System

Abstract

An object of a system according to an embodiment is to enable a user to collectively perform a process from meal suggestion to route guidance with a simple conversation.SOLUTION: A system according to an embodiment includes a meal suggestion unit, a review display unit, and a route guidance unit. The meal suggestion unit analyzes a natural language instruction from the user and suggests an appropriate restaurant or cafe. The review display unit displays a review of the restaurant or cafe suggested by the meal suggestion unit. The route guidance unit provides a map showing a route to the location of the restaurant or the cafe displayed by the review display unit and information on a train to take.SELECTED DRAWING: Figure 1

Description

[Technical Field]

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

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

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

[0004] Conventional technology requires users to switch between multiple apps to gather information, which can be stressful, especially for users who are not used to using smartphones.

[0005] The system according to the embodiment aims to enable users to receive everything from meal suggestions to route guidance all at once through simple conversation. [Means for solving the problem]

[0006] The system according to the embodiment includes a meal suggestion unit, a review display unit, and a route guidance unit. The meal suggestion unit analyzes natural language commands from a user and suggests appropriate restaurants and cafes. The review display unit displays reviews of the restaurants and cafes suggested by the meal suggestion unit. The route guidance unit provides a map showing the route to the restaurant or cafe displayed by the review display unit and information on the train to take. [Effects of the Invention]

[0007] The system according to the embodiment allows the user to receive everything from meal suggestions to route guidance all at once through simple conversation. [Brief explanation of the drawings]

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

[0025] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

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

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

[0028] (Example 1) The super AI app according to the embodiment of the present invention is a system that allows users to obtain necessary information through simple conversations, eliminating the need to use multiple apps. This reduces stress for users and supports a more comfortable life.

[0029] A super AI app according to an embodiment includes a meal suggestion unit, a review display unit, and a route guidance unit. The meal suggestion unit analyzes natural language commands from a user and suggests appropriate restaurants and cafes. For example, if a user says, "I want to eat Japanese food," the meal suggestion unit searches for nearby Japanese restaurants and displays recommendations by menu. Alternatively, if a user says, "I want to eat Italian food," the meal suggestion unit can search for nearby Italian restaurants and display recommendations by menu. Alternatively, if a user says, "I want to eat a delicious lunch," the meal suggestion unit uses a generation AI to understand the request and suggest appropriate restaurants and cafes. The review display unit displays reviews of restaurants and cafes suggested by the meal suggestion unit. For example, the review display unit provides reviews in the form, "This restaurant has a very good reputation, and its lunch menu is especially popular." Alternatively, if a user asks, "What's it like?", the review display unit can display reviews of the restaurant or cafe. The review display unit also analyzes review information using a pre-finished model and provides appropriate information to the user. The route guidance unit provides a map showing the route to the restaurant or cafe displayed by the review display unit and information about the train to take. For example, the route guidance unit may provide guidance such as, "If you take this route, you'll arrive in 10 minutes on foot." Furthermore, when a user asks, "How about going now?", the route guidance unit can provide a map showing the route to the location and information about the train to take. The route guidance unit also analyzes map data and traffic information to suggest the optimal route. This allows the super AI app according to the embodiment to provide necessary information through simple conversations, eliminating the need for users to use multiple apps. For example, since a user can access everything from meal suggestions to route guidance and weather information in a single app, even those unfamiliar with smartphones can easily use it. This reduces user stress and supports a more comfortable lifestyle.

[0030] The meal suggestion unit can learn the user's past eating history and preferences and make individually customized suggestions. The meal suggestion unit, for example, collects data on restaurants the user has visited in the past and menu items the user has ordered, and makes meal suggestions for the next meal based on that information. For example, if the user has previously preferred Japanese food, the unit will preferentially suggest Japanese restaurants for the next meal. The meal suggestion unit can also learn the user's preferences and make individually customized suggestions. For example, if the user prefers a particular dish, the unit will preferentially suggest restaurants that serve that dish. The meal suggestion unit can also learn the user's past eating history and preferences and make individually customized suggestions. This enables more appropriate meal suggestions to be made based on the user's past eating history and preferences.

[0031] The meal suggestion unit can make healthy meal suggestions taking into account the user's current health condition and nutritional balance. The meal suggestion unit, for example, collects the user's health data (e.g., weight, blood pressure, blood sugar level) and makes healthy meal suggestions based on that data. For example, it can suggest low-carbohydrate menus to a user with a high blood sugar level. The meal suggestion unit can also make healthy meal suggestions taking into account the user's nutritional balance. For example, it can suggest menus that take into account the balance of calories, vitamins, and minerals. The meal suggestion unit can also make healthy meal suggestions taking into account the user's current health condition and nutritional balance. This makes it possible to make healthy meal suggestions based on the user's health condition and nutritional balance.

[0032] In addition to meal suggestions, the meal suggestion unit can also provide recipes and cooking methods to support cooking at home. For example, the meal suggestion unit can provide a recipe for making the same dish at home based on a suggested restaurant menu. For example, the meal suggestion unit can automatically generate a recipe for a dish that the user likes and provide detailed instructions on cooking steps. The meal suggestion unit can also provide cooking methods to support cooking at home. For example, it can provide video tutorials or step-by-step guides. The meal suggestion unit can also provide recipes and cooking methods to support cooking at home. This allows support for the user when cooking at home.

[0033] When making meal suggestions, the meal suggestion unit can also provide inventory information from nearby supermarkets and grocery stores to support the purchase of ingredients. For example, the meal suggestion unit collects inventory information for ingredients needed for the suggested dishes from nearby supermarkets and grocery stores and provides it to the user. For example, it displays which stores a particular ingredient can be purchased at. The meal suggestion unit can also provide inventory information from supermarkets and grocery stores to support the purchase of ingredients. For example, it updates inventory information in real time and provides it to the user. The meal suggestion unit can also support the purchase of ingredients. This makes it possible to support the user when purchasing ingredients.

[0034] The review display unit can evaluate the reliability of reviews and prioritize displaying highly reliable reviews. The review display unit, for example, builds a system for evaluating reliability based on the review poster's past posting history and ratings. For example, it prioritizes displaying reviews by highly reliable posters. The review display unit can also analyze the content of a review and evaluate its reliability. For example, if the content of a review is specific and detailed, it is determined to be highly reliable. The review display unit can also prioritize displaying highly reliable reviews. This allows the user to obtain more accurate information by preferentially displaying highly reliable reviews.

[0035] The review display unit can summarize the content of the review, allowing the user to understand it in a short time. The review display unit can summarize the content of the review using, for example, natural language processing technology, allowing the user to understand it in a short time. For example, a long review can be summarized in a few lines. The review display unit can also extract and summarize important points of the review. For example, it can pick out particularly important information in the review and summarize it based on that. The review display unit can also summarize the content of the review, allowing the user to understand it in a short time. This allows the user to understand the content of the review in a short time.

[0036] The review display unit can display photos and videos posted by users in addition to reviews, thereby providing visual information. The review display unit, for example, displays photos and videos related to reviews, allowing users to obtain information visually. For example, it displays photos of dishes and videos of the interior of the restaurant. The review display unit can also display photos and videos posted by users. For example, it displays photos and videos taken by users to provide visual information. The review display unit can also display photos and videos to provide visual information. In this way, by providing visual information, users can obtain more specific information.

[0037] The review display unit can simultaneously display ratings and comments from other users when displaying a review, thereby providing an overall rating. The review display unit, for example, builds a system that simultaneously displays ratings and comments from other users when displaying a review. For example, it displays "likes" and comments on a review. The review display unit can also display ratings and comments from other users. For example, it can aggregate ratings and comments on a review and provide an overall rating. The review display unit can also simultaneously display ratings and comments from other users when displaying a review. This makes it possible to provide an overall rating by simultaneously displaying ratings and comments from other users.

[0038] The route guidance unit can propose the optimal route according to the user's means of transportation and preferences. The route guidance unit proposes the optimal route, for example, taking into consideration the user's means of transportation (walking, bicycle, car, etc.). For example, when walking, it will preferentially propose roads that are easy to walk on. The route guidance unit can also propose the optimal route taking into consideration the user's preferences. For example, it will propose roads with beautiful scenery or quiet roads. The route guidance unit can also propose the optimal route according to the user's means of transportation and preferences. This makes it possible to propose the optimal route according to the user's means of transportation and preferences.

[0039] The route guidance unit can reflect real-time traffic information and provide a route that can be reached in the shortest time. The route guidance unit, for example, collects real-time traffic information and builds a system that proposes a route that can be reached in the shortest time. For example, the route guidance unit adjusts the route by reflecting traffic congestion and accident information. The route guidance unit can also reflect real-time traffic information and provide a route that can be reached in the shortest time. For example, the latest traffic information is obtained using a traffic API and reflected in the route. The route guidance unit can also provide a route that can be reached in the shortest time. In this way, the route that can be reached in the shortest time can be provided by reflecting real-time traffic information.

[0040] The route guidance unit can also suggest tourist spots and rest areas along the way in addition to route guidance. For example, the route guidance unit adds information about tourist spots and rest areas to the route guidance, allowing the user to enjoy themselves along the way. For example, it can suggest parks and cafes along the route. The route guidance unit can also suggest tourist spots and rest areas. For example, it can suggest popular tourist spots and places suitable for resting. The route guidance unit can also suggest tourist spots and rest areas along the way in addition to route guidance. This allows the user to enjoy themselves while traveling by suggesting tourist spots and rest areas along the way.

[0041] The route guidance unit can support health management by displaying the user's number of steps and calories burned when providing route guidance. The route guidance unit, for example, builds a system that displays the user's number of steps and calories burned when providing route guidance. For example, the route guidance unit calculates calories burned based on the distance and time required for the route. The route guidance unit can also display the number of steps and calories burned to support health management. For example, the number of steps is measured and displayed using a pedometer or a smartphone sensor. The route guidance unit can also display the number of steps and calories burned when providing route guidance. This makes it possible to display the number of steps and calories burned to support health management of the user.

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

[0043] The super AI app can also suggest activities other than meals based on the user's hobbies and interests. For example, if the user is a movie lover, it can suggest movies currently showing at nearby cinemas. If the user is a sports enthusiast, it can suggest nearby sporting events or gyms. Furthermore, if the user is interested in art, it can provide information on nearby museums and galleries. This allows it to make suggestions based on the user's diverse interests, improving their quality of life.

[0044] The super AI app can learn a user's past travel history and preferences and make individually customized travel suggestions. For example, it can collect data on tourist spots and accommodations that the user has visited in the past and use that information to make travel suggestions for the next trip. For example, if the user has previously visited beach resorts, it can prioritize beach resorts for the next trip. Also, if the user likes a particular activity, it can prioritize travel destinations that offer that activity. This makes it possible to make more appropriate travel suggestions based on the user's past travel history and preferences.

[0045] The super AI app can suggest healthy activities based on the user's current health status and fitness goals. For example, it collects the user's health data (e.g., heart rate, steps, calories burned) and suggests healthy activities based on that data. For example, if the user wants to lose weight, it can suggest activities that burn a lot of calories. Or, if the user wants to relax, it can suggest yoga or meditation classes. This makes it possible to suggest healthy activities based on the user's health status and fitness goals.

[0046] To help users manage their schedules, the Super AI app can enhance calendar functions and manage tasks, such as sending reminders and task management. For example, it can automatically analyze a user's schedule and send reminders for important appointments. It can also manage tasks set by the user and notify them when deadlines are approaching. It can also suggest tasks at optimal times based on the user's schedule. This makes schedule management more efficient and reduces stress for users.

[0047] The super AI app can provide individually customized learning plans to support users' learning. For example, it can suggest optimal learning plans based on the user's learning history and goals. It can also monitor the user's progress and adjust the learning plan as needed. Furthermore, it can provide appropriate support and resources if the user experiences difficulties during learning. This improves the user's learning efficiency and makes it easier to achieve their goals.

[0048] The processing flow of the first embodiment will be briefly explained below.

[0049] Step 1: The meal suggestion unit analyzes natural language commands from the user and suggests appropriate restaurants and cafes. For example, if the user says, "I want to eat Japanese food," it will search for nearby Japanese restaurants and display recommendations by menu. If the user says, "I want to eat Italian food," it will search for nearby Italian restaurants and display recommendations by menu. Furthermore, if the user says, "I want to eat a delicious lunch," the generation AI will understand the request and suggest appropriate restaurants and cafes. Step 2: The review display unit displays reviews of restaurants and cafes suggested by the meal suggestion unit. For example, it provides reviews in the form of "This restaurant has a very good reputation, and its lunch menu is especially popular." It can also display reviews of restaurants and cafes when the user asks "What's it like?" Furthermore, it uses a pre-finished model to analyze the review information and provide appropriate information to the user. Step 3: The route guidance unit provides a map showing the route to the restaurant or cafe displayed by the review display unit, as well as information about the train to take. For example, it may provide guidance such as, "If you take this route, you'll arrive in 10 minutes on foot." Also, if the user asks, "How about going now?", it can provide a map showing the route to the location and information about the train to take. It also analyzes map data and traffic information to suggest the optimal route.

[0050] (Example 2) The super AI app according to the embodiment of the present invention is a system that allows users to obtain necessary information through simple conversations, eliminating the need to use multiple apps. This reduces stress for users and supports a more comfortable life.

[0051] A super AI app according to an embodiment includes a meal suggestion unit, a review display unit, and a route guidance unit. The meal suggestion unit analyzes natural language commands from a user and suggests appropriate restaurants and cafes. For example, if a user says, "I want to eat Japanese food," the meal suggestion unit searches for nearby Japanese restaurants and displays recommendations by menu. Alternatively, if a user says, "I want to eat Italian food," the meal suggestion unit can search for nearby Italian restaurants and display recommendations by menu. Alternatively, if a user says, "I want to eat a delicious lunch," the meal suggestion unit uses a generation AI to understand the request and suggest appropriate restaurants and cafes. The review display unit displays reviews of restaurants and cafes suggested by the meal suggestion unit. For example, the review display unit provides reviews in the form, "This restaurant has a very good reputation, and its lunch menu is especially popular." Alternatively, if a user asks, "What's it like?", the review display unit can display reviews of the restaurant or cafe. The review display unit also analyzes review information using a pre-finished model and provides appropriate information to the user. The route guidance unit provides a map showing the route to the restaurant or cafe displayed by the review display unit and information about the train to take. For example, the route guidance unit may provide guidance such as, "If you take this route, you'll arrive in 10 minutes on foot." Furthermore, when a user asks, "How about going now?", the route guidance unit can provide a map showing the route to the location and information about the train to take. The route guidance unit also analyzes map data and traffic information to suggest the optimal route. This allows the super AI app according to the embodiment to provide necessary information through simple conversations, eliminating the need for users to use multiple apps. For example, since a user can access everything from meal suggestions to route guidance and weather information in a single app, even those unfamiliar with smartphones can easily use it. This reduces user stress and supports a more comfortable lifestyle.

[0052] The meal suggestion unit can learn the user's past eating history and preferences and make individually customized suggestions. The meal suggestion unit, for example, collects data on restaurants the user has visited in the past and menu items the user has ordered, and makes meal suggestions for the next meal based on that information. For example, if the user has previously preferred Japanese food, the unit will preferentially suggest Japanese restaurants for the next meal. The meal suggestion unit can also learn the user's preferences and make individually customized suggestions. For example, if the user prefers a particular dish, the unit will preferentially suggest restaurants that serve that dish. The meal suggestion unit can also learn the user's past eating history and preferences and make individually customized suggestions. This enables more appropriate meal suggestions to be made based on the user's past eating history and preferences.

[0053] The meal suggestion unit can make healthy meal suggestions taking into account the user's current health condition and nutritional balance. The meal suggestion unit, for example, collects the user's health data (e.g., weight, blood pressure, blood sugar level) and makes healthy meal suggestions based on that data. For example, it can suggest low-carbohydrate menus to a user with a high blood sugar level. The meal suggestion unit can also make healthy meal suggestions taking into account the user's nutritional balance. For example, it can suggest menus that take into account the balance of calories, vitamins, and minerals. The meal suggestion unit can also make healthy meal suggestions taking into account the user's current health condition and nutritional balance. This makes it possible to make healthy meal suggestions based on the user's health condition and nutritional balance.

[0054] The meal suggestion unit can use the emotion estimation function to make meal suggestions that match the user's current mood. The meal suggestion unit, for example, analyzes the user's facial expressions and voice to estimate the user's current mood. For example, if the user is tired, the meal suggestion unit can suggest a relaxing cafe or light meal. The meal suggestion unit can also use the emotion estimation function to make meal suggestions that match the user's current mood. For example, if the user is energetic, the meal suggestion unit can suggest a meal that will energize the user. The meal suggestion unit can also make meal suggestions that match the user's current mood. This makes it possible to make meal suggestions that match the user's mood.

[0055] In addition to meal suggestions, the meal suggestion unit can also provide recipes and cooking methods to support cooking at home. For example, the meal suggestion unit can provide a recipe for making the same dish at home based on a suggested restaurant menu. For example, the meal suggestion unit can automatically generate a recipe for a dish that the user likes and provide detailed instructions on cooking steps. The meal suggestion unit can also provide cooking methods to support cooking at home. For example, it can provide video tutorials or step-by-step guides. The meal suggestion unit can also provide recipes and cooking methods to support cooking at home. This allows support for the user when cooking at home.

[0056] When making meal suggestions, the meal suggestion unit can also provide inventory information from nearby supermarkets and grocery stores to support the purchase of ingredients. For example, the meal suggestion unit collects inventory information for ingredients needed for the suggested dishes from nearby supermarkets and grocery stores and provides it to the user. For example, it displays which stores a particular ingredient can be purchased at. The meal suggestion unit can also provide inventory information from supermarkets and grocery stores to support the purchase of ingredients. For example, it updates inventory information in real time and provides it to the user. The meal suggestion unit can also support the purchase of ingredients. This makes it possible to support the user when purchasing ingredients.

[0057] The meal suggestion unit can use the emotion estimation function to analyze in real time how the user feels about the meal suggestion and adjust the suggestion content. The meal suggestion unit, for example, analyzes the facial expression and voice of the user when receiving the meal suggestion and estimates the emotion in real time. For example, if the user is dissatisfied with the suggestion, the meal suggestion unit makes a different suggestion. The meal suggestion unit can also use the emotion estimation function to analyze the user's emotion in real time and adjust the suggestion content. For example, if the user is happy, the meal suggestion unit continues to make similar suggestions. The meal suggestion unit can also adjust the suggestion content based on the user's emotion. This allows the suggestion content to be dynamically adjusted based on the user's emotion.

[0058] The review display unit can evaluate the reliability of reviews and prioritize displaying highly reliable reviews. The review display unit, for example, builds a system for evaluating reliability based on the review poster's past posting history and ratings. For example, it prioritizes displaying reviews by highly reliable posters. The review display unit can also analyze the content of a review and evaluate its reliability. For example, if the content of a review is specific and detailed, it is determined to be highly reliable. The review display unit can also prioritize displaying highly reliable reviews. This allows the user to obtain more accurate information by preferentially displaying highly reliable reviews.

[0059] The review display unit can summarize the content of the review, allowing the user to understand it in a short time. The review display unit can summarize the content of the review using, for example, natural language processing technology, allowing the user to understand it in a short time. For example, a long review can be summarized in a few lines. The review display unit can also extract and summarize important points of the review. For example, it can pick out particularly important information in the review and summarize it based on that. The review display unit can also summarize the content of the review, allowing the user to understand it in a short time. This allows the user to understand the content of the review in a short time.

[0060] The review display unit can use the emotion estimation function to analyze the emotional tone of the review and provide appropriate information to the user. The review display unit, for example, analyzes the text of the review and evaluates the emotional tone using the emotion estimation function. For example, reviews with a positive tone are preferentially displayed. The review display unit can also analyze the emotional tone and provide appropriate information to the user. For example, reviews with a negative tone are displayed to call attention to the review. The review display unit can also use the emotion estimation function to analyze the emotional tone of the review and provide appropriate information to the user. In this way, appropriate information can be provided to the user by analyzing the emotional tone of the review.

[0061] The review display unit can display photos and videos posted by users in addition to reviews, thereby providing visual information. The review display unit, for example, displays photos and videos related to reviews, allowing users to obtain information visually. For example, it displays photos of dishes and videos of the interior of the restaurant. The review display unit can also display photos and videos posted by users. For example, it displays photos and videos taken by users to provide visual information. The review display unit can also display photos and videos to provide visual information. In this way, by providing visual information, users can obtain more specific information.

[0062] The review display unit can simultaneously display ratings and comments from other users when displaying a review, thereby providing an overall rating. The review display unit, for example, builds a system that simultaneously displays ratings and comments from other users when displaying a review. For example, it displays "likes" and comments on a review. The review display unit can also display ratings and comments from other users. For example, it can aggregate ratings and comments on a review and provide an overall rating. The review display unit can also simultaneously display ratings and comments from other users when displaying a review. This makes it possible to provide an overall rating by simultaneously displaying ratings and comments from other users.

[0063] The review display unit can use the emotion estimation function to collect users' emotional reactions to reviews and adjust the display order of the reviews. The review display unit, for example, builds a system that collects users' emotional reactions to reviews in real time and adjusts the display order of reviews based on that data. For example, reviews with a large number of positive emotional reactions are preferentially displayed. The review display unit can also use the emotion estimation function to collect users' emotional reactions and adjust the display order of reviews. For example, reviews with a large number of negative emotional reactions are displayed later. The review display unit can also adjust the display order of reviews based on the users' emotional reactions. This allows the display order of reviews to be adjusted based on the users' emotional reactions.

[0064] The route guidance unit can propose the optimal route according to the user's means of transportation and preferences. The route guidance unit proposes the optimal route, for example, taking into consideration the user's means of transportation (walking, bicycle, car, etc.). For example, when walking, it will preferentially propose roads that are easy to walk on. The route guidance unit can also propose the optimal route taking into consideration the user's preferences. For example, it will propose roads with beautiful scenery or quiet roads. The route guidance unit can also propose the optimal route according to the user's means of transportation and preferences. This makes it possible to propose the optimal route according to the user's means of transportation and preferences.

[0065] The route guidance unit can reflect real-time traffic information and provide a route that can be reached in the shortest time. The route guidance unit, for example, collects real-time traffic information and builds a system that proposes a route that can be reached in the shortest time. For example, the route guidance unit adjusts the route by reflecting traffic congestion and accident information. The route guidance unit can also reflect real-time traffic information and provide a route that can be reached in the shortest time. For example, the latest traffic information is obtained using a traffic API and reflected in the route. The route guidance unit can also provide a route that can be reached in the shortest time. In this way, the route that can be reached in the shortest time can be provided by reflecting real-time traffic information.

[0066] The route guidance unit can use the emotion estimation function to consider the user's stress level and suggest a less stressful route. The route guidance unit, for example, analyzes the user's facial expressions and voice to build a system that estimates the stress level. For example, if the user is feeling stressed, it can suggest a route that goes along a quiet road or through a park. The route guidance unit can also use the emotion estimation function to consider the user's stress level and suggest a less stressful route. For example, it can suggest a route that allows the user to relax. The route guidance unit can also consider the user's stress level and suggest a less stressful route. In this way, it is possible to suggest a less stressful route in consideration of the user's stress level.

[0067] The route guidance unit can also suggest tourist spots and rest areas along the way in addition to route guidance. For example, the route guidance unit adds information about tourist spots and rest areas to the route guidance, allowing the user to enjoy themselves along the way. For example, it can suggest parks and cafes along the route. The route guidance unit can also suggest tourist spots and rest areas. For example, it can suggest popular tourist spots and places suitable for resting. The route guidance unit can also suggest tourist spots and rest areas along the way in addition to route guidance. This allows the user to enjoy themselves while traveling by suggesting tourist spots and rest areas along the way.

[0068] The route guidance unit can support health management by displaying the user's number of steps and calories burned when providing route guidance. The route guidance unit, for example, builds a system that displays the user's number of steps and calories burned when providing route guidance. For example, the route guidance unit calculates calories burned based on the distance and time required for the route. The route guidance unit can also display the number of steps and calories burned to support health management. For example, the number of steps is measured and displayed using a pedometer or a smartphone sensor. The route guidance unit can also display the number of steps and calories burned when providing route guidance. This makes it possible to display the number of steps and calories burned to support health management of the user.

[0069] The route guidance unit can use the emotion estimation function to analyze the user's emotional response to route guidance in real time and adjust the guidance content. The route guidance unit, for example, builds a system that collects the user's emotional response to route guidance in real time and adjusts the guidance content based on the data. For example, if the user is feeling anxious, detailed guidance is provided. The route guidance unit can also use the emotion estimation function to analyze the user's emotional response in real time and adjust the guidance content. For example, the guidance content is changed so that the user feels at ease. The route guidance unit can also adjust the guidance content based on the user's emotional response. This makes it possible to dynamically adjust the route guidance content based on the user's emotional response.

[0070] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0071] The super AI app can also suggest activities other than meals based on the user's hobbies and interests. For example, if the user is a movie lover, it can suggest movies currently showing at nearby cinemas. If the user is a sports enthusiast, it can suggest nearby sporting events or gyms. Furthermore, if the user is interested in art, it can provide information on nearby museums and galleries. This allows it to make suggestions based on the user's diverse interests, improving their quality of life.

[0072] The super AI app can learn a user's past travel history and preferences and make individually customized travel suggestions. For example, it can collect data on tourist spots and accommodations that the user has visited in the past and use that information to make travel suggestions for the next trip. For example, if the user has previously visited beach resorts, it can prioritize beach resorts for the next trip. Also, if the user likes a particular activity, it can prioritize travel destinations that offer that activity. This makes it possible to make more appropriate travel suggestions based on the user's past travel history and preferences.

[0073] The super AI app can suggest healthy activities based on the user's current health status and fitness goals. For example, it collects the user's health data (e.g., heart rate, steps, calories burned) and suggests healthy activities based on that data. For example, if the user wants to lose weight, it can suggest activities that burn a lot of calories. Or, if the user wants to relax, it can suggest yoga or meditation classes. This makes it possible to suggest healthy activities based on the user's health status and fitness goals.

[0074] The super AI app can use its emotion estimation function to suggest entertainment that matches the user's current mood. For example, it can analyze the user's facial expressions and voice to estimate their current mood. For example, if the user is tired, it can suggest relaxing movies and music. If the user is energetic, it can also suggest action movies and upbeat music. This makes it possible to suggest entertainment that matches the user's mood.

[0075] The super AI app can estimate the user's emotions and suggest appropriate relaxation methods based on the estimated emotions. For example, if the user is feeling stressed, it can provide meditation or deep breathing guides. If the user wants to relax, it can also suggest aromatherapy or massage methods. Furthermore, if the user is feeling anxious, it can provide relaxing music or natural sounds. In this way, it can suggest appropriate relaxation methods based on the user's emotions.

[0076] The super AI app can estimate the user's emotions and suggest appropriate reading lists based on the estimated emotions. For example, if the user is feeling down, it can suggest uplifting books or books with positive content. If the user wants to relax, it can suggest relaxing novels or essays. Furthermore, if the user is excited, it can suggest thrilling mysteries or action novels. In this way, it can suggest appropriate reading lists based on the user's emotions.

[0077] The super AI app can estimate the user's emotions and suggest an appropriate exercise program based on the estimated emotions. For example, if the user is feeling stressed, it can suggest a relaxing yoga or stretching program. If the user is energetic, it can suggest a high-intensity interval training (HIIT) or running program. Furthermore, if the user wants to relax, it can suggest a light walking or meditation program. In this way, it can suggest an appropriate exercise program based on the user's emotions.

[0078] To help users manage their schedules, the Super AI app can enhance calendar functions and manage tasks, such as sending reminders and task management. For example, it can automatically analyze a user's schedule and send reminders for important appointments. It can also manage tasks set by the user and notify them when deadlines are approaching. It can also suggest tasks at optimal times based on the user's schedule. This makes schedule management more efficient and reduces stress for users.

[0079] The super AI app can provide individually customized learning plans to support users' learning. For example, it can suggest optimal learning plans based on the user's learning history and goals. It can also monitor the user's progress and adjust the learning plan as needed. Furthermore, it can provide appropriate support and resources if the user experiences difficulties during learning. This improves the user's learning efficiency and makes it easier to achieve their goals.

[0080] The super AI app can estimate the user's emotions and suggest a music playlist that is suitable for the user based on the estimated emotions. For example, if the user wants to relax, it can suggest relaxing music. If the user wants to cheer up, it can suggest upbeat music. Furthermore, if the user wants to concentrate, it can suggest music that will help improve concentration. In this way, it can suggest an appropriate music playlist based on the user's emotions.

[0081] The processing flow of the second embodiment will be briefly explained below.

[0082] Step 1: The meal suggestion unit analyzes natural language commands from the user and suggests appropriate restaurants and cafes. For example, if the user says, "I want to eat Japanese food," it will search for nearby Japanese restaurants and display recommendations by menu. If the user says, "I want to eat Italian food," it will search for nearby Italian restaurants and display recommendations by menu. Furthermore, if the user says, "I want to eat a delicious lunch," the generation AI will understand the request and suggest appropriate restaurants and cafes. Step 2: The review display unit displays reviews of restaurants and cafes suggested by the meal suggestion unit. For example, it provides reviews in the form of "This restaurant has a very good reputation, and its lunch menu is especially popular." It can also display reviews of restaurants and cafes when the user asks "What's it like?" Furthermore, it uses a pre-finished model to analyze the review information and provide appropriate information to the user. Step 3: The route guidance unit provides a map showing the route to the restaurant or cafe displayed by the review display unit, as well as information about the train to take. For example, it may provide guidance such as, "If you take this route, you'll arrive in 10 minutes on foot." Also, if the user asks, "How about going now?", it can provide a map showing the route to the location and information about the train to take. It also analyzes map data and traffic information to suggest the optimal route.

[0083] 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.

[0084] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (Internet Search<URL: https: / / openai.com / blog / chatgpt> Examples of generative AIs include the data generation model 58, such as a neural network model (e.g., a neural network model), and a neural network model (e.g., a neural network model). The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating speech, text data indicating text, and image data indicating an image is also input to the data generation model 58. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specification processing unit 290 performs the above-mentioned specification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0085] 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.

[0086] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0087] 3, the data processing system 210 includes the data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0088] 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.

[0089] 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.

[0090] 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.

[0091] 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).

[0092] 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.

[0093] 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.

[0094] 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.

[0095] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

[0096] In the smart glasses 214, the specific processing is performed by the processor 46. A specific processing program 60 is stored in the storage 50. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. Note that the smart glasses 214 may have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59.

[0097] 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.

[0098] 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.

[0099] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0100] 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.

[0101] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0102] 5, the data processing system 310 includes the data processing device 12 and a headset type terminal 314. An example of the data processing device 12 is a server.

[0103] 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.

[0104] 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.

[0105] 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.

[0106] 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).

[0107] 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.

[0108] Fig. 6 shows an example of the main functions of the data processing device 12 and the headset type terminal 314. As shown in Fig. 6, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0109] 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.

[0110] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

[0111] In the headset type terminal 314, the identification process is performed by the processor 46. A identification program 60 is stored in the storage 50. The processor 46 reads the identification program 60 from the storage 50 and executes the read identification program 60 on the RAM 48. The identification process is realized by the processor 46 operating as a control unit 46A in accordance with the identification program 60 executed on the RAM 48. Note that the headset type terminal 314 may also have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59.

[0112] 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.

[0113] 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.

[0114] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0115] 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.

[0116] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0117] 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.

[0118] 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.

[0119] 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.

[0120] 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.

[0121] 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).

[0122] 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.

[0123] 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.

[0124] 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.

[0125] 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.

[0126] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

[0127] In the robot 414, the processor 46 performs the identification process. A identification program 60 is stored in the storage 50. The processor 46 reads the identification program 60 from the storage 50 and executes the read identification program 60 on the RAM 48. The identification process is realized by the processor 46 operating as a control unit 46A in accordance with the identification program 60 executed on the RAM 48. The robot 414 may have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59.

[0128] 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.

[0129] 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.

[0130] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0131] 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.

[0132] 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.

[0133] 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.

[0134] 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.

[0135] 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).

[0136] 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.

[0137] 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."

[0138] 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.

[0139] 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.

[0140] 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.

[0141] 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.

[0142] 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.

[0143] 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.

[0144] 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.

[0145] 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.

[0146] 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.

[0147] 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.

[0148] The above-described description and illustrations are a detailed explanation of the parts related to the technology of the present disclosure and are merely an example of the technology of the present disclosure. For example, the above description of the configuration, functions, actions, and effects is an explanation of an example of the configuration, functions, actions, and effects of the parts related to the technology of the present disclosure. Therefore, it goes without saying that unnecessary parts may be deleted, new elements may be added, or replacements may be made to the above-described description and illustrations within the scope of the gist of the technology of the present disclosure. Furthermore, to avoid confusion and facilitate understanding of the parts related to the technology of the present disclosure, the above-described description and illustrations omit explanations of common technical knowledge that do not require particular explanation to enable the implementation of the technology of the present disclosure.

[0149] All publications, patent applications, and technical standards mentioned in this specification are herein incorporated by reference to the same extent as if each individual publication, patent application, or technical standard was specifically and individually indicated to be incorporated by reference. [Explanation of symbols]

[0150] 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 meal suggestion unit that analyzes natural language commands from the user and suggests appropriate restaurants and cafes; a review display unit that displays reviews of restaurants and cafes suggested by the meal suggestion unit; a route guidance unit that provides a map showing the route to the restaurant or cafe displayed by the review display unit and information on the train to take. A system characterized by:

2. The meal suggestion unit Learn about the user's past eating history and preferences to provide personalized recommendations 2. The system of claim 1.

3. The meal suggestion unit In addition to meal suggestions, it also provides recipes and cooking methods to help you cook at home.

2. The system of claim 1.

4. The review display unit Evaluate the reliability of the reviews and display highly reliable reviews preferentially 2. The system of claim 1.

5. The route guidance unit Proposing the optimal route according to the user's means of transportation and preferences 2. The system of claim 1.

6. The meal suggestion unit Providing meal suggestions tailored to the user's current mood 2. The system of claim 1.

7. The review display unit Analyze the emotional tone of the review and provide relevant information to the user 2. The system of claim 1.

8. The route guidance unit Consider the user's stress level and suggest a less stressful route 2. The system of claim 1.

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