System

Abstract

An object of the system according to the embodiment is to grasp road conditions and danger information in real time during driving and improve safety.SOLUTION: A system according to an embodiment includes a smartphone installation unit, a generated AI car navigation unit, a camera unit, and an alert unit. The smartphone installation unit installs a smartphone on a dashboard of a vehicle. The generation AI car navigation unit uses generation AI car navigation by using a smartphone. The camera unit collects road conditions using a camera of a smartphone. The alert unit alerts the driver on the basis of the information analyzed by the generated AI car navigation 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] With conventional technology, it is difficult to grasp road conditions and hazard information in real time while driving, and there is room for improvement in improving safety.

[0005] The system according to the embodiment aims to improve safety by grasping road conditions and hazard information in real time while driving. [Means for solving the problem]

[0006] The system according to the embodiment includes a smartphone installation unit, a generated AI car navigation unit, a camera unit, and an alert unit. The smartphone installation unit installs a smartphone on the dashboard of a car. The generated AI car navigation unit uses the smartphone to utilize the generated AI car navigation. The camera unit collects road conditions using the smartphone's camera. The alert unit issues an alert to the driver based on information analyzed by the generated AI car navigation unit. [Effects of the Invention]

[0007] The system according to the embodiment can grasp road conditions and danger information in real time while driving, thereby improving safety. [Brief explanation of the drawings]

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

[0028] (Example 1) The car navigation system according to the embodiment of the present invention is a system in which a smartphone is installed on the dashboard of a car, and a generation AI analyzes location information and real-time road conditions and provides information to the driver via voice or the smartphone screen. This allows the driver to easily obtain necessary information while driving, improving driving safety and convenience.

[0029] A car navigation system according to an embodiment includes a smartphone installation unit, an AI car navigation unit, a camera unit, and an alert unit. The smartphone installation unit installs a smartphone on the dashboard of a car. For example, the smartphone is secured using a holder. The smartphone installation unit may also include a power supply function for charging the smartphone. The AI ​​car navigation unit uses the smartphone to provide AI car navigation. For example, the AI ​​analyzes questions and instructions from the driver and provides appropriate information. The AI ​​uses a text generation AI (e.g., LLM) or a multimodal generation AI to provide information to the driver via voice or the smartphone screen. The camera unit collects road conditions using the smartphone camera. For example, the smartphone camera captures images of the road ahead, and the AI ​​analyzes the images to predict dangerous situations. The alert unit alerts the driver based on the information analyzed by the AI ​​car navigation unit. For example, the alert unit warns the driver using audio or visual alerts. This allows the car navigation system according to an embodiment to easily obtain necessary information while driving, thereby improving driving safety and convenience.

[0030] The generative AI car navigation unit can analyze the driver's past driving history and provide navigation tailored to the driver's driving style. For example, the generative AI car navigation unit analyzes the driver's past driving history to learn the driver's preferred routes and routes that the driver wants to avoid. For example, it identifies roads that the driver frequently uses and roads that the driver tends to avoid, and customizes navigation based on that. The generative AI car navigation unit also provides advice tailored to the driver's driving style based on the driver's past driving data. For example, it suggests driving methods to avoid sudden braking and sudden acceleration. The generative AI car navigation unit also analyzes the driver's past driving history and suggests rest spots and service areas that the driver prefers. For example, it reflects the driver's favorite cafes and gas stations in the navigation. This makes it possible to provide navigation tailored to the driver's individual driving style.

[0031] The generating AI car navigation unit can monitor the driver's health data and provide driving advice according to the driver's physical condition. For example, the generating AI car navigation unit monitors the driver's heart rate and advises the driver to stop driving if an abnormality is detected. For example, if the heart rate rises sharply, the generating AI car navigation unit will encourage the driver to take a break. The generating AI car navigation unit can also monitor the driver's blood pressure and provide appropriate advice if an abnormality is detected. For example, if blood pressure is high, the generating AI car navigation unit will suggest breathing techniques to help the driver relax. The generating AI car navigation unit can also analyze the driver's health data and provide driving advice according to the driver's physical condition. For example, if the driver is tired, the generating AI car navigation unit will encourage the driver to take a break. This makes it possible to provide driving advice according to the driver's physical condition.

[0032] The generative AI car navigation unit can adjust the temperature or humidity inside the car to provide a comfortable driving environment for the driver. For example, the generative AI car navigation unit monitors the temperature inside the car and automatically adjusts the air conditioner to keep the driver comfortable. For example, it turns on the air conditioner if the inside of the car becomes hot. The generative AI car navigation unit also monitors the humidity inside the car and automatically adjusts the humidifier or dehumidifier to maintain appropriate humidity. For example, it turns on the humidifier if the inside of the car is dry. The generative AI car navigation unit also analyzes the temperature and humidity inside the car and makes automatic adjustments to provide a comfortable driving environment. For example, it learns the temperature and humidity that the driver finds comfortable and adjusts based on that. This allows for a comfortable driving environment.

[0033] The generating AI car navigation unit can analyze the content of the driver's question and provide more accurate information by referring to past questions or answers. For example, the generating AI car navigation unit analyzes the content of the driver's question and provides highly accurate information by referring to a database of past questions and answers. For example, if a similar question has been asked in the past, it provides information based on the answer. The generating AI car navigation unit also analyzes the content of the driver's question and builds a system that provides information by referring to related past questions and answers. For example, it generates the optimal answer based on the past question history. The generating AI car navigation unit also analyzes the content of the driver's question and provides highly accurate information based on past question and answer data. For example, it learns past data and generates the optimal answer. This makes it possible to provide highly accurate information by referring to past questions and answers.

[0034] The generative AI car navigation unit can infer the intent of the driver's question and proactively provide the necessary information. For example, the generative AI car navigation unit builds a system in which the generative AI infers the intent of the driver's question and proactively provides the necessary information. For example, if a driver asks, "What's the traffic situation up ahead?", the generative AI car navigation unit will not only provide traffic information but also suggest detour routes. The generative AI car navigation unit also analyzes the intent of the driver's question and proactively provides related information. For example, if a driver asks, "What restaurants are nearby?", the unit will also provide restaurant ratings and menus. The generative AI car navigation unit also infers the intent of the driver's question and proactively provides the necessary information. For example, if a driver asks, "Where are gas stations?", the unit will not only provide the location of the nearest gas station, but also its opening hours and price information. This makes it possible to infer the intent of the driver's question and proactively provide the necessary information.

[0035] The generative AI car navigation unit can also provide visual information in response to questions from the driver. For example, the generative AI car navigation unit builds a system in which the generative AI also provides visual information in response to questions from the driver. For example, in response to the question, "Where are the restaurants nearby?", the location of the restaurant is displayed on a map. The generative AI car navigation unit also provides visual information in response to questions from the driver. For example, in response to the question, "What is the traffic situation up ahead?", congested roads are displayed on a map. The generative AI car navigation unit also provides visual information in response to questions from the driver. For example, in response to the question, "Where are the gas stations?", an image of the nearest gas station is displayed. This makes it possible to provide visual information in response to questions from the driver.

[0036] The generating AI car navigation unit can provide relevant video content in response to the driver's questions, making it easier to understand visually. For example, the generating AI car navigation unit builds a system in which the generating AI provides relevant video content in response to the driver's questions. For example, in response to the question, "How do I change a tire?", a video showing the procedure for changing a tire is displayed. The generating AI car navigation unit also provides relevant video content in response to the driver's questions. For example, in response to the question, "How do I change the engine oil?", a video showing the procedure for changing the engine oil is displayed. The generating AI car navigation unit also provides relevant video content in response to the driver's questions. For example, in response to the question, "How do I clean the interior of the car?", a video showing the procedure for cleaning the interior of the car is displayed. In this way, relevant video content can be provided in response to the driver's questions, making it easier to understand visually.

[0037] The camera unit can analyze the smartphone camera footage and provide detailed information such as road deterioration and potholes. The camera unit, for example, analyzes the smartphone camera footage to identify the road deterioration. For example, it detects cracks and potholes in the road and provides that information to the driver. The camera unit also uses the smartphone camera footage to analyze the road surface condition and identify areas of deterioration. For example, it detects peeling pavement and unevenness and warns the driver. The generation AI also analyzes the smartphone camera footage and provides detailed information such as road deterioration and potholes. For example, it displays areas of road deterioration on a map to alert the driver. This makes it possible to provide detailed information such as road deterioration and potholes.

[0038] The camera unit can analyze the smartphone camera footage, predict the movement of pedestrians or bicycles, and issue a warning to the driver. The camera unit, for example, analyzes the smartphone camera footage and predicts the movement of pedestrians. For example, if a pedestrian is about to cross a crosswalk, it issues a warning to the driver. The camera unit also uses the smartphone camera footage to predict the movement of bicycles and issue a warning to the driver. For example, if there is a possibility that a bicycle will suddenly run out into the road, it will warn the driver. The generation AI also analyzes the smartphone camera footage, predicts the movement of pedestrians and bicycles, and issues a warning to the driver. For example, if there is a possibility that a pedestrian will suddenly run out into the road, it will issue a warning to the driver. This makes it possible to predict the movement of pedestrians and bicycles and issue a warning to the driver.

[0039] The camera unit can analyze the smartphone camera footage, predict weather changes or deterioration in visibility, and provide advice to the driver. The camera unit, for example, analyzes the smartphone camera footage and predicts weather changes. For example, it provides advice to the driver before it starts to rain. The camera unit also uses the smartphone camera footage to predict deterioration in visibility and provide advice to the driver. For example, it warns the driver before fog appears. The generation AI also analyzes the smartphone camera footage, predicts weather changes or deterioration in visibility, and provides advice to the driver. For example, it provides advice to the driver before it starts to snow. This makes it possible to predict weather changes or deterioration in visibility and provide advice to the driver.

[0040] The camera unit can analyze the smartphone camera image, recognize road signs or traffic lights, and provide appropriate driving instructions to the driver. The camera unit, for example, analyzes the smartphone camera image and recognizes road signs. For example, it recognizes speed limit signs and instructs the driver to drive at an appropriate speed. The camera unit also uses the smartphone camera image to recognize traffic lights and provide appropriate driving instructions to the driver. For example, it urges the driver to slow down before the light turns red. The generation AI also analyzes the smartphone camera image, recognizes road signs and traffic lights, and provides appropriate driving instructions to the driver. For example, it recognizes stop signs and urges the driver to stop. This makes it possible to recognize road signs and traffic lights and provide appropriate driving instructions to the driver.

[0041] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0042] Car navigation systems can also be equipped with a health management unit that monitors the driver's health condition. For example, it can measure the driver's heart rate and blood pressure in real time and issue an alert to stop driving if an abnormality is detected. The health management unit can also analyze the driver's fatigue level and suggest appropriate times to take a break. For example, if the driver has been driving for a long period of time, it can issue an alert to encourage them to take a break. This makes it possible to provide driving advice based on the driver's health condition.

[0043] The car navigation system can also include an entertainment unit that provides customized entertainment content based on the driver's preferences and past driving history. For example, it can automatically play music or podcasts that the driver likes. The entertainment unit can also analyze the driver's past listening history and suggest new content. For example, it can suggest new songs or popular podcasts in the driver's favorite genre. This can improve the entertainment experience while driving.

[0044] A car navigation system can also be equipped with a health management unit that monitors the driver's health condition and provides driving advice according to the driver's physical condition. For example, it can measure the driver's heart rate and blood pressure in real time and issue an alert to stop driving if an abnormality is detected. The health management unit can also analyze the driver's fatigue level and suggest appropriate times to take a break. For example, if the driver has been driving for a long period of time, it can issue an alert to encourage them to take a break. This makes it possible to provide driving advice according to the driver's health condition.

[0045] The car navigation system may further include a question analysis unit that analyzes the content of the driver's question and provides highly accurate information by referring to past questions and answers. For example, if the driver asks, "What's the traffic situation up ahead?", the system provides traffic information based on past data. The question analysis unit can also analyze the content of the driver's question and provide information by referring to related past questions and answers. For example, if the driver asks, "What are some restaurants nearby?", the system generates the most appropriate answer based on the past question history. This allows the system to provide highly accurate information by referring to past questions and answers.

[0046] The car navigation system can further include a question intention guessing unit that guesses the intent of the driver's question and provides necessary information in advance. For example, if the driver asks, "What's the traffic situation up ahead?", the system will not only provide traffic information but also suggest detour routes. The question intention guessing unit can also analyze the intent of the driver's question and provide related information in advance. For example, if the driver asks, "What restaurants are nearby?", the system will also provide restaurant ratings and menus. This allows the system to guess the intent of the driver's question and provide necessary information in advance.

[0047] The car navigation system may further include a visual information providing unit that provides visual information in response to a driver's question. For example, in response to the question, "What are the restaurants nearby?", the location of the restaurant is displayed on a map. The visual information providing unit may also provide visual information in response to a driver's question. For example, in response to the question, "What is the traffic situation up ahead?", congested roads are displayed on a map. In this way, visual information can be provided in response to the driver's question.

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

[0049] Step 1: The smartphone installation unit installs the smartphone on the dashboard of the car. For example, the smartphone is fixed using a holder. The smartphone installation unit may also have a power supply function for charging the smartphone. Step 2: The generating AI car navigation unit uses the generating AI car navigation system on a smartphone. For example, the generating AI analyzes questions and instructions from the driver and provides appropriate information. The generating AI uses text generating AI (e.g., LLM) and multimodal generating AI to provide information to the driver via voice or the smartphone screen. Step 3: The camera unit collects road conditions using the smartphone camera. For example, the smartphone camera captures the road conditions ahead, and the generation AI analyzes the images to predict dangerous situations. Step 4: The alert unit issues an alert to the driver based on the information analyzed by the generation AI car navigation unit. For example, the alert is issued using an audio or visual alert.

[0050] (Example 2) The car navigation system according to the embodiment of the present invention is a system in which a smartphone is installed on the dashboard of a car, and a generation AI analyzes location information and real-time road conditions and provides information to the driver via voice or the smartphone screen. This allows the driver to easily obtain necessary information while driving, improving driving safety and convenience.

[0051] A car navigation system according to an embodiment includes a smartphone installation unit, an AI car navigation unit, a camera unit, and an alert unit. The smartphone installation unit installs a smartphone on the dashboard of a car. For example, the smartphone is secured using a holder. The smartphone installation unit may also include a power supply function for charging the smartphone. The AI ​​car navigation unit uses the smartphone to provide AI car navigation. For example, the AI ​​analyzes questions and instructions from the driver and provides appropriate information. The AI ​​uses a text generation AI (e.g., LLM) or a multimodal generation AI to provide information to the driver via voice or the smartphone screen. The camera unit collects road conditions using the smartphone camera. For example, the smartphone camera captures images of the road ahead, and the AI ​​analyzes the images to predict dangerous situations. The alert unit alerts the driver based on the information analyzed by the AI ​​car navigation unit. For example, the alert unit warns the driver using audio or visual alerts. This allows the car navigation system according to an embodiment to easily obtain necessary information while driving, thereby improving driving safety and convenience.

[0052] The generative AI car navigation unit can analyze the driver's past driving history and provide navigation tailored to the driver's driving style. For example, the generative AI car navigation unit analyzes the driver's past driving history to learn the driver's preferred routes and routes that the driver wants to avoid. For example, it identifies roads that the driver frequently uses and roads that the driver tends to avoid, and customizes navigation based on that. The generative AI car navigation unit also provides advice tailored to the driver's driving style based on the driver's past driving data. For example, it suggests driving methods to avoid sudden braking and sudden acceleration. The generative AI car navigation unit also analyzes the driver's past driving history and suggests rest spots and service areas that the driver prefers. For example, it reflects the driver's favorite cafes and gas stations in the navigation. This makes it possible to provide navigation tailored to the driver's individual driving style.

[0053] The generating AI car navigation unit can analyze the driver's tone of voice or vocabulary to estimate the driver's stress level and provide appropriate advice. For example, the generating AI car navigation unit analyzes the driver's tone of voice to estimate the stress level. For example, if the driver's voice gets higher or faster, it determines that stress is increasing and provides advice to relax. The generating AI car navigation unit also analyzes the driver's vocabulary to estimate the stress level. For example, if the driver frequently uses negative language, it determines that stress is increasing and encourages the driver to use more positive language. The generating AI car navigation unit also analyzes the driver's tone of voice and vocabulary to provide driving advice according to the driver's stress level. For example, if stress is increasing, it advises the driver to take deep breaths. This makes it possible to provide appropriate advice according to the driver's stress level.

[0054] The generating AI car navigation unit can use the emotion estimation function to provide relaxing music or encouraging messages according to the driver's emotional state. For example, the generating AI car navigation unit estimates the driver's emotional state and provides relaxing music. For example, if the driver is feeling stressed, it plays music with a relaxing effect. The generating AI car navigation unit also uses the emotion estimation function to provide encouraging messages according to the driver's emotional state. For example, if the driver is feeling anxious, it will offer words of encouragement. The generating AI car navigation unit also analyzes the driver's emotional state and provides relaxing music or encouraging messages according to the emotional state. For example, if the driver is tired, it will provide an encouraging message. This makes it possible to provide relaxing music or encouraging messages according to the driver's emotional state.

[0055] The generating AI car navigation unit can monitor the driver's health data and provide driving advice according to the driver's physical condition. For example, the generating AI car navigation unit monitors the driver's heart rate and advises the driver to stop driving if an abnormality is detected. For example, if the heart rate rises sharply, the generating AI car navigation unit will encourage the driver to take a break. The generating AI car navigation unit can also monitor the driver's blood pressure and provide appropriate advice if an abnormality is detected. For example, if blood pressure is high, the generating AI car navigation unit will suggest breathing techniques to help the driver relax. The generating AI car navigation unit can also analyze the driver's health data and provide driving advice according to the driver's physical condition. For example, if the driver is tired, the generating AI car navigation unit will encourage the driver to take a break. This makes it possible to provide driving advice according to the driver's physical condition.

[0056] The generative AI car navigation unit can adjust the temperature or humidity inside the car to provide a comfortable driving environment for the driver. For example, the generative AI car navigation unit monitors the temperature inside the car and automatically adjusts the air conditioner to keep the driver comfortable. For example, it turns on the air conditioner if the inside of the car becomes hot. The generative AI car navigation unit also monitors the humidity inside the car and automatically adjusts the humidifier or dehumidifier to maintain appropriate humidity. For example, it turns on the humidifier if the inside of the car is dry. The generative AI car navigation unit also analyzes the temperature and humidity inside the car and makes automatic adjustments to provide a comfortable driving environment. For example, it learns the temperature and humidity that the driver finds comfortable and adjusts based on that. This allows for a comfortable driving environment.

[0057] The generative AI car navigation unit can use the emotion estimation function to suggest driving routes that correspond to the emotional state of the driver. For example, the generative AI car navigation unit estimates the emotional state of the driver and suggests scenic routes that are relaxing. For example, if the driver is feeling stressed, the unit will guide the driver to a route rich in nature. The generative AI car navigation unit also uses the emotion estimation function to suggest quiet routes that correspond to the emotional state of the driver. For example, if the driver is tired, the unit will guide the driver to a quiet route with little traffic. The generative AI car navigation unit also analyzes the emotional state of the driver and suggests driving routes that correspond to the emotional state. For example, if the driver wants to relax, the unit will guide the driver to a scenic route. This makes it possible to suggest driving routes that correspond to the emotional state of the driver.

[0058] The generating AI car navigation unit can analyze the content of the driver's question and provide more accurate information by referring to past questions or answers. For example, the generating AI car navigation unit analyzes the content of the driver's question and provides highly accurate information by referring to a database of past questions and answers. For example, if a similar question has been asked in the past, it provides information based on the answer. The generating AI car navigation unit also analyzes the content of the driver's question and builds a system that provides information by referring to related past questions and answers. For example, it generates the optimal answer based on the past question history. The generating AI car navigation unit also analyzes the content of the driver's question and provides highly accurate information based on past question and answer data. For example, it learns past data and generates the optimal answer. This makes it possible to provide highly accurate information by referring to past questions and answers.

[0059] The generative AI car navigation unit can infer the intent of the driver's question and proactively provide the necessary information. For example, the generative AI car navigation unit builds a system in which the generative AI infers the intent of the driver's question and proactively provides the necessary information. For example, if a driver asks, "What's the traffic situation up ahead?", the generative AI car navigation unit will not only provide traffic information but also suggest detour routes. The generative AI car navigation unit also analyzes the intent of the driver's question and proactively provides related information. For example, if a driver asks, "What restaurants are nearby?", the unit will also provide restaurant ratings and menus. The generative AI car navigation unit also infers the intent of the driver's question and proactively provides the necessary information. For example, if a driver asks, "Where are gas stations?", the unit will not only provide the location of the nearest gas station, but also its opening hours and price information. This makes it possible to infer the intent of the driver's question and proactively provide the necessary information.

[0060] The generating AI car navigation unit uses the emotion estimation function to analyze the emotional state of the driver when the question is asked and can respond in an appropriate tone. For example, the generating AI car navigation unit analyzes the emotional state of the driver when the question is asked and responds in an appropriate tone. For example, if the driver is irritated, it responds in a calm tone. The generating AI car navigation unit also uses the emotion estimation function to analyze the emotional state of the driver when the question is asked and responds in a tone that corresponds to the emotion. For example, if the driver is feeling anxious, it responds in a reassuring tone. The generating AI car navigation unit also analyzes the emotional state of the driver and responds in a tone that corresponds to the emotion at the time of the question. For example, if the driver is relaxed, it responds in a friendly tone. This makes it possible to respond in an appropriate tone that corresponds to the driver's emotional state.

[0061] The generative AI car navigation unit can also provide visual information in response to questions from the driver. For example, the generative AI car navigation unit builds a system in which the generative AI also provides visual information in response to questions from the driver. For example, in response to the question, "Where are the restaurants nearby?", the location of the restaurant is displayed on a map. The generative AI car navigation unit also provides visual information in response to questions from the driver. For example, in response to the question, "What is the traffic situation up ahead?", congested roads are displayed on a map. The generative AI car navigation unit also provides visual information in response to questions from the driver. For example, in response to the question, "Where are the gas stations?", an image of the nearest gas station is displayed. This makes it possible to provide visual information in response to questions from the driver.

[0062] The generating AI car navigation unit can provide relevant video content in response to the driver's questions, making it easier to understand visually. For example, the generating AI car navigation unit builds a system in which the generating AI provides relevant video content in response to the driver's questions. For example, in response to the question, "How do I change a tire?", a video showing the procedure for changing a tire is displayed. The generating AI car navigation unit also provides relevant video content in response to the driver's questions. For example, in response to the question, "How do I change the engine oil?", a video showing the procedure for changing the engine oil is displayed. The generating AI car navigation unit also provides relevant video content in response to the driver's questions. For example, in response to the question, "How do I clean the interior of the car?", a video showing the procedure for cleaning the interior of the car is displayed. In this way, relevant video content can be provided in response to the driver's questions, making it easier to understand visually.

[0063] The generating AI car navigation unit uses the emotion estimation function to prioritize answers according to the driver's emotional state, and can provide information with a high level of urgency first. For example, the generating AI car navigation unit builds a system in which the generating AI analyzes the driver's emotional state and provides information with a high level of urgency first. For example, if the driver is anxious, the most important information is provided first. The generating AI car navigation unit also uses the emotion estimation function to prioritize answers according to the driver's emotional state. For example, if the driver is feeling anxious, the system provides information to reassure the driver first. The generating AI car navigation unit also analyzes the driver's emotional state and provides information with a high level of urgency first. For example, if the driver is in a panic, the system provides the most important information quickly. This allows the generating AI car navigation unit to prioritize answers according to the driver's emotional state and provide information with a high level of urgency first.

[0064] The camera unit can analyze the smartphone camera footage and provide detailed information such as road deterioration and potholes. The camera unit, for example, analyzes the smartphone camera footage to identify the road deterioration. For example, it detects cracks and potholes in the road and provides that information to the driver. The camera unit also uses the smartphone camera footage to analyze the road surface condition and identify areas of deterioration. For example, it detects peeling pavement and unevenness and warns the driver. The generation AI also analyzes the smartphone camera footage and provides detailed information such as road deterioration and potholes. For example, it displays areas of road deterioration on a map to alert the driver. This makes it possible to provide detailed information such as road deterioration and potholes.

[0065] The camera unit can analyze the smartphone camera footage, predict the movement of pedestrians or bicycles, and issue a warning to the driver. The camera unit, for example, analyzes the smartphone camera footage and predicts the movement of pedestrians. For example, if a pedestrian is about to cross a crosswalk, it issues a warning to the driver. The camera unit also uses the smartphone camera footage to predict the movement of bicycles and issue a warning to the driver. For example, if there is a possibility that a bicycle will suddenly run out into the road, it will warn the driver. The generation AI also analyzes the smartphone camera footage, predicts the movement of pedestrians and bicycles, and issues a warning to the driver. For example, if there is a possibility that a pedestrian will suddenly run out into the road, it will issue a warning to the driver. This makes it possible to predict the movement of pedestrians and bicycles and issue a warning to the driver.

[0066] The camera unit can use the emotion estimation function to adjust the timing or method of warning according to the driver's emotional state. For example, the generation AI in the camera unit analyzes the driver's emotional state and adjusts the timing of the warning. For example, if the driver is relaxed, the warning is delayed slightly. The camera unit also uses the emotion estimation function to adjust the method of warning according to the driver's emotional state. For example, if the driver is feeling stressed, the warning is given in a gentle tone. The generation AI also analyzes the driver's emotional state and adjusts the timing and method of warning. For example, if the driver is impatient, a quick and clear warning is given. This makes it possible to adjust the timing and method of warning according to the driver's emotional state.

[0067] The camera unit can analyze the smartphone camera footage, predict weather changes or deterioration in visibility, and provide advice to the driver. The camera unit, for example, analyzes the smartphone camera footage and predicts weather changes. For example, it provides advice to the driver before it starts to rain. The camera unit also uses the smartphone camera footage to predict deterioration in visibility and provide advice to the driver. For example, it warns the driver before fog appears. The generation AI also analyzes the smartphone camera footage, predicts weather changes or deterioration in visibility, and provides advice to the driver. For example, it provides advice to the driver before it starts to snow. This makes it possible to predict weather changes or deterioration in visibility and provide advice to the driver.

[0068] The camera unit can analyze the smartphone camera image, recognize road signs or traffic lights, and provide appropriate driving instructions to the driver. The camera unit, for example, analyzes the smartphone camera image and recognizes road signs. For example, it recognizes speed limit signs and instructs the driver to drive at an appropriate speed. The camera unit also uses the smartphone camera image to recognize traffic lights and provide appropriate driving instructions to the driver. For example, it urges the driver to slow down before the light turns red. The generation AI also analyzes the smartphone camera image, recognizes road signs and traffic lights, and provides appropriate driving instructions to the driver. For example, it recognizes stop signs and urges the driver to stop. This makes it possible to recognize road signs and traffic lights and provide appropriate driving instructions to the driver.

[0069] The camera unit can use the emotion estimation function to customize the content of the warning sound or warning message according to the driver's emotional state. For example, the generation AI in the camera unit analyzes the driver's emotional state and customizes the content of the warning sound. For example, if the driver is relaxed, a gentle warning sound is used. The camera unit also uses the emotion estimation function to customize the content of the warning message according to the driver's emotional state. For example, if the driver is feeling stressed, a warning message is provided in a gentle tone. The generation AI also analyzes the driver's emotional state and customizes the content of the warning sound or warning message. For example, if the driver is impatient, a quick and clear warning message is provided. This makes it possible to customize the content of the warning sound or warning message according to the driver's emotional state.

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

[0071] Car navigation systems can also be equipped with a health management unit that monitors the driver's health condition. For example, it can measure the driver's heart rate and blood pressure in real time and issue an alert to stop driving if an abnormality is detected. The health management unit can also analyze the driver's fatigue level and suggest appropriate times to take a break. For example, if the driver has been driving for a long period of time, it can issue an alert to encourage them to take a break. This makes it possible to provide driving advice based on the driver's health condition.

[0072] The car navigation system can also include an entertainment unit that provides customized entertainment content based on the driver's preferences and past driving history. For example, it can automatically play music or podcasts that the driver likes. The entertainment unit can also analyze the driver's past listening history and suggest new content. For example, it can suggest new songs or popular podcasts in the driver's favorite genre. This can improve the entertainment experience while driving.

[0073] The car navigation system may further include an emotion advice unit that provides driving advice according to the driver's emotional state. For example, if the driver is feeling stressed, the emotion advice unit may suggest deep breathing techniques to relax. The emotion advice unit may also suggest driving routes according to the driver's emotional state. For example, if the driver wants to relax, the emotion advice unit may guide the driver to a scenic route. This allows the system to provide appropriate driving advice according to the driver's emotional state.

[0074] The car navigation system may further include an entertainment unit that provides entertainment content according to the driver's emotional state. For example, if the driver is feeling stressed, the entertainment unit may play relaxing music. The entertainment unit may also provide encouraging messages according to the driver's emotional state. For example, if the driver is feeling anxious, the entertainment unit may offer encouraging words. In this way, entertainment content according to the driver's emotional state can be provided.

[0075] A car navigation system can also be equipped with a health management unit that monitors the driver's health condition and provides driving advice according to the driver's physical condition. For example, it can measure the driver's heart rate and blood pressure in real time and issue an alert to stop driving if an abnormality is detected. The health management unit can also analyze the driver's fatigue level and suggest appropriate times to take a break. For example, if the driver has been driving for a long period of time, it can issue an alert to encourage them to take a break. This makes it possible to provide driving advice according to the driver's health condition.

[0076] The car navigation system may further include an emotional route suggestion unit that suggests a driving route according to the emotional state of the driver. For example, if the driver is feeling stressed, a scenic route that allows the driver to relax may be suggested. The emotional route suggestion unit may also suggest a quiet route according to the emotional state of the driver. For example, if the driver is tired, a quiet route with little traffic may be suggested. In this way, a driving route can be suggested according to the emotional state of the driver.

[0077] The car navigation system may further include a question analysis unit that analyzes the content of the driver's question and provides highly accurate information by referring to past questions and answers. For example, if the driver asks, "What's the traffic situation up ahead?", the system provides traffic information based on past data. The question analysis unit can also analyze the content of the driver's question and provide information by referring to related past questions and answers. For example, if the driver asks, "What are some restaurants nearby?", the system generates the most appropriate answer based on the past question history. This allows the system to provide highly accurate information by referring to past questions and answers.

[0078] The car navigation system can further include a question intention guessing unit that guesses the intent of the driver's question and provides necessary information in advance. For example, if the driver asks, "What's the traffic situation up ahead?", the system will not only provide traffic information but also suggest detour routes. The question intention guessing unit can also analyze the intent of the driver's question and provide related information in advance. For example, if the driver asks, "What restaurants are nearby?", the system will also provide restaurant ratings and menus. This allows the system to guess the intent of the driver's question and provide necessary information in advance.

[0079] The car navigation system may further include an emotion priority setting unit that sets the priority of answers according to the emotional state of the driver and provides information with a high degree of urgency first. For example, if the driver is feeling anxious, the most important information is provided first. The emotion priority setting unit may also set the priority of answers according to the emotional state of the driver. For example, if the driver is feeling anxious, information to reassure the driver is provided first. In this way, the priority of answers according to the emotional state of the driver can be set and information with a high degree of urgency can be provided first.

[0080] The car navigation system may further include a visual information providing unit that provides visual information in response to a driver's question. For example, in response to the question, "What are the restaurants nearby?", the location of the restaurant is displayed on a map. The visual information providing unit may also provide visual information in response to a driver's question. For example, in response to the question, "What is the traffic situation up ahead?", congested roads are displayed on a map. In this way, visual information can be provided in response to the driver's question.

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

[0082] Step 1: The smartphone installation unit installs the smartphone on the dashboard of the car. For example, the smartphone is fixed using a holder. The smartphone installation unit may also have a power supply function for charging the smartphone. Step 2: The generating AI car navigation unit uses the generating AI car navigation system on a smartphone. For example, the generating AI analyzes questions and instructions from the driver and provides appropriate information. The generating AI uses text generating AI (e.g., LLM) and multimodal generating AI to provide information to the driver via voice or the smartphone screen. Step 3: The camera unit collects road conditions using the smartphone camera. For example, the smartphone camera captures the road conditions ahead, and the generation AI analyzes the images to predict dangerous situations. Step 4: The alert unit issues an alert to the driver based on the information analyzed by the generation AI car navigation unit. For example, the alert is issued using an audio or visual alert.

[0083] The specific processing unit 290 transmits the result of the specific processing to the smart device 14. In the smart device 14, the control unit 46A causes the output device 40 to output the result of the specific processing. The microphone 38B acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

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

[0085] Furthermore, the processing by the data processing system 10 described above is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart device 14, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart device 14. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the smart device 14 or an external device, and the smart device 14 acquires or collects information necessary for processing from the data processing device 12 or an external device.

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

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

[0088] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0089] The smart glasses 214 include a computer 36, a microphone 238, a speaker 240, a camera 42, and a communication I / F 44. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, and the camera 42 are also connected to the bus 52.

[0090] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0091] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0092] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0093] Fig. 4 shows an example of the main functions of the data processing device 12 and the smart glasses 214. As shown in Fig. 4, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0094] The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0095] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0096] In the smart glasses 214, the specific processing is performed by the processor 46. A specific processing program 60 is stored in the storage 50. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. The smart glasses 214 also have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0097] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0098] The specific processing unit 290 transmits the result of the specific processing to the smart glasses 214. In the smart glasses 214, the control unit 46A causes the speaker 240 to output the result of the specific processing. The microphone 238 acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

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

[0100] The data processing system 210 according to the second embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 210 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart glasses 214, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart glasses 214. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the smart glasses 214 or an external device, etc., and the smart glasses 214 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

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

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

[0103] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0104] The headset type terminal 314 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication I / F 44, and a display 343. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, the camera 42, and the display 343 are also connected to the bus 52.

[0105] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0106] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0107] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

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

[0109] The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0110] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0111] In the headset type terminal 314, 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 headset type terminal 314 has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can also perform processing similar to that of the specific processing unit 290 using these models.

[0112] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0113] The specific processing unit 290 transmits the result of the specific processing to the headset type terminal 314. In the headset type terminal 314, the control unit 46A causes the speaker 240 and the display 343 to output the result of the specific processing. The microphone 238 acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

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

[0115] The data processing system 310 according to the third embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 310 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the headset type terminal 314, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the headset type terminal 314. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the headset type terminal 314 or an external device, etc., and the headset type terminal 314 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

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

[0117] 7, 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.

[0118] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0119] The robot 414 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication I / F 44, and a control target 443. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, the camera 42, and the control target 443 are also connected to the bus 52.

[0120] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0121] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS image sensor or a CCD image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0122] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0123] The control object 443 includes a display device, LEDs in the eyes, and motors that drive the arms, hands, and feet. The posture and gestures of the robot 414 are controlled by controlling the motors of the arms, hands, and feet. Some of the emotions of the robot 414 can be expressed by controlling these motors. In addition, the facial expressions of the robot 414 can also be expressed by controlling the light emission state of the LEDs in the eyes of the robot 414.

[0124] Fig. 8 shows an example of the main functions of the data processing device 12 and the robot 414. As shown in Fig. 8, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0125] The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0126] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0127] In the robot 414, the specific processing is performed by the processor 46. A specific processing program 60 is stored in the storage 50. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. The robot 414 has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can also perform processing similar to that of the specific processing unit 290 using these models.

[0128] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0129] The specific processing unit 290 transmits the result of the specific processing to the robot 414. In the robot 414, the control unit 46A causes the speaker 240 and the control target 443 to output the result of the specific processing. The microphone 238 acquires voice indicating a user input regarding the result of the specific processing. The control unit 46A transmits voice data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the voice data.

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

[0131] The data processing system 410 according to the fourth embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 410 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the robot 414, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the robot 414. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the robot 414 or an external device, etc., and the robot 414 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0132] The emotion identification model 59 as an emotion engine may determine the user's emotion according to a specific mapping. Specifically, the emotion identification model 59 may determine the user's emotion according to an emotion map (see FIG. 9), which is a specific mapping. Similarly, the emotion identification model 59 may determine the robot's emotion, and the identification processing unit 290 may perform identification processing using the robot's emotion.

[0133] FIG. 9 illustrates an emotion map 400 on which multiple emotions are mapped. In the emotion map 400, emotions are arranged in concentric circles radiating from the center. Emotions closer to the center of the concentric circles are more primitive. Emotions representing states and behaviors arising from a state of mind are arranged on the outer edges of the concentric circles. The concept of emotion encompasses both emotions and mental states. Emotions generally generated from reactions occurring in the brain are arranged on the left side of the concentric circles. Emotions generally induced by situational judgment are arranged on the right side of the concentric circles. Emotions generally generated from reactions occurring in the brain and induced by situational judgment are arranged on the upper and lower sides of the concentric circles. Furthermore, the emotion of "pleasure" is arranged on the upper side of the concentric circles, and the emotion of "discomfort" is arranged on the lower side. In this way, in the emotion map 400, multiple emotions are mapped based on the structure by which emotions are generated, and emotions that tend to occur simultaneously are mapped close to each other.

[0134] These emotions are distributed in the 3 o'clock direction on emotion map 400, and typically fluctuate between relief and anxiety. In the right half of emotion map 400, situational awareness dominates over internal sensations, resulting in a sense of calm.

[0135] The inside of emotion map 400 represents what is going on in the mind, and the outside of emotion map 400 represents behavior, so the further you go outside emotion map 400, the more visible the emotions become (the more they are expressed in behavior).

[0136] Human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, a state of discomfort is expressed, and when they approach the ideal, a state of pleasure is expressed. Emotions can also be created for robots, cars, and motorcycles, based on various balances, such as posture and remaining battery life. When these balances deviate from the ideal, a state of discomfort is expressed, and when they approach the ideal, a state of pleasure is expressed. An emotion map can be generated, for example, based on Dr. Mitsuyoshi's emotion map (Research on speech emotion recognition and brain physiological signal analysis systems for emotions, Tokushima University, doctoral dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map lists emotions belonging to the area called "reaction," where sensation is dominant. The right half of the emotion map lists emotions belonging to the area called "situation," where situational awareness is dominant.

[0137] The emotion map defines two emotions that promote learning. One is a negative emotion on the situation side, around the middle of "repentance" or "reflection." In other words, this occurs when the robot experiences negative emotions such as "I never want to feel this way again" or "I don't want to be scolded again." The other is a positive emotion on the response side, around "desire." In other words, this occurs when the robot experiences positive feelings such as "I want more" or "I want to know more."

[0138] The emotion identification model 59 inputs user input into a pre-trained neural network, obtains emotion values ​​indicating each emotion shown in the emotion map 400, and determines the user's emotion. This neural network is pre-trained based on multiple pieces of training data that are combinations of user input and emotion values ​​indicating each emotion shown in the emotion map 400. Furthermore, this neural network is trained so that emotions that are located close to each other have similar values, as in the emotion map 900 shown in FIG. 10. FIG. 10 shows an example in which multiple emotions, "relieved," "calm," and "reassuring," have similar emotion values.

[0139] In the above embodiment, an example was given in which a specific process is performed by one computer 22, but the technology disclosed herein is not limited to this, and distributed processing of the specific process may be performed by multiple computers including computer 22.

[0140] In the above embodiment, an example in which the specific processing program 56 is stored in the storage 32 has been described, but the technology of the present disclosure is not limited to this. For example, the specific processing program 56 may be stored in a portable, computer-readable, non-transitory storage medium such as a USB (Universal Serial Bus) memory. The specific processing program 56 stored in the non-transitory storage medium is installed in the computer 22 of the data processing device 12. The processor 28 executes the specific processing in accordance with the specific processing program 56.

[0141] Alternatively, the specific processing program 56 may be stored in a storage device such as a server connected to the data processing device 12 via the network 54, and the specific processing program 56 may be downloaded and installed on the computer 22 in response to a request from the data processing device 12.

[0142] It is not necessary to store all of the specific processing program 56 in a storage device such as a server connected to the data processing device 12 via the network 54, or to store all of the specific processing program 56 in the storage 32; only a portion of the specific processing program 56 may be stored.

[0143] The hardware resource for executing a specific process can be any of the following types of processors: A processor, for example, is a CPU, which is a general-purpose processor that functions as a hardware resource for executing a specific process by executing software, i.e., a program. A processor also includes a dedicated electrical circuit, such as an FPGA (Field-Programmable Gate Array), a PLD (Programmable Logic Device), or an ASIC (Application Specific Integrated Circuit), which is a processor with a circuit configuration designed specifically for executing a specific process. Each processor has built-in or connected memory, and each processor uses the memory to execute the specific process.

[0144] The hardware resource that executes the specific process may be configured with one of these various processors, or may be configured with a combination of two or more processors of the same or different types (for example, a combination of multiple FPGAs, or a combination of a CPU and an FPGA). Also, the hardware resource that executes the specific process may be a single processor.

[0145] As an example of a system configured with a single processor, first, one processor is configured by combining one or more CPUs and software, and this processor functions as a hardware resource that executes a specific process. Second, there is a system that uses a processor that realizes the functions of an entire system including multiple hardware resources that execute a specific process on a single IC chip, as typified by SoC (System-on-a-chip). In this way, a specific process is realized using one or more of the above-mentioned various processors as hardware resources.

[0146] Furthermore, the hardware structure of these various processors can be, more specifically, an electric circuit that combines circuit elements such as semiconductor devices. The specific processing described above is merely an example. Therefore, it goes without saying that unnecessary steps may be deleted, new steps may be added, or the processing order may be rearranged, without departing from the spirit of the invention.

[0147] In the above example, the first to fourth embodiments have been described separately, but some or all of these embodiments may be combined. The smart device 14, smart glasses 214, headset terminal 314, and robot 414 are merely examples, and they may be combined, or other devices may be used. In the above example, the first and second embodiments have been described separately, but they may be combined.

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

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

[0150] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Device 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robot

Claims

1. A smartphone installation unit that installs the smartphone on the dashboard of the car, A generated AI car navigation unit that uses the generated AI car navigation system using the smartphone; A camera unit that collects road conditions using the camera of the smartphone; An alert unit that issues an alert to the driver based on the information analyzed by the generation AI car navigation unit. A system characterized by:

2. The generating AI car navigation unit Analyzing the driver's past driving history and providing navigation tailored to the driver's driving style 2. The system of claim 1.

3. The generating AI car navigation unit Analyzing the driver's tone of voice or language to estimate the driver's stress level and provide appropriate advice 2. The system of claim 1.

4. The generating AI car navigation unit providing relaxing music or encouraging messages according to the driver's emotional state; 2. The system of claim 1.

5. The generating AI car navigation unit Monitor the health data of the driver and provide driving advice according to the driver's physical condition 2. The system of claim 1.

Images