system

Abstract

The system according to the embodiment aims to efficiently provide remote care support. [Solution] A system according to an embodiment includes a receiving unit, an analyzing unit, a generating unit, and a transmitting unit. The receiving unit receives an instruction. The analyzing unit analyzes the instruction received by the receiving unit. The generating unit generates a command based on the instruction analyzed by the analyzing unit. The transmitting unit transmits the command generated by the generating unit.

Description

[Technical Field]

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

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

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

[0004] With conventional technology, there was a problem in that it was difficult to efficiently perform individual care tasks when providing remote care support.

[0005] The system according to the embodiment aims to efficiently provide remote care support. [Means for solving the problem]

[0006] A system according to an embodiment includes a receiving unit, an analyzing unit, a generating unit, and a transmitting unit. The receiving unit receives an instruction. The analyzing unit analyzes the instruction received by the receiving unit. The generating unit generates a command based on the instruction analyzed by the analyzing unit. The transmitting unit transmits the command generated by the generating unit. [Effects of the Invention]

[0007] The system according to the embodiment can efficiently provide remote care support. [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) A care support system according to an embodiment of the present invention applies generative AI and IoT to remote care support. In this system, a care manager issues instructions in Japanese via a smartphone, and a generative AI analyzes the instructions and issues commands for IoT devices in the user's home. This allows the care manager to efficiently support caregiving for multiple users. This allows the care manager to remotely manage users' air conditioners and prevent heatstroke. The care manager can also remotely operate users' robot vacuum cleaners to provide cleaning services. For example, if a care manager issues an instruction from their smartphone, such as "Set User A's air conditioner to 25 degrees," the generative AI analyzes the instruction, generates a command to set User A's air conditioner to 25 degrees, and sends it to the IoT-enabled air conditioner. This allows the care manager to remotely manage users' air conditioners and prevent heatstroke. Similarly, if a care manager issues an instruction from their smartphone, such as "Start User B's robot vacuum cleaner," the generative AI analyzes the instruction, generates a command to start User B's robot vacuum cleaner, and sends it to the IoT-enabled robot vacuum cleaner. This allows the care manager to remotely provide cleaning services to the user, thereby reducing the burden on the care manager and improving the quality of life of the user.

[0029] A care support system according to an embodiment includes a reception unit, an analysis unit, a generation unit, and a transmission unit. The reception unit receives instructions issued by a care manager via a smartphone. The instructions include, but are not limited to, voice instructions, text instructions, and gesture instructions. The reception unit receives voice instructions using, for example, voice recognition technology. The reception unit can also receive text instructions using text analysis technology. The reception unit can also receive gesture instructions using image analysis technology. The analysis unit analyzes the instructions received by the reception unit. The analysis is performed using, for example, natural language processing technology, but is not limited to, for example. For example, the analysis unit analyzes voice instructions using natural language processing technology. The analysis unit can also analyze gesture instructions using image analysis technology. The analysis unit can also analyze voice instructions using voice analysis technology. The generation unit generates commands based on the instructions analyzed by the analysis unit. The commands include, but are not limited to, device control commands and system operation commands. For example, the generation unit generates device control commands. The generation unit can also generate system operation commands. The generation unit can also use a generation AI to generate optimal commands according to the situation and environment of each user. For example, the generation unit inputs an instruction such as "Set user A's air conditioner to 25 degrees" to the generation AI, and the generation AI analyzes the instruction and generates a command. The transmission unit transmits the command generated by the generation unit. Transmission is performed, for example, using wireless communication technology, but is not limited to this example. For example, the transmission unit transmits the command using Wi-Fi. The transmission unit can also transmit the command using Bluetooth (registered trademark). The transmission unit can also transmit the command using Zigbee. This allows the care support system according to the embodiment to efficiently perform a series of processes from receiving instructions to analysis, command generation, and transmission.

[0030] The transmitting unit can transmit commands to control air conditioning equipment. Air conditioning equipment includes, but is not limited to, for example, air conditioners, fans, and heaters. The transmitting unit transmits, for example, commands to control air conditioners. The transmitting unit can also transmit commands to control fans. The transmitting unit can also transmit commands to control heaters. This allows air conditioning equipment to be controlled remotely. Some or all of the above-described processing in the transmitting unit may be performed using, for example, AI, or may be performed without using AI. For example, the transmitting unit inputs the commands generated by the generating unit into an AI model, and the AI ​​model transmits the commands.

[0031] The transmitting unit can transmit commands to control cleaning appliances. Examples of cleaning appliances include, but are not limited to, vacuum cleaners, robot vacuum cleaners, and mops. The transmitting unit transmits, for example, commands to control vacuum cleaners. The transmitting unit can also transmit commands to control robot vacuum cleaners. The transmitting unit can also transmit commands to control mops. This allows the cleaning appliances to be controlled remotely. Some or all of the above-described processing in the transmitting unit may be performed using, for example, AI, or may be performed without using AI. For example, the transmitting unit inputs the commands generated by the generating unit into an AI model, and the AI ​​model transmits the commands.

[0032] The transmitting unit can transmit a command to control a lighting device. Examples of lighting devices include, but are not limited to, LED lights, fluorescent lights, and smart lights. The transmitting unit can transmit, for example, a command to control an LED light. The transmitting unit can also transmit a command to control a fluorescent light. The transmitting unit can also transmit a command to control a smart light. This allows the lighting device to be controlled remotely. Some or all of the above-described processing in the transmitting unit may be performed using, for example, AI, or may be performed without using AI. For example, the transmitting unit inputs the command generated by the generating unit into an AI model, and the AI ​​model transmits the command.

[0033] The transmitting unit can transmit commands to control a television. Televisions include, but are not limited to, smart televisions, LCD televisions, and plasma televisions, for example. The transmitting unit transmits commands to control, for example, a smart television. The transmitting unit can also transmit commands to control an LCD television. The transmitting unit can also transmit commands to control a plasma television. This allows the television to be controlled remotely. Some or all of the above-described processing in the transmitting unit may be performed using, for example, AI, or may be performed without using AI. For example, the transmitting unit inputs the commands generated by the generating unit into an AI model, and the AI ​​model transmits the commands.

[0034] The analysis unit can generate appropriate commands according to each user's situation and environment. Examples of the user's situation and environment include, but are not limited to, the indoor temperature, the user's activity status, and the ambient noise level. For example, the analysis unit generates a command to adjust the temperature setting of an air conditioner based on the indoor temperature. The analysis unit can also generate a command to adjust the brightness of lighting based on the user's activity status. The analysis unit can also generate a command to adjust the volume based on the ambient noise level. This makes it possible to generate optimal commands according to the user's situation and environment. 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 inputs data on the user's situation and environment into a generation AI, which then generates optimal commands.

[0035] The reception unit can analyze the care manager's past instruction history and select an appropriate reception method. For example, the reception unit preferentially suggests instruction methods (voice, text, etc.) that the care manager has frequently used in the past. The reception unit can also analyze patterns of instructions given by the care manager in the past and automatically select the optimal reception method. The reception unit can also suggest a reception method suitable for a specific time period based on the care manager's past instruction history. This makes it possible to select the optimal reception method based on the 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 inputs the care manager's past instruction history data into a generation AI, which then selects the optimal reception method.

[0036] When receiving instructions, the reception unit can filter instructions based on the care manager's current work status and areas of interest. For example, the reception unit prioritizes receiving only instructions related to the work currently being performed by the care manager. The reception unit can also prioritize receiving related instructions based on the care manager's areas of interest. The reception unit can also grasp the care manager's work status in real time and receive instructions at an appropriate time. This makes it possible to filter instructions based on the work status and areas of interest. Some or all of the above-mentioned processing in the reception unit may be performed using AI, for example, or may be performed without using AI. For example, the reception unit inputs the care manager's work status data into a generation AI, which then filters out optimal instructions.

[0037] When receiving instructions, the reception unit can select an appropriate reception means depending on the input method of the care manager. For example, when the care manager issues instructions by voice, the reception unit receives the instructions using voice recognition technology. Furthermore, when the care manager issues instructions by text, the reception unit can also receive the instructions using text analysis technology. Furthermore, when the care manager issues instructions using an image, the reception unit can also receive the instructions using image analysis technology. This makes it possible to select the optimal reception means depending on the input method. 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 inputs the care manager's input data to a generation AI, and the generation AI selects the optimal reception means.

[0038] When receiving instructions, the reception unit can prioritize receiving highly relevant instructions by taking into account the geographical location information of the care manager. For example, if the care manager is in a specific area, the reception unit can prioritize receiving instructions related to that area. Furthermore, if the care manager is traveling, the reception unit can also prioritize receiving related instructions based on the current location. Furthermore, if the care manager is in a specific facility, the reception unit can also prioritize receiving instructions related to that facility. This makes it possible to prioritize receiving highly relevant instructions based on the geographical location information. 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 inputs the geographical location information data of the care manager to a generation AI, which then selects optimal instructions.

[0039] When receiving instructions, the reception unit can analyze the social media activity of the care manager and receive related instructions. The reception unit can receive related instructions based on, for example, information shared by the care manager on social media. The reception unit can also analyze the social media activity of the care manager and prioritize receiving related instructions. The reception unit can also receive related instructions by referring to the activity of the care manager's friends on social media. This makes it possible to receive related instructions based on social media activity. Some or all of the above-described processing in the reception unit can be performed using, for example, AI, or can be performed without using AI. For example, the reception unit inputs the social media activity data of the care manager into a generation AI, which selects optimal instructions.

[0040] When receiving instructions, the reception unit can customize the reception method by reflecting the care manager's past feedback. For example, the reception unit suggests the optimal reception method based on feedback provided by the care manager in the past. The reception unit can also preferentially select a specific reception method based on the care manager's past feedback. The reception unit can also customize the reception method by reflecting the care manager's feedback in real time. This makes it possible to customize the reception method based on past feedback. Some or all of the above-mentioned processing in the reception unit may be performed using AI, for example, or may be performed without using AI. For example, the reception unit inputs the care manager's past feedback data into a generation AI, which then selects the optimal reception method.

[0041] When analyzing an instruction, the analysis unit can adjust the level of detail of the analysis based on the importance of the instruction. For example, the analysis unit performs a detailed analysis on an instruction with high importance and provides a highly accurate result. The analysis unit can also perform a simplified analysis on an instruction with low importance and provide a quick result. The analysis unit can also appropriately allocate analysis resources according to the importance of the instruction. This makes it possible to adjust the level of detail of the analysis according to the importance 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 inputs instruction importance data to a generation AI, which then selects the optimal analysis method.

[0042] When analyzing an instruction, the analysis unit can apply an appropriate analysis algorithm depending on the category of the instruction. For example, the analysis unit can apply an analysis algorithm specialized for temperature adjustment to an instruction to control an air conditioner. The analysis unit can also apply an algorithm specialized for cleaning pattern analysis to an instruction to control a robot vacuum cleaner. The analysis unit can also apply an analysis algorithm specialized for lighting adjustment to an instruction to control a lighting device. This makes it possible to apply the optimal 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 inputs instruction category data to a generation AI, and the generation AI selects the optimal analysis algorithm.

[0043] When analyzing instructions, the analysis unit can improve the accuracy of the analysis by referring to the care manager's past analysis results. The analysis unit can improve the accuracy of the current analysis, for example, based on the analysis results previously performed by the care manager. The analysis unit can also extract specific patterns from the care manager's past analysis results to improve the analysis accuracy. The analysis unit can also improve the analysis accuracy by referring to the care manager's past analysis results in real time. This makes it possible to improve the analysis accuracy based on the past analysis results. Some or all of the above-mentioned processing in the analysis unit can be performed using, for example, AI, or can be performed without using AI. For example, the analysis unit inputs the care manager's past analysis result data into the generation AI, which then selects the optimal analysis method.

[0044] When analyzing instructions, the analysis unit can determine the priority of analysis based on the time of submission of the instructions. For example, the analysis unit can prioritize analysis of instructions with high urgency and provide results quickly. The analysis unit can also prioritize analysis of instructions submitted earlier and provide results sequentially. The analysis unit can also appropriately allocate analysis resources based on the time of submission. This makes it possible to determine the priority of analysis based on the time of submission. 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 inputs data on the time of submission of instructions to a generation AI, and the generation AI determines the optimal analysis order.

[0045] When analyzing instructions, the analysis unit can adjust the order of analysis based on the relevance of the instructions. For example, the analysis unit prioritizes analysis of highly relevant instructions and provides results quickly. The analysis unit can also postpone analysis of less relevant instructions and provide results efficiently. The analysis unit can also appropriately allocate analysis resources based on the relevance of the instructions. This makes it possible to adjust 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 inputs instruction relevance data to a generation AI, and the generation AI determines the optimal analysis order.

[0046] When analyzing instructions, the analysis unit can adjust the use of technical terminology in the analysis according to the expertise level of the care manager. For example, if the care manager has a high level of expertise, the analysis unit can provide the analysis results using a lot of technical terminology. Furthermore, if the care manager has a low level of expertise, the analysis unit can provide the analysis results in simple language. Furthermore, the analysis unit can adjust the way in which the analysis results are expressed according to the expertise level of the care manager. This makes it possible to adjust the way in which the analysis results are expressed according to the expertise level. 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 inputs the expertise level data of the care manager into the generation AI, which then selects the optimal way to express the analysis results.

[0047] When generating a command, the generation unit can adjust the level of detail of the generation based on the importance of the instruction. For example, the generation unit generates a detailed command for an instruction with high importance and performs processing with high accuracy. The generation unit can also generate a simplified command for an instruction with low importance and perform processing quickly. The generation unit can also appropriately allocate generation resources according to the importance of the instruction. This makes it possible to adjust the level of detail of the generation according to the importance of the instruction. Some or all of the above-mentioned processing in the generation unit may be performed using AI, for example, or may be performed without using AI. For example, the generation unit inputs instruction importance data to a generation AI, which then selects the optimal generation method.

[0048] When generating a command, the generation unit can apply different generation algorithms depending on the category of the instruction. For example, the generation unit can apply a generation algorithm specialized for temperature adjustment to an instruction to control an air conditioner. The generation unit can also apply an algorithm specialized for cleaning pattern generation to an instruction to control a robot vacuum cleaner. The generation unit can also apply a generation algorithm specialized for lighting adjustment to an instruction to control a lighting device. This makes it possible to apply the optimal generation algorithm depending on the category of the instruction. Some or all of the above-mentioned processing in the generation unit may be performed using, for example, AI, or may be performed without using AI. For example, the generation unit inputs instruction category data to a generation AI, and the generation AI selects the optimal generation algorithm.

[0049] When generating a command, the generation unit can improve the accuracy of generation by referring to the care manager's past generation results. The generation unit, for example, improves the current generation accuracy based on the care manager's past generation results. The generation unit can also extract specific patterns from the care manager's past generation results to improve the generation accuracy. The generation unit can also improve the generation accuracy by referring to the care manager's past generation results in real time. This makes it possible to improve the generation accuracy based on the past generation results. Some or all of the above-mentioned processing in the generation unit may be performed using AI, for example, or may be performed without using AI. For example, the generation unit inputs the care manager's past generation result data into the generation AI, and the generation AI selects the optimal generation method.

[0050] When generating commands, the generation unit can determine the generation priority based on the submission time of the instructions. For example, the generation unit can generate instructions with a high degree of urgency first and process them quickly. The generation unit can also generate instructions with an older submission time first and process them sequentially. The generation unit can also appropriately allocate generation resources based on the submission time. This makes it possible to determine the generation priority based on the submission time. Some or all of the above-mentioned processing in the generation unit may be performed using, for example, AI, or may be performed without using AI. For example, the generation unit inputs instruction submission time data into the generation AI, and the generation AI determines the optimal generation order.

[0051] When generating commands, the generation unit can adjust the order of generation based on the relevance of instructions. For example, the generation unit can prioritize generating highly relevant instructions and process them quickly. The generation unit can also postpone generating less relevant instructions and process them efficiently. The generation unit can also appropriately allocate generation resources based on the relevance of instructions. This makes it possible to adjust the order of generation based on the relevance of instructions. Some or all of the above-mentioned processing in the generation unit may be performed using, for example, AI, or may be performed without using AI. For example, the generation unit inputs instruction relevance data to a generation AI, and the generation AI determines the optimal generation order.

[0052] When generating a command, the generation unit can adjust the use of technical terminology in the generation according to the expertise level of the care manager. For example, if the expertise level of the care manager is high, the generation unit generates a command using a lot of technical terminology. Furthermore, if the expertise level of the care manager is low, the generation unit can also generate a command using simple language. Furthermore, the generation unit can adjust the expression method of the command according to the expertise level of the care manager. This makes it possible to adjust the expression method of the generated result according to the expertise level. Some or all of the above-mentioned processing in the generation unit may be performed using, for example, AI, or may be performed without using AI. For example, the generation unit inputs the expertise level data of the care manager into the generation AI, and the generation AI selects the optimal expression method of the generated result.

[0053] When transmitting a command, the transmitting unit can adjust the level of detail of the transmission based on the situation and environment of each user. For example, the transmitting unit can transmit detailed commands according to the user's situation and perform highly accurate control. The transmitting unit can also transmit simplified commands according to the user's environment and perform rapid control. The transmitting unit can also appropriately allocate transmission resources based on the situation and environment of each user. This makes it possible to adjust the level of detail of the transmission according to the user's situation and environment. Some or all of the above-described processing in the transmitting unit may be performed using, or without, AI, for example. For example, the transmitting unit inputs user situation and environmental data into a generating AI, which then selects the optimal transmission method.

[0054] When transmitting a command, the transmission unit can improve the accuracy of transmission by referring to the user's past transmission results. The transmission unit can improve the current transmission accuracy, for example, based on the user's past transmission results. The transmission unit can also extract a specific pattern from the user's past transmission results to improve the transmission accuracy. The transmission unit can also improve the transmission accuracy by referring to the user's past transmission results in real time. This makes it possible to improve the transmission accuracy based on the past transmission results. Some or all of the above-described processing in the transmission unit may be performed using, for example, AI, or may be performed without using AI. For example, the transmission unit inputs the user's past transmission result data into a generation AI, which then selects the optimal transmission method.

[0055] When transmitting a command, the transmitting unit can customize the transmission means based on the user's current living situation. For example, when the user is at home, the transmitting unit can transmit detailed commands to perform highly accurate control. Furthermore, when the user is out, the transmitting unit can transmit simplified commands to perform quick control. Furthermore, the transmitting unit can appropriately customize the transmission means based on the user's current living situation. This makes it possible to customize the transmission means according to the living situation. Some or all of the above-described processing in the transmitting unit may be performed using, for example, AI, or may be performed without using AI. For example, the transmitting unit inputs the user's living situation data into a generating AI, which then selects the optimal transmission means.

[0056] When transmitting a command, the transmitting unit can select an appropriate transmission method taking into account the user's geographical location information. For example, if the user is in a specific area, the transmitting unit selects the transmission method that is optimal for that area. Furthermore, if the user is moving, the transmitting unit can also select the optimal transmission method based on the user's current location. Furthermore, if the user is in a specific facility, the transmitting unit can also select the optimal transmission method for that facility. In this way, the optimal transmission method can be selected based on the geographical location information. Some or all of the above-described processing in the transmitting unit may be performed using, for example, AI, or may be performed without using AI. For example, the transmitting unit inputs the user's geographical location information data into a generating AI, which then selects the optimal transmission method.

[0057] When transmitting a command, the transmitting unit can analyze the user's social media activity and suggest a transmission means. The transmitting unit can, for example, suggest the optimal transmission means based on information shared by the user on social media. The transmitting unit can also analyze the user's social media activity content and suggest the optimal transmission means. The transmitting unit can also suggest the optimal transmission means based on the activity of the user's friends on social media. This makes it possible to suggest the optimal transmission means based on social media activity. Some or all of the above-described processing in the transmitting unit may be performed using, for example, AI, or may be performed without using AI. For example, the transmitting unit inputs the user's social media activity data into a generating AI, which then selects the optimal transmission means.

[0058] When transmitting a command, the transmitting unit can customize the transmission method by reflecting the user's past feedback. For example, the transmitting unit can suggest the optimal transmission method based on feedback provided by the user in the past. The transmitting unit can also preferentially select a specific transmission method based on the user's past feedback. The transmitting unit can also customize the transmission method by reflecting the user's feedback in real time. This makes it possible to customize the transmission method based on the past feedback. Some or all of the above-described processing in the transmitting unit may be performed using, for example, AI, or may be performed without using AI. For example, the transmitting unit inputs the user's past feedback data into a generating AI, which then selects the optimal transmission method.

[0059] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0060] When transmitting a command to control an air conditioner, the transmitting unit can suggest optimal settings by taking into account the user's past usage history. For example, if the user has preferred a particular temperature setting in the past, the transmitting unit will preferentially suggest that setting. Also, if the user has used a particular temperature setting during a particular time period, the transmitting unit can also suggest optimal settings for that time period. Furthermore, if the user has used a particular temperature setting during a particular season in the past, the transmitting unit can also suggest optimal settings for that season. This allows the transmitting unit to suggest optimal settings based on the user's past usage history.

[0061] When transmitting commands to control the cleaning device, the transmission unit can propose an optimal cleaning schedule taking into account the user's lifestyle patterns. For example, if the user is often away from home during a specific time period, the transmission unit can propose a cleaning schedule that suits that time period. Also, if the user is often away from home on a specific day of the week, the transmission unit can also propose a cleaning schedule that suits that day of the week. Furthermore, if the user is often away from home during a specific season, the transmission unit can also propose a cleaning schedule that suits that season. In this way, the transmission unit can propose an optimal cleaning schedule based on the user's lifestyle patterns.

[0062] When transmitting a command to control a lighting device, the transmitting unit can propose optimal lighting settings taking into consideration the user's activity status. For example, if the user is reading, the transmitting unit can propose a brightness suitable for reading. Also, if the user is relaxing, the transmitting unit can propose a brightness suitable for relaxation. Furthermore, if the user is trying to sleep, the transmitting unit can propose a brightness suitable for sleeping. In this way, the transmitting unit can propose optimal lighting settings based on the user's activity status.

[0063] When transmitting a command to control the television, the transmitting unit can suggest optimal channels and programs by taking into account the user's viewing history. For example, if the user has previously preferred to watch programs of a particular genre, the transmitting unit will preferentially suggest programs of that genre. Also, if the user has watched a particular channel during a particular time period, the transmitting unit can suggest the optimal channel for that time period. Furthermore, if the user has watched programs of a particular genre during a particular season, the transmitting unit can suggest the optimal program for that season. This allows the transmitting unit to suggest optimal channels and programs based on the user's viewing history.

[0064] The processing flow of the first embodiment will be briefly explained below.

[0065] Step 1: The reception unit accepts instructions issued by the care manager via smartphone. Instructions include voice instructions, text instructions, and gesture instructions. The reception unit accepts voice instructions using voice recognition technology, text instructions using text analysis technology, and gesture instructions using image analysis technology. Step 2: The analysis unit analyzes the instruction received by the reception unit. The analysis is performed using natural language processing technology, image analysis technology, and voice analysis technology. Step 3: The generator generates commands based on the instructions analyzed by the analyzer. These commands include device control commands and system operation commands. The generator can also use generation AI to generate optimal commands based on each user's situation and environment. Step 4: The transmitter transmits the command generated by the generator using a wireless communication technology such as Wi-Fi, Bluetooth, or Zigbee.

[0066] (Example 2) A care support system according to an embodiment of the present invention applies generative AI and IoT to remote care support. In this system, a care manager issues instructions in Japanese via a smartphone, and a generative AI analyzes the instructions and issues commands for IoT devices in the user's home. This allows the care manager to efficiently support caregiving for multiple users. This allows the care manager to remotely manage users' air conditioners and prevent heatstroke. The care manager can also remotely operate users' robot vacuum cleaners to provide cleaning services. For example, if a care manager issues an instruction from their smartphone, such as "Set User A's air conditioner to 25 degrees," the generative AI analyzes the instruction, generates a command to set User A's air conditioner to 25 degrees, and sends it to the IoT-enabled air conditioner. This allows the care manager to remotely manage users' air conditioners and prevent heatstroke. Similarly, if a care manager issues an instruction from their smartphone, such as "Start User B's robot vacuum cleaner," the generative AI analyzes the instruction, generates a command to start User B's robot vacuum cleaner, and sends it to the IoT-enabled robot vacuum cleaner. This allows the care manager to remotely provide cleaning services to the user, thereby reducing the burden on the care manager and improving the quality of life of the user.

[0067] A care support system according to an embodiment includes a reception unit, an analysis unit, a generation unit, and a transmission unit. The reception unit receives instructions issued by a care manager via a smartphone. The instructions include, but are not limited to, voice instructions, text instructions, and gesture instructions. The reception unit receives voice instructions using, for example, voice recognition technology. The reception unit can also receive text instructions using text analysis technology. The reception unit can also receive gesture instructions using image analysis technology. The analysis unit analyzes the instructions received by the reception unit. The analysis is performed using, for example, natural language processing technology, but is not limited to, for example. For example, the analysis unit analyzes voice instructions using natural language processing technology. The analysis unit can also analyze gesture instructions using image analysis technology. The analysis unit can also analyze voice instructions using voice analysis technology. The generation unit generates commands based on the instructions analyzed by the analysis unit. The commands include, but are not limited to, device control commands and system operation commands. For example, the generation unit generates device control commands. The generation unit can also generate system operation commands. The generation unit can also use a generation AI to generate optimal commands according to the situation and environment of each user. For example, the generation unit inputs an instruction such as "Set user A's air conditioner to 25 degrees" to the generation AI, and the generation AI analyzes the instruction and generates a command. The transmission unit transmits the command generated by the generation unit. Transmission is performed using, for example, wireless communication technology, but is not limited to this example. For example, the transmission unit transmits the command using Wi-Fi. The transmission unit can also transmit the command using Bluetooth. The transmission unit can also transmit the command using Zigbee. As a result, the care support system according to the embodiment can efficiently perform a series of processes from receiving instructions to analyzing, generating commands, and transmitting them.

[0068] The transmitting unit can transmit commands to control air conditioning equipment. Air conditioning equipment includes, but is not limited to, for example, air conditioners, fans, and heaters. The transmitting unit transmits, for example, commands to control air conditioners. The transmitting unit can also transmit commands to control fans. The transmitting unit can also transmit commands to control heaters. This allows air conditioning equipment to be controlled remotely. Some or all of the above-described processing in the transmitting unit may be performed using, for example, AI, or may be performed without using AI. For example, the transmitting unit inputs the commands generated by the generating unit into an AI model, and the AI ​​model transmits the commands.

[0069] The transmitting unit can transmit commands to control cleaning appliances. Examples of cleaning appliances include, but are not limited to, vacuum cleaners, robot vacuum cleaners, and mops. The transmitting unit transmits, for example, commands to control vacuum cleaners. The transmitting unit can also transmit commands to control robot vacuum cleaners. The transmitting unit can also transmit commands to control mops. This allows the cleaning appliances to be controlled remotely. Some or all of the above-described processing in the transmitting unit may be performed using, for example, AI, or may be performed without using AI. For example, the transmitting unit inputs the commands generated by the generating unit into an AI model, and the AI ​​model transmits the commands.

[0070] The transmitting unit can transmit a command to control a lighting device. Examples of lighting devices include, but are not limited to, LED lights, fluorescent lights, and smart lights. The transmitting unit can transmit, for example, a command to control an LED light. The transmitting unit can also transmit a command to control a fluorescent light. The transmitting unit can also transmit a command to control a smart light. This allows the lighting device to be controlled remotely. Some or all of the above-described processing in the transmitting unit may be performed using, for example, AI, or may be performed without using AI. For example, the transmitting unit inputs the command generated by the generating unit into an AI model, and the AI ​​model transmits the command.

[0071] The transmitting unit can transmit commands to control a television. Televisions include, but are not limited to, smart televisions, LCD televisions, and plasma televisions, for example. The transmitting unit transmits commands to control, for example, a smart television. The transmitting unit can also transmit commands to control an LCD television. The transmitting unit can also transmit commands to control a plasma television. This allows the television to be controlled remotely. Some or all of the above-described processing in the transmitting unit may be performed using, for example, AI, or may be performed without using AI. For example, the transmitting unit inputs the commands generated by the generating unit into an AI model, and the AI ​​model transmits the commands.

[0072] The analysis unit can generate appropriate commands according to each user's situation and environment. Examples of the user's situation and environment include, but are not limited to, the indoor temperature, the user's activity status, and the ambient noise level. For example, the analysis unit generates a command to adjust the temperature setting of an air conditioner based on the indoor temperature. The analysis unit can also generate a command to adjust the brightness of lighting based on the user's activity status. The analysis unit can also generate a command to adjust the volume based on the ambient noise level. This makes it possible to generate optimal commands according to the user's situation and environment. 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 inputs data on the user's situation and environment into a generation AI, which then generates optimal commands.

[0073] The reception unit can estimate the user's emotions and adjust the timing of receiving instructions based on the estimated user emotions. For example, if the care manager is feeling stressed, the reception unit can temporarily delay receiving instructions to provide time for the care manager to relax. Furthermore, if the care manager is relaxed, the reception unit can immediately accept instructions and quickly start processing. Furthermore, if the care manager is in a hurry, the reception unit can prioritize receiving instructions and quickly process them. This allows the timing of receiving instructions to be adjusted according to the user's emotions. The emotion estimation is realized using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI may be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the reception unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the reception unit inputs facial expression data of the care manager into the generation AI, which then estimates the emotion and adjusts the timing of receiving instructions.

[0074] The reception unit can analyze the care manager's past instruction history and select an appropriate reception method. For example, the reception unit preferentially suggests instruction methods (voice, text, etc.) that the care manager has frequently used in the past. The reception unit can also analyze patterns of instructions given by the care manager in the past and automatically select the optimal reception method. The reception unit can also suggest a reception method suitable for a specific time period based on the care manager's past instruction history. This makes it possible to select the optimal reception method based on the 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 inputs the care manager's past instruction history data into a generation AI, which then selects the optimal reception method.

[0075] When receiving instructions, the reception unit can filter instructions based on the care manager's current work status and areas of interest. For example, the reception unit prioritizes receiving only instructions related to the work currently being performed by the care manager. The reception unit can also prioritize receiving related instructions based on the care manager's areas of interest. The reception unit can also grasp the care manager's work status in real time and receive instructions at an appropriate time. This makes it possible to filter instructions based on the work status and areas of interest. Some or all of the above-mentioned processing in the reception unit may be performed using AI, for example, or may be performed without using AI. For example, the reception unit inputs the care manager's work status data into a generation AI, which then filters out optimal instructions.

[0076] When receiving instructions, the reception unit can select an appropriate reception means depending on the input method of the care manager. For example, when the care manager issues instructions by voice, the reception unit receives the instructions using voice recognition technology. Furthermore, when the care manager issues instructions by text, the reception unit can also receive the instructions using text analysis technology. Furthermore, when the care manager issues instructions using an image, the reception unit can also receive the instructions using image analysis technology. This makes it possible to select the optimal reception means depending on the input method. 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 inputs the care manager's input data to a generation AI, and the generation AI selects the optimal reception means.

[0077] The reception unit can estimate the user's emotions and determine the priority of instructions to be received based on the estimated user's emotions. For example, when the care manager is stressed, the reception unit prioritizes receiving instructions with a high level of importance. Furthermore, when the care manager is relaxed, the reception unit can also receive instructions with a normal priority. Furthermore, when the care manager is in a hurry, the reception unit can also prioritize receiving instructions with a high level of urgency. This allows the priority of instructions to be determined according to the user's emotions. The emotion estimation is realized using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI may be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the reception unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the reception unit inputs facial expression data of the care manager into the generation AI, which then estimates the emotion and determines the priority of instructions.

[0078] When receiving instructions, the reception unit can prioritize receiving highly relevant instructions by taking into account the geographical location information of the care manager. For example, if the care manager is in a specific area, the reception unit can prioritize receiving instructions related to that area. Furthermore, if the care manager is traveling, the reception unit can also prioritize receiving related instructions based on the current location. Furthermore, if the care manager is in a specific facility, the reception unit can also prioritize receiving instructions related to that facility. This makes it possible to prioritize receiving highly relevant instructions based on the geographical location information. 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 inputs the geographical location information data of the care manager to a generation AI, which then selects optimal instructions.

[0079] When receiving instructions, the reception unit can analyze the social media activity of the care manager and receive related instructions. The reception unit can receive related instructions based on, for example, information shared by the care manager on social media. The reception unit can also analyze the social media activity of the care manager and prioritize receiving related instructions. The reception unit can also receive related instructions by referring to the activity of the care manager's friends on social media. This makes it possible to receive related instructions based on social media activity. Some or all of the above-described processing in the reception unit can be performed using, for example, AI, or can be performed without using AI. For example, the reception unit inputs the social media activity data of the care manager into a generation AI, which selects optimal instructions.

[0080] When receiving instructions, the reception unit can customize the reception method by reflecting the care manager's past feedback. For example, the reception unit suggests the optimal reception method based on feedback provided by the care manager in the past. The reception unit can also preferentially select a specific reception method based on the care manager's past feedback. The reception unit can also customize the reception method by reflecting the care manager's feedback in real time. This makes it possible to customize the reception method based on past feedback. Some or all of the above-mentioned processing in the reception unit may be performed using AI, for example, or may be performed without using AI. For example, the reception unit inputs the care manager's past feedback data into a generation AI, which then selects the optimal reception method.

[0081] The analysis unit can estimate the user's emotions and adjust the instruction analysis method based on the estimated user emotions. For example, if the care manager is stressed, the analysis unit can select a simple analysis method and provide quick results. Alternatively, if the care manager is relaxed, the analysis unit can select a detailed analysis method and provide highly accurate results. Alternatively, if the care manager is in a hurry, the analysis unit can prioritize analyzing instructions with high urgency. This allows the instruction analysis method to be adjusted according to the user's emotions. The emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the analysis unit can be performed using, for example, an AI, or without an AI. For example, the analysis unit inputs facial expression data of the care manager into the generation AI, which then estimates the emotion and adjusts the instruction analysis method.

[0082] When analyzing an instruction, the analysis unit can adjust the level of detail of the analysis based on the importance of the instruction. For example, the analysis unit performs a detailed analysis on an instruction with high importance and provides a highly accurate result. The analysis unit can also perform a simplified analysis on an instruction with low importance and provide a quick result. The analysis unit can also appropriately allocate analysis resources according to the importance of the instruction. This makes it possible to adjust the level of detail of the analysis according to the importance 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 inputs instruction importance data to a generation AI, which then selects the optimal analysis method.

[0083] When analyzing an instruction, the analysis unit can apply an appropriate analysis algorithm depending on the category of the instruction. For example, the analysis unit can apply an analysis algorithm specialized for temperature adjustment to an instruction to control an air conditioner. The analysis unit can also apply an algorithm specialized for cleaning pattern analysis to an instruction to control a robot vacuum cleaner. The analysis unit can also apply an analysis algorithm specialized for lighting adjustment to an instruction to control a lighting device. This makes it possible to apply the optimal 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 inputs instruction category data to a generation AI, and the generation AI selects the optimal analysis algorithm.

[0084] When analyzing instructions, the analysis unit can improve the accuracy of the analysis by referring to the care manager's past analysis results. The analysis unit can improve the accuracy of the current analysis, for example, based on the analysis results previously performed by the care manager. The analysis unit can also extract specific patterns from the care manager's past analysis results to improve the analysis accuracy. The analysis unit can also improve the analysis accuracy by referring to the care manager's past analysis results in real time. This makes it possible to improve the analysis accuracy based on the past analysis results. Some or all of the above-mentioned processing in the analysis unit can be performed using, for example, AI, or can be performed without using AI. For example, the analysis unit inputs the care manager's past analysis result data into the generation AI, which then selects the optimal analysis method.

[0085] The analysis unit can estimate the user's emotions and adjust the length of the analysis based on the estimated user's emotions. For example, if the care manager is stressed, the analysis unit can complete the analysis in a short time. Alternatively, if the care manager is relaxed, the analysis unit can perform a detailed analysis and provide the results over time. Alternatively, if the care manager is in a hurry, the analysis unit can perform the analysis quickly and provide the results in a short time. This allows the length of the analysis to be adjusted according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above-described processing in the analysis unit may be performed using AI, or may be performed without AI. For example, the analysis unit inputs the care manager's facial expression data into the generation AI, which then estimates the emotion and adjusts the length of the analysis.

[0086] When analyzing instructions, the analysis unit can determine the priority of analysis based on the time of submission of the instructions. For example, the analysis unit can prioritize analysis of instructions with high urgency and provide results quickly. The analysis unit can also prioritize analysis of instructions submitted earlier and provide results sequentially. The analysis unit can also appropriately allocate analysis resources based on the time of submission. This makes it possible to determine the priority of analysis based on the time of submission. 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 inputs data on the time of submission of instructions to a generation AI, and the generation AI determines the optimal analysis order.

[0087] When analyzing instructions, the analysis unit can adjust the order of analysis based on the relevance of the instructions. For example, the analysis unit prioritizes analysis of highly relevant instructions and provides results quickly. The analysis unit can also postpone analysis of less relevant instructions and provide results efficiently. The analysis unit can also appropriately allocate analysis resources based on the relevance of the instructions. This makes it possible to adjust 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 inputs instruction relevance data to a generation AI, and the generation AI determines the optimal analysis order.

[0088] When analyzing instructions, the analysis unit can adjust the use of technical terminology in the analysis according to the expertise level of the care manager. For example, if the care manager has a high level of expertise, the analysis unit can provide the analysis results using a lot of technical terminology. Furthermore, if the care manager has a low level of expertise, the analysis unit can provide the analysis results in simple language. Furthermore, the analysis unit can adjust the way in which the analysis results are expressed according to the expertise level of the care manager. This makes it possible to adjust the way in which the analysis results are expressed according to the expertise level. 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 inputs the expertise level data of the care manager into the generation AI, which then selects the optimal way to express the analysis results.

[0089] The generation unit can estimate the user's emotions and adjust the command generation method based on the estimated user's emotions. For example, if the care manager is stressed, the generation unit can generate simple commands and process them quickly. Furthermore, if the care manager is relaxed, the generation unit can generate detailed commands and process them with high accuracy. Furthermore, if the care manager is in a hurry, the generation unit can prioritize generating commands with high urgency. This allows the command generation method to be adjusted according to the user's emotions. The emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be, for example, a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above-described processing in the generation unit may be performed using AI, or may be performed without AI. For example, the generation unit inputs facial expression data of the care manager into the generation AI, which then estimates the emotion and adjusts the command generation method.

[0090] When generating a command, the generation unit can adjust the level of detail of the generation based on the importance of the instruction. For example, the generation unit generates a detailed command for an instruction with high importance and performs processing with high accuracy. The generation unit can also generate a simplified command for an instruction with low importance and perform processing quickly. The generation unit can also appropriately allocate generation resources according to the importance of the instruction. This makes it possible to adjust the level of detail of the generation according to the importance of the instruction. Some or all of the above-mentioned processing in the generation unit may be performed using AI, for example, or may be performed without using AI. For example, the generation unit inputs instruction importance data to a generation AI, which then selects the optimal generation method.

[0091] When generating a command, the generation unit can apply different generation algorithms depending on the category of the instruction. For example, the generation unit can apply a generation algorithm specialized for temperature adjustment to an instruction to control an air conditioner. The generation unit can also apply an algorithm specialized for cleaning pattern generation to an instruction to control a robot vacuum cleaner. The generation unit can also apply a generation algorithm specialized for lighting adjustment to an instruction to control a lighting device. This makes it possible to apply the optimal generation algorithm depending on the category of the instruction. Some or all of the above-mentioned processing in the generation unit may be performed using, for example, AI, or may be performed without using AI. For example, the generation unit inputs instruction category data to a generation AI, and the generation AI selects the optimal generation algorithm.

[0092] When generating a command, the generation unit can improve the accuracy of generation by referring to the care manager's past generation results. The generation unit, for example, improves the current generation accuracy based on the care manager's past generation results. The generation unit can also extract specific patterns from the care manager's past generation results to improve the generation accuracy. The generation unit can also improve the generation accuracy by referring to the care manager's past generation results in real time. This makes it possible to improve the generation accuracy based on the past generation results. Some or all of the above-mentioned processing in the generation unit may be performed using AI, for example, or may be performed without using AI. For example, the generation unit inputs the care manager's past generation result data into the generation AI, and the generation AI selects the optimal generation method.

[0093] The generation unit can estimate the user's emotions and determine the priority of commands to be generated based on the estimated user emotions. For example, if the care manager is stressed, the generation unit can generate commands with a higher priority. Furthermore, if the care manager is relaxed, the generation unit can generate commands with a normal priority. Furthermore, if the care manager is in a hurry, the generation unit can generate commands with a higher priority. This allows the priority of commands to be determined according to the user's emotions. The emotion estimation is realized using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI may be, for example, a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above-described processing in the generation unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the generation unit inputs facial expression data of the care manager into the generation AI, which then estimates the emotion and determines the priority of commands.

[0094] When generating commands, the generation unit can determine the generation priority based on the submission time of the instructions. For example, the generation unit can generate instructions with a high degree of urgency first and process them quickly. The generation unit can also generate instructions with an older submission time first and process them sequentially. The generation unit can also appropriately allocate generation resources based on the submission time. This makes it possible to determine the generation priority based on the submission time. Some or all of the above-mentioned processing in the generation unit may be performed using, for example, AI, or may be performed without using AI. For example, the generation unit inputs instruction submission time data into the generation AI, and the generation AI determines the optimal generation order.

[0095] When generating commands, the generation unit can adjust the order of generation based on the relevance of instructions. For example, the generation unit can prioritize generating highly relevant instructions and process them quickly. The generation unit can also postpone generating less relevant instructions and process them efficiently. The generation unit can also appropriately allocate generation resources based on the relevance of instructions. This makes it possible to adjust the order of generation based on the relevance of instructions. Some or all of the above-mentioned processing in the generation unit may be performed using, for example, AI, or may be performed without using AI. For example, the generation unit inputs instruction relevance data to a generation AI, and the generation AI determines the optimal generation order.

[0096] When generating a command, the generation unit can adjust the use of technical terminology in the generation according to the expertise level of the care manager. For example, if the expertise level of the care manager is high, the generation unit generates a command using a lot of technical terminology. Furthermore, if the expertise level of the care manager is low, the generation unit can also generate a command using simple language. Furthermore, the generation unit can adjust the expression method of the command according to the expertise level of the care manager. This makes it possible to adjust the expression method of the generated result according to the expertise level. Some or all of the above-mentioned processing in the generation unit may be performed using, for example, AI, or may be performed without using AI. For example, the generation unit inputs the expertise level data of the care manager into the generation AI, and the generation AI selects the optimal expression method of the generated result.

[0097] The transmission unit can estimate the user's emotions and adjust the command transmission method based on the estimated user's emotions. For example, if the care manager is stressed, the transmission unit selects a simple transmission method and quickly transmits commands. Furthermore, if the care manager is relaxed, the transmission unit can select a detailed transmission method and transmit commands with high accuracy. Furthermore, if the care manager is in a hurry, the transmission unit can prioritize sending commands with high urgency. This allows the command transmission method to be adjusted according to the user's emotions. The emotion estimation is realized using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI may be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the transmission unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the transmission unit inputs facial expression data of the care manager into the generation AI, which then estimates the emotion and adjusts the command transmission method.

[0098] When transmitting a command, the transmitting unit can adjust the level of detail of the transmission based on the situation and environment of each user. For example, the transmitting unit can transmit detailed commands according to the user's situation and perform highly accurate control. The transmitting unit can also transmit simplified commands according to the user's environment and perform rapid control. The transmitting unit can also appropriately allocate transmission resources based on the situation and environment of each user. This makes it possible to adjust the level of detail of the transmission according to the user's situation and environment. Some or all of the above-described processing in the transmitting unit may be performed using, or without, AI, for example. For example, the transmitting unit inputs user situation and environmental data into a generating AI, which then selects the optimal transmission method.

[0099] When transmitting a command, the transmission unit can improve the accuracy of transmission by referring to the user's past transmission results. The transmission unit can improve the current transmission accuracy, for example, based on the user's past transmission results. The transmission unit can also extract a specific pattern from the user's past transmission results to improve the transmission accuracy. The transmission unit can also improve the transmission accuracy by referring to the user's past transmission results in real time. This makes it possible to improve the transmission accuracy based on the past transmission results. Some or all of the above-described processing in the transmission unit may be performed using, for example, AI, or may be performed without using AI. For example, the transmission unit inputs the user's past transmission result data into a generation AI, which then selects the optimal transmission method.

[0100] When transmitting a command, the transmitting unit can customize the transmission means based on the user's current living situation. For example, when the user is at home, the transmitting unit can transmit detailed commands to perform highly accurate control. Furthermore, when the user is out, the transmitting unit can transmit simplified commands to perform quick control. Furthermore, the transmitting unit can appropriately customize the transmission means based on the user's current living situation. This makes it possible to customize the transmission means according to the living situation. Some or all of the above-described processing in the transmitting unit may be performed using, for example, AI, or may be performed without using AI. For example, the transmitting unit inputs the user's living situation data into a generating AI, which then selects the optimal transmission means.

[0101] The transmission unit can estimate the user's emotions and determine the priority of commands to be transmitted based on the estimated user's emotions. For example, if the care manager is stressed, the transmission unit can prioritize transmitting commands with a high level of importance. Furthermore, if the care manager is relaxed, the transmission unit can also transmit commands with a normal priority. Furthermore, if the care manager is in a hurry, the transmission unit can also transmit commands with a high level of urgency with the highest priority. This allows the priority of commands to be determined according to the user's emotions. The emotion estimation is realized using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI may be, for example, a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above-described processing in the transmission unit may be performed using, for example, AI, or may be performed without using AI. For example, the transmission unit inputs facial expression data of the care manager into the generation AI, which then estimates the emotion and determines the priority of commands.

[0102] When transmitting a command, the transmitting unit can select an appropriate transmission method taking into account the user's geographical location information. For example, if the user is in a specific area, the transmitting unit selects the transmission method that is optimal for that area. Furthermore, if the user is moving, the transmitting unit can also select the optimal transmission method based on the user's current location. Furthermore, if the user is in a specific facility, the transmitting unit can also select the optimal transmission method for that facility. In this way, the optimal transmission method can be selected based on the geographical location information. Some or all of the above-described processing in the transmitting unit may be performed using, for example, AI, or may be performed without using AI. For example, the transmitting unit inputs the user's geographical location information data into a generating AI, which then selects the optimal transmission method.

[0103] When transmitting a command, the transmitting unit can analyze the user's social media activity and suggest a transmission means. The transmitting unit can, for example, suggest the optimal transmission means based on information shared by the user on social media. The transmitting unit can also analyze the user's social media activity content and suggest the optimal transmission means. The transmitting unit can also suggest the optimal transmission means based on the activity of the user's friends on social media. This makes it possible to suggest the optimal transmission means based on social media activity. Some or all of the above-described processing in the transmitting unit may be performed using, for example, AI, or may be performed without using AI. For example, the transmitting unit inputs the user's social media activity data into a generating AI, which then selects the optimal transmission means.

[0104] When transmitting a command, the transmitting unit can customize the transmission method by reflecting the user's past feedback. For example, the transmitting unit can suggest the optimal transmission method based on feedback provided by the user in the past. The transmitting unit can also preferentially select a specific transmission method based on the user's past feedback. The transmitting unit can also customize the transmission method by reflecting the user's feedback in real time. This makes it possible to customize the transmission method based on the past feedback. Some or all of the above-described processing in the transmitting unit may be performed using, for example, AI, or may be performed without using AI. For example, the transmitting unit inputs the user's past feedback data into a generating AI, which then selects the optimal transmission method. === Hard Collateral 1-1 === Each of the multiple elements including the above-mentioned reception unit, analysis unit, generation unit, and transmission unit is realized, for example, by at least one of the smart device 14 and the data processing device 12. For example, the reception unit is realized by the control unit 46A of the smart device 14 and receives instructions issued by the care manager via a smartphone. The analysis unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and analyzes the instructions received by the reception unit. The generation unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and generates a command based on the analyzed instruction. The transmission unit is realized, for example, by the communication I / F 44 of the smart device 14 and transmits the generated command. === Hard Collateral 1-2 === Each of the multiple elements including the above-described reception unit, analysis unit, generation unit, and transmission unit is realized, for example, by at least one of the smart glasses 214 and the data processing device 12. For example, the reception unit is realized by the control unit 46A of the smart glasses 214 and receives instructions issued by the care manager from the smartphone. The analysis unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and analyzes the instructions received by the reception unit. The generation unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and generates a command based on the analyzed instruction. The transmission unit is realized, for example, by the communication I / F 44 of the smart glasses 214 and transmits the generated command. === Hard Collateral 1-3 === Each of the multiple elements including the above-mentioned reception unit, analysis unit, generation unit, and transmission unit is realized, for example, by at least one of the headset type terminal 314 and the data processing device 12. For example, the reception unit is realized by the control unit 46A of the headset type terminal 314 and receives instructions issued by the care manager from a smartphone. The analysis unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and analyzes the instructions received by the reception unit. The generation unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and generates a command based on the analyzed instruction. The transmission unit is realized, for example, by the communication I / F 44 of the headset type terminal 314 and transmits the generated command. === Hard Collateral 1-4 === Each of the multiple elements including the above-described reception unit, analysis unit, generation unit, and transmission unit is realized, for example, by at least one of the robot 414 and the data processing device 12. For example, the reception unit is realized by the control unit 46A of the robot 414 and receives instructions issued by the care manager via a smartphone. The analysis unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and analyzes the instructions received by the reception unit. The generation unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and generates a command based on the analyzed instruction. The transmission unit is realized, for example, by the communication I / F 44 of the robot 414 and transmits the generated command.

[0105] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0106] When receiving voice instructions from the care manager, the reception unit can analyze the tone and speed of the voice to estimate the emotional state of the care manager. For example, if the care manager is in a hurry, the voice speed often increases, so the reception unit analyzes the speed and determines that the care manager is in a hurry. Also, if the care manager is feeling stressed, the voice tone often increases, so the reception unit can analyze the tone and determine that the care manager is stressed. Furthermore, if the care manager is relaxed, the voice tone often decreases and the voice speed often decreases, so the reception unit can analyze the tone and speed and determine that the care manager is relaxed. This allows the reception unit to take appropriate action according to the emotional state of the care manager.

[0107] When transmitting a command to control an air conditioner, the transmitting unit can suggest optimal settings by taking into account the user's past usage history. For example, if the user has preferred a particular temperature setting in the past, the transmitting unit will preferentially suggest that setting. Also, if the user has used a particular temperature setting during a particular time period, the transmitting unit can also suggest optimal settings for that time period. Furthermore, if the user has used a particular temperature setting during a particular season in the past, the transmitting unit can also suggest optimal settings for that season. This allows the transmitting unit to suggest optimal settings based on the user's past usage history.

[0108] When transmitting commands to control the cleaning device, the transmission unit can propose an optimal cleaning schedule taking into account the user's lifestyle patterns. For example, if the user is often away from home during a specific time period, the transmission unit can propose a cleaning schedule that suits that time period. Also, if the user is often away from home on a specific day of the week, the transmission unit can also propose a cleaning schedule that suits that day of the week. Furthermore, if the user is often away from home during a specific season, the transmission unit can also propose a cleaning schedule that suits that season. In this way, the transmission unit can propose an optimal cleaning schedule based on the user's lifestyle patterns.

[0109] When transmitting a command to control a lighting device, the transmitting unit can propose optimal lighting settings taking into consideration the user's activity status. For example, if the user is reading, the transmitting unit can propose a brightness suitable for reading. Also, if the user is relaxing, the transmitting unit can propose a brightness suitable for relaxation. Furthermore, if the user is trying to sleep, the transmitting unit can propose a brightness suitable for sleeping. In this way, the transmitting unit can propose optimal lighting settings based on the user's activity status.

[0110] When transmitting a command to control the television, the transmitting unit can suggest optimal channels and programs by taking into account the user's viewing history. For example, if the user has previously preferred to watch programs of a particular genre, the transmitting unit will preferentially suggest programs of that genre. Also, if the user has watched a particular channel during a particular time period, the transmitting unit can suggest the optimal channel for that time period. Furthermore, if the user has watched programs of a particular genre during a particular season, the transmitting unit can suggest the optimal program for that season. This allows the transmitting unit to suggest optimal channels and programs based on the user's viewing history.

[0111] The analysis unit can take the user's emotional state into consideration when generating appropriate commands according to each user's situation and environment. For example, if the user is feeling stressed, the analysis unit can generate commands to provide a relaxing environment. Also, if the user is relaxed, the analysis unit can generate commands to maintain that state. Furthermore, if the user is in a hurry, the analysis unit can generate commands to respond quickly. This allows the analysis unit to generate optimal commands according to the user's emotional state.

[0112] The reception unit can estimate the emotion of the care manager and adjust the method of receiving instructions based on the estimated emotion. For example, if the care manager is feeling stressed, the reception unit can prioritize receiving simple instructions. Also, if the care manager is relaxed, the reception unit can prioritize receiving detailed instructions. Furthermore, if the care manager is in a hurry, the reception unit can prioritize receiving instructions with a high degree of urgency. In this way, the reception unit can provide the optimal method of receiving instructions according to the emotional state of the care manager.

[0113] The reception unit can analyze the care manager's past instruction history and take the care manager's emotional state into consideration when selecting an appropriate reception method. For example, it can preferentially suggest instruction methods that the care manager used when he or she was feeling stressed in the past. It can also suggest instruction methods that the care manager used when he or she was relaxed in the past. It can also suggest instruction methods that the care manager used when he or she was in a hurry in the past. This allows the reception unit to select the optimal reception method based on the care manager's emotional state and past instruction history.

[0114] When receiving instructions, the reception unit can take into account the emotional state of the care manager when filtering instructions based on the care manager's current work situation and areas of interest. For example, if the care manager is feeling stressed, only instructions with high importance can be received with priority. Also, if the care manager is relaxed, instructions can be received with normal priority. Furthermore, if the care manager is in a hurry, instructions with high urgency can be received with top priority. This allows the reception unit to optimally filter instructions according to the emotional state of the care manager.

[0115] The reception unit can take the emotional state of the care manager into consideration when selecting an appropriate reception means according to the care manager's input method when receiving instructions. For example, if the care manager is feeling stressed, a simple input method can be preferentially suggested. Also, if the care manager is relaxed, a detailed input method can be suggested. Furthermore, if the care manager is in a hurry, a quick input method can be suggested. In this way, the reception unit can provide the optimal reception means according to the emotional state of the care manager.

[0116] The processing flow of the second embodiment will be briefly explained below.

[0117] Step 1: The reception unit accepts instructions issued by the care manager via smartphone. Instructions include voice instructions, text instructions, and gesture instructions. The reception unit accepts voice instructions using voice recognition technology, text instructions using text analysis technology, and gesture instructions using image analysis technology. Step 2: The analysis unit analyzes the instruction received by the reception unit. The analysis is performed using natural language processing technology, image analysis technology, and voice analysis technology. Step 3: The generator generates commands based on the instructions analyzed by the analyzer. These commands include device control commands and system operation commands. The generator can also use generation AI to generate optimal commands based on each user's situation and environment. Step 4: The transmitter transmits the command generated by the generator using a wireless communication technology such as Wi-Fi, Bluetooth, or Zigbee.

[0118] 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.

[0119] 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.

[0120] 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.

[0121] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0122] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0123] 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.

[0124] 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.

[0125] 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.

[0126] 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.

[0127] 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).

[0128] 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.

[0129] 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.

[0130] 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.

[0131] 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.

[0132] 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.

[0133] 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.

[0134] 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.

[0135] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0136] 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.

[0137] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0138] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0139] 5, the data processing system 310 includes the data processing device 12 and a headset type terminal 314. An example of the data processing device 12 is a server.

[0140] 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.

[0141] 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.

[0142] 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.

[0143] 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).

[0144] 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.

[0145] 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.

[0146] 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.

[0147] 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.

[0148] 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.

[0149] 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.

[0150] 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.

[0151] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0152] 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.

[0153] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0154] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0155] 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.

[0156] 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.

[0157] 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.

[0158] 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.

[0159] 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).

[0160] 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.

[0161] 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.

[0162] 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.

[0163] 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.

[0164] 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.

[0165] 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.

[0166] 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.

[0167] 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.

[0168] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0169] 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.

[0170] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0171] 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.

[0172] 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.

[0173] 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.

[0174] 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).

[0175] Human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, a state of discomfort is expressed, and when they approach the ideal, a state of pleasure is expressed. Emotions can also be created for robots, cars, and motorcycles, based on various balances, such as posture and remaining battery life. When these balances deviate from the ideal, a state of discomfort is expressed, and when they approach the ideal, a state of pleasure is expressed. An emotion map can be generated, for example, based on Dr. Mitsuyoshi's emotion map (Research on speech emotion recognition and brain physiological signal analysis systems for emotions, Tokushima University, doctoral dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map lists emotions belonging to the area called "reaction," where sensation is dominant. The right half of the emotion map lists emotions belonging to the area called "situation," where situational awareness is dominant.

[0176] 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."

[0177] 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.

[0178] 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.

[0179] 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.

[0180] 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.

[0181] 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.

[0182] 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.

[0183] 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.

[0184] 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.

[0185] 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.

[0186] 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.

[0187] 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.

[0188] 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.

[0189] [Explanation of symbols]

[0190] 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 instructions; an analysis unit that analyzes the instruction received by the reception unit; a generation unit that generates a command based on the instruction analyzed by the analysis unit; a transmission unit that transmits the command generated by the generation unit; Equipped with A system characterized by:

2. The transmission unit Sending commands to control air conditioning equipment 2. The system of claim 1.

3. The transmission unit Send commands to control cleaning equipment 2. The system of claim 1.

4. The transmission unit Sending commands to control lighting devices 2. The system of claim 1.

5. The transmission unit Send commands to control your TV 2. The system of claim 1.

6. The analysis unit Generate appropriate commands according to each user's situation and environment 2. The system of claim 1.

7. The reception unit Estimates the user's emotions and adjusts the timing of accepting instructions based on the estimated user emotions.

2. The system of claim 1.

8. The reception unit Analyze the care manager's past instruction history and select the appropriate reception method 2. The system of claim 1.

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