System

Abstract

An object of a system according to an embodiment is to analyze data from a plurality of services and propose a restaurant that matches a condition of a user.SOLUTION: A system includes a collection unit, an analysis unit, and a proposal unit. The collection unit collects data related to restaurants from a plurality of services. The analysis unit analyzes the data collected by the collection unit, and specifies a restaurant that matches a condition of the user. The proposal unit proposes the eating / drinking place specified by the analysis unit to the user.SELECTED DRAWING: Figure 1

Description

[Technical Field]

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

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

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

[0004] Conventional technologies do not adequately integrate data from multiple services to identify restaurants that meet a user's criteria, and there is room for improvement.

[0005] The system according to the embodiment aims to analyze data from multiple services and suggest restaurants that meet the user's requirements. [Means for solving the problem]

[0006] The system according to the embodiment includes a collection unit, an analysis unit, and a suggestion unit. The collection unit collects data related to restaurants from multiple services. The analysis unit analyzes the data collected by the collection unit and identifies restaurants that meet the user's criteria. The suggestion unit suggests the restaurants identified by the analysis unit to the user. [Effects of the Invention]

[0007] The system according to the embodiment can analyze data from multiple services and suggest restaurants that meet the user's requirements. [Brief explanation of the drawings]

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

[0028] (Example 1) A recommendation system according to an embodiment of the present invention proposes restaurants that meet a user's requirements based on data collected from various services. The recommendation system collects restaurant-related data from multiple services, analyzes it using AI, and identifies and recommends restaurants that meet the user's requirements. For example, the recommendation system collects detailed data, such as restaurant ratings, menus, price ranges, business hours, and locations, from restaurant review sites, reservation sites, and social media. Next, the recommendation system analyzes the collected data using AI to identify restaurants that meet the user's requirements. For example, if a user enters criteria such as "Japanese food," "budget under 3,000 yen," and "within a five-minute walk from the station," the AI ​​identifies restaurants that meet these criteria. Finally, the recommendation system displays the identified restaurants on the user's smartphone or computer. This allows the user to easily find a restaurant that meets their requirements. This allows the recommendation system to efficiently suggest restaurants that meet the user's requirements. For example, the system can accommodate a variety of situations, such as a busy businessman who wants to have a quick lunch or a family dinner. Furthermore, by collecting data from multiple services, the system can provide a wider range of options. For example, it is possible to suggest restaurants that are highly rated or restaurants with specific menu items that are popular, tailored to the user's needs.

[0029] A proposal system according to an embodiment includes a collection unit, an analysis unit, and a proposal unit. The collection unit collects data about restaurants from multiple services. For example, the collection unit collects detailed data about restaurants, such as their ratings, menus, price ranges, business hours, and locations, from restaurant review sites, reservation sites, and social media. The collection unit can collect data using, for example, an API. The collection unit can also collect data from websites using scraping technology. The collection unit can also obtain data directly from databases. The analysis unit analyzes the data collected by the collection unit and identifies restaurants that meet the user's criteria. The analysis unit analyzes the data using, for example, AI. The AI ​​identifies restaurants based on the criteria entered by the user. For example, if the user enters criteria such as "Japanese food," "budget under 3,000 yen," and "within a five-minute walk from the station," the AI ​​identifies restaurants that meet these criteria. The proposal unit suggests restaurants identified by the analysis unit to the user. For example, the proposal unit displays the identified restaurants on the user's smartphone or computer. The proposal unit can display the proposal results in a list format or on a map, for example. As a result, the recommendation system according to the embodiment can efficiently recommend restaurants that meet the user's requirements.

[0030] The proposed system includes a reception unit that receives user conditions. The reception unit receives the user conditions. The user conditions include, for example, budget, type of food, location, and business hours. The reception unit allows the user to input the conditions using, for example, a smartphone or a PC. The reception unit can also receive conditions using voice input. For example, the user can input conditions such as "Japanese food," "budget under 3,000 yen," and "within a five-minute walk from the station" using voice. The reception unit can also receive conditions using image input. For example, the user can upload a photo of the food and set the conditions based on the photo. This allows the user's conditions to be received efficiently.

[0031] The collection unit can collect data on restaurant ratings, menus, price ranges, business hours, and locations. The collection unit collects detailed data such as restaurant ratings, menus, price ranges, business hours, and locations from, for example, restaurant rating sites, reservation sites, social media, etc. Ratings include, for example, the number of stars, review content, and rating reliability. Menus include, for example, the type of food, price, and special menu items. Price ranges include, for example, low, medium, and high prices. Business hours include, for example, lunch time, dinner time, and 24-hour service. Locations include, for example, the city name, area, and distance from a station. The collection unit can collect data using, for example, an API. The collection unit can also collect data from websites using scraping technology. Furthermore, the collection unit can obtain data directly from a database. This allows for more accurate recommendations by collecting detailed restaurant data.

[0032] The analysis unit can identify restaurants based on the conditions entered by the user. The analysis unit analyzes data using, for example, AI. The AI ​​identifies restaurants based on the conditions entered by the user. For example, if a user enters conditions such as "Japanese food," "budget under 3,000 yen," and "within a five-minute walk from the station," the AI ​​will identify restaurants that meet these conditions. The AI ​​can analyze the user's conditions using, for example, natural language processing technology. The AI ​​can also identify restaurants based on past data using machine learning algorithms. Furthermore, the AI ​​can use data mining technology to extract useful information from collected data and identify restaurants. This allows the AI ​​to accurately identify restaurants that meet the user's conditions.

[0033] The suggestion unit can display the identified restaurants on the user's smartphone or computer. For example, the suggestion unit can display the suggested results in a list format or on a map. Examples of list formats include ranking format and alphabetical order. For example, the locations of restaurants can be displayed on a map using Google (registered trademark) Maps or a proprietary map display system. For example, when a user enters conditions on a smartphone, the suggestion unit displays the results of an analysis by an AI on the smartphone. Also, when a user enters conditions on a computer, the suggestion unit displays the results of an analysis by an AI on the computer. This allows the user to easily check suggested restaurants.

[0034] The suggestion unit can display the suggestion results in list format or on a map. The suggestion unit can display the suggestion results in list format or on a map, for example. List formats include, for example, ranking format or alphabetical order. The locations of restaurants can be displayed on the map using, for example, Google Maps or a proprietary map display system. For example, when a user enters conditions on a smartphone, the suggestion unit displays the results of the AI's analysis in list format or on a map. Also, when a user enters conditions on a computer, the suggestion unit displays the results of the AI's analysis in list format or on a map. This makes it easier for users to visually check the suggestion results.

[0035] The collection unit can analyze past data collection history and select the optimal collection method. The collection unit analyzes past data collection history and selects the optimal collection method. For example, from past data collection history, AI can identify that data collection is effective during a specific time period, and collect data during that time period. In addition, AI can analyze past data collection history and identify that data from a specific service is useful, and prioritize collection of that service. In addition, based on past data collection history, AI can identify that a specific data format is suitable for analysis, and collect data in that format. This makes it possible to collect data efficiently by utilizing past data collection history.

[0036] When collecting data, the collection unit can filter the data based on the user's current areas of interest and past search history. When collecting data, the collection unit filters the data based on the user's current areas of interest and past search history. For example, the collection unit can prioritize collecting data on related restaurants based on keywords that the user has searched for in the past. It can also filter data on related restaurants based on the user's current areas of interest (e.g., Japanese cuisine, Italian cuisine). It can also analyze the user's past search history and collect data on new restaurants that may be of interest to the user. This makes it possible to collect data based on the user's interests.

[0037] The collection unit can select the optimal collection means depending on the user's input method when collecting data. The collection unit selects the optimal collection means depending on the user's input method (voice, text, image, etc.) when collecting data. For example, if the user uses voice input, data can be collected using voice recognition technology. Also, if the user uses text input, data can be collected using text analysis technology. Also, if the user uses image input, data can be collected using image recognition technology. This makes it possible to collect data depending on the user's input method.

[0038] When collecting data, the collection unit can prioritize collecting highly relevant data based on the user's geographical location information. When collecting data, the collection unit prioritizes collecting highly relevant data taking into account the user's geographical location information. For example, the collection unit prioritizes collecting data about restaurants close to the user's current location. It can also prioritize collecting data about restaurants close to places the user plans to visit. It can also prioritize collecting data about highly relevant restaurants based on the user's past movement history. This makes it possible to collect data based on the user's geographical location information.

[0039] The collection unit can analyze the user's social media activities and collect related data when collecting data. The collection unit analyzes the user's social media activities and collects related data when collecting data. For example, the collection unit collects data about places where the user has checked in on social media. The collection unit can also analyze the content of the user's social media posts and collect data about related restaurants. The collection unit can also collect data about related restaurants based on the activities of the user's friends on social media. This makes it possible to collect data based on the user's social media activities.

[0040] The collection unit can customize the collection method by reflecting the user's past feedback when collecting data. The collection unit customizes the collection method by reflecting the user's past feedback when collecting data. For example, the collection unit can adjust the type of data to be collected based on feedback provided by the user in the past. The collection unit can also analyze the user's past feedback and optimize the collection method. The collection unit can also prioritize data collection from specific services by reflecting the user's feedback. This makes it possible to collect data based on user feedback.

[0041] The analysis unit can adjust the level of detail of the analysis based on the importance of the data during analysis. The analysis unit adjusts the level of detail of the analysis based on the importance of the data during analysis. For example, detailed analysis is performed on data with high importance. Also, simplified analysis can be performed on data with low importance. Furthermore, the analysis priority can be determined according to the importance of the data. This makes it possible to perform analysis according to the importance of the data.

[0042] The analysis unit can apply different analysis algorithms depending on the data category during analysis. The analysis unit can apply different analysis algorithms depending on the data category during analysis. For example, to restaurant rating data, an algorithm that analyzes the average value and distribution of ratings can be applied. Also, to menu data, an algorithm that analyzes the ranking of popular menu items can be applied. Also, to price range data, an algorithm that analyzes the price distribution can be applied. This makes it possible to perform analysis according to the data category.

[0043] The analysis unit can improve the accuracy of the analysis by referring to the user's past analysis results during analysis. The analysis unit can improve the accuracy of the analysis by referring to the user's past analysis results during analysis. For example, the analysis algorithm can be adjusted based on feedback provided by the user in the past. The analysis unit can also analyze the user's past analysis results to improve the accuracy of the analysis. The analysis unit can also determine the priority of the analysis by referring to the user's past analysis results. In this way, the analysis accuracy can be improved by utilizing the past analysis results.

[0044] The analysis unit can determine the analysis priority based on the time of data collection during analysis. The analysis unit can determine the analysis priority based on the time of data collection during analysis. For example, the analysis unit can prioritize the most recent data. It is also possible to analyze the most recent data while referring to past data. It is also possible to adjust the analysis priority according to the time of data collection. This makes it possible to perform analysis based on the time of data collection.

[0045] The analysis unit can adjust the order of analysis based on the relevance of the data during analysis. The analysis unit can adjust the order of analysis based on the relevance of the data during analysis. For example, highly relevant data can be analyzed first. Also, less relevant data can be analyzed later. The order of analysis can also be adjusted according to the relevance of the data. This makes it possible to perform analysis based on the relevance of the data.

[0046] The analysis unit can adjust the use of technical terms in the analysis according to the user's level of expertise during analysis. The analysis unit can adjust the use of technical terms in the analysis according to the user's level of expertise during analysis. For example, the analysis unit can provide analysis results in simple language to a user with little expertise. On the other hand, the analysis unit can provide analysis results using detailed technical terms to a user with expertise. The analysis unit can also adjust the way in which the analysis results are expressed according to the user's level of expertise. This makes it possible to provide analysis results according to the user's level of expertise.

[0047] The suggestion unit can adjust the level of detail of the suggestion based on the importance of the restaurant when making a suggestion. The suggestion unit adjusts the level of detail of the suggestion based on the importance of the restaurant when making a suggestion. For example, a detailed suggestion is made for a restaurant with a high importance. Also, a simplified suggestion can be made for a restaurant with a low importance. Furthermore, the priority of the suggestion can be determined according to the importance of the restaurant. This makes it possible to make suggestions according to the importance of the restaurant.

[0048] The suggestion unit can apply different suggestion algorithms depending on the category of the restaurant when making a suggestion. The suggestion unit applies different suggestion algorithms depending on the category of the restaurant when making a suggestion. For example, a suggestion algorithm specialized for Japanese cuisine can be applied to a Japanese restaurant. Also, a suggestion algorithm specialized for Italian cuisine can be applied to an Italian restaurant. Also, a suggestion algorithm specialized for cafes can be applied to a cafe. This makes it possible to make suggestions according to the category of the restaurant.

[0049] The suggestion unit can improve the accuracy of the suggestion by referring to the user's past suggestion results when making a suggestion. ... For example, the suggestion algorithm can be adjusted based on feedback provided by the user in the past. The suggestion unit can also analyze the user's past suggestion results to improve the accuracy of the suggestion. The suggestion unit can also determine the priority of the suggestion by referring to the user's past suggestion results. In this way, the suggestion unit can improve the accuracy of the suggestion by utilizing the past suggestion results.

[0050] The suggestion unit can determine the priority of suggestions based on the ratings of restaurants when making suggestions. The suggestion unit determines the priority of suggestions based on the ratings of restaurants when making suggestions. For example, highly rated restaurants can be suggested first. Also, restaurants with low ratings can be suggested later. The priority of suggestions can also be adjusted according to the ratings of restaurants. This makes it possible to make suggestions based on the ratings of restaurants.

[0051] The suggestion unit can adjust the order of suggestions based on the relevance of the restaurants when making suggestions. The suggestion unit adjusts the order of suggestions based on the relevance of the restaurants when making suggestions. For example, the restaurant most relevant to the user's conditions is suggested first. Also, restaurants with less relevance can be suggested later. The order of suggestions can also be adjusted according to the relevance of the restaurants. This makes it possible to make suggestions based on the relevance of the restaurants.

[0052] The suggestion unit can adjust the use of technical terminology in the proposal according to the user's level of expertise when making a proposal. The suggestion unit can adjust the use of technical terminology in the proposal according to the user's level of expertise when making a proposal. For example, the suggestion unit can make a proposal using simple language for a user with little expertise. Also, the suggestion unit can make a proposal using detailed technical terminology for a user with expertise. The suggestion unit can also adjust the way the proposal is expressed according to the user's level of expertise. This makes it possible to make a proposal according to the user's level of expertise.

[0053] When receiving conditions, the reception unit can select the optimal input method based on the user's past condition input history. When receiving conditions, the reception unit selects the optimal input method by referring to the user's past condition input history. For example, conditions that the user has frequently input in the past can be automatically displayed as candidates. It can also preferentially suggest input methods (voice, text, etc.) that the user has used in the past. It can also predict and suggest conditions to be used in a specific time period based on the user's past input history. This makes it possible to efficiently input conditions by utilizing the user's past condition input history.

[0054] The reception unit can customize the input content based on the user's current situation when receiving the conditions. The reception unit customizes the input content based on the user's current situation when receiving the conditions. For example, if the user is in their current location, conditions related to that location can be automatically suggested. Also, if the user is participating in a specific event, conditions related to that event can be suggested. Also, optimal conditions can be suggested based on the user's current situation (e.g., time of day, weather). This makes it possible to input conditions according to the user's current situation.

[0055] The reception unit can improve the input method by reflecting user feedback when receiving conditions. The reception unit improves the input method by reflecting user feedback when receiving conditions. For example, the input interface can be improved based on feedback previously provided by the user. The input procedure can also be optimized by reflecting user feedback. The input method can also be customized by referring to user feedback. This makes it possible to input conditions based on user feedback.

[0056] The reception unit can select the optimal input method in consideration of the user's geographical location information when receiving the conditions. The reception unit selects the optimal input method in consideration of the user's geographical location information when receiving the conditions. For example, the reception unit can preferentially suggest conditions related to places close to the user's current location. It can also preferentially suggest conditions related to places the user plans to visit. It can also preferentially suggest highly relevant conditions based on the user's past movement history. This makes it possible to input conditions based on the user's geographical location information.

[0057] The reception unit can analyze the user's social media activity and suggest related conditions when receiving the conditions. The reception unit can analyze the user's social media activity and suggest related conditions when receiving the conditions. For example, the reception unit can suggest conditions related to places where the user has checked in on social media. The reception unit can also analyze the content of the user's social media posts and suggest related conditions. The reception unit can also suggest related conditions by taking into account the activities of the user's friends on social media. This makes it possible to suggest conditions based on the user's social media activity.

[0058] The reception unit can customize the input method by reflecting the user's past feedback when receiving the conditions. The reception unit customizes the input method by reflecting the user's past feedback when receiving the conditions. For example, the input interface is customized based on feedback provided by the user in the past. The input procedure can also be optimized by reflecting the user's feedback. The input method can also be customized by referring to the user's feedback. This makes it possible to input conditions based on the user's feedback.

[0059] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0060] The recommendation system can also analyze the user's past behavior history and collect data on restaurants the user has visited in the past. For example, it can collect data such as ratings, menus, and price ranges of restaurants the user has visited in the past, and make new suggestions based on this data. It can also collect data on nearby restaurants based on the location information of restaurants the user has visited in the past. It can also analyze reviews of restaurants the user has visited in the past and estimate the user's preferences. This makes it possible to make more personalized suggestions by utilizing the user's past behavior history.

[0061] The collection unit can also collect data on seasonal menus and limited-time events at restaurants. For example, it can collect information on cherry blossom-themed menus in the spring and beer garden events in the summer. It can also collect information on special menus and events for specific holidays. It can also collect information on new menus and renewals at restaurants and provide users with the latest information. This makes it possible to make suggestions according to the season and events.

[0062] The suggestion unit can also suggest nearby restaurants in real time based on the user's current location information. For example, if the user is looking for a restaurant within walking distance of their current location, suggestions can be made based on that information. Also, if the user moves to a specific area, restaurants within that area can be suggested. Furthermore, if the user takes a specific route, restaurants along that route can be suggested. This makes it possible to make suggestions based on the user's movement.

[0063] The collection unit can also collect data on the hygiene evaluation and safety of restaurants. For example, it can collect data on the hygiene management status and past hygiene problems of restaurants and provide it to the user. It can also collect data on the safety of restaurants (e.g., fire prevention measures, evacuation routes) and suggest restaurants that users can use with peace of mind. It can also collect data on the health status and hygiene education status of restaurant employees. This makes it possible to suggest safe and hygienic restaurants to users.

[0064] The analysis unit can also make suggestions based on the user's meal frequency and meal times. For example, if the user frequently eats out for lunch, it can suggest restaurants suitable for lunch. If the user often eats out late at night, it can suggest restaurants that are open late at night. Furthermore, if the user often eats out on a specific day of the week, it can suggest restaurants that are open on that day. This makes it possible to make suggestions based on the user's eating patterns.

[0065] The processing flow of the first embodiment will be briefly explained below.

[0066] Step 1: The collection unit collects data about restaurants from multiple services. For example, it collects detailed data such as restaurant ratings, menus, price ranges, opening hours, and locations from restaurant review sites, reservation sites, and social media. The collection unit can collect data using APIs. It can also collect data from websites using scraping technology. It can also obtain data directly from databases. Step 2: The analysis unit analyzes the data collected by the collection unit and identifies restaurants that meet the user's criteria. The analysis unit uses AI to analyze the data and identify restaurants based on the criteria entered by the user. For example, if the user enters criteria such as "Japanese food," "budget under 3,000 yen," and "within a five-minute walk from the station," the AI ​​will identify restaurants that meet these criteria. Step 3: The suggestion unit suggests the restaurants identified by the analysis unit to the user. The suggestion unit displays the identified restaurants on the user's smartphone or computer. The suggestion unit can display the suggestion results in list format or on a map.

[0067] (Example 2) A recommendation system according to an embodiment of the present invention proposes restaurants that meet a user's requirements based on data collected from various services. The recommendation system collects restaurant-related data from multiple services, analyzes it using AI, and identifies and recommends restaurants that meet the user's requirements. For example, the recommendation system collects detailed data, such as restaurant ratings, menus, price ranges, business hours, and locations, from restaurant review sites, reservation sites, and social media. Next, the recommendation system analyzes the collected data using AI to identify restaurants that meet the user's requirements. For example, if a user enters criteria such as "Japanese food," "budget under 3,000 yen," and "within a five-minute walk from the station," the AI ​​identifies restaurants that meet these criteria. Finally, the recommendation system displays the identified restaurants on the user's smartphone or computer. This allows the user to easily find a restaurant that meets their requirements. This allows the recommendation system to efficiently suggest restaurants that meet the user's requirements. For example, the system can accommodate a variety of situations, such as a busy businessman who wants to have a quick lunch or a family dinner. Furthermore, by collecting data from multiple services, the system can provide a wider range of options. For example, it is possible to suggest restaurants that are highly rated or restaurants with specific menu items that are popular, tailored to the user's needs.

[0068] A proposal system according to an embodiment includes a collection unit, an analysis unit, and a proposal unit. The collection unit collects data about restaurants from multiple services. For example, the collection unit collects detailed data about restaurants, such as their ratings, menus, price ranges, business hours, and locations, from restaurant review sites, reservation sites, and social media. The collection unit can collect data using, for example, an API. The collection unit can also collect data from websites using scraping technology. The collection unit can also obtain data directly from databases. The analysis unit analyzes the data collected by the collection unit and identifies restaurants that meet the user's criteria. The analysis unit analyzes the data using, for example, AI. The AI ​​identifies restaurants based on the criteria entered by the user. For example, if the user enters criteria such as "Japanese food," "budget under 3,000 yen," and "within a five-minute walk from the station," the AI ​​identifies restaurants that meet these criteria. The proposal unit suggests restaurants identified by the analysis unit to the user. For example, the proposal unit displays the identified restaurants on the user's smartphone or computer. The proposal unit can display the proposal results in a list format or on a map, for example. As a result, the recommendation system according to the embodiment can efficiently recommend restaurants that meet the user's requirements.

[0069] The proposed system includes a reception unit that receives user conditions. The reception unit receives the user conditions. The user conditions include, for example, budget, type of food, location, and business hours. The reception unit allows the user to input the conditions using, for example, a smartphone or a PC. The reception unit can also receive conditions using voice input. For example, the user can input conditions such as "Japanese food," "budget under 3,000 yen," and "within a five-minute walk from the station" using voice. The reception unit can also receive conditions using image input. For example, the user can upload a photo of the food and set the conditions based on the photo. This allows the user's conditions to be received efficiently.

[0070] The collection unit can collect data on restaurant ratings, menus, price ranges, business hours, and locations. The collection unit collects detailed data such as restaurant ratings, menus, price ranges, business hours, and locations from, for example, restaurant rating sites, reservation sites, social media, etc. Ratings include, for example, the number of stars, review content, and rating reliability. Menus include, for example, the type of food, price, and special menu items. Price ranges include, for example, low, medium, and high prices. Business hours include, for example, lunch time, dinner time, and 24-hour service. Locations include, for example, the city name, area, and distance from a station. The collection unit can collect data using, for example, an API. The collection unit can also collect data from websites using scraping technology. Furthermore, the collection unit can obtain data directly from a database. This allows for more accurate recommendations by collecting detailed restaurant data.

[0071] The analysis unit can identify restaurants based on the conditions entered by the user. The analysis unit analyzes data using, for example, AI. The AI ​​identifies restaurants based on the conditions entered by the user. For example, if a user enters conditions such as "Japanese food," "budget under 3,000 yen," and "within a five-minute walk from the station," the AI ​​will identify restaurants that meet these conditions. The AI ​​can analyze the user's conditions using, for example, natural language processing technology. The AI ​​can also identify restaurants based on past data using machine learning algorithms. Furthermore, the AI ​​can use data mining technology to extract useful information from collected data and identify restaurants. This allows the AI ​​to accurately identify restaurants that meet the user's conditions.

[0072] The suggestion unit can display the identified restaurants on the user's smartphone or computer. For example, the suggestion unit can display the suggested results in a list format or on a map. Examples of list formats include ranking format and alphabetical order. For example, the locations of restaurants can be displayed on a map using Google Maps or a proprietary map display system. For example, when a user enters conditions on a smartphone, the suggestion unit displays the results of an analysis by AI on the smartphone. Also, when a user enters conditions on a computer, the suggestion unit displays the results of an analysis by AI on the computer. This allows the user to easily check suggested restaurants.

[0073] The suggestion unit can display the suggestion results in list format or on a map. The suggestion unit can display the suggestion results in list format or on a map, for example. List formats include, for example, ranking format or alphabetical order. The locations of restaurants can be displayed on the map using, for example, Google Maps or a proprietary map display system. For example, when a user enters conditions on a smartphone, the suggestion unit displays the results of the AI's analysis in list format or on a map. Also, when a user enters conditions on a computer, the suggestion unit displays the results of the AI's analysis in list format or on a map. This makes it easier for users to visually check the suggestion results.

[0074] The proposed system includes a collection unit that estimates a user's emotions and adjusts the timing of data collection based on the estimated user emotions. The collection unit estimates the user's emotions and adjusts the timing of data collection based on the estimated user emotions. For example, if the user is feeling stressed, the AI ​​reduces the frequency of data collection to reduce the user's burden. Alternatively, if the user is relaxed, the AI ​​can increase the frequency of data collection to collect more detailed information. Alternatively, if the user is in a hurry, the AI ​​can quickly collect data and provide results in a short time. This allows the timing of data collection to be optimized according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. The generative AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI.

[0075] The collection unit can analyze past data collection history and select the optimal collection method. The collection unit analyzes past data collection history and selects the optimal collection method. For example, from past data collection history, AI can identify that data collection is effective during a specific time period, and collect data during that time period. In addition, AI can analyze past data collection history and identify that data from a specific service is useful, and prioritize collection of that service. In addition, based on past data collection history, AI can identify that a specific data format is suitable for analysis, and collect data in that format. This makes it possible to collect data efficiently by utilizing past data collection history.

[0076] When collecting data, the collection unit can filter the data based on the user's current areas of interest and past search history. When collecting data, the collection unit filters the data based on the user's current areas of interest and past search history. For example, the collection unit can prioritize collecting data on related restaurants based on keywords that the user has searched for in the past. It can also filter data on related restaurants based on the user's current areas of interest (e.g., Japanese cuisine, Italian cuisine). It can also analyze the user's past search history and collect data on new restaurants that may be of interest to the user. This makes it possible to collect data based on the user's interests.

[0077] The collection unit can select the optimal collection means depending on the user's input method when collecting data. The collection unit selects the optimal collection means depending on the user's input method (voice, text, image, etc.) when collecting data. For example, if the user uses voice input, data can be collected using voice recognition technology. Also, if the user uses text input, data can be collected using text analysis technology. Also, if the user uses image input, data can be collected using image recognition technology. This makes it possible to collect data depending on the user's input method.

[0078] The collection unit can estimate the user's emotions and determine the priority of data to be collected based on the estimated user emotions. The collection unit can estimate the user's emotions and determine the priority of data to be collected based on the estimated user emotions. For example, if the user is feeling stressed, the AI ​​can prioritize collecting data on restaurants where the user can relax. Also, if the user is relaxed, the AI ​​can prioritize collecting data on restaurants where the user can have a new experience. Also, if the user is in a hurry, the AI ​​can prioritize collecting data on restaurants where the user can provide service quickly. This enables more appropriate data collection by prioritizing data according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. The generative AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI.

[0079] When collecting data, the collection unit can prioritize collecting highly relevant data based on the user's geographical location information. When collecting data, the collection unit prioritizes collecting highly relevant data taking into account the user's geographical location information. For example, the collection unit prioritizes collecting data about restaurants close to the user's current location. It can also prioritize collecting data about restaurants close to places the user plans to visit. It can also prioritize collecting data about highly relevant restaurants based on the user's past movement history. This makes it possible to collect data based on the user's geographical location information.

[0080] The collection unit can analyze the user's social media activities and collect related data when collecting data. The collection unit analyzes the user's social media activities and collects related data when collecting data. For example, the collection unit collects data about places where the user has checked in on social media. The collection unit can also analyze the content of the user's social media posts and collect data about related restaurants. The collection unit can also collect data about related restaurants based on the activities of the user's friends on social media. This makes it possible to collect data based on the user's social media activities.

[0081] The collection unit can customize the collection method by reflecting the user's past feedback when collecting data. The collection unit customizes the collection method by reflecting the user's past feedback when collecting data. For example, the collection unit can adjust the type of data to be collected based on feedback provided by the user in the past. The collection unit can also analyze the user's past feedback and optimize the collection method. The collection unit can also prioritize data collection from specific services by reflecting the user's feedback. This makes it possible to collect data based on user feedback.

[0082] The analysis unit can estimate the user's emotions and adjust the way the analysis is presented based on the estimated user's emotions. The analysis unit can estimate the user's emotions and adjust the way the analysis is presented based on the estimated user's emotions. For example, if the user is feeling stressed, a simple, highly visible analysis result can be provided. If the user is relaxed, a detailed analysis result can be provided. If the user is in a hurry, a concise analysis result that focuses on the main points can be provided. This makes it possible to present the analysis result according to the user's emotions. Emotion estimation is realized using an emotion estimation function, for example, using an emotion engine or generation AI. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to these examples.

[0083] The analysis unit can adjust the level of detail of the analysis based on the importance of the data during analysis. The analysis unit adjusts the level of detail of the analysis based on the importance of the data during analysis. For example, detailed analysis is performed on data with high importance. Also, simplified analysis can be performed on data with low importance. Furthermore, the analysis priority can be determined according to the importance of the data. This makes it possible to perform analysis according to the importance of the data.

[0084] The analysis unit can apply different analysis algorithms depending on the data category during analysis. The analysis unit can apply different analysis algorithms depending on the data category during analysis. For example, to restaurant rating data, an algorithm that analyzes the average value and distribution of ratings can be applied. Also, to menu data, an algorithm that analyzes the ranking of popular menu items can be applied. Also, to price range data, an algorithm that analyzes the price distribution can be applied. This makes it possible to perform analysis according to the data category.

[0085] The analysis unit can improve the accuracy of the analysis by referring to the user's past analysis results during analysis. The analysis unit can improve the accuracy of the analysis by referring to the user's past analysis results during analysis. For example, the analysis algorithm can be adjusted based on feedback provided by the user in the past. The analysis unit can also analyze the user's past analysis results to improve the accuracy of the analysis. The analysis unit can also determine the priority of the analysis by referring to the user's past analysis results. In this way, the analysis accuracy can be improved by utilizing the past analysis results.

[0086] The analysis unit can estimate the user's emotions and adjust the length of the analysis based on the estimated user emotions. The analysis unit can estimate the user's emotions and adjust the length of the analysis based on the estimated user emotions. For example, if the user is feeling stressed, a short and to-the-point analysis result can be provided. If the user is relaxed, a detailed analysis result can be provided. If the user is in a hurry, a concise analysis result can be provided. This makes it possible to adjust the length of the analysis result according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generation AI. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to these examples.

[0087] The analysis unit can determine the analysis priority based on the time of data collection during analysis. The analysis unit can determine the analysis priority based on the time of data collection during analysis. For example, the analysis unit can prioritize the most recent data. It is also possible to analyze the most recent data while referring to past data. It is also possible to adjust the analysis priority according to the time of data collection. This makes it possible to perform analysis based on the time of data collection.

[0088] The analysis unit can adjust the order of analysis based on the relevance of the data during analysis. The analysis unit can adjust the order of analysis based on the relevance of the data during analysis. For example, highly relevant data can be analyzed first. Also, less relevant data can be analyzed later. The order of analysis can also be adjusted according to the relevance of the data. This makes it possible to perform analysis based on the relevance of the data.

[0089] The analysis unit can adjust the use of technical terms in the analysis according to the user's level of expertise during analysis. The analysis unit can adjust the use of technical terms in the analysis according to the user's level of expertise during analysis. For example, the analysis unit can provide analysis results in simple language to a user with little expertise. On the other hand, the analysis unit can provide analysis results using detailed technical terms to a user with expertise. The analysis unit can also adjust the way in which the analysis results are expressed according to the user's level of expertise. This makes it possible to provide analysis results according to the user's level of expertise.

[0090] The suggestion unit can estimate the user's emotions and adjust the way suggestions are expressed based on the estimated user emotions. The suggestion unit can estimate the user's emotions and adjust the way suggestions are expressed based on the estimated user emotions. For example, if the user is feeling stressed, a simple, highly visible suggestion can be made. If the user is relaxed, a detailed suggestion can be made. If the user is in a hurry, a concise suggestion that focuses on the main points can be made. This makes it possible to express suggestions according to the user's emotions. Emotion estimation is realized using an emotion estimation function, for example, using an emotion engine or generation AI. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to these examples.

[0091] The suggestion unit can adjust the level of detail of the suggestion based on the importance of the restaurant when making a suggestion. The suggestion unit adjusts the level of detail of the suggestion based on the importance of the restaurant when making a suggestion. For example, a detailed suggestion is made for a restaurant with a high importance. Also, a simplified suggestion can be made for a restaurant with a low importance. Furthermore, the priority of the suggestion can be determined according to the importance of the restaurant. This makes it possible to make suggestions according to the importance of the restaurant.

[0092] The suggestion unit can apply different suggestion algorithms depending on the category of the restaurant when making a suggestion. The suggestion unit applies different suggestion algorithms depending on the category of the restaurant when making a suggestion. For example, a suggestion algorithm specialized for Japanese cuisine can be applied to a Japanese restaurant. Also, a suggestion algorithm specialized for Italian cuisine can be applied to an Italian restaurant. Also, a suggestion algorithm specialized for cafes can be applied to a cafe. This makes it possible to make suggestions according to the category of the restaurant.

[0093] The suggestion unit can improve the accuracy of the suggestion by referring to the user's past suggestion results when making a suggestion. ... For example, the suggestion algorithm can be adjusted based on feedback provided by the user in the past. The suggestion unit can also analyze the user's past suggestion results to improve the accuracy of the suggestion. The suggestion unit can also determine the priority of the suggestion by referring to the user's past suggestion results. In this way, the suggestion unit can improve the accuracy of the suggestion by utilizing the past suggestion results.

[0094] The suggestion unit can estimate the user's emotion and adjust the length of the suggestion based on the estimated user's emotion. The suggestion unit can estimate the user's emotion and adjust the length of the suggestion based on the estimated user's emotion. For example, if the user is feeling stressed, a short and to-the-point suggestion can be made. If the user is relaxed, a detailed suggestion can be made. If the user is in a hurry, a concise suggestion can be made. This makes it possible to adjust the length of the suggestion according to the user's emotion. The emotion estimation is realized using an emotion estimation function, for example, using an emotion engine or a generation AI. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to these examples.

[0095] The suggestion unit can determine the priority of suggestions based on the ratings of restaurants when making suggestions. The suggestion unit determines the priority of suggestions based on the ratings of restaurants when making suggestions. For example, highly rated restaurants can be suggested first. Also, restaurants with low ratings can be suggested later. The priority of suggestions can also be adjusted according to the ratings of restaurants. This makes it possible to make suggestions based on the ratings of restaurants.

[0096] The suggestion unit can adjust the order of suggestions based on the relevance of the restaurants when making suggestions. The suggestion unit adjusts the order of suggestions based on the relevance of the restaurants when making suggestions. For example, the restaurant most relevant to the user's conditions is suggested first. Also, restaurants with less relevance can be suggested later. The order of suggestions can also be adjusted according to the relevance of the restaurants. This makes it possible to make suggestions based on the relevance of the restaurants.

[0097] The suggestion unit can adjust the use of technical terminology in the proposal according to the user's level of expertise when making a proposal. The suggestion unit can adjust the use of technical terminology in the proposal according to the user's level of expertise when making a proposal. For example, the suggestion unit can make a proposal using simple language for a user with little expertise. Also, the suggestion unit can make a proposal using detailed technical terminology for a user with expertise. The suggestion unit can also adjust the way the proposal is expressed according to the user's level of expertise. This makes it possible to make a proposal according to the user's level of expertise.

[0098] The reception unit can estimate the user's emotions and adjust the condition input method based on the estimated user emotions. The reception unit can estimate the user's emotions and adjust the condition input method based on the estimated user emotions. For example, if the user is feeling stressed, a simple interface can be provided to minimize input steps. Alternatively, if the user is relaxed, detailed input options can be provided and a customizable input method can be suggested. Alternatively, if the user is in a hurry, voice input can be prioritized to allow conditions to be input quickly. This makes it possible to adjust the condition input method according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI.

[0099] When receiving conditions, the reception unit can select the optimal input method based on the user's past condition input history. When receiving conditions, the reception unit selects the optimal input method by referring to the user's past condition input history. For example, conditions that the user has frequently input in the past can be automatically displayed as candidates. It can also preferentially suggest input methods (voice, text, etc.) that the user has used in the past. It can also predict and suggest conditions to be used in a specific time period based on the user's past input history. This makes it possible to efficiently input conditions by utilizing the user's past condition input history.

[0100] The reception unit can customize the input content based on the user's current situation when receiving the conditions. The reception unit customizes the input content based on the user's current situation when receiving the conditions. For example, if the user is in their current location, conditions related to that location can be automatically suggested. Also, if the user is participating in a specific event, conditions related to that event can be suggested. Also, optimal conditions can be suggested based on the user's current situation (e.g., time of day, weather). This makes it possible to input conditions according to the user's current situation.

[0101] The reception unit can improve the input method by reflecting user feedback when receiving conditions. The reception unit improves the input method by reflecting user feedback when receiving conditions. For example, the input interface can be improved based on feedback previously provided by the user. The input procedure can also be optimized by reflecting user feedback. The input method can also be customized by referring to user feedback. This makes it possible to input conditions based on user feedback.

[0102] The reception unit can estimate the user's emotions and determine the priority of conditions based on the estimated user emotions. The reception unit can estimate the user's emotions and determine the priority of conditions based on the estimated user emotions. For example, if the user is feeling stressed, conditions that allow the user to relax can be preferentially suggested. Also, if the user is relaxed, conditions that allow the user to have a new experience can be preferentially suggested. Also, if the user is in a hurry, conditions that allow the user to quickly provide service can be preferentially suggested. In this way, the priority of conditions can be determined according to the user's emotions. Emotion estimation is realized using an emotion estimation function using, for example, an emotion engine or a generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI.

[0103] The reception unit can select the optimal input method in consideration of the user's geographical location information when receiving the conditions. The reception unit selects the optimal input method in consideration of the user's geographical location information when receiving the conditions. For example, the reception unit can preferentially suggest conditions related to places close to the user's current location. It can also preferentially suggest conditions related to places the user plans to visit. It can also preferentially suggest highly relevant conditions based on the user's past movement history. This makes it possible to input conditions based on the user's geographical location information.

[0104] The reception unit can analyze the user's social media activity and suggest related conditions when receiving the conditions. The reception unit can analyze the user's social media activity and suggest related conditions when receiving the conditions. For example, the reception unit can suggest conditions related to places where the user has checked in on social media. The reception unit can also analyze the content of the user's social media posts and suggest related conditions. The reception unit can also suggest related conditions by taking into account the activities of the user's friends on social media. This makes it possible to suggest conditions based on the user's social media activity.

[0105] The reception unit can customize the input method by reflecting the user's past feedback when receiving the conditions. The reception unit customizes the input method by reflecting the user's past feedback when receiving the conditions. For example, the input interface is customized based on feedback provided by the user in the past. The input procedure can also be optimized by reflecting the user's feedback. The input method can also be customized by referring to the user's feedback. This makes it possible to input conditions based on the user's feedback. === Hard Collateral 1-1 === Each of the multiple elements including the collection unit, analysis unit, proposal unit, and reception unit described above is realized, for example, by at least one of the smart device 14 and the data processing device 12. For example, the collection unit collects data via the communication I / F 44 of the smart device 14, and the data is analyzed by the specific processing unit 290 of the data processing device 12. The analysis unit is realized by the specific processing unit 290 of the data processing device 12, and the proposal unit is realized by the control unit 46A of the smart device 14. The reception unit receives the user's conditions using the touch panel 38A or microphone 38B of the smart device 14. === Hard Collateral 1-2 === Each of the multiple elements including the above-mentioned collection unit, analysis unit, proposal unit, and reception unit is realized, for example, by at least one of the smart glasses 214 and the data processing device 12. For example, the collection unit collects data via the communication I / F 44 of the smart glasses 214, and the data is analyzed by the specific processing unit 290 of the data processing device 12. The analysis unit is realized by the specific processing unit 290 of the data processing device 12, and the proposal unit is realized by the control unit 46A of the smart glasses 214. The reception unit receives the user's conditions using the microphone 238 of the smart glasses 214. === Hard Collateral 1-3 === Each of the multiple elements including the above-mentioned collection unit, analysis unit, proposal unit, and reception unit is realized, for example, by at least one of the headset type terminal 314 and the data processing device 12. For example, the collection unit collects data via the communication I / F 44 of the headset type terminal 314, and the data is analyzed by the specific processing unit 290 of the data processing device 12. The analysis unit is realized by the specific processing unit 290 of the data processing device 12, and the proposal unit is realized by the control unit 46A of the headset type terminal 314. The reception unit receives the user's conditions using the microphone 238 of the headset type terminal 314. === Hard Collateral 1-4 === Each of the multiple elements including the above-mentioned collection unit, analysis unit, proposal unit, and reception unit is realized, for example, by at least one of the robot 414 and the data processing device 12. For example, the collection unit collects data via the communication I / F 44 of the robot 414, and the data is analyzed by the specific processing unit 290 of the data processing device 12. The analysis unit is realized by the specific processing unit 290 of the data processing device 12, and the proposal unit is realized by the control unit 46A of the robot 414. The reception unit receives the user's conditions using the microphone 238 of the robot 414.

[0106] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0107] The recommendation system can also analyze the user's past behavior history and collect data on restaurants the user has visited in the past. For example, it can collect data such as ratings, menus, and price ranges of restaurants the user has visited in the past, and make new suggestions based on this data. It can also collect data on nearby restaurants based on the location information of restaurants the user has visited in the past. It can also analyze reviews of restaurants the user has visited in the past and estimate the user's preferences. This makes it possible to make more personalized suggestions by utilizing the user's past behavior history.

[0108] The reception unit can also suggest conditions based on the user's current mood and physical condition. For example, if the user is tired, it can suggest restaurants with a relaxing atmosphere. If the user is energetic, it can suggest lively restaurants. Furthermore, if the user is allergic to a specific ingredient, it can suggest restaurants that cater to allergies based on that information. This makes it possible to make suggestions based on the user's current condition.

[0109] The collection unit can also collect data on seasonal menus and limited-time events at restaurants. For example, it can collect information on cherry blossom-themed menus in the spring and beer garden events in the summer. It can also collect information on special menus and events for specific holidays. It can also collect information on new menus and renewals at restaurants and provide users with the latest information. This makes it possible to make suggestions according to the season and events.

[0110] The analysis unit can also customize the suggested restaurant menus based on the user's dietary preferences and allergy information. For example, if the user is vegetarian, restaurants that offer vegetarian menus can be suggested first. Also, if the user is allergic to a specific ingredient, restaurants that offer menus that do not use that ingredient can be suggested. Furthermore, restaurants that offer preferred menus can be identified based on the user's past eating history. This makes it possible to suggest restaurants that meet the user's dietary preferences.

[0111] The suggestion unit can also suggest nearby restaurants in real time based on the user's current location information. For example, if the user is looking for a restaurant within walking distance of their current location, suggestions can be made based on that information. Also, if the user moves to a specific area, restaurants within that area can be suggested. Furthermore, if the user takes a specific route, restaurants along that route can be suggested. This makes it possible to make suggestions based on the user's movement.

[0112] The recommendation system can also analyze users' social media posts to infer their preferences and interests. For example, it can collect data on restaurants that users have rated as "delicious" on social media and suggest similar restaurants. It can also analyze photos of food that users have shared on social media and suggest restaurants that serve those dishes. It can also make suggestions to users based on data on restaurants rated by the user's friends. This makes it possible to make suggestions based on the user's social media activity.

[0113] The collection unit can also collect data on the hygiene evaluation and safety of restaurants. For example, it can collect data on the hygiene management status and past hygiene problems of restaurants and provide it to the user. It can also collect data on the safety of restaurants (e.g., fire prevention measures, evacuation routes) and suggest restaurants that users can use with peace of mind. It can also collect data on the health status and hygiene education status of restaurant employees. This makes it possible to suggest safe and hygienic restaurants to users.

[0114] The analysis unit can also make suggestions based on the user's meal frequency and meal times. For example, if the user frequently eats out for lunch, it can suggest restaurants suitable for lunch. If the user often eats out late at night, it can suggest restaurants that are open late at night. Furthermore, if the user often eats out on a specific day of the week, it can suggest restaurants that are open on that day. This makes it possible to make suggestions based on the user's eating patterns.

[0115] The suggestion unit can also estimate the user's emotions and adjust the timing of suggestions based on the estimated user emotions. For example, if the user is feeling stressed, priority is given to suggesting restaurants where the user can relax. Also, if the user is relaxed, the suggestion unit can suggest restaurants where the user can have a new experience. Furthermore, if the user is in a hurry, the suggestion unit can suggest restaurants that can provide quick service. This makes it possible to adjust the timing of suggestions according to the user's emotions.

[0116] The recommendation system can also make suggestions taking into account the user's health condition. For example, if the user is on a diet, it can suggest restaurants that offer low-calorie menus. Also, if the user needs to consume a specific nutrient, it can suggest restaurants that offer menus containing that nutrient. Furthermore, if the user has a specific health condition (e.g., high blood pressure, diabetes), it can suggest restaurants that offer menus suitable for that condition. This makes it possible to make suggestions based on the user's health condition.

[0117] The processing flow of the second embodiment will be briefly explained below.

[0118] Step 1: The collection unit collects data about restaurants from multiple services. For example, it collects detailed data such as restaurant ratings, menus, price ranges, opening hours, and locations from restaurant review sites, reservation sites, and social media. The collection unit can collect data using APIs. It can also collect data from websites using scraping technology. It can also obtain data directly from databases. Step 2: The analysis unit analyzes the data collected by the collection unit and identifies restaurants that meet the user's criteria. The analysis unit uses AI to analyze the data and identify restaurants based on the criteria entered by the user. For example, if the user enters criteria such as "Japanese food," "budget under 3,000 yen," and "within a five-minute walk from the station," the AI ​​will identify restaurants that meet these criteria. Step 3: The suggestion unit suggests the restaurants identified by the analysis unit to the user. The suggestion unit displays the identified restaurants on the user's smartphone or computer. The suggestion unit can display the suggestion results in list format or on a map.

[0119] 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.

[0120] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (registered trademark) (Internet search engine).<URL: https: / / openai.com / blog / chatgpt> Examples of 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.

[0121] 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.

[0122] The correspondence between each part and the device or control part is not limited to the above example, and various modifications are possible.

[0123] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0124] 3, the data processing system 210 includes a data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0125] 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.

[0126] 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.

[0127] 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.

[0128] 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).

[0129] 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.

[0130] 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.

[0131] 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.

[0132] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0133] In the smart glasses 214, the specific processing is performed by the processor 46. A specific processing program 60 is stored in the storage 50. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. The smart glasses 214 also have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0134] 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.

[0135] 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.

[0136] 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 AI 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.

[0137] 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.

[0138] The correspondence between each part and the device or control part is not limited to the above example, and various modifications are possible.

[0139] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0140] 5, the data processing system 310 includes the data processing device 12 and a headset terminal 314. An example of the data processing device 12 is a server.

[0141] 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.

[0142] 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.

[0143] 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.

[0144] 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).

[0145] 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.

[0146] Fig. 6 shows an example of the main functions of the data processing device 12 and the headset 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.

[0147] 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.

[0148] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0149] In the headset type terminal 314, the identification process is performed by the processor 46. A identification program 60 is stored in the storage 50. The processor 46 reads the identification program 60 from the storage 50 and executes the read identification program 60 on the RAM 48. The identification process is realized by the processor 46 operating as a control unit 46A in accordance with the identification program 60 executed on the RAM 48. Note that the headset type terminal 314 has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can also perform processing similar to that of the identification processing unit 290 using these models.

[0150] 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.

[0151] 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.

[0152] 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 AI 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.

[0153] 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.

[0154] The correspondence between each part and the device or control part is not limited to the above example, and various modifications are possible.

[0155] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0156] 7, a data processing system 410 includes a data processing device 12 and a robot 414. An example of the data processing device 12 is a server.

[0157] 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.

[0158] 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.

[0159] 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.

[0160] 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).

[0161] 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.

[0162] 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.

[0163] 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.

[0164] 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.

[0165] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0166] In the robot 414, the processor 46 performs the identification process. The storage 50 stores the identification program 60. The processor 46 reads the identification program 60 from the storage 50 and executes the read identification program 60 on the RAM 48. The identification process is realized by the processor 46 operating as the control unit 46A in accordance with the identification program 60 executed on the RAM 48. The robot 414 also has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can perform the same process as the identification processing unit 290 using these models.

[0167] 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.

[0168] 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.

[0169] 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 AI 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.

[0170] 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.

[0171] The correspondence between each part and the device or control part is not limited to the above example, and various modifications are possible.

[0172] 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.

[0173] 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.

[0174] 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.

[0175] 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).

[0176] 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.

[0177] 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."

[0178] 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.

[0179] 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.

[0180] 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.

[0181] 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.

[0182] 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.

[0183] 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.

[0184] 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.

[0185] 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.

[0186] 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.

[0187] 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.

[0188] 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, in order to avoid confusion and to 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.

[0189] 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.

[0190] [Explanation of symbols]

[0191] 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 collection unit that collects data about restaurants from multiple services; an analysis unit that analyzes the data collected by the collection unit and identifies restaurants that meet the user's requirements; a suggestion unit that suggests the restaurant identified by the analysis unit to the user. A system characterized by:

2. A reception unit is provided to receive user conditions.

2. The system of claim 1.

3. The collecting unit Collect restaurant ratings, menus, price ranges, opening hours, and location data 2. The system of claim 1.

4. The analysis unit Identify restaurants based on user-entered criteria 2. The system of claim 1.

5. The proposal unit The identified restaurants are displayed on the user's smartphone or computer.

2. The system of claim 1.

6. The proposal unit Display the results in a list or on a map 2. The system of claim 1.

7. The collecting unit Inferring user emotions and adjusting the timing of data collection based on the estimated user emotions 2. The system of claim 1.

8. The collecting unit Analyze past data collection history and select the optimal collection method 2. The system of claim 1.

9. The collecting unit When collecting data, filter it based on the user's current interests and past search history.

2. The system of claim 1.

Images