System

Abstract

An object of the system according to the embodiment is to dynamically adjust traveling according to a road surface condition.SOLUTION: A system according to an embodiment includes a sensor unit, an interaction unit, and a control unit. The sensor unit detects a road surface condition. The interaction unit interacts with the user on the basis of the road surface condition detected by the sensor unit. The control unit adjusts traveling based on the user's response obtained by the interaction unit.SELECTED DRAWING: Figure 1

Description

[Technical Field]

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

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

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

[0004] The prior art does not provide a power wheelchair that dynamically adjusts its travel according to road conditions, and there is room for improvement.

[0005] The system according to the embodiment aims to dynamically adjust driving depending on road conditions. [Means for solving the problem]

[0006] The system according to the embodiment includes a sensor unit, a dialogue unit, and a control unit. The sensor unit detects road surface conditions. The dialogue unit dialogues with a user based on the road surface conditions detected by the sensor unit. The control unit adjusts driving based on the user's response obtained by the dialogue unit. [Effects of the Invention]

[0007] Embodiments of the system can dynamically adjust driving depending on road conditions. [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 electric wheelchair system according to an embodiment of the present invention is a system that interacts with a user and dynamically adjusts its travel according to road surface conditions. In this system, the user gets into the electric wheelchair and begins traveling. Next, the electric wheelchair system detects road surface conditions using a built-in sensor. For example, various road surface conditions, such as slippery roads or the presence of steps, are detected. The electric wheelchair system then interactively provides information to the user based on the detected road surface conditions. For example, the system may ask the user, "There is a step ahead. Do you want to slow down?" If the user responds, the electric wheelchair system adjusts the travel based on the user's response. For example, if the user responds "yes," the system may slow down the speed to safely overcome the step. Furthermore, the electric wheelchair system periodically interacts with the user while traveling to enhance the user's sense of security. For example, the system may say, "Your current speed is safe. Please travel with confidence." This provides the user with a sense of security, reducing anxiety about traveling in the electric wheelchair and allowing the user to travel outdoors with peace of mind. For example, the electric wheelchair system may ask the user to get into the electric wheelchair and begin traveling. Next, the electric wheelchair system detects road surface conditions using built-in sensors. For example, it detects various road surface conditions, such as whether the road is slippery or whether there are steps. The electric wheelchair system then provides information to the user in an interactive format based on the detected road surface conditions. For example, it prompts the user with a message such as, "There is a step ahead. Do you want to slow down?" If the user responds, the electric wheelchair system adjusts the driving based on the response. For example, if the user responds "Yes," the electric wheelchair system slows down the speed to safely overcome the step. The electric wheelchair system also periodically interacts with the user while driving to enhance their sense of security. For example, it provides the user with a message such as, "Your current speed is safe. Please drive with confidence." This provides the user with a sense of security, reducing their anxiety about driving the electric wheelchair and allowing them to drive outdoors with peace of mind. This allows the electric wheelchair system to ensure the user's safety while enhancing their sense of security.For example, by interacting with the user and dynamically adjusting the driving depending on road conditions, users of electric wheelchairs can drive outdoors more comfortably.

[0029] The electric wheelchair system according to the embodiment includes a sensor unit, a dialogue unit, and a control unit. The sensor unit detects road surface conditions. Examples of road surface conditions include, but are not limited to, wet roads, frozen roads, and gravel roads. The sensor unit detects road surface irregularities using, for example, an ultrasonic sensor. The sensor unit can also detect road surface conditions visually using a camera. For example, the sensor unit can detect road surface irregularities using an ultrasonic sensor. The sensor unit can also detect road surface conditions visually using a camera. Furthermore, the sensor unit can optimize sensor operation by taking weather information into account when detecting road surface conditions. For example, in rainy weather, the sensor sensitivity can be increased to make it easier to detect slippery road surfaces. The dialogue unit interacts with the user based on the road surface conditions detected by the sensor unit. The dialogue can be performed using, for example, voice dialogue, text dialogue, gesture dialogue, or other methods, but is not limited to these examples. The dialogue unit recognizes the user's voice using voice recognition technology, for example, and interacts with the user. The dialogue unit can also periodically send messages that provide the user with a sense of security. For example, the dialogue unit can send a message such as, "The current speed is safe. Please drive safely." The control unit adjusts the driving behavior based on the user's response obtained by the dialogue unit. Driving behavior adjustments include, but are not limited to, speed adjustment, direction changes, and braking. For example, the control unit can achieve safe driving by adjusting the speed. The control unit can also avoid obstacles by changing direction. Furthermore, the control unit can optimize the speed and direction by taking into account the friction coefficient of the road surface when adjusting the driving behavior. For example, if the road surface is slippery, the speed can be reduced to ensure safe driving. In this way, the electric wheelchair system according to the embodiment can ensure the user's safety while enhancing the user's sense of security. For example, by interacting with the user and dynamically adjusting the driving behavior according to the road surface conditions, the electric wheelchair user can drive outdoors more comfortably.

[0030] The sensor unit includes an ultrasonic sensor. The ultrasonic sensor operates, for example, in a frequency range of 20 kHz to 40 kHz. The ultrasonic sensor has a detection distance range of 0.2 m to 5 m. The ultrasonic sensor also has a detection accuracy of ±1 cm. This improves the detection accuracy of road surface conditions by using the ultrasonic sensor. For example, the ultrasonic sensor can detect unevenness in the road surface with high accuracy. The ultrasonic sensor can also provide stable detection performance even in rainy weather or at night. Furthermore, the ultrasonic sensor operates with low power consumption, thereby extending the battery life of the electric wheelchair.

[0031] The sensor unit includes a camera. The camera is, for example, a high-resolution camera with a resolution of 1920 x 1080 pixels. The camera is, for example, equipped with a wide-angle lens with a viewing angle of 120 degrees. The camera also has a frame rate of 30 fps. This enables visual detection of road surface conditions. For example, the camera can visually detect unevenness and obstacles on the road surface. The camera can also demonstrate highly accurate detection performance day and night. Furthermore, the camera can analyze road surface conditions in real time using image processing technology. For example, the camera can detect unevenness and obstacles on the road surface with high accuracy. The camera can also demonstrate highly accurate detection performance day and night. Furthermore, the camera can analyze road surface conditions in real time using image processing technology.

[0032] The dialogue unit uses speech recognition technology. The speech recognition technology is, for example, deep learning-based speech recognition technology. The speech recognition technology uses, for example, keyword spotting technology. The speech recognition technology also includes, for example, noise canceling technology. As a result, the use of speech recognition technology enables natural dialogue with a user. For example, the speech recognition technology can recognize a user's speech with high accuracy. The speech recognition technology can also demonstrate stable recognition performance even in noisy environments. Furthermore, the speech recognition technology can support multiple languages. For example, the speech recognition technology can recognize a user's speech with high accuracy. The speech recognition technology can also demonstrate stable recognition performance even in noisy environments. Furthermore, the speech recognition technology can support multiple languages.

[0033] The control unit adjusts the speed. For example, the control unit adjusts the speed to achieve safe driving. Speed ​​adjustment includes, for example, acceleration, deceleration, constant speed driving, etc., but is not limited to these examples. For example, the control unit achieves smooth acceleration during acceleration. Furthermore, the control unit can achieve smooth deceleration during deceleration. Furthermore, the control unit can maintain a stable speed during constant speed driving. This enables safe driving by adjusting the speed. For example, the control unit achieves smooth acceleration during acceleration. Furthermore, the control unit can achieve smooth deceleration during deceleration. Furthermore, the control unit can maintain a stable speed during constant speed driving.

[0034] The control unit changes direction. For example, the control unit can avoid an obstacle by changing direction. Changing direction includes, but is not limited to, for example, a steering operation, the use of a turn signal, etc. For example, the control unit changes direction using a steering operation. Furthermore, the control unit can change direction using a turn signal. Furthermore, the control unit can change direction to avoid an obstacle. In this way, the obstacle can be avoided by changing direction. For example, the control unit changes direction using a steering operation. Furthermore, the control unit can change direction using a turn signal. Furthermore, the control unit can change direction to avoid an obstacle.

[0035] The dialogue unit periodically transmits a message that provides a sense of security to the user. For example, the dialogue unit periodically transmits a message that provides a sense of security to the user. Examples of messages that provide a sense of security include, but are not limited to, voice messages, text messages, and visual messages. For example, the dialogue unit transmits a message such as, "Your current speed is safe. Please drive safely." The dialogue unit can also transmit an appropriate message depending on the user's state. Furthermore, the dialogue unit can adjust the content of the message based on user feedback. This allows the user to feel more secure by periodically transmitting the message. For example, the dialogue unit transmits a message such as, "Your current speed is safe. Please drive safely." The dialogue unit can also transmit an appropriate message depending on the user's state. Furthermore, the dialogue unit can adjust the content of the message based on user feedback.

[0036] The sensor unit adjusts the operation of the sensor based on weather information when detecting road surface conditions. For example, in rainy weather, the sensor unit increases the sensitivity of the sensor to make it easier to detect slippery road surfaces. For example, in sunny weather, the sensor unit can return the sensitivity of the sensor to normal and acquire standard road surface information. Furthermore, on snowy days, the sensor unit adjusts the sensitivity of the sensor to accurately detect snow and ice conditions. This improves the detection accuracy of road surface conditions by taking weather information into consideration. Some or all of the above-described processing in the sensor unit may be performed using, for example, AI, or may be performed without using AI. For example, the sensor unit can input weather data to the generation AI and cause the generation AI to adjust the operation of the sensor according to the weather.

[0037] When detecting road surface conditions, the sensor unit analyzes the surrounding sound environment and adjusts the sensor's operation. For example, when the surroundings are quiet, the sensor unit increases the sensor's sensitivity to detect subtle changes in the road surface. For example, when the surroundings are noisy, the sensor unit adjusts the sensor's sensitivity to eliminate noise and acquire accurate road surface information. Furthermore, when the surrounding sound environment fluctuates, the sensor unit can dynamically adjust the sensor's sensitivity to acquire optimal road surface information. This allows noise to be eliminated and accurate road surface information to be acquired by taking the surrounding sound environment into consideration. Some or all of the above-described processing in the sensor unit may be performed, for example, using AI or without AI. For example, the sensor unit may input sound environment data to a generation AI and cause the generation AI to adjust the sensor's operation according to the sound environment.

[0038] When detecting road surface conditions, the sensor unit adjusts the sensor operation based on the user's past driving history. The sensor unit adjusts the sensor sensitivity based on, for example, information about routes the user has taken in the past. The sensor unit can, for example, predict specific road surface conditions from the user's past driving history and optimize the sensor operation. The sensor unit can also dynamically adjust the sensor sensitivity by referring to road surface conditions the user has experienced in the past. This allows the sensor operation to be optimized by referring to the past driving history. Some or all of the above-described processing in the sensor unit may be performed using, for example, AI, or may be performed without using AI. For example, the sensor unit can input past driving history data to a generation AI and have the generation AI adjust the sensor operation according to the driving history.

[0039] When detecting road surface conditions, the sensor unit adjusts the operation of the sensor based on ambient temperature information. For example, when the temperature is high, the sensor unit adjusts the sensitivity of the sensor to accurately detect changes in the road surface. For example, when the temperature is low, the sensor unit can increase the sensitivity of the sensor to make it easier to detect frozen road surfaces. Furthermore, when the temperature changes rapidly, the sensor unit can dynamically adjust the sensitivity of the sensor to obtain optimal road surface information. This improves the accuracy of detecting road surface conditions by taking ambient temperature information into consideration. Some or all of the above-described processing in the sensor unit may be performed using, for example, AI, or may be performed without using AI. For example, the sensor unit can input temperature data to the generation AI and have the generation AI adjust the operation of the sensor according to the temperature.

[0040] The sensor unit optimizes sensor operation by predicting the movements of other traffic participants when detecting road surface conditions. For example, when another traffic participant is approaching, the sensor unit increases the sensitivity of the sensor to quickly acquire road surface information. For example, when another traffic participant is moving away, the sensor unit can return the sensitivity of the sensor to normal. The sensor unit can also predict the movements of other traffic participants and dynamically adjust the sensor operation. This allows the sensor operation to be optimized by predicting the movements of other traffic participants. Some or all of the above-described processing in the sensor unit may be performed using, for example, AI, or may be performed without using AI. For example, the sensor unit can input movement data of traffic participants to a generation AI and have the generation AI adjust the sensor operation in accordance with the movement.

[0041] When detecting road surface conditions, the sensor unit adjusts sensor operation based on the user's health condition. For example, if the user is tired, the sensor unit increases sensor sensitivity to acquire detailed road surface information. For example, if the user is healthy, the sensor unit can return sensor sensitivity to normal. The sensor unit can also dynamically adjust sensor sensitivity according to the user's health condition. This allows sensor operation to be optimized by taking the user's health condition into consideration. Some or all of the above-mentioned processing in the sensor unit may be performed using AI, for example, or may be performed without using AI. For example, the sensor unit can input health data into the generation AI and have the generation AI adjust the sensor operation according to the user's health condition.

[0042] During a dialogue, the dialogue unit provides appropriate dialogue content based on the user's past response history. For example, if the user previously responded "yes," the dialogue unit asks the same question in a similar situation. For example, if the user previously responded "no," the dialogue unit can make a different suggestion. The dialogue unit can also analyze the user's past response history and provide optimal dialogue content. This makes it possible to provide more appropriate dialogue content by referring to the past response history. Some or all of the above-described processing in the dialogue unit may be performed using, or without, AI, for example. For example, the dialogue unit can input past response history data into a generation AI and cause the generation AI to provide dialogue content according to the response history.

[0043] The dialogue unit customizes the dialogue content during dialogue, taking into account the user's current activity status. For example, when the user is driving, the dialogue unit provides concise dialogue content. For example, when the user is stopped, the dialogue unit can provide detailed dialogue content. The dialogue unit can also dynamically customize the dialogue content according to the user's current activity status. This enables more appropriate dialogue by customizing the dialogue content according to the current activity status. Some or all of the above-described processing in the dialogue unit may be performed using, for example, AI, or may be performed without using AI. For example, the dialogue unit can input activity status data to a generation AI and cause the generation AI to customize the dialogue content according to the activity status.

[0044] The dialogue unit provides multilingual dialogue according to the user's language setting during dialogue. The dialogue unit automatically sets the dialogue language based on, for example, the language setting of the user's device. The dialogue unit can provide a language switching function when the user uses multiple languages. Furthermore, when the user selects a specific language, the dialogue unit can provide dialogue in that language. This enables multilingual dialogue. Some or all of the above-mentioned processing in the dialogue unit may be performed using, for example, AI, or may be performed without using AI. For example, the dialogue unit can input language setting data to a generation AI and cause the generation AI to provide dialogue content according to the language setting.

[0045] The dialogue unit provides appropriate information based on the user's geographical location information during dialogue. For example, when the user approaches a specific location, the dialogue unit provides information about the location. For example, when the user approaches a destination, the dialogue unit can provide information about the destination. The dialogue unit can also dynamically provide related information based on the user's geographical location information. This enables more appropriate dialogue by providing related information based on the geographical location information. Some or all of the above-described processing in the dialogue unit may be performed using, for example, AI, or may be performed without using AI. For example, the dialogue unit can input geographical location data to a generation AI and cause the generation AI to provide dialogue content according to the location information.

[0046] During a dialogue, the dialogue unit analyzes the user's social media activity and provides related dialogue content. For example, the dialogue unit provides information about places where the user has checked in on social media. For example, the dialogue unit can analyze the content posted by the user on social media and provide related dialogue content. The dialogue unit can also provide related dialogue content by referring to the activities of the user's friends on social media. In this way, more appropriate dialogue content can be provided by analyzing social media activity. Some or all of the above-described processing in the dialogue unit may be performed using, for example, AI, or may be performed without using AI. For example, the dialogue unit can input social media data into a generation AI and cause the generation AI to provide dialogue content according to the social media activity.

[0047] During the dialogue, the dialogue unit changes the use of technical terms in the dialogue content according to the user's level of expertise. For example, if the user has technical knowledge, the dialogue unit uses technical terms to conduct the dialogue. For example, if the user does not have technical knowledge, the dialogue unit can conduct the dialogue using simple language. The dialogue unit can also dynamically adjust the use of technical terms in the dialogue content according to the user's level of expertise. This enables more appropriate dialogue by adjusting the dialogue content according to the user's level of expertise. Some or all of the above-mentioned processing in the dialogue unit may be performed using, for example, AI, or may be performed without using AI. For example, the dialogue unit can input technical knowledge data to a generation AI and cause the generation AI to provide dialogue content according to the level of expertise.

[0048] When adjusting driving, the control unit adjusts the speed and direction based on the friction coefficient of the road surface. For example, if the road surface is slippery, the control unit reduces the speed to ensure safe driving. For example, if the road surface is dry, the control unit can drive at a normal speed. The control unit can also dynamically adjust the speed and direction according to the friction coefficient of the road surface. This allows for safer driving by taking the friction coefficient of the road surface into consideration. Some or all of the above-mentioned processing in the control unit may be performed using, for example, AI, or may be performed without using AI. For example, the control unit can input friction coefficient data to the generation AI and cause the generation AI to perform driving adjustments according to the friction coefficient.

[0049] When adjusting driving, the control unit predicts the movements of other traffic participants and adjusts driving. For example, when other traffic participants are approaching, the control unit reduces the speed to ensure safe driving. For example, when other traffic participants are moving away, the control unit can drive at a normal speed. Furthermore, the control unit can predict the movements of other traffic participants and dynamically adjust the speed and direction. This enables safer driving by predicting the movements of other traffic participants. Some or all of the above-mentioned processing in the control unit may be performed using, for example, AI, or may be performed without using AI. For example, the control unit can input movement data of traffic participants to the generation AI and have the generation AI execute driving adjustments according to the movements.

[0050] When adjusting driving, the control unit selects an appropriate driving method based on the user's past driving history. The control unit selects the optimal driving method based on, for example, information about routes the user has taken in the past. The control unit can, for example, predict specific road conditions from the user's past driving history and select the optimal driving method. The control unit can also select the optimal driving method by referring to driving methods the user has experienced in the past. This makes it possible to select a more appropriate driving method by referring to the past driving history. Some or all of the above-mentioned processing in the control unit may be performed using, for example, AI, or may be performed without using AI. For example, the control unit can input past driving history data into the generation AI and cause the generation AI to select a driving method according to the driving history.

[0051] When adjusting driving, the control unit adjusts the driving method based on ambient temperature information. For example, when the temperature is high, the control unit adjusts the speed while monitoring the motor temperature. For example, when the temperature is low, the control unit can adjust the driving method taking into account battery efficiency. Furthermore, when the temperature changes suddenly, the control unit can dynamically adjust the driving method to maintain optimal performance. This allows for more appropriate driving by taking ambient temperature information into account. Some or all of the above-mentioned processing in the control unit may be performed using AI, for example, or may be performed without using AI. For example, the control unit can input temperature data into the generation AI and have the generation AI perform driving adjustments according to the temperature.

[0052] When adjusting the running method, the control unit adjusts the running method taking into account the user's health condition. For example, if the user is tired, the control unit reduces the speed to ensure safe running. For example, if the user is healthy, the control unit can run at a normal speed. The control unit can also dynamically adjust the speed and direction according to the user's health condition. This allows for safer running by adjusting the running method according to the user's health condition. Some or all of the above-mentioned processing in the control unit may be performed using AI, for example, or may be performed without using AI. For example, the control unit can input health data into the generation AI and have the generation AI adjust the running method according to the user's health condition.

[0053] When adjusting driving, the control unit selects an appropriate driving route based on the user's geographical location information. For example, when the user approaches a specific location, the control unit selects a driving route appropriate for that location. For example, when the user approaches a destination, the control unit can select the shortest route. The control unit can also dynamically select an optimal driving route based on the user's geographical location information. This enables more efficient driving by selecting an optimal driving route based on the geographical location information. Some or all of the above-mentioned processing in the control unit may be performed using, for example, AI, or may be performed without using AI. For example, the control unit can input geographical location data to the generation AI and cause the generation AI to select a driving route according to the location information.

[0054] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0055] The electric wheelchair system may further include a health management unit that monitors the user's health condition. The health management unit may, for example, measure the user's heart rate and blood pressure in real time, and if an abnormality is detected, issue a warning to the user through the dialogue unit. The health management unit may also instruct the control unit to adjust the traveling speed based on the user's health condition. For example, if the user's heart rate is high, the control unit may reduce the speed to ensure safe traveling. Furthermore, the health management unit may record the user's health data and periodically transmit it to a medical institution. This allows the user's health condition to be constantly monitored and appropriate measures to be taken.

[0056] The electric wheelchair system can further include a tourist information unit that provides surrounding tourist information based on the user's location information. For example, when the user approaches a tourist spot, the tourist information unit can provide audio guidance about the history and highlights of the tourist spot. The tourist information unit can also suggest customized tourist routes based on the user's interests and preferences. Furthermore, the tourist information unit can provide information about nearby restaurants and cafes based on the user's location information. This allows the user to enjoy tourist spots more and achieve a comfortable travel experience.

[0057] The electric wheelchair system may further include a route suggestion unit that analyzes the user's past driving data and suggests an optimal driving route. The route suggestion unit may suggest an optimal driving route, for example, based on information about routes the user has taken in the past. The route suggestion unit may also suggest a customized route according to the user's preferences and destination. Furthermore, the route suggestion unit may dynamically adjust the optimal route taking into account real-time traffic information. This allows the user to reach their destination efficiently and comfortably.

[0058] The electric wheelchair system may further include an exercise suggestion unit that suggests an appropriate exercise program based on the user's health condition. The exercise suggestion unit may, for example, monitor the user's heart rate and activity level and suggest an appropriate exercise program. The exercise suggestion unit may also provide a customized exercise program based on the user's health goals. Furthermore, the exercise suggestion unit may record the user's exercise history and provide feedback on the user's progress. This allows the user to maintain a healthy lifestyle and enjoy a comfortable riding experience.

[0059] The electric wheelchair system may further include an emergency notification unit that notifies emergency contacts based on the user's location information. For example, the emergency notification unit may notify emergency contacts if the user stays in a specific area for a long period of time or if abnormal movement is detected. The emergency notification unit may also track the user's location information in real time, enabling a prompt response in the event of an emergency. Furthermore, the emergency notification unit may monitor the user's health condition and notify a medical institution if an abnormality is detected. This ensures the user's safety and allows for a prompt response.

[0060] The processing flow of the first embodiment will be briefly explained below.

[0061] Step 1: The sensor unit detects road surface conditions. Road surface conditions include, for example, wet roads, frozen roads, and gravel roads. The sensor unit can detect road surface irregularities and visual road conditions using ultrasonic sensors and cameras. It can also optimize sensor operation by taking weather information into account. For example, in rainy weather, the sensor's sensitivity can be increased to make it easier to detect slippery road surfaces. Step 2: The dialogue unit engages in dialogue with the user based on the road surface conditions detected by the sensor unit. The dialogue can be performed using voice dialogue, text dialogue, gesture dialogue, or other methods. The dialogue unit uses voice recognition technology to recognize the user's voice and engage in dialogue. It can also periodically send messages to provide the user with a sense of security. For example, it can send a message such as, "Your current speed is safe. Please drive safely." Step 3: The control unit adjusts the driving based on the user's response obtained by the dialogue unit. Driving adjustments include speed adjustment, direction changes, braking, etc. The control unit can achieve safe driving by adjusting the speed and avoid obstacles by changing direction. It can also optimize the speed and direction by taking into account the friction coefficient of the road surface. For example, if the road surface is slippery, the speed will be reduced to ensure safe driving.

[0062] (Example 2) An electric wheelchair system according to an embodiment of the present invention is a system that interacts with a user and dynamically adjusts its travel according to road surface conditions. In this system, the user gets into the electric wheelchair and begins traveling. Next, the electric wheelchair system detects road surface conditions using a built-in sensor. For example, various road surface conditions, such as slippery roads or the presence of steps, are detected. The electric wheelchair system then interactively provides information to the user based on the detected road surface conditions. For example, the system may ask the user, "There is a step ahead. Do you want to slow down?" If the user responds, the electric wheelchair system adjusts the travel based on the user's response. For example, if the user responds "yes," the system may slow down the speed to safely overcome the step. Furthermore, the electric wheelchair system periodically interacts with the user while traveling to enhance the user's sense of security. For example, the system may say, "Your current speed is safe. Please travel with confidence." This provides the user with a sense of security, reducing anxiety about traveling in the electric wheelchair and allowing the user to travel outdoors with peace of mind. For example, the electric wheelchair system may ask the user to get into the electric wheelchair and begin traveling. Next, the electric wheelchair system detects road surface conditions using built-in sensors. For example, it detects various road surface conditions, such as whether the road is slippery or whether there are steps. The electric wheelchair system then provides information to the user in an interactive format based on the detected road surface conditions. For example, it prompts the user with a message such as, "There is a step ahead. Do you want to slow down?" If the user responds, the electric wheelchair system adjusts the driving based on the response. For example, if the user responds "Yes," the electric wheelchair system slows down the speed to safely overcome the step. The electric wheelchair system also periodically interacts with the user while driving to enhance their sense of security. For example, it provides the user with a message such as, "Your current speed is safe. Please drive with confidence." This provides the user with a sense of security, reducing their anxiety about driving the electric wheelchair and allowing them to drive outdoors with peace of mind. This allows the electric wheelchair system to ensure the user's safety while enhancing their sense of security.For example, by interacting with the user and dynamically adjusting the driving depending on road conditions, users of electric wheelchairs can drive outdoors more comfortably.

[0063] The electric wheelchair system according to the embodiment includes a sensor unit, a dialogue unit, and a control unit. The sensor unit detects road surface conditions. Examples of road surface conditions include, but are not limited to, wet roads, frozen roads, and gravel roads. The sensor unit detects road surface irregularities using, for example, an ultrasonic sensor. The sensor unit can also detect road surface conditions visually using a camera. For example, the sensor unit can detect road surface irregularities using an ultrasonic sensor. The sensor unit can also detect road surface conditions visually using a camera. Furthermore, the sensor unit can optimize sensor operation by taking weather information into account when detecting road surface conditions. For example, in rainy weather, the sensor sensitivity can be increased to make it easier to detect slippery road surfaces. The dialogue unit interacts with the user based on the road surface conditions detected by the sensor unit. The dialogue can be performed using, for example, voice dialogue, text dialogue, gesture dialogue, or other methods, but is not limited to these examples. The dialogue unit recognizes the user's voice using voice recognition technology, for example, and interacts with the user. The dialogue unit can also periodically send messages that provide the user with a sense of security. For example, the dialogue unit can send a message such as, "The current speed is safe. Please drive safely." The control unit adjusts the driving behavior based on the user's response obtained by the dialogue unit. Driving behavior adjustments include, but are not limited to, speed adjustment, direction changes, and braking. For example, the control unit can achieve safe driving by adjusting the speed. The control unit can also avoid obstacles by changing direction. Furthermore, the control unit can optimize the speed and direction by taking into account the friction coefficient of the road surface when adjusting the driving behavior. For example, if the road surface is slippery, the speed can be reduced to ensure safe driving. In this way, the electric wheelchair system according to the embodiment can ensure the user's safety while enhancing the user's sense of security. For example, by interacting with the user and dynamically adjusting the driving behavior according to the road surface conditions, the electric wheelchair user can drive outdoors more comfortably.

[0064] The sensor unit includes an ultrasonic sensor. The ultrasonic sensor operates, for example, in a frequency range of 20 kHz to 40 kHz. The ultrasonic sensor has a detection distance range of 0.2 m to 5 m. The ultrasonic sensor also has a detection accuracy of ±1 cm. This improves the detection accuracy of road surface conditions by using the ultrasonic sensor. For example, the ultrasonic sensor can detect unevenness in the road surface with high accuracy. The ultrasonic sensor can also provide stable detection performance even in rainy weather or at night. Furthermore, the ultrasonic sensor operates with low power consumption, thereby extending the battery life of the electric wheelchair.

[0065] The sensor unit includes a camera. The camera is, for example, a high-resolution camera with a resolution of 1920 x 1080 pixels. The camera is, for example, equipped with a wide-angle lens with a viewing angle of 120 degrees. The camera also has a frame rate of 30 fps. This enables visual detection of road surface conditions. For example, the camera can visually detect unevenness and obstacles on the road surface. The camera can also demonstrate highly accurate detection performance day and night. Furthermore, the camera can analyze road surface conditions in real time using image processing technology. For example, the camera can detect unevenness and obstacles on the road surface with high accuracy. The camera can also demonstrate highly accurate detection performance day and night. Furthermore, the camera can analyze road surface conditions in real time using image processing technology.

[0066] The dialogue unit uses speech recognition technology. The speech recognition technology is, for example, deep learning-based speech recognition technology. The speech recognition technology uses, for example, keyword spotting technology. The speech recognition technology also includes, for example, noise canceling technology. As a result, the use of speech recognition technology enables natural dialogue with a user. For example, the speech recognition technology can recognize a user's speech with high accuracy. The speech recognition technology can also demonstrate stable recognition performance even in noisy environments. Furthermore, the speech recognition technology can support multiple languages. For example, the speech recognition technology can recognize a user's speech with high accuracy. The speech recognition technology can also demonstrate stable recognition performance even in noisy environments. Furthermore, the speech recognition technology can support multiple languages.

[0067] The control unit adjusts the speed. For example, the control unit adjusts the speed to achieve safe driving. Speed ​​adjustment includes, for example, acceleration, deceleration, constant speed driving, etc., but is not limited to these examples. For example, the control unit achieves smooth acceleration during acceleration. Furthermore, the control unit can achieve smooth deceleration during deceleration. Furthermore, the control unit can maintain a stable speed during constant speed driving. This enables safe driving by adjusting the speed. For example, the control unit achieves smooth acceleration during acceleration. Furthermore, the control unit can achieve smooth deceleration during deceleration. Furthermore, the control unit can maintain a stable speed during constant speed driving.

[0068] The control unit changes direction. For example, the control unit can avoid an obstacle by changing direction. Changing direction includes, but is not limited to, for example, a steering operation, the use of a turn signal, etc. For example, the control unit changes direction using a steering operation. Furthermore, the control unit can change direction using a turn signal. Furthermore, the control unit can change direction to avoid an obstacle. In this way, the obstacle can be avoided by changing direction. For example, the control unit changes direction using a steering operation. Furthermore, the control unit can change direction using a turn signal. Furthermore, the control unit can change direction to avoid an obstacle.

[0069] The dialogue unit periodically transmits a message that provides a sense of security to the user. For example, the dialogue unit periodically transmits a message that provides a sense of security to the user. Examples of messages that provide a sense of security include, but are not limited to, voice messages, text messages, and visual messages. For example, the dialogue unit transmits a message such as, "Your current speed is safe. Please drive safely." The dialogue unit can also transmit an appropriate message depending on the user's state. Furthermore, the dialogue unit can adjust the content of the message based on user feedback. This allows the user to feel more secure by periodically transmitting the message. For example, the dialogue unit transmits a message such as, "Your current speed is safe. Please drive safely." The dialogue unit can also transmit an appropriate message depending on the user's state. Furthermore, the dialogue unit can adjust the content of the message based on user feedback.

[0070] The sensor unit estimates the user's emotions and adjusts the sensitivity of the sensor based on the estimated user emotions. For example, if the user is nervous, the sensor unit increases the sensitivity of the sensor to acquire more detailed road surface information. For example, if the user is relaxed, the sensor unit can return the sensitivity of the sensor to normal and acquire standard road surface information. Furthermore, if the user is in a hurry, the sensor unit can temporarily reduce the sensitivity of the sensor to quickly acquire road surface information. This allows more appropriate road surface information to be acquired by adjusting the sensitivity of the sensor according to the user's emotions. Emotion estimation is realized using an emotion estimation function, for example, using an emotion engine or a generation 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 sensor unit may be performed using, for example, an AI. For example, the sensor unit may input the user's facial expression data into the generation AI and cause the generation AI to estimate the emotion.

[0071] The sensor unit adjusts the operation of the sensor based on weather information when detecting road surface conditions. For example, in rainy weather, the sensor unit increases the sensitivity of the sensor to make it easier to detect slippery road surfaces. For example, in sunny weather, the sensor unit can return the sensitivity of the sensor to normal and acquire standard road surface information. Furthermore, on snowy days, the sensor unit adjusts the sensitivity of the sensor to accurately detect snow and ice conditions. This improves the detection accuracy of road surface conditions by taking weather information into consideration. Some or all of the above-described processing in the sensor unit may be performed using, for example, AI, or may be performed without using AI. For example, the sensor unit can input weather data to the generation AI and cause the generation AI to adjust the operation of the sensor according to the weather.

[0072] When detecting road surface conditions, the sensor unit analyzes the surrounding sound environment and adjusts the sensor's operation. For example, when the surroundings are quiet, the sensor unit increases the sensor's sensitivity to detect subtle changes in the road surface. For example, when the surroundings are noisy, the sensor unit adjusts the sensor's sensitivity to eliminate noise and acquire accurate road surface information. Furthermore, when the surrounding sound environment fluctuates, the sensor unit can dynamically adjust the sensor's sensitivity to acquire optimal road surface information. This allows noise to be eliminated and accurate road surface information to be acquired by taking the surrounding sound environment into consideration. Some or all of the above-described processing in the sensor unit may be performed, for example, using AI or without AI. For example, the sensor unit may input sound environment data to a generation AI and cause the generation AI to adjust the sensor's operation according to the sound environment.

[0073] When detecting road surface conditions, the sensor unit adjusts the sensor operation based on the user's past driving history. The sensor unit adjusts the sensor sensitivity based on, for example, information about routes the user has taken in the past. The sensor unit can, for example, predict specific road surface conditions from the user's past driving history and optimize the sensor operation. The sensor unit can also dynamically adjust the sensor sensitivity by referring to road surface conditions the user has experienced in the past. This allows the sensor operation to be optimized by referring to the past driving history. Some or all of the above-described processing in the sensor unit may be performed using, for example, AI, or may be performed without using AI. For example, the sensor unit can input past driving history data to a generation AI and have the generation AI adjust the sensor operation according to the driving history.

[0074] The sensor unit estimates the user's emotion and switches the sensor's operation mode based on the estimated user's emotion. For example, if the user is nervous, the sensor unit switches the sensor's operation mode to high-sensitivity mode. For example, if the user is relaxed, the sensor unit can return the sensor's operation mode to normal mode. Furthermore, if the user is in a hurry, the sensor unit can switch the sensor's operation mode to quick mode. This allows more appropriate road surface information to be acquired by switching the sensor's operation mode according to the user's emotion. Emotion estimation is realized using an emotion estimation function, for example, using an emotion engine or a generation 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 sensor unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the sensor unit may input the user's facial expression data into the generation AI and cause the generation AI to estimate the emotion.

[0075] When detecting road surface conditions, the sensor unit adjusts the operation of the sensor based on ambient temperature information. For example, when the temperature is high, the sensor unit adjusts the sensitivity of the sensor to accurately detect changes in the road surface. For example, when the temperature is low, the sensor unit can increase the sensitivity of the sensor to make it easier to detect frozen road surfaces. Furthermore, when the temperature changes rapidly, the sensor unit can dynamically adjust the sensitivity of the sensor to obtain optimal road surface information. This improves the accuracy of detecting road surface conditions by taking ambient temperature information into consideration. Some or all of the above-described processing in the sensor unit may be performed using, for example, AI, or may be performed without using AI. For example, the sensor unit can input temperature data to the generation AI and have the generation AI adjust the operation of the sensor according to the temperature.

[0076] The sensor unit optimizes sensor operation by predicting the movements of other traffic participants when detecting road surface conditions. For example, when another traffic participant is approaching, the sensor unit increases the sensitivity of the sensor to quickly acquire road surface information. For example, when another traffic participant is moving away, the sensor unit can return the sensitivity of the sensor to normal. The sensor unit can also predict the movements of other traffic participants and dynamically adjust the sensor operation. This allows the sensor operation to be optimized by predicting the movements of other traffic participants. Some or all of the above-described processing in the sensor unit may be performed using, for example, AI, or may be performed without using AI. For example, the sensor unit can input movement data of traffic participants to a generation AI and have the generation AI adjust the sensor operation in accordance with the movement.

[0077] When detecting road surface conditions, the sensor unit adjusts sensor operation based on the user's health condition. For example, if the user is tired, the sensor unit increases sensor sensitivity to acquire detailed road surface information. For example, if the user is healthy, the sensor unit can return sensor sensitivity to normal. The sensor unit can also dynamically adjust sensor sensitivity according to the user's health condition. This allows sensor operation to be optimized by taking the user's health condition into consideration. Some or all of the above-mentioned processing in the sensor unit may be performed using AI, for example, or may be performed without using AI. For example, the sensor unit can input health data into the generation AI and have the generation AI adjust the sensor operation according to the user's health condition.

[0078] The dialogue unit estimates the user's emotions and adjusts the tone of the dialogue based on the estimated user emotions. For example, if the user is nervous, the dialogue unit may use a calm tone. For example, if the user is relaxed, the dialogue unit may use a bright tone. Furthermore, if the user is in a hurry, the dialogue unit may use a quick and concise tone. This allows for more appropriate dialogue by adjusting the tone of the dialogue according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI may be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above-described processing in the dialogue unit may be performed using AI, or may be performed without AI. For example, the dialogue unit may input the user's voice data into the generation AI and cause the generation AI to estimate emotions.

[0079] During a dialogue, the dialogue unit provides appropriate dialogue content based on the user's past response history. For example, if the user previously responded "yes," the dialogue unit asks the same question in a similar situation. For example, if the user previously responded "no," the dialogue unit can make a different suggestion. The dialogue unit can also analyze the user's past response history and provide optimal dialogue content. This makes it possible to provide more appropriate dialogue content by referring to the past response history. Some or all of the above-described processing in the dialogue unit may be performed using, or without, AI, for example. For example, the dialogue unit can input past response history data into a generation AI and cause the generation AI to provide dialogue content according to the response history.

[0080] The dialogue unit customizes the dialogue content during dialogue, taking into account the user's current activity status. For example, when the user is driving, the dialogue unit provides concise dialogue content. For example, when the user is stopped, the dialogue unit can provide detailed dialogue content. The dialogue unit can also dynamically customize the dialogue content according to the user's current activity status. This enables more appropriate dialogue by customizing the dialogue content according to the current activity status. Some or all of the above-described processing in the dialogue unit may be performed using, for example, AI, or may be performed without using AI. For example, the dialogue unit can input activity status data to a generation AI and cause the generation AI to customize the dialogue content according to the activity status.

[0081] The dialogue unit provides multilingual dialogue according to the user's language setting during dialogue. The dialogue unit automatically sets the dialogue language based on, for example, the language setting of the user's device. The dialogue unit can provide a language switching function when the user uses multiple languages. Furthermore, when the user selects a specific language, the dialogue unit can provide dialogue in that language. This enables multilingual dialogue. Some or all of the above-mentioned processing in the dialogue unit may be performed using, for example, AI, or may be performed without using AI. For example, the dialogue unit can input language setting data to a generation AI and cause the generation AI to provide dialogue content according to the language setting.

[0082] The dialogue unit estimates the user's emotions and adjusts the frequency of dialogue based on the estimated user emotions. For example, if the user is nervous, the dialogue unit increases the frequency of dialogue to provide a sense of security. For example, if the user is relaxed, the dialogue unit can return the frequency of dialogue to normal. Furthermore, if the user is in a hurry, the dialogue unit can reduce the frequency of dialogue to support quick driving. This enables more appropriate dialogue by adjusting the frequency of dialogue according to the user's emotions. Emotion estimation is realized using an emotion estimation function, for example, an emotion engine or a generation 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-mentioned processing in the dialogue unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the dialogue unit may input the user's voice data into the generation AI and cause the generation AI to estimate emotions.

[0083] The dialogue unit provides appropriate information based on the user's geographical location information during dialogue. For example, when the user approaches a specific location, the dialogue unit provides information about the location. For example, when the user approaches a destination, the dialogue unit can provide information about the destination. The dialogue unit can also dynamically provide related information based on the user's geographical location information. This enables more appropriate dialogue by providing related information based on the geographical location information. Some or all of the above-described processing in the dialogue unit may be performed using, for example, AI, or may be performed without using AI. For example, the dialogue unit can input geographical location data to a generation AI and cause the generation AI to provide dialogue content according to the location information.

[0084] During a dialogue, the dialogue unit analyzes the user's social media activity and provides related dialogue content. For example, the dialogue unit provides information about places where the user has checked in on social media. For example, the dialogue unit can analyze the content posted by the user on social media and provide related dialogue content. The dialogue unit can also provide related dialogue content by referring to the activities of the user's friends on social media. In this way, more appropriate dialogue content can be provided by analyzing social media activity. Some or all of the above-described processing in the dialogue unit may be performed using, for example, AI, or may be performed without using AI. For example, the dialogue unit can input social media data into a generation AI and cause the generation AI to provide dialogue content according to the social media activity.

[0085] During the dialogue, the dialogue unit changes the use of technical terms in the dialogue content according to the user's level of expertise. For example, if the user has technical knowledge, the dialogue unit uses technical terms to conduct the dialogue. For example, if the user does not have technical knowledge, the dialogue unit can conduct the dialogue using simple language. The dialogue unit can also dynamically adjust the use of technical terms in the dialogue content according to the user's level of expertise. This enables more appropriate dialogue by adjusting the dialogue content according to the user's level of expertise. Some or all of the above-mentioned processing in the dialogue unit may be performed using, for example, AI, or may be performed without using AI. For example, the dialogue unit can input technical knowledge data to a generation AI and cause the generation AI to provide dialogue content according to the level of expertise.

[0086] The control unit estimates the user's emotions and changes the driving adjustment method based on the estimated user emotions. For example, if the user is nervous, the control unit reduces the speed to ensure safe driving. For example, if the user is relaxed, the control unit can drive at a normal speed. Furthermore, if the user is in a hurry, the control unit can increase the speed to ensure quick driving. This allows for a safer and more comfortable driving by changing the driving adjustment method according to the user's emotions. Emotion estimation is realized using an emotion estimation function, for example, using an emotion engine or a generation AI. The generation AI may be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above-mentioned processing in the control unit may be performed using an AI, for example, or without an AI. For example, the control unit may input the user's facial expression data into the generation AI and cause the generation AI to estimate the emotion.

[0087] When adjusting driving, the control unit adjusts the speed and direction based on the friction coefficient of the road surface. For example, if the road surface is slippery, the control unit reduces the speed to ensure safe driving. For example, if the road surface is dry, the control unit can drive at a normal speed. The control unit can also dynamically adjust the speed and direction according to the friction coefficient of the road surface. This allows for safer driving by taking the friction coefficient of the road surface into consideration. Some or all of the above-mentioned processing in the control unit may be performed using, for example, AI, or may be performed without using AI. For example, the control unit can input friction coefficient data to the generation AI and cause the generation AI to perform driving adjustments according to the friction coefficient.

[0088] When adjusting driving, the control unit predicts the movements of other traffic participants and adjusts driving. For example, when other traffic participants are approaching, the control unit reduces the speed to ensure safe driving. For example, when other traffic participants are moving away, the control unit can drive at a normal speed. Furthermore, the control unit can predict the movements of other traffic participants and dynamically adjust the speed and direction. This enables safer driving by predicting the movements of other traffic participants. Some or all of the above-mentioned processing in the control unit may be performed using, for example, AI, or may be performed without using AI. For example, the control unit can input movement data of traffic participants to the generation AI and have the generation AI execute driving adjustments according to the movements.

[0089] When adjusting driving, the control unit selects an appropriate driving method based on the user's past driving history. The control unit selects the optimal driving method based on, for example, information about routes the user has taken in the past. The control unit can, for example, predict specific road conditions from the user's past driving history and select the optimal driving method. The control unit can also select the optimal driving method by referring to driving methods the user has experienced in the past. This makes it possible to select a more appropriate driving method by referring to the past driving history. Some or all of the above-mentioned processing in the control unit may be performed using, for example, AI, or may be performed without using AI. For example, the control unit can input past driving history data into the generation AI and cause the generation AI to select a driving method according to the driving history.

[0090] The control unit estimates the user's emotions and determines driving priorities based on the estimated user emotions. For example, if the user is nervous, the control unit prioritizes safety when driving. For example, if the user is relaxed, the control unit can prioritize comfort when driving. Furthermore, if the user is in a hurry, the control unit can prioritize speed when driving. This enables more appropriate driving by determining driving priorities according to the user's emotions. Emotion estimation is realized using an emotion estimation function, for example, using an emotion engine or a generation AI. The generation AI may be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above-mentioned processing in the control unit may be performed using AI, for example, or without AI. For example, the control unit may input the user's facial expression data into the generation AI and cause the generation AI to estimate emotions.

[0091] When adjusting driving, the control unit adjusts the driving method based on ambient temperature information. For example, when the temperature is high, the control unit adjusts the speed while monitoring the motor temperature. For example, when the temperature is low, the control unit can adjust the driving method taking into account battery efficiency. Furthermore, when the temperature changes suddenly, the control unit can dynamically adjust the driving method to maintain optimal performance. This allows for more appropriate driving by taking ambient temperature information into account. Some or all of the above-mentioned processing in the control unit may be performed using AI, for example, or may be performed without using AI. For example, the control unit can input temperature data into the generation AI and have the generation AI perform driving adjustments according to the temperature.

[0092] When adjusting the running method, the control unit adjusts the running method taking into account the user's health condition. For example, if the user is tired, the control unit reduces the speed to ensure safe running. For example, if the user is healthy, the control unit can run at a normal speed. The control unit can also dynamically adjust the speed and direction according to the user's health condition. This allows for safer running by adjusting the running method according to the user's health condition. Some or all of the above-mentioned processing in the control unit may be performed using AI, for example, or may be performed without using AI. For example, the control unit can input health data into the generation AI and have the generation AI adjust the running method according to the user's health condition.

[0093] When adjusting driving, the control unit selects an appropriate driving route based on the user's geographical location information. For example, when the user approaches a specific location, the control unit selects a driving route appropriate for that location. For example, when the user approaches a destination, the control unit can select the shortest route. The control unit can also dynamically select an optimal driving route based on the user's geographical location information. This enables more efficient driving by selecting an optimal driving route based on the geographical location information. Some or all of the above-mentioned processing in the control unit may be performed using, for example, AI, or may be performed without using AI. For example, the control unit can input geographical location data to the generation AI and cause the generation AI to select a driving route according to the location information. === Hard Collateral 1-1 === Each of the multiple elements, including the sensor unit, dialogue unit, and control 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 sensor unit detects road surface conditions using the camera 42 and ultrasonic sensor of the smart device 14, and processes the detected data using the control unit 46A. The dialogue unit engages in dialogue with the user using the microphone 38B and speaker 40B of the smart device 14, and processes the dialogue content using the control unit 46A. The control unit adjusts driving using the specific processing unit 290 of the data processing device 12, optimizing, for example, speed and direction. Each of the elements, the sensor unit, dialogue unit, and control unit, is also realized by the specific processing unit 290 of the data processing device 12. === Hard Collateral 1-2 === Each of the multiple elements, including the sensor unit, dialogue unit, and control 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 sensor unit detects road surface conditions using the camera 42 and ultrasonic sensor of the smart glasses 214, and processes the detected data using the control unit 46A. The dialogue unit engages in dialogue with the user using the microphone 238 and speaker 240 of the smart glasses 214, and processes the dialogue content using the control unit 46A. The control unit adjusts driving using the specific processing unit 290 of the data processing device 12, optimizing, for example, speed and direction. Each of the elements, the sensor unit, dialogue unit, and control unit, is also realized by the specific processing unit 290 of the data processing device 12. === Hard Collateral 1-3 === Each of the multiple elements including the sensor unit, dialogue unit, and control unit described above is realized, for example, by at least one of the headset terminal 314 and the data processing device 12. For example, the sensor unit detects road surface conditions using the camera 42 and ultrasonic sensor of the headset terminal 314, and processes the detected data by the control unit 46A. The dialogue unit engages in dialogue with the user using the microphone 238 and speaker 240 of the headset terminal 314, and processes the dialogue content by the control unit 46A. The control unit adjusts driving by a specific processing unit 290 of the data processing device 12, optimizing, for example, speed and direction. Each of the elements of the sensor unit, dialogue unit, and control unit is also realized by the specific processing unit 290 of the data processing device 12. === Hard Collateral 1-4 === Each of the multiple elements including the sensor unit, dialogue unit, and control unit described above is realized, for example, by at least one of the robot 414 and the data processing device 12. For example, the sensor unit detects road surface conditions using the camera 42 and ultrasonic sensor of the robot 414, and the control unit 46A processes the detected data. The dialogue unit engages in dialogue with a user using the microphone 238 and speaker 240 of the robot 414, and the control unit 46A processes the dialogue content. The control unit adjusts the traveling by the specific processing unit 290 of the data processing device 12, optimizing, for example, the speed and direction. Each of the elements of the sensor unit, dialogue unit, and control unit is also realized by the specific processing unit 290 of the data processing device 12.

[0094] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0095] The electric wheelchair system may further include a health management unit that monitors the user's health condition. The health management unit may, for example, measure the user's heart rate and blood pressure in real time, and if an abnormality is detected, issue a warning to the user through the dialogue unit. The health management unit may also instruct the control unit to adjust the traveling speed based on the user's health condition. For example, if the user's heart rate is high, the control unit may reduce the speed to ensure safe traveling. Furthermore, the health management unit may record the user's health data and periodically transmit it to a medical institution. This allows the user's health condition to be constantly monitored and appropriate measures to be taken.

[0096] The electric wheelchair system may further include an entertainment unit that estimates the user's emotions and plays music based on the estimated emotions. For example, the entertainment unit may play calming music when the user is relaxed, and music with a relaxing effect when the user is tense. The entertainment unit may also automatically generate an appropriate playlist based on the user's musical preferences. Furthermore, the entertainment unit may dynamically adjust the genre and tempo of the music according to changes in the user's emotions. This allows the system to provide music that matches the user's emotions, resulting in a comfortable riding experience.

[0097] The electric wheelchair system can further include a tourist information unit that provides surrounding tourist information based on the user's location information. For example, when the user approaches a tourist spot, the tourist information unit can provide audio guidance about the history and highlights of the tourist spot. The tourist information unit can also suggest customized tourist routes based on the user's interests and preferences. Furthermore, the tourist information unit can provide information about nearby restaurants and cafes based on the user's location information. This allows the user to enjoy tourist spots more and achieve a comfortable travel experience.

[0098] The electric wheelchair system may further include a dialogue adjustment unit that estimates the user's emotions and adjusts the content of the dialogue based on the estimated emotions. For example, the dialogue adjustment unit may send a reassuring message if the user is feeling anxious, or may dialogue in a light-hearted tone if the user is relaxed. The dialogue adjustment unit may also dynamically adjust the frequency and content of the dialogue in response to changes in the user's emotions. Furthermore, the dialogue adjustment unit may provide appropriate advice or suggestions based on the user's emotions. This allows for dialogue that is tailored to the user's emotions, resulting in a more comfortable riding experience.

[0099] The electric wheelchair system may further include a route suggestion unit that analyzes the user's past driving data and suggests an optimal driving route. The route suggestion unit may suggest an optimal driving route, for example, based on information about routes the user has taken in the past. The route suggestion unit may also suggest a customized route according to the user's preferences and destination. Furthermore, the route suggestion unit may dynamically adjust the optimal route taking into account real-time traffic information. This allows the user to reach their destination efficiently and comfortably.

[0100] The electric wheelchair system may further include a driving mode switching unit that estimates the user's emotions and switches the driving mode based on the estimated emotions. For example, if the user is nervous, the driving mode switching unit may switch to a safe mode and reduce the speed. If the user is relaxed, the driving mode switching unit may return to a normal mode and provide a comfortable ride. Furthermore, if the user is in a hurry, the driving mode switching unit may switch to a fast mode and increase the speed for quick driving. This allows the system to provide a driving mode that corresponds to the user's emotions, thereby achieving a safer and more comfortable driving experience.

[0101] The electric wheelchair system may further include an exercise suggestion unit that suggests an appropriate exercise program based on the user's health condition. The exercise suggestion unit may, for example, monitor the user's heart rate and activity level and suggest an appropriate exercise program. The exercise suggestion unit may also provide a customized exercise program based on the user's health goals. Furthermore, the exercise suggestion unit may record the user's exercise history and provide feedback on the user's progress. This allows the user to maintain a healthy lifestyle and enjoy a comfortable riding experience.

[0102] The electric wheelchair system may further include a lighting adjustment unit that estimates the user's emotions and adjusts the lighting based on the estimated emotions. For example, the lighting adjustment unit may provide soft lighting when the user is relaxed and calm lighting when the user is tense. The lighting adjustment unit may also dynamically adjust the color and brightness of the lighting in response to changes in the user's emotions. Furthermore, the lighting adjustment unit may provide customized lighting settings based on the user's preferences. This allows the lighting to be tailored to the user's emotions, resulting in a comfortable riding experience.

[0103] The electric wheelchair system may further include an emergency notification unit that notifies emergency contacts based on the user's location information. For example, the emergency notification unit may notify emergency contacts if the user stays in a specific area for a long period of time or if abnormal movement is detected. The emergency notification unit may also track the user's location information in real time, enabling a prompt response in the event of an emergency. Furthermore, the emergency notification unit may monitor the user's health condition and notify a medical institution if an abnormality is detected. This ensures the user's safety and allows for a prompt response.

[0104] The electric wheelchair system may further include a seat adjustment unit that estimates the user's emotions and adjusts the comfort of the seat based on the estimated emotions. For example, if the user is relaxed, the seat adjustment unit can adjust the seat angle and cushion hardness to provide a comfortable sitting experience. If the user is tense, the seat adjustment unit can adjust the seat angle to enhance the relaxation effect. Furthermore, the seat adjustment unit can dynamically adjust the seat settings in response to changes in the user's emotions. This allows the seat to be adjusted to suit the user's emotions, resulting in a more comfortable riding experience.

[0105] The processing flow of the second embodiment will be briefly explained below.

[0106] Step 1: The sensor unit detects road surface conditions. Road surface conditions include, for example, wet roads, frozen roads, and gravel roads. The sensor unit can detect road surface irregularities and visual road conditions using ultrasonic sensors and cameras. It can also optimize sensor operation by taking weather information into account. For example, in rainy weather, the sensor's sensitivity can be increased to make it easier to detect slippery road surfaces. Step 2: The dialogue unit engages in dialogue with the user based on the road surface conditions detected by the sensor unit. The dialogue can be performed using voice dialogue, text dialogue, gesture dialogue, or other methods. The dialogue unit uses voice recognition technology to recognize the user's voice and engage in dialogue. It can also periodically send messages to provide the user with a sense of security. For example, it can send a message such as, "Your current speed is safe. Please drive safely." Step 3: The control unit adjusts the driving based on the user's response obtained by the dialogue unit. Driving adjustments include speed adjustment, direction changes, braking, etc. The control unit can achieve safe driving by adjusting the speed and avoid obstacles by changing direction. It can also optimize the speed and direction by taking into account the friction coefficient of the road surface. For example, if the road surface is slippery, the speed will be reduced to ensure safe driving.

[0107] 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.

[0108] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (registered trademark) (Internet search engine).<URL: https: / / openai.com / blog / chatgpt> Examples of generative AIs include the data generation model 58, such as a neural network model (e.g., a neural network model), and a neural network model (e.g., a neural network model). The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating speech, text data indicating text, and image data indicating an image is also input to the data generation model 58. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specification processing unit 290 performs the above-mentioned specification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0109] 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.

[0110] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0111] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0112] 3, the data processing system 210 includes a data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0113] 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.

[0114] 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.

[0115] 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.

[0116] 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).

[0117] 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.

[0118] 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.

[0119] 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.

[0120] 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.

[0121] 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.

[0122] 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.

[0123] 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.

[0124] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0125] 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.

[0126] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0127] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0128] 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.

[0129] 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.

[0130] 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.

[0131] 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.

[0132] 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).

[0133] 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.

[0134] 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.

[0135] 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.

[0136] 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.

[0137] 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.

[0138] 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.

[0139] 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.

[0140] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0141] 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.

[0142] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0143] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0144] 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.

[0145] 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.

[0146] 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.

[0147] 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.

[0148] 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).

[0149] 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.

[0150] 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.

[0151] 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.

[0152] 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.

[0153] 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.

[0154] 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.

[0155] 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.

[0156] 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.

[0157] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0158] 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.

[0159] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0160] 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.

[0161] 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.

[0162] 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.

[0163] 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).

[0164] 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.

[0165] 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."

[0166] 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.

[0167] 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.

[0168] 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.

[0169] 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.

[0170] 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.

[0171] 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.

[0172] 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.

[0173] 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.

[0174] 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.

[0175] 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.

[0176] 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.

[0177] 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.

[0178] [Explanation of symbols]

[0179] 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 sensor unit that detects road surface conditions; a dialogue unit that dialogues with a user based on the road surface condition detected by the sensor unit; and a control unit that adjusts the driving based on the user's response obtained by the dialogue unit. A system characterized by:

2. The sensor unit Equipped with an ultrasonic sensor 2. The system of claim 1.

3. The sensor unit Equipped with a camera 2. The system of claim 1.

4. The dialogue unit Using voice recognition technology 2. The system of claim 1.

5. The control unit Adjust the speed 2. The system of claim 1.

6. The control unit Change direction 2. The system of claim 1.

7. The dialogue unit Send regular reassurance messages to users 2. The system of claim 1.

8. The sensor unit The user's emotion is estimated, and the sensitivity of the sensor is adjusted based on the estimated user's emotion.

2. The system of claim 1.

9. The sensor unit When detecting road surface conditions, adjust sensor operation based on weather information 2. The system of claim 1.

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