaurant's past sales data and seasonal menu change history. For example, the information collection unit collects the restaurant's past sales data and analyzes seasonal sales trends. For example, it identifies menu items that are popular in the summer and menu items that sell well in the winter. The information collection unit also collects the restaurant's menu change history and analyzes seasonal menu fluctuations. For example, it identifies the tendency for new menu items to be added in the spring and the tendency for specific menu items to be removed in the fall. The information collection unit also compares the sales data with the menu change history to identify menu items that are good value in specific seasons. For example, it can suggest menu items that offer many discounts in specific seasons based on past data. This makes it possible to propose optimal meal plans based on past data.

[0032] The information gathering unit can also collect information about nearby events or traffic conditions and reflect this in meal suggestions. The information gathering unit, for example, collects information about nearby events and predicts how crowded restaurants will be. For example, based on information about upcoming concerts or sporting events, the information gathering unit makes suggestions to avoid restaurants that are expected to be crowded. The information gathering unit also collects traffic conditions in real time and suggests restaurants that are easy to access. For example, it analyzes traffic congestion information and the operation status of public transportation to select restaurants that are easily accessible. The information gathering unit also combines event information and traffic conditions to suggest optimal meal plans. For example, it suggests restaurants that are easy to access after an event has ended or restaurants with good transportation access. This makes it possible to suggest optimal meal plans that take event information and traffic conditions into consideration.

[0033] The information collection unit can compare the meal history and preferences of other users and analyze trends. For example, the information collection unit collects the meal history of other users and analyzes common preferences and trends. For example, it identifies periods when particular dishes or restaurants are popular. The information collection unit also compares the meal history of other users based on the user's preferences and analyzes trends. For example, it suggests restaurants that are frequently visited by users with the same preferences. The information collection unit also suggests the latest popular menus and restaurants based on trend analysis. For example, it prioritizes suggesting new restaurants and menus that have recently been talked about. This makes it possible to analyze trends and propose optimal meal plans.

[0034] The analysis unit can also take into account information about the restaurant's environmental sounds and atmosphere. For example, the analysis unit collects and analyzes the restaurant's environmental sounds to evaluate whether the atmosphere is quiet or lively. For example, the analysis unit analyzes audio data and makes suggestions suitable for a user who prefers quiet restaurants. The analysis unit also collects and analyzes information about the restaurant's atmosphere. For example, the analysis unit suggests restaurants with a relaxing atmosphere based on information about the interior and lighting. The analysis unit also comprehensively analyzes information about the environmental sounds and atmosphere to select a restaurant that suits the user's preferences. For example, the analysis unit suggests a quiet restaurant to a user who wants to enjoy a meal in a quiet environment. This makes it possible to propose an optimal meal plan that takes into account the restaurant's environmental sounds and atmosphere.

[0035] The analysis unit can also take into account the user's past dietary history and health data. For example, the analysis unit collects and analyzes the user's past dietary history to understand the user's preferences and dietary trends. For example, suggestions are made based on data on menus previously ordered and restaurants visited. The analysis unit also collects and analyzes the user's health data to propose health-conscious meal plans. For example, it proposes menus that take calories and nutritional balance into consideration. The analysis unit also comprehensively analyzes the user's past dietary history and health data to propose a meal plan that suits the user's preferences and health condition. For example, it proposes low-calorie menus to a user who is on a diet. This makes it possible to propose an optimal meal plan that takes the user's past dietary history and health data into consideration.

[0036] The analysis unit can cluster reviews and ratings from other users and find common trends. For example, the analysis unit collects reviews and ratings from other users and uses a clustering algorithm to analyze common trends. For example, it identifies restaurants that have high ratings for specific dishes or services. The analysis unit also suggests restaurants with common trends based on the clustering results. For example, it groups restaurants with the same rating trends and suggests them to the user. The analysis unit also uses a clustering algorithm to identify restaurants that suit the user's preferences. For example, it suggests restaurants that have been highly rated by users with the same rating trends. This makes it possible to find common trends and suggest optimal meal plans.

[0037] The analysis unit can also take into account special menus and limited-time menus offered by restaurants. For example, the analysis unit collects and analyzes information about special menus and limited-time menus offered by restaurants. For example, seasonal menus and event-specific menus are reflected in the proposals. The analysis unit also proposes an optimal meal plan for the user based on the information about the special menus and limited-time menus. For example, it prioritizes proposals of menus that are only offered in specific seasons. The analysis unit also analyzes the special menus and limited-time menus offered by restaurants to propose menus that suit the user's preferences. For example, if the user likes a particular dish, it proposes a special menu that includes that dish. This makes it possible to propose an optimal meal plan that takes special menus and limited-time menus into consideration.

[0038] The suggestion unit can also take into account the user's schedule and plans. For example, the suggestion unit collects the user's schedule and takes it into consideration when making suggestions. For example, if the user is busy, the suggestion unit suggests restaurants where a meal can be eaten in a short time. The suggestion unit also suggests optimal meal plans based on the user's schedule. For example, if the user plans to attend a specific event, the suggestion unit suggests restaurants that are easily accessible before or after the event. The suggestion unit also builds a system that takes into account the user's schedule and plans and makes suggestions. For example, the suggestion unit works in conjunction with the user's calendar app to suggest meal plans that match the plans. This makes it possible to suggest optimal meal plans that take the user's schedule and plans into consideration.

[0039] The suggestion unit can reflect the user's past feedback. For example, the suggestion unit collects the user's past feedback and reflects it when making suggestions. For example, it prioritizes suggesting restaurants and menus that the user has given high ratings to in the past. The suggestion unit also adjusts the content of the suggestions based on the user's feedback. For example, it excludes from the suggestions restaurants and menus that the user has been dissatisfied with in the past. The suggestion unit also builds a system that reflects the user's past feedback. For example, it analyzes the user's evaluation data and customizes the content of the suggestions. This makes it possible to suggest an optimal meal plan that reflects the user's past feedback.

[0040] The suggestion unit can also take into account the preferences of the user's friends and family. For example, the suggestion unit collects the preferences of the user's friends and family and takes them into consideration when making suggestions. For example, if the user is dining with friends or family, the suggestion unit suggests restaurants that match those preferences. The suggestion unit also suggests optimal meal plans based on the preferences of the user's friends and family. For example, if the user's friends or family like a particular dish, the suggestion unit suggests menus that include that dish. The suggestion unit also builds a system that takes into account the preferences of the user's friends and family and makes suggestions. For example, the suggestion unit collects data on the user's friends and family and customizes the content of the suggestions. This makes it possible to suggest optimal meal plans that take into account the preferences of the user's friends and family.

[0041] The suggestion unit can take into account the user's current location and means of transportation. For example, the suggestion unit collects the user's current location and takes it into consideration when making suggestions. For example, the suggestion unit suggests restaurants that are easily accessible from the user's current location. The suggestion unit also suggests an optimal meal plan based on the user's means of transportation. For example, if the user is traveling by car, the suggestion unit suggests restaurants with parking lots. The suggestion unit also builds a system that makes suggestions taking into account the user's current location and means of transportation. For example, the suggestion unit suggests restaurants that are easily accessible based on the user's location information. This makes it possible to suggest an optimal meal plan that takes into account the user's current location and means of transportation.

[0042] The suggestion unit can reflect real-time user feedback. For example, the suggestion unit collects real-time user feedback and reflects it when updating the suggestion. For example, if the user is dissatisfied with the suggestion, the suggestion unit adjusts the suggestion content based on that feedback. The suggestion unit also updates the suggestion content in real time based on user feedback. For example, if the user gives a high rating to the suggestion, the suggestion content is strengthened based on that rating. The suggestion unit also builds a system that reflects real-time user feedback. For example, the suggestion unit analyzes user rating data and customizes the suggestion content. This makes it possible to suggest an optimal meal plan that reflects the user's real-time feedback.

[0043] The suggestion unit can also take into account restaurant inventory and reservation status. For example, the suggestion unit collects restaurant inventory status in real time and takes it into account when updating the suggestion. For example, if a specific menu item is sold out, the suggestion unit adjusts the suggestion content based on that information. The suggestion unit also collects restaurant reservation status and takes it into account when updating the suggestion. For example, restaurants that are fully booked may be excluded from the suggestion and restaurants with available seats may be suggested preferentially. The suggestion unit also builds a system that updates the suggestion content in real time based on inventory and reservation status. For example, if inventory changes after a user receives a suggestion, the suggestion content is updated to reflect that information. This makes it possible to suggest an optimal meal plan that takes into account restaurant inventory and reservation status.

[0044] The suggestion unit can reflect the latest reviews and ratings of other users. For example, the suggestion unit collects the latest reviews and ratings of other users and reflects them when updating the suggestions. For example, it prioritizes suggesting restaurants that have recently received high ratings. The suggestion unit also updates the content of the suggestions in real time based on the latest reviews and ratings. For example, it excludes restaurants that have recently received low ratings from the suggestions. The suggestion unit also builds a system that reflects the latest reviews and ratings of other users. For example, it collects the latest rating data from review sites and customizes the content of the suggestions. This makes it possible to suggest optimal meal plans that reflect the latest reviews and ratings of other users.

[0045] The suggestion unit can take into account changes in the seasons and weather. The suggestion unit, for example, takes into account changes in the seasons and updates the suggestion. For example, it suggests cold dishes and light meals in the summer, and hot dishes and hearty menus in the winter. The suggestion unit also collects weather changes in real time and updates the suggestion. For example, it suggests indoor restaurants on rainy days, and restaurants with terrace seating on sunny days. The suggestion unit also builds a system that updates the suggestion content in real time based on changes in the seasons and weather. For example, it collects weather forecast data and adjusts the suggestion content. This makes it possible to suggest optimal meal plans that take changes in the seasons and weather into account.

[0046] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0047] The information collection unit can also propose an individually customized meal plan based on the user's past eating history and preferences. For example, it can prioritize restaurants and menus that the user has previously rated highly. The information collection unit can also propose appropriate menus taking into account the user's allergy information and dietary restrictions. For example, it can propose restaurants that offer gluten-free or vegan menus. The information collection unit can also analyze the user's eating history and propose healthy meal plans. For example, it can propose menus that take calories and nutritional balance into consideration. This makes it possible to propose the optimal meal plan according to the user's individual needs.

[0048] The suggestion unit can also propose optimal meal plans taking into account the user's schedule and plans. For example, if the user is busy, it can suggest restaurants where a meal can be eaten in a short time. The suggestion unit can also propose optimal meal plans based on the user's schedule. For example, if the user plans to attend a specific event, it can suggest restaurants that are easily accessible before or after the event. The suggestion unit can also build a system that makes suggestions taking into account the user's schedule and plans. For example, it can link with the user's calendar app to suggest meal plans that match the plans. This makes it possible to propose optimal meal plans taking into account the user's schedule and plans.

[0049] The suggestion unit can also propose an optimal meal plan by taking into account the preferences of the user's friends and family. For example, if the user is dining with friends or family, the suggestion unit can suggest a restaurant that suits their preferences. The suggestion unit can also propose an optimal meal plan based on the preferences of the user's friends and family. For example, if the user's friends or family like a particular dish, the suggestion unit can suggest a menu that includes that dish. The suggestion unit can also build a system that makes suggestions by taking into account the preferences of the user's friends and family. For example, the suggestion unit can collect data on the user's friends and family and customize the suggestions. This makes it possible to propose an optimal meal plan that takes into account the preferences of the user's friends and family.

[0050] The suggestion unit can also propose an optimal meal plan taking into account the user's current location and mode of transportation. For example, it can suggest restaurants that are easily accessible from the user's current location. The suggestion unit can also propose an optimal meal plan based on the user's mode of transportation. For example, if the user is traveling by car, it can suggest restaurants with parking lots. The suggestion unit can also build a system that makes suggestions taking into account the user's current location and mode of transportation. For example, it can suggest restaurants that are easily accessible based on the user's location information. This makes it possible to propose an optimal meal plan taking into account the user's current location and mode of transportation.

[0051] The suggestion unit can also reflect real-time user feedback. For example, the suggestion unit collects real-time user feedback and reflects it when updating the suggestion. For example, if the user is dissatisfied with the suggestion, the suggestion unit adjusts the suggestion content based on that feedback. The suggestion unit can also update the suggestion content in real time based on user feedback. For example, if the user gives a high rating to the suggestion, the suggestion content is strengthened based on that rating. The suggestion unit can also build a system that reflects real-time user feedback. For example, the suggestion unit analyzes user rating data and customizes the suggestion content. This makes it possible to suggest an optimal meal plan that reflects the user's real-time feedback.

[0052] The suggestion unit can also propose optimal meal plans by taking into account restaurant inventory and reservation status. For example, the suggestion unit can collect restaurant inventory status in real time and take it into consideration when updating the proposal. For example, if a specific menu item is sold out, the suggestion unit can adjust the suggestion content based on that information. The suggestion unit can also collect restaurant reservation status and take it into consideration when updating the suggestion. For example, restaurants that are fully booked can be excluded from the suggestions, and restaurants with available seats can be suggested first. The suggestion unit can also build a system that updates the suggestion content in real time based on inventory and reservation status. For example, if inventory changes after a user receives a suggestion, the suggestion content can be updated to reflect that information. This makes it possible to propose optimal meal plans that take into account restaurant inventory and reservation status.

[0053] The suggestion unit can also reflect the latest reviews and ratings of other users. For example, the latest reviews and ratings of other users are collected and reflected when updating the suggestions. For example, restaurants that have recently received high ratings are given priority in the suggestions. The suggestion unit can also update the suggestions in real time based on the latest reviews and ratings. For example, restaurants that have recently received low ratings are excluded from the suggestions. The suggestion unit can also build a system that reflects the latest reviews and ratings of other users. For example, the latest rating data is collected from review sites and the suggestions are customized. This makes it possible to suggest optimal meal plans that reflect the latest reviews and ratings of other users.

[0054] The processing flow of the first embodiment will be briefly explained below.

[0055] Step 1: The information gathering unit collects information such as restaurant menus, prices, coupons, and reviews on the Internet. For example, the information gathering unit obtains menu information from the restaurant's official website, collects discount information from coupon sites, and collects user ratings from review sites. Step 2: The analysis unit analyzes the information collected by the information collection unit and calculates the best value meal for that day near the user's home. For example, the analysis unit comprehensively considers menu prices, coupon discount rates, and review ratings to suggest the most cost-effective meal. The analysis unit also calculates a meal plan taking into account the user's preferences and conditions. Step 3: The suggestion unit proposes the optimal meal plan calculated by the analysis unit to the user. For example, the suggestion unit notifies the user of the proposal via a smartphone app or website and automatically updates the proposal content.

[0056] (Example 2) The meal recommendation system according to an embodiment of the present invention is a system that collects and analyzes information on the Internet and recommends the best value meal of the day near the user's home, thereby enabling the user to easily find the best value meal of the day near their home.

[0057] A meal suggestion system according to an embodiment includes an information collection unit, an analysis unit, and a suggestion unit. The information collection unit collects information such as restaurant menus, prices, coupons, and reviews on the Internet. For example, the information collection unit acquires menu information from the restaurant's official website. The information collection unit can also collect discount information from coupon sites. The information collection unit can also collect user ratings from review sites. For example, the information collection unit acquires the latest menu information from the restaurant's official website, collects discount coupons from coupon sites, and collects user ratings from review sites. The analysis unit analyzes the information collected by the information collection unit and calculates the best meal deal for that day near the user's home. For example, the analysis unit comprehensively considers menu prices, coupon discount rates, and review ratings to suggest the most cost-effective meal. The analysis unit can also calculate a meal plan taking into account the user's preferences and conditions. The analysis unit calculates an optimal meal plan based on the collected information. For example, the analysis unit comprehensively considers menu prices, coupon discount rates, and review ratings to calculate an optimal meal plan taking into account the user's preferences and conditions. The suggestion unit suggests the optimal meal plan calculated by the analysis unit to the user. For example, the suggestion unit notifies the user of the suggestion through a smartphone app. The suggestion unit can also notify the user of the suggestion through a website. The suggestion unit can also automatically update the suggestion content. For example, the suggestion unit notifies the user of the suggestion through a smartphone app, notifies the user of the suggestion through a website, and automatically updates the suggestion content. In this way, the meal suggestion system according to the embodiment allows the user to easily find the best value meal of the day near their home. For example, the user can receive suggestions through a smartphone app and a website, and the suggestion content is automatically updated, allowing the user to always find the optimal meal plan based on the latest information.

[0058] The information collection unit can also collect social media posts from restaurants or real-time user feedback. For example, the generation AI collects posts from official social media accounts of restaurants to obtain information about menu changes and special events. For example, it analyzes posts on Facebook and Instagram to collect the latest menus and discount information. The information collection unit also analyzes posts on review sites and forums to collect real-time user feedback. For example, it collects the latest reviews on Yelp and TripAdvisor to reflect restaurant ratings and user opinions. The information collection unit also analyzes hashtags on social media to collect trending information about specific restaurants and menus. For example, it analyzes posts with hashtags like #lunch and #dinner to identify popular menus and restaurants. This allows the system to recommend optimal meal plans based on the latest information.

[0059] The information collection unit can compare the data with the restaurant's past sales data and seasonal menu change history. For example, the information collection unit collects the restaurant's past sales data and analyzes seasonal sales trends. For example, it identifies menu items that are popular in the summer and menu items that sell well in the winter. The information collection unit also collects the restaurant's menu change history and analyzes seasonal menu fluctuations. For example, it identifies the tendency for new menu items to be added in the spring and the tendency for specific menu items to be removed in the fall. The information collection unit also compares the sales data with the menu change history to identify menu items that are good value in specific seasons. For example, it can suggest menu items that offer many discounts in specific seasons based on past data. This makes it possible to propose optimal meal plans based on past data.

[0060] The information collection unit can use the emotion estimation function to select a restaurant based on the user's emotions. The information collection unit, for example, analyzes the user's past reviews and feedback and calculates an emotion score. For example, it prioritizes suggesting restaurants with a high percentage of positive emotions. The information collection unit also analyzes the user's real-time emotional state and selects a restaurant based on the emotion. For example, if the user is feeling stressed, it suggests a restaurant with a relaxing atmosphere. The information collection unit also uses the emotion estimation function to suggest a menu that matches the user's emotions. For example, if the user is feeling happy, it suggests a special dessert or a celebratory menu. This makes it possible to suggest the optimal restaurant based on the user's emotions.

[0061] The information gathering unit can also collect information about nearby events or traffic conditions and reflect this in meal suggestions. The information gathering unit, for example, collects information about nearby events and predicts how crowded restaurants will be. For example, based on information about upcoming concerts or sporting events, the information gathering unit makes suggestions to avoid restaurants that are expected to be crowded. The information gathering unit also collects traffic conditions in real time and suggests restaurants that are easy to access. For example, it analyzes traffic congestion information and the operation status of public transportation to select restaurants that are easily accessible. The information gathering unit also combines event information and traffic conditions to suggest optimal meal plans. For example, it suggests restaurants that are easy to access after an event has ended or restaurants with good transportation access. This makes it possible to suggest optimal meal plans that take event information and traffic conditions into consideration.

[0062] The information collection unit can compare the meal history and preferences of other users and analyze trends. For example, the information collection unit collects the meal history of other users and analyzes common preferences and trends. For example, it identifies periods when particular dishes or restaurants are popular. The information collection unit also compares the meal history of other users based on the user's preferences and analyzes trends. For example, it suggests restaurants that are frequently visited by users with the same preferences. The information collection unit also suggests the latest popular menus and restaurants based on trend analysis. For example, it prioritizes suggesting new restaurants and menus that have recently been talked about. This makes it possible to analyze trends and propose optimal meal plans.

[0063] The information collection unit can estimate the user's emotions in real time when they are entering text and make suggestions that will elicit positive emotions. The information collection unit, for example, analyzes the user's facial expressions and voice when they are entering text and estimates the emotions in real time. For example, it analyzes the user's emotions using a camera or microphone and makes positive suggestions when it detects negative emotions. The information collection unit also uses the emotion estimation function to provide an interface that elicits positive emotions when the user is entering text. For example, it presents encouraging messages and success stories. The information collection unit also provides feedback in real time based on the emotion estimation data when the user is entering text and offers advice that will strengthen positive emotions. For example, it displays appropriate encouragement or praise based on the input content. This makes it possible to make positive suggestions based on the user's emotions.

[0064] The analysis unit can also take into account information about the restaurant's environmental sounds and atmosphere. For example, the analysis unit collects and analyzes the restaurant's environmental sounds to evaluate whether the atmosphere is quiet or lively. For example, the analysis unit analyzes audio data and makes suggestions suitable for a user who prefers quiet restaurants. The analysis unit also collects and analyzes information about the restaurant's atmosphere. For example, the analysis unit suggests restaurants with a relaxing atmosphere based on information about the interior and lighting. The analysis unit also comprehensively analyzes information about the environmental sounds and atmosphere to select a restaurant that suits the user's preferences. For example, the analysis unit suggests a quiet restaurant to a user who wants to enjoy a meal in a quiet environment. This makes it possible to propose an optimal meal plan that takes into account the restaurant's environmental sounds and atmosphere.

[0065] The analysis unit can also take into account the user's past dietary history and health data. For example, the analysis unit collects and analyzes the user's past dietary history to understand the user's preferences and dietary trends. For example, suggestions are made based on data on menus previously ordered and restaurants visited. The analysis unit also collects and analyzes the user's health data to propose health-conscious meal plans. For example, it proposes menus that take calories and nutritional balance into consideration. The analysis unit also comprehensively analyzes the user's past dietary history and health data to propose a meal plan that suits the user's preferences and health condition. For example, it proposes low-calorie menus to a user who is on a diet. This makes it possible to propose an optimal meal plan that takes the user's past dietary history and health data into consideration.

[0066] The analysis unit can use the emotion estimation function to propose an optimal meal plan based on the user's emotions. The analysis unit, for example, analyzes the user's emotional state in real time and proposes an optimal meal plan based on the emotions. For example, if the user is feeling stressed, it proposes a menu that will help the user relax. The analysis unit also uses the emotion estimation function to select a restaurant that matches the user's emotions. For example, if the user is feeling happy, it proposes a special dessert or celebratory menu. The analysis unit also proposes a meal plan that matches the user's emotions based on the user's emotion data. For example, if the user is tired, it proposes a menu that will replenish energy. In this way, it is possible to propose an optimal meal plan based on the user's emotions.

[0067] The analysis unit can cluster reviews and ratings from other users and find common trends. For example, the analysis unit collects reviews and ratings from other users and uses a clustering algorithm to analyze common trends. For example, it identifies restaurants that have high ratings for specific dishes or services. The analysis unit also suggests restaurants with common trends based on the clustering results. For example, it groups restaurants with the same rating trends and suggests them to the user. The analysis unit also uses a clustering algorithm to identify restaurants that suit the user's preferences. For example, it suggests restaurants that have been highly rated by users with the same rating trends. This makes it possible to find common trends and suggest optimal meal plans.

[0068] The analysis unit can also take into account special menus and limited-time menus offered by restaurants. For example, the analysis unit collects and analyzes information about special menus and limited-time menus offered by restaurants. For example, seasonal menus and event-specific menus are reflected in the proposals. The analysis unit also proposes an optimal meal plan for the user based on the information about the special menus and limited-time menus. For example, it prioritizes proposals of menus that are only offered in specific seasons. The analysis unit also analyzes the special menus and limited-time menus offered by restaurants to propose menus that suit the user's preferences. For example, if the user likes a particular dish, it proposes a special menu that includes that dish. This makes it possible to propose an optimal meal plan that takes special menus and limited-time menus into consideration.

[0069] The analysis unit can use the emotion estimation function to suggest variations in meal plans based on the user's emotions. The analysis unit, for example, analyzes the user's emotional state in real time and suggests multiple meal plans based on the emotions. For example, if the user is feeling stressed, the analysis unit suggests multiple relaxing menus. The analysis unit also uses the emotion estimation function to select multiple restaurants that match the user's emotions. For example, if the user is feeling happy, the analysis unit suggests multiple special desserts or celebratory menus. The analysis unit also suggests variations in meal plans that match the user's emotions based on the user's emotion data. For example, if the user is tired, the analysis unit suggests multiple energy-replenishing menus. In this way, multiple meal plans can be suggested based on the user's emotions.

[0070] The suggestion unit can also take into account the user's schedule and plans. For example, the suggestion unit collects the user's schedule and takes it into consideration when making suggestions. For example, if the user is busy, the suggestion unit suggests restaurants where a meal can be eaten in a short time. The suggestion unit also suggests optimal meal plans based on the user's schedule. For example, if the user plans to attend a specific event, the suggestion unit suggests restaurants that are easily accessible before or after the event. The suggestion unit also builds a system that takes into account the user's schedule and plans and makes suggestions. For example, the suggestion unit works in conjunction with the user's calendar app to suggest meal plans that match the plans. This makes it possible to suggest optimal meal plans that take the user's schedule and plans into consideration.

[0071] The suggestion unit can reflect the user's past feedback. For example, the suggestion unit collects the user's past feedback and reflects it when making suggestions. For example, it prioritizes suggesting restaurants and menus that the user has given high ratings to in the past. The suggestion unit also adjusts the content of the suggestions based on the user's feedback. For example, it excludes from the suggestions restaurants and menus that the user has been dissatisfied with in the past. The suggestion unit also builds a system that reflects the user's past feedback. For example, it analyzes the user's evaluation data and customizes the content of the suggestions. This makes it possible to suggest an optimal meal plan that reflects the user's past feedback.

[0072] The suggestion unit can use the emotion estimation function to select a method for presenting suggestions based on the user's emotions. The suggestion unit, for example, analyzes the user's emotional state in real time and selects an optimal method for presenting suggestions based on the emotions. For example, if the user is feeling stressed, the suggestion unit selects a method for presenting suggestions that will help the user relax. The suggestion unit also uses the emotion estimation function to select a method for presenting suggestions that matches the user's emotions. For example, if the user is feeling happy, the suggestion unit suggests a special dessert or a celebratory menu. The suggestion unit also selects a method for presenting suggestions that matches the user's emotions based on the user's emotion data. For example, if the user is tired, the suggestion unit suggests a menu that will replenish energy. In this way, the optimal method for presenting suggestions can be selected based on the user's emotions.

[0073] The suggestion unit can also take into account the preferences of the user's friends and family. For example, the suggestion unit collects the preferences of the user's friends and family and takes them into consideration when making suggestions. For example, if the user is dining with friends or family, the suggestion unit suggests restaurants that match those preferences. The suggestion unit also suggests optimal meal plans based on the preferences of the user's friends and family. For example, if the user's friends or family like a particular dish, the suggestion unit suggests menus that include that dish. The suggestion unit also builds a system that takes into account the preferences of the user's friends and family and makes suggestions. For example, the suggestion unit collects data on the user's friends and family and customizes the content of the suggestions. This makes it possible to suggest optimal meal plans that take into account the preferences of the user's friends and family.

[0074] The suggestion unit can take into account the user's current location and means of transportation. For example, the suggestion unit collects the user's current location and takes it into consideration when making suggestions. For example, the suggestion unit suggests restaurants that are easily accessible from the user's current location. The suggestion unit also suggests an optimal meal plan based on the user's means of transportation. For example, if the user is traveling by car, the suggestion unit suggests restaurants with parking lots. The suggestion unit also builds a system that makes suggestions taking into account the user's current location and means of transportation. For example, the suggestion unit suggests restaurants that are easily accessible based on the user's location information. This makes it possible to suggest an optimal meal plan that takes into account the user's current location and means of transportation.

[0075] The suggestion unit can adjust the timing of suggestions based on the user's emotions using the emotion estimation function. The suggestion unit, for example, analyzes the user's emotional state in real time and adjusts the optimal timing of suggestions based on the emotions. For example, if the user is feeling stressed, the suggestion unit makes suggestions at a time when the user can relax. The suggestion unit also uses the emotion estimation function to adjust the timing of suggestions to match the user's emotions. For example, if the user is feeling happy, the suggestion unit suggests a special dessert or a celebratory menu. The suggestion unit also adjusts the timing of suggestions to match the user's emotions based on the user's emotion data. For example, if the user is tired, the suggestion unit suggests a menu that will replenish energy. In this way, the optimal timing of suggestions can be adjusted based on the user's emotions.

[0076] The suggestion unit can reflect real-time user feedback. For example, the suggestion unit collects real-time user feedback and reflects it when updating the suggestion. For example, if the user is dissatisfied with the suggestion, the suggestion unit adjusts the suggestion content based on that feedback. The suggestion unit also updates the suggestion content in real time based on user feedback. For example, if the user gives a high rating to the suggestion, the suggestion content is strengthened based on that rating. The suggestion unit also builds a system that reflects real-time user feedback. For example, the suggestion unit analyzes user rating data and customizes the suggestion content. This makes it possible to suggest an optimal meal plan that reflects the user's real-time feedback.

[0077] The suggestion unit can also take into account restaurant inventory and reservation status. For example, the suggestion unit collects restaurant inventory status in real time and takes it into account when updating the suggestion. For example, if a specific menu item is sold out, the suggestion unit adjusts the suggestion content based on that information. The suggestion unit also collects restaurant reservation status and takes it into account when updating the suggestion. For example, restaurants that are fully booked may be excluded from the suggestion and restaurants with available seats may be suggested preferentially. The suggestion unit also builds a system that updates the suggestion content in real time based on inventory and reservation status. For example, if inventory changes after a user receives a suggestion, the suggestion content is updated to reflect that information. This makes it possible to suggest an optimal meal plan that takes into account restaurant inventory and reservation status.

[0078] The suggestion unit can adjust the update frequency of suggestions based on the user's emotions using the emotion estimation function. The suggestion unit, for example, analyzes the user's emotional state in real time and adjusts the update frequency of suggestions based on the emotions. For example, if the user is feeling stressed, suggestions are not made frequently. The suggestion unit also uses the emotion estimation function to adjust the update frequency of suggestions to match the user's emotions. For example, if the user is feeling happy, new suggestions are made frequently. The suggestion unit also builds a system that adjusts the update frequency of suggestions to match the user's emotions based on the user's emotion data. For example, if the user is tired, the frequency of suggestions is reduced. This makes it possible to adjust the update frequency of suggestions to an optimal level based on the user's emotions.

[0079] The suggestion unit can reflect the latest reviews and ratings of other users. For example, the suggestion unit collects the latest reviews and ratings of other users and reflects them when updating the suggestions. For example, it prioritizes suggesting restaurants that have recently received high ratings. The suggestion unit also updates the content of the suggestions in real time based on the latest reviews and ratings. For example, it excludes restaurants that have recently received low ratings from the suggestions. The suggestion unit also builds a system that reflects the latest reviews and ratings of other users. For example, it collects the latest rating data from review sites and customizes the content of the suggestions. This makes it possible to suggest optimal meal plans that reflect the latest reviews and ratings of other users.

[0080] The suggestion unit can take into account changes in the seasons and weather. The suggestion unit, for example, takes into account changes in the seasons and updates the suggestion. For example, it suggests cold dishes and light meals in the summer, and hot dishes and hearty menus in the winter. The suggestion unit also collects weather changes in real time and updates the suggestion. For example, it suggests indoor restaurants on rainy days, and restaurants with terrace seating on sunny days. The suggestion unit also builds a system that updates the suggestion content in real time based on changes in the seasons and weather. For example, it collects weather forecast data and adjusts the suggestion content. This makes it possible to suggest optimal meal plans that take changes in the seasons and weather into account.

[0081] The suggestion unit can use the emotion estimation function to adjust the updated content of the suggestions based on the user's emotions. For example, the suggestion unit analyzes the user's emotional state in real time and adjusts the updated content of the suggestions based on the emotions. For example, if the user is feeling stressed, the suggestion unit suggests a menu that will help the user relax. The suggestion unit also uses the emotion estimation function to adjust the updated content of the suggestions to match the user's emotions. For example, if the user is feeling happy, the suggestion unit suggests a special dessert or a celebratory menu. The suggestion unit also builds a system that adjusts the updated content of the suggestions to match the user's emotions based on the user's emotion data. For example, if the user is tired, the suggestion unit suggests a menu that will replenish energy. This makes it possible to adjust the updated content of the suggestions optimally based on the user's emotions.

[0082] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0083] The information collection unit can also propose an individually customized meal plan based on the user's past eating history and preferences. For example, it can prioritize restaurants and menus that the user has previously rated highly. The information collection unit can also propose appropriate menus taking into account the user's allergy information and dietary restrictions. For example, it can propose restaurants that offer gluten-free or vegan menus. The information collection unit can also analyze the user's eating history and propose healthy meal plans. For example, it can propose menus that take calories and nutritional balance into consideration. This makes it possible to propose the optimal meal plan according to the user's individual needs.

[0084] The analysis unit can also analyze the user's emotional state in real time and suggest an optimal meal plan based on that emotion. For example, if the user is feeling stressed, it can suggest a menu that will help them relax. The analysis unit can also use the emotion estimation function to select a restaurant that matches the user's emotions. For example, if the user is feeling happy, it can suggest a special dessert or celebratory menu. The analysis unit can also suggest a meal plan that matches the user's emotions based on the user's emotional data. For example, if the user is tired, it can suggest a menu that will replenish energy. In this way, it is possible to suggest an optimal meal plan based on the user's emotions.

[0085] The suggestion unit can also propose optimal meal plans taking into account the user's schedule and plans. For example, if the user is busy, it can suggest restaurants where a meal can be eaten in a short time. The suggestion unit can also propose optimal meal plans based on the user's schedule. For example, if the user plans to attend a specific event, it can suggest restaurants that are easily accessible before or after the event. The suggestion unit can also build a system that makes suggestions taking into account the user's schedule and plans. For example, it can link with the user's calendar app to suggest meal plans that match the plans. This makes it possible to propose optimal meal plans taking into account the user's schedule and plans.

[0086] The suggestion unit can also propose an optimal meal plan by taking into account the preferences of the user's friends and family. For example, if the user is dining with friends or family, the suggestion unit can suggest a restaurant that suits their preferences. The suggestion unit can also propose an optimal meal plan based on the preferences of the user's friends and family. For example, if the user's friends or family like a particular dish, the suggestion unit can suggest a menu that includes that dish. The suggestion unit can also build a system that makes suggestions by taking into account the preferences of the user's friends and family. For example, the suggestion unit can collect data on the user's friends and family and customize the suggestions. This makes it possible to propose an optimal meal plan that takes into account the preferences of the user's friends and family.

[0087] The suggestion unit can also propose an optimal meal plan taking into account the user's current location and mode of transportation. For example, it can suggest restaurants that are easily accessible from the user's current location. The suggestion unit can also propose an optimal meal plan based on the user's mode of transportation. For example, if the user is traveling by car, it can suggest restaurants with parking lots. The suggestion unit can also build a system that makes suggestions taking into account the user's current location and mode of transportation. For example, it can suggest restaurants that are easily accessible based on the user's location information. This makes it possible to propose an optimal meal plan taking into account the user's current location and mode of transportation.

[0088] The suggestion unit can also use the emotion estimation function to adjust the timing of suggestions based on the user's emotions. For example, the suggestion unit analyzes the user's emotional state in real time and adjusts the optimal timing of suggestions based on the emotions. For example, if the user is feeling stressed, the suggestion unit makes suggestions at a time when the user can relax. The suggestion unit can also use the emotion estimation function to adjust the timing of suggestions to match the user's emotions. For example, if the user is feeling happy, the suggestion unit can suggest a special dessert or a celebratory menu. The suggestion unit can also adjust the timing of suggestions to match the user's emotions based on the user's emotion data. For example, if the user is tired, the suggestion unit can suggest a menu that will replenish energy. In this way, the optimal timing of suggestions can be adjusted based on the user's emotions.

[0089] The suggestion unit can also reflect real-time user feedback. For example, the suggestion unit collects real-time user feedback and reflects it when updating the suggestion. For example, if the user is dissatisfied with the suggestion, the suggestion unit adjusts the suggestion content based on that feedback. The suggestion unit can also update the suggestion content in real time based on user feedback. For example, if the user gives a high rating to the suggestion, the suggestion content is strengthened based on that rating. The suggestion unit can also build a system that reflects real-time user feedback. For example, the suggestion unit analyzes user rating data and customizes the suggestion content. This makes it possible to suggest an optimal meal plan that reflects the user's real-time feedback.

[0090] The suggestion unit can also propose optimal meal plans by taking into account restaurant inventory and reservation status. For example, the suggestion unit can collect restaurant inventory status in real time and take it into consideration when updating the proposal. For example, if a specific menu item is sold out, the suggestion unit can adjust the suggestion content based on that information. The suggestion unit can also collect restaurant reservation status and take it into consideration when updating the suggestion. For example, restaurants that are fully booked can be excluded from the suggestions, and restaurants with available seats can be suggested first. The suggestion unit can also build a system that updates the suggestion content in real time based on inventory and reservation status. For example, if inventory changes after a user receives a suggestion, the suggestion content can be updated to reflect that information. This makes it possible to propose optimal meal plans that take into account restaurant inventory and reservation status.

[0091] The suggestion unit can also use the emotion estimation function to adjust the update frequency of suggestions based on the user's emotions. For example, the suggestion unit can analyze the user's emotional state in real time and adjust the update frequency of suggestions based on the emotions. For example, if the user is feeling stressed, suggestions can be made less frequently. The suggestion unit can also use the emotion estimation function to adjust the update frequency of suggestions to match the user's emotions. For example, if the user is feeling happy, new suggestions can be made more frequently. The suggestion unit can also build a system that adjusts the update frequency of suggestions to match the user's emotions based on the user's emotion data. For example, if the user is tired, the frequency of suggestions can be reduced. This makes it possible to adjust the update frequency of suggestions to an optimal level based on the user's emotions.

[0092] The suggestion unit can also reflect the latest reviews and ratings of other users. For example, the latest reviews and ratings of other users are collected and reflected when updating the suggestions. For example, restaurants that have recently received high ratings are given priority in the suggestions. The suggestion unit can also update the suggestions in real time based on the latest reviews and ratings. For example, restaurants that have recently received low ratings are excluded from the suggestions. The suggestion unit can also build a system that reflects the latest reviews and ratings of other users. For example, the latest rating data is collected from review sites and the suggestions are customized. This makes it possible to suggest optimal meal plans that reflect the latest reviews and ratings of other users.

[0093] The processing flow of the second embodiment will be briefly explained below.

[0094] Step 1: The information gathering unit collects information such as restaurant menus, prices, coupons, and reviews on the Internet. For example, the information gathering unit obtains menu information from the restaurant's official website, collects discount information from coupon sites, and collects user ratings from review sites. Step 2: The analysis unit analyzes the information collected by the information collection unit and calculates the best value meal for that day near the user's home. For example, the analysis unit comprehensively considers menu prices, coupon discount rates, and review ratings to suggest the most cost-effective meal. The analysis unit also calculates a meal plan taking into account the user's preferences and conditions. Step 3: The suggestion unit proposes the optimal meal plan calculated by the analysis unit to the user. For example, the suggestion unit notifies the user of the proposal via a smartphone app or website and automatically updates the proposal content.

[0095] 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.

[0096] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (Internet Search<URL: https: / / openai.com / blog / chatgpt> Examples of generative AIs include the data generation model 58, such as a neural network model (e.g., a neural network model), and a neural network model (e.g., a neural network model). The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating speech, text data indicating text, and image data indicating an image is also input to the data generation model 58. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specification processing unit 290 performs the above-mentioned specification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0097] 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.

[0098] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0099] 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.

[0100] 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.

[0101] 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.

[0102] 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.

[0103] 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).

[0104] 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.

[0105] 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.

[0106] 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.

[0107] 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.

[0108] 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.

[0109] 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.

[0110] 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.

[0111] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0112] 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.

[0113] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0114] 5, the data processing system 310 includes the data processing device 12 and a headset type terminal 314. An example of the data processing device 12 is a server.

[0115] 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.

[0116] 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.

[0117] 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.

[0118] 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).

[0119] 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.

[0120] 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.

[0121] 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.

[0122] 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.

[0123] In the headset type terminal 314, the specific processing is performed by the processor 46. A specific processing program 60 is stored in the storage 50. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. Note that the headset type terminal 314 has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can also perform processing similar to that of the specific processing unit 290 using these models.

[0124] 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.

[0125] 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.

[0126] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0127] 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.

[0128] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0129] 7, the data processing system 410 includes a data processing device 12 and a robot 414. An example of the data processing device 12 is a server.

[0130] 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.

[0131] 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.

[0132] 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.

[0133] 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).

[0134] 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.

[0135] 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.

[0136] 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.

[0137] 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.

[0138] 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.

[0139] In the robot 414, the specific processing is performed by the processor 46. A specific processing program 60 is stored in the storage 50. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. The robot 414 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 processing similar to that of the specific processing unit 290 using these models.

[0140] 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.

[0141] 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.

[0142] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0143] 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.

[0144] 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.

[0145] 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.

[0146] 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.

[0147] 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).

[0148] 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.

[0149] 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."

[0150] 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.

[0151] 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.

[0152] 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.

[0153] 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.

[0154] 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.

[0155] 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.

[0156] 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.

[0157] 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.

[0158] 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.

[0159] 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.

[0160] 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.

[0161] All publications, patent applications, and technical standards mentioned in this specification are herein incorporated by reference to the same extent as if each individual publication, patent application, or technical standard was specifically and individually indicated to be incorporated by reference. [Explanation of symbols]

[0162] 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. an information gathering department that collects information such as restaurant menus, prices, coupons, and reviews on the Internet; an analysis unit that analyzes the information collected by the information collection unit and calculates the best value meal for that day near the user's home; a suggestion unit that suggests the optimal meal plan calculated by the analysis unit to the user. A system characterized by:

2. The information collecting unit Also collect restaurant social media posts or real-time user feedback 2. The system of claim 1.

3. The information collecting unit Match historical restaurant sales data and seasonal menu changes 2. The system of claim 1.

4. The information collecting unit Selecting restaurants based on user sentiment 2. The system of claim 1.

5. The information collecting unit Information about nearby events or traffic conditions is also collected and reflected in meal suggestions.

2. The system of claim 1.

6. The information collecting unit Compare your diet history and preferences with other users to analyze trends 2. The system of claim 1.

7. The information collecting unit Estimates the user's emotions in real time as they type, and makes suggestions that elicit positive emotions 2. The system of claim 1.

8. The analysis unit Consider information about the restaurant's ambient sounds and atmosphere 2. The system of claim 1.

Images