system

Abstract

The system according to this embodiment aims to generate the cause of a device malfunction and the correction procedure using voice input during operation, and to reflect this information in a data management device. [Solution] The system according to the embodiment comprises a reception unit, a generation unit, and a reflection unit. The reception unit receives voice input. The generation unit generates the cause of the device malfunction and a correction procedure based on the information received by the reception unit. The reflection unit reflects the information generated by the generation unit into a data management device.

Description

Technical Field

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

Background Art

[0002] Patent Document 1 discloses a method for controlling a persona chatbot, which is performed by at least one processor, and includes steps of receiving a user utterance, adding the user utterance to a prompt including an instruction sentence related to an explanation of a 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

Summary of the Invention

Problems to be Solved by the Invention

[0004] In the conventional technology, the creation of working design documents and work instructions is complicated, and there is a risk of accuracy and time problems.

[0005] The system according to the embodiment aims to generate the cause of a device failure and a correction procedure using voice input during work and reflect them in a data management device.

Means for Solving the Problems

[0006] The system according to the embodiment includes a reception unit, a generation unit, and a reflection unit. The reception unit receives voice input. The generation unit generates the cause of a device failure and a correction procedure based on the information received by the reception unit. The reflection unit reflects the information generated by the generation unit in a data management device. [Effects of the Invention]

[0007] The system according to this embodiment can generate the cause of a device malfunction and the correction procedure using voice input during operation, and reflect this information in the data management device. [Brief explanation of the drawing]

[0008] [Figure 1] This is a conceptual diagram showing an example of the configuration of a data processing system according to the first embodiment. [Figure 2] This is a conceptual diagram showing an example of the essential functions of a data processing device and a smart device according to the first embodiment. [Figure 3] This is a conceptual diagram showing an example of the configuration of a data processing system according to the second embodiment. [Figure 4] This is a conceptual diagram showing an example of the main functions of a data processing device and smart glasses according to the second embodiment. [Figure 5] This is a conceptual diagram showing an example of the configuration of a data processing system according to the third embodiment. [Figure 6] This is a conceptual diagram showing an example of the main functions of a data processing device and a headset-type terminal according to the third embodiment. [Figure 7] This is a conceptual diagram showing an example of the configuration of a data processing system according to the fourth embodiment. [Figure 8] This is a conceptual diagram showing an example of the main functions of a data processing device and a robot according to the fourth embodiment. [Figure 9] This shows an emotion map where multiple emotions are mapped. [Figure 10] This shows an emotion map where multiple emotions are mapped. [Modes for carrying out the invention]

[0009] Hereinafter, an example of an embodiment of the system relating to the technology of this disclosure will be described with reference to the attached drawings.

[0010] First, let's explain the terminology used in the following explanation.

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

[0012] In the following embodiments, signed RAM (Random Access Memory) is a memory that temporarily stores information and is used as work memory by the processor.

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

[0014] In the following embodiments, the signed communication interface (I / F) is an interface that includes a communication processor and an antenna. The communication interface manages communication between multiple computers. Examples of communication standards applicable to the communication interface include wireless communication standards such as 5G (5th Generation Mobile Communication System), Wi-Fi (registered trademark), or Bluetooth (registered trademark).

[0015] In the following embodiments, "A and / or B" is synonymous with "at least one of A and B". That is, "A and / or B" means that it may be only A, only B, or a combination of A and B. Also, in this specification, when expressing three or more matters connected by "and / or", the same concept as "A and / or B" is applied.

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

[0017] As shown in FIG. 1, the 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, the RAM 30, and the storage 32 are connected to a bus 34. Also, the database 24 and the communication I / F 26 are 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. Also, the reception device 38, the output device 40, and the camera 42 are connected to the bus 52.

[0020] The reception device 38 is equipped with a touch panel 38A and a microphone 38B, and accepts user input. The touch panel 38A accepts user input via touch by detecting contact with an object (e.g., a pen or finger). The microphone 38B accepts user input via voice by detecting the user's voice. The control unit 46A transmits data indicating the user input received by the touch panel 38A and microphone 38B to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 (see Figure 2) acquires the data indicating the user input.

[0021] The output device 40 includes a display 40A and a speaker 40B, and presents data to the user by outputting the data in a form perceptible to the user (e.g., audio and / or text). The display 40A displays visible information such as text and images according to instructions from the processor 46. The speaker 40B outputs audio according to instructions from the processor 46. The camera 42 is a small digital camera equipped with an optical system such as a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor.

[0022] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various types of information between processor 46 and processor 28 via network 54.

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

[0024] As shown in Figure 2, in the data processing device 12, a specific processing 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" related to the technology of this 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 processing is realized by the processor 28 operating as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0025] Storage 32 stores the data generation model 58 and the 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 the user's emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0026] In the smart device 14, specific processing is performed by the processor 46. The storage 50 stores a specific processing program 60. The specific processing program 60 is used in conjunction 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 a control unit 46A according to the specific processing program 60 executed on the RAM 48. The smart device 14 also has a data generation model 58 and an emotion identification model 59, similar to the data generation model and emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0027] Furthermore, other devices besides the data processing device 12 may also 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 processing results (such as prediction results) using the data generation model 58 by communicating with the server device having the data generation model 58. The data processing device 12 may also be a server device or a terminal device owned by a user (e.g., a mobile phone, robot, home appliance, etc.). Next, an example of processing by the data processing system 10 according to the first embodiment will be described.

[0028] (Example of form 1) The system according to an embodiment of the present invention is designed to solve the problems of accuracy and time involved in creating design documents and work instructions during work, which are cumbersome. This system allows workers to use voice input during work to inquire in real time with a generating AI about the cause of equipment malfunctions and correction procedures. This system can be linked with a "data management device" to centrally manage all data. Data obtained from the field via voice input is automatically reflected in the "data management device," and data can be updated instantly remotely. This dramatically improves work efficiency and enhances safety and accuracy. It also reduces the burden on engineers and promotes the smooth progress of the entire operation. For example, during work, a worker can use voice input to inquire in real time with a generating AI about the cause of equipment malfunctions and correction procedures. At this time, the generating AI generates optimal design documents and work instructions based on the information input using voice recognition technology. For example, if a worker voice inputs, "What is the cause of this device error?", the generating AI will analyze the cause of the error and propose a correction procedure. Next, the design documents and work instructions proposed by the generating AI are centrally managed in conjunction with the "data management device." This system automatically reflects data obtained via voice input from the field into a "data management device," allowing for immediate data updates from remote locations. For example, when a worker uses voice input on-site, that information is instantly reflected in the "data management device," and remote engineers can check the information in real time. This system dramatically improves work efficiency and enhances safety and accuracy. For instance, by allowing workers to use voice input on-site, necessary information can be quickly obtained, making it easier to identify and correct the cause of errors. It also reduces the burden on engineers and promotes the smooth progress of the entire operation. For example, engineers can check data remotely and take necessary actions quickly. Furthermore, this system has a diverse customer base not only in the telecommunications industry but also in the infrastructure and engineering industries, and is expected to have a market size of several hundred million dollars in the growing IoT and automation markets. For example, in the infrastructure industry, on-site work efficiency will be improved, and in the engineering industry, design documents and work instructions can be created more quickly.Thus, the present invention solves the problems of accuracy and time involved in creating design documents and work instructions during work, dramatically improving work efficiency and enhancing safety and precision.

[0029] The system according to the embodiment comprises a reception unit, a generation unit, and a reflection unit. The reception unit receives voice input. The reception unit can receive voice input using, for example, a microphone. The reception unit can also convert voice input into text data using speech recognition technology. For example, the reception unit analyzes the voice using speech recognition software and saves it as text data. Furthermore, the reception unit has a function to learn the voice characteristics of a specific speaker in order to improve the accuracy of voice input. For example, the reception unit improves the accuracy of speech recognition by accumulating voice data of workers and learning the voice characteristics of individual workers. The generation unit uses a generation AI to generate the cause of the device malfunction and correction procedures based on the information received by the reception unit. For example, the generation AI can analyze the voice input, identify the cause of the device malfunction, and propose correction procedures. For example, the generation unit can receive a prompt such as "What is the cause of this device error?", analyze the cause of the error, and propose correction procedures. The generation unit can also use the generation AI to generate optimal design documents and work instructions. For example, the generation unit generates design documents and work instructions based on voice input using a generation AI and provides them to the worker. The reflection unit reflects the information generated by the generation unit into the data management device. The reflection unit can, for example, immediately reflect data obtained through voice input into the data management device. For example, the reflection unit sends the information received through voice input to the data management device and immediately updates the data. The reflection unit can also perform data updates remotely. For example, the reflection unit allows a remote engineer to access the data management device and update the data. As a result, the system according to the embodiment improves work efficiency by receiving voice input, generating the cause of the device malfunction and correction procedures, and reflecting them in the data management device.

[0030] The reception unit accepts voice input. For example, the reception unit can accept voice input using a microphone. Specifically, the microphone is highly sensitive and equipped with noise-canceling capabilities to reduce ambient noise. This enables clear voice input even in noisy work environments. Furthermore, the reception unit can convert voice input into text data using speech recognition technology. The speech recognition technology employs the latest deep learning-based speech recognition algorithms, achieving high recognition accuracy. For example, the reception unit analyzes speech using speech recognition software and saves it as text data. The speech recognition software analyzes the waveform data of the speech, identifying phonemes and words to generate accurate text data. In addition, the reception unit has a function to learn the voice characteristics of specific speakers in order to improve the accuracy of voice input. For example, the reception unit accumulates voice data from workers and learns the voice characteristics of individual workers to improve the accuracy of speech recognition. This makes it easier to recognize specialized terminology and pronunciation habits frequently used by specific workers, further improving the accuracy of voice input.

[0031] The generation unit uses a generation AI to generate the cause of the device malfunction and the correction procedure based on the information received by the reception unit. For example, the generation unit's generation AI can analyze voice input, identify the cause of the device malfunction, and propose a correction procedure. The generation AI has learned from a vast dataset and can quickly and accurately identify the cause by referring to past malfunction cases and correction procedures. For example, the generation unit can receive a prompt from the generation AI, such as "What is the cause of this device error?", analyze the cause of the error, and propose a correction procedure. The generation AI uses natural language processing technology to understand the prompt, search for relevant information, and generate the optimal answer. The generation unit can also use the generation AI to generate optimal design documents and work instructions. For example, the generation unit can have the generation AI generate design documents and work instructions based on voice input and provide them to the worker. The generation AI automatically fills in specific content based on design document and work instruction templates, quickly generating high-quality documents. This allows the generation unit to reduce the burden on workers and significantly improve work efficiency.

[0032] The reflection unit reflects the information generated by the generation unit to the data management device. For example, the reflection unit can immediately reflect data obtained through voice input to the data management device. Specifically, the reflection unit sends text data and correction procedures received from the generation unit to the data management device and updates the database. This ensures that the latest information is always reflected in the data management device, allowing workers to proceed with their work based on the most up-to-date information. The reflection unit can also perform remote data updates. For example, the reflection unit allows remote engineers to access the data management device and update the data. Remote access is performed using secure communication protocols, ensuring the confidentiality and integrity of the data. This allows the reflection unit to update data even from geographically distant locations, enabling flexible responses even in situations requiring rapid action. Furthermore, the reflection unit has a data version control function, allowing users to refer back to past data. This allows tracking the data change history and reverting to previous states as needed. This ensures data consistency and reliability, improving the overall operational efficiency of the system.

[0033] The generation unit can generate optimal design documents and work instructions using generation AI. For example, the generation unit's generation AI can analyze voice input and generate optimal design documents and work instructions. For example, the generation unit's generation AI can receive a prompt such as "What is the cause of this device error?", analyze the cause of the error, and propose a correction procedure. The generation unit can also generate design documents and work instructions using generation AI. For example, the generation unit's generation AI can generate design documents and work instructions based on voice input and provide them to the worker. As a result, the efficiency and accuracy of work are improved by generating optimal design documents and work instructions using generation AI.

[0034] The generation unit can analyze the cause of an error using a generation AI and propose a correction procedure. For example, the generation unit's generation AI can analyze voice input, identify the cause of the error, and propose a correction procedure. For instance, the generation unit's generation AI can receive a prompt such as "What is the cause of this device error?", analyze the cause of the error, and propose a correction procedure. The generation unit can also use the generation AI to analyze the cause of an error and propose a correction procedure. For example, the generation unit's generation AI can analyze the cause of an error based on voice input and propose a correction procedure. This enables rapid error correction by having the generation AI analyze the cause of the error and propose a correction procedure.

[0035] The reflection unit can instantly reflect data obtained through voice input to the data management device. For example, the reflection unit can transmit the information received through voice input to the data management device and immediately update the data. For example, the reflection unit can transmit the information received through voice input to the data management device and immediately update the data. The reflection unit can also instantly reflect data obtained through voice input to the data management device. For example, the reflection unit can transmit the information received through voice input to the data management device and immediately update the data. This enables centralized data management and immediate updates by instantly reflecting data obtained through voice input to the data management device.

[0036] The data update unit can perform remote data updates. For example, the data update unit allows a remote engineer to access the data management device and update the data. The data update unit can also perform remote data updates. For example, the data update unit allows a remote engineer to access the data management device and update the data. This enables remote data management by allowing remote data updates.

[0037] The reception unit can analyze the worker's past voice input history and select the optimal reception method. For example, the reception unit can prioritize receiving voice commands that the worker has frequently used in the past. The reception unit can also predict and receive voice commands to be used during specific time periods based on the worker's past voice input history. The reception unit can also analyze the worker's past voice input history and suggest the most efficient voice input method. By analyzing past voice input history, the reception unit can select the optimal reception method and improve work efficiency. Some or all of the above processing in the reception unit may be performed using AI, for example, or without AI.

[0038] The reception unit can filter voice input based on the worker's current work status and areas of interest. For example, the reception unit may only accept voice input related to the work the worker is currently performing. The reception unit can also prioritize receiving relevant voice input based on the worker's areas of interest. The reception unit can also analyze the worker's current work status and filter to receive the most relevant voice input. By filtering voice input based on the worker's current work status and areas of interest, the reception unit prioritizes receiving highly relevant information. Some or all of the above processing in the reception unit may be performed using AI, for example, or without AI.

[0039] The reception unit can prioritize receiving voice inputs that are highly relevant, taking into account the worker's geographical location information. For example, if the worker is in a specific location, the reception unit will prioritize receiving voice inputs related to that location. The reception unit can also filter and receive relevant voice inputs based on the worker's current location. The reception unit can also analyze the worker's geographical location information and prioritize receiving the most relevant voice inputs. By considering the worker's geographical location information, the reception unit prioritizes receiving highly relevant voice inputs. Some or all of the above processing in the reception unit may be performed using AI, for example, or without AI.

[0040] The reception unit can analyze the worker's social media activity when receiving voice input and accept relevant voice input. For example, the reception unit can prioritize accepting relevant voice input based on the worker's social media activity. The reception unit can also analyze the worker's social media activity and suggest the most suitable voice input. The reception unit can also filter and accept relevant voice input based on the worker's social media activity. For example, the reception unit filters and accepts relevant voice input based on the worker's social media activity. This allows for the priority acceptance of relevant voice input by analyzing the worker's social media activity. Some or all of the above processing in the reception unit may be performed using AI, for example, or without AI.

[0041] The generation unit can adjust the level of detail of the information it generates based on the importance of the device's malfunction cause during generation. For example, the generation unit generates detailed information for malfunction causes of high importance. The generation unit can also generate concise information for malfunction causes of low importance. The generation unit can also dynamically adjust the level of detail of the information according to the importance of the malfunction cause. By adjusting the level of detail of the information based on the importance of the malfunction cause, the necessary information is provided at an appropriate level of detail. Some or all of the above-described processes in the generation unit may be performed using, for example, a generation AI, or without using a generation AI.

[0042] The generation unit can apply different generation algorithms depending on the category of the device during generation. For example, the generation unit can select the optimal generation algorithm depending on the category of the device. For example, the generation unit can select the optimal generation algorithm depending on the category of the device. The generation unit can also apply different generation algorithms for each category of device to generate optimal information. For example, the generation unit can apply different generation algorithms for each category of device to generate optimal information. The generation unit can also dynamically switch generation algorithms based on the category of the device. For example, the generation unit dynamically switches generation algorithms based on the category of the device. This allows the generation unit to generate appropriate information by applying the optimal generation algorithm according to the category of the device. Some or all of the above-described processes in the generation unit may be performed using, for example, a generation AI, or without using a generation AI.

[0043] The generation unit can determine the priority of the information to be generated based on the timing of the device malfunction during generation. For example, the generation unit will prioritize generating information about recently occurring malfunctions. The generation unit can also generate information about past malfunctions as needed. The generation unit can also dynamically adjust the priority of information based on the timing of the malfunctions. By determining the priority of information based on the timing of the malfunctions, important information is provided preferentially. Some or all of the above processing in the generation unit may be performed using, for example, a generation AI, or without using a generation AI.

[0044] The generation unit can adjust the order of information generated based on the relationships between devices during generation. For example, the generation unit can prioritize generating information that is highly relevant to the devices. The generation unit can also postpone the generation of information that is less relevant to the devices. The generation unit can also dynamically adjust the order of information generation based on the relationships between devices. By doing so, the generation unit prioritizes providing information that is highly relevant by adjusting the order of information generation based on the relationships between devices. Some or all of the above processing in the generation unit may be performed using, for example, a generation AI, or without using a generation AI.

[0045] The reflection unit can select the optimal reflection method by referring to past data reflection history when reflecting data. For example, the reflection unit can select the optimal reflection method based on data reflection methods used in the past. For example, the reflection unit can select the optimal reflection method based on data reflection methods used in the past. The reflection unit can also propose the most efficient reflection method from past data reflection history. For example, the reflection unit can propose the most efficient reflection method from past data reflection history. The reflection unit can also analyze the data reflection history and dynamically select the optimal reflection method. For example, the reflection unit analyzes the data reflection history and dynamically selects the optimal reflection method. This improves the efficiency of data reflection by selecting the optimal reflection method by referring to past data reflection history. Some or all of the above processing in the reflection unit may be performed using AI, for example, or without using AI.

[0046] The reflection unit can customize the means of reflection based on the worker's current work status when reflecting data. For example, the reflection unit can prioritize reflecting data related to the work the worker is currently performing. The reflection unit can also analyze the worker's current work status and propose the optimal means of reflection. The reflection unit can also dynamically customize the means of data reflection based on the worker's current work status. For example, the reflection unit can dynamically customize the means of data reflection based on the worker's current work status. This enables efficient data reflection by customizing the means of data reflection based on the worker's current work status. Some or all of the above processing in the reflection unit may be performed using AI, for example, or without using AI.

[0047] The reflection unit can select the optimal reflection method when reflecting data, taking into account the worker's geographical location information. For example, if a worker is in a specific location, the reflection unit will prioritize reflecting data related to that location. The reflection unit can also propose the optimal data reflection method based on the worker's current location. The reflection unit can also analyze the worker's geographical location information and select the optimal data reflection method. This allows for efficient data reflection by selecting the optimal data reflection method while considering the worker's geographical location information. Some or all of the above processing in the reflection unit may be performed using AI, for example, or without AI.

[0048] The reflection unit can analyze the worker's social media activity and propose a method for reflection when reflecting data. For example, the reflection unit can propose the optimal data reflection method based on the worker's social media activity. The reflection unit can also analyze the worker's social media activity and prioritize the reflection of relevant data. For example, the reflection unit analyzes the worker's social media activity and prioritizes the reflection of relevant data. The reflection unit can also select the optimal data reflection method based on the worker's social media activity. For example, the reflection unit selects the optimal data reflection method based on the worker's social media activity. This enables efficient data reflection by proposing the optimal data reflection method through analysis of the worker's social media activity. Some or all of the above processing in the reflection unit may be performed using AI, for example, or without using AI.

[0049] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0050] The generation unit can analyze the cause of an error using generation AI and propose a correction procedure. For example, the generation AI can analyze voice input, identify the cause of the error, and propose a correction procedure. It can also analyze the cause of an error and propose a correction procedure using generation AI. Furthermore, by having the generation AI analyze the cause of an error based on voice input and propose a correction procedure, rapid error correction becomes possible.

[0051] The reflection unit can instantly reflect data obtained through voice input to the data management device. For example, it can transmit information received through voice input to the data management device and immediately update the data. It can also instantly reflect data obtained through voice input to the data management device. Furthermore, by transmitting information received through voice input to the data management device and immediately updating the data, centralized data management and instant updates become possible.

[0052] The data update unit allows for remote data updates. For example, a remote engineer can access the data management device and update the data. Furthermore, remote data updates can be performed remotely. This enables remote data management by allowing remote engineers to access the data management device and update the data.

[0053] The generation unit can adjust the level of detail of the information generated based on the severity of the device malfunction cause during generation. For example, it can generate detailed information for high-severity malfunction causes, and concise information for low-severity malfunction causes. Furthermore, it can dynamically adjust the level of detail of the information according to the severity of the malfunction cause. This allows the necessary information to be provided with the appropriate level of detail by adjusting the level of detail based on the severity of the malfunction cause.

[0054] The generation unit can apply different generation algorithms depending on the device category during generation. For example, it can select the optimal generation algorithm based on the device category. It can also apply different generation algorithms for each device category to generate optimal information. Furthermore, it can dynamically switch generation algorithms based on the device category. This allows for the generation of appropriate information by applying the optimal generation algorithm according to the device category.

[0055] The data reflection unit can select the optimal reflection method by referring to past data reflection history when reflecting data. For example, it can select the optimal reflection method based on data reflection methods used in the past. It can also propose the most efficient reflection method from past data reflection history. Furthermore, it can analyze the data reflection history and dynamically select the optimal reflection method. As a result, by referring to past data reflection history, the optimal reflection method can be selected, improving the efficiency of data reflection.

[0056] The following briefly describes the processing flow for example form 1.

[0057] Step 1: The reception unit accepts voice input. For example, it can accept voice input using a microphone. It can also convert voice input into text data using speech recognition technology. Furthermore, it has a function to learn the voice characteristics of specific speakers, and by accumulating voice data from workers and learning the voice characteristics of individual workers, the accuracy of speech recognition can be improved. Step 2: The generation unit generates the cause of the device malfunction and the correction procedure based on the information received by the reception unit. Using the generation AI, it can analyze voice input to identify the cause of the device malfunction and propose a correction procedure. It can also generate optimal design documents and work instructions. Step 3: The reflection unit reflects the information generated by the generation unit to the data management device. Data obtained via voice input can be immediately reflected to the data management device, and data updates can also be performed remotely.

[0058] (Example of form 2) The system according to an embodiment of the present invention is designed to solve the problems of accuracy and time involved in creating design documents and work instructions during work, which are cumbersome. This system allows workers to use voice input during work to inquire in real time with a generating AI about the cause of equipment malfunctions and correction procedures. This system can be linked with a "data management device" to centrally manage all data. Data obtained from the field via voice input is automatically reflected in the "data management device," and data can be updated instantly remotely. This dramatically improves work efficiency and enhances safety and accuracy. It also reduces the burden on engineers and promotes the smooth progress of the entire operation. For example, during work, a worker can use voice input to inquire in real time with a generating AI about the cause of equipment malfunctions and correction procedures. At this time, the generating AI generates optimal design documents and work instructions based on the information input using voice recognition technology. For example, if a worker voice inputs, "What is the cause of this device error?", the generating AI will analyze the cause of the error and propose a correction procedure. Next, the design documents and work instructions proposed by the generating AI are centrally managed in conjunction with the "data management device." This system automatically reflects data obtained via voice input from the field into a "data management device," allowing for immediate data updates from remote locations. For example, when a worker uses voice input on-site, that information is instantly reflected in the "data management device," and remote engineers can check the information in real time. This system dramatically improves work efficiency and enhances safety and accuracy. For instance, by allowing workers to use voice input on-site, necessary information can be quickly obtained, making it easier to identify and correct the cause of errors. It also reduces the burden on engineers and promotes the smooth progress of the entire operation. For example, engineers can check data remotely and take necessary actions quickly. Furthermore, this system has a diverse customer base not only in the telecommunications industry but also in the infrastructure and engineering industries, and is expected to have a market size of several hundred million dollars in the growing IoT and automation markets. For example, in the infrastructure industry, on-site work efficiency will be improved, and in the engineering industry, design documents and work instructions can be created more quickly.Thus, the present invention solves the problems of accuracy and time involved in creating design documents and work instructions during work, dramatically improving work efficiency and enhancing safety and precision.

[0059] The system according to the embodiment comprises a reception unit, a generation unit, and a reflection unit. The reception unit receives voice input. The reception unit can receive voice input using, for example, a microphone. The reception unit can also convert voice input into text data using speech recognition technology. For example, the reception unit analyzes the voice using speech recognition software and saves it as text data. Furthermore, the reception unit has a function to learn the voice characteristics of a specific speaker in order to improve the accuracy of voice input. For example, the reception unit improves the accuracy of speech recognition by accumulating voice data of workers and learning the voice characteristics of individual workers. The generation unit uses a generation AI to generate the cause of the device malfunction and correction procedures based on the information received by the reception unit. For example, the generation AI can analyze the voice input, identify the cause of the device malfunction, and propose correction procedures. For example, the generation unit can receive a prompt such as "What is the cause of this device error?", analyze the cause of the error, and propose correction procedures. The generation unit can also use the generation AI to generate optimal design documents and work instructions. For example, the generation unit generates design documents and work instructions based on voice input using a generation AI and provides them to the worker. The reflection unit reflects the information generated by the generation unit into the data management device. The reflection unit can, for example, immediately reflect data obtained through voice input into the data management device. For example, the reflection unit sends the information received through voice input to the data management device and immediately updates the data. The reflection unit can also perform data updates remotely. For example, the reflection unit allows a remote engineer to access the data management device and update the data. As a result, the system according to the embodiment improves work efficiency by receiving voice input, generating the cause of the device malfunction and correction procedures, and reflecting them in the data management device.

[0060] The reception unit accepts voice input. For example, the reception unit can accept voice input using a microphone. Specifically, the microphone is highly sensitive and equipped with noise-canceling capabilities to reduce ambient noise. This enables clear voice input even in noisy work environments. Furthermore, the reception unit can convert voice input into text data using speech recognition technology. The speech recognition technology employs the latest deep learning-based speech recognition algorithms, achieving high recognition accuracy. For example, the reception unit analyzes speech using speech recognition software and saves it as text data. The speech recognition software analyzes the waveform data of the speech, identifying phonemes and words to generate accurate text data. In addition, the reception unit has a function to learn the voice characteristics of specific speakers in order to improve the accuracy of voice input. For example, the reception unit accumulates voice data from workers and learns the voice characteristics of individual workers to improve the accuracy of speech recognition. This makes it easier to recognize specialized terminology and pronunciation habits frequently used by specific workers, further improving the accuracy of voice input.

[0061] The generation unit uses a generation AI to generate the cause of the device malfunction and the correction procedure based on the information received by the reception unit. For example, the generation unit's generation AI can analyze voice input, identify the cause of the device malfunction, and propose a correction procedure. The generation AI has learned from a vast dataset and can quickly and accurately identify the cause by referring to past malfunction cases and correction procedures. For example, the generation unit can receive a prompt from the generation AI, such as "What is the cause of this device error?", analyze the cause of the error, and propose a correction procedure. The generation AI uses natural language processing technology to understand the prompt, search for relevant information, and generate the optimal answer. The generation unit can also use the generation AI to generate optimal design documents and work instructions. For example, the generation unit can have the generation AI generate design documents and work instructions based on voice input and provide them to the worker. The generation AI automatically fills in specific content based on design document and work instruction templates, quickly generating high-quality documents. This allows the generation unit to reduce the burden on workers and significantly improve work efficiency.

[0062] The reflection unit reflects the information generated by the generation unit to the data management device. For example, the reflection unit can immediately reflect data obtained through voice input to the data management device. Specifically, the reflection unit sends text data and correction procedures received from the generation unit to the data management device and updates the database. This ensures that the latest information is always reflected in the data management device, allowing workers to proceed with their work based on the most up-to-date information. The reflection unit can also perform remote data updates. For example, the reflection unit allows remote engineers to access the data management device and update the data. Remote access is performed using secure communication protocols, ensuring the confidentiality and integrity of the data. This allows the reflection unit to update data even from geographically distant locations, enabling flexible responses even in situations requiring rapid action. Furthermore, the reflection unit has a data version control function, allowing users to refer back to past data. This allows tracking the data change history and reverting to previous states as needed. This ensures data consistency and reliability, improving the overall operational efficiency of the system.

[0063] The generation unit can generate optimal design documents and work instructions using generation AI. For example, the generation unit's generation AI can analyze voice input and generate optimal design documents and work instructions. For example, the generation unit's generation AI can receive a prompt such as "What is the cause of this device error?", analyze the cause of the error, and propose a correction procedure. The generation unit can also generate design documents and work instructions using generation AI. For example, the generation unit's generation AI can generate design documents and work instructions based on voice input and provide them to the worker. As a result, the efficiency and accuracy of work are improved by generating optimal design documents and work instructions using generation AI.

[0064] The generation unit can analyze the cause of an error using a generation AI and propose a correction procedure. For example, the generation unit's generation AI can analyze voice input, identify the cause of the error, and propose a correction procedure. For instance, the generation unit's generation AI can receive a prompt such as "What is the cause of this device error?", analyze the cause of the error, and propose a correction procedure. The generation unit can also use the generation AI to analyze the cause of an error and propose a correction procedure. For example, the generation unit's generation AI can analyze the cause of an error based on voice input and propose a correction procedure. This enables rapid error correction by having the generation AI analyze the cause of the error and propose a correction procedure.

[0065] The reflection unit can instantly reflect data obtained through voice input to the data management device. For example, the reflection unit can transmit the information received through voice input to the data management device and immediately update the data. For example, the reflection unit can transmit the information received through voice input to the data management device and immediately update the data. The reflection unit can also instantly reflect data obtained through voice input to the data management device. For example, the reflection unit can transmit the information received through voice input to the data management device and immediately update the data. This enables centralized data management and immediate updates by instantly reflecting data obtained through voice input to the data management device.

[0066] The data update unit can perform remote data updates. For example, the data update unit allows a remote engineer to access the data management device and update the data. The data update unit can also perform remote data updates. For example, the data update unit allows a remote engineer to access the data management device and update the data. This enables remote data management by allowing remote data updates.

[0067] The reception desk can estimate the worker's emotions and adjust the timing of voice input reception based on the estimated emotions. For example, if the worker is stressed, the reception desk can delay the timing of voice input reception and wait until the worker is relaxed. The reception desk can also speed up the timing of voice input reception if the worker is concentrating, allowing for quicker information retrieval. The reception desk can also adjust the timing of voice input reception if the worker is tired, timing it to coincide with the worker's break. This reduces the burden on workers by adjusting the timing of voice input reception according to their emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) and multimodal generation AI.

[0068] The reception unit can analyze the worker's past voice input history and select the optimal reception method. For example, the reception unit can prioritize receiving voice commands that the worker has frequently used in the past. The reception unit can also predict and receive voice commands to be used during specific time periods based on the worker's past voice input history. The reception unit can also analyze the worker's past voice input history and suggest the most efficient voice input method. By analyzing past voice input history, the reception unit can select the optimal reception method and improve work efficiency. Some or all of the above processing in the reception unit may be performed using AI, for example, or without AI.

[0069] The reception unit can filter voice input based on the worker's current work status and areas of interest. For example, the reception unit may only accept voice input related to the work the worker is currently performing. The reception unit can also prioritize receiving relevant voice input based on the worker's areas of interest. The reception unit can also analyze the worker's current work status and filter to receive the most relevant voice input. By filtering voice input based on the worker's current work status and areas of interest, the reception unit prioritizes receiving highly relevant information. Some or all of the above processing in the reception unit may be performed using AI, for example, or without AI.

[0070] The reception desk can estimate the worker's emotions and determine the priority of voice input to be received based on the estimated emotions. For example, if the worker is stressed, the reception desk will prioritize important voice input. For example, if the worker is stressed, the reception desk will prioritize important voice input. For example, if the worker is relaxed, the reception desk will prioritize normal voice input. For example, if the worker is relaxed, the reception desk will prioritize normal voice input. For example, if the worker is in a hurry, the reception desk will prioritize urgent voice input. For example, if the worker is in a hurry, the reception desk will prioritize urgent voice input. In this way, by determining the priority of voice input based on the worker's emotions, important information is received preferentially. Emotion estimation is achieved using emotion estimation functions, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI.

[0071] The reception unit can prioritize receiving voice inputs that are highly relevant, taking into account the worker's geographical location information. For example, if the worker is in a specific location, the reception unit will prioritize receiving voice inputs related to that location. The reception unit can also filter and receive relevant voice inputs based on the worker's current location. The reception unit can also analyze the worker's geographical location information and prioritize receiving the most relevant voice inputs. By considering the worker's geographical location information, the reception unit prioritizes receiving highly relevant voice inputs. Some or all of the above processing in the reception unit may be performed using AI, for example, or without AI.

[0072] The reception unit can analyze the worker's social media activity when receiving voice input and accept relevant voice input. For example, the reception unit can prioritize accepting relevant voice input based on the worker's social media activity. The reception unit can also analyze the worker's social media activity and suggest the most suitable voice input. The reception unit can also filter and accept relevant voice input based on the worker's social media activity. For example, the reception unit filters and accepts relevant voice input based on the worker's social media activity. This allows for the priority acceptance of relevant voice input by analyzing the worker's social media activity. Some or all of the above processing in the reception unit may be performed using AI, for example, or without AI.

[0073] The generation unit can estimate the worker's emotions and adjust the expression of the design documents and work instructions it generates based on the estimated emotions. For example, if the worker is stressed, the generation unit will use a simple and easy-to-understand expression. For example, if the worker is relaxed, the generation unit will use an expression that includes detailed explanations. For example, if the worker is relaxed, the generation unit will use an expression that includes detailed explanations. For example, if the worker is in a hurry, the generation unit will use a concise expression that gets straight to the point. For example, if the worker is in a hurry, the generation unit will use a concise expression that gets straight to the point. In this way, by adjusting the expression of the design documents and work instructions based on the worker's emotions, information that is easy for the worker to understand is provided. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or a generation AI. The generation AI is a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to these examples.

[0074] The generation unit can adjust the level of detail of the information it generates based on the importance of the device's malfunction cause during generation. For example, the generation unit generates detailed information for malfunction causes of high importance. The generation unit can also generate concise information for malfunction causes of low importance. The generation unit can also dynamically adjust the level of detail of the information according to the importance of the malfunction cause. By adjusting the level of detail of the information based on the importance of the malfunction cause, the necessary information is provided at an appropriate level of detail. Some or all of the above-described processes in the generation unit may be performed using, for example, a generation AI, or without using a generation AI.

[0075] The generation unit can apply different generation algorithms depending on the category of the device during generation. For example, the generation unit can select the optimal generation algorithm depending on the category of the device. For example, the generation unit can select the optimal generation algorithm depending on the category of the device. The generation unit can also apply different generation algorithms for each category of device to generate optimal information. For example, the generation unit can apply different generation algorithms for each category of device to generate optimal information. The generation unit can also dynamically switch generation algorithms based on the category of the device. For example, the generation unit dynamically switches generation algorithms based on the category of the device. This allows the generation unit to generate appropriate information by applying the optimal generation algorithm according to the category of the device. Some or all of the above-described processes in the generation unit may be performed using, for example, a generation AI, or without using a generation AI.

[0076] The generation unit can estimate the worker's emotions and adjust the length of the design documents and work instructions it generates based on the estimated emotions. For example, if the worker is stressed, the generation unit can generate short, concise design documents and work instructions. The generation unit can also generate longer design documents and work instructions with more detailed explanations if the worker is relaxed. Furthermore, if the worker is in a hurry, the generation unit can generate shorter design documents and work instructions for quick understanding. This allows the system to provide the worker with an appropriate amount of information by adjusting the length of the design documents and work instructions based on their emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or a generation AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) and multimodal generation AI.

[0077] The generation unit can determine the priority of the information to be generated based on the timing of the device malfunction during generation. For example, the generation unit will prioritize generating information about recently occurring malfunctions. The generation unit can also generate information about past malfunctions as needed. The generation unit can also dynamically adjust the priority of information based on the timing of the malfunctions. By determining the priority of information based on the timing of the malfunctions, important information is provided preferentially. Some or all of the above processing in the generation unit may be performed using, for example, a generation AI, or without using a generation AI.

[0078] The generation unit can adjust the order of information generated based on the relationships between devices during generation. For example, the generation unit can prioritize generating information that is highly relevant to the devices. The generation unit can also postpone the generation of information that is less relevant to the devices. The generation unit can also dynamically adjust the order of information generation based on the relationships between devices. By doing so, the generation unit prioritizes providing information that is highly relevant by adjusting the order of information generation based on the relationships between devices. Some or all of the above processing in the generation unit may be performed using, for example, a generation AI, or without using a generation AI.

[0079] The reflection unit can estimate the worker's emotions and adjust the data reflection method based on the estimated emotions. For example, if the worker is stressed, the reflection unit can delay data reflection and wait until the worker is relaxed. The reflection unit can also reflect data quickly if the worker is focused. For example, if the worker is focused, the reflection unit can reflect data quickly. The reflection unit can also adjust data reflection if the worker is tired and reflect the data at a time when the worker is taking a break. For example, if the worker is tired, the reflection unit can adjust data reflection and reflect the data at a time when the worker is taking a break. In this way, the burden on the worker is reduced by adjusting the data reflection method based on the worker's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI.

[0080] The reflection unit can select the optimal reflection method by referring to past data reflection history when reflecting data. For example, the reflection unit can select the optimal reflection method based on data reflection methods used in the past. For example, the reflection unit can select the optimal reflection method based on data reflection methods used in the past. The reflection unit can also propose the most efficient reflection method from past data reflection history. For example, the reflection unit can propose the most efficient reflection method from past data reflection history. The reflection unit can also analyze the data reflection history and dynamically select the optimal reflection method. For example, the reflection unit analyzes the data reflection history and dynamically selects the optimal reflection method. This improves the efficiency of data reflection by selecting the optimal reflection method by referring to past data reflection history. Some or all of the above processing in the reflection unit may be performed using AI, for example, or without using AI.

[0081] The reflection unit can customize the means of reflection based on the worker's current work status when reflecting data. For example, the reflection unit can prioritize reflecting data related to the work the worker is currently performing. The reflection unit can also analyze the worker's current work status and propose the optimal means of reflection. The reflection unit can also dynamically customize the means of data reflection based on the worker's current work status. For example, the reflection unit can dynamically customize the means of data reflection based on the worker's current work status. This enables efficient data reflection by customizing the means of data reflection based on the worker's current work status. Some or all of the above processing in the reflection unit may be performed using AI, for example, or without using AI.

[0082] The reflection unit can estimate the worker's emotions and determine the priority of data reflection based on the estimated worker's emotions. For example, if the worker is stressed, the reflection unit will prioritize reflecting important data. For example, if the worker is stressed, the reflection unit will prioritize reflecting important data. For example, if the worker is relaxed, the reflection unit will prioritize reflecting normal data. For example, if the worker is relaxed, the reflection unit will prioritize reflecting normal data. For example, if the worker is in a hurry, the reflection unit will prioritize reflecting urgent data. For example, if the worker is in a hurry, the reflection unit will prioritize reflecting urgent data. In this way, by determining the priority of data reflection based on the worker's emotions, important data is prioritized. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI.

[0083] The reflection unit can select the optimal reflection method when reflecting data, taking into account the worker's geographical location information. For example, if a worker is in a specific location, the reflection unit will prioritize reflecting data related to that location. The reflection unit can also propose the optimal data reflection method based on the worker's current location. The reflection unit can also analyze the worker's geographical location information and select the optimal data reflection method. This allows for efficient data reflection by selecting the optimal data reflection method while considering the worker's geographical location information. Some or all of the above processing in the reflection unit may be performed using AI, for example, or without AI.

[0084] The reflection unit can analyze the worker's social media activity and propose a method for reflection when reflecting data. For example, the reflection unit can propose the optimal data reflection method based on the worker's social media activity. The reflection unit can also analyze the worker's social media activity and prioritize the reflection of relevant data. For example, the reflection unit analyzes the worker's social media activity and prioritizes the reflection of relevant data. The reflection unit can also select the optimal data reflection method based on the worker's social media activity. For example, the reflection unit selects the optimal data reflection method based on the worker's social media activity. This enables efficient data reflection by proposing the optimal data reflection method through analysis of the worker's social media activity. Some or all of the above processing in the reflection unit may be performed using AI, for example, or without using AI.

[0085] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0086] The reception desk can estimate the worker's emotions and adjust the timing of voice input reception based on the estimated emotions. For example, if a worker is stressed, the timing of voice input reception can be delayed, waiting until the worker is relaxed. Conversely, if a worker is concentrating, the timing of voice input reception can be advanced to allow for quick information retrieval. Furthermore, if a worker is tired, the timing of voice input reception can be adjusted to coincide with the worker's break. In this way, the burden on workers can be reduced by adjusting the timing of voice input reception according to their emotions.

[0087] The generation unit can analyze the cause of an error using generation AI and propose a correction procedure. For example, the generation AI can analyze voice input, identify the cause of the error, and propose a correction procedure. It can also analyze the cause of an error and propose a correction procedure using generation AI. Furthermore, by having the generation AI analyze the cause of an error based on voice input and propose a correction procedure, rapid error correction becomes possible.

[0088] The reflection unit can instantly reflect data obtained through voice input to the data management device. For example, it can transmit information received through voice input to the data management device and immediately update the data. It can also instantly reflect data obtained through voice input to the data management device. Furthermore, by transmitting information received through voice input to the data management device and immediately updating the data, centralized data management and instant updates become possible.

[0089] The data update unit allows for remote data updates. For example, a remote engineer can access the data management device and update the data. Furthermore, remote data updates can be performed remotely. This enables remote data management by allowing remote engineers to access the data management device and update the data.

[0090] The reception system can estimate the worker's emotions and determine the priority of voice input to be received based on those emotions. For example, if the worker is stressed, important voice input will be prioritized. If the worker is relaxed, normal voice input may be prioritized. Furthermore, if the worker is in a hurry, urgent voice input may be prioritized. In this way, by prioritizing voice input based on the worker's emotions, important information can be received preferentially.

[0091] The generation unit can estimate the worker's emotions and adjust the expression of the generated design documents and work instructions based on those emotions. For example, if the worker is stressed, a simple and easy-to-understand expression is used. If the worker is relaxed, an expression that includes detailed explanations can be used. Furthermore, if the worker is in a hurry, a concise expression that gets straight to the point can be used. In this way, by adjusting the expression of design documents and work instructions based on the worker's emotions, information that is easy for the worker to understand can be provided.

[0092] The generation unit can adjust the level of detail of the information generated based on the severity of the device malfunction cause during generation. For example, it can generate detailed information for high-severity malfunction causes, and concise information for low-severity malfunction causes. Furthermore, it can dynamically adjust the level of detail of the information according to the severity of the malfunction cause. This allows the necessary information to be provided with the appropriate level of detail by adjusting the level of detail based on the severity of the malfunction cause.

[0093] The generation unit can apply different generation algorithms depending on the device category during generation. For example, it can select the optimal generation algorithm based on the device category. It can also apply different generation algorithms for each device category to generate optimal information. Furthermore, it can dynamically switch generation algorithms based on the device category. This allows for the generation of appropriate information by applying the optimal generation algorithm according to the device category.

[0094] The reflection unit can estimate the worker's emotions and adjust the data reflection method based on the estimated emotions. For example, if the worker is stressed, the data reflection can be delayed until the worker is relaxed. Conversely, if the worker is focused, the data reflection can be performed quickly. Furthermore, if the worker is tired, the data reflection can be adjusted to coincide with the worker's break. In this way, the burden on workers can be reduced by adjusting the data reflection method based on their emotions.

[0095] The data reflection unit can select the optimal reflection method by referring to past data reflection history when reflecting data. For example, it can select the optimal reflection method based on data reflection methods used in the past. It can also propose the most efficient reflection method from past data reflection history. Furthermore, it can analyze the data reflection history and dynamically select the optimal reflection method. As a result, by referring to past data reflection history, the optimal reflection method can be selected, improving the efficiency of data reflection.

[0096] The following briefly describes the processing flow for example form 2.

[0097] Step 1: The reception unit accepts voice input. For example, it can accept voice input using a microphone. It can also convert voice input into text data using speech recognition technology. Furthermore, it has a function to learn the voice characteristics of specific speakers, and by accumulating voice data from workers and learning the voice characteristics of individual workers, the accuracy of speech recognition can be improved. Step 2: The generation unit generates the cause of the device malfunction and the correction procedure based on the information received by the reception unit. Using the generation AI, it can analyze voice input to identify the cause of the device malfunction and propose a correction procedure. It can also generate optimal design documents and work instructions. Step 3: The reflection unit reflects the information generated by the generation unit to the data management device. Data obtained via voice input can be immediately reflected to the data management device, and data updates can also be performed remotely.

[0098] 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 user input for the result of the specific processing. The control unit 46A transmits the audio data indicating 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.

[0099] Data generation model 58 is a form of so-called generative AI (Artificial Intelligence). An example of data generation model 58 is ChatGPT (registered trademark) (Internet search).<URL: https: / / openai.com / blog / chatgpt> Examples of generative AI include text generation AI, image generation AI, and multimodal generation AI. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and with inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats from audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVMs), k-means clustering, convolutional neural networks (CNNs), recurrent neural networks (RNNs), generative adversarial networks (GANs), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each of the above parts is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example.Furthermore, processing performed by AI, including generative AI, may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by AI, including generative AI.

[0100] Furthermore, the processing performed by the data processing system 10 described above is carried out by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart device 14, but it may also be carried out by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart device 14. In addition, 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.

[0101] Each of the multiple elements described above, including the reception unit, generation unit, and reflection unit, is implemented in at least one of the smart device 14 and the data processing unit 12. For example, the reception unit receives voice input using the microphone 38B of the smart device 14 and converts it into text data using speech recognition technology by the control unit 46A. The generation unit is implemented in the specific processing unit 290 of the data processing unit 12 and generates the cause of the device malfunction and correction procedures using generation AI. The reflection unit is implemented in the specific processing unit 290 of the data processing unit 12 and immediately reflects the generated information in the data management device. The correspondence between each unit and the device or control unit is not limited to the example described above and can be changed in various ways.

[0102] [Second Embodiment] Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment.

[0103] As shown in Figure 3, the data processing system 210 includes a data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0104] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN and / or LAN.

[0105] The smart glasses 214 include a computer 36, a microphone 238, a speaker 240, a camera 42, and a communication interface 44. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, and camera 42 are also connected to the bus 52.

[0106] The microphone 238 receives voice signals from the user and accepts instructions from the user. The microphone 238 captures the voice signals from the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0107] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, which captures images of the area around the user (for example, an imaging range defined by a field of view equivalent to the field of vision of a typical healthy person).

[0108] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0109] Figure 4 shows an example of the main functions of the data processing device 12 and the smart glasses 214. As shown in Figure 4, the data processing device 12 performs specific processing by the processor 28. The storage 32 stores the specific processing program 56.

[0110] The processor 28 reads a 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 acting as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0111] Storage 32 stores the data generation model 58 and the 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 the user's emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0112] In the smart glasses 214, specific processing is performed by the processor 46. The storage 50 stores a specific processing program 60. 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 acting as a control unit 46A according to the specific processing program 60 executed on the RAM 48. The smart glasses 214 also have a data generation model 58 and an emotion identification model 59, similar to the data generation model and emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0113] Furthermore, other devices besides the data processing device 12 may also 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 obtains processing results (such as prediction results) using the data generation model 58 by communicating with the server device that has the data generation model 58. Also, the data processing device 12 may be a server device or a terminal device owned by the user (for example, a mobile phone, robot, home appliance, etc.).

[0114] 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 user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0115] The data generation model 58 is a so-called generative AI. An example of a 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 is input with prompts containing instructions, and inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats such as audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVM), k-means clustering, convolutional neural networks (CNN), recurrent neural networks (RNN), generative adversarial networks (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each part described above is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example. Also, processing performed by an AI including a generative AI may be replaced by rule-based processing, and rule-based processing may be replaced by processing performed by an AI including a generative AI.

[0116] 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 performed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart glasses 214, but it may also be performed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart glasses 214. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the smart glasses 214 or an external device, and the smart glasses 214 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0117] Each of the multiple elements, including the reception unit, generation unit, and reflection unit described above, is implemented in at least one of the smart glasses 214 and the data processing unit 12. For example, the reception unit receives voice input using the microphone 238 of the smart glasses 214 and converts it into text data using speech recognition technology by the control unit 46A. The generation unit is implemented in the specific processing unit 290 of the data processing unit 12 and generates the cause of the device malfunction and correction procedures using generation AI. The reflection unit is implemented in the specific processing unit 290 of the data processing unit 12 and immediately reflects the generated information in the data management device. The correspondence between each unit and the device or control unit is not limited to the example described above and can be changed in various ways.

[0118] [Third Embodiment] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[0119] As shown in Figure 5, the data processing system 310 includes a data processing device 12 and a headset terminal 314. An example of the data processing device 12 is a server.

[0120] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN and / or LAN.

[0121] The headset terminal 314 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication interface 44, and a display 343. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, camera 42, and display 343 are also connected to the bus 52.

[0122] The microphone 238 receives voice signals from the user and accepts instructions from the user. The microphone 238 captures the voice signals from the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0123] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, which captures images of the area around the user (for example, an imaging range defined by a field of view equivalent to the field of vision of a typical healthy person).

[0124] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0125] Figure 6 shows an example of the main functions of the data processing device 12 and the headset terminal 314. As shown in Figure 6, the data processing device 12 performs specific processing using the processor 28. The storage 32 stores the specific processing program 56.

[0126] The processor 28 reads a 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 acting as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0127] Storage 32 stores the data generation model 58 and the 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 the user's emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0128] In the headset terminal 314, specific processing is performed by the processor 46. The storage 50 stores a specific program 60. The processor 46 reads the specific program 60 from the storage 50 and executes the read specific program 60 on the RAM 48. The specific processing is realized by the processor 46 acting as a control unit 46A according to the specific program 60 executed on the RAM 48. The headset terminal 314 also has a data generation model 58 and an emotion identification model 59, similar to the data generation model and emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0129] Furthermore, other devices besides the data processing device 12 may also 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 obtains processing results (such as prediction results) using the data generation model 58 by communicating with the server device that has the data generation model 58. Also, the data processing device 12 may be a server device or a terminal device owned by the user (for example, a mobile phone, robot, home appliance, etc.).

[0130] The specific processing unit 290 transmits the result of the specific processing to the headset terminal 314. In the headset terminal 314, the control unit 46A causes the speaker 240 and display 343 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0131] The data generation model 58 is a so-called generative AI. An example of a 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 is input with prompts containing instructions, and inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats such as audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVM), k-means clustering, convolutional neural networks (CNN), recurrent neural networks (RNN), generative adversarial networks (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each part described above is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example. Also, processing performed by an AI including a generative AI may be replaced by rule-based processing, and rule-based processing may be replaced by processing performed by an AI including a generative AI.

[0132] 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 performed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the headset terminal 314, but may also be performed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the headset terminal 314. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the headset terminal 314 or an external device, and the headset terminal 314 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0133] Each of the multiple elements described above, including the reception unit, generation unit, and reflection unit, is implemented in at least one of the headset terminal 314 and the data processing unit 12. For example, the reception unit receives voice input using the microphone 238 of the headset terminal 314 and converts it into text data using speech recognition technology by the control unit 46A. The generation unit is implemented in the specific processing unit 290 of the data processing unit 12 and generates the cause of the device malfunction and correction procedures using generation AI. The reflection unit is implemented in the specific processing unit 290 of the data processing unit 12 and immediately reflects the generated information in the data management device. The correspondence between each unit and the device or control unit is not limited to the example described above and can be changed in various ways.

[0134] [Fourth Embodiment] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[0135] As shown in Figure 7, the data processing system 410 includes a data processing device 12 and a robot 414. An example of the data processing device 12 is a server.

[0136] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN and / or LAN.

[0137] The robot 414 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication interface 44, and a controlled object 443. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, camera 42, and controlled object 443 are also connected to the bus 52.

[0138] The microphone 238 receives voice signals from the user and accepts instructions from the user. The microphone 238 captures the voice signals from the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0139] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS image sensor or CCD image sensor, which captures images of the area around the user (for example, an imaging range defined by a field of view equivalent to the field of vision of a typical healthy person).

[0140] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0141] The controlled 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 robot 414's emotions can be expressed by controlling these motors. The robot 414's facial expressions can also be expressed by controlling the illumination state of the LEDs in its eyes.

[0142] Figure 8 shows an example of the main functions of the data processing device 12 and the robot 414. As shown in Figure 8, the data processing device 12 performs specific processing using the processor 28. The storage 32 stores the specific processing program 56.

[0143] The processor 28 reads a 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 acting as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0144] Storage 32 stores the data generation model 58 and the 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 the user's emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0145] In robot 414, specific processing is performed by processor 46. A specific program 60 is stored in storage 50. Processor 46 reads the specific program 60 from storage 50 and executes it on RAM 48. The specific processing is achieved by processor 46 acting as a control unit 46A according to the specific program 60 executed on RAM 48. Robot 414 also has data generation model 58 and emotion identification model 59, similar to those of the robot, and can perform processing similar to that of the specific processing unit 290 using these models.

[0146] Furthermore, other devices besides the data processing device 12 may also 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 obtains processing results (such as prediction results) using the data generation model 58 by communicating with the server device that has the data generation model 58. Also, the data processing device 12 may be a server device or a terminal device owned by the user (for example, a mobile phone, robot, home appliance, etc.).

[0147] 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 controlled object 443 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0148] The data generation model 58 is a so-called generative AI. An example of a 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 is input with prompts containing instructions, and inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats such as audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVM), k-means clustering, convolutional neural networks (CNN), recurrent neural networks (RNN), generative adversarial networks (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each part described above is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example. Also, processing performed by an AI including a generative AI may be replaced by rule-based processing, and rule-based processing may be replaced by processing performed by an AI including a generative AI.

[0149] 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 performed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the robot 414, but it may also be performed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the robot 414. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the robot 414 or an external device, and the robot 414 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0150] Each of the multiple elements, including the reception unit, generation unit, and reflection unit described above, is implemented in, for example, at least one of the robot 414 and the data processing unit 12. For example, the reception unit receives voice input using the microphone 238 of the robot 414 and converts it into text data using speech recognition technology by the control unit 46A. The generation unit is implemented in, for example, the specific processing unit 290 of the data processing unit 12 and generates the cause of the device malfunction and correction procedures using generation AI. The reflection unit is implemented in, for example, the specific processing unit 290 of the data processing unit 12 and immediately reflects the generated information in the data management device. The correspondence between each unit and the device or control unit is not limited to the example described above and can be changed in various ways.

[0151] Furthermore, the emotion identification model 59, acting 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 a specific mapping, which is an emotion map (see Figure 9). Similarly, the emotion identification model 59 may also determine the robot's emotion, and the identification processing unit 290 may perform identification processing using the robot's emotion.

[0152] Figure 9 shows the emotion map 400, in which multiple emotions are mapped. In the emotion map 400, emotions are arranged in concentric circles radiating from the center. The closer to the center of the concentric circles, the more primitive the emotions are located. Further out of the concentric circles, emotions representing states and actions arising from mental states are located. Emotion is a concept that includes feelings and mental states. On the left side of the concentric circles, emotions that are generally generated from reactions occurring in the brain are located. On the right side of the concentric circles, emotions that are generally induced by situational judgment are located. Above and below the concentric circles, emotions that are generally generated from reactions occurring in the brain and induced by situational judgment are located. In addition, the emotion of "pleasure" is located on the upper side of the concentric circles, and the emotion of "displeasure" is located on the lower side. Thus, in the emotion map 400, multiple emotions are mapped based on the structure in which emotions arise, and emotions that are likely to occur simultaneously are mapped close together.

[0153] These emotions are distributed at the 3 o'clock position on the Emotion Map 400, and usually fluctuate between feelings of security and anxiety. In the right half of the Emotion Map 400, situational awareness takes precedence over internal feelings, resulting in a calm impression.

[0154] The inside of the Emotion Map 400 represents inner thoughts, while the outside represents actions. Therefore, the further you go from the outside of the Emotion Map 400, the more visible (expressed in actions) your emotions become.

[0155] Here, human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, it results in discomfort, and when they approach the ideal, it results in pleasure. Similarly, in robots, cars, and motorcycles, emotions can be created based on various balances, such as posture and battery level. When these balances deviate from the ideal, it results in discomfort, and when they approach the ideal, it results in pleasure. The emotion map can be generated based, for example, on Dr. Mitsuyoshi's emotion map (Research on a system for analyzing brain physiological signals of speech emotion recognition and emotion, Tokushima University, doctoral dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map contains emotions belonging to a region called "response," where sensation is dominant. The right half of the emotion map contains emotions belonging to a region called "situation," where situational awareness is dominant.

[0156] The emotion map defines two emotions that promote learning. One is the emotion around the middle of the negative "repentance" and "reflection" on the situation side. In other words, it is 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 the emotion around the positive "desire" on the reaction side. In other words, it is when the robot has positive feelings such as "I want more" or "I want to know more."

[0157] The emotion identification model 59 inputs user input into a pre-trained neural network, obtains emotion values ​​representing each emotion shown in the emotion map 400, and determines the user's emotion. This neural network is pre-trained based on multiple training data sets, which are combinations of user input and emotion values ​​representing each emotion shown in the emotion map 400. Furthermore, this neural network is trained so that emotions located close together have similar values, as shown in the emotion map 900 in Figure 10. Figure 10 shows an example where multiple emotions such as "reassured," "calm," and "confident" have similar emotion values.

[0158] In the above embodiment, an example was given in which a specific process is performed by a single computer 22. However, the technology of this disclosure is not limited thereto, and a distributed processing method for the specific process may be used, which includes computer 22 and multiple other computers.

[0159] In the above embodiment, an example was given in which the specific processing program 56 is stored in the storage 32, but the technology of this disclosure is not limited thereto. For example, the specific processing program 56 may be stored in a portable, computer-readable, non-temporary storage medium such as a USB (Universal Serial Bus) memory. The specific processing program 56 stored in the non-temporary storage medium is installed in the computer 22 of the data processing device 12. The processor 28 executes specific processing according to the specific processing program 56.

[0160] 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.

[0161] Furthermore, it is not necessary to store the entirety 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 the entirety of the specific processing program 56 in the storage 32; it is acceptable to store only a portion of the specific processing program 56.

[0162] The following types of processors can be used as hardware resources to perform specific processing. Examples of processors include a CPU, a general-purpose processor that functions as a hardware resource to perform specific processing by executing software, i.e., a program. Other examples of processors include dedicated electrical circuits, such as FPGAs (Field-Programmable Gate Arrays), PLDs (Programmable Logic Devices), or ASICs (Application Specific Integrated Circuits), which have circuit configurations specifically designed to perform specific processing. All of these processors have built-in or connected memory, and all of them perform specific processing by using memory.

[0163] The hardware resource that performs a specific process may consist of one of these various processors, or it may consist of 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). Alternatively, the hardware resource that performs a specific process may consist of a single processor.

[0164] Examples of configurations using a single processor include, firstly, a configuration in which one or more CPUs and software are combined to form a single processor, and this processor functions as a hardware resource that performs a specific process. Secondly, there is a configuration using a processor that realizes the functions of the entire system, including multiple hardware resources that perform a specific process, on a single IC chip, as exemplified by SoCs (System-on-a-chip). In this way, a specific process is realized using one or more of the above types of processors as hardware resources.

[0165] Furthermore, the hardware structure of these various processors can more specifically utilize electrical circuits that combine circuit elements such as semiconductor devices. Also, the specific processing described above is merely an example. Therefore, it goes without saying that unnecessary steps can be deleted, new steps added, or the processing order rearranged, as long as it does not deviate from the main purpose.

[0166] Furthermore, although the above-described examples were divided into four embodiments, some or all of these embodiments may be combined. Also, the smart device 14, smart glasses 214, headset terminal 314, and robot 414 are just examples, and they may be combined, or other devices may be used. Also, although the above-described examples were divided into two embodiments, Embodiment 1 and Embodiment 2, these may be combined.

[0167] The descriptions and illustrations presented above are detailed explanations of the technical aspects of this disclosure and are merely examples of the technical aspects. For example, the above descriptions of the structure, function, operation, and effect are examples of the structure, function, operation, and effect of the technical aspects of this disclosure. Therefore, it goes without saying that you may delete unnecessary parts, add new elements, or replace elements in the descriptions and illustrations presented above, as long as you do not deviate from the essence of the technical aspects of this disclosure. Furthermore, in order to avoid confusion and facilitate understanding of the technical aspects of this disclosure, explanations of common technical knowledge and other things that do not require special explanation to enable the implementation of the technical aspects of this disclosure have been omitted from the descriptions and illustrations presented above.

[0168] All documents, patent applications, and technical standards described herein are incorporated by reference to the same extent as if each individual document, patent application, and technical standard were specifically and individually noted to be incorporated by reference.

[0169] (Note 1) A reception desk that accepts voice input, Based on the information received by the reception unit, a generation unit generates the cause of the device malfunction and a correction procedure, The system includes a reflection unit that reflects the information generated by the generation unit to a data management device. A system characterized by the following features. (Note 2) The generating unit is The AI ​​generates optimal design documents and work instructions. The system described in Appendix 1, characterized by the features described herein. (Note 3) The generating unit is The generated AI analyzes the cause of the error and proposes a corrective procedure. The system described in Appendix 1, characterized by the features described herein. (Note 4) The aforementioned reflection unit is, Data obtained via voice input is immediately reflected in the data management device. The system described in Appendix 1, characterized by the features described herein. (Note 5) The aforementioned reflection unit is, Perform data updates remotely. The system described in Appendix 1, characterized by the features described herein. (Note 6) The aforementioned reception unit is The system estimates the worker's emotions and adjusts the timing of voice input acceptance based on the estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 7) The aforementioned reception unit is The system analyzes the worker's past voice input history to select the most suitable reception method. The system described in Appendix 1, characterized by the features described herein. (Note 8) The aforementioned reception unit is When receiving voice input, filtering is performed based on the worker's current work status and areas of interest. The system described in Appendix 1, characterized by the features described herein. (Note 9) The aforementioned reception unit is The system estimates the worker's emotions and determines the priority of incoming voice input based on those emotions. The system described in Appendix 1, characterized by the features described herein. (Note 10) The aforementioned reception unit is When receiving voice input, the system prioritizes receiving voice input that is highly relevant, taking into account the worker's geographical location. The system described in Appendix 1, characterized by the features described herein. (Note 11) The aforementioned reception unit is When receiving voice input, the system analyzes the worker's social media activity and accepts relevant voice input. The system described in Appendix 1, characterized by the features described herein. (Note 12) The generating unit is The system estimates the worker's emotions and adjusts the way design documents and work instructions are expressed based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 13) The generating unit is During generation, the level of detail of the generated information is adjusted based on the severity of the cause of the device malfunction. The system described in Appendix 1, characterized by the features described herein. (Note 14) The generating unit is During generation, different generation algorithms are applied depending on the device category. The system described in Appendix 1, characterized by the features described herein. (Note 15) The generating unit is It estimates the worker's emotions and adjusts the length of the design documents and work instructions generated based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 16) The generating unit is During generation, the priority of the information to be generated is determined based on when the device malfunction occurred. The system described in Appendix 1, characterized by the features described herein. (Note 17) The generating unit is During generation, the order of the generated information is adjusted based on the relationships between devices. The system described in Appendix 1, characterized by the features described herein. (Note 18) The aforementioned reflection unit is, The system estimates the worker's emotions and adjusts how the data is reflected based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 19) The aforementioned reflection unit is, When updating data, the system selects the optimal update method by referring to past data update history. The system described in Appendix 1, characterized by the features described herein. (Note 20) The aforementioned reflection unit is, When updating data, the method of updating is customized based on the worker's current work status. The system described in Appendix 1, characterized by the features described herein. (Note 21) The aforementioned reflection unit is, The system estimates the worker's emotions and determines the priority of data reflection based on the estimated worker's emotions. The system described in Appendix 1, characterized by the features described herein. (Note 22) The aforementioned reflection unit is, When updating data, the optimal method of updating is selected, taking into account the geographical location information of the worker. The system described in Appendix 1, characterized by the features described herein. (Note 23) The aforementioned reflection unit is, When updating data, we analyze the social media activity of workers and propose methods for updating it. The system described in Appendix 1, characterized by the features described herein. [Explanation of symbols]

[0170] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Devices 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robots

Claims

1. A reception desk that accepts voice input, Based on the information received by the reception unit, a generation unit generates the cause of the device malfunction and a correction procedure, The system includes a reflection unit that reflects the information generated by the generation unit to a data management device. A system characterized by the following features.

2. The generating unit is The AI ​​generates optimal design documents and work instructions. The system according to feature 1.

3. The generating unit is The AI ​​generates data to analyze the cause of errors and proposes corrective steps. The system according to feature 1.

4. The aforementioned reflection unit is, Data obtained via voice input is immediately reflected in the data management device. The system according to feature 1.

5. The aforementioned reflection unit is, Perform data updates remotely. The system according to feature 1.

6. The aforementioned reception unit is The system estimates the worker's emotions and adjusts the timing of voice input acceptance based on the estimated emotions. The system according to feature 1.

7. The aforementioned reception unit is The system analyzes the worker's past voice input history to select the most suitable reception method. The system according to feature 1.

8. The aforementioned reception unit is When receiving voice input, filtering is performed based on the worker's current work status and areas of interest. The system according to feature 1.

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