System

Abstract

An object of a system according to an embodiment is to analyze words and facial expressions of a driver and a passenger and to provide more comfortable driving.SOLUTION: A system according to an embodiment includes a word analysis unit, a facial expression analysis unit, and a driving adjustment unit. The word analysis unit analyzes words of the driver and the passenger. The facial expression analysis unit analyzes facial expressions of the driver and the passenger. The driving adjustment unit adjusts driving based on results analyzed by the word analysis unit and the facial expression analysis 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] Although the safety of self-driving cars has improved with conventional technology, passengers still face the problem of feeling unsafe.

[0005] The system according to the embodiment aims to provide a safer driving experience by analyzing the words and facial expressions of the driver and passengers. [Means for solving the problem]

[0006] The system according to the embodiment includes a word analysis unit, a facial expression analysis unit, and a driving adjustment unit. The word analysis unit analyzes the words of the driver and passengers. The facial expression analysis unit analyzes the facial expressions of the driver and passengers. The driving adjustment unit adjusts the driving based on the results of the analysis by the word analysis unit and the facial expression analysis unit. [Effects of the Invention]

[0007] The system according to the embodiment can analyze the words and facial expressions of the driver and passengers, providing a more reassuring driving experience. [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) A system according to an embodiment of the present invention is a system that analyzes the words and facial expressions of the driver and passengers in an autonomous vehicle and provides a more reassuring driving experience based on the results of the analysis, thereby increasing the psychological sense of security of the driver and passengers.

[0029] The system according to the embodiment includes a word analysis unit, an expression analysis unit, and a driving adjustment unit. The word analysis unit analyzes the words of the driver and passengers. For example, the word analysis unit converts the statements of the driver and passengers into text data using voice recognition technology. The word analysis unit can also analyze the content of the statements using natural language processing technology. The word analysis unit can also analyze the emotional intensity of the statements and prioritize responses to statements that express particularly strong emotions. For example, voice recognition technology analyzes the statements of the driver and passengers in real time and converts them into text data. Natural language processing technology analyzes the content of the statements and understands the intention of the statements. Analyzing the emotional intensity of the statements allows for quick responses to statements that express particularly strong emotions. The expression analysis unit analyzes the facial expressions of the driver and passengers. For example, the expression analysis unit detects the facial expressions of the driver and passengers using face recognition technology. The expression analysis unit can also analyze the emotions of facial expressions using an emotion estimation algorithm. The expression analysis unit can also analyze subtle changes in facial expressions to more quickly detect changes in the emotions of passengers. For example, facial recognition technology analyzes the facial expressions of the driver and passengers in real time to estimate their emotions. An emotion estimation algorithm analyzes changes in facial expressions and evaluates the intensity of emotions. By analyzing subtle changes in facial expressions, changes in passenger emotions can be quickly detected. The driving adjustment unit adjusts driving based on the results of the analysis by the word analysis unit and the facial expression analysis unit. For example, the driving adjustment unit may reduce speed or avoid sudden steering maneuvers to enhance passenger comfort. The driving adjustment unit may also analyze road conditions and traffic information in real time to select an optimal driving pattern. The driving adjustment unit may also adjust driving taking into account the dynamic characteristics of the vehicle. For example, the driving adjustment unit may adjust speed to enhance passenger comfort. By analyzing road conditions and traffic information, an optimal driving pattern can be selected. By taking into account the dynamic characteristics of the vehicle, safer driving can be achieved. As a result, the system according to the embodiment can enhance the psychological comfort of the driver and passengers. For example, the system may analyze the words and facial expressions of the driver and passengers and adjust driving based on the results to enable passengers to enjoy a relaxed ride.The system can also learn the individual comfort patterns of the driver and passengers and customize driving accordingly, providing a more personalized experience.

[0030] The word analysis unit analyzes background sounds of a speech to more accurately understand the intention of the speech. The word analysis unit, for example, analyzes background sounds of a speech to more accurately understand the intention of the speech. For example, if music is playing loudly in the car, the speech content is analyzed taking into account the influence of that noise. The word analysis unit also analyzes background sounds of a speech to more accurately understand the intention of the speech. For example, if there is loud outside noise, the speech content is analyzed taking into account the influence of that noise. The word analysis unit also analyzes background sounds of a speech to more accurately understand the intention of the speech. For example, if there is a lot of conversation in the car, the speech content is analyzed taking into account the influence of that noise. This allows the intention of the speech to be more accurately understood.

[0031] The language analysis unit can analyze the content of utterances in multiple languages, making it possible to accommodate passengers who speak different languages. The language analysis unit, for example, analyzes the content of utterances in multiple languages, making it possible to accommodate passengers who speak different languages. For example, it supports multiple languages ​​such as English and Chinese. The language analysis unit can also analyze the content of utterances in multiple languages, making it possible to accommodate passengers who speak different languages. For example, if a passenger speaks a language other than Japanese, it analyzes the utterances in that language. The language analysis unit can also analyze the content of utterances in multiple languages, making it possible to accommodate passengers who speak different languages. For example, if a passenger speaks French, it analyzes the utterances in that language. This makes it possible to accommodate passengers who speak different languages.

[0032] The word analysis unit can estimate the passenger's preferences and interests based on the content of the utterances and automatically adjust the entertainment system. The word analysis unit, for example, estimates the passenger's preferences and interests based on the content of the utterances and automatically adjusts the entertainment system. For example, changing the music genre. The word analysis unit can also estimate the passenger's preferences and interests based on the content of the utterances and automatically adjust the entertainment system. For example, changing the movie genre. The word analysis unit can also estimate the passenger's preferences and interests based on the content of the utterances and automatically adjust the entertainment system. For example, changing the radio channel. This makes it possible to provide entertainment that suits the passenger's preferences and interests.

[0033] The facial expression analysis unit, when analyzing facial expressions, also takes into account the direction of the face and the direction of the gaze, and can identify factors that will attract the passenger's attention. For example, the facial expression analysis unit, when analyzing facial expressions, also takes into account the direction of the face and the direction of the gaze, and can identify factors that will attract the passenger's attention. For example, it analyzes where the gaze is directed. Furthermore, the facial expression analysis unit, when analyzing facial expressions, also takes into account the direction of the face and the direction of the gaze, and can identify factors that will attract the passenger's attention. For example, it analyzes which direction the face is facing. Furthermore, the facial expression analysis unit, when analyzing facial expressions, also takes into account the direction of the face and the direction of the gaze, and can identify factors that will attract the passenger's attention. For example, it analyzes whether the gaze is directed towards a specific object. This makes it possible to identify factors that will attract the passenger's attention.

[0034] The facial expression analysis unit can estimate the passenger's level of fatigue based on facial expression analysis and suggest a break. The facial expression analysis unit can, for example, estimate the passenger's level of fatigue based on facial expression analysis and suggest a break. For example, it can analyze dark circles under the eyes and the weight of the eyelids. The facial expression analysis unit can also estimate the passenger's level of fatigue based on facial expression analysis and suggest a break. For example, it can analyze the frequency of yawning and the degree to which the eyes are open. The facial expression analysis unit can also estimate the passenger's level of fatigue based on facial expression analysis and suggest a break. For example, it can analyze the degree of tension in the facial muscles. This makes it possible to estimate the passenger's level of fatigue and suggest an appropriate break.

[0035] The facial expression analysis unit can automatically adjust the lighting and temperature inside the car based on the facial expression analysis, thereby providing a comfortable environment. The facial expression analysis unit can automatically adjust the lighting and temperature inside the car based on the facial expression analysis, thereby providing a comfortable environment. For example, if a relaxed facial expression is detected, the lighting can be changed to a warmer color. The facial expression analysis unit can also automatically adjust the lighting and temperature inside the car based on the facial expression analysis, thereby providing a comfortable environment. For example, if a tired facial expression is detected, the temperature can be slightly lowered. The facial expression analysis unit can also automatically adjust the lighting and temperature inside the car based on the facial expression analysis, thereby providing a comfortable environment. For example, if a tense facial expression is detected, the lighting can be softened. This can provide a comfortable in-car environment.

[0036] The driving adjustment unit can analyze road conditions and traffic information in real time when adjusting driving and select an optimal driving pattern. For example, when adjusting driving, the driving adjustment unit analyzes road conditions and traffic information in real time and selects an optimal driving pattern. For example, the driving adjustment unit changes the route based on congestion information. Furthermore, when adjusting driving, the driving adjustment unit analyzes road conditions and traffic information in real time and selects an optimal driving pattern. For example, the driving adjustment unit adjusts the speed based on accident information. Furthermore, when adjusting driving, the driving adjustment unit analyzes road conditions and traffic information in real time and selects an optimal driving pattern. For example, the driving style is changed based on weather information. In this way, an optimal driving pattern can be selected.

[0037] The driving adjustment unit can take into account the dynamic characteristics of the vehicle when adjusting the driving. The driving adjustment unit, for example, takes into account the dynamic characteristics of the vehicle when adjusting the driving. For example, it analyzes the wear state of the tires and avoids sudden braking. The driving adjustment unit also takes into account the dynamic characteristics of the vehicle when adjusting the driving. For example, it analyzes the remaining amount of fuel and drives in a fuel-efficient manner. The driving adjustment unit also takes into account the dynamic characteristics of the vehicle when adjusting the driving. For example, it analyzes the temperature of the engine and drives in a way that avoids overload. This makes it possible to adjust the driving in consideration of the dynamic characteristics of the vehicle.

[0038] The driving adjustment unit can monitor the health condition of passengers and drive the vehicle according to their health condition. The driving adjustment unit, for example, monitors the health condition of passengers and drives the vehicle according to their health condition. For example, if the heart rate is high, the driving adjustment unit drives more gently. The driving adjustment unit also monitors the health condition of passengers and drives the vehicle according to their health condition. For example, if the body temperature is high, the driving adjustment unit adjusts the temperature inside the vehicle. The driving adjustment unit also monitors the health condition of passengers and drives the vehicle according to their health condition. For example, if the stress level is high, the driving adjustment unit drives more smoothly. This makes it possible to drive the vehicle according to the health condition of passengers.

[0039] The driving adjustment unit can suggest the optimal route to the destination and reduce stress for passengers. The driving adjustment unit, for example, suggests the optimal route to the destination and reduces stress for passengers. For example, it selects a route that avoids traffic jams. The driving adjustment unit can also suggest the optimal route to the destination and reduce stress for passengers. For example, it can select a route with good scenery. The driving adjustment unit can also suggest the optimal route to the destination and reduce stress for passengers. For example, it can select a route with many rest stops. In this way, the optimal route can be suggested and stress for passengers can be reduced.

[0040] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0041] The system may further include a music selection unit. The music selection unit can select appropriate music according to the passenger's preferences and current mood. For example, if the passenger wants to relax, it can select calm music. On the other hand, if the passenger wants to cheer up, it can select upbeat music. Furthermore, the music selection unit can learn the passenger's past music selection history and provide more personalized music. This allows the system to provide music that matches the passenger's mood, creating a comfortable in-car environment.

[0042] The system may further include a scent provider. The scent provider can provide an appropriate scent depending on the passenger's emotions and mood. For example, if the passenger wants to relax, it can provide a lavender scent. Alternatively, if the passenger wants to improve their concentration, it can provide a peppermint scent. Furthermore, the scent provider can learn the passenger's past scent preferences and provide a more personalized scent. This allows the scent provider to provide a scent that matches the passenger's mood, creating a comfortable in-car environment.

[0043] The system can also be equipped with a seat adjustment unit. The seat adjustment unit can automatically adjust the seat position and angle according to the passenger's body type and posture. For example, if the passenger becomes tired after a long drive, the seat can be reclined. The seat height and angle can also be fine-tuned to allow the passenger to maintain a comfortable posture. Furthermore, the seat adjustment unit can learn the passenger's past seat adjustment history and provide more personalized seat adjustments. This makes it possible to create a comfortable seating environment that matches the passenger's body type and posture.

[0044] The system may further include a temperature control unit. The temperature control unit can automatically adjust the temperature inside the vehicle according to the passenger's perceived temperature and the outside temperature. For example, it can make the vehicle warmer on cold days and cooler on hot days. It can also adjust the temperature individually based on the passenger's perceived temperature. Furthermore, the temperature control unit can learn the passenger's past temperature adjustment history and provide more personalized temperature adjustment. This makes it possible to create a comfortable in-vehicle environment that matches the passenger's perceived temperature.

[0045] The system may further include a lighting adjustment unit. The lighting adjustment unit can automatically adjust the lighting inside the vehicle according to the passenger's mood and the time of day. For example, it can provide soft lighting at night and bright lighting during the day. It can also provide warm lighting when the passenger wants to relax based on the passenger's mood. Furthermore, the lighting adjustment unit can learn the passenger's past lighting adjustment history and provide more personalized lighting. This makes it possible to create a comfortable in-vehicle environment that matches the passenger's mood and the time of day.

[0046] The system may further include an entertainment provider. The entertainment provider can provide appropriate entertainment according to the passenger's preferences and mood. For example, it can provide movies, music, games, etc. The entertainment content can also be adjusted to help the passenger relax. Furthermore, the entertainment provider can learn the passenger's past entertainment history and provide more personalized entertainment. This makes it possible to provide comfortable entertainment that matches the passenger's preferences and mood.

[0047] The processing flow of the first embodiment will be briefly explained below.

[0048] Step 1: The word analysis unit analyzes the words of the driver and passengers. For example, the word analysis unit uses voice recognition technology to convert the driver's and passengers' comments into text data. It can also use natural language processing technology to analyze the content of the comments and the emotional intensity of the comments. It prioritizes responses to comments that show particularly strong emotions. Step 2: The facial expression analysis unit analyzes the facial expressions of the driver and passengers. For example, the facial expression analysis unit uses face recognition technology to detect the facial expressions of the driver and passengers, and then analyzes the emotions in the expressions using an emotion estimation algorithm. It can also analyze subtle changes in facial expressions to quickly detect changes in the passengers' emotions. Step 3: The driving adjustment unit adjusts the driving behavior based on the results of the analysis by the speech analysis unit and facial expression analysis unit. For example, the driving adjustment unit can increase passengers' sense of security by reducing speed and avoiding sudden steering. It can also analyze road conditions and traffic information in real time to select the optimal driving pattern. It can also adjust the driving behavior taking into account the vehicle's dynamic characteristics.

[0049] (Example 2) A system according to an embodiment of the present invention is a system that analyzes the words and facial expressions of the driver and passengers in an autonomous vehicle and provides a more reassuring driving experience based on the results of the analysis, thereby increasing the psychological sense of security of the driver and passengers.

[0050] The system according to the embodiment includes a word analysis unit, an expression analysis unit, and a driving adjustment unit. The word analysis unit analyzes the words of the driver and passengers. For example, the word analysis unit converts the statements of the driver and passengers into text data using voice recognition technology. The word analysis unit can also analyze the content of the statements using natural language processing technology. The word analysis unit can also analyze the emotional intensity of the statements and prioritize responses to statements that express particularly strong emotions. For example, voice recognition technology analyzes the statements of the driver and passengers in real time and converts them into text data. Natural language processing technology analyzes the content of the statements and understands the intention of the statements. Analyzing the emotional intensity of the statements allows for quick responses to statements that express particularly strong emotions. The expression analysis unit analyzes the facial expressions of the driver and passengers. For example, the expression analysis unit detects the facial expressions of the driver and passengers using face recognition technology. The expression analysis unit can also analyze the emotions of facial expressions using an emotion estimation algorithm. The expression analysis unit can also analyze subtle changes in facial expressions to more quickly detect changes in the emotions of passengers. For example, facial recognition technology analyzes the facial expressions of the driver and passengers in real time to estimate their emotions. An emotion estimation algorithm analyzes changes in facial expressions and evaluates the intensity of emotions. By analyzing subtle changes in facial expressions, changes in passenger emotions can be quickly detected. The driving adjustment unit adjusts driving based on the results of the analysis by the word analysis unit and the facial expression analysis unit. For example, the driving adjustment unit may reduce speed or avoid sudden steering maneuvers to enhance passenger comfort. The driving adjustment unit may also analyze road conditions and traffic information in real time to select an optimal driving pattern. The driving adjustment unit may also adjust driving taking into account the dynamic characteristics of the vehicle. For example, the driving adjustment unit may adjust speed to enhance passenger comfort. By analyzing road conditions and traffic information, an optimal driving pattern can be selected. By taking into account the dynamic characteristics of the vehicle, safer driving can be achieved. As a result, the system according to the embodiment can enhance the psychological comfort of the driver and passengers. For example, the system may analyze the words and facial expressions of the driver and passengers and adjust driving based on the results to enable passengers to enjoy a relaxed ride.The system can also learn the individual comfort patterns of the driver and passengers and customize driving accordingly, providing a more personalized experience.

[0051] The word analysis unit can analyze the emotional intensity of a utterance and prioritize responses to utterances that show particularly strong emotions. The word analysis unit, for example, analyzes the emotional intensity of a utterance and prioritizes responses to utterances that show particularly strong emotions. For example, it detects words that show strong emotions such as "scary" or "dangerous" and immediately adjusts driving. The word analysis unit also analyzes the emotional intensity of a utterance and prioritizes responses to utterances that show particularly strong emotions. For example, it detects utterances ...I'm in a hurry" and immediately adjusts driving, for example. The word analysis unit also analyzes the emotional intensity of a utterance and prioritizes responses to utterances that show particularly strong emotions. For example, it detects utterances that show "I want to feel safe" and immediately adjusts driving, for example. This allows for a quick response to utterances that show particularly strong emotions.

[0052] The word analysis unit analyzes background sounds of a speech to more accurately understand the intention of the speech. The word analysis unit, for example, analyzes background sounds of a speech to more accurately understand the intention of the speech. For example, if music is playing loudly in the car, the speech content is analyzed taking into account the influence of that noise. The word analysis unit also analyzes background sounds of a speech to more accurately understand the intention of the speech. For example, if there is loud outside noise, the speech content is analyzed taking into account the influence of that noise. The word analysis unit also analyzes background sounds of a speech to more accurately understand the intention of the speech. For example, if there is a lot of conversation in the car, the speech content is analyzed taking into account the influence of that noise. This allows the intention of the speech to be more accurately understood.

[0053] The word analysis unit can use the emotion estimation function to analyze the emotion of the utterance in real time and adjust driving to bring out positive emotions. The word analysis unit, for example, uses the emotion estimation function to analyze the emotion of the utterance in real time and adjust driving to bring out positive emotions. For example, in response to a relaxed utterance, the driver drives more gently. The word analysis unit also uses the emotion estimation function to analyze the emotion of the utterance in real time and adjust driving to bring out positive emotions. For example, in response to a joyful utterance, the driver drives more smoothly. The word analysis unit also uses the emotion estimation function to analyze the emotion of the utterance in real time and adjust driving to bring out positive emotions. For example, in response to a reassuring utterance, the driver drives more safely. This makes it possible to adjust driving to bring out positive emotions.

[0054] The language analysis unit can analyze the content of utterances in multiple languages, making it possible to accommodate passengers who speak different languages. The language analysis unit, for example, analyzes the content of utterances in multiple languages, making it possible to accommodate passengers who speak different languages. For example, it supports multiple languages ​​such as English and Chinese. The language analysis unit can also analyze the content of utterances in multiple languages, making it possible to accommodate passengers who speak different languages. For example, if a passenger speaks a language other than Japanese, it analyzes the utterances in that language. The language analysis unit can also analyze the content of utterances in multiple languages, making it possible to accommodate passengers who speak different languages. For example, if a passenger speaks French, it analyzes the utterances in that language. This makes it possible to accommodate passengers who speak different languages.

[0055] The word analysis unit can estimate the passenger's preferences and interests based on the content of the utterances and automatically adjust the entertainment system. The word analysis unit, for example, estimates the passenger's preferences and interests based on the content of the utterances and automatically adjusts the entertainment system. For example, changing the music genre. The word analysis unit can also estimate the passenger's preferences and interests based on the content of the utterances and automatically adjust the entertainment system. For example, changing the movie genre. The word analysis unit can also estimate the passenger's preferences and interests based on the content of the utterances and automatically adjust the entertainment system. For example, changing the radio channel. This makes it possible to provide entertainment that suits the passenger's preferences and interests.

[0056] The word analysis unit can use the emotion estimation function to analyze the emotion of the utterance and make driving adjustments to reduce the stress level of the passenger. The word analysis unit, for example, uses the emotion estimation function to analyze the emotion of the utterance and make driving adjustments to reduce the stress level of the passenger. For example, in response to a utterance that expresses stress, the driving is made more gentle. The word analysis unit also uses the emotion estimation function to analyze the emotion of the utterance and make driving adjustments to reduce the stress level of the passenger. For example, in response to a utterance that expresses tension, the driving is made safer. The word analysis unit also uses the emotion estimation function to analyze the emotion of the utterance and make driving adjustments to reduce the stress level of the passenger. For example, in response to a utterance that expresses anxiety, the driving is made smoother. This makes it possible to make driving adjustments to reduce the stress level of the passenger.

[0057] The facial expression analysis unit analyzes subtle changes in facial expressions, allowing for earlier detection of changes in the passenger's emotions. The facial expression analysis unit, for example, analyzes subtle changes in facial expressions, allowing for earlier detection of changes in the passenger's emotions. For example, it analyzes the movement of the eyebrows and the degree to which the corners of the mouth are turned up. The facial expression analysis unit also analyzes subtle changes in facial expressions, allowing for earlier detection of changes in the passenger's emotions. For example, it analyzes the movement of the eyes and the size of the pupils. The facial expression analysis unit also analyzes subtle changes in facial expressions, allowing for earlier detection of changes in the passenger's emotions. For example, it analyzes the movement of the cheek muscles. This allows for earlier detection of changes in the passenger's emotions.

[0058] The facial expression analysis unit, when analyzing facial expressions, also takes into account the direction of the face and the direction of the gaze, and can identify factors that will attract the passenger's attention. For example, the facial expression analysis unit, when analyzing facial expressions, also takes into account the direction of the face and the direction of the gaze, and can identify factors that will attract the passenger's attention. For example, it analyzes where the gaze is directed. Furthermore, the facial expression analysis unit, when analyzing facial expressions, also takes into account the direction of the face and the direction of the gaze, and can identify factors that will attract the passenger's attention. For example, it analyzes which direction the face is facing. Furthermore, the facial expression analysis unit, when analyzing facial expressions, also takes into account the direction of the face and the direction of the gaze, and can identify factors that will attract the passenger's attention. For example, it analyzes whether the gaze is directed towards a specific object. This makes it possible to identify factors that will attract the passenger's attention.

[0059] The facial expression analysis unit can use the emotion estimation function to analyze facial expressions in real time and adjust driving to bring out positive emotions. The facial expression analysis unit, for example, uses the emotion estimation function to analyze facial expressions in real time and adjust driving to bring out positive emotions. For example, when a smile is detected, the driving is made smoother. The facial expression analysis unit also uses the emotion estimation function to analyze facial expressions in real time and adjust driving to bring out positive emotions. For example, when a relaxed facial expression is detected, the driving is made more gentle. The facial expression analysis unit also uses the emotion estimation function to analyze facial expressions in real time and adjust driving to bring out positive emotions. For example, when a relieved facial expression is detected, the driving is made safer. This makes it possible to adjust driving to bring out positive emotions.

[0060] The facial expression analysis unit can estimate the passenger's level of fatigue based on facial expression analysis and suggest a break. The facial expression analysis unit can, for example, estimate the passenger's level of fatigue based on facial expression analysis and suggest a break. For example, it can analyze dark circles under the eyes and the weight of the eyelids. The facial expression analysis unit can also estimate the passenger's level of fatigue based on facial expression analysis and suggest a break. For example, it can analyze the frequency of yawning and the degree to which the eyes are open. The facial expression analysis unit can also estimate the passenger's level of fatigue based on facial expression analysis and suggest a break. For example, it can analyze the degree of tension in the facial muscles. This makes it possible to estimate the passenger's level of fatigue and suggest an appropriate break.

[0061] The facial expression analysis unit can automatically adjust the lighting and temperature inside the car based on the facial expression analysis, thereby providing a comfortable environment. The facial expression analysis unit can automatically adjust the lighting and temperature inside the car based on the facial expression analysis, thereby providing a comfortable environment. For example, if a relaxed facial expression is detected, the lighting can be changed to a warmer color. The facial expression analysis unit can also automatically adjust the lighting and temperature inside the car based on the facial expression analysis, thereby providing a comfortable environment. For example, if a tired facial expression is detected, the temperature can be slightly lowered. The facial expression analysis unit can also automatically adjust the lighting and temperature inside the car based on the facial expression analysis, thereby providing a comfortable environment. For example, if a tense facial expression is detected, the lighting can be softened. This can provide a comfortable in-car environment.

[0062] The facial expression analysis unit can use the emotion estimation function to analyze the emotion of the facial expression and make driving adjustments to increase the passenger's level of relaxation. The facial expression analysis unit, for example, uses the emotion estimation function to analyze the emotion of the facial expression and make driving adjustments to increase the passenger's level of relaxation. For example, if a relaxed expression is detected, the driving is made more gentle. The facial expression analysis unit also uses the emotion estimation function to analyze the emotion of the facial expression and make driving adjustments to increase the passenger's level of relaxation. For example, if a smiling face is detected, the driving is made smoother. The facial expression analysis unit also uses the emotion estimation function to analyze the emotion of the facial expression and make driving adjustments to increase the passenger's level of relaxation. For example, if a relieved expression is detected, the driving is made safer. This makes it possible to make driving adjustments to increase the passenger's level of relaxation.

[0063] The driving adjustment unit can analyze road conditions and traffic information in real time when adjusting driving and select an optimal driving pattern. For example, when adjusting driving, the driving adjustment unit analyzes road conditions and traffic information in real time and selects an optimal driving pattern. For example, the driving adjustment unit changes the route based on congestion information. Furthermore, when adjusting driving, the driving adjustment unit analyzes road conditions and traffic information in real time and selects an optimal driving pattern. For example, the driving adjustment unit adjusts the speed based on accident information. Furthermore, when adjusting driving, the driving adjustment unit analyzes road conditions and traffic information in real time and selects an optimal driving pattern. For example, the driving style is changed based on weather information. In this way, an optimal driving pattern can be selected.

[0064] The driving adjustment unit can take into account the dynamic characteristics of the vehicle when adjusting the driving. The driving adjustment unit, for example, takes into account the dynamic characteristics of the vehicle when adjusting the driving. For example, it analyzes the wear state of the tires and avoids sudden braking. The driving adjustment unit also takes into account the dynamic characteristics of the vehicle when adjusting the driving. For example, it analyzes the remaining amount of fuel and drives in a fuel-efficient manner. The driving adjustment unit also takes into account the dynamic characteristics of the vehicle when adjusting the driving. For example, it analyzes the temperature of the engine and drives in a way that avoids overload. This makes it possible to adjust the driving in consideration of the dynamic characteristics of the vehicle.

[0065] The driving adjustment unit can use the emotion estimation function to evaluate the effect of the driving adjustment in real time and make driving adjustments to bring out positive emotions. The driving adjustment unit, for example, uses the emotion estimation function to evaluate the effect of the driving adjustment in real time and make driving adjustments to bring out positive emotions. For example, if a relaxed facial expression is detected, the driving is made more gentle. The driving adjustment unit also uses the emotion estimation function to evaluate the effect of the driving adjustment in real time and make driving adjustments to bring out positive emotions. For example, if a smiling face is detected, the driving is made smoother. The driving adjustment unit also uses the emotion estimation function to evaluate the effect of the driving adjustment in real time and make driving adjustments to bring out positive emotions. For example, if a relieved facial expression is detected, the driving is made safer. This makes it possible to make driving adjustments to bring out positive emotions.

[0066] The driving adjustment unit can monitor the health condition of passengers and drive the vehicle according to their health condition. The driving adjustment unit, for example, monitors the health condition of passengers and drives the vehicle according to their health condition. For example, if the heart rate is high, the driving adjustment unit drives more gently. The driving adjustment unit also monitors the health condition of passengers and drives the vehicle according to their health condition. For example, if the body temperature is high, the driving adjustment unit adjusts the temperature inside the vehicle. The driving adjustment unit also monitors the health condition of passengers and drives the vehicle according to their health condition. For example, if the stress level is high, the driving adjustment unit drives more smoothly. This makes it possible to drive the vehicle according to the health condition of passengers.

[0067] The driving adjustment unit can suggest the optimal route to the destination and reduce stress for passengers. The driving adjustment unit, for example, suggests the optimal route to the destination and reduces stress for passengers. For example, it selects a route that avoids traffic jams. The driving adjustment unit can also suggest the optimal route to the destination and reduce stress for passengers. For example, it can select a route with good scenery. The driving adjustment unit can also suggest the optimal route to the destination and reduce stress for passengers. For example, it can select a route with many rest stops. In this way, the optimal route can be suggested and stress for passengers can be reduced.

[0068] The driving adjustment unit can use the emotion estimation function to evaluate the effect of the driving adjustment and make driving adjustments to increase the passenger's sense of security. The driving adjustment unit, for example, uses the emotion estimation function to evaluate the effect of the driving adjustment and make driving adjustments to increase the passenger's sense of security. For example, if a relaxed facial expression is detected, the driving is made gentler. The driving adjustment unit also uses the emotion estimation function to evaluate the effect of the driving adjustment and make driving adjustments to increase the passenger's sense of security. For example, if a smiling face is detected, the driving is made smoother. The driving adjustment unit also uses the emotion estimation function to evaluate the effect of the driving adjustment and make driving adjustments to increase the passenger's sense of security. For example, if a relieved facial expression is detected, the driving is made safer. This makes it possible to make driving adjustments to increase the passenger's sense of security.

[0069] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0070] The system may further include a music selection unit. The music selection unit can select appropriate music according to the passenger's preferences and current mood. For example, if the passenger wants to relax, it can select calm music. On the other hand, if the passenger wants to cheer up, it can select upbeat music. Furthermore, the music selection unit can learn the passenger's past music selection history and provide more personalized music. This allows the system to provide music that matches the passenger's mood, creating a comfortable in-car environment.

[0071] The system may further include a scent provider. The scent provider can provide an appropriate scent depending on the passenger's emotions and mood. For example, if the passenger wants to relax, it can provide a lavender scent. Alternatively, if the passenger wants to improve their concentration, it can provide a peppermint scent. Furthermore, the scent provider can learn the passenger's past scent preferences and provide a more personalized scent. This allows the scent provider to provide a scent that matches the passenger's mood, creating a comfortable in-car environment.

[0072] The system can also be equipped with a seat adjustment unit. The seat adjustment unit can automatically adjust the seat position and angle according to the passenger's body type and posture. For example, if the passenger becomes tired after a long drive, the seat can be reclined. The seat height and angle can also be fine-tuned to allow the passenger to maintain a comfortable posture. Furthermore, the seat adjustment unit can learn the passenger's past seat adjustment history and provide more personalized seat adjustments. This makes it possible to create a comfortable seating environment that matches the passenger's body type and posture.

[0073] The system may further include a temperature control unit. The temperature control unit can automatically adjust the temperature inside the vehicle according to the passenger's perceived temperature and the outside temperature. For example, it can make the vehicle warmer on cold days and cooler on hot days. It can also adjust the temperature individually based on the passenger's perceived temperature. Furthermore, the temperature control unit can learn the passenger's past temperature adjustment history and provide more personalized temperature adjustment. This makes it possible to create a comfortable in-vehicle environment that matches the passenger's perceived temperature.

[0074] The system may further include a lighting adjustment unit. The lighting adjustment unit can automatically adjust the lighting inside the vehicle according to the passenger's mood and the time of day. For example, it can provide soft lighting at night and bright lighting during the day. It can also provide warm lighting when the passenger wants to relax based on the passenger's mood. Furthermore, the lighting adjustment unit can learn the passenger's past lighting adjustment history and provide more personalized lighting. This makes it possible to create a comfortable in-vehicle environment that matches the passenger's mood and the time of day.

[0075] The system can further include a health monitoring unit. The health monitoring unit can monitor the passenger's health condition in real time and adjust driving as necessary. For example, it can measure heart rate and blood pressure and, if abnormalities are detected, moderate driving. It can also suggest that the passenger take a break if they feel tired. Furthermore, the health monitoring unit can learn from the passenger's past health data and provide more personalized health management. This allows for safe and comfortable driving that is tailored to the passenger's health condition.

[0076] The system can further include a stress detection unit. The stress detection unit can detect the passenger's stress level in real time and adjust driving as needed. For example, if the passenger's stress level is high, the driving can be made gentler. It can also provide music or fragrance to help the passenger relax. Furthermore, the stress detection unit can learn the passenger's past stress data and provide more personalized stress management. This allows for a comfortable driving experience that is tailored to the passenger's stress level.

[0077] The system may further include an emotion feedback unit. The emotion feedback unit can provide real-time feedback on the passenger's emotions and evaluate the effectiveness of driving adjustments. For example, if the passenger is relaxed, the system can drive to maintain that state. Also, if the passenger is feeling anxious, the system can identify the cause and adjust the driving. Furthermore, the emotion feedback unit can learn the passenger's past emotion data and provide more personalized emotion management. This allows for comfortable driving that is tailored to the passenger's emotions.

[0078] The system may further include a relaxation induction unit. The relaxation induction unit can adjust driving to enhance the passenger's level of relaxation. For example, it can provide relaxing music or scents. It can also adjust the seat angle and temperature to help the passenger relax. Furthermore, the relaxation induction unit can learn the passenger's past relaxation data and provide more personalized relaxation induction. This allows for a comfortable driving experience that enhances the passenger's level of relaxation.

[0079] The system may further include an entertainment provider. The entertainment provider can provide appropriate entertainment according to the passenger's preferences and mood. For example, it can provide movies, music, games, etc. The entertainment content can also be adjusted to help the passenger relax. Furthermore, the entertainment provider can learn the passenger's past entertainment history and provide more personalized entertainment. This makes it possible to provide comfortable entertainment that matches the passenger's preferences and mood.

[0080] The processing flow of the second embodiment will be briefly explained below.

[0081] Step 1: The word analysis unit analyzes the words of the driver and passengers. For example, the word analysis unit uses voice recognition technology to convert the driver's and passengers' comments into text data. It can also use natural language processing technology to analyze the content of the comments and the emotional intensity of the comments. It prioritizes responses to comments that show particularly strong emotions. Step 2: The facial expression analysis unit analyzes the facial expressions of the driver and passengers. For example, the facial expression analysis unit uses face recognition technology to detect the facial expressions of the driver and passengers, and then analyzes the emotions in the expressions using an emotion estimation algorithm. It can also analyze subtle changes in facial expressions to quickly detect changes in the passengers' emotions. Step 3: The driving adjustment unit adjusts the driving behavior based on the results of the analysis by the speech analysis unit and facial expression analysis unit. For example, the driving adjustment unit can increase passengers' sense of security by reducing speed and avoiding sudden steering. It can also analyze road conditions and traffic information in real time to select the optimal driving pattern. It can also adjust the driving behavior taking into account the vehicle's dynamic characteristics.

[0082] 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.

[0083] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (registered trademark) (Internet search engine).<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.

[0084] 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.

[0085] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0086] 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.

[0087] 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.

[0088] 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.

[0089] 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.

[0090] 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).

[0091] 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.

[0092] 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.

[0093] 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.

[0094] 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.

[0095] 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.

[0096] 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.

[0097] 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.

[0098] 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 AI 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.

[0099] 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.

[0100] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0101] 5, the data processing system 310 includes the data processing device 12 and a headset terminal 314. An example of the data processing device 12 is a server.

[0102] 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.

[0103] 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.

[0104] 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.

[0105] 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).

[0106] 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.

[0107] 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.

[0108] 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.

[0109] 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.

[0110] 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.

[0111] 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.

[0112] 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.

[0113] 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 AI 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.

[0114] 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.

[0115] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0116] 7, a data processing system 410 includes a data processing device 12 and a robot 414. An example of the data processing device 12 is a server.

[0117] 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.

[0118] 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.

[0119] 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.

[0120] 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).

[0121] 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.

[0122] 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.

[0123] 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.

[0124] 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.

[0125] 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.

[0126] 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 also 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 perform processing similar to that of the specific processing unit 290 using these models.

[0127] 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.

[0128] 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.

[0129] 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 AI 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.

[0130] 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.

[0131] 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.

[0132] 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.

[0133] 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.

[0134] 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).

[0135] Human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, a state of discomfort is expressed, and when they approach the ideal, a state of pleasure is expressed. Emotions can also be created for robots, cars, and motorcycles, based on various balances, such as posture and remaining battery life. When these balances deviate from the ideal, a state of discomfort is expressed, and when they approach the ideal, a state of pleasure is expressed. An emotion map can be generated, for example, based on Dr. Mitsuyoshi's emotion map (Research on speech emotion recognition and brain physiological signal analysis systems for emotions, Tokushima University, doctoral dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map lists emotions belonging to the "reaction" domain, where sensation is dominant. The right half of the emotion map lists emotions belonging to the "situation" domain, where situational awareness is dominant.

[0136] 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."

[0137] 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.

[0138] 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.

[0139] 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.

[0140] 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.

[0141] 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.

[0142] 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.

[0143] 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.

[0144] 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.

[0145] 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.

[0146] 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.

[0147] 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.

[0148] 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]

[0149] 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 speech analysis unit that analyzes the words of the driver and passengers, An expression analysis unit that analyzes the facial expressions of the driver and passengers; a driving adjustment unit that adjusts driving based on the results of analysis by the word analysis unit and the facial expression analysis unit. A system characterized by:

2. The word analysis unit Analyze the emotional intensity of comments and prioritize responses to comments that show particularly strong emotions The system of claim 1 .

3. The word analysis unit Analyze background sounds of speech to more accurately understand the intent of said speech The system of claim 1 .

4. The word analysis unit Analyzes the emotions expressed in speech in real time and adjusts driving to elicit positive emotions The system of claim 1 .

5. The word analysis unit Analyzes speech content in multiple languages ​​to accommodate passengers who speak different languages The system of claim 1 .

6. The word analysis unit Based on what the passengers say, the system will estimate their preferences and interests and automatically adjust the entertainment system. The system of claim 1 .

7. The word analysis unit Analyzes the emotions expressed and adjusts driving to reduce passenger stress levels The system of claim 1 .

8. The facial expression analysis unit By analyzing subtle changes in the facial expressions, changes in the passenger's emotions are detected more quickly. The system of claim 1 .

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