System

Abstract

An object of a system according to an embodiment is to efficiently and accurately respond to a request of a client.SOLUTION: A system includes a request reception unit, an analysis unit, a proposal unit, and a storage unit. The request receiving unit receives a request from a client. The analyzing unit analyzes the request received by the request receiving unit. The proposal unit proposes an optimal route or destination on the basis of a result analyzed by the analysis unit. The storage unit stores the route proposed by the proposal unit.SELECTED DRAWING: Figure 1

Description

[Technical Field]

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

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

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

[0004] Conventional technology has had the problem that responding to client requests is dependent on the operator, limiting the improvement of efficiency and accuracy.

[0005] The system according to the embodiment aims to respond to the requests of clients efficiently and accurately. [Means for solving the problem]

[0006] The system according to the embodiment includes a request receiving unit, an analysis unit, a proposal unit, and a storage unit. The request receiving unit receives a request from a client. The analysis unit analyzes the request received by the request receiving unit. The proposal unit proposes an optimal route or destination based on the results of the analysis by the analysis unit. The storage unit stores the route proposed by the proposal unit. [Effects of the Invention]

[0007] The system according to the embodiment can respond to the request of the client efficiently and accurately. [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 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 elderly service system according to an embodiment of the present invention is a system that automatically receives requests from clients, analyzes them using a generation AI, proposes optimal routes and destinations, and stores them. As a result, the elderly service system can propose optimal routes and destinations based on the client's requests and use them for future trips.

[0029] An elderly service system according to an embodiment includes a request receiving unit, an analysis unit, a proposal unit, and a memory unit. The request receiving unit receives a request from a requester. For example, the request can be received in the form of text input, voice input, or selection from options. The request receiving unit can also receive the request from the requester in real time. The analysis unit analyzes the request received by the request receiving unit. For example, the analysis unit can analyze the request using natural language processing technology to understand the requester's intent. The analysis unit can also analyze the request using a machine learning algorithm. The proposal unit proposes an optimal route and destination based on the results of the analysis by the analysis unit. For example, the optimal route and destination can be selected based on criteria such as distance, time, cost, and user preferences. The proposal unit can also present multiple options in response to the request from the requester. The memory unit stores the route proposed by the proposal unit. For example, the route can be stored in a format that can be saved in a database. The memory unit can also specify and store the type of information to be saved. This allows the optimal route and destination to be proposed based on the requester's request and be used for future visits.

[0030] The suggestion unit learns the client's past behavioral patterns and can make suggestions based on the client's preferences and habits. For example, the suggestion unit uses a generation AI to learn the client's past behavioral patterns and understand the client's favorite places and preferred time of day. For example, if the client has a habit of going to a cafe every Tuesday, the suggestion unit can suggest the most suitable cafe based on that information. The suggestion unit can also prioritize suggestions for the client's preferred places and time of day based on the client's preferences and habits. This makes it possible to make suggestions based on the client's preferences and habits.

[0031] The analysis unit monitors the requester's real-time health condition and can suggest optimal routes and destinations based on that. For example, the generation AI in the analysis unit obtains step count and heart rate data from the requester's smartwatch or fitness tracker and monitors the health condition in real time. For example, if the heart rate is high, it can suggest places where the requester can relax. The analysis unit can also suggest routes and destinations that the requester can travel comfortably based on the requester's health condition. This makes it possible to make optimal suggestions based on the requester's health condition.

[0032] The suggestion unit can present multiple options in response to a request from a client, allowing the client to select one. For example, the suggestion unit uses a generation AI to analyze the client's request and generate multiple options. For example, in response to the question "where to buy cafe au lait," the suggestion unit can present multiple nearby cafes and convenience stores. The suggestion unit can also provide detailed information about the options when the client selects them, allowing the client to select from multiple options.

[0033] The proposal unit can propose a composite route that includes the use of public transportation in accordance with the requester's request. For example, the proposal unit uses a generation AI to analyze the requester's request and propose the optimal route that includes the use of public transportation. For example, it can present a route that takes into account bus and train timetables to reach the destination in the shortest time. The proposal unit can also provide transfer information and fare information when the requester uses public transportation. This makes it possible to propose a composite route that includes the use of public transportation.

[0034] The memory unit can analyze the requester's past route data and propose a route that suits the optimal time of day and season. For example, the generation AI in the memory unit analyzes the requester's past route data and proposes a route that suits the optimal time of day. For example, it proposes a route that avoids congestion during morning rush hour. The memory unit can also propose a route that suits the season based on the requester's past route data. For example, it proposes a route that passes through famous cherry blossom viewing spots in spring. This makes it possible to propose a route that suits the optimal time of day and season based on the requester's past route data.

[0035] The memory unit can propose the optimal route, taking into account congestion and weather information, based on the requester's past route data. For example, the generation AI acquires real-time congestion data and combines it with past route data to propose the optimal route. For example, it presents a route that avoids congestion. The memory unit can also propose a route that allows the requester to travel comfortably, taking into account weather information. For example, it can propose a route with a roof on a rainy day. This makes it possible to propose the optimal route, taking into account congestion and weather information.

[0036] The memory unit can propose the optimal route based on the requester's past route data, comparing it with route data of other users. For example, the generation AI in the memory unit compares the requester's past route data with that of other users and proposes the optimal route. For example, it refers to routes preferred by other users. The memory unit can also propose a new route that the requester should try based on the route data of other users. This makes it possible to propose the optimal route by comparing it with route data of other users.

[0037] The memory unit can propose a route that includes tourist spot and event information based on the requester's past route data. For example, the generation AI analyzes the requester's past route data and proposes a route that includes tourist spot and event information. For example, it proposes a route that allows the requester to enjoy sightseeing. The memory unit can also propose a route that includes tourist spot and event information based on the requester's interests. This makes it possible to propose a route that includes tourist spot and event information.

[0038] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0039] The suggestion unit can suggest routes and destinations based on specific themes in response to the request of the client. For example, if the client requests that they "want to visit historical places," the suggestion unit can suggest routes that include historical landmarks in the area. If the client requests that they "want to enjoy nature," the suggestion unit can also suggest routes that include parks and nature reserves. Furthermore, if the client requests that they "want to go on a gourmet tour," the suggestion unit can suggest routes that include famous restaurants and cafes in the area. This makes it possible to suggest routes based on the client's specific interests and themes.

[0040] The analysis unit can obtain the requester's real-time location information and suggest the optimal route and destination based on the requester's current location. For example, if the requester is looking for the nearest cafe from their current location, the analysis unit can suggest the optimal cafe based on their current location. Also, if the requester is looking for the nearest public transportation station from their current location, the analysis unit can provide that information. Furthermore, if the requester is looking for the nearest tourist spot from their current location, the analysis unit can provide that information. This makes it possible to make real-time suggestions based on the requester's current location.

[0041] The suggestion unit can suggest a route for participating in a specific event or activity in accordance with the request of the requester. For example, if the requester requests that he or she "want to go to a concert," the suggestion unit can suggest the optimal route to the concert venue. Also, if the requester requests that he or she "want to participate in a sporting event," the suggestion unit can suggest the optimal route to the sporting event venue. Furthermore, if the requester requests that he or she "want to participate in a workshop," the suggestion unit can suggest the optimal route to the workshop venue. This makes it possible to suggest routes based on the requester's specific events or activities.

[0042] The analysis unit can suggest new activities and places for the client to try based on the client's past behavioral data. For example, it can analyze the places the client has visited and the activities they have participated in in the past to suggest places the client has not visited yet but may be interested in. It can also suggest new activities the client may enjoy based on the activities the client has participated in in the past. It can also suggest new spots near places the client has visited in the past. This makes it possible to make new suggestions based on the client's past behavioral data.

[0043] The suggestion unit can suggest routes and destinations for achieving specific health goals according to the request of the client. For example, if the client requests "I want to walk," the suggestion unit can suggest routes suitable for walking. Also, if the client requests "I want to relax," the suggestion unit can suggest places where one can relax. Furthermore, if the client requests "I want to exercise," the suggestion unit can suggest places and routes suitable for exercise. This makes it possible to suggest routes and destinations based on the client's specific health goals.

[0044] The processing flow of the first embodiment will be briefly explained below.

[0045] Step 1: The request receiving unit receives a request from a requester. For example, the request can be received in the form of text input, voice input, selection from options, etc. The request receiving unit can also receive the request from the requester in real time. Step 2: The analysis unit analyzes the request received by the request receiving unit. For example, the analysis unit analyzes the request using natural language processing technology to understand the requester's intention. The analysis unit can also analyze the request using a machine learning algorithm. Step 3: The suggestion unit proposes optimal routes and destinations based on the results of the analysis by the analysis unit. For example, it selects optimal routes and destinations based on criteria such as distance, time, cost, and user preferences. The suggestion unit can also present multiple options according to the requester's request. Step 4: The storage unit stores the route proposed by the proposal unit. For example, the storage unit stores the route in a format that is stored in a database. The storage unit can also specify the type of information to be stored.

[0046] (Example 2) The elderly service system according to an embodiment of the present invention is a system that automatically receives requests from clients, analyzes them using a generation AI, proposes optimal routes and destinations, and stores them. As a result, the elderly service system can propose optimal routes and destinations based on the client's requests and use them for future trips.

[0047] An elderly service system according to an embodiment includes a request receiving unit, an analysis unit, a proposal unit, and a memory unit. The request receiving unit receives a request from a requester. For example, the request can be received in the form of text input, voice input, or selection from options. The request receiving unit can also receive the request from the requester in real time. The analysis unit analyzes the request received by the request receiving unit. For example, the analysis unit can analyze the request using natural language processing technology to understand the requester's intent. The analysis unit can also analyze the request using a machine learning algorithm. The proposal unit proposes an optimal route and destination based on the results of the analysis by the analysis unit. For example, the optimal route and destination can be selected based on criteria such as distance, time, cost, and user preferences. The proposal unit can also present multiple options in response to the request from the requester. The memory unit stores the route proposed by the proposal unit. For example, the route can be stored in a format that can be saved in a database. The memory unit can also specify and store the type of information to be saved. This allows the optimal route and destination to be proposed based on the requester's request and be used for future visits.

[0048] The suggestion unit learns the client's past behavioral patterns and can make suggestions based on the client's preferences and habits. For example, the suggestion unit uses a generation AI to learn the client's past behavioral patterns and understand the client's favorite places and preferred time of day. For example, if the client has a habit of going to a cafe every Tuesday, the suggestion unit can suggest the most suitable cafe based on that information. The suggestion unit can also prioritize suggestions for the client's preferred places and time of day based on the client's preferences and habits. This makes it possible to make suggestions based on the client's preferences and habits.

[0049] The analysis unit monitors the requester's real-time health condition and can suggest optimal routes and destinations based on that. For example, the generation AI in the analysis unit obtains step count and heart rate data from the requester's smartwatch or fitness tracker and monitors the health condition in real time. For example, if the heart rate is high, it can suggest places where the requester can relax. The analysis unit can also suggest routes and destinations that the requester can travel comfortably based on the requester's health condition. This makes it possible to make optimal suggestions based on the requester's health condition.

[0050] The analysis unit can use the emotion estimation function to analyze the client's emotional state and suggest relaxing places to reduce stress. For example, the analysis unit uses a generation AI to analyze the client's voice and facial expressions and estimate the client's emotional state in real time. For example, if the client is feeling stressed, the analysis unit can suggest a park or cafe where the client can relax. The analysis unit can also prioritize suggesting places where the client can relax based on the client's emotional state. This makes it possible to suggest relaxing places based on the client's emotional state.

[0051] The suggestion unit can present multiple options in response to a request from a client, allowing the client to select one. For example, the suggestion unit uses a generation AI to analyze the client's request and generate multiple options. For example, in response to the question "where to buy cafe au lait," the suggestion unit can present multiple nearby cafes and convenience stores. The suggestion unit can also provide detailed information about the options when the client selects them, allowing the client to select from multiple options.

[0052] The proposal unit can propose a composite route that includes the use of public transportation in accordance with the requester's request. For example, the proposal unit uses a generation AI to analyze the requester's request and propose the optimal route that includes the use of public transportation. For example, it can present a route that takes into account bus and train timetables to reach the destination in the shortest time. The proposal unit can also provide transfer information and fare information when the requester uses public transportation. This makes it possible to propose a composite route that includes the use of public transportation.

[0053] The analysis unit uses an emotion estimation function to analyze the client's emotions in real time and make suggestions that elicit positive emotions. For example, the analysis unit uses a generative AI to analyze the client's voice and facial expressions and estimate their emotions in real time. For example, if the client is feeling anxious, the analysis unit can make suggestions that will give the client a sense of security. The analysis unit can also suggest places and activities that will evoke positive emotions for the client based on the client's emotional state. This makes it possible to make positive suggestions based on the client's emotions.

[0054] The memory unit can analyze the requester's past route data and propose a route that suits the optimal time of day and season. For example, the generation AI in the memory unit analyzes the requester's past route data and proposes a route that suits the optimal time of day. For example, it proposes a route that avoids congestion during morning rush hour. The memory unit can also propose a route that suits the season based on the requester's past route data. For example, it proposes a route that passes through famous cherry blossom viewing spots in spring. This makes it possible to propose a route that suits the optimal time of day and season based on the requester's past route data.

[0055] The memory unit can propose the optimal route, taking into account congestion and weather information, based on the requester's past route data. For example, the generation AI acquires real-time congestion data and combines it with past route data to propose the optimal route. For example, it presents a route that avoids congestion. The memory unit can also propose a route that allows the requester to travel comfortably, taking into account weather information. For example, it can propose a route with a roof on a rainy day. This makes it possible to propose the optimal route, taking into account congestion and weather information.

[0056] The memory unit can use the emotion estimation function to analyze the client's emotional data from past route use and prioritize suggesting routes that elicit positive emotions. For example, the memory unit can use the generation AI to analyze the client's emotional data from past route use and prioritize suggesting routes that elicit positive emotions. For example, it can suggest routes that allow the client to relax. The memory unit can also prioritize suggesting routes that the client enjoyed based on the client's emotional data. This allows emotional elements to be reflected in the evaluation by generating summaries that capture emotional nuances.

[0057] The memory unit can propose the optimal route based on the requester's past route data, comparing it with route data of other users. For example, the generation AI in the memory unit compares the requester's past route data with that of other users and proposes the optimal route. For example, it refers to routes preferred by other users. The memory unit can also propose a new route that the requester should try based on the route data of other users. This makes it possible to propose the optimal route by comparing it with route data of other users.

[0058] The memory unit can propose a route that includes tourist spot and event information based on the requester's past route data. For example, the generation AI analyzes the requester's past route data and proposes a route that includes tourist spot and event information. For example, it proposes a route that allows the requester to enjoy sightseeing. The memory unit can also propose a route that includes tourist spot and event information based on the requester's interests. This makes it possible to propose a route that includes tourist spot and event information.

[0059] The memory unit can use the emotion estimation function to analyze the emotional data of the client when they have used a route in the past and suggest a route that is likely to resonate with them emotionally. For example, the memory unit uses a generation AI to analyze the emotional data of the client when they have used a route in the past and suggest a route that is likely to resonate with them emotionally. For example, it can prioritize suggesting routes that the client has enjoyed. The memory unit can also suggest routes that include scenery that the client resonates with, based on the client's emotional data. This makes it possible to suggest a route that is likely to resonate with them emotionally, based on the client's emotional data.

[0060] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0061] The suggestion unit can suggest routes and destinations based on specific themes in response to the request of the client. For example, if the client requests that they "want to visit historical places," the suggestion unit can suggest routes that include historical landmarks in the area. If the client requests that they "want to enjoy nature," the suggestion unit can also suggest routes that include parks and nature reserves. Furthermore, if the client requests that they "want to go on a gourmet tour," the suggestion unit can suggest routes that include famous restaurants and cafes in the area. This makes it possible to suggest routes based on the client's specific interests and themes.

[0062] The analysis unit can obtain the requester's real-time location information and suggest the optimal route and destination based on the requester's current location. For example, if the requester is looking for the nearest cafe from their current location, the analysis unit can suggest the optimal cafe based on their current location. Also, if the requester is looking for the nearest public transportation station from their current location, the analysis unit can provide that information. Furthermore, if the requester is looking for the nearest tourist spot from their current location, the analysis unit can provide that information. This makes it possible to make real-time suggestions based on the requester's current location.

[0063] The suggestion unit can suggest a route for participating in a specific event or activity in accordance with the request of the requester. For example, if the requester requests that he or she "want to go to a concert," the suggestion unit can suggest the optimal route to the concert venue. Also, if the requester requests that he or she "want to participate in a sporting event," the suggestion unit can suggest the optimal route to the sporting event venue. Furthermore, if the requester requests that he or she "want to participate in a workshop," the suggestion unit can suggest the optimal route to the workshop venue. This makes it possible to suggest routes based on the requester's specific events or activities.

[0064] The analysis unit can suggest new activities and places for the client to try based on the client's past behavioral data. For example, it can analyze the places the client has visited and the activities they have participated in in the past to suggest places the client has not visited yet but may be interested in. It can also suggest new activities the client may enjoy based on the activities the client has participated in in the past. It can also suggest new spots near places the client has visited in the past. This makes it possible to make new suggestions based on the client's past behavioral data.

[0065] The suggestion unit can suggest routes and destinations for achieving specific health goals according to the request of the client. For example, if the client requests "I want to walk," the suggestion unit can suggest routes suitable for walking. Also, if the client requests "I want to relax," the suggestion unit can suggest places where one can relax. Furthermore, if the client requests "I want to exercise," the suggestion unit can suggest places and routes suitable for exercise. This makes it possible to suggest routes and destinations based on the client's specific health goals.

[0066] The analysis unit can use the emotion estimation function to analyze the emotional state of the client and suggest music or podcasts that will help the client relax. For example, if the client is feeling stressed, the analysis unit can suggest relaxing music. Also, if the client is feeling anxious, the analysis unit can suggest podcasts that will give the client a sense of security. Furthermore, if the client is tired, the analysis unit can suggest music that will refresh the client. This makes it possible to suggest relaxing music or podcasts based on the client's emotional state.

[0067] The suggestion unit can analyze the client's emotional state using the emotion estimation function and suggest activities and events that the client can enjoy. For example, if the client is feeling bored, the suggestion unit can suggest fun events and activities. If the client is feeling lonely, the suggestion unit can also suggest social events. Furthermore, if the client is looking for excitement, the suggestion unit can suggest activities that stimulate adrenaline. This makes it possible to suggest enjoyable activities and events based on the client's emotional state.

[0068] The analysis unit uses the emotion estimation function to analyze the emotional state of the client and can suggest places and routes where the client can relax. For example, if the client is feeling stressed, the analysis unit can suggest a route that goes through a park or nature where the client can relax. Also, if the client is feeling anxious, the analysis unit can suggest a place that gives a sense of security. Furthermore, if the client is tired, the analysis unit can suggest a place where the client can refresh themselves. This makes it possible to suggest places and routes where the client can relax based on the client's emotional state.

[0069] The suggestion unit can use the emotion estimation function to analyze the client's emotional state and suggest places and routes where the client will have positive emotions. For example, if the client is feeling sad, the suggestion unit can suggest places that will brighten the mood. If the client is feeling angry, the suggestion unit can also suggest places where the client can relax. Furthermore, if the client is feeling happy, the suggestion unit can suggest places that will further enhance that emotion. This makes it possible to suggest places and routes that will elicit positive emotions based on the client's emotional state.

[0070] The analysis unit uses the emotion estimation function to analyze the emotional state of the client and can suggest places and routes that are likely to resonate with the client emotionally. For example, if the client is looking for something moving, the analysis unit can suggest routes that include moving scenery. Also, if the client is looking for healing, the analysis unit can suggest places that have a healing effect. Furthermore, if the client is looking for fun, the analysis unit can suggest places and routes that are likely to resonate with the client emotionally. This makes it possible to suggest places and routes that are likely to resonate with the client emotionally based on the client's emotional state.

[0071] The processing flow of the second embodiment will be briefly explained below.

[0072] Step 1: The request receiving unit receives a request from a requester. For example, the request can be received in the form of text input, voice input, selection from options, etc. The request receiving unit can also receive the request from the requester in real time. Step 2: The analysis unit analyzes the request received by the request receiving unit. For example, the analysis unit analyzes the request using natural language processing technology to understand the requester's intention. The analysis unit can also analyze the request using a machine learning algorithm. Step 3: The suggestion unit proposes optimal routes and destinations based on the results of the analysis by the analysis unit. For example, it selects optimal routes and destinations based on criteria such as distance, time, cost, and user preferences. The suggestion unit can also present multiple options according to the requester's request. Step 4: The storage unit stores the route proposed by the proposal unit. For example, the storage unit stores the route in a format that is stored in a database. The storage unit can also specify the type of information to be stored.

[0073] 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.

[0074] 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.

[0075] 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.

[0076] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0077] 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.

[0078] 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.

[0079] 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.

[0080] 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.

[0081] 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).

[0082] 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.

[0083] 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.

[0084] 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.

[0085] 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.

[0086] 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.

[0087] 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.

[0088] 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.

[0089] 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.

[0090] 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.

[0091] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0092] 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.

[0093] 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.

[0094] 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.

[0095] 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.

[0096] 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).

[0097] 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.

[0098] 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.

[0099] 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.

[0100] 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.

[0101] 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.

[0102] 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.

[0103] 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.

[0104] 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.

[0105] 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.

[0106] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0107] 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.

[0108] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0109] The 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.

[0110] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0111] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS 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).

[0112] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0113] 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.

[0114] 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.

[0115] 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.

[0116] 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.

[0117] 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.

[0118] 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.

[0119] 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.

[0120] 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.

[0121] 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.

[0122] 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.

[0123] 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.

[0124] 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.

[0125] 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).

[0126] 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 "reaction" domain, where sensation is dominant. The right half of the emotion map lists emotions belonging to the "situation" domain, where situational awareness is dominant.

[0127] 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."

[0128] 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.

[0129] 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.

[0130] 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.

[0131] 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.

[0132] 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.

[0133] 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.

[0134] 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.

[0135] 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.

[0136] 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.

[0137] 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.

[0138] 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.

[0139] 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]

[0140] 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 request receiving unit that receives a request from a requester; an analysis unit that analyzes the request received by the request receiving unit; a suggestion unit that suggests an optimal route and destination based on the results of the analysis by the analysis unit; a storage unit that stores the route proposed by the proposal unit; A system characterized by:

2. The analysis unit Monitor the requester's real-time health status and suggest optimal routes and destinations based on that information 2. The system of claim 1.

3. The proposal unit Present multiple options to the requester and allow the requester to select one 2. The system of claim 1.

4. The storage unit Analyze the client's past route data and propose the best route according to the time of day and season 2. The system of claim 1.

5. The analysis unit Analyzing the client's emotional state and suggesting a relaxing place to reduce stress 2. The system of claim 1.

Images