system

Abstract

The system according to the embodiment aims to perform autonomous driving by accepting instructions in natural language. [Solution] A system according to an embodiment includes a communication unit, an autonomous driving unit, and a data analysis unit. The communication unit accepts instructions in natural language. The autonomous driving unit selects a driving route based on the instructions accepted by the communication unit. The data analysis unit analyzes data collected by the autonomous driving 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 technology has not yet fully realized a system that accepts instructions in natural language and performs autonomous driving, and there is room for improvement.

[0005] The system according to the embodiment aims to perform autonomous driving by accepting instructions in natural language. [Means for solving the problem]

[0006] The system according to the embodiment includes a communication unit, an autonomous driving unit, and a data analysis unit. The communication unit receives instructions in natural language. The autonomous driving unit selects a driving route based on the instructions received by the communication unit. The data analysis unit analyzes data collected by the autonomous driving unit. [Effects of the Invention]

[0007] The system according to the embodiment can perform autonomous driving by accepting instructions in natural language. [Brief explanation of the drawings]

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

[0028] (Example 1) An e-sports participation system according to an embodiment of the present invention enables small individual teams to participate in e-sports using AI players by having the AI ​​players converse in natural language and communicate with their team members. This system allows the AI ​​players to converse in natural language and communicate with their team members. The AI ​​players then participate in races using autonomous driving technology. The AI ​​players select optimal driving routes by collecting and analyzing data in real time during the race. The AI ​​players can also receive instructions in natural language to respond to various situations that arise during the race. This allows team members to issue strategic instructions to the AI ​​players. This system enables even small individual teams to participate in e-sports using AI players, thereby contributing to the development of motorsports technology and providing fair opportunities. For example, it is important that the AI ​​players converse in natural language and communicate with their team members. This allows the AI ​​players to understand and respond appropriately to human instructions. Another important aspect is that the AI ​​players participate in races using autonomous driving technology. This solves problems such as the cost and language barriers of real-world races. Furthermore, the AI ​​players can select optimal driving routes by collecting and analyzing data in real time. In addition, the AI ​​player can receive instructions in natural language to respond to various situations that arise during the race. This allows team members to give strategic instructions to the AI ​​player. This system allows even small individual teams to participate in e-sports with AI players, realizing technological development in motorsports and providing a fair chance for competition. As a result, the e-sports participation system allows AI players to receive instructions in natural language, select optimal driving routes, and analyze data, realizing technological development in motorsports and providing a fair chance for competition in e-sports.

[0029] An e-sports participation system according to an embodiment includes a communication unit, an autonomous driving unit, and a data analysis unit. The communication unit receives instructions from an AI player in natural language. Examples of such instructions include, but are not limited to, voice instructions and text instructions. The communication unit receives voice instructions using, for example, voice recognition technology. The communication unit can also receive text instructions using text analysis technology. For example, the voice recognition technology converts voice data into text data and transmits the instructions to the AI ​​player. The text analysis technology analyzes the text data and understands the content of the instructions. The autonomous driving unit selects a driving route based on the instructions received by the communication unit. For example, the driving route may be selected taking into consideration the shortest distance, traffic conditions, user preferences, etc., but is not limited to such examples. The autonomous driving unit scans the surrounding environment using, for example, LIDAR (Light Detection and Ranging) technology and selects the optimal driving route. The autonomous driving unit can also select a driving route using a camera or GPS (Global Positioning System). For example, LIDAR technology uses laser light to measure the distance to surrounding objects and generate a 3D map. The camera analyzes image data and understands road conditions. The GPS identifies the current location and calculates a route to the destination. The data analysis unit analyzes the data collected by the autonomous driving unit. For example, the data is analyzed using statistical analysis or a machine learning algorithm, but is not limited to these examples. The data analysis unit, for example, analyzes the driving data using statistical analysis and optimizes the driving route. The data analysis unit can also analyze the driving data using a machine learning algorithm to improve the driving performance of the AI ​​player. For example, statistical analysis calculates the average value and variance of the driving data to help optimize the driving route. The machine learning algorithm learns from large amounts of driving data and improves the accuracy of driving route selection. As a result, the e-sports participation system according to the embodiment allows the AI ​​player to receive instructions in natural language, select the optimal driving route, and analyze the data, thereby realizing technological development in motorsports and providing fair opportunities in e-sports.

[0030] The Communication Department allows AI players to converse in natural language and communicate with team members. For example, the Communication Department enables AI players to communicate with team members using voice conversations and text chats. In voice conversations, for instance, the AI ​​player uses speech recognition technology to understand what team members are saying and speech synthesis technology to respond. In text chats, the AI ​​player can also use text analysis technology to understand messages from team members and text generation technology to respond. For example, speech recognition technology converts voice data into text data and transmits it to the AI ​​player. Speech synthesis technology converts text data into voice data and responds to team members. Text analysis technology analyzes text data and understands the content of the message. Text generation technology generates an appropriate response based on the message content. This allows for smooth communication with team members by enabling AI players to converse in natural language.

[0031] The autonomous driving unit allows AI players to participate in races using autonomous driving technology. The autonomous driving unit enables AI players to participate in races using autonomous driving technologies such as LIDAR, cameras, and GPS. For example, LIDAR technology uses laser light to scan the surrounding environment and generate a 3D map. Cameras analyze image data to understand road conditions. GPS determines the current location and calculates the route to the destination. This allows AI players to overcome problems such as the costs and language barriers of real-world races by using autonomous driving technology. Some or all of the above-described processes in the autonomous driving unit may be performed using AI, or not. For example, the autonomous driving unit can input data from LIDAR, cameras, GPS, etc., into the AI ​​and have the AI ​​perform the process of selecting the optimal driving route.

[0032] The data analysis unit allows the AI ​​player to collect and analyze data in real time. The data analysis unit, for example, collects data in real time using sensors and analyzes the data using statistical analysis and machine learning algorithms. For example, the sensors collect data such as speed, position, and acceleration while driving. Statistical analysis calculates the average value and variance of the collected data and uses this data to optimize the driving route. The machine learning algorithm learns from large amounts of driving data and improves the accuracy of driving route selection. This allows the AI ​​player to collect and analyze data in real time and select the optimal driving route. Some or all of the above-mentioned processing in the data analysis unit may be performed using AI, for example, or may be performed without using AI. For example, the data analysis unit may input data collected by sensors into AI and have the AI ​​analyze the data.

[0033] The communication unit can receive instructions in natural language to respond to multiple situations that occur during a race. The communication unit receives instructions in natural language to respond to situations such as weather changes, traffic accidents, and road construction. For example, to respond to weather changes, the AI ​​player can receive an instruction such as, "It's starting to rain, so please slow down." To respond to a traffic accident, the AI ​​player can receive an instruction such as, "There's been an accident ahead, so please take a detour route." To respond to road construction, the AI ​​player can receive an instruction such as, "Please avoid roads under construction." In this way, by receiving instructions in natural language to respond to various situations that occur during a race, the AI ​​player can respond appropriately. Some or all of the above-described processing in the communication unit may be performed using AI, for example, or may be performed without using AI. For example, the communication unit can input situations such as weather changes and traffic accidents into the AI ​​and cause the AI ​​to execute a process of generating appropriate instructions.

[0034] The autonomous driving unit can select the most suitable driving route during a race. The autonomous driving unit selects a driving route taking into consideration, for example, the shortest distance, traffic conditions, user preferences, etc. For example, to select the shortest distance, the AI ​​player can receive an instruction such as "Please select the shortest route." The AI ​​player can also receive an instruction such as "Please select a route that avoids traffic jams" taking traffic conditions into consideration. Furthermore, the AI ​​player can receive an instruction such as "Please select a scenic route" taking user preferences into consideration. By selecting the optimal driving route during a race, the AI ​​player can efficiently progress through the race. Some or all of the above-described processing in the autonomous driving unit may be performed using, or without, an AI. For example, the autonomous driving unit can input data such as the shortest distance and traffic conditions into the AI ​​and cause the AI ​​to execute a process of selecting the optimal driving route.

[0035] The communication unit can analyze the team members' past instruction histories and select the most appropriate communication method. For example, the communication unit can analyze the patterns of instructions used by the team members in the past and suggest the most effective instruction method. For example, the communication unit can prioritize the use of words and phrases that the team members have used in the past. The communication unit can also select the optimal instruction method for a specific situation from the team members' past instruction histories. For example, the communication unit can analyze the patterns of instructions used by the team members in the past and suggest the most effective instruction method. The communication unit can also prioritize the use of words and phrases that the team members have used in the past. The communication unit can also select the optimal instruction method for a specific situation from the team members' past instruction histories. In this way, the optimal communication method is selected by analyzing the past instruction histories. Some or all of the above-mentioned processing in the communication unit may be performed using, for example, AI, or may be performed without using AI. For example, the communication unit can input past instruction history data into a generation AI and cause the generation AI to execute a process of selecting the optimal communication method.

[0036] The communications department can filter instructions based on the current situation and areas of interest of team members when receiving them. For example, if a team member is focused on the current race situation, the communications department will only accept relevant instructions. If a team member is interested in a particular technology, the communications department can also prioritize instructions related to that technology. Furthermore, it can filter out unnecessary instructions based on the current situation of the team members. For example, if a team member is focused on the current race situation, the communications department will only accept relevant instructions. If a team member is interested in a particular technology, it can also prioritize instructions related to that technology. Furthermore, it can filter out unnecessary instructions based on the current situation of the team members. This ensures that highly relevant instructions are received by filtering based on the current situation and areas of interest. Some or all of the above processing in the communications department may be performed using AI, or not. For example, the communications department can input data on the current situation and areas of interest into a generating AI and have the generating AI perform the filtering process.

[0037] The communications department can prioritize receiving instructions that are highly relevant, taking into account the geographical location information of team members. For example, if a team member is in a specific location, the communications department will prioritize receiving instructions related to that location. If a team member is on the move, the communications department can also prioritize receiving instructions related to their destination. Furthermore, if a team member is in a specific region, the communications department can also prioritize receiving instructions related to that region. In this way, by considering geographical location information, highly relevant instructions are prioritized. Some or all of the above processing in the communications department may be performed using AI, for example, or not. For example, the communications department can input geographic location data into a generating AI and have the AI ​​perform a process that prioritizes receiving instructions that are highly relevant.

[0038] When receiving an instruction, the communication unit can analyze the social media activities of the team members and accept the relevant instruction. For example, the communication unit can accept the relevant instruction based on information shared by the team members on social media. For example, the communication unit can accept the relevant instruction based on information shared by the team members on social media. The communication unit can also analyze current areas of interest from the team members' social media activities and accept the relevant instruction. Furthermore, the communication unit can also accept the relevant instruction based on information about accounts followed by the team members on social media. For example, the communication unit can accept the relevant instruction based on information shared by the team members on social media. The communication unit can also analyze current areas of interest from the team members' social media activities and accept the relevant instruction. Furthermore, the communication unit can also accept the relevant instruction based on information about accounts followed by the team members on social media. In this way, the social media activities are analyzed and the relevant instruction is accepted. Some or all of the above-described processing in the communication unit may be performed using, for example, AI, or may be performed without using AI. For example, the communication unit can input social media activity data to the generation AI and cause the generation AI to execute a process of accepting the relevant instruction.

[0039] The autonomous driving unit can analyze past driving data and select the most suitable driving route. The autonomous driving unit, for example, selects the most efficient route based on past driving data. For example, the autonomous driving unit can select the most efficient route from past driving data. It can also select a route that avoids congestion based on past driving data. It can also analyze past driving data and select the safest route. For example, the autonomous driving unit can select the most efficient route from past driving data. It can also select a route that avoids congestion based on past driving data. It can also analyze past driving data and select the safest route. In this way, the optimal driving route is selected by analyzing the past driving data. Some or all of the above-mentioned processing in the autonomous driving unit may be performed using, for example, AI, or may be performed without using AI. For example, the autonomous driving unit can input past driving data into a generation AI and cause the generation AI to execute processing to select an optimal driving route.

[0040] When selecting a driving route, the autonomous driving unit can perform filtering based on current road conditions and weather information. The autonomous driving unit selects an optimal route based on, for example, current road congestion information. For example, the autonomous driving unit can select an optimal route based on current road congestion information. The autonomous driving unit can also select an optimal route based on current weather information. A detour route can also be selected based on current road construction information. For example, the autonomous driving unit can select an optimal route based on current road congestion information. The autonomous driving unit can also select an optimal route based on current weather information. A detour route can also be selected based on current road construction information. In this way, an optimal driving route is selected by filtering based on current road conditions and weather information. Some or all of the above-described processing in the autonomous driving unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the autonomous driving unit can input current road conditions and weather information to a generation AI and cause the generation AI to perform a filtering process.

[0041] When selecting a driving route, the autonomous driving unit can select the most suitable route by taking geographical location information into consideration. The autonomous driving unit, for example, selects the shortest route from the current location to the destination. For example, the autonomous driving unit selects the shortest route from the current location to the destination. It can also select the safest route from the current location to the destination. It can also select the most efficient route from the current location to the destination. For example, the autonomous driving unit selects the shortest route from the current location to the destination. It can also select the safest route from the current location to the destination. It can also select the most efficient route from the current location to the destination. In this way, the optimal driving route is selected by taking geographical location information into consideration. Some or all of the above-described processing in the autonomous driving unit may be performed using, for example, AI, or may be performed without using AI. For example, the autonomous driving unit can input geographical location information data to a generation AI and cause the generation AI to execute processing to select an optimal route.

[0042] When selecting a driving route, the autonomous driving unit can select an optimal route by referring to related traffic information. The autonomous driving unit selects the optimal route, for example, based on real-time traffic congestion information. For example, the autonomous driving unit selects the optimal route based on real-time traffic congestion information. The autonomous driving unit can also select the optimal route by taking into account the real-time operation status of public transportation. Furthermore, the autonomous driving unit can also select a detour route based on real-time road construction information. For example, the autonomous driving unit selects the optimal route based on real-time traffic congestion information. The autonomous driving unit can also select the optimal route by taking into account the real-time operation status of public transportation. Furthermore, the autonomous driving unit can also select a detour route based on real-time road construction information. In this way, the optimal driving route is selected by referring to related traffic information. Some or all of the above-mentioned processing in the autonomous driving unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the autonomous driving unit can input traffic information data to a generation AI and cause the generation AI to execute a process of selecting an optimal route.

[0043] The data analysis unit can optimize the data analysis algorithm by referring to past data. The data analysis unit, for example, selects an optimal data analysis algorithm based on past data. For example, the data analysis unit can select an optimal data analysis algorithm based on past data. The data analysis unit can also analyze past data and optimize the data analysis algorithm. Furthermore, the data analysis accuracy can be improved by referring to past data. For example, the data analysis unit can select an optimal data analysis algorithm based on past data. The data analysis unit can also analyze past data and optimize the data analysis algorithm. Furthermore, the data analysis accuracy can be improved by referring to past data. In this way, the data analysis algorithm is optimized by referring to past data. Some or all of the above-mentioned processing in the data analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the data analysis unit can input past data to the generation AI and cause the generation AI to optimize the data analysis algorithm.

[0044] The data analysis unit can perform filtering based on the current race situation and environmental information during data analysis. For example, the data analysis unit can prioritize the analysis of relevant data based on the current race situation. It can also prioritize the analysis of relevant data based on the current environmental information. Furthermore, it can filter out unnecessary data based on the current race situation and environmental information. For example, it can prioritize the analysis of relevant data based on the current race situation. It can also prioritize the analysis of relevant data based on the current environmental information. Furthermore, it can filter out unnecessary data based on the current race situation and environmental information. As a result, by filtering based on the current race situation and environmental information, highly relevant data is prioritized for analysis. Some or all of the above processing in the data analysis unit may be performed using AI, for example, or without AI. For example, the data analysis unit can input the current race situation and environmental information into a generating AI and have the generating AI perform the filtering process.

[0045] The data analysis unit can select the optimal analysis method by considering geographical location information during data analysis. For example, the data analysis unit can select the optimal data analysis method based on current location information. For example, it can select the optimal data analysis method based on current location information. It can also select the most efficient data analysis method by considering geographical location information. Furthermore, it can select the most accurate data analysis method by considering geographical location information. For example, it can select the optimal data analysis method based on current location information. It can also select the most efficient data analysis method by considering geographical location information. Furthermore, it can select the most accurate data analysis method by considering geographical location information. In this way, the optimal data analysis method is selected by considering geographical location information. Some or all of the above processing in the data analysis unit may be performed using AI, for example, or without using AI. For example, the data analysis unit can input geographical location information data into a generating AI and have the generating AI execute the process of selecting the optimal data analysis method.

[0046] During data analysis, the data analysis unit can improve the accuracy of the analysis by referring to related past race data. The data analysis unit, for example, improves the accuracy of the data analysis based on past race data. For example, the data analysis accuracy can be improved based on past race data. The data analysis algorithm can also be optimized by referring to related past race data. The data analysis accuracy can also be improved by analyzing past race data. For example, the data analysis accuracy can be improved based on past race data. The data analysis algorithm can also be optimized by referring to related past race data. The data analysis accuracy can also be improved by analyzing past race data. In this way, the accuracy of the data analysis is improved by referring to related past race data. Some or all of the above-described processing in the data analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the data analysis unit can input past race data to the generation AI and cause the generation AI to perform processing to improve the accuracy of the data analysis.

[0047] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0048] The autonomous driving part can predict the movements of other vehicles during a race and select the optimal driving route. For example, if the vehicle ahead is likely to brake suddenly, the AI ​​player will slow down in advance. Also, if a vehicle in the adjacent lane is attempting to change lanes, the AI ​​player can predict that movement and maintain a safe distance. Furthermore, if a vehicle behind is attempting to overtake, the AI ​​player can predict that movement and take appropriate action. This allows for safer and more efficient driving by predicting the movements of other vehicles.

[0049] The data analysis unit can analyze data collected during a race in real time and provide feedback to improve the AI ​​player's performance. For example, if the AI ​​player is slowing down too much when cornering, the data analysis unit can provide that information in real time and instruct the AI ​​player to maintain an appropriate speed when cornering next time. Also, if the AI ​​player brakes too early, the data analysis unit can provide that information and instruct the AI ​​player to adjust the next braking point. Furthermore, if the AI ​​player is losing traction when accelerating, the data analysis unit can provide that information and instruct the AI ​​player to maintain traction when accelerating next time. This allows the AI ​​player's performance to improve through real-time data analysis and feedback.

[0050] The autonomous driving part can detect road conditions in real time during a race and select the optimal driving route. For example, if the road is wet, the AI ​​player will adjust its speed to avoid slipping. Also, if the road is uneven, the AI ​​player can select a route that avoids those parts. Furthermore, if the road is dry, the AI ​​player can increase its speed to obtain optimal grip. This makes it possible to select the optimal driving route according to road conditions, resulting in safe and efficient driving.

[0051] The communication unit can analyze the user's past behavioral data and provide optimal instructions. For example, it can analyze what instructions the user has given in specific situations in the past and provide optimal instructions for similar situations. It can also learn patterns of instructions that the user has preferred in the past and provide instructions based on those patterns. Furthermore, it can also provide instructions in advance for predicted situations based on the user's past behavioral data. In this way, more effective instructions can be provided by utilizing the user's past behavioral data.

[0052] The autonomous driving unit can communicate with other vehicles during a race and drive in coordination with them. For example, it can cooperate with the vehicle in front to perform draft driving, improving fuel efficiency. It can also cooperate with the vehicle next to it to smoothly change lanes. It can also cooperate with the vehicle behind it to safely overtake. This enables coordinated driving through communication with other vehicles, improving the efficiency and safety of the race.

[0053] The processing flow of the first embodiment will be briefly explained below.

[0054] Step 1: In the communication section, the AI ​​player receives instructions in natural language. Examples include voice instructions and text instructions. Voice instructions can be received using voice recognition technology, and text instructions can be received using text analysis technology. Voice recognition technology converts voice data into text data and conveys instructions to the AI ​​player. Text analysis technology analyzes the text data and understands the content of the instructions. Step 2: The autonomous driving unit selects a driving route based on the instructions received by the communication unit. For example, it selects a driving route taking into consideration the shortest distance, traffic conditions, and user preferences. It uses LIDAR technology to scan the surrounding environment and selects the optimal driving route. It can also select a driving route using a camera or GPS. LIDAR technology uses laser light to measure the distance to surrounding objects and generate a 3D map. The camera analyzes image data and understands road conditions. The GPS identifies the current location and calculates the route to the destination. Step 3: The data analysis unit analyzes the data collected by the autonomous driving unit. For example, it analyzes the data using statistical analysis and machine learning algorithms. Statistical analysis is used to analyze the driving data and optimize the driving route. Machine learning algorithms are used to analyze the driving data and improve the driving performance of the AI ​​player. Statistical analysis calculates the average and variance of the driving data and helps optimize the driving route. Machine learning algorithms learn from large amounts of driving data and improve the accuracy of driving route selection.

[0055] (Example 2) An e-sports participation system according to an embodiment of the present invention enables small individual teams to participate in e-sports using AI players by having the AI ​​players converse in natural language and communicate with their team members. This system allows the AI ​​players to converse in natural language and communicate with their team members. The AI ​​players then participate in races using autonomous driving technology. The AI ​​players select optimal driving routes by collecting and analyzing data in real time during the race. The AI ​​players can also receive instructions in natural language to respond to various situations that arise during the race. This allows team members to issue strategic instructions to the AI ​​players. This system enables even small individual teams to participate in e-sports using AI players, thereby contributing to the development of motorsports technology and providing fair opportunities. For example, it is important that the AI ​​players converse in natural language and communicate with their team members. This allows the AI ​​players to understand and respond appropriately to human instructions. Another important aspect is that the AI ​​players participate in races using autonomous driving technology. This solves problems such as the cost and language barriers of real-world races. Furthermore, the AI ​​players can select optimal driving routes by collecting and analyzing data in real time. In addition, the AI ​​player can receive instructions in natural language to respond to various situations that arise during the race. This allows team members to give strategic instructions to the AI ​​player. This system allows even small individual teams to participate in e-sports with AI players, realizing technological development in motorsports and providing a fair chance for competition. As a result, the e-sports participation system allows AI players to receive instructions in natural language, select optimal driving routes, and analyze data, realizing technological development in motorsports and providing a fair chance for competition in e-sports.

[0056] An e-sports participation system according to an embodiment includes a communication unit, an autonomous driving unit, and a data analysis unit. The communication unit receives instructions from an AI player in natural language. Examples of such instructions include, but are not limited to, voice instructions and text instructions. The communication unit receives voice instructions using, for example, voice recognition technology. The communication unit can also receive text instructions using text analysis technology. For example, the voice recognition technology converts voice data into text data and transmits the instructions to the AI ​​player. The text analysis technology analyzes the text data and understands the content of the instructions. The autonomous driving unit selects a driving route based on the instructions received by the communication unit. For example, the driving route may be selected taking into consideration the shortest distance, traffic conditions, user preferences, etc., but is not limited to such examples. The autonomous driving unit scans the surrounding environment using, for example, LIDAR (Light Detection and Ranging) technology and selects the optimal driving route. The autonomous driving unit can also select a driving route using a camera or GPS (Global Positioning System). For example, LIDAR technology uses laser light to measure the distance to surrounding objects and generate a 3D map. The camera analyzes image data and understands road conditions. The GPS identifies the current location and calculates a route to the destination. The data analysis unit analyzes the data collected by the autonomous driving unit. For example, the data is analyzed using statistical analysis or a machine learning algorithm, but is not limited to these examples. The data analysis unit, for example, analyzes the driving data using statistical analysis and optimizes the driving route. The data analysis unit can also analyze the driving data using a machine learning algorithm to improve the driving performance of the AI ​​player. For example, statistical analysis calculates the average value and variance of the driving data to help optimize the driving route. The machine learning algorithm learns from large amounts of driving data and improves the accuracy of driving route selection. As a result, the e-sports participation system according to the embodiment allows the AI ​​player to receive instructions in natural language, select the optimal driving route, and analyze the data, thereby realizing technological development in motorsports and providing fair opportunities in e-sports.

[0057] The Communication Department allows AI players to converse in natural language and communicate with team members. For example, the Communication Department enables AI players to communicate with team members using voice conversations and text chats. In voice conversations, for instance, the AI ​​player uses speech recognition technology to understand what team members are saying and speech synthesis technology to respond. In text chats, the AI ​​player can also use text analysis technology to understand messages from team members and text generation technology to respond. For example, speech recognition technology converts voice data into text data and transmits it to the AI ​​player. Speech synthesis technology converts text data into voice data and responds to team members. Text analysis technology analyzes text data and understands the content of the message. Text generation technology generates an appropriate response based on the message content. This allows for smooth communication with team members by enabling AI players to converse in natural language.

[0058] The autonomous driving unit allows AI players to participate in races using autonomous driving technology. The autonomous driving unit enables AI players to participate in races using autonomous driving technologies such as LIDAR, cameras, and GPS. For example, LIDAR technology uses laser light to scan the surrounding environment and generate a 3D map. Cameras analyze image data to understand road conditions. GPS determines the current location and calculates the route to the destination. This allows AI players to overcome problems such as the costs and language barriers of real-world races by using autonomous driving technology. Some or all of the above-described processes in the autonomous driving unit may be performed using AI, or not. For example, the autonomous driving unit can input data from LIDAR, cameras, GPS, etc., into the AI ​​and have the AI ​​perform the process of selecting the optimal driving route.

[0059] The data analysis unit allows the AI ​​player to collect and analyze data in real time. The data analysis unit, for example, collects data in real time using sensors and analyzes the data using statistical analysis and machine learning algorithms. For example, the sensors collect data such as speed, position, and acceleration while driving. Statistical analysis calculates the average value and variance of the collected data and uses this data to optimize the driving route. The machine learning algorithm learns from large amounts of driving data and improves the accuracy of driving route selection. This allows the AI ​​player to collect and analyze data in real time and select the optimal driving route. Some or all of the above-mentioned processing in the data analysis unit may be performed using AI, for example, or may be performed without using AI. For example, the data analysis unit may input data collected by sensors into AI and have the AI ​​analyze the data.

[0060] The communication unit can receive instructions in natural language to respond to multiple situations that occur during a race. The communication unit receives instructions in natural language to respond to situations such as weather changes, traffic accidents, and road construction. For example, to respond to weather changes, the AI ​​player can receive an instruction such as, "It's starting to rain, so please slow down." To respond to a traffic accident, the AI ​​player can receive an instruction such as, "There's been an accident ahead, so please take a detour route." To respond to road construction, the AI ​​player can receive an instruction such as, "Please avoid roads under construction." In this way, by receiving instructions in natural language to respond to various situations that occur during a race, the AI ​​player can respond appropriately. Some or all of the above-described processing in the communication unit may be performed using AI, for example, or may be performed without using AI. For example, the communication unit can input situations such as weather changes and traffic accidents into the AI ​​and cause the AI ​​to execute a process of generating appropriate instructions.

[0061] The autonomous driving unit can select the most suitable driving route during a race. The autonomous driving unit selects a driving route taking into consideration, for example, the shortest distance, traffic conditions, user preferences, etc. For example, to select the shortest distance, the AI ​​player can receive an instruction such as "Please select the shortest route." The AI ​​player can also receive an instruction such as "Please select a route that avoids traffic jams" taking traffic conditions into consideration. Furthermore, the AI ​​player can receive an instruction such as "Please select a scenic route" taking user preferences into consideration. By selecting the optimal driving route during a race, the AI ​​player can efficiently progress through the race. Some or all of the above-described processing in the autonomous driving unit may be performed using, or without, an AI. For example, the autonomous driving unit can input data such as the shortest distance and traffic conditions into the AI ​​and cause the AI ​​to execute a process of selecting the optimal driving route.

[0062] The communication unit can estimate the user's emotions and adjust how instructions are received based on those estimated emotions. The communication unit estimates user emotions using technologies such as voice analysis, facial recognition, and text analysis. For example, voice analysis technology analyzes the tone and speed of the user's voice to estimate emotions. Facial recognition technology captures the user's facial expressions with a camera to estimate emotions. Text analysis technology analyzes the user's text messages to estimate emotions. This allows the system to estimate user emotions and adjust how instructions are received based on those estimated emotions. For example, if the user is nervous, the system can provide an interface that accepts instructions in a calm tone. If the user is relaxed, it can provide an interface that accepts detailed instructions. Furthermore, if the user is in a hurry, it can provide an interface that accepts concise and quick instructions. By adjusting how instructions are received according to the user's emotions, more appropriate instructions can be received. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above-described processing in the communication unit may be performed using, for example, AI, or may be performed without using AI. For example, the communication unit may input voice data and facial expression data to the generation AI and have the generation AI estimate emotions.

[0063] The communication unit can analyze the team members' past instruction histories and select the most appropriate communication method. For example, the communication unit can analyze the patterns of instructions used by the team members in the past and suggest the most effective instruction method. For example, the communication unit can prioritize the use of words and phrases that the team members have used in the past. The communication unit can also select the optimal instruction method for a specific situation from the team members' past instruction histories. For example, the communication unit can analyze the patterns of instructions used by the team members in the past and suggest the most effective instruction method. The communication unit can also prioritize the use of words and phrases that the team members have used in the past. The communication unit can also select the optimal instruction method for a specific situation from the team members' past instruction histories. In this way, the optimal communication method is selected by analyzing the past instruction histories. Some or all of the above-mentioned processing in the communication unit may be performed using, for example, AI, or may be performed without using AI. For example, the communication unit can input past instruction history data into a generation AI and cause the generation AI to execute a process of selecting the optimal communication method.

[0064] The communications department can filter instructions based on the current situation and areas of interest of team members when receiving them. For example, if a team member is focused on the current race situation, the communications department will only accept relevant instructions. If a team member is interested in a particular technology, the communications department can also prioritize instructions related to that technology. Furthermore, it can filter out unnecessary instructions based on the current situation of the team members. For example, if a team member is focused on the current race situation, the communications department will only accept relevant instructions. If a team member is interested in a particular technology, it can also prioritize instructions related to that technology. Furthermore, it can filter out unnecessary instructions based on the current situation of the team members. This ensures that highly relevant instructions are received by filtering based on the current situation and areas of interest. Some or all of the above processing in the communications department may be performed using AI, or not. For example, the communications department can input data on the current situation and areas of interest into a generating AI and have the generating AI perform the filtering process.

[0065] The communication unit can estimate the user's emotions and prioritize instructions based on the estimated user emotions. The communication unit estimates the user's emotions using technologies such as voice analysis, facial expression recognition, and text analysis. For example, voice analysis technology analyzes the tone and speed of the user's voice to estimate emotions. Facial expression recognition technology captures the user's facial expressions with a camera to estimate emotions. Text analysis technology analyzes the user's text messages to estimate emotions. This allows the user's emotions to be estimated and instructions to be prioritized based on the estimated emotions. For example, if the user is nervous, important instructions can be prioritized. Also, if the user is relaxed, detailed instructions can be prioritized. Furthermore, if the user is in a hurry, instructions requiring a quick response can be prioritized. This allows important instructions to be prioritized by prioritizing instructions based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI 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 communication unit may be performed using, for example, AI, or may be performed without using AI. For example, the communication unit may input voice data and facial expression data to the generation AI and have the generation AI estimate emotions.

[0066] The communications department can prioritize receiving instructions that are highly relevant, taking into account the geographical location information of team members. For example, if a team member is in a specific location, the communications department will prioritize receiving instructions related to that location. If a team member is on the move, the communications department can also prioritize receiving instructions related to their destination. Furthermore, if a team member is in a specific region, the communications department can also prioritize receiving instructions related to that region. In this way, by considering geographical location information, highly relevant instructions are prioritized. Some or all of the above processing in the communications department may be performed using AI, for example, or not. For example, the communications department can input geographic location data into a generating AI and have the AI ​​perform a process that prioritizes receiving instructions that are highly relevant.

[0067] When receiving an instruction, the communication unit can analyze the social media activities of the team members and accept the relevant instruction. For example, the communication unit can accept the relevant instruction based on information shared by the team members on social media. For example, the communication unit can accept the relevant instruction based on information shared by the team members on social media. The communication unit can also analyze current areas of interest from the team members' social media activities and accept the relevant instruction. Furthermore, the communication unit can also accept the relevant instruction based on information about accounts followed by the team members on social media. For example, the communication unit can accept the relevant instruction based on information shared by the team members on social media. The communication unit can also analyze current areas of interest from the team members' social media activities and accept the relevant instruction. Furthermore, the communication unit can also accept the relevant instruction based on information about accounts followed by the team members on social media. In this way, the social media activities are analyzed and the relevant instruction is accepted. Some or all of the above-described processing in the communication unit may be performed using, for example, AI, or may be performed without using AI. For example, the communication unit can input social media activity data to the generation AI and cause the generation AI to execute a process of accepting the relevant instruction.

[0068] The autonomous driving unit can estimate the user's emotions and adjust the route selection method based on the estimated emotions. The autonomous driving unit estimates the user's emotions using technologies such as voice analysis, facial recognition, and text analysis. For example, voice analysis technology analyzes the tone and speed of the user's voice to estimate emotions. Facial recognition technology captures the user's facial expressions with a camera to estimate emotions. Text analysis technology analyzes the user's text messages to estimate emotions. This allows the unit to estimate the user's emotions and adjust the route selection method based on the estimated emotions. For example, if the user is relaxed, it may select a route with good scenery. If the user is in a hurry, it may select the shortest route. Furthermore, if the user is excited, it may select a challenging route. By adjusting the route selection method according to the user's emotions, a more appropriate route is selected. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above-described processes in the autonomous driving unit may be performed using AI, for example, or without AI. For example, the autonomous driving unit can input voice data and facial expression data into a generating AI and have the generating AI perform emotion estimation.

[0069] The autonomous driving unit can analyze past driving data and select the most suitable driving route. The autonomous driving unit, for example, selects the most efficient route based on past driving data. For example, the autonomous driving unit can select the most efficient route from past driving data. It can also select a route that avoids congestion based on past driving data. It can also analyze past driving data and select the safest route. For example, the autonomous driving unit can select the most efficient route from past driving data. It can also select a route that avoids congestion based on past driving data. It can also analyze past driving data and select the safest route. In this way, the optimal driving route is selected by analyzing the past driving data. Some or all of the above-mentioned processing in the autonomous driving unit may be performed using, for example, AI, or may be performed without using AI. For example, the autonomous driving unit can input past driving data into a generation AI and cause the generation AI to execute processing to select an optimal driving route.

[0070] When selecting a driving route, the autonomous driving unit can perform filtering based on current road conditions and weather information. The autonomous driving unit selects an optimal route based on, for example, current road congestion information. For example, the autonomous driving unit can select an optimal route based on current road congestion information. The autonomous driving unit can also select an optimal route based on current weather information. A detour route can also be selected based on current road construction information. For example, the autonomous driving unit can select an optimal route based on current road congestion information. The autonomous driving unit can also select an optimal route based on current weather information. A detour route can also be selected based on current road construction information. In this way, an optimal driving route is selected by filtering based on current road conditions and weather information. Some or all of the above-described processing in the autonomous driving unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the autonomous driving unit can input current road conditions and weather information to a generation AI and cause the generation AI to perform a filtering process.

[0071] The autonomous driving unit can estimate the user's emotions and determine the priority of the driving route based on the estimated emotions. The autonomous driving unit estimates the user's emotions using technologies such as voice analysis, facial recognition, and text analysis. For example, voice analysis technology analyzes the tone and speed of the user's voice to estimate emotions. Facial recognition technology captures the user's facial expressions with a camera to estimate emotions. Text analysis technology analyzes the user's text messages to estimate emotions. This allows the unit to estimate the user's emotions and determine the priority of the driving route based on the estimated emotions. For example, if the user is nervous, the safest route will be prioritized. If the user is relaxed, a route with good scenery may be prioritized. Furthermore, if the user is in a hurry, the shortest route may be prioritized. In this way, by determining the priority of the driving route according to the user's emotions, important driving routes are selected preferentially. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above-described processes in the autonomous driving unit may be performed using AI, for example, or without AI. For example, the autonomous driving unit can input voice data and facial expression data into a generating AI and have the generating AI perform emotion estimation.

[0072] When selecting a driving route, the autonomous driving unit can select the most suitable route by taking geographical location information into consideration. The autonomous driving unit, for example, selects the shortest route from the current location to the destination. For example, the autonomous driving unit selects the shortest route from the current location to the destination. It can also select the safest route from the current location to the destination. It can also select the most efficient route from the current location to the destination. For example, the autonomous driving unit selects the shortest route from the current location to the destination. It can also select the safest route from the current location to the destination. It can also select the most efficient route from the current location to the destination. In this way, the optimal driving route is selected by taking geographical location information into consideration. Some or all of the above-described processing in the autonomous driving unit may be performed using, for example, AI, or may be performed without using AI. For example, the autonomous driving unit can input geographical location information data to a generation AI and cause the generation AI to execute processing to select an optimal route.

[0073] When selecting a driving route, the autonomous driving unit can select an optimal route by referring to related traffic information. The autonomous driving unit selects the optimal route, for example, based on real-time traffic congestion information. For example, the autonomous driving unit selects the optimal route based on real-time traffic congestion information. The autonomous driving unit can also select the optimal route by taking into account the real-time operation status of public transportation. Furthermore, the autonomous driving unit can also select a detour route based on real-time road construction information. For example, the autonomous driving unit selects the optimal route based on real-time traffic congestion information. The autonomous driving unit can also select the optimal route by taking into account the real-time operation status of public transportation. Furthermore, the autonomous driving unit can also select a detour route based on real-time road construction information. In this way, the optimal driving route is selected by referring to related traffic information. Some or all of the above-mentioned processing in the autonomous driving unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the autonomous driving unit can input traffic information data to a generation AI and cause the generation AI to execute a process of selecting an optimal route.

[0074] The data analysis unit can estimate the user's emotions and adjust the data analysis method based on the estimated user's emotions. The data analysis unit estimates the user's emotions using technologies such as voice analysis, facial expression recognition, and text analysis. For example, voice analysis technology analyzes the tone and speed of the user's voice to estimate emotions. Facial expression recognition technology captures the user's facial expressions with a camera to estimate emotions. Text analysis technology analyzes the user's text messages to estimate emotions. This allows the user's emotions to be estimated and the data analysis method to be adjusted based on the estimated emotions. For example, if the user is relaxed, detailed data analysis can be performed. If the user is in a hurry, quick data analysis can be performed. Furthermore, if the user is excited, visually stimulating data analysis can be performed. This allows the data analysis method to be adjusted according to the user's emotions, resulting in more appropriate data analysis. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. The generative AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the data analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the data analysis unit may input voice data and facial expression data to the generation AI and cause the generation AI to estimate emotions.

[0075] The data analysis unit can optimize the data analysis algorithm by referring to past data. The data analysis unit, for example, selects an optimal data analysis algorithm based on past data. For example, the data analysis unit can select an optimal data analysis algorithm based on past data. The data analysis unit can also analyze past data and optimize the data analysis algorithm. Furthermore, the data analysis accuracy can be improved by referring to past data. For example, the data analysis unit can select an optimal data analysis algorithm based on past data. The data analysis unit can also analyze past data and optimize the data analysis algorithm. Furthermore, the data analysis accuracy can be improved by referring to past data. In this way, the data analysis algorithm is optimized by referring to past data. Some or all of the above-mentioned processing in the data analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the data analysis unit can input past data to the generation AI and cause the generation AI to optimize the data analysis algorithm.

[0076] The data analysis unit can perform filtering based on the current race situation and environmental information during data analysis. For example, the data analysis unit can prioritize the analysis of relevant data based on the current race situation. It can also prioritize the analysis of relevant data based on the current environmental information. Furthermore, it can filter out unnecessary data based on the current race situation and environmental information. For example, it can prioritize the analysis of relevant data based on the current race situation. It can also prioritize the analysis of relevant data based on the current environmental information. Furthermore, it can filter out unnecessary data based on the current race situation and environmental information. As a result, by filtering based on the current race situation and environmental information, highly relevant data is prioritized for analysis. Some or all of the above processing in the data analysis unit may be performed using AI, for example, or without AI. For example, the data analysis unit can input the current race situation and environmental information into a generating AI and have the generating AI perform the filtering process.

[0077] The data analysis unit can estimate the user's emotions and determine the priority of data analysis based on the estimated user emotions. The data analysis unit estimates the user's emotions using technologies such as voice analysis, facial expression recognition, and text analysis. For example, voice analysis technology analyzes the tone and speed of the user's voice to estimate emotions. Facial expression recognition technology captures the user's facial expressions with a camera to estimate emotions. Text analysis technology analyzes the user's text messages to estimate emotions. This allows the user's emotions to be estimated and the priority of data analysis to be determined based on the estimated emotions. For example, if the user is nervous, important data can be analyzed first. Alternatively, if the user is relaxed, detailed data can be analyzed first. Furthermore, if the user is in a hurry, data requiring a quick response can be analyzed first. This allows important data to be analyzed first by determining the priority of data analysis based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. The generative AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the data analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the data analysis unit may input voice data and facial expression data to the generation AI and cause the generation AI to estimate emotions.

[0078] The data analysis unit can select the optimal analysis method by considering geographical location information during data analysis. For example, the data analysis unit can select the optimal data analysis method based on current location information. For example, it can select the optimal data analysis method based on current location information. It can also select the most efficient data analysis method by considering geographical location information. Furthermore, it can select the most accurate data analysis method by considering geographical location information. For example, it can select the optimal data analysis method based on current location information. It can also select the most efficient data analysis method by considering geographical location information. Furthermore, it can select the most accurate data analysis method by considering geographical location information. In this way, the optimal data analysis method is selected by considering geographical location information. Some or all of the above processing in the data analysis unit may be performed using AI, for example, or without using AI. For example, the data analysis unit can input geographical location information data into a generating AI and have the generating AI execute the process of selecting the optimal data analysis method.

[0079] During data analysis, the data analysis unit can improve the accuracy of the analysis by referring to related past race data. The data analysis unit, for example, improves the accuracy of the data analysis based on past race data. For example, the data analysis accuracy can be improved based on past race data. The data analysis algorithm can also be optimized by referring to related past race data. The data analysis accuracy can also be improved by analyzing past race data. For example, the data analysis accuracy can be improved based on past race data. The data analysis algorithm can also be optimized by referring to related past race data. The data analysis accuracy can also be improved by analyzing past race data. In this way, the accuracy of the data analysis is improved by referring to related past race data. Some or all of the above-described processing in the data analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the data analysis unit can input past race data to the generation AI and cause the generation AI to perform processing to improve the accuracy of the data analysis. === Hard Collateral 1-1 === Each of the multiple elements including the communication unit, the automatic driving unit, and the data analysis unit described above is realized, for example, by at least one of the smart device 14 and the data processing device 12. For example, the communication unit receives instructions in natural language using the microphone 38B or the touch panel 38A of the smart device 14 and analyzes the instructions using the control unit 46A. The automatic driving unit selects a driving route using the camera 42 or GPS of the smart device 14 and is controlled by the processor 46. The data analysis unit is realized by the specific processing unit 290 of the data processing device 12 and analyzes collected data to select an optimal driving route. === Hard Collateral 1-2 === Each of the multiple elements including the communication unit, the autonomous driving unit, and the data analysis unit described above is realized, for example, by at least one of the smart glasses 214 and the data processing device 12. For example, the communication unit receives instructions in natural language using the microphone 238 and the speaker 240 of the smart glasses 214 and analyzes the content of the instructions using the control unit 46A. The autonomous driving unit selects a driving route using the camera 42 and GPS of the smart glasses 214 and is controlled by the processor 46. The data analysis unit is realized by the specific processing unit 290 of the data processing device 12 and analyzes collected data to select an optimal driving route. === Hard Collateral 1-3 === Each of the multiple elements including the communication unit, the automatic driving unit, and the data analysis unit described above is realized, for example, by at least one of the headset type terminal 314 and the data processing device 12. For example, the communication unit receives instructions in natural language using the microphone 238 and the speaker 240 of the headset type terminal 314, and analyzes the content of the instructions using the control unit 46A. The automatic driving unit selects a driving route using the camera 42 and GPS of the headset type terminal 314, and is controlled by the processor 46. The data analysis unit is realized by the specific processing unit 290 of the data processing device 12, and analyzes collected data to select an optimal driving route. === Hard Collateral 1-4 === Each of the multiple elements including the communication unit, the automatic driving unit, and the data analysis unit described above is realized, for example, by at least one of the robot 414 and the data processing device 12. For example, the communication unit receives instructions in natural language using the microphone 238 and the speaker 240 of the robot 414, and analyzes the content of the instructions using the control unit 46A. The automatic driving unit selects a driving route using the camera 42 and GPS of the robot 414, and is controlled by the processor 46. The data analysis unit is realized by the specific processing unit 290 of the data processing device 12, and analyzes collected data to select an optimal driving route.

[0080] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0081] The communication unit can estimate the user's emotions and adjust the AI ​​player's responses based on the estimated emotions. For example, if the user is excited, the AI ​​player can respond in a calm tone to calm the user. If the user is depressed, the AI ​​player can offer words of encouragement. Furthermore, if the user is having fun, the AI ​​player can share that emotion and provide a more enjoyable conversation. This enables appropriate responses according to the user's emotions, resulting in more natural communication.

[0082] The autonomous driving part can predict the movements of other vehicles during a race and select the optimal driving route. For example, if the vehicle ahead is likely to brake suddenly, the AI ​​player will slow down in advance. Also, if a vehicle in the adjacent lane is attempting to change lanes, the AI ​​player can predict that movement and maintain a safe distance. Furthermore, if a vehicle behind is attempting to overtake, the AI ​​player can predict that movement and take appropriate action. This allows for safer and more efficient driving by predicting the movements of other vehicles.

[0083] The data analysis unit can analyze data collected during a race in real time and provide feedback to improve the AI ​​player's performance. For example, if the AI ​​player is slowing down too much when cornering, the data analysis unit can provide that information in real time and instruct the AI ​​player to maintain an appropriate speed when cornering next time. Also, if the AI ​​player brakes too early, the data analysis unit can provide that information and instruct the AI ​​player to adjust the next braking point. Furthermore, if the AI ​​player is losing traction when accelerating, the data analysis unit can provide that information and instruct the AI ​​player to maintain traction when accelerating next time. This allows the AI ​​player's performance to improve through real-time data analysis and feedback.

[0084] The communication department can estimate the user's emotions and adjust the AI ​​player's strategy based on the estimated emotions. For example, if the user is nervous, the AI ​​player will adopt a risk-averse strategy. If the user is confident, the AI ​​player can adopt an aggressive strategy. Furthermore, if the user is tired, the AI ​​player can adopt a conservative strategy. This makes it possible to adjust strategies according to the user's emotions, resulting in more effective race management.

[0085] The autonomous driving part can detect road conditions in real time during a race and select the optimal driving route. For example, if the road is wet, the AI ​​player will adjust its speed to avoid slipping. Also, if the road is uneven, the AI ​​player can select a route that avoids those parts. Furthermore, if the road is dry, the AI ​​player can increase its speed to obtain optimal grip. This makes it possible to select the optimal driving route according to road conditions, resulting in safe and efficient driving.

[0086] The data analysis unit can estimate the user's emotions and adjust the way data is displayed based on the estimated emotions. For example, if the user is nervous, the data can be displayed simply, emphasizing only important information. If the user is relaxed, detailed data can be displayed, allowing the user to grasp the overall situation. Furthermore, if the user is excited, the data can be displayed using visually appealing graphics. This enables the data to be displayed in a way that corresponds to the user's emotions, resulting in more effective information provision.

[0087] The communication unit can analyze the user's past behavioral data and provide optimal instructions. For example, it can analyze what instructions the user has given in specific situations in the past and provide optimal instructions for similar situations. It can also learn patterns of instructions that the user has preferred in the past and provide instructions based on those patterns. Furthermore, it can also provide instructions in advance for predicted situations based on the user's past behavioral data. In this way, more effective instructions can be provided by utilizing the user's past behavioral data.

[0088] The autonomous driving unit can communicate with other vehicles during a race and drive in coordination with them. For example, it can cooperate with the vehicle in front to perform draft driving, improving fuel efficiency. It can also cooperate with the vehicle next to it to smoothly change lanes. It can also cooperate with the vehicle behind it to safely overtake. This enables coordinated driving through communication with other vehicles, improving the efficiency and safety of the race.

[0089] The data analysis unit can estimate the user's emotions and adjust the data analysis method based on the estimated emotions. For example, if the user is nervous, a simplified analysis can be performed to provide quick results. If the user is relaxed, a detailed analysis can be performed to provide comprehensive results. Furthermore, if the user is excited, the results can be provided in a visually appealing format. This enables a data analysis method that corresponds to the user's emotions, resulting in more effective information provision.

[0090] The communication unit can estimate the user's emotions and adjust the AI ​​player's behavior based on the estimated emotions. For example, if the user is nervous, the AI ​​player will take risk-avoiding actions. If the user is confident, the AI ​​player can take aggressive actions. Furthermore, if the user is tired, the AI ​​player can take conservative actions. This makes it possible to adjust behavior according to the user's emotions, resulting in more effective race management.

[0091] The processing flow of the second embodiment will be briefly explained below.

[0092] Step 1: In the communication section, the AI ​​player receives instructions in natural language. Examples include voice instructions and text instructions. Voice instructions can be received using voice recognition technology, and text instructions can be received using text analysis technology. Voice recognition technology converts voice data into text data and conveys instructions to the AI ​​player. Text analysis technology analyzes the text data and understands the content of the instructions. Step 2: The autonomous driving unit selects a driving route based on the instructions received by the communication unit. For example, it selects a driving route taking into consideration the shortest distance, traffic conditions, and user preferences. It uses LIDAR technology to scan the surrounding environment and selects the optimal driving route. It can also select a driving route using a camera or GPS. LIDAR technology uses laser light to measure the distance to surrounding objects and generate a 3D map. The camera analyzes image data and understands road conditions. The GPS identifies the current location and calculates the route to the destination. Step 3: The data analysis unit analyzes the data collected by the autonomous driving unit. For example, it analyzes the data using statistical analysis and machine learning algorithms. Statistical analysis is used to analyze the driving data and optimize the driving route. Machine learning algorithms are used to analyze the driving data and improve the driving performance of the AI ​​player. Statistical analysis calculates the average and variance of the driving data and helps optimize the driving route. Machine learning algorithms learn from large amounts of driving data and improve the accuracy of driving route selection.

[0093] 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.

[0094] 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.

[0095] 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.

[0096] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0097] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0098] 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.

[0099] 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.

[0100] 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.

[0101] 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.

[0102] 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).

[0103] 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.

[0104] 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.

[0105] 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.

[0106] 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.

[0107] 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.

[0108] 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.

[0109] 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.

[0110] 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.

[0111] 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.

[0112] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0113] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0114] 5, the data processing system 310 includes the data processing device 12 and a headset terminal 314. An example of the data processing device 12 is a server.

[0115] 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.

[0116] 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.

[0117] 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.

[0118] 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).

[0119] 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.

[0120] Fig. 6 shows an example of the main functions of the data processing device 12 and the headset 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.

[0121] 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.

[0122] 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.

[0123] 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.

[0124] 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.

[0125] 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.

[0126] 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.

[0127] 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.

[0128] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0129] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0130] 7, a data processing system 410 includes a data processing device 12 and a robot 414. An example of the data processing device 12 is a server.

[0131] 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.

[0132] 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.

[0133] 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.

[0134] 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).

[0135] 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.

[0136] 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.

[0137] 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.

[0138] 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.

[0139] 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.

[0140] 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.

[0141] 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.

[0142] 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.

[0143] 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.

[0144] 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.

[0145] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0146] 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.

[0147] 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.

[0148] 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.

[0149] 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).

[0150] Human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, a state of discomfort is expressed, and when they approach the ideal, a state of pleasure is expressed. Emotions can also be created for robots, cars, and motorcycles, based on various balances, such as posture and remaining battery life. When these balances deviate from the ideal, a state of discomfort is expressed, and when they approach the ideal, a state of pleasure is expressed. An emotion map can be generated, for example, based on Dr. Mitsuyoshi's emotion map (Research on speech emotion recognition and brain physiological signal analysis systems for emotions, Tokushima University, doctoral dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map lists emotions belonging to the area called "reaction," where sensation is dominant. The right half of the emotion map lists emotions belonging to the area called "situation," where situational awareness is dominant.

[0151] 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."

[0152] 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.

[0153] 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.

[0154] 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.

[0155] 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.

[0156] 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.

[0157] 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.

[0158] 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.

[0159] 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.

[0160] 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.

[0161] 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.

[0162] The above-described description and illustrations are a detailed explanation of the parts related to the technology of the present disclosure and are merely an example of the technology of the present disclosure. For example, the above description of the configuration, functions, actions, and effects is an explanation of an example of the configuration, functions, actions, and effects of the parts related to the technology of the present disclosure. Therefore, it goes without saying that unnecessary parts may be deleted, new elements may be added, or replacements may be made to the above-described description and illustrations within the scope of the gist of the technology of the present disclosure. Furthermore, to avoid confusion and facilitate understanding of the parts related to the technology of the present disclosure, the above-described description and illustrations omit explanations of common technical knowledge that do not require particular explanation to enable the implementation of the technology of the present disclosure.

[0163] 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.

[0164] [Explanation of symbols]

[0165] 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 communication unit that accepts instructions in natural language; an automatic driving unit that selects a driving route based on instructions received by the communication unit; a data analysis unit that analyzes the data collected by the automatic driving unit. A system characterized by:

2. The communication unit AI players converse in natural language and communicate with team members 2. The system of claim 1.

3. The automatic driving unit is AI players will participate in races using autonomous driving technology.

2. The system of claim 1.

4. The data analysis unit AI players collect and analyze data in real time 2. The system of claim 1.

5. The communication unit Receive natural language instructions to handle multiple situations that arise during the race 2. The system of claim 1.

6. The automatic driving unit is Choose the best route during the race 2. The system of claim 1.

7. The communication unit Estimate the user's emotions and adjust the way instructions are received based on the estimated user emotions.

2. The system of claim 1.

8. The communication unit Analyze team members' past instruction history and select the most appropriate communication method 2. The system of claim 1.

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