System

Abstract

An object of the system according to the embodiment is to provide special assistance to people with physical restrictions and to realize a barrier-free environment.SOLUTION: A system includes a reception unit, an analysis unit, and a provision unit. The reception unit receives a voice or gesture instruction from a user. The analysis unit analyzes the instruction received by the reception unit and generates information for providing assistance. The provision unit provides assistance on the basis of the information generated by the analysis unit.SELECTED DRAWING: Figure 1

Description

[Technical Field]

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

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

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

[0004] Conventional technologies have difficulty providing sufficient assistance to people with physical limitations, and there is room for improvement in realizing a barrier-free environment.

[0005] The system according to the embodiment aims to provide special assistance to people with physical limitations and to realize a barrier-free environment. [Means for solving the problem]

[0006] A system according to an embodiment includes a reception unit, an analysis unit, and a provision unit. The reception unit receives voice or gesture instructions from a user. The analysis unit analyzes the instructions received by the reception unit and generates information for providing assistance. The provision unit provides assistance based on the information generated by the analysis unit. [Effects of the Invention]

[0007] The system according to the embodiment can provide special assistance to people with physical limitations and realize a barrier-free environment. [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) In an assistance system according to an embodiment of the present invention, a user wears AR glasses and inputs instructions via voice or gestures. The LLM analyzes the instructions and provides appropriate assistance. In this assistance system, a user wears AR glasses and inputs instructions via voice or gestures. For example, a visually impaired person may say, "Tell me the way to the station." This instruction is input to the LLM via a microphone and sensors installed in the AR glasses. The LLM then analyzes the input instruction. The LLM understands the voice or gesture instruction and generates information to provide appropriate assistance. For example, in response to the instruction, "Tell me the way to the station," the LLM calculates the optimal route from the current location to the station and transmits that information to the AR glasses. The AR glasses then provide appropriate assistance to the user based on the information transmitted from the LLM. For example, if a visually impaired person wants to know the way to the station, the AR glasses analyze the surrounding environment in real time and provide navigation such as "Turn right" or "There is a crosswalk 50 meters ahead." Furthermore, to enable hearing impaired people to understand conversations, the AR glasses convert speech into text and display it visually in real time. For example, if a conversation partner says, "Hello, how are you?", the AR glasses will convert the speech into text and display it visually, allowing the hearing impaired person to understand the conversation. In this way, assistance systems help people with physical limitations to live more independently. For example, a visually impaired person can navigate the streets independently, and a hearing impaired person can understand conversations. This creates a barrier-free environment and improves the quality of life for people with physical limitations. In this way, assistance systems help people with physical limitations to live more independently. For example, a visually impaired person can navigate the streets independently, and a hearing impaired person can understand conversations. This creates a barrier-free environment and improves the quality of life for people with physical limitations.

[0029] An assistance system according to an embodiment includes a reception unit, an analysis unit, and a provision unit. The reception unit receives voice or gesture instructions from a user. For example, the reception unit uses a microphone to receive voice instructions. The reception unit can also use a camera or a sensor to receive gesture instructions. The analysis unit analyzes the instructions received by the reception unit and generates information for providing assistance. For example, the analysis unit analyzes voice instructions using voice recognition technology. The analysis unit can also analyze gesture instructions using gesture recognition technology. The analysis unit uses a large-scale language model (LLM) to understand voice or gesture instructions and generate information for providing appropriate assistance. For example, the analysis unit analyzes a voice instruction such as "Tell me the way to the station" and calculates the optimal route from the current location to the station. The provision unit provides assistance based on the information generated by the analysis unit. For example, the provision unit analyzes the surrounding environment and provides voice navigation for visually impaired persons. The provision unit can also convert voice to text and display it visually for hearing impaired persons. Some or all of the above-described processing in the providing unit may be performed using, for example, AI, or may be performed without using AI. As a result, the assistance system according to the embodiment can analyze the user's voice and gesture instructions and provide appropriate assistance, thereby helping people with physical limitations to live more independently.

[0030] The providing unit can analyze the surrounding environment for visually impaired persons and provide navigation by voice. The providing unit, for example, uses a camera or a sensor to analyze the surrounding environment for visually impaired persons. For example, the providing unit can acquire images of the surroundings using a camera and analyze the location of obstacles and the shape of roads using image recognition technology. The providing unit can also acquire distance information about the surroundings using a sensor and generate navigation information. The providing unit provides navigation by voice based on the analyzed information. For example, the providing unit can provide voice navigation such as "Turn right" or "There is a crosswalk 50 meters ahead." The providing unit can also analyze the surrounding environment in real time and provide appropriate navigation information to the user. This can help visually impaired persons navigate independently. Some or all of the above-described processing in the providing unit may be performed using, for example, AI, or may be performed without AI. For example, the providing unit can input image data acquired by a camera to a generation AI and cause the generation AI to generate navigation information from the image data.

[0031] The providing unit can convert speech into text and visually display it for the hearing impaired. The providing unit, for example, uses speech recognition technology to convert speech into text for the hearing impaired. For example, the providing unit captures speech using a microphone and converts it into text in real time using speech recognition technology. The providing unit can also use a display to visually display the converted text. For example, the providing unit displays text on the display of AR glasses to enable the hearing impaired to understand the conversation. The providing unit can also support multiple languages ​​using speech recognition technology. For example, the providing unit recognizes speech input in multiple languages, such as English, Japanese, and French, and converts it into text. This can help the hearing impaired to understand the conversation. Some or all of the above-described processing in the providing unit may be performed using AI, or may be performed without AI. For example, the providing unit can input speech data captured by a microphone to a generation AI and have the generation AI convert the speech data into text data.

[0032] The reception unit can receive voice instructions. The reception unit, for example, uses a microphone to receive voice instructions. For example, when a user gives a voice instruction such as "Tell me the way to the station," the reception unit captures the voice using the microphone. The reception unit can also use noise canceling technology to accurately receive voice instructions. For example, the reception unit filters out surrounding environmental sounds to clearly receive voice instructions. This allows the user's voice instructions to be accurately received. Some or all of the above-described processing in the reception unit may be performed using, for example, AI, or may be performed without using AI. For example, the reception unit can input voice data captured by the microphone to a generation AI and have the generation AI analyze the content of the instruction from the voice data.

[0033] The reception unit can receive gesture instructions. The reception unit uses, for example, a camera or a sensor to receive the gesture instructions. For example, when a user makes a gesture such as waving their hand, the reception unit captures the movement with a camera. The reception unit can also use image recognition technology to accurately receive gesture instructions. For example, the reception unit analyzes image data acquired by a camera and recognizes the content of the gesture. This allows the user's gesture instructions to be accurately received. Some or all of the above-described processing in the reception unit may be performed using, for example, AI, or may be performed without using AI. For example, the reception unit can input image data acquired by a camera to a generation AI and have the generation AI analyze the content of the gesture from the image data.

[0034] The analysis unit can analyze the voice instruction and generate information for providing assistance. The analysis unit, for example, uses speech recognition technology to analyze the voice instruction. For example, the analysis unit converts the voice instruction into text data and analyzes the content. The analysis unit can also understand the meaning of the voice instruction using natural language processing technology. For example, the analysis unit analyzes the voice instruction, such as "Tell me the way to the station," and calculates the optimal route from the current location to the station. The analysis unit uses a large-scale language model (LLM) to understand the voice instruction and generate information for providing appropriate assistance. For example, the analysis unit analyzes the voice instruction using an LLM model that takes the voice instruction as input and outputs navigation information. This makes it possible to analyze the voice instruction and generate information for providing appropriate assistance. Some or all of the above-described processing in the analysis unit may be performed using, for example, AI, or may be performed without AI. For example, the analysis unit can input voice data to a generation AI and cause the generation AI to generate assistance information from the voice data.

[0035] The analysis unit can analyze the gesture instruction and generate information for providing assistance. The analysis unit, for example, uses image recognition technology to analyze the gesture instruction. For example, the analysis unit can analyze image data acquired by a camera and recognize the content of the gesture. The analysis unit can also understand the meaning of the gesture instruction using motion analysis technology. For example, the analysis unit can analyze a user's hand-waving gesture and understand its meaning. The analysis unit can use a large-scale language model (LLM) to understand the gesture instruction and generate information for providing appropriate assistance. For example, the analysis unit can analyze the gesture instruction using an LLM model that inputs the gesture instruction and outputs assistance information. This makes it possible to analyze the gesture instruction and generate information for providing appropriate assistance. Some or all of the above-described processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input image data to a generation AI and cause the generation AI to generate assistance information from the image data.

[0036] The reception unit can analyze the user's past instruction history and select a reception method. The reception unit, for example, uses data mining technology or machine learning algorithms to analyze the user's past instruction history. For example, the reception unit prioritizes receiving voice instructions that the user has frequently used in the past. The reception unit can also suggest an optimal gesture based on the user's past gesture instructions. Furthermore, the reception unit can predict and suggest an instruction method to be used in a specific time period based on the user's past instruction history. In this way, the optimal reception method can be selected by analyzing the user's past instruction history. Some or all of the above-described processing in the reception unit may be performed using, for example, AI, or may be performed without using AI. For example, the reception unit can input past instruction history data into a generation AI and have the generation AI select an optimal reception method.

[0037] The reception unit can filter the user's environmental sounds to remove noise when receiving a voice instruction. The reception unit, for example, uses noise canceling technology when receiving a voice instruction. For example, when the user is in a noisy place, the reception unit filters the environmental sounds to clearly receive the voice instruction. Furthermore, when the user is in a quiet place, the reception unit can accurately receive even subtle voice instructions. Furthermore, when the user is moving, the reception unit can also filter wind noise and traffic noise to receive the voice instruction. This allows the voice instruction to be clearly received by filtering the environmental sounds and removing noise. Some or all of the above-described processing in the reception unit may be performed using, for example, AI, or may be performed without using AI. For example, the reception unit can input environmental sound data to a generation AI and cause the generation AI to remove noise.

[0038] The reception unit can improve the accuracy of gesture recognition based on the user's physical characteristics when receiving a gesture instruction. The reception unit, for example, uses a machine learning algorithm to improve the accuracy of gesture recognition by taking the user's physical characteristics into account. For example, the reception unit can learn the size and movement patterns of the user's hand to improve the accuracy of gesture recognition. The reception unit can also analyze the user's arm movement and posture to perform accurate gesture recognition. Furthermore, the reception unit can apply a customized gesture recognition algorithm based on the user's physical characteristics. This enables accurate gesture recognition by improving the accuracy of gesture recognition based on the user's physical characteristics. Some or all of the above-described processing in the reception unit can be performed using, for example, AI, or without AI. For example, the reception unit can input the user's physical characteristic data into a generation AI and cause the generation AI to improve the accuracy of gesture recognition.

[0039] When receiving a voice instruction, the reception unit can prioritize receiving highly relevant instructions by taking into account the user's geographical location information. The reception unit, for example, uses GPS technology to consider the user's geographical location information. For example, when the user is in a specific location, the reception unit can prioritize receiving instructions related to that location. Furthermore, when the user is moving, the reception unit can prioritize receiving optimal instructions based on the user's current location. Furthermore, when the user is approaching a destination, the reception unit can prioritize receiving instructions related to the destination. In this way, by taking into account the user's geographical location information, highly relevant instructions can be prioritized. Some or all of the above-described processing in the reception unit may be performed using, for example, AI, or may be performed without using AI. For example, the reception unit can input geographical location information data to a generation AI and cause the generation AI to execute highly relevant instructions at a higher priority.

[0040] When receiving a gesture instruction, the reception unit can analyze the user's social media activity and receive related instructions. The reception unit, for example, uses data mining technology to analyze the user's social media activity. For example, the reception unit can prioritize receiving instructions related to places where the user has checked in on social media. The reception unit can also analyze the content of the user's social media posts and prioritize receiving related instructions. Furthermore, the reception unit can prioritize receiving related instructions by referring to the activities of the user's friends on social media. In this way, by analyzing the user's social media activity, related instructions can be prioritized. Some or all of the above-described processing by the reception unit may be performed using, for example, AI, or may be performed without using AI. For example, the reception unit can input social media data into a generation AI and cause the generation AI to receive related instructions.

[0041] When receiving a voice instruction, the reception unit can adjust the reception method by reflecting the user's past feedback. The reception unit, for example, uses data mining technology to reflect the user's past feedback. For example, the reception unit improves the reception method for voice instructions based on feedback provided by the user in the past. The reception unit can also suggest an optimal reception method for voice instructions based on the user's past feedback. Furthermore, the reception unit can also improve the accuracy of reception of voice instructions by reflecting the user's feedback. In this way, the reception method for voice instructions can be customized by reflecting the user's past feedback. Some or all of the above-described processing in the reception unit may be performed using, for example, AI, or may be performed without using AI. For example, the reception unit can input past feedback data into a generation AI and cause the generation AI to adjust the reception method.

[0042] When analyzing instructions, the analysis unit can adjust the level of detail of the analysis based on the importance of the instructions. The analysis unit, for example, uses a machine learning algorithm to evaluate the importance of the instructions. For example, the analysis unit evaluates the urgency of the instructions and the priority of the task, and performs a detailed analysis on instructions with high importance. The analysis unit can also perform a concise analysis on instructions with low importance. Furthermore, the analysis unit can determine the priority of the analysis based on the importance of the instructions. This enables efficient analysis by adjusting the level of detail of the analysis based on the importance of the instructions. Some or all of the above-mentioned processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input instruction data to a generation AI and have the generation AI evaluate the importance and adjust the level of detail of the analysis.

[0043] When analyzing instructions, the analysis unit can apply different analysis algorithms depending on the category of the instruction. The analysis unit, for example, uses a machine learning algorithm to classify the category of the instruction. For example, the analysis unit applies an algorithm that calculates the optimal route to navigation instructions. The analysis unit can also apply a speech recognition algorithm to conversational text conversion instructions. Furthermore, the analysis unit can select an appropriate analysis algorithm for other instructions. This enables highly accurate analysis by applying an appropriate analysis algorithm depending on the category of the instruction. Some or all of the above-mentioned processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input instruction data to a generation AI and have the generation AI apply an analysis algorithm depending on the category.

[0044] When analyzing instructions, the analysis unit can improve the accuracy of the analysis by referring to the user's past analysis results. The analysis unit, for example, uses data mining technology to analyze the user's past analysis results. For example, the analysis unit can improve the accuracy of the current instruction analysis based on the user's past analysis results. The analysis unit can also suggest an optimal analysis method based on the user's past analysis results. Furthermore, the analysis unit can adjust the analysis algorithm by referring to the user's past analysis results. In this way, the accuracy of the analysis can be improved by referring to the user's past analysis results. Some or all of the above-mentioned processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input past analysis result data into the generation AI and have the generation AI improve the accuracy of the analysis.

[0045] When analyzing instructions, the analysis unit can determine the priority of analysis based on the time when the instructions were submitted. The analysis unit, for example, uses data mining technology to evaluate the time when the instructions were submitted. For example, the analysis unit prioritizes analysis of urgent instructions. The analysis unit can also postpone analysis of instructions submitted earlier. Furthermore, the analysis unit can dynamically adjust the priority of analysis based on the time when the instructions were submitted. This enables efficient analysis by determining the priority of analysis based on the time when the instructions were submitted. Some or all of the above-mentioned processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input submission time data into the generation AI and have the generation AI determine the priority of analysis.

[0046] When analyzing instructions, the analysis unit can adjust the order of analysis based on the relevance of the instructions. The analysis unit, for example, uses a machine learning algorithm to evaluate the relevance of the instructions. For example, the analysis unit prioritizes analysis of highly relevant instructions. The analysis unit can also postpone analysis of less relevant instructions. Furthermore, the analysis unit can dynamically adjust the order of analysis based on the relevance of the instructions. This enables efficient analysis by adjusting the order of analysis based on the relevance of the instructions. Some or all of the above-mentioned processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input instruction data to a generation AI and have the generation AI execute an analysis order based on the relevance.

[0047] When analyzing instructions, the analysis unit can adjust the use of technical terminology in the analysis according to the user's level of expertise. The analysis unit, for example, uses a machine learning algorithm to evaluate the user's level of expertise. For example, if the user has technical expertise, the analysis unit can provide the analysis result using technical terminology. Alternatively, if the user does not have technical expertise, the analysis unit can provide the analysis result in simple, easy-to-understand language. Furthermore, the analysis unit can adjust the way the analysis result is expressed according to the user's level of expertise. This allows for more appropriate analysis results to be provided by adjusting the use of technical terminology in the analysis according to the user's level of expertise. Some or all of the above-described processing in the analysis unit may be performed using, for example, AI, or may be performed without AI. For example, the analysis unit can input the user's level of expertise data into the generation AI to adjust the use of technical terminology.

[0048] When providing assistance, the providing unit can select a provision method by analyzing the user's past assistance history. The providing unit, for example, uses data mining technology to analyze the user's past assistance history. For example, the providing unit selects an optimal provision method based on the assistance received by the user in the past. The providing unit can also suggest an optimal provision method for a specific time period based on the user's past assistance history. Furthermore, the providing unit can analyze the user's past assistance history and select the most effective provision method. In this way, the optimal provision method can be selected by analyzing the user's past assistance history. Some or all of the above-described processing in the providing unit may be performed using, for example, AI, or may be performed without using AI. For example, the providing unit can input past assistance history data to a generation AI and cause the generation AI to select an optimal provision method.

[0049] The providing unit can adjust the means of providing assistance based on the user's current living situation when providing assistance. The providing unit, for example, uses sensor technology or data mining technology to evaluate the user's current living situation. For example, when the user is at home, the providing unit can provide assistance optimal for the home. Also, when the user is out, the providing unit can provide assistance optimal for the destination. Furthermore, the providing unit can provide customized assistance based on the user's living situation. This makes it possible to provide more appropriate assistance by adjusting the means of providing assistance based on the user's current living situation. Some or all of the above-described processing in the providing unit may be performed using, for example, AI, or may be performed without using AI. For example, the providing unit can input living situation data to a generating AI and cause the generating AI to adjust the means of providing assistance.

[0050] The providing unit can adjust the assistance providing method by reflecting user feedback when providing assistance. The providing unit, for example, uses data mining technology to reflect user feedback. For example, the providing unit improves the assistance providing method based on the feedback provided by the user. The providing unit can also suggest an optimal assistance providing method based on the user feedback. Furthermore, the providing unit can improve the accuracy of the assistance by reflecting user feedback. In this way, the assistance providing method can be improved by reflecting user feedback. Some or all of the above-described processing in the providing unit may be performed using AI, for example, or may be performed without using AI. For example, the providing unit can input feedback data to a generating AI and cause the generating AI to adjust the assistance providing method.

[0051] When providing assistance, the providing unit can select a provision method taking into account the user's geographical location information. The providing unit, for example, uses GPS technology to consider the user's geographical location information. For example, when the user is in a specific location, the providing unit can provide the optimal assistance for that location. Also, when the user is moving, the providing unit can provide the optimal assistance based on the user's current location. Furthermore, when the user is approaching a destination, the providing unit can provide assistance related to the destination. In this way, optimal assistance can be provided by taking the user's geographical location information into account. Some or all of the above-described processing in the providing unit may be performed using, for example, AI, or may be performed without using AI. For example, the providing unit can input geographical location information data to a generation AI and cause the generation AI to select the optimal provision method.

[0052] When providing assistance, the providing unit can analyze the user's social media activity and suggest a means of providing the assistance. The providing unit, for example, uses data mining technology to analyze the user's social media activity. For example, the providing unit can provide assistance related to places where the user has checked in on social media. The providing unit can also analyze the content of the user's social media posts and provide related assistance. Furthermore, the providing unit can provide related assistance by referring to the activities of the user's friends on social media. In this way, relevant assistance can be provided by analyzing the user's social media activity. Some or all of the above-described processing in the providing unit may be performed using, for example, AI, or may be performed without using AI. For example, the providing unit can input social media data into a generation AI and cause the generation AI to suggest a means of providing the assistance.

[0053] When providing assistance, the providing unit can adjust the assistance provision method by reflecting the user's past feedback. The providing unit, for example, uses data mining technology to reflect the user's past feedback. For example, the providing unit customizes the assistance provision method based on feedback provided by the user in the past. The providing unit can also suggest an optimal assistance provision method based on the user's past feedback. Furthermore, the providing unit can improve the accuracy of assistance provision by reflecting the user's feedback. In this way, the assistance provision method can be customized by reflecting the user's past feedback. Some or all of the above-mentioned processing in the providing unit may be performed using, for example, AI, or may be performed without using AI. For example, the providing unit can input feedback data to a generating AI and cause the generating AI to adjust the assistance provision method.

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

[0055] The providing unit can analyze the user's past assistance history and select a provision method. For example, the providing unit selects the optimal provision method based on the assistance the user has received in the past. The providing unit can also suggest the optimal provision method for a specific time period based on the user's past assistance history. Furthermore, the providing unit can analyze the user's past assistance history and select the most effective provision method. In this way, the optimal provision method can be selected by analyzing the user's past assistance history. Some or all of the above-described processing in the providing unit may be performed using, for example, AI, or may be performed without using AI. For example, the providing unit can input past assistance history data to a generation AI and cause the generation AI to select the optimal provision method.

[0056] When analyzing instructions, the analysis unit can adjust the level of detail of the analysis based on the importance of the instructions. For example, the analysis unit can evaluate the urgency of the instructions and the priority of the tasks, and perform a detailed analysis on instructions with high importance. It can also perform a concise analysis on instructions with low importance. Furthermore, the analysis unit can determine the priority of the analysis based on the importance of the instructions. This allows for efficient analysis by adjusting the level of detail of the analysis based on the importance of the instructions. Some or all of the above-mentioned processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input instruction data to a generation AI and have the generation AI evaluate the importance and adjust the level of detail of the analysis.

[0057] The reception unit can analyze the user's past instruction history and select the reception method. For example, the reception unit can prioritize reception of voice instructions that the user has frequently used in the past. The reception unit can also suggest the optimal gesture based on the gesture instructions that the user has used in the past. Furthermore, the reception unit can predict and suggest the instruction method to be used in a specific time period from the user's past instruction history. In this way, the optimal reception method can be selected by analyzing the user's past instruction history. Some or all of the above-mentioned processing in the reception unit may be performed using, for example, AI, or may be performed without using AI. For example, the reception unit can input past instruction history data into a generation AI and have the generation AI select the optimal reception method.

[0058] When analyzing instructions, the analysis unit can apply different analysis algorithms depending on the category of the instruction. For example, the analysis unit can apply an algorithm that calculates an optimal route to a navigation instruction. The analysis unit can also apply a voice recognition algorithm to a conversational text conversion instruction. Furthermore, the analysis unit can select an appropriate analysis algorithm for other instructions. This enables highly accurate analysis by applying an appropriate analysis algorithm depending on the category of the instruction. Some or all of the above-mentioned processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input instruction data to a generation AI and have the generation AI apply an analysis algorithm depending on the category.

[0059] The providing unit can adjust the means of providing assistance based on the user's current living situation when providing assistance. For example, when the user is at home, the providing unit can provide assistance optimal for the home. Also, when the user is out, the providing unit can provide assistance optimal for the destination. Furthermore, the providing unit can provide customized assistance based on the user's living situation. This makes it possible to provide more appropriate assistance by adjusting the means of providing assistance based on the user's current living situation. Some or all of the above-described processing in the providing unit may be performed using, for example, AI, or may be performed without using AI. For example, the providing unit can input living situation data to a generating AI and cause the generating AI to adjust the means of providing assistance.

[0060] The providing unit can adjust the assistance providing method by reflecting user feedback when providing assistance. For example, the providing unit can improve the assistance providing method based on feedback provided by the user. The providing unit can also suggest an optimal assistance providing method based on the user feedback. Furthermore, the providing unit can improve the accuracy of the assistance by reflecting user feedback. In this way, the assistance providing method can be improved by reflecting user feedback. Some or all of the above-described processing in the providing unit may be performed using AI, for example, or may be performed without using AI. For example, the providing unit can input feedback data to a generating AI and cause the generating AI to adjust the assistance providing method.

[0061] The processing flow of the first embodiment will be briefly explained below.

[0062] Step 1: The reception unit receives voice or gesture instructions from the user. For example, a microphone can be used to receive voice instructions, and a camera or sensor can be used to receive gesture instructions. Step 2: The analysis unit analyzes the instructions received by the reception unit and generates information for providing assistance. For example, it uses voice recognition technology to analyze voice instructions and gesture recognition technology to analyze gesture instructions. It also uses a large-scale language model (LLM) to understand voice and gesture instructions and generate information for providing appropriate assistance. Step 3: The provision unit provides assistance based on the information generated by the analysis unit. For example, it can analyze the surrounding environment and provide voice navigation for the visually impaired. It can also convert voice to text and display it visually for the hearing impaired. The processing in the provision unit may be performed using AI or without AI.

[0063] (Example 2) In an assistance system according to an embodiment of the present invention, a user wears AR glasses and inputs instructions via voice or gestures. The LLM analyzes the instructions and provides appropriate assistance. In this assistance system, a user wears AR glasses and inputs instructions via voice or gestures. For example, a visually impaired person may say, "Tell me the way to the station." This instruction is input to the LLM via a microphone and sensors installed in the AR glasses. The LLM then analyzes the input instruction. The LLM understands the voice or gesture instruction and generates information to provide appropriate assistance. For example, in response to the instruction, "Tell me the way to the station," the LLM calculates the optimal route from the current location to the station and transmits that information to the AR glasses. The AR glasses then provide appropriate assistance to the user based on the information transmitted from the LLM. For example, if a visually impaired person wants to know the way to the station, the AR glasses analyze the surrounding environment in real time and provide navigation such as "Turn right" or "There is a crosswalk 50 meters ahead." Furthermore, to enable hearing impaired people to understand conversations, the AR glasses convert speech into text and display it visually in real time. For example, if a conversation partner says, "Hello, how are you?", the AR glasses will convert the speech into text and display it visually, allowing the hearing impaired person to understand the conversation. In this way, assistance systems help people with physical limitations to live more independently. For example, a visually impaired person can navigate the streets independently, and a hearing impaired person can understand conversations. This creates a barrier-free environment and improves the quality of life for people with physical limitations. In this way, assistance systems help people with physical limitations to live more independently. For example, a visually impaired person can navigate the streets independently, and a hearing impaired person can understand conversations. This creates a barrier-free environment and improves the quality of life for people with physical limitations.

[0064] An assistance system according to an embodiment includes a reception unit, an analysis unit, and a provision unit. The reception unit receives voice or gesture instructions from a user. For example, the reception unit uses a microphone to receive voice instructions. The reception unit can also use a camera or a sensor to receive gesture instructions. The analysis unit analyzes the instructions received by the reception unit and generates information for providing assistance. For example, the analysis unit analyzes voice instructions using voice recognition technology. The analysis unit can also analyze gesture instructions using gesture recognition technology. The analysis unit uses a large-scale language model (LLM) to understand voice or gesture instructions and generate information for providing appropriate assistance. For example, the analysis unit analyzes a voice instruction such as "Tell me the way to the station" and calculates the optimal route from the current location to the station. The provision unit provides assistance based on the information generated by the analysis unit. For example, the provision unit analyzes the surrounding environment and provides voice navigation for visually impaired persons. The provision unit can also convert voice to text and display it visually for hearing impaired persons. Some or all of the above-described processing in the providing unit may be performed using, for example, AI, or may be performed without using AI. As a result, the assistance system according to the embodiment can analyze the user's voice and gesture instructions and provide appropriate assistance, thereby helping people with physical limitations to live more independently.

[0065] The providing unit can analyze the surrounding environment for visually impaired persons and provide navigation by voice. The providing unit, for example, uses a camera or a sensor to analyze the surrounding environment for visually impaired persons. For example, the providing unit can acquire images of the surroundings using a camera and analyze the location of obstacles and the shape of roads using image recognition technology. The providing unit can also acquire distance information about the surroundings using a sensor and generate navigation information. The providing unit provides navigation by voice based on the analyzed information. For example, the providing unit can provide voice navigation such as "Turn right" or "There is a crosswalk 50 meters ahead." The providing unit can also analyze the surrounding environment in real time and provide appropriate navigation information to the user. This can help visually impaired persons navigate independently. Some or all of the above-described processing in the providing unit may be performed using, for example, AI, or may be performed without AI. For example, the providing unit can input image data acquired by a camera to a generation AI and cause the generation AI to generate navigation information from the image data.

[0066] The providing unit can convert speech into text and visually display it for the hearing impaired. The providing unit, for example, uses speech recognition technology to convert speech into text for the hearing impaired. For example, the providing unit captures speech using a microphone and converts it into text in real time using speech recognition technology. The providing unit can also use a display to visually display the converted text. For example, the providing unit displays text on the display of AR glasses to enable the hearing impaired to understand the conversation. The providing unit can also support multiple languages ​​using speech recognition technology. For example, the providing unit recognizes speech input in multiple languages, such as English, Japanese, and French, and converts it into text. This can help the hearing impaired to understand the conversation. Some or all of the above-described processing in the providing unit may be performed using AI, or may be performed without AI. For example, the providing unit can input speech data captured by a microphone to a generation AI and have the generation AI convert the speech data into text data.

[0067] The reception unit can receive voice instructions. The reception unit, for example, uses a microphone to receive voice instructions. For example, when a user gives a voice instruction such as "Tell me the way to the station," the reception unit captures the voice using the microphone. The reception unit can also use noise canceling technology to accurately receive voice instructions. For example, the reception unit filters out surrounding environmental sounds to clearly receive voice instructions. This allows the user's voice instructions to be accurately received. Some or all of the above-described processing in the reception unit may be performed using, for example, AI, or may be performed without using AI. For example, the reception unit can input voice data captured by the microphone to a generation AI and have the generation AI analyze the content of the instruction from the voice data.

[0068] The reception unit can receive gesture instructions. The reception unit uses, for example, a camera or a sensor to receive the gesture instructions. For example, when a user makes a gesture such as waving their hand, the reception unit captures the movement with a camera. The reception unit can also use image recognition technology to accurately receive gesture instructions. For example, the reception unit analyzes image data acquired by a camera and recognizes the content of the gesture. This allows the user's gesture instructions to be accurately received. Some or all of the above-described processing in the reception unit may be performed using, for example, AI, or may be performed without using AI. For example, the reception unit can input image data acquired by a camera to a generation AI and have the generation AI analyze the content of the gesture from the image data.

[0069] The analysis unit can analyze the voice instruction and generate information for providing assistance. The analysis unit, for example, uses speech recognition technology to analyze the voice instruction. For example, the analysis unit converts the voice instruction into text data and analyzes the content. The analysis unit can also understand the meaning of the voice instruction using natural language processing technology. For example, the analysis unit analyzes the voice instruction, such as "Tell me the way to the station," and calculates the optimal route from the current location to the station. The analysis unit uses a large-scale language model (LLM) to understand the voice instruction and generate information for providing appropriate assistance. For example, the analysis unit analyzes the voice instruction using an LLM model that takes the voice instruction as input and outputs navigation information. This makes it possible to analyze the voice instruction and generate information for providing appropriate assistance. Some or all of the above-described processing in the analysis unit may be performed using, for example, AI, or may be performed without AI. For example, the analysis unit can input voice data to a generation AI and cause the generation AI to generate assistance information from the voice data.

[0070] The analysis unit can analyze the gesture instruction and generate information for providing assistance. The analysis unit, for example, uses image recognition technology to analyze the gesture instruction. For example, the analysis unit can analyze image data acquired by a camera and recognize the content of the gesture. The analysis unit can also understand the meaning of the gesture instruction using motion analysis technology. For example, the analysis unit can analyze a user's hand-waving gesture and understand its meaning. The analysis unit can use a large-scale language model (LLM) to understand the gesture instruction and generate information for providing appropriate assistance. For example, the analysis unit can analyze the gesture instruction using an LLM model that inputs the gesture instruction and outputs assistance information. This makes it possible to analyze the gesture instruction and generate information for providing appropriate assistance. Some or all of the above-described processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input image data to a generation AI and cause the generation AI to generate assistance information from the image data.

[0071] The reception unit can estimate the user's emotions and adjust the method for accepting voice and gesture instructions based on the estimated user emotions. The reception unit, for example, uses voice tone analysis or facial expression recognition technology to estimate the user's emotions. For example, the reception unit can analyze the user's voice tone to determine whether the user is nervous or relaxed. The reception unit can also analyze the user's facial expressions captured by a camera to estimate the user's emotions. The reception unit adjusts the method for accepting voice and gesture instructions based on the estimated user emotions. For example, if the user is nervous, the reception unit can prioritize the acceptance of voice instructions and respond with simple instructions. If the user is relaxed, the reception unit can accept detailed gesture instructions and provide customizable assistance. Furthermore, if the user is in a hurry, the reception unit can quickly accept voice instructions and immediately start assistance. This allows the reception unit to provide more appropriate assistance by adjusting the method for accepting voice and gesture instructions according to the user's emotions. Some or all of the above-described processing in the reception unit may be performed using, for example, AI, or may be performed without AI. For example, the reception unit can input voice tone data and facial expression data into the generation AI and have the generation AI estimate emotions.

[0072] The reception unit can analyze the user's past instruction history and select a reception method. The reception unit, for example, uses data mining technology or machine learning algorithms to analyze the user's past instruction history. For example, the reception unit prioritizes receiving voice instructions that the user has frequently used in the past. The reception unit can also suggest an optimal gesture based on the user's past gesture instructions. Furthermore, the reception unit can predict and suggest an instruction method to be used in a specific time period based on the user's past instruction history. In this way, the optimal reception method can be selected by analyzing the user's past instruction history. Some or all of the above-described processing in the reception unit may be performed using, for example, AI, or may be performed without using AI. For example, the reception unit can input past instruction history data into a generation AI and have the generation AI select an optimal reception method.

[0073] The reception unit can filter the user's environmental sounds to remove noise when receiving a voice instruction. The reception unit, for example, uses noise canceling technology when receiving a voice instruction. For example, when the user is in a noisy place, the reception unit filters the environmental sounds to clearly receive the voice instruction. Furthermore, when the user is in a quiet place, the reception unit can accurately receive even subtle voice instructions. Furthermore, when the user is moving, the reception unit can also filter wind noise and traffic noise to receive the voice instruction. This allows the voice instruction to be clearly received by filtering the environmental sounds and removing noise. Some or all of the above-described processing in the reception unit may be performed using, for example, AI, or may be performed without using AI. For example, the reception unit can input environmental sound data to a generation AI and cause the generation AI to remove noise.

[0074] The reception unit can improve the accuracy of gesture recognition based on the user's physical characteristics when receiving a gesture instruction. The reception unit, for example, uses a machine learning algorithm to improve the accuracy of gesture recognition by taking the user's physical characteristics into account. For example, the reception unit can learn the size and movement patterns of the user's hand to improve the accuracy of gesture recognition. The reception unit can also analyze the user's arm movement and posture to perform accurate gesture recognition. Furthermore, the reception unit can apply a customized gesture recognition algorithm based on the user's physical characteristics. This enables accurate gesture recognition by improving the accuracy of gesture recognition based on the user's physical characteristics. Some or all of the above-described processing in the reception unit can be performed using, for example, AI, or without AI. For example, the reception unit can input the user's physical characteristic data into a generation AI and cause the generation AI to improve the accuracy of gesture recognition.

[0075] The reception unit can estimate the user's emotions and prioritize instructions to be received based on the estimated user emotions. The reception unit, for example, uses voice tone analysis or facial expression recognition technology to estimate the user's emotions. For example, the reception unit can analyze the user's voice tone to determine whether the user is nervous or relaxed. The reception unit can also analyze the user's facial expressions captured by a camera to estimate the user's emotions. The reception unit prioritizes instructions to be received based on the estimated user 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 for more appropriate assistance to be provided by prioritizing instructions according to the user's emotions. Some or all of the above-described processing in the reception unit may be performed using, or without, AI. For example, the reception unit can input voice tone data and facial expression data into a generation AI and have the generation AI perform emotion estimation.

[0076] When receiving a voice instruction, the reception unit can prioritize receiving highly relevant instructions by taking into account the user's geographical location information. The reception unit, for example, uses GPS technology to consider the user's geographical location information. For example, when the user is in a specific location, the reception unit can prioritize receiving instructions related to that location. Furthermore, when the user is moving, the reception unit can prioritize receiving optimal instructions based on the user's current location. Furthermore, when the user is approaching a destination, the reception unit can prioritize receiving instructions related to the destination. In this way, by taking into account the user's geographical location information, highly relevant instructions can be prioritized. Some or all of the above-described processing in the reception unit may be performed using, for example, AI, or may be performed without using AI. For example, the reception unit can input geographical location information data to a generation AI and cause the generation AI to execute highly relevant instructions at a higher priority.

[0077] When receiving a gesture instruction, the reception unit can analyze the user's social media activity and receive related instructions. The reception unit, for example, uses data mining technology to analyze the user's social media activity. For example, the reception unit can prioritize receiving instructions related to places where the user has checked in on social media. The reception unit can also analyze the content of the user's social media posts and prioritize receiving related instructions. Furthermore, the reception unit can prioritize receiving related instructions by referring to the activities of the user's friends on social media. In this way, by analyzing the user's social media activity, related instructions can be prioritized. Some or all of the above-described processing by the reception unit may be performed using, for example, AI, or may be performed without using AI. For example, the reception unit can input social media data into a generation AI and cause the generation AI to receive related instructions.

[0078] When receiving a voice instruction, the reception unit can adjust the reception method by reflecting the user's past feedback. The reception unit, for example, uses data mining technology to reflect the user's past feedback. For example, the reception unit improves the reception method for voice instructions based on feedback provided by the user in the past. The reception unit can also suggest an optimal reception method for voice instructions based on the user's past feedback. Furthermore, the reception unit can also improve the accuracy of reception of voice instructions by reflecting the user's feedback. In this way, the reception method for voice instructions can be customized by reflecting the user's past feedback. Some or all of the above-described processing in the reception unit may be performed using, for example, AI, or may be performed without using AI. For example, the reception unit can input past feedback data into a generation AI and cause the generation AI to adjust the reception method.

[0079] The analysis unit can estimate the user's emotions and adjust the instruction analysis method based on the estimated user emotions. The analysis unit, for example, uses voice tone analysis or facial expression recognition technology to estimate the user's emotions. For example, the analysis unit can analyze the user's voice tone to determine whether the user is nervous or relaxed. The analysis unit can also analyze the user's facial expressions captured by a camera to estimate the user's emotions. The analysis unit adjusts the instruction analysis method based on the estimated user emotions. For example, if the user is nervous, the analysis unit can provide a concise and clear analysis result. If the user is relaxed, the analysis unit can provide a detailed analysis result. Furthermore, if the user is in a hurry, the analysis unit can perform a quick analysis and provide an immediate result. This allows for more appropriate analysis results to be provided by adjusting the instruction analysis method based on the user's emotions. Some or all of the above-described processing in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input voice tone data and facial expression data into a generation AI and have the generation AI perform emotion estimation.

[0080] When analyzing instructions, the analysis unit can adjust the level of detail of the analysis based on the importance of the instructions. The analysis unit, for example, uses a machine learning algorithm to evaluate the importance of the instructions. For example, the analysis unit evaluates the urgency of the instructions and the priority of the task, and performs a detailed analysis on instructions with high importance. The analysis unit can also perform a concise analysis on instructions with low importance. Furthermore, the analysis unit can determine the priority of the analysis based on the importance of the instructions. This enables efficient analysis by adjusting the level of detail of the analysis based on the importance of the instructions. Some or all of the above-mentioned processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input instruction data to a generation AI and have the generation AI evaluate the importance and adjust the level of detail of the analysis.

[0081] When analyzing instructions, the analysis unit can apply different analysis algorithms depending on the category of the instruction. The analysis unit, for example, uses a machine learning algorithm to classify the category of the instruction. For example, the analysis unit applies an algorithm that calculates the optimal route to navigation instructions. The analysis unit can also apply a speech recognition algorithm to conversational text conversion instructions. Furthermore, the analysis unit can select an appropriate analysis algorithm for other instructions. This enables highly accurate analysis by applying an appropriate analysis algorithm depending on the category of the instruction. Some or all of the above-mentioned processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input instruction data to a generation AI and have the generation AI apply an analysis algorithm depending on the category.

[0082] When analyzing instructions, the analysis unit can improve the accuracy of the analysis by referring to the user's past analysis results. The analysis unit, for example, uses data mining technology to analyze the user's past analysis results. For example, the analysis unit can improve the accuracy of the current instruction analysis based on the user's past analysis results. The analysis unit can also suggest an optimal analysis method based on the user's past analysis results. Furthermore, the analysis unit can adjust the analysis algorithm by referring to the user's past analysis results. In this way, the accuracy of the analysis can be improved by referring to the user's past analysis results. Some or all of the above-mentioned processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input past analysis result data into the generation AI and have the generation AI improve the accuracy of the analysis.

[0083] The analysis unit can estimate the user's emotions and adjust the display method of the analysis results based on the estimated user emotions. The analysis unit, for example, uses voice tone analysis or facial expression recognition technology to estimate the user's emotions. For example, the analysis unit can analyze the user's voice tone to determine whether the user is nervous or relaxed. The analysis unit can also analyze the user's facial expressions captured by a camera to estimate the user's emotions. The analysis unit adjusts the display method of the analysis results based on the estimated user emotions. For example, if the user is nervous, a simple, highly visible display method can be provided. If the user is relaxed, a display method including detailed information can be provided. Furthermore, if the user is in a hurry, a display method that focuses on the main points can be provided. This allows for more appropriate display by adjusting the display method of the analysis results according to the user's emotions. Some or all of the above-mentioned processing in the analysis unit may be performed using, for example, AI, or without AI. For example, the analysis unit can input voice tone data and facial expression data into a generation AI and have the generation AI perform emotion estimation.

[0084] When analyzing instructions, the analysis unit can determine the priority of analysis based on the time when the instructions were submitted. The analysis unit, for example, uses data mining technology to evaluate the time when the instructions were submitted. For example, the analysis unit prioritizes analysis of urgent instructions. The analysis unit can also postpone analysis of instructions submitted earlier. Furthermore, the analysis unit can dynamically adjust the priority of analysis based on the time when the instructions were submitted. This enables efficient analysis by determining the priority of analysis based on the time when the instructions were submitted. Some or all of the above-mentioned processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input submission time data into the generation AI and have the generation AI determine the priority of analysis.

[0085] When analyzing instructions, the analysis unit can adjust the order of analysis based on the relevance of the instructions. The analysis unit, for example, uses a machine learning algorithm to evaluate the relevance of the instructions. For example, the analysis unit prioritizes analysis of highly relevant instructions. The analysis unit can also postpone analysis of less relevant instructions. Furthermore, the analysis unit can dynamically adjust the order of analysis based on the relevance of the instructions. This enables efficient analysis by adjusting the order of analysis based on the relevance of the instructions. Some or all of the above-mentioned processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input instruction data to a generation AI and have the generation AI execute an analysis order based on the relevance.

[0086] When analyzing instructions, the analysis unit can adjust the use of technical terminology in the analysis according to the user's level of expertise. The analysis unit, for example, uses a machine learning algorithm to evaluate the user's level of expertise. For example, if the user has technical expertise, the analysis unit can provide the analysis result using technical terminology. Alternatively, if the user does not have technical expertise, the analysis unit can provide the analysis result in simple, easy-to-understand language. Furthermore, the analysis unit can adjust the way the analysis result is expressed according to the user's level of expertise. This allows for more appropriate analysis results to be provided by adjusting the use of technical terminology in the analysis according to the user's level of expertise. Some or all of the above-described processing in the analysis unit may be performed using, for example, AI, or may be performed without AI. For example, the analysis unit can input the user's level of expertise data into the generation AI to adjust the use of technical terminology.

[0087] The providing unit can estimate the user's emotions and adjust the method of providing assistance based on the estimated user emotions. The providing unit, for example, uses voice tone analysis or facial expression recognition technology to estimate the user's emotions. For example, the providing unit can analyze the user's voice tone to determine whether the user is nervous or relaxed. The providing unit can also analyze the user's facial expressions captured by a camera to estimate the user's emotions. The providing unit adjusts the method of providing assistance based on the estimated user emotions. For example, if the user is nervous, assistance can be provided in a calm voice. If the user is relaxed, assistance including detailed information can be provided. Furthermore, if the user is in a hurry, quick and concise assistance can be provided. This allows for more appropriate assistance to be provided by adjusting the method of providing assistance according to the user's emotions. Some or all of the above-described processing in the providing unit may be performed using, or without, AI. For example, the providing unit can input voice tone data and facial expression data into a generation AI and have the generation AI perform emotion estimation.

[0088] When providing assistance, the providing unit can select a provision method by analyzing the user's past assistance history. The providing unit, for example, uses data mining technology to analyze the user's past assistance history. For example, the providing unit selects an optimal provision method based on the assistance received by the user in the past. The providing unit can also suggest an optimal provision method for a specific time period based on the user's past assistance history. Furthermore, the providing unit can analyze the user's past assistance history and select the most effective provision method. In this way, the optimal provision method can be selected by analyzing the user's past assistance history. Some or all of the above-described processing in the providing unit may be performed using, for example, AI, or may be performed without using AI. For example, the providing unit can input past assistance history data to a generation AI and cause the generation AI to select an optimal provision method.

[0089] The providing unit can adjust the means of providing assistance based on the user's current living situation when providing assistance. The providing unit, for example, uses sensor technology or data mining technology to evaluate the user's current living situation. For example, when the user is at home, the providing unit can provide assistance optimal for the home. Also, when the user is out, the providing unit can provide assistance optimal for the destination. Furthermore, the providing unit can provide customized assistance based on the user's living situation. This makes it possible to provide more appropriate assistance by adjusting the means of providing assistance based on the user's current living situation. Some or all of the above-described processing in the providing unit may be performed using, for example, AI, or may be performed without using AI. For example, the providing unit can input living situation data to a generating AI and cause the generating AI to adjust the means of providing assistance.

[0090] The providing unit can adjust the assistance providing method by reflecting user feedback when providing assistance. The providing unit, for example, uses data mining technology to reflect user feedback. For example, the providing unit improves the assistance providing method based on the feedback provided by the user. The providing unit can also suggest an optimal assistance providing method based on the user feedback. Furthermore, the providing unit can improve the accuracy of the assistance by reflecting user feedback. In this way, the assistance providing method can be improved by reflecting user feedback. Some or all of the above-described processing in the providing unit may be performed using AI, for example, or may be performed without using AI. For example, the providing unit can input feedback data to a generating AI and cause the generating AI to adjust the assistance providing method.

[0091] The providing unit can estimate the user's emotions and determine the priority of assistance based on the estimated user emotions. The providing unit, for example, uses voice tone analysis or facial expression recognition technology to estimate the user's emotions. For example, the providing unit can analyze the user's voice tone to determine whether the user is nervous or relaxed. The providing unit can also analyze the user's facial expressions captured by a camera to estimate the user's emotions. The providing unit determines the priority of assistance based on the estimated user emotions. For example, if the user is nervous, important assistance can be provided preferentially. Also, if the user is relaxed, detailed assistance can be provided preferentially. Furthermore, if the user is in a hurry, assistance requiring a quick response can be provided preferentially. This allows more appropriate assistance to be provided by determining the priority of assistance according to the user's emotions. Some or all of the above-described processing in the providing unit may be performed using, for example, AI, or may be performed without AI. For example, the providing unit can input voice tone data and facial expression data into a generation AI and have the generation AI perform emotion estimation.

[0092] When providing assistance, the providing unit can select a provision method taking into account the user's geographical location information. The providing unit, for example, uses GPS technology to consider the user's geographical location information. For example, when the user is in a specific location, the providing unit can provide the optimal assistance for that location. Also, when the user is moving, the providing unit can provide the optimal assistance based on the user's current location. Furthermore, when the user is approaching a destination, the providing unit can provide assistance related to the destination. In this way, optimal assistance can be provided by taking the user's geographical location information into account. Some or all of the above-described processing in the providing unit may be performed using, for example, AI, or may be performed without using AI. For example, the providing unit can input geographical location information data to a generation AI and cause the generation AI to select the optimal provision method.

[0093] When providing assistance, the providing unit can analyze the user's social media activity and suggest a means of providing the assistance. The providing unit, for example, uses data mining technology to analyze the user's social media activity. For example, the providing unit can provide assistance related to places where the user has checked in on social media. The providing unit can also analyze the content of the user's social media posts and provide related assistance. Furthermore, the providing unit can provide related assistance by referring to the activities of the user's friends on social media. In this way, relevant assistance can be provided by analyzing the user's social media activity. Some or all of the above-described processing in the providing unit may be performed using, for example, AI, or may be performed without using AI. For example, the providing unit can input social media data into a generation AI and cause the generation AI to suggest a means of providing the assistance.

[0094] When providing assistance, the providing unit can adjust the assistance provision method by reflecting the user's past feedback. The providing unit, for example, uses data mining technology to reflect the user's past feedback. For example, the providing unit customizes the assistance provision method based on feedback provided by the user in the past. The providing unit can also suggest an optimal assistance provision method based on the user's past feedback. Furthermore, the providing unit can improve the accuracy of assistance provision by reflecting the user's feedback. In this way, the assistance provision method can be customized by reflecting the user's past feedback. Some or all of the above-mentioned processing in the providing unit may be performed using, for example, AI, or may be performed without using AI. For example, the providing unit can input feedback data to a generating AI and cause the generating AI to adjust the assistance provision method. === Hard Collateral 1-1 === Each of the multiple elements including the above-described reception unit, analysis unit, and provision unit is realized, for example, by at least one of the smart device 14 and the data processing device 12. For example, the reception unit can receive a user's voice or gestures using a microphone or camera of the smart device 14. For example, the analysis unit can be realized by the specific processing unit 290 of the data processing device 12 and analyze instructions using voice recognition technology or gesture recognition technology. For example, the provision unit can be realized by the control unit 46A of the smart device 14 and provide appropriate assistance based on the analyzed information. For example, the reception unit can estimate the user's emotions using the microphone or camera of the smart device 14 and adjust the method of receiving instructions based on the estimated emotions. === Hard Collateral 1-2 === Each of the multiple elements, including the above-described reception unit, analysis unit, and provision unit, is realized, for example, by at least one of the smart glasses 214 and the data processing device 12. For example, the reception unit can receive a user's voice or gestures using a microphone or camera of the smart glasses 214. For example, the analysis unit can be realized by the specific processing unit 290 of the data processing device 12 and analyze instructions using voice recognition technology or gesture recognition technology. For example, the provision unit can be realized by the control unit 46A of the smart glasses 214 and provide appropriate assistance based on the analyzed information. For example, the reception unit can estimate a user's emotion using a microphone or camera of the smart glasses 214 and adjust the method of receiving instructions based on the estimated emotion. === Hard Collateral 1-3 === Each of the multiple elements including the above-described reception unit, analysis unit, and provision unit is realized, for example, by at least one of the headset type terminal 314 and the data processing device 12. For example, the reception unit can receive the user's voice or gestures using a microphone or camera of the headset type terminal 314. For example, the analysis unit is realized by the specific processing unit 290 of the data processing device 12 and analyzes instructions using voice recognition technology or gesture recognition technology. For example, the provision unit is realized by the control unit 46A of the headset type terminal 314 and provides appropriate assistance based on the analyzed information. For example, the reception unit can estimate the user's emotions using the microphone or camera of the headset type terminal 314 and adjust the method of receiving instructions based on the estimated emotions. === Hard Collateral 1-4 === Each of the multiple elements including the above-described reception unit, analysis unit, and provision unit is realized, for example, by at least one of the robot 414 and the data processing device 12. For example, the reception unit can receive the user's voice or gestures using a microphone or camera of the robot 414. For example, the analysis unit is realized by the specific processing unit 290 of the data processing device 12 and analyzes instructions using voice recognition technology or gesture recognition technology. For example, the provision unit is realized by the control unit 46A of the robot 414 and provides appropriate assistance based on the analyzed information. For example, the reception unit can estimate the user's emotions using the microphone or camera of the robot 414 and adjust the method of receiving instructions based on the estimated emotions.

[0095] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0096] The analysis unit can estimate the user's emotions and adjust the display method of the analysis results based on the estimated user emotions. For example, the analysis unit can analyze the user's voice tone and facial expression to determine whether the user is nervous or relaxed. If the user is nervous, a simple, highly visible display method can be provided. If the user is relaxed, a display method including detailed information can be provided. Furthermore, if the user is in a hurry, a display method that focuses on the main points can be provided. This allows for more appropriate display by adjusting the display method of the analysis results according to the user's emotions. Some or all of the above-mentioned processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input voice tone data and facial expression data into a generation AI and have the generation AI perform emotion estimation.

[0097] The providing unit can analyze the user's past assistance history and select a provision method. For example, the providing unit selects the optimal provision method based on the assistance the user has received in the past. The providing unit can also suggest the optimal provision method for a specific time period based on the user's past assistance history. Furthermore, the providing unit can analyze the user's past assistance history and select the most effective provision method. In this way, the optimal provision method can be selected by analyzing the user's past assistance history. Some or all of the above-described processing in the providing unit may be performed using, for example, AI, or may be performed without using AI. For example, the providing unit can input past assistance history data to a generation AI and cause the generation AI to select the optimal provision method.

[0098] The reception unit can estimate the user's emotions and adjust the method for accepting voice and gesture instructions based on the estimated user emotions. For example, the reception unit can analyze the user's voice tone to determine whether the user is nervous or relaxed. The reception unit can also analyze the user's facial expressions captured by a camera to estimate emotions. If the user is nervous, the reception unit can prioritize the acceptance of voice instructions and enable the user to respond with simple instructions. If the user is relaxed, the reception unit can accept detailed gesture instructions and provide customizable assistance. Furthermore, if the user is in a hurry, the reception unit can quickly accept voice instructions and immediately start assistance. This allows for more appropriate assistance to be provided by adjusting the method for accepting voice and gesture instructions according to the user's emotions. Some or all of the above-described processing in the reception unit may be performed using, or without, AI. For example, the reception unit can input voice tone data and facial expression data into a generation AI and have the generation AI perform emotion estimation.

[0099] When analyzing instructions, the analysis unit can adjust the level of detail of the analysis based on the importance of the instructions. For example, the analysis unit can evaluate the urgency of the instructions and the priority of the tasks, and perform a detailed analysis on instructions with high importance. It can also perform a concise analysis on instructions with low importance. Furthermore, the analysis unit can determine the priority of the analysis based on the importance of the instructions. This allows for efficient analysis by adjusting the level of detail of the analysis based on the importance of the instructions. Some or all of the above-mentioned processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input instruction data to a generation AI and have the generation AI evaluate the importance and adjust the level of detail of the analysis.

[0100] The providing unit can estimate the user's emotions and adjust the method of providing assistance based on the estimated user emotions. For example, the providing unit can analyze the user's voice tone and facial expression to determine whether the user is nervous or relaxed. If the user is nervous, the providing unit can provide assistance in a calm voice. If the user is relaxed, the providing unit can provide assistance with detailed information. Furthermore, if the user is in a hurry, the providing unit can provide quick and concise assistance. This allows more appropriate assistance to be provided by adjusting the method of providing assistance according to the user's emotions. Some or all of the above-described processing in the providing unit may be performed using, for example, AI, or may be performed without using AI. For example, the providing unit can input voice tone data and facial expression data into a generation AI and have the generation AI perform emotion estimation.

[0101] The reception unit can analyze the user's past instruction history and select the reception method. For example, the reception unit can prioritize reception of voice instructions that the user has frequently used in the past. The reception unit can also suggest the optimal gesture based on the gesture instructions that the user has used in the past. Furthermore, the reception unit can predict and suggest the instruction method to be used in a specific time period from the user's past instruction history. In this way, the optimal reception method can be selected by analyzing the user's past instruction history. Some or all of the above-mentioned processing in the reception unit may be performed using, for example, AI, or may be performed without using AI. For example, the reception unit can input past instruction history data into a generation AI and have the generation AI select the optimal reception method.

[0102] When analyzing instructions, the analysis unit can apply different analysis algorithms depending on the category of the instruction. For example, the analysis unit can apply an algorithm that calculates an optimal route to a navigation instruction. The analysis unit can also apply a voice recognition algorithm to a conversational text conversion instruction. Furthermore, the analysis unit can select an appropriate analysis algorithm for other instructions. This enables highly accurate analysis by applying an appropriate analysis algorithm depending on the category of the instruction. Some or all of the above-mentioned processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input instruction data to a generation AI and have the generation AI apply an analysis algorithm depending on the category.

[0103] The providing unit can adjust the means of providing assistance based on the user's current living situation when providing assistance. For example, when the user is at home, the providing unit can provide assistance optimal for the home. Also, when the user is out, the providing unit can provide assistance optimal for the destination. Furthermore, the providing unit can provide customized assistance based on the user's living situation. This makes it possible to provide more appropriate assistance by adjusting the means of providing assistance based on the user's current living situation. Some or all of the above-described processing in the providing unit may be performed using, for example, AI, or may be performed without using AI. For example, the providing unit can input living situation data to a generating AI and cause the generating AI to adjust the means of providing assistance.

[0104] The analysis unit can estimate the user's emotions and adjust the method of analyzing instructions based on the estimated user emotions. For example, the analysis unit can analyze the user's voice tone and facial expression to determine whether the user is nervous or relaxed. If the user is nervous, the analysis unit can provide a concise and clear analysis result. If the user is relaxed, the analysis unit can provide a detailed analysis result. Furthermore, if the user is in a hurry, the analysis unit can perform a quick analysis and provide an immediate result. This allows for more appropriate analysis results to be provided by adjusting the method of analyzing instructions according to the user's emotions. Some or all of the above-mentioned processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input voice tone data and facial expression data into a generation AI and have the generation AI perform emotion estimation.

[0105] The providing unit can adjust the assistance providing method by reflecting user feedback when providing assistance. For example, the providing unit can improve the assistance providing method based on feedback provided by the user. The providing unit can also suggest an optimal assistance providing method based on the user feedback. Furthermore, the providing unit can improve the accuracy of the assistance by reflecting user feedback. In this way, the assistance providing method can be improved by reflecting user feedback. Some or all of the above-described processing in the providing unit may be performed using AI, for example, or may be performed without using AI. For example, the providing unit can input feedback data to a generating AI and cause the generating AI to adjust the assistance providing method.

[0106] The processing flow of the second embodiment will be briefly explained below.

[0107] Step 1: The reception unit receives voice or gesture instructions from the user. For example, a microphone can be used to receive voice instructions, and a camera or sensor can be used to receive gesture instructions. Step 2: The analysis unit analyzes the instructions received by the reception unit and generates information for providing assistance. For example, it uses voice recognition technology to analyze voice instructions and gesture recognition technology to analyze gesture instructions. It also uses a large-scale language model (LLM) to understand voice and gesture instructions and generate information for providing appropriate assistance. Step 3: The provision unit provides assistance based on the information generated by the analysis unit. For example, it can analyze the surrounding environment and provide voice navigation for the visually impaired. It can also convert voice to text and display it visually for the hearing impaired. The processing in the provision unit may be performed using AI or without AI.

[0108] 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.

[0109] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (registered trademark) (Internet search engine).<URL: https: / / openai.com / blog / chatgpt> Examples of generative AIs include the data generation model 58, such as a neural network model (e.g., a neural network model), and a neural network model (e.g., a neural network model). The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating speech, text data indicating text, and image data indicating an image is also input to the data generation model 58. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specification processing unit 290 performs the above-mentioned specification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0110] 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.

[0111] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0112] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0113] 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.

[0114] 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.

[0115] 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.

[0116] 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.

[0117] 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).

[0118] 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.

[0119] 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.

[0120] 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.

[0121] 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.

[0122] 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.

[0123] 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.

[0124] 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.

[0125] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AI other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0126] 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.

[0127] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0128] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0129] 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.

[0130] 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.

[0131] 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.

[0132] 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.

[0133] 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).

[0134] 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.

[0135] Fig. 6 shows an example of the main functions of the data processing device 12 and the headset type terminal 314. As shown in Fig. 6, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0136] 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.

[0137] 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.

[0138] 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.

[0139] 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.

[0140] 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.

[0141] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AI other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0142] 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.

[0143] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0144] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0145] 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.

[0146] 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.

[0147] 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.

[0148] 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.

[0149] 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).

[0150] 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.

[0151] 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.

[0152] 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.

[0153] 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.

[0154] 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.

[0155] 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.

[0156] 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.

[0157] 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.

[0158] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AI other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0159] 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.

[0160] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0161] 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.

[0162] 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.

[0163] 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.

[0164] 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).

[0165] Human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, a state of discomfort is expressed, and when they approach the ideal, a state of pleasure is expressed. Emotions can also be created for robots, cars, and motorcycles, based on various balances, such as posture and remaining battery life. When these balances deviate from the ideal, a state of discomfort is expressed, and when they approach the ideal, a state of pleasure is expressed. An emotion map can be generated, for example, based on Dr. Mitsuyoshi's emotion map (Research on speech emotion recognition and brain physiological signal analysis systems for emotions, Tokushima University, doctoral dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map lists emotions belonging to the "reaction" domain, where sensation is dominant. The right half of the emotion map lists emotions belonging to the "situation" domain, where situational awareness is dominant.

[0166] 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."

[0167] 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.

[0168] 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.

[0169] 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.

[0170] 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.

[0171] 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.

[0172] 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.

[0173] 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.

[0174] 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.

[0175] 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.

[0176] 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.

[0177] 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.

[0178] 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.

[0179] [Explanation of symbols]

[0180] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Device 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robot

Claims

1. a reception unit that receives voice or gesture instructions from a user; an analysis unit that analyzes the instruction received by the reception unit and generates information for providing assistance; a providing unit that provides assistance based on the information generated by the analyzing unit. A system characterized by:

2. The providing unit Analyzes the surrounding environment and provides voice navigation for the visually impaired 2. The system of claim 1.

3. The providing unit Converts speech to text and provides visual displays for the hearing impaired 2. The system of claim 1.

4. The reception unit Accepts voice commands 2. The system of claim 1.

5. The reception unit Accept gesture instructions 2. The system of claim 1.

6. The analysis unit Analyzes voice commands and generates information to provide assistance 2. The system of claim 1.

7. The analysis unit Analyze gesture instructions and generate information to provide assistance 2. The system of claim 1.

8. The reception unit Estimate the user's emotions and adjust the method of accepting voice and gesture instructions based on the estimated user emotions.

2. The system of claim 1.

9. The reception unit Analyze the user's past instruction history and select the reception method 2. The system of claim 1.

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