System

Abstract

An object of a system according to an embodiment is to propose an optimum stopover course based on a preference of a user.SOLUTION: A system according to an embodiment includes an input unit, a preference analysis unit, a stopover course suggestion unit, a transportation handling unit, and a real-time information provision unit. The input unit inputs a start point and a goal point. The preference analysis section analyzes a preference of the user based on the start point and the goal point input by the input section. The stopover course suggestion section suggests a stopover course on the basis of the preference of the user and the moving means analyzed by the preference analysis section. The moving means dealing portion deals with the moving means based on the drop-in route suggested by the drop-in route suggestion portion. The real-time information providing unit provides real-time information based on the moving means associated by the moving means associating 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 of making it difficult to suggest detour routes based on a user's preferences.

[0005] The system according to the embodiment aims to propose an optimal detour route based on the user's preferences. [Means for solving the problem]

[0006] The system according to the embodiment includes an input unit, a preference analysis unit, a detour course suggestion unit, a transportation means correspondence unit, and a real-time information provision unit. The input unit inputs a start point and a finish point. The preference analysis unit analyzes a user's preferences based on the start point and the finish point input by the input unit. The detour course suggestion unit suggests a detour course based on the user's preferences and the transportation means analyzed by the preference analysis unit. The transportation means correspondence unit corresponds to the transportation means based on the detour course proposed by the detour course suggestion unit. The real-time information provision unit provides real-time information based on the transportation means corresponded by the transportation means correspondence unit. [Effects of the Invention]

[0007] The system according to the embodiment can suggest optimal detour routes based on the user's preferences. [Brief explanation of the drawings]

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

[0028] (Example 1) The detour course suggestion system according to an embodiment of the present invention is a system in which a generation AI suggests an optimal detour course when a user wants to make a short detour before returning home. As a result, the detour course suggestion system suggests an optimal detour course according to the user's preferences and means of transportation, enabling the user to make a comfortable detour.

[0029] A detour course suggestion system according to an embodiment includes an input unit, a preference analysis unit, a detour course suggestion unit, a transportation mode support unit, and a real-time information provision unit. The input unit inputs a start point and a finish point. For example, a user sets a route from work to home. The preference analysis unit analyzes the user's preferences based on the start point and finish point input by the input unit. For example, the preference analysis unit analyzes the user's past behavioral history and preferences, and identifies the user's preferences based on data on cafes, parks, shopping malls, etc. that the user has previously visited. The detour course suggestion unit proposes a detour course based on the user's preferences and transportation mode analyzed by the preference analysis unit. For example, in response to a user's request such as "I want to take a break at a cafe on my way from work to home," the system proposes an optimal cafe taking into consideration the cafe's location, business hours, and congestion status. The transportation mode support unit supports transportation modes based on the detour course proposed by the detour course suggestion unit. For example, any transportation mode can be supported, such as public transportation, car, bicycle, or walking. The real-time information provision unit provides real-time information based on the transportation mode supported by the transportation mode support unit. For example, the system obtains information such as weather, traffic conditions, store opening hours, and congestion status in real time, and proposes an optimal route based on the information. As a result, the detour course suggestion system according to the embodiment proposes an optimal detour course according to the user's preferences and means of transportation, and provides real-time information, thereby enabling the user to take a comfortable detour.

[0030] The preference analysis unit can analyze the user's past behavioral history or social media postings. The preference analysis unit, for example, analyzes the user's past behavioral history. For example, the user's preferences are identified based on GPS data or app usage history. The preference analysis unit also analyzes the content of social media postings. For example, the preferences are identified based on posts that the user has "liked." In this way, by analyzing the user's past behavioral history and social media postings, more detailed preferences can be identified and optimal detour routes can be suggested.

[0031] The real-time information providing unit can provide information taking into consideration the user's current mood and physical condition. The real-time information providing unit provides information taking into consideration the user's current mood, for example. For example, if the user is tired, it provides information about places where the user can relax. The real-time information providing unit also provides information taking into consideration the user's current physical condition. For example, if the user is feeling unwell, it provides information about nearby medical facilities. In this way, providing information taking into consideration the user's current mood and physical condition can make detours more comfortable.

[0032] The detour course suggestion unit can refer to the user's past detour history and suggest places visited in the past and new places in a balanced manner. The detour course suggestion unit, for example, refers to the user's past detour history. For example, it identifies places visited by the user in the past based on GPS data or past visit history. The detour course suggestion unit also suggests new places. For example, it suggests a combination of cafes visited by the user in the past and new cafes. In this way, by referring to the user's past detour history and suggesting places visited in the past and new places in a balanced manner, it is possible to continue to attract the user's interest.

[0033] The transportation means correspondence unit can refer to the user's past history of transportation means and suggest the most suitable means. The transportation means correspondence unit, for example, refers to the user's past history of transportation means. For example, based on the past history of transportation means selection and frequency of use, the transportation means that the user has used in the past is identified. The transportation means correspondence unit also suggests the most suitable means. For example, if the user has used a car in the past, a detour route by car is suggested. In this way, by referring to the user's past history of transportation means and suggesting the most suitable means, the transportation means correspondence unit makes the user's travel more comfortable.

[0034] The detour course suggestion unit can suggest relaxing places and activities taking into consideration the user's current mood and physical condition. The detour course suggestion unit, for example, suggests relaxing places taking into consideration the user's current mood. For example, if the user is tired, it suggests a quiet cafe or park. The detour course suggestion unit also suggests relaxing activities taking into consideration the user's current physical condition. For example, if the user is feeling unwell, it suggests activities such as yoga or a walk. In this way, by suggesting relaxing places and activities taking into consideration the user's current mood and physical condition, the user can make a comfortable detour.

[0035] The real-time information providing unit can refer to the user's past behavior history and provide optimal information. The real-time information providing unit, for example, refers to the user's past behavior history. For example, it identifies places the user has visited in the past based on GPS data or app usage history. The real-time information providing unit also provides optimal information. For example, it provides information on new related places based on information on places the user has visited in the past. In this way, the user's past behavior history can be referred to and optimal information can be provided, allowing the user to make a comfortable detour.

[0036] The transportation means correspondence unit can suggest the optimal transportation means taking into consideration the user's current physical condition and mood. The transportation means correspondence unit, for example, suggests the optimal transportation means taking into consideration the user's current physical condition. For example, if the user is tired, it suggests a route using public transportation. The transportation means correspondence unit also suggests the optimal transportation means taking into consideration the user's current mood. For example, if the user wants to relax, it suggests a route with a detour on foot. In this way, suggesting the optimal transportation means taking into consideration the user's current physical condition and mood makes the user's travel more comfortable.

[0037] The detour course suggestion unit can refer to the location information of the user's friends and family and suggest a course that allows for a detour together. The detour course suggestion unit, for example, refers to the location information of the user's friends and family. For example, the location of the friends and family is identified based on GPS data or a location sharing app. The detour course suggestion unit also suggests a course that allows for a detour together. For example, if a friend is nearby, the unit suggests a course that includes a stop at a cafe together. In this way, by referring to the location information of the user's friends and family and suggesting a course that allows for a detour together, the user's detours become more enjoyable.

[0038] The real-time information providing unit can refer to the location information of the user's friends and family and provide information on ways to make a detour together. The real-time information providing unit, for example, refers to the location information of the user's friends and family. For example, the location of friends and family is identified based on GPS data or a location sharing app. The real-time information providing unit also provides information on ways to make a detour together. For example, if a friend is nearby, information on stopping by a cafe together is provided. In this way, by referring to the location information of the user's friends and family and providing information on ways to make a detour together, the user's detours become more enjoyable.

[0039] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0040] The detour course suggestion system may further include a health data acquisition unit that acquires the user's health data. For example, the system acquires heart rate, step count, sleep data, and the like from a smartwatch or fitness tracker to understand the user's health condition. This allows the system to suggest detour courses based on the user's health condition. For example, if the user is tired, the system can suggest relaxing places, and if the user is energetic, the system can suggest active activities.

[0041] The detour course suggestion system can further include a hobby analysis unit that analyzes the user's hobbies and interests. For example, the system can identify the user's hobbies and interests based on data such as events the user has attended in the past, products purchased, and books read. This makes it possible to suggest detour courses that match the user's hobbies and interests. For example, if the user is interested in art, the system can suggest nearby art museums and galleries.

[0042] The detour course suggestion system may further include a friend preference analysis unit that analyzes the preferences of the user's friends and family. For example, the preferences of the user's friends and family may be identified based on data on places they have visited or events they have attended in the past. This makes it possible to suggest detour courses that the user and their friends and family can enjoy together. For example, if the user and their friends have a common hobby, places related to that hobby may be suggested.

[0043] The detour course suggestion system may further include a purchase history analysis unit that analyzes the user's past purchase history. For example, the user's purchasing tendencies may be identified based on data on products and services purchased in the past by the user. This makes it possible to suggest detour courses that match the user's purchasing tendencies. For example, if the user has purchased products from a specific brand in the past, stores of that brand may be suggested.

[0044] The detour course suggestion system may further include a travel history analysis unit that analyzes the user's past travel history. For example, the user's travel habits may be identified based on data on travel destinations and accommodations visited by the user in the past. This makes it possible to suggest detour courses that match the user's travel habits. For example, if the user has visited a specific area in the past, it may be possible to suggest places related to that area.

[0045] The detour course suggestion system may further include an event history analysis unit that analyzes the user's past event participation history. For example, the user's interests may be identified based on data on events and workshops the user has participated in in the past. This makes it possible to suggest detour courses that match the user's interests. For example, if the user has previously attended a music festival, nearby music events may be suggested.

[0046] The processing flow of the first embodiment will be briefly explained below.

[0047] Step 1: The input unit inputs a start point and a finish point. For example, the user sets a route from work to home. Step 2: The preference analysis unit analyzes the user's preferences based on the start and finish points entered by the input unit. For example, it analyzes the user's past behavioral history and preferences, and understands the user's preferences based on data such as cafes, parks, and shopping malls that the user has visited in the past. Step 3: The detour course suggestion unit suggests detour courses based on the user's preferences and means of transportation analyzed by the preference analysis unit. For example, if a user wishes to "take a break at a cafe on the way from work to home," the unit will suggest the most suitable cafe, taking into consideration the cafe's location, business hours, and congestion status. Step 4: The transportation means handling unit handles transportation means based on the detour course proposed by the detour course suggestion unit. For example, any transportation means can be handled, such as public transportation, car, bicycle, walking, etc. Step 5: The real-time information provider provides real-time information based on the means of transportation selected by the means of transportation provider. For example, it obtains information on weather, traffic conditions, store opening hours, and congestion status in real time, and then suggests the optimal route based on that information.

[0048] (Example 2) The detour course suggestion system according to an embodiment of the present invention is a system in which a generation AI suggests an optimal detour course when a user wants to make a short detour before returning home. As a result, the detour course suggestion system suggests an optimal detour course according to the user's preferences and means of transportation, enabling the user to make a comfortable detour.

[0049] A detour course suggestion system according to an embodiment includes an input unit, a preference analysis unit, a detour course suggestion unit, a transportation mode support unit, and a real-time information provision unit. The input unit inputs a start point and a finish point. For example, a user sets a route from work to home. The preference analysis unit analyzes the user's preferences based on the start point and finish point input by the input unit. For example, the preference analysis unit analyzes the user's past behavioral history and preferences, and identifies the user's preferences based on data on cafes, parks, shopping malls, etc. that the user has previously visited. The detour course suggestion unit proposes a detour course based on the user's preferences and transportation mode analyzed by the preference analysis unit. For example, in response to a user's request such as "I want to take a break at a cafe on my way from work to home," the system proposes an optimal cafe taking into consideration the cafe's location, business hours, and congestion status. The transportation mode support unit supports transportation modes based on the detour course proposed by the detour course suggestion unit. For example, any transportation mode can be supported, such as public transportation, car, bicycle, or walking. The real-time information provision unit provides real-time information based on the transportation mode supported by the transportation mode support unit. For example, the system obtains information such as weather, traffic conditions, store opening hours, and congestion status in real time, and proposes an optimal route based on the information. As a result, the detour course suggestion system according to the embodiment proposes an optimal detour course according to the user's preferences and means of transportation, and provides real-time information, thereby enabling the user to take a comfortable detour.

[0050] The preference analysis unit can analyze the user's past behavioral history or social media postings. The preference analysis unit, for example, analyzes the user's past behavioral history. For example, the user's preferences are identified based on GPS data or app usage history. The preference analysis unit also analyzes the content of social media postings. For example, the preferences are identified based on posts that the user has "liked." In this way, by analyzing the user's past behavioral history and social media postings, more detailed preferences can be identified and optimal detour routes can be suggested.

[0051] The real-time information providing unit can provide information taking into consideration the user's current mood and physical condition. The real-time information providing unit provides information taking into consideration the user's current mood, for example. For example, if the user is tired, it provides information about places where the user can relax. The real-time information providing unit also provides information taking into consideration the user's current physical condition. For example, if the user is feeling unwell, it provides information about nearby medical facilities. In this way, providing information taking into consideration the user's current mood and physical condition can make detours more comfortable.

[0052] The detour course suggestion unit can refer to the user's past detour history and suggest places visited in the past and new places in a balanced manner. The detour course suggestion unit, for example, refers to the user's past detour history. For example, it identifies places visited by the user in the past based on GPS data or past visit history. The detour course suggestion unit also suggests new places. For example, it suggests a combination of cafes visited by the user in the past and new cafes. In this way, by referring to the user's past detour history and suggesting places visited in the past and new places in a balanced manner, it is possible to continue to attract the user's interest.

[0053] The transportation means correspondence unit can refer to the user's past history of transportation means and suggest the most suitable means. The transportation means correspondence unit, for example, refers to the user's past history of transportation means. For example, based on the past history of transportation means selection and frequency of use, the transportation means that the user has used in the past is identified. The transportation means correspondence unit also suggests the most suitable means. For example, if the user has used a car in the past, a detour route by car is suggested. In this way, by referring to the user's past history of transportation means and suggesting the most suitable means, the transportation means correspondence unit makes the user's travel more comfortable.

[0054] The detour course suggestion unit can suggest relaxing places and activities taking into consideration the user's current mood and physical condition. The detour course suggestion unit, for example, suggests relaxing places taking into consideration the user's current mood. For example, if the user is tired, it suggests a quiet cafe or park. The detour course suggestion unit also suggests relaxing activities taking into consideration the user's current physical condition. For example, if the user is feeling unwell, it suggests activities such as yoga or a walk. In this way, by suggesting relaxing places and activities taking into consideration the user's current mood and physical condition, the user can make a comfortable detour.

[0055] The real-time information providing unit can refer to the user's past behavior history and provide optimal information. The real-time information providing unit, for example, refers to the user's past behavior history. For example, it identifies places the user has visited in the past based on GPS data or app usage history. The real-time information providing unit also provides optimal information. For example, it provides information on new related places based on information on places the user has visited in the past. In this way, the user's past behavior history can be referred to and optimal information can be provided, allowing the user to make a comfortable detour.

[0056] The transportation means correspondence unit can suggest the optimal transportation means taking into consideration the user's current physical condition and mood. The transportation means correspondence unit, for example, suggests the optimal transportation means taking into consideration the user's current physical condition. For example, if the user is tired, it suggests a route using public transportation. The transportation means correspondence unit also suggests the optimal transportation means taking into consideration the user's current mood. For example, if the user wants to relax, it suggests a route with a detour on foot. In this way, suggesting the optimal transportation means taking into consideration the user's current physical condition and mood makes the user's travel more comfortable.

[0057] The detour course suggestion unit can refer to the location information of the user's friends and family and suggest a course that allows for a detour together. The detour course suggestion unit, for example, refers to the location information of the user's friends and family. For example, the location of the friends and family is identified based on GPS data or a location sharing app. The detour course suggestion unit also suggests a course that allows for a detour together. For example, if a friend is nearby, the unit suggests a course that includes a stop at a cafe together. In this way, by referring to the location information of the user's friends and family and suggesting a course that allows for a detour together, the user's detours become more enjoyable.

[0058] The real-time information providing unit can refer to the location information of the user's friends and family and provide information on ways to make a detour together. The real-time information providing unit, for example, refers to the location information of the user's friends and family. For example, the location of friends and family is identified based on GPS data or a location sharing app. The real-time information providing unit also provides information on ways to make a detour together. For example, if a friend is nearby, information on stopping by a cafe together is provided. In this way, by referring to the location information of the user's friends and family and providing information on ways to make a detour together, the user's detours become more enjoyable.

[0059] The detour course suggestion unit can analyze the user's past emotional history and suggest a detour course that will bring out the most positive emotions. The detour course suggestion unit, for example, uses an emotion estimation function to analyze the user's past emotional history. For example, the user's emotional history is identified based on social media posts, self-reporting, and facial expression analysis. The detour course suggestion unit also suggests a detour course that will bring out the most positive emotions. For example, the detour course suggestion unit suggests a detour course that will bring out the most positive emotions based on places that the user has enjoyed in the past. In this way, by analyzing the user's past emotional history and suggesting a detour course that will bring out the most positive emotions, the user's detours become more enjoyable.

[0060] The transportation means correspondence unit can analyze the user's past emotion history and suggest a transportation means that will elicit the most positive emotion. The transportation means correspondence unit, for example, uses an emotion estimation function to analyze the user's past emotion history. For example, the user's emotion history is identified based on social media posts, self-reporting, and facial expression analysis. The transportation means correspondence unit also suggests a transportation means that will elicit the most positive emotion. For example, the transportation means correspondence unit makes suggestions based on transportation means that the user has enjoyed in the past. In this way, by analyzing the user's past emotion history and suggesting a transportation means that will elicit the most positive emotion, the user's transportation becomes more comfortable.

[0061] The real-time information providing unit can analyze the user's past emotional history and provide information that will elicit the most positive emotions. The real-time information providing unit, for example, uses an emotion estimation function to analyze the user's past emotional history. For example, the real-time information providing unit identifies the user's emotional history based on social media posts, self-reporting, and facial expression analysis. The real-time information providing unit also provides information that will elicit the most positive emotions. For example, the real-time information providing unit provides information about related new places based on information about places the user has enjoyed in the past. In this way, by analyzing the user's past emotional history and providing information that will elicit the most positive emotions, the user's detours become more enjoyable.

[0062] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0063] The detour course suggestion system may further include a health data acquisition unit that acquires the user's health data. For example, the system acquires heart rate, step count, sleep data, and the like from a smartwatch or fitness tracker to understand the user's health condition. This allows the system to suggest detour courses based on the user's health condition. For example, if the user is tired, the system can suggest relaxing places, and if the user is energetic, the system can suggest active activities.

[0064] The detour course suggestion system can further include a hobby analysis unit that analyzes the user's hobbies and interests. For example, the system can identify the user's hobbies and interests based on data such as events the user has attended in the past, products purchased, and books read. This makes it possible to suggest detour courses that match the user's hobbies and interests. For example, if the user is interested in art, the system can suggest nearby art museums and galleries.

[0065] The detour course suggestion system may further include an emotion estimation unit that estimates the user's emotion and suggests a detour course based on the estimated emotion. For example, if the user is feeling stressed, it may suggest a place where the user can relax, and if the user is excited, it may suggest an active activity. This makes it possible to suggest an optimal detour course based on the user's emotion.

[0066] The detour course suggestion system may further include a friend preference analysis unit that analyzes the preferences of the user's friends and family. For example, the preferences of the user's friends and family may be identified based on data on places they have visited or events they have attended in the past. This makes it possible to suggest detour courses that the user and their friends and family can enjoy together. For example, if the user and their friends have a common hobby, places related to that hobby may be suggested.

[0067] The detour course suggestion system may further include an emotion estimation unit that estimates the user's emotion and suggests a means of transportation based on the estimated emotion. For example, if the user wants to relax, walking or cycling can be suggested, and if the user is in a hurry, public transportation or a taxi can be suggested. This makes it possible to suggest the optimal means of transportation according to the user's emotion.

[0068] The detour course suggestion system may further include a purchase history analysis unit that analyzes the user's past purchase history. For example, the user's purchasing tendencies may be identified based on data on products and services purchased in the past by the user. This makes it possible to suggest detour courses that match the user's purchasing tendencies. For example, if the user has purchased products from a specific brand in the past, stores of that brand may be suggested.

[0069] The detour course suggestion system may further include an emotion estimation unit that estimates the user's emotion and provides real-time information based on the estimated emotion. For example, if the user is tired, information on places where the user can relax can be provided, and if the user is excited, information on active activities can be provided. This makes it possible to provide optimal real-time information according to the user's emotion.

[0070] The detour course suggestion system may further include a travel history analysis unit that analyzes the user's past travel history. For example, the user's travel habits may be identified based on data on travel destinations and accommodations visited by the user in the past. This makes it possible to suggest detour courses that match the user's travel habits. For example, if the user has visited a specific area in the past, it may be possible to suggest places related to that area.

[0071] The detour course suggestion system may further include an emotion estimation unit that estimates the user's emotion and suggests detour courses with friends or family based on the estimated emotion. For example, if the user feels lonely, it may suggest places that the user can enjoy with friends or family, and if the user wants to relax alone, it may suggest quiet places. This makes it possible to suggest optimal detour courses according to the user's emotion.

[0072] The detour course suggestion system may further include an event history analysis unit that analyzes the user's past event participation history. For example, the user's interests may be identified based on data on events and workshops the user has participated in in the past. This makes it possible to suggest detour courses that match the user's interests. For example, if the user has previously attended a music festival, nearby music events may be suggested.

[0073] The processing flow of the second embodiment will be briefly explained below.

[0074] Step 1: The input unit inputs a start point and a finish point. For example, the user sets a route from work to home. Step 2: The preference analysis unit analyzes the user's preferences based on the start and finish points entered by the input unit. For example, it analyzes the user's past behavioral history and preferences, and understands the user's preferences based on data such as cafes, parks, and shopping malls that the user has visited in the past. Step 3: The detour course suggestion unit suggests detour courses based on the user's preferences and means of transportation analyzed by the preference analysis unit. For example, if a user wishes to "take a break at a cafe on the way from work to home," the unit will suggest the most suitable cafe, taking into consideration the cafe's location, business hours, and congestion status. Step 4: The transportation means handling unit handles transportation means based on the detour course proposed by the detour course suggestion unit. For example, any transportation means can be handled, such as public transportation, car, bicycle, walking, etc. Step 5: The real-time information provider provides real-time information based on the means of transportation selected by the means of transportation provider. For example, it obtains information on weather, traffic conditions, store opening hours, and congestion status in real time, and then suggests the optimal route based on that information.

[0075] 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.

[0076] 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.

[0077] 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.

[0078] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0079] 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.

[0080] 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.

[0081] 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.

[0082] 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.

[0083] 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).

[0084] 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.

[0085] 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.

[0086] 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.

[0087] 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.

[0088] 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.

[0089] 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.

[0090] 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.

[0091] 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.

[0092] 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.

[0093] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0094] 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.

[0095] 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.

[0096] 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.

[0097] 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.

[0098] 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).

[0099] 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.

[0100] 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.

[0101] 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.

[0102] 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.

[0103] 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.

[0104] 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.

[0105] 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.

[0106] 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.

[0107] 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.

[0108] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0109] 7, the data processing system 410 includes a data processing device 12 and a robot 414. An example of the data processing device 12 is a server.

[0110] 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.

[0111] 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.

[0112] 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.

[0113] 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).

[0114] 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.

[0115] 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.

[0116] 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.

[0117] 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.

[0118] 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.

[0119] 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.

[0120] 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.

[0121] 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.

[0122] 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.

[0123] 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.

[0124] 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.

[0125] 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.

[0126] 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.

[0127] 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).

[0128] 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.

[0129] 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."

[0130] 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.

[0131] 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.

[0132] 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.

[0133] 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.

[0134] 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.

[0135] 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.

[0136] 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.

[0137] 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.

[0138] 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.

[0139] 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.

[0140] 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.

[0141] 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]

[0142] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Device 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robot

Claims

1. an input section for inputting a start point and a finish point; a preference analysis unit that analyzes user preferences based on the start point and the finish point input by the input unit; a detour course suggestion unit that suggests the detour course based on the user's preference and means of transportation analyzed by the preference analysis unit; a transportation means correspondence unit that corresponds to the transportation means based on the detour course proposed by the detour course suggestion unit; a real-time information providing unit that provides real-time information based on the transportation means associated by the transportation means associated unit. A system characterized by:

2. The preference analysis unit Analyzing the user's past behavior history or social media posts 2. The system of claim 1.

3. The detour course suggestion unit By referring to the user's past detour history, the system proposes a balanced mix of previously visited and new places.

2. The system of claim 1.

4. The transportation means corresponding unit Refer to the user's past travel history and suggest the most suitable means 2. The system of claim 1.

5. The real-time information providing unit Providing information taking into account the user's current mood and physical condition 2. The system of claim 1.

6. The detour course suggestion unit Analyze the user's past emotional history and suggest detours that will bring out the most positive emotions.

2. The system of claim 1.

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