system

Abstract

The system according to the embodiment aims to dynamically correct the discrepancy between the operator's sense and the robot's operation. [Solution] A system according to an embodiment includes a receiving unit, an estimation unit, a correction unit, and an adjustment unit. The receiving unit receives a sense of discomfort through voice. The estimation unit estimates a movement deviation based on the information received by the receiving unit. The correction unit corrects a movement algorithm based on the movement deviation estimated by the estimation unit. The adjustment unit adjusts haptic feedback based on the movement algorithm corrected by the correction unit.

Description

[Technical Field]

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

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

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

[0004] With conventional technology, there is a risk of a mismatch between the operator's senses and the robot's actions when remotely operating a robot.

[0005] The system according to the embodiment aims to dynamically correct the discrepancy between the operator's sense and the robot's operation. [Means for solving the problem]

[0006] A system according to an embodiment includes a receiving unit, an estimation unit, a correction unit, and an adjustment unit. The receiving unit receives a sense of discomfort through voice. The estimation unit estimates a deviation in movement based on the information received by the receiving unit. The correction unit corrects a movement algorithm based on the deviation in movement estimated by the estimation unit. The adjustment unit adjusts the haptic feedback based on the movement algorithm corrected by the correction unit. [Effects of the Invention]

[0007] The system according to the embodiment can dynamically correct the discrepancy between the operator's sense and the robot's operation. [Brief explanation of the drawings]

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

[0028] (Example 1) A system according to an embodiment of the present invention resolves discrepancies between an operator's senses and the robot's actions during remote robot operation. In this system, the operator communicates any perceived discomfort to a generation AI via voice. The generation AI then infers the discrepancies between the operator's and the robot's actions from the subtle discomfort expressed verbally, and dynamically modifies the operation algorithm. Furthermore, the system dynamically tunes the robot's haptic feedback to match each operator's physical sensory values. This allows the system to resolve discrepancies between the operator's senses and the robot's actions, improving the accuracy and efficiency of remote robot operation. For example, this allows operators to operate robots more accurately and quickly in remote medical surgery or in dangerous environments.

[0029] The system according to the embodiment includes a reception unit, an estimation unit, a correction unit, and an adjustment unit. The reception unit receives the operator's sense of discomfort through speech. For example, the operator may express specific discomfort in words, such as "the robot's movements are slow" or "its reactions are slow." The reception unit uses a generation AI to analyze this speech information and understand the operator's sense of discomfort. The estimation unit uses the generation AI to estimate the mismatch in movement between the operator and the robot based on the subtle discomfort expressed verbally. For example, based on the discomfort that "the robot's movements are slow," the generation AI determines that the robot's movement speed needs to be adjusted. The correction unit modifies the movement algorithm based on the mismatch in movement estimated by the estimation unit. For example, the generation AI dynamically modifies the movement algorithm to realize movement that matches the operator's senses. The adjustment unit adjusts the haptic feedback to match each operator's physical sensory values. For example, if the operator feels that "the sense of touching objects is weak," the generation AI adjusts the intensity of the haptic feedback. This allows the operator to get a sense of "if I move it like this, the robot will move like this" based on the sensation of touching an object, its weight, and its physical size. As a result, the system according to the embodiment can dynamically correct the operator's sensory discomfort and adjust the haptic feedback, thereby improving the accuracy and efficiency of remote robot operation.

[0030] When receiving a feeling of discomfort, the reception unit can select an appropriate reception method by referring to the operator's past operation history. In the reception unit, for example, the generation AI selects the optimal question format based on patterns of discomfort frequently reported by the operator in the past. The generation AI can also predict discomfort in specific situations from the operator's past operation history and prepare questions in advance. Furthermore, the generation AI can select an appropriate feedback format based on words and expressions used by the operator in the past. This makes it possible to select the optimal reception method by referring to the past operation history and efficiently receive feelings of discomfort. Some or all of the above-mentioned processing in the reception unit may be performed, for example, using AI or without using AI.

[0031] When receiving a report of an anomaly, the reception unit can filter the report based on the current work status of the operator. For example, when the operator is performing a high-load task, the reception unit allows the generation AI to prioritize receiving only important reports of an anomaly. Also, when the operator is on a break, the generation AI can also prioritize receiving detailed reports of an anomaly. Furthermore, when the operator is responding to an emergency, the generation AI can also prioritize receiving brief reports of an anomaly. In this way, by filtering based on the current work status, important reports of an anomaly can be prioritized. Some or all of the above-described processing in the reception unit may be performed using AI, for example, or may be performed without using AI.

[0032] When accepting a feeling of discomfort, the acceptance unit can select an appropriate acceptance means depending on the input method of the operator. For example, when the operator uses voice input, the generation AI can accept the feeling of discomfort using voice recognition technology. Also, when the operator uses text input, the generation AI can accept the feeling of discomfort using text analysis technology. Furthermore, when the operator uses gesture input, the generation AI can accept the feeling of discomfort using gesture recognition technology. This allows for efficient acceptance of the feeling of discomfort by selecting the optimal acceptance means depending on the input method. Some or all of the above-mentioned processing in the acceptance unit may be performed using AI, for example, or may be performed without using AI.

[0033] When predicting a movement deviation, the prediction unit can improve the accuracy of the prediction by referring to the operator's past operation history. In the prediction unit, for example, the generation AI improves the accuracy of the prediction based on patterns of movement deviation reported by the operator in the past. The generation AI can also predict movement deviation in a specific situation from the operator's past operation history and improve the accuracy of the prediction. Furthermore, the generation AI can improve the accuracy of the prediction based on operation methods used by the operator in the past. In this way, the accuracy of the prediction is improved by referring to the past operation history. Some or all of the above-mentioned processing in the prediction unit may be performed, for example, using AI or may be performed without using AI.

[0034] When predicting a movement deviation, the prediction unit can adjust the prediction algorithm based on the operator's current work status. For example, when the operator is performing high-load work, the generation AI in the prediction unit adjusts the movement deviation prediction algorithm. Also, when the operator is on a break, the generation AI can make a detailed prediction of the movement deviation. Furthermore, when the operator is responding to an emergency, the generation AI can make a quick prediction of the movement deviation. As a result, by adjusting the prediction algorithm based on the current work status, more appropriate prediction of the movement deviation becomes possible. Some or all of the above-mentioned processing in the prediction unit may be performed, for example, using AI, or may be performed without using AI.

[0035] When estimating a movement misalignment, the estimation unit can select an appropriate estimation means depending on the input method of the operator. For example, when the operator uses voice input, the generation AI can estimate the movement misalignment using voice recognition technology. Also, when the operator uses text input, the generation AI can estimate the movement misalignment using text analysis technology. Furthermore, when the operator uses gesture input, the generation AI can estimate the movement misalignment using gesture recognition technology. This enables efficient estimation of movement misalignment by selecting the optimal estimation means depending on the input method. Some or all of the above-mentioned processing in the estimation unit may be performed using AI, for example, or may be performed without using AI.

[0036] When correcting a movement algorithm, the correction unit can select an appropriate correction method by referring to the operator's past operation history. In the correction unit, for example, the generation AI selects the optimal correction method based on patterns of movement errors reported by the operator in the past. The generation AI can also predict movement errors in specific situations from the operator's past operation history and select the optimal correction method. Furthermore, the generation AI can select the optimal correction method based on operation methods used by the operator in the past. This makes it possible to select the optimal correction method by referring to the past operation history and to correct the movement algorithm efficiently. Some or all of the above-mentioned processing in the correction unit may be performed, for example, using AI or without using AI.

[0037] When correcting the movement algorithm, the correction unit can adjust the correction algorithm based on the operator's current work situation. For example, when the operator is performing high-load work, the correction unit causes the generation AI to adjust the correction algorithm of the movement algorithm. Also, when the operator is on a break, the generation AI can make detailed corrections to the movement algorithm. Furthermore, when the operator is responding to an emergency, the generation AI can quickly correct the movement algorithm. This makes it possible to correct the movement algorithm more appropriately by adjusting the correction algorithm based on the current work situation. Some or all of the above-mentioned processing in the correction unit may be performed, for example, using AI or without using AI.

[0038] When correcting the movement algorithm, the correction unit can select an appropriate correction means depending on the input method of the operator. For example, when the operator uses voice input, the generation AI corrects the movement algorithm using voice recognition technology. Also, when the operator uses text input, the generation AI can correct the movement algorithm using text analysis technology. Furthermore, when the operator uses gesture input, the generation AI can correct the movement algorithm using gesture recognition technology. This enables efficient correction of the movement algorithm by selecting the optimal correction means depending on the input method. Some or all of the above-mentioned processing in the correction unit may be performed using AI, for example, or without using AI.

[0039] When adjusting the haptic feedback, the adjustment unit can select an appropriate adjustment method by referring to the operator's past operation history. For example, the adjustment unit allows the generation AI to select the optimal adjustment method based on haptic feedback patterns reported by the operator in the past. The generation AI can also predict haptic feedback in a specific situation from the operator's past operation history and select the optimal adjustment method. Furthermore, the generation AI can also select the optimal adjustment method based on operation methods used by the operator in the past. This allows the optimal adjustment method to be selected by referring to the past operation history, enabling efficient adjustment of haptic feedback. Some or all of the above-described processing in the adjustment unit may be performed, for example, using AI or without AI.

[0040] When adjusting the haptic feedback, the adjustment unit can adjust the adjustment algorithm based on the operator's current work situation. For example, when the operator is performing high-load work, the adjustment unit causes the generation AI to adjust the haptic feedback adjustment algorithm. Also, when the operator is taking a break, the generation AI can make detailed adjustments to the haptic feedback. Furthermore, when the operator is responding to an emergency, the generation AI can make quick adjustments to the haptic feedback. In this way, by adjusting the adjustment algorithm based on the current work situation, more appropriate adjustment of the haptic feedback becomes possible. Some or all of the above-described processing in the adjustment unit may be performed using AI, for example, or may be performed without using AI.

[0041] When adjusting the haptic feedback, the adjustment unit can select an appropriate adjustment means depending on the input method of the operator. For example, when the operator uses voice input, the generation AI can adjust the haptic feedback using voice recognition technology. When the operator uses text input, the generation AI can also adjust the haptic feedback using text analysis technology. Furthermore, when the operator uses gesture input, the generation AI can also adjust the haptic feedback using gesture recognition technology. This enables efficient adjustment of haptic feedback by selecting the optimal adjustment means depending on the input method. Some or all of the above-mentioned processing in the adjustment unit may be performed using AI, for example, or without using AI.

[0042] When adjusting the haptic feedback, the adjustment unit can prioritize highly relevant feedback by taking into account the operator's geographical location information. For example, if the operator is working in a specific area, the adjustment unit can prioritize haptic feedback specific to that area. Also, if the operator is moving, the adjustment unit can prioritize haptic feedback related to movement. Furthermore, if the operator is working in a specific facility, the adjustment unit can prioritize haptic feedback specific to that facility. In this way, highly relevant feedback can be adjusted by taking into account the geographical location information. Some or all of the above-described processing by the adjustment unit may be performed using AI, for example, or may be performed without using AI.

[0043] When adjusting the haptic feedback, the adjustment unit can analyze the social media activity of the operator and adjust the related feedback. For example, the adjustment unit can cause the generation AI to adjust the related feedback based on haptic feedback problems reported by the operator on social media. The adjustment unit can also analyze the content of the operator's social media posts and adjust the related feedback. Furthermore, the generation AI can also adjust the related feedback based on the activity of the operator's friends on social media. In this way, the related feedback can be adjusted by analyzing social media activity. Some or all of the above-mentioned processing in the adjustment unit may be performed, for example, using AI or without using AI.

[0044] When adjusting the haptic feedback, the adjustment unit can customize the adjustment method by reflecting past feedback from the operator. For example, the adjustment unit uses the generation AI to select the optimal adjustment method based on feedback previously provided by the operator. The adjustment unit can also customize the adjustment method for specific haptic feedback based on past feedback from the operator. Furthermore, the generation AI can adjust the adjustment method based on haptic feedback problems previously reported by the operator. In this way, by reflecting past feedback, the optimal adjustment method can be customized, enabling efficient adjustment of haptic feedback. Some or all of the above-described processing in the adjustment unit may be performed, for example, using AI or without using AI.

[0045] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0046] The estimation unit can also improve the accuracy of motion misalignment estimation by using the operator's gaze tracking data. For example, if the operator is gazing at a specific part of the body, it will prioritize estimating motion misalignment related to that part. It can also infer the operator's intention from gaze movements and have the generation AI correct motion misalignment based on that intention. Furthermore, it can analyze the duration of gaze fixation and estimate detailed motion misalignment for parts of the body the operator is gazing at for long periods of time. This makes it possible to estimate motion misalignment more accurately by using gaze tracking data.

[0047] The adjustment unit can also adjust the haptic feedback using acoustic data from the operator's work environment. For example, if the work environment is noisy, the generation AI can increase the intensity of the haptic feedback. Alternatively, if the operator is working in a quiet environment, the generation AI can provide more detailed haptic feedback. Furthermore, it can analyze the types of environmental sounds and, if a particular sound occurs frequently, prioritize and adjust the haptic feedback related to that sound. This makes it possible to adjust the haptic feedback more appropriately by using acoustic data from the work environment.

[0048] The reception unit can also recognize the operator's gestures and adjust the method of receiving discomfort based on the recognized gestures. For example, if the operator makes a gesture of raising their hand, the generation AI can immediately begin receiving discomfort. Also, if the operator makes a gesture of shaking their head, the generation AI can avoid asking detailed questions and receive discomfort in the form of a simple question. Furthermore, if the operator repeats a specific gesture, it can prioritize receiving discomfort related to that gesture. This makes it possible to receive discomfort more appropriately by recognizing gestures.

[0049] The correction unit can also select a method for correcting the movement algorithm by referencing the operator's past operation history. For example, the generation AI selects the optimal correction method based on patterns of movement errors reported by the operator in the past. The generation AI can also predict movement errors in specific situations from the operator's past operation history and select the optimal correction method. Furthermore, the generation AI can select the optimal correction method based on operation methods used by the operator in the past. This makes it possible to select the optimal correction method by referencing past operation history and to efficiently correct the movement algorithm.

[0050] The reception unit can also select an appropriate reception method by referring to the operator's past operation history. For example, the generation AI selects the optimal question format based on patterns of discomfort that the operator has frequently reported in the past. The generation AI can also predict discomfort in specific situations from the operator's past operation history and prepare questions in advance. Furthermore, the generation AI can select an appropriate feedback format based on the words and expressions used by the operator in the past. This makes it possible to select the optimal reception method by referring to the past operation history and efficiently receive discomfort.

[0051] The adjustment unit can also adjust the haptic feedback taking into account the geographical location information of the operator. For example, if the operator is working in a specific area, the adjustment unit can prioritize haptic feedback specific to that area. Also, if the operator is moving, the adjustment unit can prioritize haptic feedback related to movement. Furthermore, if the operator is working within a specific facility, the adjustment unit can prioritize haptic feedback specific to that facility. In this way, by taking the geographical location information into account, it is possible to adjust highly relevant feedback.

[0052] The processing flow of the first embodiment will be briefly explained below.

[0053] Step 1: The reception department receives the operator's verbal feedback about their discomfort. For example, the operator may express specific discomfort in words, such as "the robot's movements are slow" or "it's slow to respond." The reception department then uses generative AI to analyze this voice information and understand the operator's discomfort. Step 2: The prediction unit uses the generation AI to predict the misalignment between the operator and the robot based on subtle discomfort expressed verbally. For example, based on the discomfort that "the robot's movements are slow," the generation AI determines that the robot's movement speed needs to be adjusted. Step 3: The correction unit corrects the movement algorithm based on the movement deviation estimated by the estimation unit. For example, the generation AI dynamically corrects the movement algorithm to realize movement that matches the operator's senses. Step 4: The adjustment unit adjusts the haptic feedback to match each operator's physical sensory values. For example, if the operator feels that "the sense of touching an object is weak," the generation AI adjusts the strength of the haptic feedback. This allows the operator to get a sense of "if I move it like this, the robot will move this much," which is derived from the sense of touch, weight, and physical size of the object.

[0054] (Example 2) A system according to an embodiment of the present invention resolves discrepancies between an operator's senses and the robot's actions during remote robot operation. In this system, the operator communicates any perceived discomfort to a generation AI via voice. The generation AI then infers the discrepancies between the operator's and the robot's actions from the subtle discomfort expressed verbally, and dynamically modifies the operation algorithm. Furthermore, the system dynamically tunes the robot's haptic feedback to match each operator's physical sensory values. This allows the system to resolve discrepancies between the operator's senses and the robot's actions, improving the accuracy and efficiency of remote robot operation. For example, this allows operators to operate robots more accurately and quickly in remote medical surgery or in dangerous environments.

[0055] The system according to the embodiment includes a reception unit, an estimation unit, a correction unit, and an adjustment unit. The reception unit receives the operator's sense of discomfort through speech. For example, the operator may express specific discomfort in words, such as "the robot's movements are slow" or "its reactions are slow." The reception unit uses a generation AI to analyze this speech information and understand the operator's sense of discomfort. The estimation unit uses the generation AI to estimate the mismatch in movement between the operator and the robot based on the subtle discomfort expressed verbally. For example, based on the discomfort that "the robot's movements are slow," the generation AI determines that the robot's movement speed needs to be adjusted. The correction unit modifies the movement algorithm based on the mismatch in movement estimated by the estimation unit. For example, the generation AI dynamically modifies the movement algorithm to realize movement that matches the operator's senses. The adjustment unit adjusts the haptic feedback to match each operator's physical sensory values. For example, if the operator feels that "the sense of touching objects is weak," the generation AI adjusts the intensity of the haptic feedback. This allows the operator to get a sense of "if I move it like this, the robot will move like this" based on the sensation of touching an object, its weight, and its physical size. As a result, the system according to the embodiment can dynamically correct the operator's sensory discomfort and adjust the haptic feedback, thereby improving the accuracy and efficiency of remote robot operation.

[0056] The reception unit can estimate the operator's emotions and adjust the method for accepting the discomfort based on the estimated emotions. For example, if the operator is nervous, the generation AI can provide a voice guide to relax the operator, smoothly accepting the sensory discomfort. Also, if the operator is tired, the generation AI can accept the sensory discomfort in the form of a simple question. Furthermore, if the operator is concentrating, the generation AI can accept the sensory discomfort in the form of a request for detailed feedback. This allows for more appropriate acceptance of the discomfort by adjusting the acceptance method according to the operator's emotions. Emotion estimation is realized using an emotion estimation function, for example, using an emotion engine or generation AI. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to these examples.

[0057] When receiving a feeling of discomfort, the reception unit can select an appropriate reception method by referring to the operator's past operation history. In the reception unit, for example, the generation AI selects the optimal question format based on patterns of discomfort frequently reported by the operator in the past. The generation AI can also predict discomfort in specific situations from the operator's past operation history and prepare questions in advance. Furthermore, the generation AI can select an appropriate feedback format based on words and expressions used by the operator in the past. This makes it possible to select the optimal reception method by referring to the past operation history and efficiently receive feelings of discomfort. Some or all of the above-mentioned processing in the reception unit may be performed, for example, using AI or without using AI.

[0058] When receiving a report of an anomaly, the reception unit can filter the report based on the current work status of the operator. For example, when the operator is performing a high-load task, the reception unit allows the generation AI to prioritize receiving only important reports of an anomaly. Also, when the operator is on a break, the generation AI can also prioritize receiving detailed reports of an anomaly. Furthermore, when the operator is responding to an emergency, the generation AI can also prioritize receiving brief reports of an anomaly. In this way, by filtering based on the current work status, important reports of an anomaly can be prioritized. Some or all of the above-described processing in the reception unit may be performed using AI, for example, or may be performed without using AI.

[0059] When accepting a feeling of discomfort, the acceptance unit can select an appropriate acceptance means depending on the input method of the operator. For example, when the operator uses voice input, the generation AI can accept the feeling of discomfort using voice recognition technology. Also, when the operator uses text input, the generation AI can accept the feeling of discomfort using text analysis technology. Furthermore, when the operator uses gesture input, the generation AI can accept the feeling of discomfort using gesture recognition technology. This allows for efficient acceptance of the feeling of discomfort by selecting the optimal acceptance means depending on the input method. Some or all of the above-mentioned processing in the acceptance unit may be performed using AI, for example, or may be performed without using AI.

[0060] The estimation unit can estimate the operator's emotions and adjust the method for estimating movement misalignment based on the estimated emotions. For example, if the operator is nervous, the estimation unit adjusts the method for estimating movement misalignment so that the generation AI can reduce the tension. Also, if the operator is relaxed, the generation AI can estimate detailed movement misalignment. Furthermore, if the operator is concentrating, the generation AI can estimate movement misalignment with high accuracy. This allows for more accurate estimation of movement misalignment by adjusting the estimation method according to the operator's emotions. Emotion estimation is realized using an emotion estimation function, for example, using an emotion engine or generation AI. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to these examples.

[0061] When predicting a movement deviation, the prediction unit can improve the accuracy of the prediction by referring to the operator's past operation history. In the prediction unit, for example, the generation AI improves the accuracy of the prediction based on patterns of movement deviation reported by the operator in the past. The generation AI can also predict movement deviation in a specific situation from the operator's past operation history and improve the accuracy of the prediction. Furthermore, the generation AI can improve the accuracy of the prediction based on operation methods used by the operator in the past. In this way, the accuracy of the prediction is improved by referring to the past operation history. Some or all of the above-mentioned processing in the prediction unit may be performed, for example, using AI or may be performed without using AI.

[0062] When predicting a movement deviation, the prediction unit can adjust the prediction algorithm based on the operator's current work status. For example, when the operator is performing high-load work, the generation AI in the prediction unit adjusts the movement deviation prediction algorithm. Also, when the operator is on a break, the generation AI can make a detailed prediction of the movement deviation. Furthermore, when the operator is responding to an emergency, the generation AI can make a quick prediction of the movement deviation. As a result, by adjusting the prediction algorithm based on the current work status, more appropriate prediction of the movement deviation becomes possible. Some or all of the above-mentioned processing in the prediction unit may be performed, for example, using AI, or may be performed without using AI.

[0063] When estimating a movement misalignment, the estimation unit can select an appropriate estimation means depending on the input method of the operator. For example, when the operator uses voice input, the generation AI can estimate the movement misalignment using voice recognition technology. Also, when the operator uses text input, the generation AI can estimate the movement misalignment using text analysis technology. Furthermore, when the operator uses gesture input, the generation AI can estimate the movement misalignment using gesture recognition technology. This enables efficient estimation of movement misalignment by selecting the optimal estimation means depending on the input method. Some or all of the above-mentioned processing in the estimation unit may be performed using AI, for example, or may be performed without using AI.

[0064] The correction unit can estimate the operator's emotions and adjust the method of correcting the movement algorithm based on the estimated emotions. For example, if the operator is nervous, the correction unit adjusts the method of correcting the movement algorithm so that the generation AI reduces the tension. Also, if the operator is relaxed, the generation AI can make detailed corrections to the movement algorithm. Furthermore, if the operator is concentrating, the generation AI can make highly accurate corrections to the movement algorithm. This allows for more appropriate correction of the movement algorithm by adjusting the correction method according to the operator's emotions. Emotion estimation is realized using an emotion estimation function, for example, using an emotion engine or generation AI. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to these examples.

[0065] When correcting a movement algorithm, the correction unit can select an appropriate correction method by referring to the operator's past operation history. In the correction unit, for example, the generation AI selects the optimal correction method based on patterns of movement errors reported by the operator in the past. The generation AI can also predict movement errors in specific situations from the operator's past operation history and select the optimal correction method. Furthermore, the generation AI can select the optimal correction method based on operation methods used by the operator in the past. This makes it possible to select the optimal correction method by referring to the past operation history and to correct the movement algorithm efficiently. Some or all of the above-mentioned processing in the correction unit may be performed, for example, using AI or without using AI.

[0066] When correcting the movement algorithm, the correction unit can adjust the correction algorithm based on the operator's current work situation. For example, when the operator is performing high-load work, the correction unit causes the generation AI to adjust the correction algorithm of the movement algorithm. Also, when the operator is on a break, the generation AI can make detailed corrections to the movement algorithm. Furthermore, when the operator is responding to an emergency, the generation AI can quickly correct the movement algorithm. This makes it possible to correct the movement algorithm more appropriately by adjusting the correction algorithm based on the current work situation. Some or all of the above-mentioned processing in the correction unit may be performed, for example, using AI or without using AI.

[0067] When correcting the movement algorithm, the correction unit can select an appropriate correction means depending on the input method of the operator. For example, when the operator uses voice input, the generation AI corrects the movement algorithm using voice recognition technology. Also, when the operator uses text input, the generation AI can correct the movement algorithm using text analysis technology. Furthermore, when the operator uses gesture input, the generation AI can correct the movement algorithm using gesture recognition technology. This enables efficient correction of the movement algorithm by selecting the optimal correction means depending on the input method. Some or all of the above-mentioned processing in the correction unit may be performed using AI, for example, or without using AI.

[0068] The adjustment unit can estimate the operator's emotion and adjust the adjustment method of the haptic feedback based on the estimated emotion. For example, if the operator is nervous, the generation AI can adjust the intensity of the haptic feedback to reduce the tension. Also, if the operator is relaxed, the generation AI can provide detailed haptic feedback. Furthermore, if the operator is concentrating, the generation AI can provide highly accurate haptic feedback. This allows for more appropriate adjustment of the haptic feedback by adjusting the adjustment method according to the operator's emotion. The emotion estimation is realized using an emotion estimation function, for example, using an emotion engine or generation AI. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to these examples.

[0069] When adjusting the haptic feedback, the adjustment unit can select an appropriate adjustment method by referring to the operator's past operation history. For example, the adjustment unit allows the generation AI to select the optimal adjustment method based on haptic feedback patterns reported by the operator in the past. The generation AI can also predict haptic feedback in a specific situation from the operator's past operation history and select the optimal adjustment method. Furthermore, the generation AI can also select the optimal adjustment method based on operation methods used by the operator in the past. This allows the optimal adjustment method to be selected by referring to the past operation history, enabling efficient adjustment of haptic feedback. Some or all of the above-described processing in the adjustment unit may be performed, for example, using AI or without AI.

[0070] When adjusting the haptic feedback, the adjustment unit can adjust the adjustment algorithm based on the operator's current work situation. For example, when the operator is performing high-load work, the adjustment unit causes the generation AI to adjust the haptic feedback adjustment algorithm. Also, when the operator is taking a break, the generation AI can make detailed adjustments to the haptic feedback. Furthermore, when the operator is responding to an emergency, the generation AI can make quick adjustments to the haptic feedback. In this way, by adjusting the adjustment algorithm based on the current work situation, more appropriate adjustment of the haptic feedback becomes possible. Some or all of the above-described processing in the adjustment unit may be performed using AI, for example, or may be performed without using AI.

[0071] When adjusting the haptic feedback, the adjustment unit can select an appropriate adjustment means depending on the input method of the operator. For example, when the operator uses voice input, the generation AI can adjust the haptic feedback using voice recognition technology. When the operator uses text input, the generation AI can also adjust the haptic feedback using text analysis technology. Furthermore, when the operator uses gesture input, the generation AI can also adjust the haptic feedback using gesture recognition technology. This enables efficient adjustment of haptic feedback by selecting the optimal adjustment means depending on the input method. Some or all of the above-mentioned processing in the adjustment unit may be performed using AI, for example, or without using AI.

[0072] The adjustment unit can estimate the operator's emotions and determine the priority of the haptic feedback to be adjusted based on the estimated emotions. For example, if the operator is feeling anxious, the adjustment unit causes the generation AI to prioritize adjusting haptic feedback that is important for reducing the anxiety. Also, if the operator is relaxed, the generation AI can prioritize adjusting detailed haptic feedback. Furthermore, if the operator is concentrating, the generation AI can prioritize adjusting haptic feedback that affects the work. Thus, by determining the priority according to the operator's emotions, it becomes possible to adjust more important haptic feedback. The emotion estimation is realized using an emotion estimation function, for example, using an emotion engine or generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI.

[0073] When adjusting the haptic feedback, the adjustment unit can prioritize highly relevant feedback by taking into account the operator's geographical location information. For example, if the operator is working in a specific area, the adjustment unit can prioritize haptic feedback specific to that area. Also, if the operator is moving, the adjustment unit can prioritize haptic feedback related to movement. Furthermore, if the operator is working in a specific facility, the adjustment unit can prioritize haptic feedback specific to that facility. In this way, highly relevant feedback can be adjusted by taking into account the geographical location information. Some or all of the above-described processing by the adjustment unit may be performed using AI, for example, or may be performed without using AI.

[0074] When adjusting the haptic feedback, the adjustment unit can analyze the social media activity of the operator and adjust the related feedback. For example, the adjustment unit can cause the generation AI to adjust the related feedback based on haptic feedback problems reported by the operator on social media. The adjustment unit can also analyze the content of the operator's social media posts and adjust the related feedback. Furthermore, the generation AI can also adjust the related feedback based on the activity of the operator's friends on social media. In this way, the related feedback can be adjusted by analyzing social media activity. Some or all of the above-mentioned processing in the adjustment unit may be performed, for example, using AI or without using AI.

[0075] When adjusting the haptic feedback, the adjustment unit can customize the adjustment method by reflecting past feedback from the operator. For example, the adjustment unit uses the generation AI to select the optimal adjustment method based on feedback previously provided by the operator. The adjustment unit can also customize the adjustment method for specific haptic feedback based on past feedback from the operator. Furthermore, the generation AI can adjust the adjustment method based on haptic feedback problems previously reported by the operator. In this way, by reflecting past feedback, the optimal adjustment method can be customized, enabling efficient adjustment of haptic feedback. Some or all of the above-described processing in the adjustment unit may be performed, for example, using AI or without using AI. === Hard Collateral 1-1 === Each of the multiple elements, including the reception unit, estimation unit, correction unit, and adjustment unit, described above, is realized, for example, by at least one of the smart device 14 and the data processing device 12. For example, the reception unit receives the operator's voice using the microphone 38B of the smart device 14 and transmits the voice data to the generation AI via the control unit 46A. The estimation unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and analyzes the operator's sense of discomfort using the generation AI to estimate a movement deviation. The correction unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and dynamically corrects the movement algorithm. The adjustment unit is realized, for example, by the control unit 46A of the smart device 14 and adjusts the haptic feedback. === Hard Collateral 1-2 === Each of the multiple elements, including the above-mentioned reception unit, estimation unit, correction unit, and adjustment unit, is realized, for example, by at least one of the smart glasses 214 and the data processing device 12. For example, the reception unit receives the operator's voice using the microphone 238 of the smart glasses 214 and transmits the voice data to the generation AI via the control unit 46A. The estimation unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and analyzes the operator's sense of discomfort using the generation AI to estimate a movement deviation. The correction unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and dynamically corrects the movement algorithm. The adjustment unit is realized, for example, by the control unit 46A of the smart glasses 214 and adjusts the haptic feedback. === Hard Collateral 1-3 === Each of the multiple elements including the above-mentioned reception unit, estimation unit, correction unit, and adjustment unit is realized, for example, by at least one of the headset-type terminal 314 and the data processing device 12. For example, the reception unit receives the operator's voice using the microphone 238 of the headset-type terminal 314 and transmits the voice data to the generation AI via the control unit 46A. The estimation unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and analyzes the operator's sense of discomfort using the generation AI to estimate a movement deviation. The correction unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and dynamically corrects the movement algorithm. The adjustment unit is realized, for example, by the control unit 46A of the headset-type terminal 314 and adjusts the haptic feedback. === Hard Collateral 1-4 === Each of the multiple elements, including the above-mentioned reception unit, estimation unit, correction unit, and adjustment unit, is realized, for example, by at least one of the robot 414 and the data processing device 12. For example, the reception unit receives the operator's voice using the microphone 238 of the robot 414 and transmits the voice data to the generation AI via the control unit 46A. The estimation unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and analyzes the operator's sense of discomfort using the generation AI to estimate a movement discrepancy. The correction unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and dynamically corrects the movement algorithm. The adjustment unit is realized, for example, by the control unit 46A of the robot 414 and adjusts the haptic feedback.

[0076] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0077] The reception unit can also acquire biometric information from the operator and adjust the method for accepting discomfort based on the acquired biometric information. For example, by monitoring the operator's heart rate and electrical skin response, if the stress level is high, the generation AI can provide a voice guide to help them relax. The generation AI can also measure the operator's body temperature and sweat rate, and if fatigue is detected, accept the sensory discomfort in the form of a simple question. Furthermore, by analyzing the operator's breathing pattern, if the operator is highly focused, the generation AI can accept the sensory discomfort in the form of a request for detailed feedback. This allows for more appropriate acceptance of discomfort by adjusting the reception method based on biometric information.

[0078] The estimation unit can also improve the accuracy of motion misalignment estimation by using the operator's gaze tracking data. For example, if the operator is gazing at a specific part of the body, it will prioritize estimating motion misalignment related to that part. It can also infer the operator's intention from gaze movements and have the generation AI correct motion misalignment based on that intention. Furthermore, it can analyze the duration of gaze fixation and estimate detailed motion misalignment for parts of the body the operator is gazing at for long periods of time. This makes it possible to estimate motion misalignment more accurately by using gaze tracking data.

[0079] The correction unit can also analyze the operator's voice tone and speaking speed and adjust how the movement algorithm is corrected. For example, if the operator's voice is high and fast, it may indicate tension or impatience, so the generation AI will quickly correct the movement algorithm. On the other hand, if the operator's voice is low and slow, it may indicate relaxation, so the generation AI can make detailed corrections to the movement algorithm. Furthermore, it can analyze the intonation of the operator's voice and adjust how the movement algorithm is corrected according to changes in emotion. This makes it possible to more appropriately correct the movement algorithm by analyzing the operator's voice tone and speaking speed.

[0080] The adjustment unit can also adjust the haptic feedback using acoustic data from the operator's work environment. For example, if the work environment is noisy, the generation AI can increase the intensity of the haptic feedback. Alternatively, if the operator is working in a quiet environment, the generation AI can provide more detailed haptic feedback. Furthermore, it can analyze the types of environmental sounds and, if a particular sound occurs frequently, prioritize and adjust the haptic feedback related to that sound. This makes it possible to adjust the haptic feedback more appropriately by using acoustic data from the work environment.

[0081] The reception unit can also recognize the operator's gestures and adjust the method of receiving discomfort based on the recognized gestures. For example, if the operator makes a gesture of raising their hand, the generation AI can immediately begin receiving discomfort. Also, if the operator makes a gesture of shaking their head, the generation AI can avoid asking detailed questions and receive discomfort in the form of a simple question. Furthermore, if the operator repeats a specific gesture, it can prioritize receiving discomfort related to that gesture. This makes it possible to receive discomfort more appropriately by recognizing gestures.

[0082] The estimation unit can also estimate the operator's emotions and adjust the method for estimating movement deviations based on the estimated emotions. For example, if the operator is nervous, the generation AI adjusts the method for estimating movement deviations to reduce the tension. Also, if the operator is relaxed, the generation AI can make detailed estimations of movement deviations. Furthermore, if the operator is concentrating, the generation AI can make highly accurate estimations of movement deviations. In this way, by adjusting the estimation method according to the operator's emotions, more accurate estimations of movement deviations become possible.

[0083] The correction unit can also select a method for correcting the movement algorithm by referencing the operator's past operation history. For example, the generation AI selects the optimal correction method based on patterns of movement errors reported by the operator in the past. The generation AI can also predict movement errors in specific situations from the operator's past operation history and select the optimal correction method. Furthermore, the generation AI can select the optimal correction method based on operation methods used by the operator in the past. This makes it possible to select the optimal correction method by referencing past operation history and to efficiently correct the movement algorithm.

[0084] The adjustment unit can also estimate the operator's emotions and adjust the haptic feedback adjustment method based on the estimated emotions. For example, if the operator is nervous, the generation AI can adjust the intensity of the haptic feedback to reduce the tension. Also, if the operator is relaxed, the generation AI can provide detailed haptic feedback. Furthermore, if the operator is concentrating, the generation AI can provide highly accurate haptic feedback. This allows the adjustment method to be adjusted according to the operator's emotions, enabling more appropriate haptic feedback adjustment.

[0085] The reception unit can also select an appropriate reception method by referring to the operator's past operation history. For example, the generation AI selects the optimal question format based on patterns of discomfort that the operator has frequently reported in the past. The generation AI can also predict discomfort in specific situations from the operator's past operation history and prepare questions in advance. Furthermore, the generation AI can select an appropriate feedback format based on the words and expressions used by the operator in the past. This makes it possible to select the optimal reception method by referring to the past operation history and efficiently receive discomfort.

[0086] The adjustment unit can also adjust the haptic feedback taking into account the geographical location information of the operator. For example, if the operator is working in a specific area, the adjustment unit can prioritize haptic feedback specific to that area. Also, if the operator is moving, the adjustment unit can prioritize haptic feedback related to movement. Furthermore, if the operator is working within a specific facility, the adjustment unit can prioritize haptic feedback specific to that facility. In this way, by taking the geographical location information into account, it is possible to adjust highly relevant feedback.

[0087] The processing flow of the second embodiment will be briefly explained below.

[0088] Step 1: The reception department receives the operator's verbal feedback about their discomfort. For example, the operator may express specific discomfort in words, such as "the robot's movements are slow" or "it's slow to respond." The reception department then uses generative AI to analyze this voice information and understand the operator's discomfort. Step 2: The prediction unit uses the generation AI to predict the misalignment between the operator and the robot based on subtle discomfort expressed verbally. For example, based on the discomfort that "the robot's movements are slow," the generation AI determines that the robot's movement speed needs to be adjusted. Step 3: The correction unit corrects the movement algorithm based on the movement deviation estimated by the estimation unit. For example, the generation AI dynamically corrects the movement algorithm to realize movement that matches the operator's senses. Step 4: The adjustment unit adjusts the haptic feedback to match each operator's physical sensory values. For example, if the operator feels that "the sense of touching an object is weak," the generation AI adjusts the strength of the haptic feedback. This allows the operator to get a sense of "if I move it like this, the robot will move this much," which is derived from the sense of touch, weight, and physical size of the object.

[0089] The specific processing unit 290 transmits the result of the specific processing to the smart device 14. In the smart device 14, the control unit 46A causes the output device 40 to output the result of the specific processing. The microphone 38B acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0090] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (registered trademark) (Internet search engine).<URL: https: / / openai.com / blog / chatgpt> Examples of generative AIs include the data generation model 58, such as a neural network model (e.g., a neural network model), and a neural network model (e.g., a neural network model). The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating speech, text data indicating text, and image data indicating an image is also input to the data generation model 58. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specification processing unit 290 performs the above-mentioned specification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0091] Furthermore, the processing by the data processing system 10 described above is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart device 14, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart device 14. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the smart device 14 or an external device, and the smart device 14 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0092] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0093] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0094] 3, the data processing system 210 includes the data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0095] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0096] The smart glasses 214 include a computer 36, a microphone 238, a speaker 240, a camera 42, and a communication I / F 44. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, and the camera 42 are also connected to the bus 52.

[0097] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0098] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0099] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0100] Fig. 4 shows an example of the main functions of the data processing device 12 and the smart glasses 214. As shown in Fig. 4, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0101] The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0102] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0103] In the smart glasses 214, the specific processing is performed by the processor 46. A specific processing program 60 is stored in the storage 50. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. The smart glasses 214 also have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0104] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0105] The specific processing unit 290 transmits the result of the specific processing to the smart glasses 214. In the smart glasses 214, the control unit 46A causes the speaker 240 to output the result of the specific processing. The microphone 238 acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

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

[0107] The data processing system 210 according to the second embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 210 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart glasses 214, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart glasses 214. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the smart glasses 214 or an external device, etc., and the smart glasses 214 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

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

[0109] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0110] 5, the data processing system 310 includes the data processing device 12 and a headset type terminal 314. An example of the data processing device 12 is a server.

[0111] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0112] The headset type terminal 314 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication I / F 44, and a display 343. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, the camera 42, and the display 343 are also connected to the bus 52.

[0113] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0114] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0115] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0116] Fig. 6 shows an example of the main functions of the data processing device 12 and the headset type terminal 314. As shown in Fig. 6, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0117] The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0118] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0119] In the headset type terminal 314, the identification process is performed by the processor 46. A identification program 60 is stored in the storage 50. The processor 46 reads the identification program 60 from the storage 50 and executes the read identification program 60 on the RAM 48. The identification process is realized by the processor 46 operating as a control unit 46A in accordance with the identification program 60 executed on the RAM 48. Note that the headset type terminal 314 has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can also perform processing similar to that of the identification processing unit 290 using these models.

[0120] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0121] The specific processing unit 290 transmits the result of the specific processing to the headset type terminal 314. In the headset type terminal 314, the control unit 46A causes the speaker 240 and the display 343 to output the result of the specific processing. The microphone 238 acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0122] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0123] The data processing system 310 according to the third embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 310 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the headset type terminal 314, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the headset type terminal 314. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the headset type terminal 314 or an external device, etc., and the headset type terminal 314 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

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

[0125] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0126] 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.

[0127] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0128] The robot 414 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication I / F 44, and a control target 443. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, the camera 42, and the control target 443 are also connected to the bus 52.

[0129] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0130] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS image sensor or a CCD image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0131] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0132] The control object 443 includes a display device, LEDs in the eyes, and motors that drive the arms, hands, and feet. The posture and gestures of the robot 414 are controlled by controlling the motors of the arms, hands, and feet. Some of the emotions of the robot 414 can be expressed by controlling these motors. In addition, the facial expressions of the robot 414 can also be expressed by controlling the light emission state of the LEDs in the eyes of the robot 414.

[0133] Fig. 8 shows an example of the main functions of the data processing device 12 and the robot 414. As shown in Fig. 8, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0134] The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0135] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0136] In the robot 414, the processor 46 performs the identification process. The storage 50 stores the identification program 60. The processor 46 reads the identification program 60 from the storage 50 and executes the read identification program 60 on the RAM 48. The identification process is realized by the processor 46 operating as the control unit 46A in accordance with the identification program 60 executed on the RAM 48. The robot 414 also has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can perform the same process as the identification processing unit 290 using these models.

[0137] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0138] The specific processing unit 290 transmits the result of the specific processing to the robot 414. In the robot 414, the control unit 46A causes the speaker 240 and the control target 443 to output the result of the specific processing. The microphone 238 acquires voice indicating a user input regarding the result of the specific processing. The control unit 46A transmits voice data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the voice data.

[0139] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0140] The data processing system 410 according to the fourth embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 410 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the robot 414, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the robot 414. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the robot 414 or an external device, etc., and the robot 414 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0141] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0142] The emotion identification model 59 as an emotion engine may determine the user's emotion according to a specific mapping. Specifically, the emotion identification model 59 may determine the user's emotion according to an emotion map (see FIG. 9), which is a specific mapping. Similarly, the emotion identification model 59 may determine the robot's emotion, and the identification processing unit 290 may perform identification processing using the robot's emotion.

[0143] FIG. 9 illustrates an emotion map 400 on which multiple emotions are mapped. In the emotion map 400, emotions are arranged in concentric circles radiating from the center. Emotions closer to the center of the concentric circles are more primitive. Emotions representing states and behaviors arising from a state of mind are arranged on the outer edges of the concentric circles. The concept of emotion encompasses both emotions and mental states. Emotions generally generated from reactions occurring in the brain are arranged on the left side of the concentric circles. Emotions generally induced by situational judgment are arranged on the right side of the concentric circles. Emotions generally generated from reactions occurring in the brain and induced by situational judgment are arranged on the upper and lower sides of the concentric circles. Furthermore, the emotion of "pleasure" is arranged on the upper side of the concentric circles, and the emotion of "discomfort" is arranged on the lower side. In this way, in the emotion map 400, multiple emotions are mapped based on the structure by which emotions are generated, and emotions that tend to occur simultaneously are mapped close to each other.

[0144] These emotions are distributed in the 3 o'clock direction on emotion map 400, and typically fluctuate between relief and anxiety. In the right half of emotion map 400, situational awareness dominates over internal sensations, resulting in a sense of calm.

[0145] The inside of emotion map 400 represents what is going on in the mind, and the outside of emotion map 400 represents behavior, so the further you go outside emotion map 400, the more visible the emotions become (the more they are expressed in behavior).

[0146] Human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, a state of discomfort is expressed, and when they approach the ideal, a state of pleasure is expressed. Emotions can also be created for robots, cars, and motorcycles, based on various balances, such as posture and remaining battery life. When these balances deviate from the ideal, a state of discomfort is expressed, and when they approach the ideal, a state of pleasure is expressed. An emotion map can be generated, for example, based on Dr. Mitsuyoshi's emotion map (Research on speech emotion recognition and brain physiological signal analysis systems for emotions, Tokushima University, doctoral dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map lists emotions belonging to the area called "reaction," where sensation is dominant. The right half of the emotion map lists emotions belonging to the area called "situation," where situational awareness is dominant.

[0147] The emotion map defines two emotions that promote learning. One is a negative emotion on the situation side, around the middle of "repentance" or "reflection." In other words, this occurs when the robot experiences negative emotions such as "I never want to feel this way again" or "I don't want to be scolded again." The other is a positive emotion on the response side, around "desire." In other words, this occurs when the robot experiences positive feelings such as "I want more" or "I want to know more."

[0148] The emotion identification model 59 inputs user input into a pre-trained neural network, obtains emotion values ​​indicating each emotion shown in the emotion map 400, and determines the user's emotion. This neural network is pre-trained based on multiple pieces of training data that are combinations of user input and emotion values ​​indicating each emotion shown in the emotion map 400. Furthermore, this neural network is trained so that emotions that are located close to each other have similar values, as in the emotion map 900 shown in FIG. 10. FIG. 10 shows an example in which multiple emotions, "relieved," "calm," and "reassuring," have similar emotion values.

[0149] In the above embodiment, an example was given in which a specific process is performed by one computer 22, but the technology disclosed herein is not limited to this, and distributed processing of the specific process may be performed by multiple computers including computer 22.

[0150] In the above embodiment, an example in which the specific processing program 56 is stored in the storage 32 has been described, but the technology of the present disclosure is not limited to this. For example, the specific processing program 56 may be stored in a portable, computer-readable, non-transitory storage medium such as a USB (Universal Serial Bus) memory. The specific processing program 56 stored in the non-transitory storage medium is installed in the computer 22 of the data processing device 12. The processor 28 executes the specific processing in accordance with the specific processing program 56.

[0151] 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.

[0152] It is not necessary to store all of the specific processing program 56 in a storage device such as a server connected to the data processing device 12 via the network 54, or to store all of the specific processing program 56 in the storage 32; only a portion of the specific processing program 56 may be stored.

[0153] The hardware resource for executing a specific process can be any of the following types of processors: A processor, for example, is a CPU, which is a general-purpose processor that functions as a hardware resource for executing a specific process by executing software, i.e., a program. A processor also includes a dedicated electrical circuit, such as an FPGA (Field-Programmable Gate Array), a PLD (Programmable Logic Device), or an ASIC (Application Specific Integrated Circuit), which is a processor with a circuit configuration designed specifically for executing a specific process. Each processor has built-in or connected memory, and each processor uses the memory to execute the specific process.

[0154] The hardware resource that executes the specific process may be configured with one of these various processors, or may be configured with a combination of two or more processors of the same or different types (for example, a combination of multiple FPGAs, or a combination of a CPU and an FPGA). Also, the hardware resource that executes the specific process may be a single processor.

[0155] As an example of a system configured with a single processor, first, one processor is configured by combining one or more CPUs and software, and this processor functions as a hardware resource that executes a specific process. Second, there is a system that uses a processor that realizes the functions of an entire system including multiple hardware resources that execute a specific process on a single IC chip, as typified by SoC (System-on-a-chip). In this way, a specific process is realized using one or more of the above-mentioned various processors as hardware resources.

[0156] Furthermore, the hardware structure of these various processors can be, more specifically, an electric circuit that combines circuit elements such as semiconductor devices. The specific processing described above is merely an example. Therefore, it goes without saying that unnecessary steps may be deleted, new steps may be added, or the processing order may be rearranged, without departing from the spirit of the invention.

[0157] In the above example, the first to fourth embodiments have been described separately, but some or all of these embodiments may be combined. The smart device 14, smart glasses 214, headset terminal 314, and robot 414 are merely examples, and they may be combined, or other devices may be used. In the above example, the first and second embodiments have been described separately, but they may be combined.

[0158] The above-described description and illustrations are a detailed explanation of the parts related to the technology of the present disclosure and are merely an example of the technology of the present disclosure. For example, the above description of the configuration, functions, actions, and effects is an explanation of an example of the configuration, functions, actions, and effects of the parts related to the technology of the present disclosure. Therefore, it goes without saying that unnecessary parts may be deleted, new elements may be added, or replacements may be made to the above-described description and illustrations within the scope of the gist of the technology of the present disclosure. Furthermore, to avoid confusion and facilitate understanding of the parts related to the technology of the present disclosure, the above-described description and illustrations omit explanations of common technical knowledge that do not require particular explanation to enable the implementation of the technology of the present disclosure.

[0159] All publications, patent applications, and technical standards mentioned in this specification are herein incorporated by reference to the same extent as if each individual publication, patent application, or technical standard was specifically and individually indicated to be incorporated by reference.

[0160] [Explanation of symbols]

[0161] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Device 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robot

Claims

1. a reception unit that receives the discomfort by voice; an estimation unit that estimates a deviation in movement based on the information received by the reception unit; a correction unit that corrects a movement algorithm based on the deviation of the movement estimated by the estimation unit; an adjustment unit that adjusts the haptic feedback based on the operation algorithm corrected by the correction unit. A system characterized by:

2. The reception unit Estimate the operator's emotions and adjust how they respond to discomfort based on the estimated emotions.

2. The system of claim 1.

3. The reception unit When receiving a complaint, the appropriate method of response is selected by referring to the operator's past operation history.

2. The system of claim 1.

4. The reception unit When receiving an anomaly, filtering is performed based on the operator's current work situation.

2. The system of claim 1.

5. The reception unit When receiving a complaint, select the appropriate method of reception depending on the operator's input method.

2. The system of claim 1.

6. The estimation unit Estimate the operator's emotions and adjust the method of predicting movement deviations based on the estimated emotions.

2. The system of claim 1.

7. The estimation unit When predicting deviations in behavior, the accuracy of predictions can be improved by referring to the operator's past operation history.

2. The system of claim 1.

8. The estimation unit When predicting misalignment, the algorithm adjusts based on the operator's current working situation.

2. The system of claim 1.

9. The estimation unit When estimating the deviation of movement, select the appropriate estimation method according to the operator's input method.

2. The system of claim 1.

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