system

Abstract

The system according to the embodiment aims to automatically drive a vehicle and adjust the in-vehicle environment based on voice instructions and the state of the occupants. [Solution] A system according to an embodiment includes a reception unit, an analysis unit, a driving unit, a monitoring unit, and an adjustment unit. The reception unit receives voice instructions. The analysis unit analyzes the voice instructions received by the reception unit. The driving unit automatically drives the vehicle based on the instructions analyzed by the analysis unit. The monitoring unit monitors the state of the occupants using an in-vehicle camera. The adjustment unit adjusts the in-vehicle environment based on the state of the occupants monitored by the monitoring unit.

Description

[Technical Field]

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

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

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

[0004] Conventional technologies do not adequately adjust the in-car environment based on voice instructions or the state of the occupants when driving autonomously, and there is room for improvement.

[0005] The system according to the embodiment aims to automatically drive a vehicle and adjust the in-vehicle environment based on voice instructions and the state of the occupants. [Means for solving the problem]

[0006] The system according to the embodiment includes a reception unit, an analysis unit, a driving unit, a monitoring unit, and an adjustment unit. The reception unit receives voice instructions. The analysis unit analyzes the voice instructions received by the reception unit. The driving unit automatically drives the vehicle based on the instructions analyzed by the analysis unit. The monitoring unit monitors the state of the occupants using an in-vehicle camera. The adjustment unit adjusts the in-vehicle environment based on the state of the occupants monitored by the monitoring unit. [Effects of the Invention]

[0007] The system according to the embodiment can automatically drive the vehicle and adjust the in-vehicle environment based on voice instructions and the state of the occupants. [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 nonvolatile storage devices that store various programs, various parameters, etc. Examples of nonvolatile 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 pointer. The microphone 38B detects the user's voice to receive user input by voice. The control unit 46A transmits data indicating the user input received by the touch panel 38A and the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 (see FIG. 2) acquires the data indicating the user input.

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

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

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

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

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

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

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

[0028] (Example 1) An autonomous driving system according to an embodiment of the present invention utilizes voice recognition technology to issue instructions. This autonomous driving system allows occupants to issue voice instructions, and the vehicle automatically drives to the destination based on those instructions. The system also works in conjunction with an in-vehicle camera to analyze the occupant's condition and automatically respond by lowering the music volume or adjusting the lighting. For example, when an occupant issues a specific instruction such as "Go to a nearby restaurant" or "Go to a drugstore," the voice instruction is analyzed by the in-vehicle voice recognition system. Based on the analyzed instruction, the vehicle automatically drives to the destination. Furthermore, the in-vehicle camera monitors the occupant's facial expression and, if the occupant appears tired or sleepy, automatically responds by lowering the music volume or adjusting the lighting. This improves the comfort and safety of the occupants. For example, if the occupant appears tired, the in-vehicle camera detects this and lowers the music volume to encourage relaxation. Furthermore, if the occupant appears sleepy at night, the lighting is adjusted to wake the occupant. In this way, appropriate responses are taken according to the occupant's condition. This mechanism makes autonomous driving more comfortable and safe. Passengers can easily give instructions by voice, and the in-car camera monitors the passengers' condition and responds appropriately, allowing them to enjoy autonomous driving with peace of mind. This allows the autonomous driving system to receive and analyze passengers' voice instructions, drive autonomously, monitor the passengers' condition, and adjust the in-car environment to improve passenger comfort and safety.

[0029] An autonomous driving system according to an embodiment includes a reception unit, an analysis unit, a driving unit, a monitoring unit, and an adjustment unit. The reception unit receives voice instructions from a passenger. The voice instructions from the passenger include, but are not limited to, specific instructions such as "go to a nearby restaurant" or "go to a drugstore." The reception unit converts the voice instructions into text data using, for example, voice recognition technology and understands the content of the text data. The analysis unit analyzes the voice instructions received by the reception unit. The analysis unit analyzes the content of the voice instructions using, for example, natural language processing technology and identifies a destination. The driving unit automatically drives the vehicle based on the instructions analyzed by the analysis unit. The driving unit calculates an optimal route using, for example, a vehicle navigation system and performs automatic driving. The monitoring unit monitors the passenger's condition using an in-vehicle camera. The monitoring unit monitors the passenger's facial expression and facial expression to detect whether the passenger is tired or sleepy. The adjustment unit adjusts the in-vehicle environment based on the passenger's condition monitored by the monitoring unit. The adjustment unit automatically takes measures such as lowering the volume of music, adjusting lighting, etc. As a result, the autonomous driving system according to the embodiment can improve the comfort and safety of the occupants by receiving and analyzing voice instructions from the occupants, performing autonomous driving, monitoring the state of the occupants, and adjusting the in-vehicle environment.

[0030] The reception unit can analyze the occupant's past voice instruction history and select an appropriate reception method. For example, the reception unit can prioritize and quickly recognize voice instructions frequently used by the occupant in the past. The reception unit can also learn patterns of voice instructions used by the occupant in the past and efficiently recognize similar instructions. Furthermore, the reception unit can suggest the optimal reception method for a specific time period or situation based on the occupant's past voice instruction history. This makes it possible to select the optimal reception method and improve the accuracy of receiving voice instructions by analyzing the past voice instruction history. Some or all of the above-described processing in the reception unit may be performed using, for example, AI, or may be performed without using AI. For example, the reception unit can input past voice instruction history data into a generation AI and have the generation AI select the optimal reception method.

[0031] When receiving a voice instruction, the reception unit can perform filtering based on the current situation of the occupant and environmental sounds. For example, when the occupant is talking in the vehicle, the reception unit filters environmental sounds and recognizes only the voice instruction. In addition, when the occupant is listening to music, the reception unit can temporarily lower the volume of the music and accept the voice instruction. Furthermore, when the occupant has the window open, the reception unit can filter external noise and clearly recognize the voice instruction. In this way, by performing filtering based on the current situation of the occupant and environmental sounds, the recognition accuracy of the voice instruction can be improved. Some or all of the above-mentioned processing in the reception unit may be performed using, for example, AI, or may be performed without using AI. For example, the reception unit can input environmental sound data to a generation AI and have the generation AI perform filtering.

[0032] When receiving a voice instruction, the reception unit can prioritize receiving highly relevant instructions by taking into account the occupant's geographical location information. For example, when the occupant is in a specific area, the reception unit can prioritize receiving voice instructions related to the area. Furthermore, when the occupant is approaching a destination, the reception unit can also prioritize receiving voice instructions related to the destination. Furthermore, when the occupant is traveling on a specific route, the reception unit can also prioritize receiving voice instructions related to the route. In this way, by taking the geographical location information into account, highly relevant voice instructions can be prioritized. Some or all of the above-described processing in the reception unit may be performed using AI, for example, or may be performed without using AI. For example, the reception unit can input geographical location information data to a generation AI and cause the generation AI to select highly relevant instructions.

[0033] When receiving a voice instruction, the reception unit can analyze the occupant's social media activity and receive related instructions. For example, if the occupant mentions a specific location on social media, the reception unit can prioritize receiving voice instructions related to the location. Furthermore, if the occupant plans to attend a specific event on social media, the reception unit can prioritize receiving voice instructions related to the event. Furthermore, if the occupant indicates a specific interest on social media, the reception unit can prioritize receiving voice instructions related to the interest. Thus, by analyzing social media activity, related voice instructions can be prioritized. Some or all of the above-described processing in the reception unit may be performed using, for example, AI, or may be performed without using AI. For example, the reception unit can input social media activity data into a generation AI and cause the generation AI to select related instructions.

[0034] When analyzing a voice instruction, the analysis unit can adjust the level of detail of the analysis based on the importance of the instruction. For example, in the case of an instruction with high urgency, the analysis unit performs a detailed analysis and responds quickly. The analysis unit can also perform a normal analysis in the case of an ordinary instruction. Furthermore, in the case of an instruction with low importance, the analysis unit can perform a simplified analysis to save resources. In this way, by adjusting the level of detail of the analysis based on the importance of the instruction, resources can be used efficiently. Some or all of the above-mentioned processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input instruction importance data to the generation AI and cause the generation AI to adjust the level of detail of the analysis.

[0035] When analyzing a voice instruction, the analysis unit can apply different analysis algorithms depending on the category of the instruction. For example, in the case of an instruction related to navigation, the analysis unit can apply an analysis algorithm based on geographic information. In addition, in the case of an instruction related to entertainment, the analysis unit can also apply an analysis algorithm based on media information. Furthermore, in the case of an instruction related to vehicle operation, the analysis unit can also apply an analysis algorithm based on vehicle control information. In this way, by applying different analysis algorithms depending on the category of the instruction, the analysis accuracy can be improved. Some or all of the above-mentioned processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input instruction category data to the generation AI and cause the generation AI to apply the analysis algorithm.

[0036] When analyzing a voice instruction, the analysis unit can determine the priority of the analysis based on the time when the instruction was submitted. For example, the analysis unit prioritizes analysis for highly urgent instructions. The analysis unit can also analyze normal instructions with normal priority. Furthermore, the analysis unit can postpone analysis for instructions with low importance. In this way, by determining the priority of the analysis based on the time when the instruction was submitted, it is possible to quickly analyze highly urgent instructions. Some or all of the above-mentioned processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input data on the time when the instruction was submitted to the generation AI and have the generation AI determine the priority of the analysis.

[0037] When analyzing voice instructions, the analysis unit can adjust the order of analysis based on the relevance of the instructions. For example, the analysis unit prioritizes analysis of instructions related to the occupant's current situation. The analysis unit can also prioritize analysis of instructions related to the occupant's past instruction history. Furthermore, the analysis unit can also prioritize analysis of instructions related to the occupant's current location information. In this way, by adjusting the order of analysis based on the relevance of the instructions, highly relevant instructions can be analyzed preferentially. Some or all of the above-mentioned processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input instruction relevance data to the generation AI and cause the generation AI to adjust the order of analysis.

[0038] During autonomous driving, the driving unit can select an appropriate driving method by referring to the occupant's past driving history. For example, the driving unit selects the optimal driving method based on the occupant's preferred driving style in the past. The driving unit can also select a safe driving method from the occupant's past driving history. Furthermore, the driving unit can analyze the occupant's past driving history and select the most comfortable driving method. In this way, by referring to the past driving history, the optimal driving method can be selected, improving the comfort and safety of the occupant. Some or all of the above-mentioned processing in the driving unit may be performed using, for example, AI, or may be performed without using AI. For example, the driving unit can input past driving history data to a generation AI and have the generation AI select an optimal driving method.

[0039] During autonomous driving, the driving unit can customize driving parameters based on the occupant's current physical condition and situation. For example, if the occupant is tired, the driving unit can set gentle driving parameters. Also, if the occupant is relaxed, the driving unit can set normal driving parameters. Furthermore, if the occupant is excited, the driving unit can set cautious driving parameters. In this way, by customizing driving parameters based on the occupant's physical condition and situation, it is possible to improve the comfort and safety of the occupant. Some or all of the above-mentioned processing in the driving unit may be performed using, for example, AI, or may be performed without using AI. For example, the driving unit can input the occupant's physical condition data into the generation AI and have the generation AI customize the driving parameters.

[0040] During autonomous driving, the driving unit can select an appropriate route by taking into account the geographical location information of the occupant. For example, when the occupant is in a specific area, the driving unit selects the optimal route related to that area. Furthermore, when the occupant is approaching a destination, the driving unit can also select the shortest route. Furthermore, when the occupant is traveling on a specific route, the driving unit can also select the optimal route related to that route. In this way, by taking the geographical location information into consideration, the optimal route can be selected, thereby improving the comfort and safety of the occupant. Some or all of the above-mentioned processing in the driving unit may be performed, for example, using AI or without AI. For example, the driving unit can input geographical location information data to a generation AI and cause the generation AI to select an optimal route.

[0041] During autonomous driving, the driving unit can analyze the social media activity of the occupant and suggest driving methods. For example, if the occupant mentions a specific location on social media, the driving unit can suggest driving methods related to that location. Also, if the occupant plans to attend a specific event on social media, the driving unit can suggest driving methods related to that event. Furthermore, if the occupant indicates a specific interest on social media, the driving unit can suggest driving methods related to that interest. In this way, related driving methods can be suggested by analyzing social media activity. Some or all of the above-mentioned processing in the driving unit may be performed using, for example, AI, or may be performed without using AI. For example, the driving unit can input social media activity data to a generation AI and have the generation AI suggest driving methods.

[0042] During monitoring, the monitoring unit can select an appropriate monitoring method by referring to the occupant's past condition data. For example, if the occupant has been tired in the past, the monitoring unit can increase the frequency of monitoring by referring to that condition. Also, if the occupant has been relaxed in the past, the monitoring unit can maintain the normal monitoring method by referring to that condition. Furthermore, if the occupant has been excited in the past, the monitoring unit can adjust the accuracy of monitoring by referring to that condition. In this way, by referring to the past condition data, the optimal monitoring method can be selected and the monitoring accuracy can be improved. Some or all of the above-mentioned processing in the monitoring unit may be performed using, for example, AI, or may be performed without using AI. For example, the monitoring unit can input past condition data into the generation AI and have the generation AI select the optimal monitoring method.

[0043] During monitoring, the monitoring unit can customize the monitoring parameters based on the occupant's current physical condition and situation. For example, if the occupant is tired, the monitoring unit increases the monitoring frequency to detect small changes. The monitoring unit can also maintain normal monitoring parameters if the occupant is relaxed. Furthermore, if the occupant is excited, the monitoring unit can reduce the monitoring accuracy to prevent false detection. This makes it possible to improve monitoring accuracy by customizing the monitoring parameters based on the occupant's physical condition and situation. Some or all of the above-mentioned processing in the monitoring unit may be performed using AI, for example, or may be performed without using AI. For example, the monitoring unit can input the occupant's physical condition data into the generation AI and cause the generation AI to customize the monitoring parameters.

[0044] During monitoring, the monitoring unit can select an appropriate monitoring method by taking into account the geographical location information of the occupant. For example, when the occupant is in a specific area, the monitoring unit can prioritize monitoring of monitoring items related to that area. Furthermore, when the occupant is approaching a destination, the monitoring unit can also prioritize monitoring of monitoring items related to the destination. Furthermore, when the occupant is traveling on a specific route, the monitoring unit can also prioritize monitoring of monitoring items related to that route. In this way, by taking the geographical location information into consideration, the optimal monitoring method can be selected and monitoring accuracy can be improved. Some or all of the above-described processing in the monitoring unit may be performed using, for example, AI, or may be performed without using AI. For example, the monitoring unit can input geographical location information data to a generation AI and cause the generation AI to select an optimal monitoring method.

[0045] During monitoring, the monitoring unit can analyze the occupant's social media activity and suggest monitoring measures. For example, if the occupant mentions a specific location on social media, the monitoring unit can suggest monitoring measures related to that location. Also, if the occupant plans to attend a specific event on social media, the monitoring unit can suggest monitoring measures related to that event. Furthermore, if the occupant indicates a specific interest on social media, the monitoring unit can suggest monitoring measures related to that interest. In this way, relevant monitoring measures can be suggested by analyzing social media activity. Some or all of the above-mentioned processing in the monitoring unit may be performed using, for example, AI, or may be performed without using AI. For example, the monitoring unit can input social media activity data to a generation AI and have the generation AI execute the suggested monitoring measures.

[0046] When adjusting the in-vehicle environment, the adjustment unit can select an appropriate adjustment method by referring to the occupant's past environment setting history. For example, the adjustment unit can set an optimal volume based on the occupant's past preferred music volume. The adjustment unit can also set optimal lighting settings based on the occupant's past lighting setting history. Furthermore, the adjustment unit can analyze the occupant's past environment setting history to provide the most comfortable in-vehicle environment. As a result, by referring to the past environment setting history, an optimal adjustment method can be selected and occupant comfort can be improved. Some or all of the above-mentioned processing in the adjustment unit may be performed using, for example, AI, or may be performed without using AI. For example, the adjustment unit can input past environment setting history data to a generation AI and cause the generation AI to select an optimal adjustment method.

[0047] When adjusting the in-vehicle environment, the adjustment unit can customize the adjustment parameters based on the occupant's current physical condition and situation. For example, if the occupant is tired, the adjustment unit can lower the music volume and set the lights to a more subdued setting. The adjustment unit can also maintain a normal in-vehicle environment if the occupant is relaxed. Furthermore, if the occupant is excited, the adjustment unit can lower the music volume and adjust the lights to calm the occupant. This allows the comfort of the occupant to be improved by customizing the adjustment parameters based on the occupant's physical condition and situation. Some or all of the above-described processing in the adjustment unit may be performed using, for example, AI, or may be performed without using AI. For example, the adjustment unit can input the occupant's physical condition data into the generation AI and cause the generation AI to customize the adjustment parameters.

[0048] When adjusting the in-vehicle environment, the adjustment unit can select an appropriate adjustment method by taking into account the geographical location information of the occupant. For example, when the occupant is in a specific area, the adjustment unit can provide an optimal in-vehicle environment related to the area. Furthermore, when the occupant is approaching a destination, the adjustment unit can also provide an optimal in-vehicle environment related to the destination. Furthermore, when the occupant is traveling a specific route, the adjustment unit can also provide an optimal in-vehicle environment related to the route. In this way, by taking the geographical location information into consideration, the optimal adjustment method can be selected and the comfort of the occupant can be improved. Some or all of the above-mentioned processing in the adjustment unit may be performed using, for example, AI, or may be performed without using AI. For example, the adjustment unit can input geographical location information data to the generation AI and cause the generation AI to select the optimal adjustment method.

[0049] When adjusting the in-vehicle environment, the adjustment unit can analyze the occupant's social media activity and suggest adjustment measures. For example, if the occupant mentions specific music on social media, the adjustment unit can play that music. Furthermore, if the occupant plans to attend a specific event on social media, the adjustment unit can provide an in-vehicle environment related to that event. Furthermore, if the occupant indicates specific interests on social media, the adjustment unit can provide an in-vehicle environment related to those interests. Thus, by analyzing social media activity, relevant adjustment measures can be suggested. Some or all of the above-described processing in the adjustment unit may be performed using, for example, AI, or may be performed without using AI. For example, the adjustment unit can input social media activity data to a generation AI and have the generation AI suggest adjustment measures.

[0050] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0051] The reception unit not only accepts voice instructions from the occupant, but can also monitor the occupant's health condition and adjust the sensitivity of voice instructions based on the occupant's health condition. For example, if the occupant has a cold, the reception unit can increase the sensitivity of voice instructions so that instructions can be recognized even at low voice volumes. Alternatively, if the occupant is healthy, the reception unit can maintain normal sensitivity of voice instructions so that instructions can be received in a natural conversational tone. Furthermore, if the occupant is tired, the reception sensitivity of voice instructions can be adjusted so that only clear instructions are accepted to prevent misrecognition. This allows for more appropriate reception of voice instructions by adjusting the sensitivity of voice instructions according to the occupant's health condition.

[0052] The reception unit can analyze the occupant's past voice instruction history and select an appropriate reception method. For example, it can prioritize and quickly recognize voice instructions that the occupant has used frequently in the past. The reception unit can also learn the patterns of voice instructions that the occupant has used in the past and efficiently recognize similar instructions. Furthermore, the reception unit can suggest the optimal reception method for a specific time period or situation based on the occupant's past voice instruction history. This makes it possible to select the optimal reception method by analyzing the past voice instruction history and improve the accuracy of receiving voice instructions.

[0053] When receiving a voice instruction, the reception unit can perform filtering based on the current situation of the occupant and environmental sounds. For example, if the occupant is talking in the vehicle, the reception unit can filter environmental sounds and recognize only the voice instruction. Also, if the occupant is listening to music, the volume of the music can be temporarily lowered to receive the voice instruction. Furthermore, if the occupant has the window open, the reception unit can filter external noise and clearly recognize the voice instruction. Thus, by performing filtering based on the current situation of the occupant and environmental sounds, the recognition accuracy of the voice instruction can be improved.

[0054] When receiving a voice instruction, the reception unit can prioritize receiving highly relevant instructions by taking into account the occupant's geographical location information. For example, if the occupant is in a specific area, the reception unit can prioritize receiving voice instructions related to that area. In addition, if the occupant is approaching a destination, the reception unit can also prioritize receiving voice instructions related to the destination. Furthermore, if the occupant is traveling on a specific route, the reception unit can also prioritize receiving voice instructions related to that route. In this way, by taking into account the geographical location information, highly relevant voice instructions can be prioritized.

[0055] When receiving a voice instruction, the reception unit can analyze the occupant's social media activity and receive related instructions. For example, if the occupant mentions a specific location on social media, the reception unit can preferentially receive voice instructions related to that location. Also, if the occupant plans to attend a specific event on social media, the reception unit can preferentially receive voice instructions related to that event. Furthermore, if the occupant indicates a specific interest on social media, the reception unit can preferentially receive voice instructions related to that interest. In this way, by analyzing social media activity, it is possible to preferentially receive related voice instructions.

[0056] When analyzing a voice instruction, the analysis unit can adjust the level of detail of the analysis based on the importance of the instruction. For example, in the case of an instruction with high urgency, a detailed analysis is performed to respond quickly. The analysis unit can also perform a normal analysis in the case of an ordinary instruction. Furthermore, in the case of an instruction with low importance, the analysis unit can perform a simplified analysis to save resources. In this way, adjusting the level of detail of the analysis based on the importance of the instruction allows for efficient use of resources.

[0057] When analyzing a voice instruction, the analysis unit can apply different analysis algorithms depending on the category of the instruction. For example, in the case of an instruction related to navigation, the analysis unit can apply an analysis algorithm based on geographic information. In addition, in the case of an instruction related to entertainment, the analysis unit can also apply an analysis algorithm based on media information. Furthermore, in the case of an instruction related to vehicle operation, the analysis unit can also apply an analysis algorithm based on vehicle control information. In this way, by applying different analysis algorithms depending on the category of the instruction, the analysis accuracy can be improved.

[0058] The processing flow of the first embodiment will be briefly explained below.

[0059] Step 1: The reception unit receives a voice instruction from the occupant. The voice instruction from the occupant includes, but is not limited to, specific instructions such as "Go to a nearby restaurant" or "Go to a drugstore." The reception unit converts the voice instruction into text data using voice recognition technology and understands the content of the text data. Step 2: The analysis unit analyzes the voice instruction received by the reception unit. The analysis unit analyzes the content of the voice instruction using natural language processing technology and identifies the destination. Step 3: The driving unit automatically drives the vehicle based on the instructions analyzed by the analysis unit. The driving unit calculates the optimal route using the vehicle's navigation system and performs automatic driving. Step 4: The monitoring unit monitors the state of the occupants using the in-car camera. The monitoring unit monitors the occupants' complexions and expressions to detect if they appear tired or sleepy. Step 5: The adjustment unit adjusts the in-vehicle environment based on the occupant status monitored by the monitoring unit. The adjustment unit automatically takes measures such as lowering the music volume or adjusting the lighting.

[0060] (Example 2) An autonomous driving system according to an embodiment of the present invention utilizes voice recognition technology to issue instructions. This autonomous driving system allows occupants to issue voice instructions, and the vehicle automatically drives to the destination based on those instructions. The system also works in conjunction with an in-vehicle camera to analyze the occupant's condition and automatically respond by lowering the music volume or adjusting the lighting. For example, when an occupant issues a specific instruction such as "Go to a nearby restaurant" or "Go to a drugstore," the voice instruction is analyzed by the in-vehicle voice recognition system. Based on the analyzed instruction, the vehicle automatically drives to the destination. Furthermore, the in-vehicle camera monitors the occupant's facial expression and, if the occupant appears tired or sleepy, automatically responds by lowering the music volume or adjusting the lighting. This improves the comfort and safety of the occupants. For example, if the occupant appears tired, the in-vehicle camera detects this and lowers the music volume to encourage relaxation. Furthermore, if the occupant appears sleepy at night, the lighting is adjusted to wake the occupant. In this way, appropriate responses are taken according to the occupant's condition. This mechanism makes autonomous driving more comfortable and safe. Passengers can easily give instructions by voice, and the in-car camera monitors the passengers' condition and responds appropriately, allowing them to enjoy autonomous driving with peace of mind. This allows the autonomous driving system to receive and analyze passengers' voice instructions, drive autonomously, monitor the passengers' condition, and adjust the in-car environment to improve passenger comfort and safety.

[0061] An autonomous driving system according to an embodiment includes a reception unit, an analysis unit, a driving unit, a monitoring unit, and an adjustment unit. The reception unit receives voice instructions from a passenger. The voice instructions from the passenger include, but are not limited to, specific instructions such as "go to a nearby restaurant" or "go to a drugstore." The reception unit converts the voice instructions into text data using, for example, voice recognition technology and understands the content of the text data. The analysis unit analyzes the voice instructions received by the reception unit. The analysis unit analyzes the content of the voice instructions using, for example, natural language processing technology and identifies a destination. The driving unit automatically drives the vehicle based on the instructions analyzed by the analysis unit. The driving unit calculates an optimal route using, for example, a vehicle navigation system and performs automatic driving. The monitoring unit monitors the passenger's condition using an in-vehicle camera. The monitoring unit monitors the passenger's facial expression and facial expression to detect whether the passenger is tired or sleepy. The adjustment unit adjusts the in-vehicle environment based on the passenger's condition monitored by the monitoring unit. The adjustment unit automatically takes measures such as lowering the volume of music, adjusting lighting, etc. As a result, the autonomous driving system according to the embodiment can improve the comfort and safety of the occupants by receiving and analyzing voice instructions from the occupants, performing autonomous driving, monitoring the state of the occupants, and adjusting the in-vehicle environment.

[0062] The reception unit can estimate the occupant's emotions and adjust the sensitivity to voice instructions based on the estimated emotions. For example, if the occupant is stressed, the reception unit can increase the sensitivity to voice instructions and recognize instructions even at a low voice volume. Furthermore, if the occupant is relaxed, the reception unit can maintain the normal sensitivity to voice instructions and accept instructions in a natural conversational tone. Furthermore, if the occupant is excited, the reception unit can decrease the sensitivity to voice instructions and accept only clear instructions to prevent misrecognition. This allows for more appropriate reception of voice instructions by adjusting the sensitivity to voice instructions according to the occupant's emotions. The emotion estimation is realized using an emotion estimation function, such as an emotion engine or a generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the reception unit can be performed using, for example, an AI. For example, the reception unit can input the occupant's emotion data into the generation AI and have the generation AI execute emotion estimation.

[0063] The reception unit can analyze the occupant's past voice instruction history and select an appropriate reception method. For example, the reception unit can prioritize and quickly recognize voice instructions frequently used by the occupant in the past. The reception unit can also learn patterns of voice instructions used by the occupant in the past and efficiently recognize similar instructions. Furthermore, the reception unit can suggest the optimal reception method for a specific time period or situation based on the occupant's past voice instruction history. This makes it possible to select the optimal reception method and improve the accuracy of receiving voice instructions by analyzing the past voice instruction history. Some or all of the above-described processing in the reception unit may be performed using, for example, AI, or may be performed without using AI. For example, the reception unit can input past voice instruction history data into a generation AI and have the generation AI select the optimal reception method.

[0064] When receiving a voice instruction, the reception unit can perform filtering based on the current situation of the occupant and environmental sounds. For example, when the occupant is talking in the vehicle, the reception unit filters environmental sounds and recognizes only the voice instruction. In addition, when the occupant is listening to music, the reception unit can temporarily lower the volume of the music and accept the voice instruction. Furthermore, when the occupant has the window open, the reception unit can filter external noise and clearly recognize the voice instruction. In this way, by performing filtering based on the current situation of the occupant and environmental sounds, the recognition accuracy of the voice instruction can be improved. Some or all of the above-mentioned processing in the reception unit may be performed using, for example, AI, or may be performed without using AI. For example, the reception unit can input environmental sound data to a generation AI and have the generation AI perform filtering.

[0065] The reception unit can estimate the occupant's emotions and determine the priority of received voice instructions based on the estimated emotions. For example, if the occupant is nervous, the reception unit can prioritize receiving urgent voice instructions. Furthermore, if the occupant is relaxed, the reception unit can prioritize receiving normal voice instructions. Furthermore, if the occupant is excited, the reception unit can prioritize receiving important voice instructions. In this way, by determining the priority of voice instructions based on the occupant's emotions, it is possible to prioritize receiving urgent instructions. The emotion estimation is realized 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 reception unit may be performed using an AI, for example, or without an AI. For example, the reception unit can input the occupant's emotion data into the generation AI and cause the generation AI to determine the priority of the voice instructions.

[0066] When receiving a voice instruction, the reception unit can prioritize receiving highly relevant instructions by taking into account the occupant's geographical location information. For example, when the occupant is in a specific area, the reception unit can prioritize receiving voice instructions related to the area. Furthermore, when the occupant is approaching a destination, the reception unit can also prioritize receiving voice instructions related to the destination. Furthermore, when the occupant is traveling on a specific route, the reception unit can also prioritize receiving voice instructions related to the route. In this way, by taking the geographical location information into account, highly relevant voice instructions can be prioritized. Some or all of the above-described processing in the reception unit may be performed using AI, for example, or may be performed without using AI. For example, the reception unit can input geographical location information data to a generation AI and cause the generation AI to select highly relevant instructions.

[0067] When receiving a voice instruction, the reception unit can analyze the occupant's social media activity and receive related instructions. For example, if the occupant mentions a specific location on social media, the reception unit can prioritize receiving voice instructions related to the location. Furthermore, if the occupant plans to attend a specific event on social media, the reception unit can prioritize receiving voice instructions related to the event. Furthermore, if the occupant indicates a specific interest on social media, the reception unit can prioritize receiving voice instructions related to the interest. Thus, by analyzing social media activity, related voice instructions can be prioritized. Some or all of the above-described processing in the reception unit may be performed using, for example, AI, or may be performed without using AI. For example, the reception unit can input social media activity data into a generation AI and cause the generation AI to select related instructions.

[0068] The analysis unit can estimate the occupant's emotions and adjust the accuracy of the analysis based on the estimated emotions. For example, if the occupant is nervous, the analysis unit increases the analysis accuracy to prevent misrecognition. The analysis unit can also maintain normal analysis accuracy when the occupant is relaxed. Furthermore, if the occupant is excited, the analysis unit can decrease the analysis accuracy to prevent misrecognition. This makes it possible to prevent misrecognition by adjusting the analysis accuracy based on the occupant'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-mentioned processing in the analysis unit may be performed using an AI, for example, or without an AI. For example, the analysis unit can input the occupant's emotion data into the generation AI and have the generation AI adjust the analysis accuracy.

[0069] When analyzing a voice instruction, the analysis unit can adjust the level of detail of the analysis based on the importance of the instruction. For example, in the case of an instruction with high urgency, the analysis unit performs a detailed analysis and responds quickly. The analysis unit can also perform a normal analysis in the case of an ordinary instruction. Furthermore, in the case of an instruction with low importance, the analysis unit can perform a simplified analysis to save resources. In this way, by adjusting the level of detail of the analysis based on the importance of the instruction, resources can be used efficiently. Some or all of the above-mentioned processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input instruction importance data to the generation AI and cause the generation AI to adjust the level of detail of the analysis.

[0070] When analyzing a voice instruction, the analysis unit can apply different analysis algorithms depending on the category of the instruction. For example, in the case of an instruction related to navigation, the analysis unit can apply an analysis algorithm based on geographic information. In addition, in the case of an instruction related to entertainment, the analysis unit can also apply an analysis algorithm based on media information. Furthermore, in the case of an instruction related to vehicle operation, the analysis unit can also apply an analysis algorithm based on vehicle control information. In this way, by applying different analysis algorithms depending on the category of the instruction, the analysis accuracy can be improved. Some or all of the above-mentioned processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input instruction category data to the generation AI and cause the generation AI to apply the analysis algorithm.

[0071] The analysis unit can estimate the occupant's emotions and adjust the analysis speed based on the estimated emotions. For example, if the occupant is in a hurry, the analysis unit can increase the analysis speed and respond quickly. The analysis unit can also maintain a normal analysis speed if the occupant is relaxed. Furthermore, if the occupant is excited, the analysis unit can also slow down the analysis speed to prevent erroneous recognition. This enables rapid and accurate analysis by adjusting the analysis speed based on the occupant's emotions. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above-described processing in the analysis unit can be performed using, for example, an AI. For example, the analysis unit can input the occupant's emotion data into the generation AI and have the generation AI adjust the analysis speed.

[0072] When analyzing a voice instruction, the analysis unit can determine the priority of the analysis based on the time when the instruction was submitted. For example, the analysis unit prioritizes analysis for highly urgent instructions. The analysis unit can also analyze normal instructions with normal priority. Furthermore, the analysis unit can postpone analysis for instructions with low importance. In this way, by determining the priority of the analysis based on the time when the instruction was submitted, it is possible to quickly analyze highly urgent instructions. Some or all of the above-mentioned processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input data on the time when the instruction was submitted to the generation AI and have the generation AI determine the priority of the analysis.

[0073] When analyzing voice instructions, the analysis unit can adjust the order of analysis based on the relevance of the instructions. For example, the analysis unit prioritizes analysis of instructions related to the occupant's current situation. The analysis unit can also prioritize analysis of instructions related to the occupant's past instruction history. Furthermore, the analysis unit can also prioritize analysis of instructions related to the occupant's current location information. In this way, by adjusting the order of analysis based on the relevance of the instructions, highly relevant instructions can be analyzed preferentially. Some or all of the above-mentioned processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input instruction relevance data to the generation AI and cause the generation AI to adjust the order of analysis.

[0074] The driving unit can estimate the emotions of the occupants and adjust the driving style based on the estimated emotions. For example, if the occupants are nervous, the driving unit can adopt a gentle driving style. Furthermore, if the occupants are relaxed, the driving unit can maintain a normal driving style. Furthermore, if the occupants are excited, the driving unit can adopt a cautious driving style. Thus, by adjusting the driving style based on the emotions of the occupants, the comfort and safety of the occupants can be improved. The emotion estimation is realized using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be, for example, 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 driving unit can be performed using, for example, an AI, or can be performed without using an AI. For example, the driving unit can input the emotion data of the occupants into the generation AI and have the generation AI adjust the driving style.

[0075] During autonomous driving, the driving unit can select an appropriate driving method by referring to the occupant's past driving history. For example, the driving unit selects the optimal driving method based on the occupant's preferred driving style in the past. The driving unit can also select a safe driving method from the occupant's past driving history. Furthermore, the driving unit can analyze the occupant's past driving history and select the most comfortable driving method. In this way, by referring to the past driving history, the optimal driving method can be selected, improving the comfort and safety of the occupant. Some or all of the above-mentioned processing in the driving unit may be performed using, for example, AI, or may be performed without using AI. For example, the driving unit can input past driving history data to a generation AI and have the generation AI select an optimal driving method.

[0076] During autonomous driving, the driving unit can customize driving parameters based on the occupant's current physical condition and situation. For example, if the occupant is tired, the driving unit can set gentle driving parameters. Also, if the occupant is relaxed, the driving unit can set normal driving parameters. Furthermore, if the occupant is excited, the driving unit can set cautious driving parameters. In this way, by customizing driving parameters based on the occupant's physical condition and situation, it is possible to improve the comfort and safety of the occupant. Some or all of the above-mentioned processing in the driving unit may be performed using, for example, AI, or may be performed without using AI. For example, the driving unit can input the occupant's physical condition data into the generation AI and have the generation AI customize the driving parameters.

[0077] The driving unit can estimate the emotions of the occupants and determine driving priorities based on the estimated emotions. For example, if the occupants are nervous, the driving unit can prioritize execution of driving instructions with high urgency. Furthermore, if the occupants are relaxed, the driving unit can also prioritize execution of normal driving instructions. Furthermore, if the occupants are excited, the driving unit can also prioritize execution of important driving instructions. Thus, by determining driving priorities based on the emotions of the occupants, driving instructions with high urgency can be prioritized. The emotion estimation is realized using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI may be, for example, 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 driving unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the driving unit can input emotion data of the occupants into the generation AI and have the generation AI determine driving priorities.

[0078] During autonomous driving, the driving unit can select an appropriate route by taking into account the geographical location information of the occupant. For example, when the occupant is in a specific area, the driving unit selects the optimal route related to that area. Furthermore, when the occupant is approaching a destination, the driving unit can also select the shortest route. Furthermore, when the occupant is traveling on a specific route, the driving unit can also select the optimal route related to that route. In this way, by taking the geographical location information into consideration, the optimal route can be selected, thereby improving the comfort and safety of the occupant. Some or all of the above-mentioned processing in the driving unit may be performed, for example, using AI or without AI. For example, the driving unit can input geographical location information data to a generation AI and cause the generation AI to select an optimal route.

[0079] During autonomous driving, the driving unit can analyze the social media activity of the occupant and suggest driving methods. For example, if the occupant mentions a specific location on social media, the driving unit can suggest driving methods related to that location. Also, if the occupant plans to attend a specific event on social media, the driving unit can suggest driving methods related to that event. Furthermore, if the occupant indicates a specific interest on social media, the driving unit can suggest driving methods related to that interest. In this way, related driving methods can be suggested by analyzing social media activity. Some or all of the above-mentioned processing in the driving unit may be performed using, for example, AI, or may be performed without using AI. For example, the driving unit can input social media activity data to a generation AI and have the generation AI suggest driving methods.

[0080] The monitoring unit can estimate the occupant's emotions and adjust the monitoring accuracy based on the estimated emotions. For example, if the occupant is tense, the monitoring unit increases the monitoring accuracy to detect small changes. The monitoring unit can also maintain normal monitoring accuracy when the occupant is relaxed. Furthermore, if the occupant is excited, the monitoring unit can decrease the monitoring accuracy to prevent false positives. In this way, false positives can be prevented by adjusting the monitoring accuracy based on the occupant's emotions. Emotion estimation is realized using an emotion estimation function, for example, using an emotion engine or a generative AI. The generative 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 monitoring unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the monitoring unit can input the occupant's emotion data into the generative AI and cause the generative AI to adjust the monitoring accuracy.

[0081] During monitoring, the monitoring unit can select an appropriate monitoring method by referring to the occupant's past condition data. For example, if the occupant has been tired in the past, the monitoring unit can increase the frequency of monitoring by referring to that condition. Also, if the occupant has been relaxed in the past, the monitoring unit can maintain the normal monitoring method by referring to that condition. Furthermore, if the occupant has been excited in the past, the monitoring unit can adjust the accuracy of monitoring by referring to that condition. In this way, by referring to the past condition data, the optimal monitoring method can be selected and the monitoring accuracy can be improved. Some or all of the above-mentioned processing in the monitoring unit may be performed using, for example, AI, or may be performed without using AI. For example, the monitoring unit can input past condition data into the generation AI and have the generation AI select the optimal monitoring method.

[0082] During monitoring, the monitoring unit can customize the monitoring parameters based on the occupant's current physical condition and situation. For example, if the occupant is tired, the monitoring unit increases the monitoring frequency to detect small changes. The monitoring unit can also maintain normal monitoring parameters if the occupant is relaxed. Furthermore, if the occupant is excited, the monitoring unit can reduce the monitoring accuracy to prevent false detection. This makes it possible to improve monitoring accuracy by customizing the monitoring parameters based on the occupant's physical condition and situation. Some or all of the above-mentioned processing in the monitoring unit may be performed using AI, for example, or may be performed without using AI. For example, the monitoring unit can input the occupant's physical condition data into the generation AI and cause the generation AI to customize the monitoring parameters.

[0083] The monitoring unit can estimate the emotions of the occupants and determine monitoring priorities based on the estimated emotions. For example, if the occupant is nervous, the monitoring unit can prioritize monitoring of highly urgent monitoring items. Furthermore, if the occupant is relaxed, the monitoring unit can also prioritize monitoring of normal monitoring items. Furthermore, if the occupant is excited, the monitoring unit can also prioritize monitoring of important monitoring items. Thus, by determining monitoring priorities based on the occupant's emotions, it is possible to prioritize monitoring of highly urgent monitoring items. The 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-described processing in the monitoring unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the monitoring unit can input the occupant's emotion data into the generation AI and have the generation AI determine the monitoring priorities.

[0084] During monitoring, the monitoring unit can select an appropriate monitoring method by taking into account the geographical location information of the occupant. For example, when the occupant is in a specific area, the monitoring unit can prioritize monitoring of monitoring items related to that area. Furthermore, when the occupant is approaching a destination, the monitoring unit can also prioritize monitoring of monitoring items related to the destination. Furthermore, when the occupant is traveling on a specific route, the monitoring unit can also prioritize monitoring of monitoring items related to that route. In this way, by taking the geographical location information into consideration, the optimal monitoring method can be selected and monitoring accuracy can be improved. Some or all of the above-described processing in the monitoring unit may be performed using, for example, AI, or may be performed without using AI. For example, the monitoring unit can input geographical location information data to a generation AI and cause the generation AI to select an optimal monitoring method.

[0085] During monitoring, the monitoring unit can analyze the occupant's social media activity and suggest monitoring measures. For example, if the occupant mentions a specific location on social media, the monitoring unit can suggest monitoring measures related to that location. Also, if the occupant plans to attend a specific event on social media, the monitoring unit can suggest monitoring measures related to that event. Furthermore, if the occupant indicates a specific interest on social media, the monitoring unit can suggest monitoring measures related to that interest. In this way, relevant monitoring measures can be suggested by analyzing social media activity. Some or all of the above-mentioned processing in the monitoring unit may be performed using, for example, AI, or may be performed without using AI. For example, the monitoring unit can input social media activity data to a generation AI and have the generation AI execute the suggested monitoring measures.

[0086] The adjustment unit can estimate the emotions of the occupants and determine how to adjust the in-vehicle environment based on the estimated emotions. For example, if the occupants are tense, the adjustment unit can lower the volume of music so that they can relax. Furthermore, if the occupants are relaxed, the adjustment unit can also maintain a normal in-vehicle environment. Furthermore, if the occupants are excited, the adjustment unit can adjust the lights so that they can calm down. Thus, by determining how to adjust the in-vehicle environment based on the emotions of the occupants, the comfort of the occupants can be improved. The emotion estimation is realized using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be, for example, 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 adjustment unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the adjustment unit can input the emotion data of the occupants into the generation AI and have the generation AI determine how to adjust the in-vehicle environment.

[0087] When adjusting the in-vehicle environment, the adjustment unit can select an appropriate adjustment method by referring to the occupant's past environment setting history. For example, the adjustment unit can set an optimal volume based on the occupant's past preferred music volume. The adjustment unit can also set optimal lighting settings based on the occupant's past lighting setting history. Furthermore, the adjustment unit can analyze the occupant's past environment setting history to provide the most comfortable in-vehicle environment. As a result, by referring to the past environment setting history, an optimal adjustment method can be selected and occupant comfort can be improved. Some or all of the above-mentioned processing in the adjustment unit may be performed using, for example, AI, or may be performed without using AI. For example, the adjustment unit can input past environment setting history data to a generation AI and cause the generation AI to select an optimal adjustment method.

[0088] When adjusting the in-vehicle environment, the adjustment unit can customize the adjustment parameters based on the occupant's current physical condition and situation. For example, if the occupant is tired, the adjustment unit can lower the music volume and set the lights to a more subdued setting. The adjustment unit can also maintain a normal in-vehicle environment if the occupant is relaxed. Furthermore, if the occupant is excited, the adjustment unit can lower the music volume and adjust the lights to calm the occupant. This allows the comfort of the occupant to be improved by customizing the adjustment parameters based on the occupant's physical condition and situation. Some or all of the above-described processing in the adjustment unit may be performed using, for example, AI, or may be performed without using AI. For example, the adjustment unit can input the occupant's physical condition data into the generation AI and cause the generation AI to customize the adjustment parameters.

[0089] The adjustment unit can estimate the emotions of the occupants and determine the priority of adjustments to the in-vehicle environment based on the estimated emotions. For example, if the occupants are nervous, the adjustment unit can prioritize adjusting the volume of music to help them relax. Furthermore, if the occupants are relaxed, the adjustment unit can also maintain a normal in-vehicle environment. Furthermore, if the occupants are excited, the adjustment unit can prioritize adjusting lights to help them calm down. Thus, by determining the priority of adjustments to the in-vehicle environment based on the emotions of the occupants, adjustments with high urgency can be prioritized. The estimation of emotions is realized using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above-described processing in the adjustment unit can be performed using, for example, an AI. For example, the adjustment unit can input the emotion data of the occupants into the generation AI and cause the generation AI to determine the priority of adjustments to the in-vehicle environment.

[0090] When adjusting the in-vehicle environment, the adjustment unit can select an appropriate adjustment method by taking into account the geographical location information of the occupant. For example, when the occupant is in a specific area, the adjustment unit can provide an optimal in-vehicle environment related to the area. Furthermore, when the occupant is approaching a destination, the adjustment unit can also provide an optimal in-vehicle environment related to the destination. Furthermore, when the occupant is traveling a specific route, the adjustment unit can also provide an optimal in-vehicle environment related to the route. In this way, by taking the geographical location information into consideration, the optimal adjustment method can be selected and the comfort of the occupant can be improved. Some or all of the above-mentioned processing in the adjustment unit may be performed using, for example, AI, or may be performed without using AI. For example, the adjustment unit can input geographical location information data to the generation AI and cause the generation AI to select the optimal adjustment method.

[0091] When adjusting the in-vehicle environment, the adjustment unit can analyze the occupant's social media activity and suggest adjustment measures. For example, if the occupant mentions specific music on social media, the adjustment unit can play that music. Furthermore, if the occupant plans to attend a specific event on social media, the adjustment unit can provide an in-vehicle environment related to that event. Furthermore, if the occupant indicates specific interests on social media, the adjustment unit can provide an in-vehicle environment related to those interests. Thus, by analyzing social media activity, relevant adjustment measures can be suggested. Some or all of the above-described processing in the adjustment unit may be performed using, for example, AI, or may be performed without using AI. For example, the adjustment unit can input social media activity data to a generation AI and have the generation AI suggest adjustment measures. === Hard Collateral 1-1 === Each of the multiple elements including the above-mentioned reception unit, analysis unit, driving unit, monitoring unit, and adjustment unit is realized, for example, by at least one of the smart device 14 and the data processing device 12. For example, the reception unit is realized by the microphone 38B and control unit 46A of the smart device 14 and receives voice instructions from the occupant. The analysis unit is realized by the specific processing unit 290 of the data processing device 12 and analyzes the voice instructions. The driving unit is realized by the specific processing unit 290 of the data processing device 12 and automatically drives the vehicle. The monitoring unit is realized by the camera 42 and control unit 46A of the smart device 14 and monitors the state of the occupant. The adjustment unit is realized by the specific processing unit 290 of the data processing device 12 and adjusts the in-vehicle environment. === Hard Collateral 1-2 === Each of the multiple elements including the above-mentioned reception unit, analysis unit, driving unit, monitoring unit, and adjustment unit is realized, for example, by at least one of the smart glasses 214 and the data processing device 12. For example, the reception unit is realized by the microphone 238 and the control unit 46A of the smart glasses 214 and receives voice instructions from the occupant. The analysis unit is realized by the specific processing unit 290 of the data processing device 12 and analyzes the voice instructions. The driving unit is realized by the specific processing unit 290 of the data processing device 12 and automatically drives the vehicle. The monitoring unit is realized by the camera 42 and the control unit 46A of the smart glasses 214 and monitors the state of the occupant. The adjustment unit is realized by the specific processing unit 290 of the data processing device 12 and adjusts the in-vehicle environment. === Hard Collateral 1-3 === Each of the multiple elements including the above-mentioned reception unit, analysis unit, driving unit, monitoring unit, and adjustment unit is realized, for example, by at least one of the headset type terminal 314 and the data processing device 12. For example, the reception unit is realized by the microphone 238 and control unit 46A of the headset type terminal 314 and receives voice instructions from the occupant. The analysis unit is realized by the specific processing unit 290 of the data processing device 12 and analyzes the voice instructions. The driving unit is realized by the specific processing unit 290 of the data processing device 12 and automatically drives the vehicle. The monitoring unit is realized by the camera 42 and control unit 46A of the headset type terminal 314 and monitors the state of the occupant. The adjustment unit is realized by the specific processing unit 290 of the data processing device 12 and adjusts the in-vehicle environment. === Hard Collateral 1-4 === Each of the multiple elements including the above-mentioned reception unit, analysis unit, driving unit, monitoring unit, and adjustment unit is realized, for example, by at least one of the robot 414 and the data processing device 12. For example, the reception unit is realized by the microphone 238 and control unit 46A of the robot 414 and receives voice instructions from the occupant. The analysis unit is realized by the specific processing unit 290 of the data processing device 12 and analyzes the voice instructions. The driving unit is realized by the specific processing unit 290 of the data processing device 12 and automatically drives the vehicle. The monitoring unit is realized by the camera 42 and control unit 46A of the robot 414 and monitors the state of the occupant. The adjustment unit is realized by the specific processing unit 290 of the data processing device 12 and adjusts the in-vehicle environment.

[0092] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0093] The reception unit not only accepts voice instructions from the occupant, but can also monitor the occupant's health condition and adjust the sensitivity of voice instructions based on the occupant's health condition. For example, if the occupant has a cold, the reception unit can increase the sensitivity of voice instructions so that instructions can be recognized even at low voice volumes. Alternatively, if the occupant is healthy, the reception unit can maintain normal sensitivity of voice instructions so that instructions can be received in a natural conversational tone. Furthermore, if the occupant is tired, the reception sensitivity of voice instructions can be adjusted so that only clear instructions are accepted to prevent misrecognition. This allows for more appropriate reception of voice instructions by adjusting the sensitivity of voice instructions according to the occupant's health condition.

[0094] The reception unit can estimate the emotions of the occupant and adjust the sensitivity to receiving voice instructions based on the estimated emotions. For example, if the occupant is feeling stressed, the sensitivity to receiving voice instructions can be increased so that instructions can be recognized even at a low volume. Alternatively, if the occupant is relaxed, the sensitivity to receiving voice instructions can be maintained at a normal level so that instructions can be received in a natural conversational tone. Furthermore, if the occupant is excited, the sensitivity to receiving voice instructions can be decreased so that only clear instructions can be received to prevent misrecognition. In this way, by adjusting the sensitivity to receiving voice instructions according to the occupant's emotions, more appropriate voice instructions can be received.

[0095] The reception unit can analyze the occupant's past voice instruction history and select an appropriate reception method. For example, it can prioritize and quickly recognize voice instructions that the occupant has used frequently in the past. The reception unit can also learn the patterns of voice instructions that the occupant has used in the past and efficiently recognize similar instructions. Furthermore, the reception unit can suggest the optimal reception method for a specific time period or situation based on the occupant's past voice instruction history. This makes it possible to select the optimal reception method by analyzing the past voice instruction history and improve the accuracy of receiving voice instructions.

[0096] When receiving a voice instruction, the reception unit can perform filtering based on the current situation of the occupant and environmental sounds. For example, if the occupant is talking in the vehicle, the reception unit can filter environmental sounds and recognize only the voice instruction. Also, if the occupant is listening to music, the volume of the music can be temporarily lowered to receive the voice instruction. Furthermore, if the occupant has the window open, the reception unit can filter external noise and clearly recognize the voice instruction. Thus, by performing filtering based on the current situation of the occupant and environmental sounds, the recognition accuracy of the voice instruction can be improved.

[0097] The reception unit can estimate the emotion of the occupant and determine the priority of voice instructions to be received based on the estimated emotion. For example, if the occupant is nervous, it can prioritize receiving urgent voice instructions. Also, if the occupant is relaxed, it can prioritize receiving normal voice instructions. Furthermore, if the occupant is excited, it can prioritize receiving important voice instructions. In this way, by determining the priority of voice instructions based on the emotion of the occupant, it is possible to prioritize receiving urgent instructions.

[0098] When receiving a voice instruction, the reception unit can prioritize receiving highly relevant instructions by taking into account the occupant's geographical location information. For example, if the occupant is in a specific area, the reception unit can prioritize receiving voice instructions related to that area. In addition, if the occupant is approaching a destination, the reception unit can also prioritize receiving voice instructions related to the destination. Furthermore, if the occupant is traveling on a specific route, the reception unit can also prioritize receiving voice instructions related to that route. In this way, by taking into account the geographical location information, highly relevant voice instructions can be prioritized.

[0099] When receiving a voice instruction, the reception unit can analyze the occupant's social media activity and receive related instructions. For example, if the occupant mentions a specific location on social media, the reception unit can preferentially receive voice instructions related to that location. Also, if the occupant plans to attend a specific event on social media, the reception unit can preferentially receive voice instructions related to that event. Furthermore, if the occupant indicates a specific interest on social media, the reception unit can preferentially receive voice instructions related to that interest. In this way, by analyzing social media activity, it is possible to preferentially receive related voice instructions.

[0100] The analysis unit can estimate the emotions of the occupant and adjust the accuracy of the analysis based on the estimated emotions. For example, if the occupant is nervous, the analysis accuracy can be increased to prevent erroneous recognition. The analysis unit can also maintain normal analysis accuracy if the occupant is relaxed. Furthermore, the analysis unit can also lower the analysis accuracy if the occupant is excited to prevent erroneous recognition. In this way, erroneous recognition can be prevented by adjusting the analysis accuracy based on the emotions of the occupant.

[0101] When analyzing a voice instruction, the analysis unit can adjust the level of detail of the analysis based on the importance of the instruction. For example, in the case of an instruction with high urgency, a detailed analysis is performed to respond quickly. The analysis unit can also perform a normal analysis in the case of an ordinary instruction. Furthermore, in the case of an instruction with low importance, the analysis unit can perform a simplified analysis to save resources. In this way, adjusting the level of detail of the analysis based on the importance of the instruction allows for efficient use of resources.

[0102] When analyzing a voice instruction, the analysis unit can apply different analysis algorithms depending on the category of the instruction. For example, in the case of an instruction related to navigation, the analysis unit can apply an analysis algorithm based on geographic information. In addition, in the case of an instruction related to entertainment, the analysis unit can also apply an analysis algorithm based on media information. Furthermore, in the case of an instruction related to vehicle operation, the analysis unit can also apply an analysis algorithm based on vehicle control information. In this way, by applying different analysis algorithms depending on the category of the instruction, the analysis accuracy can be improved.

[0103] The processing flow of the second embodiment will be briefly explained below.

[0104] Step 1: The reception unit receives a voice instruction from the occupant. The voice instruction from the occupant includes, but is not limited to, specific instructions such as "Go to a nearby restaurant" or "Go to a drugstore." The reception unit converts the voice instruction into text data using voice recognition technology and understands the content of the text data. Step 2: The analysis unit analyzes the voice instruction received by the reception unit. The analysis unit analyzes the content of the voice instruction using natural language processing technology and identifies the destination. Step 3: The driving unit automatically drives the vehicle based on the instructions analyzed by the analysis unit. The driving unit calculates the optimal route using the vehicle's navigation system and performs automatic driving. Step 4: The monitoring unit monitors the state of the occupants using the in-car camera. The monitoring unit monitors the occupants' complexions and expressions to detect if they appear tired or sleepy. Step 5: The adjustment unit adjusts the in-vehicle environment based on the occupant status monitored by the monitoring unit. The adjustment unit automatically takes measures such as lowering the music volume or adjusting the lighting.

[0105] 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.

[0106] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (registered trademark) (Internet search engine).<URL: https: / / openai.com / blog / chatgpt> Examples of the generative AI include a neural network (NN) and a neural network (NN). The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image (e.g., still image data or video data). The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt, and outputs the inference result in one or more data formats of voice data, text data, image data, etc. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specification processing unit 290 performs the above-mentioned specification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and may perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processing of each of the above-mentioned parts is performed by an AI, the processing may be performed in part or entirely by the AI, but is not limited to these examples. The processing performed by an AI including the generative AI may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by an AI including the generative AI.

[0107] 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.

[0108] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0109] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0110] 3, the data processing system 210 includes the data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0111] 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.

[0112] 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.

[0113] 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.

[0114] 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).

[0115] 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.

[0116] 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.

[0117] 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.

[0118] 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.

[0119] 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.

[0120] 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.

[0121] 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.

[0122] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt including an instruction, as well as inference data such as audio data indicating speech, text data indicating text, and image data indicating an image (e.g., still image data or video data). The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt, and outputs the inference result in one or more data formats, such as audio data, text data, and image data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation models 58 include AIs other than the generation AI. Examples of AIs other than the generation AI include, but are not limited to, linear regression, logistic regression, decision trees, random forests, support vector machines (SVMs), k-means clustering, convolutional neural networks (CNNs), recurrent neural networks (RNNs), generative adversarial networks (GANs), and naive Bayes. These AIs can perform various types of processing, but are not limited to these examples. The AI ​​may also be an AI agent. When the processing of each of the above-described parts is performed by an AI, the processing may be performed in part or entirely by the AI, but is not limited to these examples. Processing performed by an AI, including the generation AI, may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by an AI, including the generation AI.

[0123] 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.

[0124] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0125] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0126] 5, the data processing system 310 includes the data processing device 12 and a headset type terminal 314. An example of the data processing device 12 is a server.

[0127] 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.

[0128] 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.

[0129] 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.

[0130] 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).

[0131] 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.

[0132] 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.

[0133] The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0134] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0135] In the 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.

[0136] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0137] The specific processing unit 290 transmits the result of the specific processing to the 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.

[0138] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt including an instruction, as well as inference data such as audio data indicating speech, text data indicating text, and image data indicating an image (e.g., still image data or video data). The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt, and outputs the inference result in one or more data formats, such as audio data, text data, and image data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation models 58 include AIs other than the generation AI. Examples of AIs other than the generation AI include, but are not limited to, linear regression, logistic regression, decision trees, random forests, support vector machines (SVMs), k-means clustering, convolutional neural networks (CNNs), recurrent neural networks (RNNs), generative adversarial networks (GANs), and naive Bayes. These AIs can perform various types of processing, but are not limited to these examples. The AI ​​may also be an AI agent. When the processing of each of the above-described parts is performed by an AI, the processing may be performed in part or entirely by the AI, but is not limited to these examples. Processing performed by an AI, including the generation AI, may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by an AI, including the generation AI.

[0139] The data processing system 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.

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

[0141] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0142] 7, the data processing system 410 includes a data processing device 12 and a robot 414. An example of the data processing device 12 is a server.

[0143] 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.

[0144] 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.

[0145] 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.

[0146] 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).

[0147] 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.

[0148] 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.

[0149] 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.

[0150] 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.

[0151] 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.

[0152] 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.

[0153] 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.

[0154] 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.

[0155] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt including an instruction, as well as inference data such as audio data indicating speech, text data indicating text, and image data indicating an image (e.g., still image data or video data). The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt, and outputs the inference result in one or more data formats, such as audio data, text data, and image data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation models 58 include AIs other than the generation AI. Examples of AIs other than the generation AI include, but are not limited to, linear regression, logistic regression, decision trees, random forests, support vector machines (SVMs), k-means clustering, convolutional neural networks (CNNs), recurrent neural networks (RNNs), generative adversarial networks (GANs), and naive Bayes. These AIs can perform various types of processing, but are not limited to these examples. The AI ​​may also be an AI agent. When the processing of each of the above-described parts is performed by an AI, the processing may be performed in part or entirely by the AI, but is not limited to these examples. Processing performed by an AI, including the generation AI, may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by an AI, including the generation AI.

[0156] 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.

[0157] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0158] 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.

[0159] 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.

[0160] 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.

[0161] 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).

[0162] Human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, a state of discomfort is expressed, and when they approach the ideal, a state of pleasure is expressed. Emotions can also be created for robots, cars, and motorcycles, based on various balances, such as posture and remaining battery life. When these balances deviate from the ideal, a state of discomfort is expressed, and when they approach the ideal, a state of pleasure is expressed. An emotion map can be generated, for example, based on Dr. Mitsuyoshi's emotion map (Research on speech emotion recognition and brain physiological signal analysis systems for emotions, Tokushima University, doctoral dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map lists emotions belonging to the "reaction" domain, where sensation is dominant. The right half of the emotion map lists emotions belonging to the "situation" domain, where situational awareness is dominant.

[0163] 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."

[0164] 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.

[0165] 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.

[0166] 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.

[0167] 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.

[0168] 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.

[0169] 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.

[0170] 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.

[0171] 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.

[0172] 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.

[0173] 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.

[0174] 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, in order to avoid confusion and to 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.

[0175] 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.

[0176] [Explanation of symbols]

[0177] 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 reception unit that receives voice instructions; an analysis unit that analyzes the voice instruction received by the reception unit; a driving unit that automatically drives a vehicle based on the instructions analyzed by the analysis unit; a monitoring unit that monitors the state of occupants using an in-vehicle camera; an adjusting unit that adjusts the in-vehicle environment based on the state of the occupant monitored by the monitoring unit; A system characterized by:

2. The reception unit Estimates the passenger's emotions and adjusts the sensitivity of voice instructions based on the estimated emotions. The system of claim 1 .

3. The reception unit Analyze the passenger's past voice command history and select the appropriate reception method The system of claim 1 .

4. The reception unit When receiving voice commands, filtering is performed based on the passenger's current situation and environmental sounds. The system of claim 1 .

5. The reception unit Estimates the emotions of passengers and determines the priority of voice commands to be accepted based on the estimated emotions. The system of claim 1 .

6. The reception unit When receiving voice commands, the system prioritizes relevant commands based on the passenger's geographic location. The system of claim 1 .

7. The reception unit When receiving voice commands, the system analyzes the passenger's social media activity and receives relevant commands. The system of claim 1 .

8. The analysis unit Estimate the emotions of the passengers and adjust the accuracy of the analysis based on the estimated emotions. The system of claim 1 .

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