System

Abstract

An object of a system according to an embodiment is to efficiently propose a vacant seat status, a menu, and an amount of money of a nearby store on the basis of content desired by a user.SOLUTION: A system includes a desired content input unit, an analysis unit, a current location specification unit, a vacant seat confirmation unit, a sales confirmation unit, a food ingredient stock confirmation unit, a proposal generation unit, and a notification unit. The desired content input unit inputs desired content of the user. The analysis unit analyzes the desired content input by the desired content input unit. The current location identification unit identifies a current location of a user. The vacant seat confirmation unit confirms a vacant seat status of the nearby store on the basis of the current location specified by the current location specification unit. The sales confirmation unit confirms the sales achievement status of the store. The ingredient stock confirmation part confirms the expiration date of the ingredient stock of the store. A proposal generation part proposes a proper amount and menu on the basis of information obtained by the vacant seat confirmation part, the sales confirmation part and the food stock confirmation part. The notification unit notifies the user of the proposal content generated by the proposal generation unit.SELECTED DRAWING: Figure 1

Description

[Technical Field]

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

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

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

[0004] Conventional technology has had the problem that it takes time for users to find a store that meets their desired conditions, making it difficult for them to make an efficient selection.

[0005] The system according to the embodiment aims to efficiently suggest the availability of seats, menus, and prices of nearby restaurants based on the user's preferences. [Means for solving the problem]

[0006] The system according to the embodiment comprises a desired content input unit, an analysis unit, a current location determination unit, a seat availability confirmation unit, a sales confirmation unit, a food ingredient inventory confirmation unit, a proposal generation unit, and a notification unit. The desired content input unit inputs the user's desired content. The analysis unit analyzes the desired content input by the desired content input unit. The current location determination unit determines the user's current location. The seat availability confirmation unit checks the seat availability of nearby restaurants based on the current location determined by the current location determination unit. The sales confirmation unit checks the restaurant's sales achievement status. The food ingredient inventory confirmation unit checks the expiration date of the restaurant's food ingredient inventory. The proposal generation unit proposes an appropriate price and menu based on the information obtained by the seat availability confirmation unit, sales confirmation unit, and food ingredient inventory confirmation unit. The notification unit notifies the user of the proposal generated by the proposal generation unit. [Effects of the Invention]

[0007] The system according to the embodiment can efficiently suggest the availability of seats, menus, and prices of nearby restaurants based on the user's preferences. [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 the user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

[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 may have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59.

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

[0028] (Example 1) A proposal system according to an embodiment of the present invention allows a user to input their desired details and receive suggestions for seating availability, menus, and prices for after-parties from restaurants near their current location. This system utilizes a generation AI to automatically suggest appropriate prices and menus based on factors such as restaurant seating availability, sales achievement, and expiration dates for food stock. This allows the proposal system to suggest appropriate prices and menus based on the user's wishes, taking into account factors such as seating availability, sales achievement, and expiration dates for food stock at nearby restaurants.

[0029] The proposal system according to the embodiment includes a desired content input unit, an analysis unit, a current location identification unit, a seat availability confirmation unit, a sales confirmation unit, a food ingredient inventory confirmation unit, a proposal generation unit, and a notification unit. The desired content input unit inputs the user's desired content. For example, the user inputs specific requests such as "I want to eat Japanese food," "My budget is under 3,000 yen," and "I'd like to use the restaurant from 8 p.m." The analysis unit analyzes the desired content input by the desired content input unit. For example, a generation AI analyzes the user's desired content and makes appropriate suggestions. The current location identification unit identifies the user's current location. For example, the current location is identified using GPS data. The seat availability confirmation unit checks the seat availability of nearby restaurants based on the current location identified by the current location identification unit. For example, the current location information is collected in real time. The sales confirmation unit checks the restaurant's sales achievement status. For example, the sales confirmation unit grasps the current achievement status of the restaurant's sales target. The food ingredient inventory confirmation unit checks the expiration date of the restaurant's food ingredient inventory. For example, the food ingredient inventory confirmation unit suggests a menu that prioritizes the use-by date of ingredients close to their expiration date. The proposal generation unit proposes an appropriate price and menu based on information obtained by the seat availability confirmation unit, sales confirmation unit, and ingredient inventory confirmation unit. For example, the proposal may be in the form of, "Today's recommended menu is a specially priced Japanese meal set using ingredients with an approaching expiration date. The price is 2,500 yen." The notification unit notifies the user of the proposal content generated by the proposal generation unit. For example, the proposal content may be notified to the user's smartphone so that the user can check it. This enables the proposal system according to the embodiment to propose an appropriate price and menu based on the user's wishes, taking into consideration the seat availability, sales achievement status, and expiration date of ingredient inventory at nearby restaurants.

[0030] The desired content input unit analyzes the user's past usage history, learns preferences and tendencies, and can make more personalized suggestions. The desired content input unit, for example, analyzes the user's past usage history and learns preferences and tendencies. For example, if a user has previously preferred Japanese food, it will preferentially suggest Japanese food. The desired content input unit can also suggest menus with a high repeat rate based on the user's past ordering history. For example, it will suggest menus that have been ordered many times in the past. This makes it possible to make more personalized suggestions based on the user's past usage history.

[0031] The desired content input section allows the generation AI to ask questions in real time in response to the user's input, further specifying the desired content. For example, if the user inputs "I want to eat Japanese food," the generation AI will ask "What kind of Japanese food would you like specifically?" to elicit more specific requests. Also, if the user inputs "My budget is under 3,000 yen," the desired content input section can ask "Does that budget include drinks?" to confirm more detailed requests. Furthermore, if the user inputs "I'd like to start at 8 p.m.", the generation AI can ask "How many people will be coming?" to confirm the number of people. This allows the user's desired content to be further specified, enabling more appropriate suggestions.

[0032] The desired content input unit can analyze the user's voice input and understand the desired content using voice recognition technology. For example, if the user vocally inputs "I want to eat Japanese food," the desired content input unit converts it into text using voice recognition technology and analyzes the desired content. In addition, if the user vocally inputs "My budget is 3,000 yen or less," the desired content input unit can also convert it into text using voice recognition technology and analyze the desired content. Furthermore, if the user vocally inputs "I would like to use it from 8 p.m.", the desired content input unit can also convert it into text using voice recognition technology and analyze the desired content. This makes it possible to analyze the user's voice input and understand the desired content.

[0033] The desired content input unit can analyze the content of the user's social media posts and make suggestions that reflect the user's current mood and interests. The desired content input unit, for example, analyzes the content of the user's social media posts and identifies the user's current mood and interests from recent posts. For example, if there have been many recent posts about Japanese food, the desired content input unit can suggest Japanese food. The desired content input unit can also analyze the content of the user's social media posts and identify the user's current mood and interests from recent posts. For example, if there have been many recent posts about cafes, the desired content input unit can suggest cafes. The desired content input unit can also analyze the content of the user's social media posts and identify the user's current mood and interests from recent posts. For example, if there have been many recent posts about events, the desired content input unit can suggest events. This makes it possible to analyze the content of the user's social media posts and make suggestions that reflect the user's current mood and interests.

[0034] The vacancy confirmation unit can analyze the congestion level of surrounding stores in real time and preferentially suggest stores that offer a comfortable environment. The vacancy confirmation unit, for example, analyzes the congestion level of surrounding stores in real time and preferentially suggests stores that are not crowded. For example, it lists stores that are less crowded and suggests them to the user. The vacancy confirmation unit can also analyze the congestion level of surrounding stores in real time and preferentially suggest stores that offer a comfortable environment. For example, it suggests stores that offer a comfortable environment taking into consideration the seating arrangement and the atmosphere inside the store. Furthermore, the vacancy confirmation unit can analyze the congestion level of surrounding stores in real time and make suggestions according to the user's preferences. For example, if a user prefers a quiet environment, it suggests stores that are less crowded. This makes it possible to analyze the congestion level of surrounding stores in real time and preferentially suggest stores that offer a comfortable environment.

[0035] The vacancy confirmation unit can suggest restaurants with good accessibility by taking into account the user's means of transportation and traffic conditions. The vacancy confirmation unit, for example, suggests restaurants with good accessibility by taking into account the user's means of transportation. For example, it suggests nearby restaurants for a user who travels by foot, and suggests restaurants with parking for a user who travels by car. The vacancy confirmation unit can also suggest restaurants with good accessibility by taking into account traffic conditions. For example, it suggests restaurants with easy accessibility by selecting a route with less traffic congestion. Furthermore, the vacancy confirmation unit can also suggest an optimal route by taking into account the user's means of transportation and traffic conditions. For example, it suggests restaurants with easy accessibility from the nearest station or bus stop for a user who uses public transportation. This makes it possible to suggest restaurants with good accessibility by taking into account the user's means of transportation and traffic conditions.

[0036] The vacancy confirmation unit can analyze reviews and ratings of nearby restaurants and prioritize suggesting restaurants with high ratings. The vacancy confirmation unit, for example, analyzes reviews and ratings of nearby restaurants and prioritize suggesting restaurants with high ratings. For example, it lists restaurants with high user ratings and makes suggestions. The vacancy confirmation unit can also analyze reviews and ratings of nearby restaurants and prioritize suggesting restaurants with high ratings. For example, it suggests restaurants with high star ratings or restaurants with good comments. Furthermore, the vacancy confirmation unit can analyze reviews and ratings of nearby restaurants and prioritize suggesting restaurants with high ratings. For example, it suggests highly rated restaurants that match the user's preferences. This makes it possible to analyze reviews and ratings of nearby restaurants and prioritize suggesting highly rated restaurants.

[0037] The vacancy check unit can make suggestions by taking into consideration not only the user's current location but also data on places visited in the past. The vacancy check unit can make suggestions by taking into consideration not only the user's current location but also data on places visited in the past. For example, similar restaurants are suggested based on data on restaurants visited in the past. The vacancy check unit can also make suggestions by taking into consideration not only the user's current location but also data on places visited in the past. For example, restaurants that suit the user's preferences are suggested based on data on restaurants visited in the past. The vacancy check unit can also make suggestions by taking into consideration not only the user's current location but also data on places visited in the past. For example, restaurants that suit the user's preferences are suggested based on data on restaurants visited in the past. This makes it possible to make suggestions by taking into consideration not only the user's current location but also data on places visited in the past.

[0038] The sales confirmation unit can analyze the store's sales data and propose special discounts during times when sales are low. The sales confirmation unit can, for example, analyze the store's sales data and propose special discounts during times when sales are low. For example, it can propose discount menus during weekday afternoons or late at night. The sales confirmation unit can also analyze the store's sales data and propose special discounts during times when sales are low. For example, it can propose discount menus during specific times of the day. The sales confirmation unit can also analyze the store's sales data and propose special discounts during times when sales are low. For example, it can propose discount menus during specific days of the week or times of the day. This makes it possible to analyze the store's sales data and propose special discounts during times when sales are low.

[0039] The ingredient inventory checking unit can propose menus taking into consideration not only the expiration date of ingredients but also the quality and freshness of ingredients. The ingredient inventory checking unit, for example, proposes menus taking into consideration not only the expiration date of ingredients but also the quality and freshness of ingredients. For example, it preferentially proposes menus using highly fresh ingredients. The ingredient inventory checking unit can also propose menus taking into consideration not only the expiration date of ingredients but also the quality and freshness of ingredients. For example, it proposes menus using high-quality ingredients. The ingredient inventory checking unit can also propose menus taking into consideration not only the expiration date of ingredients but also the quality and freshness of ingredients. For example, it proposes menus using ingredients that are close to their expiration date. This makes it possible to propose menus taking into consideration not only the expiration date of ingredients but also the quality and freshness of ingredients.

[0040] The ingredient inventory confirmation unit can analyze the restaurant's inventory data and propose seasonal limited menus and special menus. The ingredient inventory confirmation unit can, for example, analyze the restaurant's inventory data and propose seasonal limited menus. For example, it can propose special menus using seasonal ingredients. The ingredient inventory confirmation unit can also analyze the restaurant's inventory data and propose special menus. For example, it can propose event-only menus and chef-recommended menus. The ingredient inventory confirmation unit can also analyze the restaurant's inventory data and propose seasonal limited menus and special menus. For example, it can propose special menus using seasonal ingredients and event-only menus. This makes it possible to analyze the restaurant's inventory data and propose seasonal limited menus and special menus.

[0041] The sales confirmation unit can analyze the degree of achievement of the store's sales target in real time and make suggestions toward achieving the target. The sales confirmation unit, for example, can analyze the degree of achievement of the store's sales target in real time and make suggestions toward achieving the target. For example, if sales have not reached the target, it can propose a special discount. The sales confirmation unit can also analyze the degree of achievement of the store's sales target in real time and make suggestions toward achieving the target. For example, if sales have not reached the target, it can propose a promotion. The sales confirmation unit can also analyze the degree of achievement of the store's sales target in real time and make suggestions toward achieving the target. For example, if sales have not reached the target, it can propose a special event. This makes it possible to analyze the degree of achievement of the store's sales target in real time and make suggestions toward achieving the target.

[0042] The proposal generation unit can propose a menu with optimal cost performance according to the user's budget. The proposal generation unit, for example, proposes a menu with optimal cost performance according to the user's budget. For example, if the budget is 3,000 yen or less, the proposal unit proposes the menu with the highest satisfaction within that range. The proposal generation unit can also propose a menu with optimal cost performance according to the user's budget. For example, if the budget is 5,000 yen or less, the proposal unit proposes the menu with the highest satisfaction within that range. The proposal generation unit can also propose a menu with optimal cost performance according to the user's budget. For example, if the budget is 10,000 yen or less, the proposal unit proposes the menu with the highest satisfaction within that range. This makes it possible to propose a menu with optimal cost performance according to the user's budget.

[0043] The suggestion generation unit can analyze the nutritional value and calorie information of the menu and make suggestions suitable for health-conscious users. The suggestion generation unit can, for example, analyze the nutritional value and calorie information of the menu and make suggestions suitable for health-conscious users. For example, it can suggest a low-calorie, nutritionally balanced menu. The suggestion generation unit can also analyze the nutritional value and calorie information of the menu and make suggestions suitable for health-conscious users. For example, it can suggest a high-protein, low-fat menu. The suggestion generation unit can also analyze the nutritional value and calorie information of the menu and make suggestions suitable for health-conscious users. For example, it can suggest a menu that is rich in vitamins and minerals. This makes it possible to analyze the nutritional value and calorie information of the menu and make suggestions suitable for health-conscious users.

[0044] The suggestion generation unit can analyze the user's past order history and suggest menus with a high repeat rate. The suggestion generation unit, for example, analyzes the user's past order history and suggests menus with a high repeat rate. For example, it re-suggests menus that have been ordered many times in the past. The suggestion generation unit can also analyze the user's past order history and suggest menus with a high repeat rate. For example, it can suggest menus that the user is particularly fond of. The suggestion generation unit can also analyze the user's past order history and suggest menus with a high repeat rate. For example, it can suggest menus that the user is particularly fond of and re-suggest menus that the user has ordered many times in the past. This makes it possible to analyze the user's past order history and suggest menus with a high repeat rate.

[0045] The proposal generation unit can propose limited menus in consideration of special events and campaign information of the restaurant. The proposal generation unit can propose limited menus in consideration of special events and campaign information of the restaurant. For example, it proposes a special menu that is only available for a limited time. The proposal generation unit can also propose limited menus in consideration of special events and campaign information of the restaurant. For example, it proposes an event-only menu or a chef's recommended menu. The proposal generation unit can also propose limited menus in consideration of special events and campaign information of the restaurant. For example, it proposes a special menu that is only available for a limited time or a menu that is only available for an event. This makes it possible to propose limited menus in consideration of special events and campaign information of the restaurant.

[0046] The notification unit can cooperate with the user's calendar app to automatically add the suggested content to the schedule. The notification unit, for example, cooperates with the user's calendar app to automatically add the suggested content to the schedule. For example, the suggested reservation time is automatically registered in the calendar. The notification unit can also cooperate with the user's calendar app to automatically add the suggested content to the schedule. For example, the suggested event or activity is automatically registered in the calendar. The notification unit can also cooperate with the user's calendar app to automatically add the suggested content to the schedule. For example, the suggested reservation time or event is automatically registered in the calendar. This makes it possible to cooperate with the user's calendar app to automatically add the suggested content to the schedule.

[0047] The notification unit can synchronize the suggestion content with multiple devices so that it can be viewed anywhere. The notification unit can, for example, synchronize the suggestion content with multiple devices so that it can be viewed anywhere. For example, the suggestion content is displayed on a smartphone or a smart watch. The notification unit can also synchronize the suggestion content with multiple devices so that it can be viewed anywhere. For example, the suggestion content is displayed on a tablet or a PC. The notification unit can also synchronize the suggestion content with multiple devices so that it can be viewed anywhere. For example, the suggestion content is displayed on a smartphone, a smart watch, a tablet, or a PC. This makes it possible to synchronize the suggestion content with multiple devices so that it can be viewed anywhere.

[0048] The notification unit may add a function to share the suggested content on a social networking site, allowing the suggested content to be shared with friends and family. The notification unit may add a function to share the suggested content on a social networking site, for example, by posting suggested restaurants and menus on the social networking site. The notification unit may also add a function to share the suggested content on a social networking site, allowing the suggested content to be shared with friends and family. For example, the notification unit may post suggested events and activities on the social networking site. The notification unit may also add a function to share the suggested content on a social networking site, allowing the suggested content to be shared with friends and family. For example, the notification unit may post suggested restaurants, menus, events and activities on the social networking site. This may add a function to share the suggested content on a social networking site, allowing the suggested content to be shared with friends and family.

[0049] The notification unit can also suggest other events and activities that suit the user's preferences based on the content of the proposal. The notification unit, for example, suggests other events and activities that suit the user's preferences based on the content of the proposal. For example, it suggests an event that will be held near the proposed store. The notification unit can also suggest other events and activities that suit the user's preferences based on the content of the proposal. For example, it suggests an activity that will be held near the proposed store. The notification unit can also suggest other events and activities that suit the user's preferences based on the content of the proposal. For example, it suggests an event or activity that will be held near the proposed store. This makes it possible to suggest other events and activities that suit the user's preferences based on the content of the proposal.

[0050] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0051] The proposal system can further include a health management unit that monitors the user's health condition. For example, it can obtain the user's heart rate and stress level from a smartwatch or fitness tracker and make suggestions based on this. For example, if the user's heart rate is high, it can suggest a relaxing shop, and if the stress level is high, it can suggest a refreshing activity. This makes it possible to make suggestions based on the user's health condition.

[0052] The proposal system can further include an allergy management unit that takes into account the user's dietary restrictions and allergy information. For example, if the user is allergic to a particular ingredient, it can propose a menu that does not contain that ingredient. Also, if the user is on a diet, it can propose low-calorie or low-sugar menus. Furthermore, if the user is vegetarian or vegan, it can propose corresponding menus. This makes it possible to make proposals that take into account the user's dietary restrictions and allergy information.

[0053] The suggestion system can also be equipped with a review analysis unit that takes into account the user's past reviews and ratings. For example, it can prioritize suggestions of restaurants and menus that the user has previously given high ratings. It can also exclude restaurants and menus that the user has previously given low ratings. It can also analyze the content of the user's reviews and make suggestions taking into account the user's particularly favorite points. This makes it possible to make suggestions based on the user's past reviews and ratings.

[0054] The recommendation system can also be equipped with a travel history analysis unit that takes into account the user's travel history. For example, it can suggest restaurants that offer similar atmospheres and cuisine based on data on travel destinations and tourist spots visited by the user in the past. It can also suggest restaurants that recreate cuisine from travel destinations that the user particularly liked. Furthermore, it can analyze the user's travel history and suggest destinations for the next trip. This makes it possible to make suggestions based on the user's travel history.

[0055] The recommendation system can further include a hobby analysis unit that takes into account the user's hobbies and interests. For example, if the user likes music or movies, related events and shops can be suggested. Also, if the user likes sports or outdoor activities, related activities and shops can be suggested. Furthermore, the system can analyze the user's hobbies and interests and make suggestions that will spark new hobbies or interests. This makes it possible to make suggestions based on the user's hobbies and interests.

[0056] The processing flow of the first embodiment will be briefly explained below.

[0057] Step 1: The user inputs their desired content into the desired content input section. For example, the user inputs specific requests such as "I want to eat Japanese food," "My budget is 3,000 yen or less," and "I want to start from 8 p.m." Step 2: The analysis unit analyzes the desired content entered by the desired content input unit. For example, the generation AI analyzes the user's desired content and makes appropriate suggestions. Step 3: The current location determination unit determines the current location of the user, for example, by using GPS data. Step 4: The seat availability check unit checks seat availability at nearby restaurants based on the current location identified by the current location identification unit, for example, by collecting seat availability information at restaurants in real time. Step 5: The sales confirmation unit checks the store's sales achievement status. For example, it checks the current achievement status of the store's sales target. Step 6: The food stock confirmation unit checks the expiration dates of the food stock in the restaurant. For example, it proposes a menu that prioritizes the use of food items with an approaching expiration date. Step 7: The proposal generation unit proposes an appropriate price and menu based on the information obtained by the seat availability confirmation unit, sales confirmation unit, and ingredient inventory confirmation unit. For example, it may propose something like, "Today's recommended menu is a Japanese meal set at a special price using ingredients that are close to their expiration date. The price is 2,500 yen." Step 8: The notification unit notifies the user of the proposal content generated by the proposal generation unit, for example, by notifying the user of the proposal content via a smartphone of the user so that the user can check it.

[0058] (Example 2) A proposal system according to an embodiment of the present invention allows a user to input their desired details and receive suggestions for seating availability, menus, and prices for after-parties from restaurants near their current location. This system utilizes a generation AI to automatically suggest appropriate prices and menus based on factors such as restaurant seating availability, sales achievement, and expiration dates for food stock. This allows the proposal system to suggest appropriate prices and menus based on the user's wishes, taking into account factors such as seating availability, sales achievement, and expiration dates for food stock at nearby restaurants.

[0059] The proposal system according to the embodiment includes a desired content input unit, an analysis unit, a current location identification unit, a seat availability confirmation unit, a sales confirmation unit, a food ingredient inventory confirmation unit, a proposal generation unit, and a notification unit. The desired content input unit inputs the user's desired content. For example, the user inputs specific requests such as "I want to eat Japanese food," "My budget is under 3,000 yen," and "I'd like to use the restaurant from 8 p.m." The analysis unit analyzes the desired content input by the desired content input unit. For example, a generation AI analyzes the user's desired content and makes appropriate suggestions. The current location identification unit identifies the user's current location. For example, the current location is identified using GPS data. The seat availability confirmation unit checks the seat availability of nearby restaurants based on the current location identified by the current location identification unit. For example, the current location information is collected in real time. The sales confirmation unit checks the restaurant's sales achievement status. For example, the sales confirmation unit grasps the current achievement status of the restaurant's sales target. The food ingredient inventory confirmation unit checks the expiration date of the restaurant's food ingredient inventory. For example, the food ingredient inventory confirmation unit suggests a menu that prioritizes the use-by date of ingredients close to their expiration date. The proposal generation unit proposes an appropriate price and menu based on information obtained by the seat availability confirmation unit, sales confirmation unit, and ingredient inventory confirmation unit. For example, the proposal may be in the form of, "Today's recommended menu is a specially priced Japanese meal set using ingredients with an approaching expiration date. The price is 2,500 yen." The notification unit notifies the user of the proposal content generated by the proposal generation unit. For example, the proposal content may be notified to the user's smartphone so that the user can check it. This enables the proposal system according to the embodiment to propose an appropriate price and menu based on the user's wishes, taking into consideration the seat availability, sales achievement status, and expiration date of ingredient inventory at nearby restaurants.

[0060] The desired content input unit analyzes the user's past usage history, learns preferences and tendencies, and can make more personalized suggestions. The desired content input unit, for example, analyzes the user's past usage history and learns preferences and tendencies. For example, if a user has previously preferred Japanese food, it will preferentially suggest Japanese food. The desired content input unit can also suggest menus with a high repeat rate based on the user's past ordering history. For example, it will suggest menus that have been ordered many times in the past. This makes it possible to make more personalized suggestions based on the user's past usage history.

[0061] The desired content input section allows the generation AI to ask questions in real time in response to the user's input, further specifying the desired content. For example, if the user inputs "I want to eat Japanese food," the generation AI will ask "What kind of Japanese food would you like specifically?" to elicit more specific requests. Also, if the user inputs "My budget is under 3,000 yen," the desired content input section can ask "Does that budget include drinks?" to confirm more detailed requests. Furthermore, if the user inputs "I'd like to start at 8 p.m.", the generation AI can ask "How many people will be coming?" to confirm the number of people. This allows the user's desired content to be further specified, enabling more appropriate suggestions.

[0062] The desired content input unit can use the emotion estimation function to analyze the emotion of the user when entering information and make suggestions according to the user's stress or fatigue level. For example, the desired content input unit can analyze the emotion of the user when entering information and, if stress is high, suggest a restaurant where the user can relax. For example, it can suggest a Japanese restaurant with a quiet atmosphere. The desired content input unit can also analyze the emotion of the user when entering information and, if fatigue is high, suggest a restaurant where the user can refresh. For example, it can suggest a cafe or spa where the user can relax. Furthermore, the desired content input unit can analyze the emotion of the user when entering information and make suggestions to bring out positive emotions. For example, it can suggest fun events or activities. This makes it possible to make suggestions according to the user's emotions.

[0063] The desired content input unit can analyze the user's voice input and understand the desired content using voice recognition technology. For example, if the user vocally inputs "I want to eat Japanese food," the desired content input unit converts it into text using voice recognition technology and analyzes the desired content. In addition, if the user vocally inputs "My budget is 3,000 yen or less," the desired content input unit can also convert it into text using voice recognition technology and analyze the desired content. Furthermore, if the user vocally inputs "I would like to use it from 8 p.m.", the desired content input unit can also convert it into text using voice recognition technology and analyze the desired content. This makes it possible to analyze the user's voice input and understand the desired content.

[0064] The desired content input unit can analyze the content of the user's social media posts and make suggestions that reflect the user's current mood and interests. The desired content input unit, for example, analyzes the content of the user's social media posts and identifies the user's current mood and interests from recent posts. For example, if there have been many recent posts about Japanese food, the desired content input unit can suggest Japanese food. The desired content input unit can also analyze the content of the user's social media posts and identify the user's current mood and interests from recent posts. For example, if there have been many recent posts about cafes, the desired content input unit can suggest cafes. The desired content input unit can also analyze the content of the user's social media posts and identify the user's current mood and interests from recent posts. For example, if there have been many recent posts about events, the desired content input unit can suggest events. This makes it possible to analyze the content of the user's social media posts and make suggestions that reflect the user's current mood and interests.

[0065] The desired content input unit can use the emotion estimation function to analyze the user's emotion at the time of input in real time and make suggestions to bring out positive emotions. For example, the desired content input unit can analyze the user's emotion at the time of input in real time, and if the user's emotion is strong negative, make suggestions to bring out positive emotions. For example, it can suggest relaxing shops and fun events. The desired content input unit can also analyze the user's emotion at the time of input in real time, and if the user's emotion is strong positive, make suggestions to maintain that emotion. For example, it can suggest fun activities and events. Furthermore, the desired content input unit can analyze the user's emotion at the time of input in real time and make suggestions according to changes in emotion. For example, if the emotion changes from negative to positive, it can make suggestions to maintain that change. This makes it possible to analyze the user's emotion in real time and make suggestions to bring out positive emotions.

[0066] The vacancy confirmation unit can analyze the congestion level of surrounding stores in real time and preferentially suggest stores that offer a comfortable environment. The vacancy confirmation unit, for example, analyzes the congestion level of surrounding stores in real time and preferentially suggests stores that are not crowded. For example, it lists stores that are less crowded and suggests them to the user. The vacancy confirmation unit can also analyze the congestion level of surrounding stores in real time and preferentially suggest stores that offer a comfortable environment. For example, it suggests stores that offer a comfortable environment taking into consideration the seating arrangement and the atmosphere inside the store. Furthermore, the vacancy confirmation unit can analyze the congestion level of surrounding stores in real time and make suggestions according to the user's preferences. For example, if a user prefers a quiet environment, it suggests stores that are less crowded. This makes it possible to analyze the congestion level of surrounding stores in real time and preferentially suggest stores that offer a comfortable environment.

[0067] The vacancy confirmation unit can suggest restaurants with good accessibility by taking into account the user's means of transportation and traffic conditions. The vacancy confirmation unit, for example, suggests restaurants with good accessibility by taking into account the user's means of transportation. For example, it suggests nearby restaurants for a user who travels by foot, and suggests restaurants with parking for a user who travels by car. The vacancy confirmation unit can also suggest restaurants with good accessibility by taking into account traffic conditions. For example, it suggests restaurants with easy accessibility by selecting a route with less traffic congestion. Furthermore, the vacancy confirmation unit can also suggest an optimal route by taking into account the user's means of transportation and traffic conditions. For example, it suggests restaurants with easy accessibility from the nearest station or bus stop for a user who uses public transportation. This makes it possible to suggest restaurants with good accessibility by taking into account the user's means of transportation and traffic conditions.

[0068] The vacant seat checking unit can use the emotion estimation function to suggest restaurants with an atmosphere that matches the user's current mood. The vacant seat checking unit, for example, analyzes the user's current mood using the emotion estimation function and suggests restaurants with an atmosphere that matches the mood. For example, a quiet cafe is suggested for a user who is in a relaxing mood. The vacant seat checking unit can also analyze the user's current mood using the emotion estimation function and suggest restaurants with an atmosphere that matches the mood. For example, a lively restaurant is suggested for a user who is in a happy mood. The vacant seat checking unit can also analyze the user's current mood using the emotion estimation function and suggest restaurants with an atmosphere that matches the mood. For example, a quiet cafe is suggested for a user who is in a happy mood, and a lively restaurant is suggested for a user who is in a happy mood. This makes it possible to suggest restaurants with an atmosphere that matches the user's current mood.

[0069] The vacancy confirmation unit can analyze reviews and ratings of nearby restaurants and prioritize suggesting restaurants with high ratings. The vacancy confirmation unit, for example, analyzes reviews and ratings of nearby restaurants and prioritize suggesting restaurants with high ratings. For example, it lists restaurants with high user ratings and makes suggestions. The vacancy confirmation unit can also analyze reviews and ratings of nearby restaurants and prioritize suggesting restaurants with high ratings. For example, it suggests restaurants with high star ratings or restaurants with good comments. Furthermore, the vacancy confirmation unit can analyze reviews and ratings of nearby restaurants and prioritize suggesting restaurants with high ratings. For example, it suggests highly rated restaurants that match the user's preferences. This makes it possible to analyze reviews and ratings of nearby restaurants and prioritize suggesting highly rated restaurants.

[0070] The vacancy check unit can make suggestions by taking into consideration not only the user's current location but also data on places visited in the past. The vacancy check unit can make suggestions by taking into consideration not only the user's current location but also data on places visited in the past. For example, similar restaurants are suggested based on data on restaurants visited in the past. The vacancy check unit can also make suggestions by taking into consideration not only the user's current location but also data on places visited in the past. For example, restaurants that suit the user's preferences are suggested based on data on restaurants visited in the past. The vacancy check unit can also make suggestions by taking into consideration not only the user's current location but also data on places visited in the past. For example, restaurants that suit the user's preferences are suggested based on data on restaurants visited in the past. This makes it possible to make suggestions by taking into consideration not only the user's current location but also data on places visited in the past.

[0071] The vacancy confirmation unit can use the emotion estimation function to suggest relaxing establishments or lively establishments based on the user's current emotion. The vacancy confirmation unit, for example, analyzes the user's current emotion using the emotion estimation function and suggests relaxing establishments based on that emotion. For example, it can suggest quiet cafes or spas. The vacancy confirmation unit can also analyze the user's current emotion using the emotion estimation function and suggest lively establishments based on that emotion. For example, it can suggest lively restaurants or bars. The vacancy confirmation unit can also analyze the user's current emotion using the emotion estimation function and suggest relaxing establishments or lively establishments based on that emotion. For example, it can suggest quiet cafes or spas and lively restaurants or bars. This makes it possible to suggest relaxing establishments or lively establishments based on the user's current emotion.

[0072] The sales confirmation unit can analyze the store's sales data and propose special discounts during times when sales are low. The sales confirmation unit can, for example, analyze the store's sales data and propose special discounts during times when sales are low. For example, it can propose discount menus during weekday afternoons or late at night. The sales confirmation unit can also analyze the store's sales data and propose special discounts during times when sales are low. For example, it can propose discount menus during specific times of the day. The sales confirmation unit can also analyze the store's sales data and propose special discounts during times when sales are low. For example, it can propose discount menus during specific days of the week or times of the day. This makes it possible to analyze the store's sales data and propose special discounts during times when sales are low.

[0073] The ingredient inventory checking unit can propose menus taking into consideration not only the expiration date of ingredients but also the quality and freshness of ingredients. The ingredient inventory checking unit, for example, proposes menus taking into consideration not only the expiration date of ingredients but also the quality and freshness of ingredients. For example, it preferentially proposes menus using highly fresh ingredients. The ingredient inventory checking unit can also propose menus taking into consideration not only the expiration date of ingredients but also the quality and freshness of ingredients. For example, it proposes menus using high-quality ingredients. The ingredient inventory checking unit can also propose menus taking into consideration not only the expiration date of ingredients but also the quality and freshness of ingredients. For example, it proposes menus using ingredients that are close to their expiration date. This makes it possible to propose menus taking into consideration not only the expiration date of ingredients but also the quality and freshness of ingredients.

[0074] The ingredient inventory confirmation unit can use the emotion estimation function to suggest a menu using ingredients that match the user's preferences. The ingredient inventory confirmation unit can, for example, use the emotion estimation function to suggest a menu using ingredients that match the user's preferences. For example, the menu is customized based on ingredients that the user likes. The ingredient inventory confirmation unit can also use the emotion estimation function to suggest a menu using ingredients that match the user's preferences. For example, a menu that matches the preferences is suggested based on the user's past order history. Furthermore, the ingredient inventory confirmation unit can also use the emotion estimation function to suggest a menu using ingredients that match the user's preferences. For example, a special menu using ingredients that match the user's preferences is suggested. This makes it possible to suggest a menu using ingredients that match the user's preferences.

[0075] The ingredient inventory confirmation unit can analyze the restaurant's inventory data and propose seasonal limited menus and special menus. The ingredient inventory confirmation unit can, for example, analyze the restaurant's inventory data and propose seasonal limited menus. For example, it can propose special menus using seasonal ingredients. The ingredient inventory confirmation unit can also analyze the restaurant's inventory data and propose special menus. For example, it can propose event-only menus and chef-recommended menus. The ingredient inventory confirmation unit can also analyze the restaurant's inventory data and propose seasonal limited menus and special menus. For example, it can propose special menus using seasonal ingredients and event-only menus. This makes it possible to analyze the restaurant's inventory data and propose seasonal limited menus and special menus.

[0076] The sales confirmation unit can analyze the degree of achievement of the store's sales target in real time and make suggestions toward achieving the target. The sales confirmation unit, for example, can analyze the degree of achievement of the store's sales target in real time and make suggestions toward achieving the target. For example, if sales have not reached the target, it can propose a special discount. The sales confirmation unit can also analyze the degree of achievement of the store's sales target in real time and make suggestions toward achieving the target. For example, if sales have not reached the target, it can propose a promotion. The sales confirmation unit can also analyze the degree of achievement of the store's sales target in real time and make suggestions toward achieving the target. For example, if sales have not reached the target, it can propose a special event. This makes it possible to analyze the degree of achievement of the store's sales target in real time and make suggestions toward achieving the target.

[0077] The sales confirmation unit can use the emotion estimation function to suggest special events and campaigns based on the user's emotions. The sales confirmation unit, for example, uses the emotion estimation function to suggest special events based on the user's emotions. For example, if the user has positive emotions, it suggests a special dinner event. The sales confirmation unit can also use the emotion estimation function to suggest campaigns based on the user's emotions. For example, if the user has positive emotions, it suggests a special discount campaign. The sales confirmation unit can also use the emotion estimation function to suggest special events and campaigns based on the user's emotions. For example, if the user has positive emotions, it suggests a special dinner event or a discount campaign. This makes it possible to suggest special events and campaigns based on the user's emotions.

[0078] The proposal generation unit can propose a menu with optimal cost performance according to the user's budget. The proposal generation unit, for example, proposes a menu with optimal cost performance according to the user's budget. For example, if the budget is 3,000 yen or less, the proposal unit proposes the menu with the highest satisfaction within that range. The proposal generation unit can also propose a menu with optimal cost performance according to the user's budget. For example, if the budget is 5,000 yen or less, the proposal unit proposes the menu with the highest satisfaction within that range. The proposal generation unit can also propose a menu with optimal cost performance according to the user's budget. For example, if the budget is 10,000 yen or less, the proposal unit proposes the menu with the highest satisfaction within that range. This makes it possible to propose a menu with optimal cost performance according to the user's budget.

[0079] The suggestion generation unit can analyze the nutritional value and calorie information of the menu and make suggestions suitable for health-conscious users. The suggestion generation unit can, for example, analyze the nutritional value and calorie information of the menu and make suggestions suitable for health-conscious users. For example, it can suggest a low-calorie, nutritionally balanced menu. The suggestion generation unit can also analyze the nutritional value and calorie information of the menu and make suggestions suitable for health-conscious users. For example, it can suggest a high-protein, low-fat menu. The suggestion generation unit can also analyze the nutritional value and calorie information of the menu and make suggestions suitable for health-conscious users. For example, it can suggest a menu that is rich in vitamins and minerals. This makes it possible to analyze the nutritional value and calorie information of the menu and make suggestions suitable for health-conscious users.

[0080] The suggestion generation unit can use the emotion estimation function to suggest a special menu that matches the user's mood. The suggestion generation unit, for example, uses the emotion estimation function to suggest a special menu that matches the user's mood. For example, if the user feels like relaxing, the suggestion generation unit suggests herbal tea with a relaxing effect. The suggestion generation unit can also use the emotion estimation function to suggest a special menu that matches the user's mood. For example, if the user feels like cheering up, the suggestion generation unit suggests a menu that is suitable for replenishing energy. The suggestion generation unit can also use the emotion estimation function to suggest a special menu that matches the user's mood. For example, if the user feels like relaxing, the suggestion generation unit suggests herbal tea with a relaxing effect, and if the user feels like cheering up, the suggestion generation unit suggests a menu that is suitable for replenishing energy. In this way, it is possible to use the emotion estimation function to suggest a special menu that matches the user's mood.

[0081] The suggestion generation unit can analyze the user's past order history and suggest menus with a high repeat rate. The suggestion generation unit, for example, analyzes the user's past order history and suggests menus with a high repeat rate. For example, it re-suggests menus that have been ordered many times in the past. The suggestion generation unit can also analyze the user's past order history and suggest menus with a high repeat rate. For example, it can suggest menus that the user is particularly fond of. The suggestion generation unit can also analyze the user's past order history and suggest menus with a high repeat rate. For example, it can suggest menus that the user is particularly fond of and re-suggest menus that the user has ordered many times in the past. This makes it possible to analyze the user's past order history and suggest menus with a high repeat rate.

[0082] The proposal generation unit can propose limited menus in consideration of special events and campaign information of the restaurant. The proposal generation unit can propose limited menus in consideration of special events and campaign information of the restaurant. For example, it proposes a special menu that is only available for a limited time. The proposal generation unit can also propose limited menus in consideration of special events and campaign information of the restaurant. For example, it proposes an event-only menu or a chef's recommended menu. The proposal generation unit can also propose limited menus in consideration of special events and campaign information of the restaurant. For example, it proposes a special menu that is only available for a limited time or a menu that is only available for an event. This makes it possible to propose limited menus in consideration of special events and campaign information of the restaurant.

[0083] The suggestion generation unit can use the emotion estimation function to suggest a surprise menu or a special service based on the user's emotion. The suggestion generation unit, for example, uses the emotion estimation function to suggest a surprise menu based on the user's emotion. For example, if the user has positive emotions, it suggests a special dessert. The suggestion generation unit can also use the emotion estimation function to suggest a special service based on the user's emotion. For example, if the user has positive emotions, it provides a special service. The suggestion generation unit can also use the emotion estimation function to suggest a surprise menu or a special service based on the user's emotion. For example, if the user has positive emotions, it suggests a special dessert or a special service. This makes it possible to suggest a surprise menu or a special service based on the user's emotion using the emotion estimation function.

[0084] The notification unit can cooperate with the user's calendar app to automatically add the suggested content to the schedule. The notification unit, for example, cooperates with the user's calendar app to automatically add the suggested content to the schedule. For example, the suggested reservation time is automatically registered in the calendar. The notification unit can also cooperate with the user's calendar app to automatically add the suggested content to the schedule. For example, the suggested event or activity is automatically registered in the calendar. The notification unit can also cooperate with the user's calendar app to automatically add the suggested content to the schedule. For example, the suggested reservation time or event is automatically registered in the calendar. This makes it possible to cooperate with the user's calendar app to automatically add the suggested content to the schedule.

[0085] The notification unit can synchronize the suggestion content with multiple devices so that it can be viewed anywhere. The notification unit can, for example, synchronize the suggestion content with multiple devices so that it can be viewed anywhere. For example, the suggestion content is displayed on a smartphone or a smart watch. The notification unit can also synchronize the suggestion content with multiple devices so that it can be viewed anywhere. For example, the suggestion content is displayed on a tablet or a PC. The notification unit can also synchronize the suggestion content with multiple devices so that it can be viewed anywhere. For example, the suggestion content is displayed on a smartphone, a smart watch, a tablet, or a PC. This makes it possible to synchronize the suggestion content with multiple devices so that it can be viewed anywhere.

[0086] The notification unit can use the emotion estimation function to select a notification method according to the user's emotion and send a message that elicits positive emotion. The notification unit, for example, uses the emotion estimation function to select a notification method according to the user's emotion. For example, it sends a message that elicits positive emotion. The notification unit can also use the emotion estimation function to select a notification method according to the user's emotion and send a message that elicits positive emotion. For example, it sends a notification during a time period when the user is relaxed. The notification unit can also use the emotion estimation function to select a notification method according to the user's emotion and send a message that elicits positive emotion. For example, it sends a notification during a time period when the user is relaxed and sends a message that elicits positive emotion. This makes it possible to use the emotion estimation function to select a notification method according to the user's emotion and send a message that elicits positive emotion.

[0087] The notification unit may add a function to share the suggested content on a social networking site, allowing the suggested content to be shared with friends and family. The notification unit may add a function to share the suggested content on a social networking site, for example, by posting suggested restaurants and menus on the social networking site. The notification unit may also add a function to share the suggested content on a social networking site, allowing the suggested content to be shared with friends and family. For example, the notification unit may post suggested events and activities on the social networking site. The notification unit may also add a function to share the suggested content on a social networking site, allowing the suggested content to be shared with friends and family. For example, the notification unit may post suggested restaurants, menus, events and activities on the social networking site. This may add a function to share the suggested content on a social networking site, allowing the suggested content to be shared with friends and family.

[0088] The notification unit can also suggest other events and activities that suit the user's preferences based on the content of the proposal. The notification unit, for example, suggests other events and activities that suit the user's preferences based on the content of the proposal. For example, it suggests an event that will be held near the proposed store. The notification unit can also suggest other events and activities that suit the user's preferences based on the content of the proposal. For example, it suggests an activity that will be held near the proposed store. The notification unit can also suggest other events and activities that suit the user's preferences based on the content of the proposal. For example, it suggests an event or activity that will be held near the proposed store. This makes it possible to suggest other events and activities that suit the user's preferences based on the content of the proposal.

[0089] The notification unit can use the emotion estimation function to optimize the timing and content of notifications based on the user's emotion. The notification unit, for example, uses the emotion estimation function to optimize the timing of notifications based on the user's emotion. For example, the notification unit sends a notification during a time period when the user is relaxed. The notification unit can also use the emotion estimation function to optimize the content of notifications based on the user's emotion. For example, the notification unit sends a positive message during a time period when the user is relaxed. The notification unit can also use the emotion estimation function to optimize the timing and content of notifications based on the user's emotion. For example, the notification unit sends a positive message during a time period when the user is relaxed. This makes it possible to use the emotion estimation function to optimize the timing and content of notifications based on the user's emotion.

[0090] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0091] The proposal system can further include a health management unit that monitors the user's health condition. For example, it can obtain the user's heart rate and stress level from a smartwatch or fitness tracker and make suggestions based on this. For example, if the user's heart rate is high, it can suggest a relaxing shop, and if the stress level is high, it can suggest a refreshing activity. This makes it possible to make suggestions based on the user's health condition.

[0092] The proposal system can further include an allergy management unit that takes into account the user's dietary restrictions and allergy information. For example, if the user is allergic to a particular ingredient, it can propose a menu that does not contain that ingredient. Also, if the user is on a diet, it can propose low-calorie or low-sugar menus. Furthermore, if the user is vegetarian or vegan, it can propose corresponding menus. This makes it possible to make proposals that take into account the user's dietary restrictions and allergy information.

[0093] The suggestion system can also be equipped with a review analysis unit that takes into account the user's past reviews and ratings. For example, it can prioritize suggestions of restaurants and menus that the user has previously given high ratings. It can also exclude restaurants and menus that the user has previously given low ratings. It can also analyze the content of the user's reviews and make suggestions taking into account the user's particularly favorite points. This makes it possible to make suggestions based on the user's past reviews and ratings.

[0094] The recommendation system can also be equipped with a travel history analysis unit that takes into account the user's travel history. For example, it can suggest restaurants that offer similar atmospheres and cuisine based on data on travel destinations and tourist spots visited by the user in the past. It can also suggest restaurants that recreate cuisine from travel destinations that the user particularly liked. Furthermore, it can analyze the user's travel history and suggest destinations for the next trip. This makes it possible to make suggestions based on the user's travel history.

[0095] The recommendation system can further include a hobby analysis unit that takes into account the user's hobbies and interests. For example, if the user likes music or movies, related events and shops can be suggested. Also, if the user likes sports or outdoor activities, related activities and shops can be suggested. Furthermore, the system can analyze the user's hobbies and interests and make suggestions that will spark new hobbies or interests. This makes it possible to make suggestions based on the user's hobbies and interests.

[0096] The proposed system can also estimate the user's emotions and suggest relaxing shops and activities based on the estimated emotions. For example, if the user is feeling stressed, it can suggest a quiet cafe or spa. If the user is tired, it can suggest a refreshing activity or massage. Furthermore, if the user is feeling positive, it can suggest fun events and activities. This makes it possible to make suggestions based on the user's emotions.

[0097] The proposed system can also estimate the user's emotions and suggest special services or surprises based on the estimated emotions. For example, if the user is feeling positive, it can suggest special desserts or drinks. If the user is feeling negative, it can suggest relaxing services or surprises. Furthermore, it can suggest special events or campaigns based on the user's emotions. This makes it possible to suggest special services and surprises based on the user's emotions.

[0098] The proposed system can also estimate the user's emotions and select the optimal notification method based on the estimated emotions. For example, it can send notifications during times when the user is relaxing and send messages that elicit positive emotions. If the user is feeling stressed, it can also notify them of shops or activities that will help them relax. Furthermore, it can optimize the timing and content of notifications according to the user's emotions. This makes it possible to select the optimal notification method based on the user's emotions.

[0099] The proposed system can further estimate the user's emotions and suggest special menus that match the user's mood based on the estimated emotions. For example, if the user feels like relaxing, it can suggest relaxing herbal tea or light meals. If the user feels like energizing, it can also suggest menus that are suitable for replenishing energy. Furthermore, it can suggest special desserts or drinks depending on the user's emotions. This makes it possible to suggest special menus based on the user's emotions.

[0100] The proposed system can also estimate the user's emotions and suggest shops with an atmosphere that matches the user's mood based on the estimated emotions. For example, if the user is in a relaxing mood, it can suggest quiet cafes and restaurants. If the user is in a fun mood, it can suggest lively bars and restaurants. It can also suggest special events and activities based on the user's emotions. This makes it possible to suggest shops with an atmosphere that matches the user's emotions.

[0101] The processing flow of the second embodiment will be briefly explained below.

[0102] Step 1: The user inputs their desired content into the desired content input section. For example, the user inputs specific requests such as "I want to eat Japanese food," "My budget is 3,000 yen or less," and "I want to start from 8 p.m." Step 2: The analysis unit analyzes the desired content entered by the desired content input unit. For example, the generation AI analyzes the user's desired content and makes appropriate suggestions. Step 3: The current location determination unit determines the current location of the user, for example, by using GPS data. Step 4: The seat availability check unit checks seat availability at nearby restaurants based on the current location identified by the current location identification unit, for example, by collecting seat availability information at restaurants in real time. Step 5: The sales confirmation unit checks the store's sales achievement status. For example, it checks the current achievement status of the store's sales target. Step 6: The food stock confirmation unit checks the expiration dates of the food stock in the restaurant. For example, it proposes a menu that prioritizes the use of food items with an approaching expiration date. Step 7: The proposal generation unit proposes an appropriate price and menu based on the information obtained by the seat availability confirmation unit, sales confirmation unit, and ingredient inventory confirmation unit. For example, it may propose something like, "Today's recommended menu is a Japanese meal set at a special price using ingredients that are close to their expiration date. The price is 2,500 yen." Step 8: The notification unit notifies the user of the proposal content generated by the proposal generation unit, for example, by notifying the user of the proposal content via a smartphone of the user so that the user can check it.

[0103] 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.

[0104] 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.

[0105] 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.

[0106] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0107] 3, the data processing system 210 includes the data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0108] 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.

[0109] 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.

[0110] 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.

[0111] 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).

[0112] 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.

[0113] 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.

[0114] 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.

[0115] 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 the user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

[0116] 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. Note that the smart glasses 214 may have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59.

[0117] 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.

[0118] 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.

[0119] 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.

[0120] 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.

[0121] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0122] 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.

[0123] 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.

[0124] 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.

[0125] 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.

[0126] 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).

[0127] 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.

[0128] 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.

[0129] 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.

[0130] 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 the user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

[0131] In the headset type terminal 314, the identification process is performed by the processor 46. A identification program 60 is stored in the storage 50. The processor 46 reads the identification program 60 from the storage 50 and executes the read identification program 60 on the RAM 48. The identification process is realized by the processor 46 operating as a control unit 46A in accordance with the identification program 60 executed on the RAM 48. Note that the headset type terminal 314 may also have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59.

[0132] 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.

[0133] 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.

[0134] 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.

[0135] 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.

[0136] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0137] 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.

[0138] 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.

[0139] 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.

[0140] 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.

[0141] 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).

[0142] 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.

[0143] 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.

[0144] 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.

[0145] 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.

[0146] 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 the user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

[0147] In the robot 414, the processor 46 performs the identification process. A identification program 60 is stored in the storage 50. The processor 46 reads the identification program 60 from the storage 50 and executes the read identification program 60 on the RAM 48. The identification process is realized by the processor 46 operating as a control unit 46A in accordance with the identification program 60 executed on the RAM 48. The robot 414 may have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59.

[0148] 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.

[0149] 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.

[0150] 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.

[0151] 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.

[0152] 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.

[0153] 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.

[0154] 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.

[0155] 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).

[0156] 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.

[0157] 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."

[0158] 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.

[0159] 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.

[0160] 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.

[0161] 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.

[0162] 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.

[0163] 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.

[0164] 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.

[0165] 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.

[0166] 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.

[0167] 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.

[0168] 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.

[0169] 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]

[0170] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Device 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robot

Claims

1. a desired content input section for inputting desired content of the user; an analysis unit that analyzes the desired content input by the desired content input unit; a current location determination unit that determines a current location of a user; a seat availability confirmation unit that checks seat availability at nearby stores based on the current location identified by the current location identification unit; A sales confirmation department that checks the store's sales achievement status, A food inventory check section that checks the expiration dates of food ingredients in the store, a proposal generation unit that proposes appropriate prices and menus based on the information obtained by the vacant seat confirmation unit, the sales confirmation unit, and the food stock confirmation unit; a notification unit that notifies the user of the content of the proposal generated by the proposal generation unit. A system characterized by:

2. The desired content input unit Analyzing the content of the user's social media posts and making the suggestions that reflect their mood and interests 2. The system of claim 1.

3. The vacant seat confirmation unit The degree of congestion of the surrounding stores is analyzed in real time, and the store that provides a comfortable environment is given priority in the suggestion.

2. The system of claim 1.

4. The sales confirmation unit Analyzing the sales data of the store and offering special discounts during times when sales are low 2. The system of claim 1.

5. The proposal generation unit The menu with the best cost performance is proposed according to the user's budget.

2. The system of claim 1.

6. The notification unit A notification method is selected according to the user's emotions, and a message that elicits the positive emotions is sent.

2. The system of claim 1.

7. The desired content input unit Analyzing the user's emotions at the time of input and making the suggestions according to the user's stress and fatigue level 2. The system of claim 1.

8. The vacant seat confirmation unit The store with an atmosphere that matches the user's mood is suggested.

2. The system of claim 1.

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