System

Abstract

An object of a system according to an embodiment is to flexibly change a route during driving and provide peripheral information.SOLUTION: A system according to an embodiment includes an interaction unit, a route change unit, and a surrounding information providing unit. The interaction unit changes the route through interaction with the user. The route change unit changes the route based on the instruction received by the interaction unit. The peripheral information providing unit provides peripheral information in accordance with a route during driving.SELECTED DRAWING: Figure 1

Description

[Technical Field]

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

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

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

[0004] Conventional technologies have had the problem of not being flexible enough to change routes or provide surrounding information while driving.

[0005] The system according to the embodiment aims to flexibly change the route during driving and provide surrounding information. [Means for solving the problem]

[0006] The system according to the embodiment includes a dialogue unit, a route change unit, and a surrounding information providing unit. The dialogue unit changes the route through dialogue with the user. The route change unit changes the route based on instructions received by the dialogue unit. The surrounding information providing unit provides surrounding information tailored to the route being driven. [Effects of the Invention]

[0007] The system according to the embodiment can flexibly change the route during driving and provide surrounding information. [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 assistant car navigation system according to the embodiment of the present invention changes the route through dialogue with the user and provides surrounding information tailored to the route during driving. This allows the assistant car navigation system to quickly respond to unexpected traffic congestion and road conditions, improving the enjoyment and convenience of driving.

[0029] An assistant car navigation system according to an embodiment includes a dialogue unit, a route change unit, and a surrounding area information providing unit. The dialogue unit changes the route through dialogue with the user. For example, if the user says, "I want to avoid traffic jams," the dialogue unit accepts the instruction. Also, if the user says, "Tell me about nearby tourist spots," the dialogue unit accepts the instruction. The route change unit changes the route based on the instruction accepted by the dialogue unit. For example, in the route change unit, the generation AI analyzes current traffic conditions and proposes an optimal detour route. Also, in the route change unit, the generation AI can analyze traffic congestion and accident information and notify the user, "The current route is congested. Would you like to suggest a detour route?" The surrounding area information providing unit provides surrounding area information tailored to the route being driven. For example, the generation AI searches for and suggests surrounding tourist spots based on current location information. Also, the generation AI can analyze the user's preferences and past history and suggest detours during the drive. For example, if the user says, "Find a good restaurant," the generation AI can suggest the optimal restaurant based on the user's preferences. This allows the assistant car navigation system according to the embodiment to flexibly change routes and provide surrounding information through dialogue with the user, for example, by quickly responding to unexpected traffic congestion and suggesting detours to enhance the enjoyment of driving.

[0030] The route change unit can analyze the user's past driving history and suggest routes that are optimal for the user's driving style. For example, the generation AI analyzes the user's past driving history and suggests routes that are optimal for the user's driving style based on data such as driving speed and frequency of stops. For example, it selects routes with fewer sharp curves and fewer traffic lights. This makes it possible to suggest routes that are optimal for the user's driving style.

[0031] The route change unit can analyze the user's real-time biometric information and suggest a route that suits the user's condition. For example, the generation AI can monitor the user's heart rate in real time, and if the heart rate is high, suggest a relaxing route. For example, it can select a road with little traffic or a road with good scenery. This makes it possible to suggest a route that suits the user's condition.

[0032] The route change unit can analyze the user's music playlist and suggest routes that match the music genre. For example, the generation AI analyzes the user's music playlist, and if there is a lot of relaxing music, the route change unit suggests a scenic route. For example, it selects a route that goes along the seaside or a mountain path. This makes it possible to suggest routes that match the music genre.

[0033] The route change unit can analyze weather information and suggest the optimal route depending on the weather. For example, the generation AI analyzes weather information and suggests roads that are less slippery and have good drainage in rainy weather. For example, it selects routes that are well paved and include many tunnels. This makes it possible to suggest the optimal route depending on the weather.

[0034] The surrounding area information providing unit can analyze the user's past search history and provide surrounding area information based on the user's interests. For example, the generating AI analyzes the user's past search history and provides surrounding area information based on the user's genre of interest (restaurants, tourist spots, etc.). For example, similar restaurants are suggested based on the genre of restaurants the user has searched for in the past. This makes it possible to provide surrounding area information based on the user's interests.

[0035] The surrounding information providing unit can analyze the user's social media posts and provide surrounding information based on the user's current interests. For example, the generating AI analyzes the user's social media posts and provides surrounding information based on the user's current interests (events, trends, etc.). For example, it suggests places related to events recently posted by the user. This makes it possible to provide surrounding information based on the user's current interests.

[0036] The surrounding information providing unit can analyze the user's calendar information and provide surrounding information that matches the user's schedule. For example, the generating AI analyzes the user's calendar information and provides surrounding information that matches the user's schedule. For example, it can suggest cafes or restaurants where users can relax before or after a meeting. This makes it possible to provide surrounding information that matches the user's schedule.

[0037] The surrounding information provision unit can analyze the location information of the user's friends and family and suggest meeting up if they are nearby. For example, the generation AI can analyze the location information of the user's friends and family and suggest meeting up if they are nearby. For example, if a friend is at a nearby cafe, the system will suggest that cafe. This makes it possible to suggest meeting up if the user's friends or family are nearby.

[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 assistant car navigation system can also be equipped with a posture monitoring unit that monitors the user's posture while driving and suggests resting if the posture is poor. For example, if the user's posture is poor after a long drive, the system will suggest a nearby rest spot. The posture monitoring unit can also analyze the user's fatigue level and notify the user of appropriate rest times. This helps maintain the user's health and supports a comfortable drive.

[0040] The assistant car navigation system can also be equipped with a food and drink monitoring unit that monitors the user's food and drink consumption while driving and suggests appropriate hydration and meal timings. For example, if the user is not hydrated after a long drive, the system can suggest nearby convenience stores or cafes. The food and drink monitoring unit can also analyze the user's meal timings and suggest appropriate dining spots. This helps maintain the user's health and support a comfortable drive.

[0041] The assistant car navigation system can also be equipped with a gaze monitoring unit that monitors the user's gaze while driving and warns the user if the user's gaze is not focused. For example, if the user's gaze is averted from the road, the system will issue a voice warning. The gaze monitoring unit can also analyze the user's gaze data and provide information on where the user's gaze is focused. This can support the user's safe driving.

[0042] The assistant car navigation system can also be equipped with a temperature monitoring unit that monitors the temperature inside the vehicle while the user is driving and suggests appropriate temperature adjustments. For example, if the vehicle is too hot, it will suggest using the air conditioner, and if it is too cold, it will suggest using the heater. The temperature monitoring unit can also learn the user's comfortable temperature and suggest the optimal temperature setting. This can support the user in having a comfortable drive.

[0043] The assistant car navigation system can also be equipped with a fatigue analysis unit that analyzes the user's level of fatigue while driving and suggests appropriate rest times. For example, if fatigue accumulates after a long drive, the system will suggest nearby rest spots. The fatigue analysis unit can also suggest spots where the user can refresh themselves based on their level of fatigue. This helps maintain the user's health and supports a comfortable drive.

[0044] The assistant car navigation system can also include a visual analysis unit that analyzes the user's visual information while driving and suggests a visually enjoyable route. For example, it can suggest a scenic route or a route that passes through tourist spots. The visual analysis unit can also suggest spots that can be enjoyed while driving based on the user's visual preferences. This can improve the user's visual enjoyment.

[0045] The processing flow of the first embodiment will be briefly explained below.

[0046] Step 1: The dialogue unit changes the route through dialogue with the user. For example, if the user says, "I want to avoid traffic jams," the dialogue unit accepts that instruction. Also, if the user says, "Tell me about nearby tourist spots," the dialogue unit accepts that instruction as well. Step 2: The route change unit changes the route based on the instructions received by the dialogue unit. For example, the route change unit uses the generation AI to analyze current traffic conditions and propose the optimal detour route. It can also analyze traffic congestion and accident information and notify the user, "The current route is congested. Would you like to suggest a detour route?" Step 3: The surrounding information provider provides surrounding information tailored to the route being driven. For example, the generating AI in the surrounding information provider searches for and suggests nearby tourist spots based on the current location information. It can also analyze the user's preferences and past history to suggest detours during the drive. For example, if the user says, "Find a good restaurant," the generating AI in the surrounding information provider will suggest the best restaurant based on the user's preferences.

[0047] (Example 2) The assistant car navigation system according to the embodiment of the present invention changes the route through dialogue with the user and provides surrounding information tailored to the route during driving. This allows the assistant car navigation system to quickly respond to unexpected traffic congestion and road conditions, improving the enjoyment and convenience of driving.

[0048] An assistant car navigation system according to an embodiment includes a dialogue unit, a route change unit, and a surrounding area information providing unit. The dialogue unit changes the route through dialogue with the user. For example, if the user says, "I want to avoid traffic jams," the dialogue unit accepts the instruction. Also, if the user says, "Tell me about nearby tourist spots," the dialogue unit accepts the instruction. The route change unit changes the route based on the instruction accepted by the dialogue unit. For example, in the route change unit, the generation AI analyzes current traffic conditions and proposes an optimal detour route. Also, in the route change unit, the generation AI can analyze traffic congestion and accident information and notify the user, "The current route is congested. Would you like to suggest a detour route?" The surrounding area information providing unit provides surrounding area information tailored to the route being driven. For example, the generation AI searches for and suggests surrounding tourist spots based on current location information. Also, the generation AI can analyze the user's preferences and past history and suggest detours during the drive. For example, if the user says, "Find a good restaurant," the generation AI can suggest the optimal restaurant based on the user's preferences. This allows the assistant car navigation system according to the embodiment to flexibly change routes and provide surrounding information through dialogue with the user, for example, by quickly responding to unexpected traffic congestion and suggesting detours to enhance the enjoyment of driving.

[0049] The route change unit can analyze the user's past driving history and suggest routes that are optimal for the user's driving style. For example, the generation AI analyzes the user's past driving history and suggests routes that are optimal for the user's driving style based on data such as driving speed and frequency of stops. For example, it selects routes with fewer sharp curves and fewer traffic lights. This makes it possible to suggest routes that are optimal for the user's driving style.

[0050] The route change unit can analyze the user's real-time biometric information and suggest a route that suits the user's condition. For example, the generation AI can monitor the user's heart rate in real time, and if the heart rate is high, suggest a relaxing route. For example, it can select a road with little traffic or a road with good scenery. This makes it possible to suggest a route that suits the user's condition.

[0051] The route change unit can use the emotion estimation function to analyze the user's emotional state and suggest a route to reduce stress. For example, the route change unit uses the emotion estimation function to analyze the user's emotional state from their facial expressions and voice, and suggests a relaxing route if they are under high stress. For example, the route change unit selects a scenic road or a route that passes through a park. This makes it possible to suggest a route that suits the user's emotional state.

[0052] The route change unit can analyze the user's music playlist and suggest routes that match the music genre. For example, the generation AI analyzes the user's music playlist, and if there is a lot of relaxing music, the route change unit suggests a scenic route. For example, it selects a route that goes along the seaside or a mountain path. This makes it possible to suggest routes that match the music genre.

[0053] The route change unit can analyze weather information and suggest the optimal route depending on the weather. For example, the generation AI analyzes weather information and suggests roads that are less slippery and have good drainage in rainy weather. For example, it selects routes that are well paved and include many tunnels. This makes it possible to suggest the optimal route depending on the weather.

[0054] The route change unit can use the emotion estimation function to suggest a scenic route that allows the user to relax. The route change unit, for example, uses the emotion estimation function to analyze the user's emotional state and suggest a scenic route that allows the user to relax. For example, the route change unit selects a route that goes along the seaside or a mountain path. This allows the route change unit to suggest a scenic route that allows the user to relax.

[0055] The surrounding area information providing unit can analyze the user's past search history and provide surrounding area information based on the user's interests. For example, the generating AI analyzes the user's past search history and provides surrounding area information based on the user's genre of interest (restaurants, tourist spots, etc.). For example, similar restaurants are suggested based on the genre of restaurants the user has searched for in the past. This makes it possible to provide surrounding area information based on the user's interests.

[0056] The surrounding information providing unit can analyze the user's social media posts and provide surrounding information based on the user's current interests. For example, the generating AI analyzes the user's social media posts and provides surrounding information based on the user's current interests (events, trends, etc.). For example, it suggests places related to events recently posted by the user. This makes it possible to provide surrounding information based on the user's current interests.

[0057] The surrounding information providing unit can use the emotion estimation function to suggest refreshment spots according to the user's emotional state. For example, the surrounding information providing unit uses the emotion estimation function to analyze the user's emotional state and suggest spots where the user can refresh. For example, if the user is under high stress, the surrounding information providing unit can suggest parks or cafes. This makes it possible to suggest refreshment spots according to the user's emotional state.

[0058] The surrounding information providing unit can analyze the user's calendar information and provide surrounding information that matches the user's schedule. For example, the generating AI analyzes the user's calendar information and provides surrounding information that matches the user's schedule. For example, it can suggest cafes or restaurants where users can relax before or after a meeting. This makes it possible to provide surrounding information that matches the user's schedule.

[0059] The surrounding information provision unit can analyze the location information of the user's friends and family and suggest meeting up if they are nearby. For example, the generation AI can analyze the location information of the user's friends and family and suggest meeting up if they are nearby. For example, if a friend is at a nearby cafe, the system will suggest that cafe. This makes it possible to suggest meeting up if the user's friends or family are nearby.

[0060] The peripheral information providing unit can use the emotion estimation function to provide the user with enjoyable event information in real time. For example, the peripheral information providing unit uses the emotion estimation function to analyze the user's emotional state and provide enjoyable event information in real time. For example, if the emotion is positive, music festivals and sporting events are suggested. This allows the user to receive enjoyable event information in real time.

[0061] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0062] The assistant car navigation system can also be equipped with a posture monitoring unit that monitors the user's posture while driving and suggests resting if the posture is poor. For example, if the user's posture is poor after a long drive, the system will suggest a nearby rest spot. The posture monitoring unit can also analyze the user's fatigue level and notify the user of appropriate rest times. This helps maintain the user's health and supports a comfortable drive.

[0063] The assistant car navigation system can also be equipped with a food and drink monitoring unit that monitors the user's food and drink consumption while driving and suggests appropriate hydration and meal timings. For example, if the user is not hydrated after a long drive, the system can suggest nearby convenience stores or cafes. The food and drink monitoring unit can also analyze the user's meal timings and suggest appropriate dining spots. This helps maintain the user's health and support a comfortable drive.

[0064] The assistant car navigation system can also include a voice analysis unit that analyzes voice commands given by the user while driving and changes the route or provides surrounding information based on the voice command. For example, if the user says, "Tell me where the next gas station is," the voice analysis unit will accept the command and suggest the most suitable gas station. The voice analysis unit can also estimate the user's emotions from the user's voice and make suggestions based on the emotions. This can improve user convenience.

[0065] The assistant car navigation system can also be equipped with a gaze monitoring unit that monitors the user's gaze while driving and warns the user if the user's gaze is not focused. For example, if the user's gaze is averted from the road, the system will issue a voice warning. The gaze monitoring unit can also analyze the user's gaze data and provide information on where the user's gaze is focused. This can support the user's safe driving.

[0066] The assistant car navigation system can also include a music analysis unit that analyzes the user's music playlist while driving and provides surrounding information tailored to the music genre. For example, if there is a lot of relaxing music, the system can suggest scenic routes and relaxing spots. The music analysis unit can also provide information about events that can be enjoyed while driving based on the user's music preferences. This can improve the user's driving experience.

[0067] The assistant car navigation system can also be equipped with a temperature monitoring unit that monitors the temperature inside the vehicle while the user is driving and suggests appropriate temperature adjustments. For example, if the vehicle is too hot, it will suggest using the air conditioner, and if it is too cold, it will suggest using the heater. The temperature monitoring unit can also learn the user's comfortable temperature and suggest the optimal temperature setting. This can support the user in having a comfortable drive.

[0068] The assistant car navigation system can also include a music suggestion unit that analyzes the user's emotional state while driving and suggests music that matches the user's emotions. For example, if the user is feeling stressed, the system suggests relaxing music, and if the user is feeling positive, the system suggests upbeat music. The music suggestion unit can also provide event information that can be enjoyed while driving based on the user's emotional state. This allows the system to provide music and event information that matches the user's emotional state.

[0069] The assistant car navigation system can also be equipped with a fatigue analysis unit that analyzes the user's level of fatigue while driving and suggests appropriate rest times. For example, if fatigue accumulates after a long drive, the system will suggest nearby rest spots. The fatigue analysis unit can also suggest spots where the user can refresh themselves based on their level of fatigue. This helps maintain the user's health and supports a comfortable drive.

[0070] The assistant car navigation system may also include a refreshment suggestion unit that analyzes the user's emotional state while driving and suggests refreshment spots according to the user's emotions. For example, if the user is feeling stressed, it suggests parks or cafes, and if the user is feeling relaxed, it suggests tourist spots. The refreshment suggestion unit may also provide information about events that can be enjoyed while driving based on the user's emotional state. This allows the system to provide information about refreshment spots and events according to the user's emotional state.

[0071] The assistant car navigation system can also include a visual analysis unit that analyzes the user's visual information while driving and suggests a visually enjoyable route. For example, it can suggest a scenic route or a route that passes through tourist spots. The visual analysis unit can also suggest spots that can be enjoyed while driving based on the user's visual preferences. This can improve the user's visual enjoyment.

[0072] The processing flow of the second embodiment will be briefly explained below.

[0073] Step 1: The dialogue unit changes the route through dialogue with the user. For example, if the user says, "I want to avoid traffic jams," the dialogue unit accepts that instruction. Also, if the user says, "Tell me about nearby tourist spots," the dialogue unit accepts that instruction as well. Step 2: The route change unit changes the route based on the instructions received by the dialogue unit. For example, the route change unit uses the generation AI to analyze current traffic conditions and propose the optimal detour route. It can also analyze traffic congestion and accident information and notify the user, "The current route is congested. Would you like to suggest a detour route?" Step 3: The surrounding information provider provides surrounding information tailored to the route being driven. For example, the generating AI in the surrounding information provider searches for and suggests nearby tourist spots based on the current location information. It can also analyze the user's preferences and past history to suggest detours during the drive. For example, if the user says, "Find a good restaurant," the generating AI in the surrounding information provider will suggest the best restaurant based on the user's preferences.

[0074] 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.

[0075] 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.

[0076] 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.

[0077] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0078] 3, the data processing system 210 includes the data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0079] 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.

[0080] 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.

[0081] 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.

[0082] 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).

[0083] 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.

[0084] 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.

[0085] 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.

[0086] 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.

[0087] 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.

[0088] 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.

[0089] 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.

[0090] 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.

[0091] 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.

[0092] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0093] 5, the data processing system 310 includes the data processing device 12 and a headset type terminal 314. An example of the data processing device 12 is a server.

[0094] 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.

[0095] 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.

[0096] 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.

[0097] 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).

[0098] 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.

[0099] 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.

[0100] 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.

[0101] 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.

[0102] 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.

[0103] 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.

[0104] 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.

[0105] 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.

[0106] 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.

[0107] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0108] 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.

[0109] 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.

[0110] 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.

[0111] 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.

[0112] 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).

[0113] 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.

[0114] 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.

[0115] 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.

[0116] 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.

[0117] 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.

[0118] 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. Note that 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.

[0119] 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.

[0120] 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.

[0121] 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.

[0122] 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.

[0123] 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.

[0124] 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.

[0125] 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.

[0126] 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).

[0127] 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.

[0128] 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."

[0129] 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.

[0130] 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.

[0131] 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.

[0132] 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.

[0133] 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.

[0134] 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.

[0135] 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.

[0136] 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.

[0137] 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.

[0138] 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.

[0139] 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.

[0140] 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]

[0141] 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 dialogue unit that changes the route through dialogue with a user; a route change unit that changes the route based on an instruction received by the dialogue unit; and a surrounding information providing unit that provides surrounding information according to the route during driving. A system characterized by:

2. The route change unit Analyzing real-time biological information of the user and proposing a route according to the user's condition 2. The system of claim 1.

3. The route change unit Analyze the user's music playlist and suggest routes based on the music genre.

2. The system of claim 1.

4. The surrounding information providing unit Analyzing the user's past search history and providing surrounding information based on the user's interests 2. The system of claim 1.

5. The route change unit Analyzing the user's emotional state and suggesting routes to reduce stress 2. The system of claim 1.

6. The surrounding information providing unit Suggesting a refreshing spot according to the emotional state of the user 2. The system of claim 1.

7. The route change unit Suggest scenic routes that allow the user to relax 2. The system of claim 1.

8. The surrounding information providing unit Providing real-time information about events that users can enjoy 2. The system of claim 1.

Images