system

Abstract

The system according to the embodiment aims to estimate the psychological state of the driver and passengers and adjust the driving style based on that. [Solution] According to an embodiment, the system includes a collection unit, an analysis unit, and an adjustment unit. The collection unit collects facial expressions or words of the driver or passenger. The analysis unit infers the psychological state based on the data collected by the collection unit. The adjustment unit adjusts the driving style based on the psychological state inferred by the analysis unit.

Description

[Technical Field]

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

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

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

[0004] Conventional technologies do not adjust driving styles taking into account the psychological state of the driver and passengers, and there is room for improvement.

[0005] The system according to the embodiment aims to estimate the psychological state of the driver and passengers and adjust the driving style based on that. [Means for solving the problem]

[0006] The system according to the embodiment includes a collection unit, an analysis unit, and an adjustment unit. The collection unit collects facial expressions or words of the driver or passenger. The analysis unit infers a psychological state based on the data collected by the collection unit. The adjustment unit adjusts the driving style based on the psychological state inferred by the analysis unit. [Effects of the Invention]

[0007] The system according to the embodiment can estimate the psychological state of the driver and passengers and adjust their driving style accordingly. [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) An autonomous driving system according to an embodiment of the present invention not only improves vehicle safety using autonomous driving technology, but also predicts the psychological state of the driver and passengers, providing a more reassuring driving experience. This autonomous driving system collects the facial expressions and words of the driver and passengers, which are then analyzed by a generation AI to predict their psychological state and adjust the driving style accordingly. For example, sensors installed in the vehicle collect the facial expressions and words of the driver and passengers. The generation AI then analyzes the collected data to predict the psychological state of the driver and passengers. For example, if a passenger looks anxious, the generation AI analyzes their facial expressions and identifies the cause of the anxiety. Furthermore, the generation AI adjusts the driving style based on the predicted psychological state. For example, if a passenger feels anxious, the generation AI takes appropriate action, such as slowing down the vehicle. This system enables driving that is tailored to the psychological state of the driver and passengers, providing a more reassuring driving experience. For example, if a passenger feels relaxed, the system continues driving normally, but if they feel anxious, the system takes appropriate action, such as slowing down the vehicle. Furthermore, the generation AI has a high ability to understand context, as it can read what the driver and passengers want from their words and facial expressions. This system will grasp the psychological state of the driver and passengers in real time and provide an appropriate driving style, thereby enabling a more reassuring driving experience.As a result, the autonomous driving system will be able to provide a driving style that suits the psychological state of the driver and passengers, enabling a more reassuring driving experience.

[0029] An autonomous driving system according to an embodiment includes a collection unit, an analysis unit, and an adjustment unit. The collection unit collects facial expressions or words of a driver or passenger. The collection unit can collect facial expressions and words using, for example, a camera or a microphone installed in the vehicle. The collection unit can also capture the facial expressions of the driver or passenger in real time and store them as digital data. For example, the collection unit can capture the driver's facial expressions using a camera and store them as image data. The collection unit can also record the passenger's words using a microphone and store them as audio data. The analysis unit infers the psychological state based on the data collected by the collection unit. For example, the analysis unit can analyze the collected data using a generation AI and infer the psychological state of the driver or passenger. For example, the analysis unit can use the generation AI to analyze facial expression data and infer the driver's stress level. The analysis unit can also use the generation AI to analyze audio data and infer the passenger's relaxation level. The analysis unit can also use the generation AI to analyze text data and infer the driver's or passenger's emotions. For example, the analysis unit uses speech recognition technology to convert words into text data using the generation AI, and analyzes the text data to infer emotions. The adjustment unit adjusts the driving style based on the psychological state inferred by the analysis unit. The adjustment unit can, for example, adjust the speed of the vehicle. For example, the adjustment unit slows down the vehicle if the driver is nervous. The adjustment unit can also maintain a normal driving style if the passenger is relaxed. Furthermore, the adjustment unit can adjust the driving style to encourage the driver to take a break if the driver is tired. For example, the adjustment unit displays a message encouraging the driver to take a break if it is inferred that the driver is tired. This allows the autonomous driving system according to the embodiment to provide a driving style that corresponds to the psychological state of the driver and passengers.

[0030] The collection unit can collect facial expressions or words of the driver or passengers using sensors installed in the vehicle. The collection unit, for example, collects the driver's facial expressions using a camera installed in the vehicle. For example, the collection unit captures the driver's facial expressions in real time using the camera and saves them as image data. The collection unit can also collect the words of passengers using a microphone installed in the vehicle. For example, the collection unit records the passengers' words using the microphone and saves them as audio data. The collection unit can also collect location information of the driver and passengers using a position sensor installed in the vehicle. For example, the collection unit collects the driver's location information using a position sensor and saves it as digital data. In this way, the facial expressions and words of the driver and passengers can be accurately collected by using the sensors in the vehicle. Some or all of the above-mentioned processing in the collection unit may be performed using, for example, a generation AI, or may be performed without using a generation AI. For example, the collection unit can input image data acquired by a camera to the generation AI and cause the generation AI to analyze facial expressions from the image data.

[0031] The analysis unit can analyze the collected data and infer the psychological state of the driver or passenger. For example, the analysis unit analyzes collected image data and infers the psychological state from the driver's facial expression. For example, the analysis unit analyzes the image data using a generation AI and infers the driver's stress level. The analysis unit can also analyze collected audio data and infer the psychological state from the passenger's words. For example, the analysis unit analyzes the audio data using a generation AI and infers the passenger's level of relaxation. The analysis unit can also analyze collected text data and infer the emotions of the driver or passenger. For example, the analysis unit analyzes the text data using a generation AI and infers the emotions of the driver or passenger. In this way, the psychological state of the driver or passenger can be accurately inferred by analyzing the collected data. Some or all of the above-mentioned processing in the analysis unit may be performed using, for example, a generation AI, or may be performed without using a generation AI. For example, the analysis unit can input collected data into a generation AI and have the generation AI analyze the data.

[0032] The adjustment unit can adjust the vehicle speed based on the estimated psychological state. For example, the adjustment unit slows down the vehicle speed if the driver is nervous. For example, the adjustment unit slows down the vehicle speed if it is estimated that the driver's stress level is high. The adjustment unit can also maintain a normal driving style if the passenger is relaxed. For example, the adjustment unit maintains the vehicle speed if it is estimated that the passenger is highly relaxed. Furthermore, the adjustment unit can adjust the driving style to encourage the driver to take a break if the driver is tired. For example, the adjustment unit displays a message encouraging the driver to take a break if it is estimated that the driver is tired. This allows for more reassuring driving by adjusting the vehicle speed based on the driver's psychological state. Some or all of the above-described processing in the adjustment unit may be performed using, or without, a generation AI. For example, the adjustment unit can input the estimated psychological state into the generation AI and cause the generation AI to adjust the driving style.

[0033] The collection unit can analyze past facial expression or speech data of the driver or passenger and select the optimal collection method. For example, the collection unit can identify, from past data, time periods when specific facial expressions or speech frequently appear and concentrate collection on those time periods. For example, the collection unit can analyze past facial expression data and identify changes in facial expressions during specific time periods. The collection unit can also detect signs of the appearance of specific emotions based on past data and strengthen collection when such signs appear. For example, the collection unit can analyze past speech data and detect signs of the appearance of specific emotions. Furthermore, the collection unit can analyze past data and optimize the collection method for specific situations. For example, the collection unit can optimize the collection method for facial expressions or speech under specific situations based on past data. This allows the analysis of past data to select the optimal collection method and efficiently collect data. Some or all of the above-mentioned processing in the collection unit can be performed using, for example, a generation AI, or can be performed without using a generation AI. For example, the collection unit can input past data into a generation AI and have the generation AI select the optimal collection method.

[0034] When collecting facial expressions or words, the collection unit can filter the data based on the driver's or passenger's current physical condition or stress level. For example, if the driver is at a high stress level, the collection unit filters the collected data to extract only important information. For example, if the collection unit estimates that the driver's stress level is high, the collection unit filters facial expression and word data to prioritize collecting information related to stress. Furthermore, if a passenger is in poor physical condition, the collection unit can filter the collected data to prioritize collecting information related to their physical condition. For example, if the collection unit estimates that the passenger is in poor physical condition, the collection unit filters facial expression and word data to prioritize collecting information related to their physical condition. Furthermore, if the driver is relaxed, the collection unit can filter the collected data to collect information related to their relaxed state. For example, if the collection unit estimates that the driver is highly relaxed, the collection unit filters facial expression and word data to collect information related to their relaxed state. Thus, by filtering based on their physical condition or stress level, only important information can be efficiently collected. Some or all of the above-described processing in the collection unit may be performed, for example, using a generation AI, or may be performed without using a generation AI. For example, the collection unit can input estimated physical condition and stress level into the generation AI and leave the filtering to the generation AI.

[0035] When collecting facial expressions or words, the collection unit can prioritize collecting highly relevant data by taking into account the geographical location information of the driver or passenger. For example, if the driver is nervous at a specific location, the collection unit prioritizes collecting facial expressions and words at that location. For example, if the collection unit estimates that the driver is nervous at a specific intersection, the collection unit prioritizes collecting data on facial expressions and words at that intersection. Furthermore, if a passenger is at a tourist spot, the collection unit can prioritize collecting facial expressions and words related to the enjoyment of that location. For example, if the collection unit estimates that the passenger is at a tourist spot, the collection unit prioritizes collecting data on facial expressions and words related to the enjoyment of that location. Furthermore, if the driver is stuck in traffic, the collection unit can prioritize collecting facial expressions and words related to stress in that situation. For example, if the collection unit estimates that the driver is stuck in traffic, the collection unit prioritizes collecting data on facial expressions and words related to stress in that situation. This allows for efficient collection of highly relevant data by taking geographical location information into account. Some or all of the above-described processing by the collection unit may be performed, for example, using a generation AI, or may be performed without using a generation AI. For example, the collection unit can input geographical location information to the generation AI and cause the generation AI to collect highly relevant data.

[0036] The collection unit can analyze the social media activity of the driver or passenger when collecting facial expressions or words, and collect related data. For example, if the driver posts on social media that he or she is feeling stressed, the collection unit collects facial expressions and words related to the stress. For example, if the driver posts on social media that he or she is feeling stressed, the collection unit collects data on facial expressions and words related to the stress. Furthermore, if a passenger posts on social media that he or she is having fun, the collection unit can also collect facial expressions and words related to the fun. For example, if the passenger posts on social media that he or she is having fun, the collection unit collects data on facial expressions and words related to the fun. Furthermore, if the driver posts on social media that he or she is tired, the collection unit can also collect facial expressions and words related to the fatigue. For example, if the driver posts on social media that he or she is tired, the collection unit collects data on facial expressions and words related to the fatigue. This allows for efficient collection of related data by analyzing social media activity. Some or all of the above-mentioned processing in the collection unit may be performed, for example, using a generation AI, or may be performed without using a generation AI. For example, the collection unit can input social media posting data into the generation AI and cause the generation AI to collect related data.

[0037] During analysis, the analysis unit can adjust the level of detail of the analysis based on the importance of the collected data. For example, the analysis unit performs a detailed analysis on data with high importance. For example, if the analysis unit estimates that the driver's stress level is high, the analysis unit performs a detailed analysis. The analysis unit can also perform a simplified analysis on data with low importance. For example, if the analysis unit estimates that the passenger's level of relaxation is high, the analysis unit performs a simplified analysis. Furthermore, the analysis unit can also perform an analysis with an appropriate level of detail on data with medium importance. For example, if the analysis unit estimates that the driver's fatigue level is medium, the analysis unit performs an analysis with an appropriate level of detail. This allows for efficient analysis by adjusting the level of detail of the analysis based on the importance of the data. Some or all of the above-described processing in the analysis unit may be performed using, or without, a generation AI. For example, the analysis unit may input the importance of the collected data to the generation AI and cause the generation AI to adjust the level of detail of the analysis.

[0038] During analysis, the analysis unit can apply different analysis algorithms depending on the data category. For example, the analysis unit applies a facial expression recognition algorithm to facial expression data. For example, the analysis unit applies a facial expression recognition algorithm to analyze the driver's facial expression data. The analysis unit can also apply a natural language processing algorithm to word data. For example, the analysis unit applies a natural language processing algorithm to analyze the passenger's word data. The analysis unit can also apply a biosignal analysis algorithm to physical condition data. For example, the analysis unit applies a biosignal analysis algorithm to analyze the driver's physical condition data. This improves analysis accuracy by applying an appropriate analysis algorithm depending on the data category. Some or all of the above-mentioned processing in the analysis unit may be performed using, or without, a generation AI. For example, the analysis unit can input the data category into the generation AI and cause the generation AI to apply an appropriate analysis algorithm.

[0039] During analysis, the analysis unit can determine the priority of analysis based on the time when the data was collected. The analysis unit, for example, prioritizes analysis of the most recent data. For example, the analysis unit prioritizes analysis of the driver's most recent stress level data. The analysis unit can also prioritize analysis of current data while referring to past data. For example, the analysis unit prioritizes analysis of current relaxation level data while referring to past relaxation level data of passengers. The analysis unit can also prioritize analysis of data collected during a specific time period. For example, the analysis unit prioritizes analysis of stress level data collected during a specific time period. In this way, by determining the priority of analysis based on the time when the data was collected, the most recent data can be prioritized for analysis. Some or all of the above-described processing in the analysis unit may be performed using, or without, the generation AI. For example, the analysis unit can input the time when the data was collected into the generation AI and have the generation AI determine the analysis priority.

[0040] During analysis, the analysis unit can adjust the order of analysis based on the relevance of the data. For example, the analysis unit prioritizes analysis of highly relevant data. For example, the analysis unit prioritizes analysis of facial expression data that is highly relevant to the driver's stress level data. The analysis unit can also postpone analysis of less relevant data. For example, the analysis unit postpones analysis of voice data that is less relevant to the passenger's relaxation level data. Furthermore, the analysis unit can analyze data with a moderate degree of relevance in an appropriate order. For example, the analysis unit analyzes text data that is moderately relevant to the driver's fatigue level data in an appropriate order. This allows for efficient analysis by adjusting the order of analysis based on the relevance of the data. Some or all of the above-described processing in the analysis unit may be performed using, or without, a generation AI. For example, the analysis unit can input the relevance of the data to the generation AI and have the generation AI adjust the order of analysis.

[0041] During adjustment, the adjustment unit can analyze the past driving history of the driver or passenger and select the optimal adjustment method. The adjustment unit, for example, selects the optimal driving style for a specific situation from the past driving history. For example, the adjustment unit analyzes the driver's past driving history and selects the optimal driving style for a specific situation. The adjustment unit can also select a driving style that matches the driver's preferences based on the past driving history. For example, the adjustment unit analyzes the driver's past driving history and selects a driving style that matches the driver's preferences. The adjustment unit can also analyze the past driving history and select a driving style that matches the passenger's preferences. For example, the adjustment unit analyzes the passenger's past driving history and selects a driving style that matches the passenger's preferences. In this way, the optimal driving style can be selected by analyzing the past driving history. Some or all of the above-described processing in the adjustment unit may be performed using, or without, a generation AI. For example, the adjustment unit can input the past driving history into the generation AI and cause the generation AI to select the optimal adjustment method.

[0042] During adjustment, the adjustment unit can customize the driving style based on the current physical condition or stress level of the driver or passenger. For example, if the driver has a high stress level, the adjustment unit adjusts the driving style to a more relaxing one. For example, if the adjustment unit estimates that the driver's stress level is high, the adjustment unit slows down the vehicle's speed and adjusts the driving style to a more relaxing one. The adjustment unit can also adjust the driving style to a more comfortable one if the passenger is in poor physical condition. For example, if the adjustment unit estimates that the passenger is in poor physical condition, the adjustment unit slows down the vehicle's speed and adjusts the driving style to a more comfortable one. Furthermore, if the driver is tired, the adjustment unit can adjust the driving style to encourage rest. For example, if the adjustment unit estimates that the driver is tired, the adjustment unit displays a message encouraging the driver to take a rest. This allows for a more comfortable driving experience by customizing the driving style based on the physical condition or stress level. Some or all of the above-described processing in the adjustment unit may be performed using, or without, a generation AI. For example, the adjustment unit may input estimated physical condition and stress level into the generation AI and cause the generation AI to customize the driving style.

[0043] During adjustment, the adjustment unit can select an optimal driving style by taking into account the geographical location information of the driver or passenger. For example, if the driver is in an urban area, the adjustment unit selects a driving style suitable for the urban area. For example, if the adjustment unit estimates that the driver is in an urban area, the adjustment unit selects a driving style suitable for the urban area. Furthermore, if the passenger is in a tourist destination, the adjustment unit can select a driving style suitable for the tourist destination. For example, if the adjustment unit estimates that the passenger is in a tourist destination, the adjustment unit selects a driving style suitable for the tourist destination. Furthermore, if the driver is on a highway, the adjustment unit can select a driving style suitable for the highway. For example, if the adjustment unit estimates that the driver is on a highway, the adjustment unit selects a driving style suitable for the highway. In this way, the optimal driving style can be provided by taking the geographical location information into consideration. Some or all of the above-described processing in the adjustment unit may be performed using, or without, the generation AI. For example, the adjustment unit can input geographical location information to the generation AI and cause the generation AI to select an optimal driving style.

[0044] During adjustment, the adjustment unit can analyze the social media activity of the driver or passenger to suggest a driving style. For example, if the driver posts on social media that he or she is stressed, the adjustment unit can suggest a driving style that reduces that stress. For example, if the driver posts on social media that he or she is stressed, the adjustment unit can suggest a driving style that reduces that stress. Furthermore, if a passenger posts on social media that he or she is having fun, the adjustment unit can suggest a driving style that maintains that fun. For example, if the passenger posts on social media that he or she is having fun, the adjustment unit can suggest a driving style that maintains that fun. Furthermore, if the driver posts on social media that he or she is tired, the adjustment unit can suggest a driving style that reduces that fatigue. For example, if the driver posts on social media that he or she is tired, the adjustment unit can suggest a driving style that reduces that fatigue. In this way, a more appropriate driving style can be suggested by analyzing social media activity. Some or all of the above-described processing in the adjustment unit may be performed using, or without, a generation AI. For example, the adjustment unit can input social media posting data to the generation AI and cause the generation AI to suggest a driving style.

[0045] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0046] The analysis unit can take into account past driving history and behavioral patterns when predicting the psychological state of the driver and passengers. For example, the analysis unit can refer to the driver's history of feeling stressed in specific situations in the past and predict stress when a similar situation occurs. The analysis unit can also analyze situations in which passengers were relaxed in the past and predict their level of relaxation when a similar situation occurs again. Furthermore, the analysis unit can analyze the driver's past driving style and reaction time to predict the psychological state according to the current driving situation. This makes it possible to utilize past data to more accurately predict psychological states.

[0047] The collection unit can collect biometric data of the driver and passengers and use it to infer their psychological state. For example, the collection unit can collect biometric data such as heart rate and skin galvanic response to infer stress levels. The collection unit can also monitor breathing patterns to infer relaxation levels. Furthermore, the collection unit can measure body temperature and sweat rate to detect changes in emotions. This makes it possible to understand psychological states from a more multifaceted perspective by utilizing biometric data.

[0048] The analysis unit can take external environmental data into consideration when estimating the psychological state of the driver and passengers. For example, the analysis unit can refer to weather data to predict the stress the driver will feel in bad weather. The analysis unit can also analyze traffic situation data to estimate the anxiety passengers will feel in traffic jams. Furthermore, the analysis unit can use road condition data to estimate the driver's level of tension on rough roads. In this way, utilizing external environmental data makes it possible to more accurately estimate psychological states.

[0049] The analysis unit can utilize in-car voice data when inferring the psychological state of the driver and passengers. For example, the analysis unit can analyze the driver's tone of voice and speaking style to infer their stress level. The analysis unit can also analyze the content of passenger conversations to infer their emotions. Furthermore, the analysis unit can collect in-car voice data over the long term and track changes in their psychological state. This makes it possible to utilize in-car voice data to more accurately infer their psychological state.

[0050] The processing flow of the first embodiment will be briefly explained below.

[0051] Step 1: The collection unit collects facial expressions or words of the driver or passengers. The collection unit can collect facial expressions and words using a camera or microphone installed in the vehicle. The collection unit can also capture the facial expressions of the driver or passengers in real time and save them as digital data. For example, the driver's facial expression can be captured using a camera and saved as image data. The words of passengers can also be recorded using a microphone and saved as audio data. Step 2: The analysis unit infers the psychological state based on the data collected by the collection unit. The analysis unit can analyze the collected data using the generation AI and infer the psychological state of the driver and passengers. For example, the generation AI can analyze facial expression data and infer the driver's stress level. The generation AI can also analyze voice data and infer the passenger's level of relaxation. Furthermore, the generation AI can analyze text data and infer the emotions of the driver and passengers. For example, the generation AI can use voice recognition technology to convert words into text data, and then analyze the text data to infer emotions. Step 3: The adjustment unit adjusts the driving style based on the psychological state estimated by the analysis unit. The adjustment unit can adjust the speed of the vehicle. For example, if the driver is nervous, the adjustment unit can slow down the vehicle. Also, if the passenger is relaxed, the adjustment unit can maintain a normal driving style. Furthermore, if the driver is tired, the adjustment unit can adjust the driving style to encourage rest. For example, if it is estimated that the driver is tired, a message encouraging the driver to take a rest is displayed.

[0052] (Example 2) An autonomous driving system according to an embodiment of the present invention not only improves vehicle safety using autonomous driving technology, but also predicts the psychological state of the driver and passengers, providing a more reassuring driving experience. This autonomous driving system collects the facial expressions and words of the driver and passengers, which are then analyzed by a generation AI to predict their psychological state and adjust the driving style accordingly. For example, sensors installed in the vehicle collect the facial expressions and words of the driver and passengers. The generation AI then analyzes the collected data to predict the psychological state of the driver and passengers. For example, if a passenger looks anxious, the generation AI analyzes their facial expressions and identifies the cause of the anxiety. Furthermore, the generation AI adjusts the driving style based on the predicted psychological state. For example, if a passenger feels anxious, the generation AI takes appropriate action, such as slowing down the vehicle. This system enables driving that is tailored to the psychological state of the driver and passengers, providing a more reassuring driving experience. For example, if a passenger feels relaxed, the system continues driving normally, but if they feel anxious, the system takes appropriate action, such as slowing down the vehicle. Furthermore, the generation AI has a high ability to understand context, as it can read what the driver and passengers want from their words and facial expressions. This system will grasp the psychological state of the driver and passengers in real time and provide an appropriate driving style, thereby enabling a more reassuring driving experience.As a result, the autonomous driving system will be able to provide a driving style that suits the psychological state of the driver and passengers, enabling a more reassuring driving experience.

[0053] An autonomous driving system according to an embodiment includes a collection unit, an analysis unit, and an adjustment unit. The collection unit collects facial expressions or words of a driver or passenger. The collection unit can collect facial expressions and words using, for example, a camera or a microphone installed in the vehicle. The collection unit can also capture the facial expressions of the driver or passenger in real time and store them as digital data. For example, the collection unit can capture the driver's facial expressions using a camera and store them as image data. The collection unit can also record the passenger's words using a microphone and store them as audio data. The analysis unit infers the psychological state based on the data collected by the collection unit. For example, the analysis unit can analyze the collected data using a generation AI and infer the psychological state of the driver or passenger. For example, the analysis unit can use the generation AI to analyze facial expression data and infer the driver's stress level. The analysis unit can also use the generation AI to analyze audio data and infer the passenger's relaxation level. The analysis unit can also use the generation AI to analyze text data and infer the driver's or passenger's emotions. For example, the analysis unit uses speech recognition technology to convert words into text data using the generation AI, and analyzes the text data to infer emotions. The adjustment unit adjusts the driving style based on the psychological state inferred by the analysis unit. The adjustment unit can, for example, adjust the speed of the vehicle. For example, the adjustment unit slows down the vehicle if the driver is nervous. The adjustment unit can also maintain a normal driving style if the passenger is relaxed. Furthermore, the adjustment unit can adjust the driving style to encourage the driver to take a break if the driver is tired. For example, the adjustment unit displays a message encouraging the driver to take a break if it is inferred that the driver is tired. This allows the autonomous driving system according to the embodiment to provide a driving style that corresponds to the psychological state of the driver and passengers.

[0054] The collection unit can collect facial expressions or words of the driver or passengers using sensors installed in the vehicle. The collection unit, for example, collects the driver's facial expressions using a camera installed in the vehicle. For example, the collection unit captures the driver's facial expressions in real time using the camera and saves them as image data. The collection unit can also collect the words of passengers using a microphone installed in the vehicle. For example, the collection unit records the passengers' words using the microphone and saves them as audio data. The collection unit can also collect location information of the driver and passengers using a position sensor installed in the vehicle. For example, the collection unit collects the driver's location information using a position sensor and saves it as digital data. In this way, the facial expressions and words of the driver and passengers can be accurately collected by using the sensors in the vehicle. Some or all of the above-mentioned processing in the collection unit may be performed using, for example, a generation AI, or may be performed without using a generation AI. For example, the collection unit can input image data acquired by a camera to the generation AI and cause the generation AI to analyze facial expressions from the image data.

[0055] The analysis unit can analyze the collected data and infer the psychological state of the driver or passenger. For example, the analysis unit analyzes collected image data and infers the psychological state from the driver's facial expression. For example, the analysis unit analyzes the image data using a generation AI and infers the driver's stress level. The analysis unit can also analyze collected audio data and infer the psychological state from the passenger's words. For example, the analysis unit analyzes the audio data using a generation AI and infers the passenger's level of relaxation. The analysis unit can also analyze collected text data and infer the emotions of the driver or passenger. For example, the analysis unit analyzes the text data using a generation AI and infers the emotions of the driver or passenger. In this way, the psychological state of the driver or passenger can be accurately inferred by analyzing the collected data. Some or all of the above-mentioned processing in the analysis unit may be performed using, for example, a generation AI, or may be performed without using a generation AI. For example, the analysis unit can input collected data into a generation AI and have the generation AI analyze the data.

[0056] The adjustment unit can adjust the vehicle speed based on the estimated psychological state. For example, the adjustment unit slows down the vehicle speed if the driver is nervous. For example, the adjustment unit slows down the vehicle speed if it is estimated that the driver's stress level is high. The adjustment unit can also maintain a normal driving style if the passenger is relaxed. For example, the adjustment unit maintains the vehicle speed if it is estimated that the passenger is highly relaxed. Furthermore, the adjustment unit can adjust the driving style to encourage the driver to take a break if the driver is tired. For example, the adjustment unit displays a message encouraging the driver to take a break if it is estimated that the driver is tired. This allows for more reassuring driving by adjusting the vehicle speed based on the driver's psychological state. Some or all of the above-described processing in the adjustment unit may be performed using, or without, a generation AI. For example, the adjustment unit can input the estimated psychological state into the generation AI and cause the generation AI to adjust the driving style.

[0057] The collection unit can estimate the emotions of the driver or passenger and adjust the frequency of facial expression or word collection based on the estimated emotions. For example, if the driver is tense, the collection unit increases the collection frequency to track changes in emotions in real time. For example, if the collection unit estimates that the driver's stress level is high, it sets the frequency of facial expression and word collection in seconds. Furthermore, if the passenger is relaxed, the collection unit can also reduce the collection frequency to reduce the load on the system. For example, if the collection unit estimates that the passenger is highly relaxed, it sets the frequency of facial expression and word collection in minutes. Furthermore, if the driver is tired, the collection unit can set the collection frequency to a medium level to efficiently collect necessary information. For example, if the collection unit estimates that the driver is tired, it sets the frequency of facial expression and word collection in tens of seconds. This allows for adjusting the collection frequency based on emotions, thereby efficiently collecting necessary information while reducing the load on the system. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or generative AI. The generation AI may be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the collection unit may be performed using the generation AI, or may be performed without using the generation AI. For example, the collection unit may input estimated emotions to the generation AI and cause the generation AI to adjust the collection frequency.

[0058] The collection unit can analyze past facial expression or speech data of the driver or passenger and select the optimal collection method. For example, the collection unit can identify, from past data, time periods when specific facial expressions or speech frequently appear and concentrate collection on those time periods. For example, the collection unit can analyze past facial expression data and identify changes in facial expressions during specific time periods. The collection unit can also detect signs of the appearance of specific emotions based on past data and strengthen collection when such signs appear. For example, the collection unit can analyze past speech data and detect signs of the appearance of specific emotions. Furthermore, the collection unit can analyze past data and optimize the collection method for specific situations. For example, the collection unit can optimize the collection method for facial expressions or speech under specific situations based on past data. This allows the analysis of past data to select the optimal collection method and efficiently collect data. Some or all of the above-mentioned processing in the collection unit can be performed using, for example, a generation AI, or can be performed without using a generation AI. For example, the collection unit can input past data into a generation AI and have the generation AI select the optimal collection method.

[0059] When collecting facial expressions or words, the collection unit can filter the data based on the driver's or passenger's current physical condition or stress level. For example, if the driver is at a high stress level, the collection unit filters the collected data to extract only important information. For example, if the collection unit estimates that the driver's stress level is high, the collection unit filters facial expression and word data to prioritize collecting information related to stress. Furthermore, if a passenger is in poor physical condition, the collection unit can filter the collected data to prioritize collecting information related to their physical condition. For example, if the collection unit estimates that the passenger is in poor physical condition, the collection unit filters facial expression and word data to prioritize collecting information related to their physical condition. Furthermore, if the driver is relaxed, the collection unit can filter the collected data to collect information related to their relaxed state. For example, if the collection unit estimates that the driver is highly relaxed, the collection unit filters facial expression and word data to collect information related to their relaxed state. Thus, by filtering based on their physical condition or stress level, only important information can be efficiently collected. Some or all of the above-described processing in the collection unit may be performed, for example, using a generation AI, or may be performed without using a generation AI. For example, the collection unit can input estimated physical condition and stress level into the generation AI and leave the filtering to the generation AI.

[0060] The collection unit can estimate the emotions of the driver or passenger and determine the priority of data to be collected based on the estimated emotions. For example, if the driver is feeling anxious, the collection unit prioritizes collecting facial expressions and words related to anxiety. For example, if the collection unit estimates that the driver's anxiety level is high, the collection unit prioritizes collecting data on facial expressions and words related to anxiety. Furthermore, if the passenger is having fun, the collection unit can prioritize collecting facial expressions and words related to enjoyment. For example, if the collection unit estimates that the passenger's enjoyment level is high, the collection unit prioritizes collecting data on facial expressions and words related to enjoyment. Furthermore, if the driver is tired, the collection unit can prioritize collecting facial expressions and words related to fatigue. For example, if the collection unit estimates that the driver's fatigue level is high, the collection unit prioritizes collecting data on facial expressions and words related to fatigue. In this way, by prioritizing data based on emotions, important data can be collected preferentially. Emotion estimation is realized using an emotion estimation function, for example, an emotion engine or a generative AI. The generative AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the collection unit may be performed using, for example, the generation AI, or may be performed without using the generation AI. For example, the collection unit may input estimated emotions to the generation AI and have the generation AI determine the priority of the data.

[0061] When collecting facial expressions or words, the collection unit can prioritize collecting highly relevant data by taking into account the geographical location information of the driver or passenger. For example, if the driver is nervous at a specific location, the collection unit prioritizes collecting facial expressions and words at that location. For example, if the collection unit estimates that the driver is nervous at a specific intersection, the collection unit prioritizes collecting data on facial expressions and words at that intersection. Furthermore, if a passenger is at a tourist spot, the collection unit can prioritize collecting facial expressions and words related to the enjoyment of that location. For example, if the collection unit estimates that the passenger is at a tourist spot, the collection unit prioritizes collecting data on facial expressions and words related to the enjoyment of that location. Furthermore, if the driver is stuck in traffic, the collection unit can prioritize collecting facial expressions and words related to stress in that situation. For example, if the collection unit estimates that the driver is stuck in traffic, the collection unit prioritizes collecting data on facial expressions and words related to stress in that situation. This allows for efficient collection of highly relevant data by taking geographical location information into account. Some or all of the above-described processing by the collection unit may be performed, for example, using a generation AI, or may be performed without using a generation AI. For example, the collection unit can input geographical location information to the generation AI and cause the generation AI to collect highly relevant data.

[0062] The collection unit can analyze the social media activity of the driver or passenger when collecting facial expressions or words, and collect related data. For example, if the driver posts on social media that he or she is feeling stressed, the collection unit collects facial expressions and words related to the stress. For example, if the driver posts on social media that he or she is feeling stressed, the collection unit collects data on facial expressions and words related to the stress. Furthermore, if a passenger posts on social media that he or she is having fun, the collection unit can also collect facial expressions and words related to the fun. For example, if the passenger posts on social media that he or she is having fun, the collection unit collects data on facial expressions and words related to the fun. Furthermore, if the driver posts on social media that he or she is tired, the collection unit can also collect facial expressions and words related to the fatigue. For example, if the driver posts on social media that he or she is tired, the collection unit collects data on facial expressions and words related to the fatigue. This allows for efficient collection of related data by analyzing social media activity. Some or all of the above-mentioned processing in the collection unit may be performed, for example, using a generation AI, or may be performed without using a generation AI. For example, the collection unit can input social media posting data into the generation AI and cause the generation AI to collect related data.

[0063] The analysis unit can estimate the emotions of the driver or passenger and adjust the way the analysis is presented based on the estimated emotions. For example, if the driver is tense, the analysis unit displays the analysis results in a simple, easy-to-understand format. For example, if the analysis unit estimates that the driver's level of tension is high, the analysis unit displays a concise summary of the analysis results. The analysis unit can also display detailed analysis results if the passenger is relaxed. For example, if the analysis unit estimates that the passenger is highly relaxed, the analysis unit displays detailed analysis results. Furthermore, if the driver is tired, the analysis unit can also display a concise summary of the analysis results. For example, if the analysis unit estimates that the driver is tired, the analysis unit displays a concise summary of the analysis results. This allows the analysis results to be presented in a more understandable manner by adjusting the way the analysis is presented based on emotions. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to these examples. Some or all of the above-mentioned processing in the analysis unit can be performed using, for example, the generation AI, or without the generation AI. For example, the analysis unit can input the estimated emotion into the generation AI and have the generation AI adjust the way the analysis is expressed.

[0064] During analysis, the analysis unit can adjust the level of detail of the analysis based on the importance of the collected data. For example, the analysis unit performs a detailed analysis on data with high importance. For example, if the analysis unit estimates that the driver's stress level is high, the analysis unit performs a detailed analysis. The analysis unit can also perform a simplified analysis on data with low importance. For example, if the analysis unit estimates that the passenger's level of relaxation is high, the analysis unit performs a simplified analysis. Furthermore, the analysis unit can also perform an analysis with an appropriate level of detail on data with medium importance. For example, if the analysis unit estimates that the driver's fatigue level is medium, the analysis unit performs an analysis with an appropriate level of detail. This allows for efficient analysis by adjusting the level of detail of the analysis based on the importance of the data. Some or all of the above-described processing in the analysis unit may be performed using, or without, a generation AI. For example, the analysis unit may input the importance of the collected data to the generation AI and cause the generation AI to adjust the level of detail of the analysis.

[0065] During analysis, the analysis unit can apply different analysis algorithms depending on the data category. For example, the analysis unit applies a facial expression recognition algorithm to facial expression data. For example, the analysis unit applies a facial expression recognition algorithm to analyze the driver's facial expression data. The analysis unit can also apply a natural language processing algorithm to word data. For example, the analysis unit applies a natural language processing algorithm to analyze the passenger's word data. The analysis unit can also apply a biosignal analysis algorithm to physical condition data. For example, the analysis unit applies a biosignal analysis algorithm to analyze the driver's physical condition data. This improves analysis accuracy by applying an appropriate analysis algorithm depending on the data category. Some or all of the above-mentioned processing in the analysis unit may be performed using, or without, a generation AI. For example, the analysis unit can input the data category into the generation AI and cause the generation AI to apply an appropriate analysis algorithm.

[0066] The analysis unit can estimate the emotions of the driver or passenger and adjust the length of the analysis based on the estimated emotions. For example, if the driver is in a hurry, the analysis unit provides a short and concise analysis result. For example, if the analysis unit estimates that the driver is in a hurry, the analysis unit provides a concise summary of the analysis result. The analysis unit can also provide a detailed analysis result if the passenger is relaxed. For example, if the analysis unit estimates that the passenger is relaxed, the analysis unit provides a detailed analysis result. The analysis unit can also provide a concise and easy-to-understand analysis result if the driver is nervous. For example, if the analysis unit estimates that the driver is nervous, the analysis unit provides a concise summary of the analysis result. This allows the analysis result to be provided at an appropriate length by adjusting the length of the analysis based on the emotions. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to these examples. Some or all of the above-mentioned processing in the analysis unit can be performed using, for example, the generation AI, or without the generation AI. For example, the analysis unit can input the estimated emotion into the generation AI and have the generation AI adjust the length of the analysis.

[0067] During analysis, the analysis unit can determine the priority of analysis based on the time when the data was collected. The analysis unit, for example, prioritizes analysis of the most recent data. For example, the analysis unit prioritizes analysis of the driver's most recent stress level data. The analysis unit can also prioritize analysis of current data while referring to past data. For example, the analysis unit prioritizes analysis of current relaxation level data while referring to past relaxation level data of passengers. The analysis unit can also prioritize analysis of data collected during a specific time period. For example, the analysis unit prioritizes analysis of stress level data collected during a specific time period. In this way, by determining the priority of analysis based on the time when the data was collected, the most recent data can be prioritized for analysis. Some or all of the above-described processing in the analysis unit may be performed using, or without, the generation AI. For example, the analysis unit can input the time when the data was collected into the generation AI and have the generation AI determine the analysis priority.

[0068] During analysis, the analysis unit can adjust the order of analysis based on the relevance of the data. For example, the analysis unit prioritizes analysis of highly relevant data. For example, the analysis unit prioritizes analysis of facial expression data that is highly relevant to the driver's stress level data. The analysis unit can also postpone analysis of less relevant data. For example, the analysis unit postpones analysis of voice data that is less relevant to the passenger's relaxation level data. Furthermore, the analysis unit can analyze data with a moderate degree of relevance in an appropriate order. For example, the analysis unit analyzes text data that is moderately relevant to the driver's fatigue level data in an appropriate order. This allows for efficient analysis by adjusting the order of analysis based on the relevance of the data. Some or all of the above-described processing in the analysis unit may be performed using, or without, a generation AI. For example, the analysis unit can input the relevance of the data to the generation AI and have the generation AI adjust the order of analysis.

[0069] The adjustment unit can estimate the emotions of the driver or passenger and determine how to adjust the driving style based on the estimated emotions. For example, the adjustment unit slows down the vehicle speed when the driver is nervous. For example, the adjustment unit slows down the vehicle speed when it is estimated that the driver's level of tension is high. The adjustment unit can also maintain a normal driving style when the passenger is relaxed. For example, the adjustment unit maintains the vehicle speed when it is estimated that the passenger is highly relaxed. Furthermore, the adjustment unit can adjust the driving style to encourage the driver to take a break when the driver is tired. For example, the adjustment unit displays a message encouraging the driver to take a break when it is estimated that the driver is tired. In this way, by determining how to adjust the driving style based on emotions, a more appropriate driving style can be provided. Emotion estimation is realized using an emotion estimation function, for example, using an emotion engine or a generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-mentioned processing in the adjustment unit can be performed using, for example, the generation AI, or without the generation AI. For example, the adjustment unit can input the estimated emotions into the generation AI and have the generation AI decide how to adjust the driving style.

[0070] During adjustment, the adjustment unit can analyze the past driving history of the driver or passenger and select the optimal adjustment method. The adjustment unit, for example, selects the optimal driving style for a specific situation from the past driving history. For example, the adjustment unit analyzes the driver's past driving history and selects the optimal driving style for a specific situation. The adjustment unit can also select a driving style that matches the driver's preferences based on the past driving history. For example, the adjustment unit analyzes the driver's past driving history and selects a driving style that matches the driver's preferences. The adjustment unit can also analyze the past driving history and select a driving style that matches the passenger's preferences. For example, the adjustment unit analyzes the passenger's past driving history and selects a driving style that matches the passenger's preferences. In this way, the optimal driving style can be selected by analyzing the past driving history. Some or all of the above-described processing in the adjustment unit may be performed using, or without, a generation AI. For example, the adjustment unit can input the past driving history into the generation AI and cause the generation AI to select the optimal adjustment method.

[0071] During adjustment, the adjustment unit can customize the driving style based on the current physical condition or stress level of the driver or passenger. For example, if the driver has a high stress level, the adjustment unit adjusts the driving style to a more relaxing one. For example, if the adjustment unit estimates that the driver's stress level is high, the adjustment unit slows down the vehicle's speed and adjusts the driving style to a more relaxing one. The adjustment unit can also adjust the driving style to a more comfortable one if the passenger is in poor physical condition. For example, if the adjustment unit estimates that the passenger is in poor physical condition, the adjustment unit slows down the vehicle's speed and adjusts the driving style to a more comfortable one. Furthermore, if the driver is tired, the adjustment unit can adjust the driving style to encourage rest. For example, if the adjustment unit estimates that the driver is tired, the adjustment unit displays a message encouraging the driver to take a rest. This allows for a more comfortable driving experience by customizing the driving style based on the physical condition or stress level. Some or all of the above-described processing in the adjustment unit may be performed using, or without, a generation AI. For example, the adjustment unit may input estimated physical condition and stress level into the generation AI and cause the generation AI to customize the driving style.

[0072] The adjustment unit can estimate the emotions of the driver or passenger and prioritize driving styles based on the estimated emotions. For example, if the driver is feeling anxious, the adjustment unit prioritizes a driving style that reduces anxiety. For example, if the adjustment unit estimates that the driver's anxiety level is high, the adjustment unit prioritizes a driving style that reduces anxiety. Furthermore, if the passenger is enjoying themselves, the adjustment unit can prioritize a driving style that maintains enjoyment. For example, if the adjustment unit estimates that the passenger's enjoyment level is high, the adjustment unit prioritizes a driving style that maintains enjoyment. Furthermore, if the driver is tired, the adjustment unit can prioritize a driving style that reduces fatigue. For example, if the adjustment unit estimates that the driver's fatigue level is high, the adjustment unit prioritizes a driving style that reduces fatigue. In this way, by prioritizing driving styles based on emotions, a more appropriate driving style can be provided. Emotion estimation is realized using an emotion estimation function, for example, using an emotion engine or generative AI. The generative AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the adjustment unit may be performed using, for example, the generation AI, or may be performed without using the generation AI. For example, the adjustment unit may input estimated emotions to the generation AI and cause the generation AI to determine the priority of driving styles.

[0073] During adjustment, the adjustment unit can select an optimal driving style by taking into account the geographical location information of the driver or passenger. For example, if the driver is in an urban area, the adjustment unit selects a driving style suitable for the urban area. For example, if the adjustment unit estimates that the driver is in an urban area, the adjustment unit selects a driving style suitable for the urban area. Furthermore, if the passenger is in a tourist destination, the adjustment unit can select a driving style suitable for the tourist destination. For example, if the adjustment unit estimates that the passenger is in a tourist destination, the adjustment unit selects a driving style suitable for the tourist destination. Furthermore, if the driver is on a highway, the adjustment unit can select a driving style suitable for the highway. For example, if the adjustment unit estimates that the driver is on a highway, the adjustment unit selects a driving style suitable for the highway. In this way, the optimal driving style can be provided by taking the geographical location information into consideration. Some or all of the above-described processing in the adjustment unit may be performed using, or without, the generation AI. For example, the adjustment unit can input geographical location information to the generation AI and cause the generation AI to select an optimal driving style.

[0074] During adjustment, the adjustment unit can analyze the social media activity of the driver or passenger to suggest a driving style. For example, if the driver posts on social media that he or she is stressed, the adjustment unit can suggest a driving style that reduces that stress. For example, if the driver posts on social media that he or she is stressed, the adjustment unit can suggest a driving style that reduces that stress. Furthermore, if a passenger posts on social media that he or she is having fun, the adjustment unit can suggest a driving style that maintains that fun. For example, if the passenger posts on social media that he or she is having fun, the adjustment unit can suggest a driving style that maintains that fun. Furthermore, if the driver posts on social media that he or she is tired, the adjustment unit can suggest a driving style that reduces that fatigue. For example, if the driver posts on social media that he or she is tired, the adjustment unit can suggest a driving style that reduces that fatigue. In this way, a more appropriate driving style can be suggested by analyzing social media activity. Some or all of the above-described processing in the adjustment unit may be performed using, or without, a generation AI. For example, the adjustment unit can input social media posting data to the generation AI and cause the generation AI to suggest a driving style. === Hard Collateral 1-1 === Each of the multiple elements including the collection unit, analysis unit, and adjustment unit described above is realized, for example, by at least one of the smart device 14 and the data processing device 12. For example, the collection unit collects facial expressions and words of the driver and passengers using the camera 42 and microphone 38B of the smart device 14. The analysis unit is realized, for example, by the specific processing unit 290 of the data processing device 12, and analyzes the collected data using a generation AI to infer a psychological state. The adjustment unit is realized, for example, by the specific processing unit 290 of the data processing device 12, and adjusts the driving style based on the inferred psychological state. === Hard Collateral 1-2 === Each of the multiple elements, including the collection unit, analysis unit, and adjustment unit, described above, is realized, for example, by at least one of the smart glasses 214 and the data processing device 12. For example, the collection unit collects facial expressions and words of the driver and passengers using the camera 42 and microphone 238 of the smart glasses 214. The analysis unit is realized, for example, by the specific processing unit 290 of the data processing device 12, and analyzes the collected data using a generative AI to infer a psychological state. The adjustment unit is realized, for example, by the specific processing unit 290 of the data processing device 12, and adjusts the driving style based on the inferred psychological state. === Hard Collateral 1-3 === Each of the multiple elements including the collection unit, analysis unit, and adjustment unit described above is realized, for example, by at least one of the headset-type terminal 314 and the data processing device 12. For example, the collection unit collects facial expressions and words of the driver and passengers using the camera 42 and microphone 238 of the headset-type terminal 314. The analysis unit is realized, for example, by the specific processing unit 290 of the data processing device 12, and analyzes the collected data using a generation AI to infer a psychological state. The adjustment unit is realized, for example, by the specific processing unit 290 of the data processing device 12, and adjusts the driving style based on the inferred psychological state. === Hard Collateral 1-4 === Each of the multiple elements including the collection unit, analysis unit, and adjustment unit described above is realized, for example, by at least one of the robot 414 and the data processing device 12. For example, the collection unit collects facial expressions and words of the driver and passengers using the camera 42 and microphone 238 of the robot 414. The analysis unit is realized, for example, by the specific processing unit 290 of the data processing device 12, and analyzes the collected data using a generative AI to infer the psychological state. The adjustment unit is realized, for example, by the specific processing unit 290 of the data processing device 12, and adjusts the driving style based on the inferred psychological state.

[0075] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0076] The analysis unit can take into account past driving history and behavioral patterns when predicting the psychological state of the driver and passengers. For example, the analysis unit can refer to the driver's history of feeling stressed in specific situations in the past and predict stress when a similar situation occurs. The analysis unit can also analyze situations in which passengers were relaxed in the past and predict their level of relaxation when a similar situation occurs again. Furthermore, the analysis unit can analyze the driver's past driving style and reaction time to predict the psychological state according to the current driving situation. This makes it possible to utilize past data to more accurately predict psychological states.

[0077] The collection unit can collect biometric data of the driver and passengers and use it to infer their psychological state. For example, the collection unit can collect biometric data such as heart rate and skin galvanic response to infer stress levels. The collection unit can also monitor breathing patterns to infer relaxation levels. Furthermore, the collection unit can measure body temperature and sweat rate to detect changes in emotions. This makes it possible to understand psychological states from a more multifaceted perspective by utilizing biometric data.

[0078] The adjustment unit can adjust the in-car environment based on the psychological state of the driver and passengers. For example, if the driver is nervous, the adjustment unit can dim the lights in the car to provide a relaxing environment. If the passengers are relaxed, the adjustment unit can change the music in the car to maintain a comfortable atmosphere. Furthermore, if the driver is tired, the adjustment unit can activate the seat massage function. In this way, by adjusting the in-car environment, a comfortable driving experience can be provided according to the driver's psychological state.

[0079] The analysis unit can take external environmental data into consideration when estimating the psychological state of the driver and passengers. For example, the analysis unit can refer to weather data to predict the stress the driver will feel in bad weather. The analysis unit can also analyze traffic situation data to estimate the anxiety passengers will feel in traffic jams. Furthermore, the analysis unit can use road condition data to estimate the driver's level of tension on rough roads. In this way, utilizing external environmental data makes it possible to more accurately estimate psychological states.

[0080] The collection unit can adjust the in-car entertainment system based on the psychological state of the driver and passengers. For example, if the driver is tense, the collection unit can play relaxing music. If the passengers are bored, the collection unit can provide interesting content. Furthermore, if the driver is tired, the collection unit can display refreshing videos. In this way, by adjusting the entertainment system, a comfortable driving experience can be provided according to the psychological state.

[0081] The adjustment unit can adjust the guidance method of the navigation system based on the psychological state of the driver or passenger. For example, if the driver is nervous, the adjustment unit can provide simple and easy-to-understand guidance. If the passenger is relaxed, the adjustment unit can also provide detailed tourist information. Furthermore, if the driver is tired, the adjustment unit can also guide the driver to rest spots. In this way, by adjusting the guidance method of the navigation system, appropriate guidance can be provided according to the psychological state.

[0082] The analysis unit can utilize social media data when inferring the psychological state of the driver or passengers. For example, the analysis unit can analyze the content posted by the driver on social media to infer their current psychological state. The analysis unit can also infer their emotions by referencing photos and comments shared by passengers on social media. Furthermore, the analysis unit can analyze the social media activity history of the driver and passengers to grasp long-term trends in their psychological state. This makes it possible to utilize social media data to more accurately infer their psychological state.

[0083] The collection unit can adjust the temperature inside the vehicle based on the psychological state of the driver and passengers. For example, if the driver is nervous, the collection unit can lower the temperature inside the vehicle to provide a relaxing environment. The collection unit can also maintain a comfortable temperature when the passengers are relaxed. Furthermore, if the driver is tired, the collection unit can moderately adjust the temperature to provide a comfortable environment. In this way, by adjusting the temperature inside the vehicle, a comfortable driving experience can be provided according to the driver's psychological state.

[0084] The adjustment unit can adjust the lighting inside the vehicle based on the psychological state of the driver and passengers. For example, if the driver is nervous, the adjustment unit can dim the lighting to provide a relaxing environment. If the passengers are relaxed, the adjustment unit can also maintain bright lighting. Furthermore, if the driver is tired, the adjustment unit can provide soft lighting to create a comfortable environment. Thus, by adjusting the lighting inside the vehicle, a comfortable driving experience can be provided according to the driver's psychological state.

[0085] The analysis unit can utilize in-car voice data when inferring the psychological state of the driver and passengers. For example, the analysis unit can analyze the driver's tone of voice and speaking style to infer their stress level. The analysis unit can also analyze the content of passenger conversations to infer their emotions. Furthermore, the analysis unit can collect in-car voice data over the long term and track changes in their psychological state. This makes it possible to utilize in-car voice data to more accurately infer their psychological state.

[0086] The processing flow of the second embodiment will be briefly explained below.

[0087] Step 1: The collection unit collects facial expressions or words of the driver or passengers. The collection unit can collect facial expressions and words using a camera or microphone installed in the vehicle. The collection unit can also capture the facial expressions of the driver or passengers in real time and save them as digital data. For example, the driver's facial expression can be captured using a camera and saved as image data. The words of passengers can also be recorded using a microphone and saved as audio data. Step 2: The analysis unit infers the psychological state based on the data collected by the collection unit. The analysis unit can analyze the collected data using the generation AI and infer the psychological state of the driver and passengers. For example, the generation AI can analyze facial expression data and infer the driver's stress level. The generation AI can also analyze voice data and infer the passenger's level of relaxation. Furthermore, the generation AI can analyze text data and infer the emotions of the driver and passengers. For example, the generation AI can use voice recognition technology to convert words into text data, and then analyze the text data to infer emotions. Step 3: The adjustment unit adjusts the driving style based on the psychological state estimated by the analysis unit. The adjustment unit can adjust the speed of the vehicle. For example, if the driver is nervous, the adjustment unit can slow down the vehicle. Also, if the passenger is relaxed, the adjustment unit can maintain a normal driving style. Furthermore, if the driver is tired, the adjustment unit can adjust the driving style to encourage rest. For example, if it is estimated that the driver is tired, a message encouraging the driver to take a rest is displayed.

[0088] 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.

[0089] 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 the generative AI include a neural network (NN) and a neural network (NN). 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 (e.g., still image data or video data). 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 one or more data formats of voice data, text data, image data, etc. 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 may perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processing of each of the above-mentioned parts is performed by an AI, the processing may be performed in part or entirely by the AI, but is not limited to these examples. The processing performed by an AI including the generative AI may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by an AI including the generative AI.

[0090] 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.

[0091] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0092] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0093] 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.

[0094] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0095] The 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.

[0096] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0097] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0098] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0099] Fig. 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.

[0100] The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0101] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate 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.

[0102] 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.

[0103] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0104] The specific processing unit 290 transmits the result of the specific processing to the 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.

[0105] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt including an instruction, as well as inference data such as audio data indicating speech, text data indicating text, and image data indicating an image (e.g., still image data or video data). 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 one or more data formats, such as audio data, text data, and image 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 models 58 include AIs other than the generation AI. Examples of AIs other than the generation AI include, but are not limited to, linear regression, logistic regression, decision trees, random forests, support vector machines (SVMs), k-means clustering, convolutional neural networks (CNNs), recurrent neural networks (RNNs), generative adversarial networks (GANs), and naive Bayes. These AIs can perform various types of processing, but are not limited to these examples. The AI ​​may also be an AI agent. When the processing of each of the above-described parts is performed by an AI, the processing may be performed in part or entirely by the AI, but is not limited to these examples. Processing performed by an AI, including the generation AI, may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by an AI, including the generation AI.

[0106] 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.

[0107] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0108] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0109] 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.

[0110] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0111] The 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.

[0112] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0113] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (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).

[0114] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0115] 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.

[0116] The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0117] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate 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.

[0118] In the headset type terminal 314, the identification process is performed by the processor 46. A identification program 60 is stored in the storage 50. The processor 46 reads the identification program 60 from the storage 50 and executes the read identification program 60 on the RAM 48. The identification process is realized by the processor 46 operating as a control unit 46A in accordance with the identification program 60 executed on the RAM 48. Note that the headset type terminal 314 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 identification processing unit 290 using these models.

[0119] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0120] The specific processing unit 290 transmits the result of the specific processing to the 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.

[0121] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt including an instruction, as well as inference data such as audio data indicating speech, text data indicating text, and image data indicating an image (e.g., still image data or video data). 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 one or more data formats, such as audio data, text data, and image 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 models 58 include AIs other than the generation AI. Examples of AIs other than the generation AI include, but are not limited to, linear regression, logistic regression, decision trees, random forests, support vector machines (SVMs), k-means clustering, convolutional neural networks (CNNs), recurrent neural networks (RNNs), generative adversarial networks (GANs), and naive Bayes. These AIs can perform various types of processing, but are not limited to these examples. The AI ​​may also be an AI agent. When the processing of each of the above-described parts is performed by an AI, the processing may be performed in part or entirely by the AI, but is not limited to these examples. Processing performed by an AI, including the generation AI, may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by an AI, including the generation AI.

[0122] 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.

[0123] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0124] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0125] 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.

[0126] 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.

[0127] 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.

[0128] 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.

[0129] 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).

[0130] 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.

[0131] 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.

[0132] 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.

[0133] 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.

[0134] 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.

[0135] In the robot 414, the processor 46 performs the identification process. The storage 50 stores the identification program 60. The processor 46 reads the identification program 60 from the storage 50 and executes the read identification program 60 on the RAM 48. The identification process is realized by the processor 46 operating as the control unit 46A in accordance with the identification 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 the same process as the identification processing unit 290 using these models.

[0136] 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.

[0137] 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.

[0138] 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 including an instruction, as well as inference data such as audio data indicating speech, text data indicating text, and image data indicating an image (e.g., still image data or video data). 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 one or more data formats, such as audio data, text data, and image 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 models 58 include AIs other than the generation AI. Examples of AIs other than the generation AI include, but are not limited to, linear regression, logistic regression, decision trees, random forests, support vector machines (SVMs), k-means clustering, convolutional neural networks (CNNs), recurrent neural networks (RNNs), generative adversarial networks (GANs), and naive Bayes. These AIs can perform various types of processing, but are not limited to these examples. The AI ​​may also be an AI agent. When the processing of each of the above-described parts is performed by an AI, the processing may be performed in part or entirely by the AI, but is not limited to these examples. Processing performed by an AI, including the generation AI, may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by an AI, including the generation AI.

[0139] 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.

[0140] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0141] 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.

[0142] 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.

[0143] 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.

[0144] 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).

[0145] Human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, a state of discomfort is expressed, and when they approach the ideal, a state of pleasure is expressed. Emotions can also be created for robots, cars, and motorcycles, based on various balances, such as posture and remaining battery life. When these balances deviate from the ideal, a state of discomfort is expressed, and when they approach the ideal, a state of pleasure is expressed. An emotion map can be generated, for example, based on Dr. Mitsuyoshi's emotion map (Research on speech emotion recognition and brain physiological signal analysis systems for emotions, Tokushima University, doctoral dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map lists emotions belonging to the area called "reaction," where sensation is dominant. The right half of the emotion map lists emotions belonging to the area called "situation," where situational awareness is dominant.

[0146] 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."

[0147] 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.

[0148] 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.

[0149] 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.

[0150] 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.

[0151] 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.

[0152] 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.

[0153] 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.

[0154] 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.

[0155] 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.

[0156] 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.

[0157] 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.

[0158] 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.

[0159] [Explanation of symbols]

[0160] 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 collection unit that collects facial expressions or words of the driver or passenger; an analysis unit that infers a psychological state based on the data collected by the collection unit; an adjustment unit that adjusts a driving style based on the psychological state estimated by the analysis unit; Equipped with A system characterized by:

2. The collecting unit Collecting facial expressions or words of the driver or passengers using sensors installed inside the vehicle 2. The system of claim 1.

3. The analysis unit Analyze the collected data to infer the psychological state of the driver or passengers 2. The system of claim 1.

4. The adjustment unit Adjusting the car's speed based on the inferred state of mind 2. The system of claim 1.

5. The collecting unit Estimate the emotions of the driver or passenger and adjust the frequency of facial expression or speech collection based on the estimated emotions.

2. The system of claim 1.

6. The collecting unit Analyze the driver's or passenger's past facial expressions or verbal data to select the optimal collection method 2. The system of claim 1.

7. The collecting unit When collecting facial expressions or words, filtering is performed based on the driver's or passenger's current physical condition or stress level.

2. The system of claim 1.

8. The collecting unit Estimate the emotions of the driver or passenger and prioritize data collection based on the estimated emotions 2. The system of claim 1.

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