System

Abstract

An object of the system according to the embodiment is to standardize a procedure of a cleaning operation, evaluate a cleaning result, and provide feedback.SOLUTION: A system according to an embodiment includes AR devices, an AI evaluator, and a feedback provider. The AR device guides the cleaning procedure for the cleaning worker. The AI evaluator evaluates the cleaned result. The feedback providing unit provides feedback to the cleaning worker.SELECTED DRAWING: Figure 1

Description

[Technical Field]

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

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

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

[0004] With conventional technology, there was variation in cleaning procedures and results, making it difficult to pay fair wages and improve skills.

[0005] The system according to the embodiment aims to standardize the procedures for cleaning work, evaluate the cleaning results and provide feedback. [Means for solving the problem]

[0006] The system according to the embodiment includes an AR device, an AI evaluation unit, and a feedback providing unit. The AR device guides a cleaning worker through a cleaning procedure. The AI ​​evaluation unit evaluates the cleaning result. The feedback providing unit provides feedback to the cleaning worker. [Effects of the Invention]

[0007] The system according to the embodiment can standardize the cleaning procedure, evaluate the cleaning results and provide feedback. [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 the user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

[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 may have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59.

[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) The cleaning system according to an embodiment of the present invention utilizes AR technology and AI evaluation to solve problems in the hospital cleaning market, providing fair wages, improving skills, and providing a safe and comfortable restroom environment. As a result, the cleaning system can improve the skills of cleaning workers, provide fair wages, and provide a uniform "safe and comfortable restroom environment" to hospitals.

[0029] A cleaning system according to an embodiment includes an AR device, an AI evaluation unit, and a feedback provision unit. The AR device guides cleaning workers through cleaning procedures. For example, the AR device visually shows cleaning workers wearing the device the areas to be cleaned and the procedures to be performed. The AR device also displays detailed cleaning procedures in real time, helping cleaning workers perform tasks according to standardized procedures. The AI ​​evaluation unit evaluates the cleaning results. For example, a camera or sensor is used to capture images of the area to be cleaned, and the image data is analyzed by AI. The AI ​​determines the degree of completion and cleanliness of the cleaning and provides an appropriate evaluation. The feedback provision unit provides feedback to the cleaning worker. For example, it specifically indicates areas where cleaning was inadequate and areas that need improvement. This allows cleaning workers to improve their skills. The cleaning system thus improves the skills of cleaning workers, provides fair wages, and provides hospitals with a uniform "safe and comfortable restroom environment."

[0030] The AR device is equipped with an audio guide function and can guide the user through cleaning procedures by combining visual and audio information. For example, the AR device can add an audio guide function to guide the user through cleaning procedures using visual and audio information. For example, the device can visually show the toilet bowl cleaning procedure while simultaneously providing audio instructions such as "Next, wipe the rim of the toilet bowl." This combination of visual and audio information makes it easier for the user to understand the cleaning procedures.

[0031] The AR device has a customization function based on the cleaning worker's past work history, and can provide cleaning procedures optimized for each individual worker. For example, the AR device can provide cleaning procedures optimized for each individual worker based on the cleaning worker's past work history. For example, the device can provide guidance focusing on areas where mistakes have been made frequently in the past. This improves cleaning efficiency by providing procedures optimized for each individual worker.

[0032] The AR device can visually show not only cleaning procedures but also how to use and maintain cleaning tools. For example, the AR device can visually show how to use cleaning tools along with cleaning procedures. For example, it can display how to use a mop or how to change the water in a bucket. This visually shows how to use and maintain cleaning tools, thereby improving the efficiency of cleaning work.

[0033] The AR device can make cleaning procedure guidelines available in different languages, making them accessible to foreign workers. For example, the AR device can make cleaning procedure guidelines available in multiple languages, making them accessible to foreign workers. For example, the device can display them in English, Chinese, Spanish, etc. This makes it possible for foreign workers to use the guidelines by supporting different languages.

[0034] The AI ​​evaluation unit can add an analysis of the cleaning worker's movements and include efficient movements in the evaluation criteria. For example, the AI ​​evaluation unit can add an analysis of the cleaning worker's movements and include efficient movements in the evaluation criteria. For example, it can analyze how a mop is moved or how a vacuum cleaner is used and evaluate efficient movements. In this way, by including efficient movements in the evaluation criteria, the efficiency of cleaning work can be improved.

[0035] The AI ​​evaluation unit detects the microbial level of the cleaning area and can judge the cleanliness level in more detail. The AI ​​evaluation unit, for example, detects the microbial level of the cleaning area and judges the cleanliness level in more detail. For example, it detects the presence of microorganisms using UV light or special sensors. This allows the cleanliness level to be judged in more detail by detecting the microbial level.

[0036] The AI ​​evaluation unit can be applied to cleaning areas within the hospital other than toilets, improving overall cleaning quality.The AI ​​evaluation unit can be applied to cleaning areas within the hospital other than toilets, improving overall cleaning quality. For example, AI evaluates the cleaning results of patient rooms and corridors.By applying this to cleaning areas other than toilets, overall cleaning quality can be improved.

[0037] The AI ​​evaluation unit visualizes the cleaning worker's skill map and can track the progress of individual skill improvement. The AI ​​evaluation unit visualizes the cleaning worker's skill map and can track the progress of individual skill improvement. For example, the evaluation score of each worker is displayed in a graph. This makes it possible to track the progress of individual skill improvement by visualizing it as a skill map.

[0038] The feedback providing unit can include personalized advice based on the cleaning worker's past performance data. For example, the feedback providing unit includes personalized advice based on the cleaning worker's past performance data. For example, the feedback providing unit may focus advice on areas where the cleaning worker has made many mistakes in the past. In this way, including personalized advice promotes improvement of the cleaning worker's skills.

[0039] The feedback providing unit can provide the feedback content as a video tutorial to make it easier to understand visually. For example, the feedback providing unit can provide the feedback content as a video tutorial to make it easier to understand visually. For example, the feedback providing unit can show a cleaning procedure in the form of a video, and visually explain specific operations. In this way, the use of the video tutorial can make the feedback content easier to understand visually.

[0040] The feedback providing unit can provide feedback as regular training sessions to continuously improve skills. For example, the feedback providing unit can provide feedback as regular training sessions to continuously improve skills. For example, the feedback providing unit can share feedback in weekly training sessions. This allows the cleaning worker's skills to continuously improve through regular training sessions.

[0041] The feedback providing unit can share the feedback content with other cleaning workers to form a community for learning best practices. For example, the feedback providing unit can share the feedback content with other cleaning workers to form a community for learning best practices. For example, the feedback can be shared in an online forum. In this way, by sharing the feedback content, cleaning workers can learn best practices and promote skill improvement.

[0042] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0043] The cleaning system can also be equipped with a health management unit that monitors the health of cleaning workers. For example, it measures heart rate and body temperature, and if an abnormality is detected, it displays an alert urging workers to take a break. This helps maintain the health of cleaning workers and improve work efficiency.

[0044] The cleaning system can further include an action recording unit that records the actions of the cleaning worker and plays them back later for self-evaluation. For example, the cleaning worker's actions during cleaning work can be filmed with a camera and played back later to identify areas for improvement. This allows the cleaning worker to self-evaluate and improve their skills.

[0045] The cleaning system can also include an environmental adjustment unit to maintain a comfortable working environment for the cleaning worker. For example, the system can appropriately adjust the temperature and humidity of the work area to provide a comfortable working environment, thereby improving the work efficiency of the cleaning worker.

[0046] The cleaning system can also be equipped with a time management unit that manages the working hours of cleaning workers. For example, it can record the start and end times of work and ensure appropriate rest periods. This reduces the fatigue of cleaning workers and improves their work efficiency.

[0047] The cleaning system may further include a comparative evaluation unit for comparing the work performance of each cleaning worker with that of other workers. For example, the evaluation scores of each worker may be compared, and workers who demonstrate excellent performance may be rewarded. This increases the motivation of cleaning workers and improves overall work efficiency.

[0048] The processing flow of the first embodiment will be briefly explained below.

[0049] Step 1: The AR device guides the cleaning worker through the cleaning procedure. For example, the AR device visually shows the areas and procedures to be cleaned to the cleaning worker wearing the device. The AR device also displays the details of the cleaning procedure in real time, helping the cleaning worker to follow the standardized procedure. Step 2: The AI ​​evaluation unit evaluates the cleaning results. For example, a camera or sensor is used to photograph the condition of the cleaning area, and the image data is analyzed by the AI. The AI ​​determines the degree of completion and cleanliness of the cleaning and makes an appropriate evaluation. Step 3: The feedback provider provides feedback to the cleaner. For example, it specifically indicates areas where cleaning was inadequate and areas where improvement is needed. This allows the cleaner to improve their skills.

[0050] (Example 2) The cleaning system according to an embodiment of the present invention utilizes AR technology and AI evaluation to solve problems in the hospital cleaning market, providing fair wages, improving skills, and providing a safe and comfortable restroom environment. As a result, the cleaning system can improve the skills of cleaning workers, provide fair wages, and provide a uniform "safe and comfortable restroom environment" to hospitals.

[0051] A cleaning system according to an embodiment includes an AR device, an AI evaluation unit, and a feedback provision unit. The AR device guides cleaning workers through cleaning procedures. For example, the AR device visually shows cleaning workers wearing the device the areas to be cleaned and the procedures to be performed. The AR device also displays detailed cleaning procedures in real time, helping cleaning workers perform tasks according to standardized procedures. The AI ​​evaluation unit evaluates the cleaning results. For example, a camera or sensor is used to capture images of the area to be cleaned, and the image data is analyzed by AI. The AI ​​determines the degree of completion and cleanliness of the cleaning and provides an appropriate evaluation. The feedback provision unit provides feedback to the cleaning worker. For example, it specifically indicates areas where cleaning was inadequate and areas that need improvement. This allows cleaning workers to improve their skills. The cleaning system thus improves the skills of cleaning workers, provides fair wages, and provides hospitals with a uniform "safe and comfortable restroom environment."

[0052] The AR device is equipped with an audio guide function and can guide the user through cleaning procedures by combining visual and audio information. For example, the AR device can add an audio guide function to guide the user through cleaning procedures using visual and audio information. For example, the device can visually show the toilet bowl cleaning procedure while simultaneously providing audio instructions such as "Next, wipe the rim of the toilet bowl." This combination of visual and audio information makes it easier for the user to understand the cleaning procedures.

[0053] The AR device has a customization function based on the cleaning worker's past work history, and can provide cleaning procedures optimized for each individual worker. For example, the AR device can provide cleaning procedures optimized for each individual worker based on the cleaning worker's past work history. For example, the device can provide guidance focusing on areas where mistakes have been made frequently in the past. This improves cleaning efficiency by providing procedures optimized for each individual worker.

[0054] The AR device is equipped with an emotion estimation function and can monitor the stress level of cleaning workers in real time and display advice on how to relax if stress levels increase. For example, the AR device uses the emotion estimation function to monitor the stress level of cleaning workers in real time. For example, if stress levels increase, the device displays advice such as "Take a deep breath and relax." This reduces the stress of cleaning workers and improves their work efficiency.

[0055] The AR device can visually show not only cleaning procedures but also how to use and maintain cleaning tools. For example, the AR device can visually show how to use cleaning tools along with cleaning procedures. For example, it can display how to use a mop or how to change the water in a bucket. This visually shows how to use and maintain cleaning tools, thereby improving the efficiency of cleaning work.

[0056] The AR device can make cleaning procedure guidelines available in different languages, making them accessible to foreign workers. For example, the AR device can make cleaning procedure guidelines available in multiple languages, making them accessible to foreign workers. For example, the device can display them in English, Chinese, Spanish, etc. This makes it possible for foreign workers to use the guidelines by supporting different languages.

[0057] The AR device is equipped with an emotion estimation function and can provide positive feedback in real time to motivate cleaning workers. For example, the AR device uses the emotion estimation function to provide positive feedback to motivate cleaning workers. For example, it displays "Great job!" This increases the motivation of cleaning workers and improves their work efficiency.

[0058] The AI ​​evaluation unit can add an analysis of the cleaning worker's movements and include efficient movements in the evaluation criteria. For example, the AI ​​evaluation unit can add an analysis of the cleaning worker's movements and include efficient movements in the evaluation criteria. For example, it can analyze how a mop is moved or how a vacuum cleaner is used and evaluate efficient movements. In this way, by including efficient movements in the evaluation criteria, the efficiency of cleaning work can be improved.

[0059] The AI ​​evaluation unit detects the microbial level of the cleaning area and can judge the cleanliness level in more detail. The AI ​​evaluation unit, for example, detects the microbial level of the cleaning area and judges the cleanliness level in more detail. For example, it detects the presence of microorganisms using UV light or special sensors. This allows the cleanliness level to be judged in more detail by detecting the microbial level.

[0060] The AI ​​evaluation unit uses an emotion estimation function to reflect the emotional state of the cleaning worker in the evaluation, and can award bonus points if the positive emotion is high. The AI ​​evaluation unit, for example, uses an emotion estimation function to reflect the emotional state of the cleaning worker in the evaluation. For example, it can award bonus points if the positive emotion is high. In this way, by awarding bonus points if the positive emotion is high, the motivation of the cleaning worker is improved.

[0061] The AI ​​evaluation unit can be applied to cleaning areas within the hospital other than toilets, improving overall cleaning quality.The AI ​​evaluation unit can be applied to cleaning areas within the hospital other than toilets, improving overall cleaning quality. For example, AI evaluates the cleaning results of patient rooms and corridors.By applying this to cleaning areas other than toilets, overall cleaning quality can be improved.

[0062] The AI ​​evaluation unit visualizes the cleaning worker's skill map and can track the progress of individual skill improvement. The AI ​​evaluation unit visualizes the cleaning worker's skill map and can track the progress of individual skill improvement. For example, the evaluation score of each worker is displayed in a graph. This makes it possible to track the progress of individual skill improvement by visualizing it as a skill map.

[0063] The AI ​​evaluation unit uses the emotion estimation function to provide evaluation feedback based on the emotional state of the cleaning worker, thereby maintaining motivation. The AI ​​evaluation unit, for example, uses the emotion estimation function to provide evaluation feedback based on the emotional state of the cleaning worker. For example, if positive emotions are high, the AI ​​evaluation unit may display "Great job." In this way, by providing evaluation feedback based on the emotional state, the motivation of the cleaning worker can be maintained.

[0064] The feedback providing unit can include personalized advice based on the cleaning worker's past performance data. For example, the feedback providing unit includes personalized advice based on the cleaning worker's past performance data. For example, the feedback providing unit may focus advice on areas where the cleaning worker has made many mistakes in the past. In this way, including personalized advice promotes improvement of the cleaning worker's skills.

[0065] The feedback providing unit can provide the feedback content as a video tutorial to make it easier to understand visually. For example, the feedback providing unit can provide the feedback content as a video tutorial to make it easier to understand visually. For example, the feedback providing unit can show a cleaning procedure in the form of a video, and visually explain specific operations. In this way, the use of the video tutorial can make the feedback content easier to understand visually.

[0066] The feedback providing unit can use the emotion estimation function to consider the emotional state of the cleaning worker when providing feedback and use expressions that elicit positive emotions. For example, the feedback providing unit can use the emotion estimation function to consider the emotional state of the cleaning worker when providing feedback and use expressions that elicit positive emotions. For example, it can display "Great job!". In this way, the use of expressions that elicit positive emotions improves the motivation of the cleaning worker.

[0067] The feedback providing unit can provide feedback as regular training sessions to continuously improve skills. For example, the feedback providing unit can provide feedback as regular training sessions to continuously improve skills. For example, the feedback providing unit can share feedback in weekly training sessions. This allows the cleaning worker's skills to continuously improve through regular training sessions.

[0068] The feedback providing unit can share the feedback content with other cleaning workers to form a community for learning best practices. For example, the feedback providing unit can share the feedback content with other cleaning workers to form a community for learning best practices. For example, the feedback can be shared in an online forum. In this way, by sharing the feedback content, cleaning workers can learn best practices and promote skill improvement.

[0069] The feedback providing unit can use the emotion estimation function to monitor the emotional state of the cleaning worker in real time when providing feedback and provide feedback at an appropriate timing. The feedback providing unit can, for example, use the emotion estimation function to monitor the emotional state of the cleaning worker in real time when providing feedback and provide feedback at an appropriate timing. For example, feedback can be provided when stress is low. In this way, by providing feedback at an appropriate timing, the motivation of the cleaning worker can be maintained.

[0070] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0071] The cleaning system can also be equipped with a health management unit that monitors the health of cleaning workers. For example, it measures heart rate and body temperature, and if an abnormality is detected, it displays an alert urging workers to take a break. This helps maintain the health of cleaning workers and improve work efficiency.

[0072] The cleaning system can further include an action recording unit that records the actions of the cleaning worker and plays them back later for self-evaluation. For example, the cleaning worker's actions during cleaning work can be filmed with a camera and played back later to identify areas for improvement. This allows the cleaning worker to self-evaluate and improve their skills.

[0073] The cleaning system can also include an environmental adjustment unit to maintain a comfortable working environment for the cleaning worker. For example, the system can appropriately adjust the temperature and humidity of the work area to provide a comfortable working environment, thereby improving the work efficiency of the cleaning worker.

[0074] The cleaning system can also be equipped with a time management unit that manages the working hours of cleaning workers. For example, it can record the start and end times of work and ensure appropriate rest periods. This reduces the fatigue of cleaning workers and improves their work efficiency.

[0075] The cleaning system may further include a comparative evaluation unit for comparing the work performance of each cleaning worker with that of other workers. For example, the evaluation scores of each worker may be compared, and workers who demonstrate excellent performance may be rewarded. This increases the motivation of cleaning workers and improves overall work efficiency.

[0076] The cleaning system also includes a stress management module that monitors the emotional state of the cleaning worker and can provide advice on how to relax when stress levels rise. For example, it displays messages recommending deep breathing or light stretching. This reduces the stress of the cleaning worker and improves their work efficiency.

[0077] The cleaning system also includes a motivational component that can provide messages based on the emotional state of the cleaners to elicit positive emotions. For example, messages such as "Great job!" or "Your efforts are appreciated" are displayed. This increases the motivation of the cleaners and improves their work efficiency.

[0078] The cleaning system also includes a feedback providing unit based on the emotional state of the cleaning worker, and can provide feedback according to the worker's emotional state. For example, if stress levels are high, the system can provide gentle feedback, while if positive emotions are high, the system can suggest specific areas for improvement. This provides appropriate feedback according to the cleaning worker's emotional state, promoting skill improvement.

[0079] The cleaning system also includes a performance evaluation module that evaluates the performance of cleaning workers based on their emotional state, allowing it to adjust the evaluation based on their emotional state. For example, the system can moderate the evaluation when workers are highly stressed and severe the evaluation when workers are highly positive. This allows for fair evaluation based on the cleaning workers' emotional state, maintaining their motivation.

[0080] The cleaning system further includes a training provider based on the emotional state of the cleaning worker, and can provide training according to the emotional state. For example, if stress is high, training for relaxation is provided, and if positive emotions are high, training for skill improvement is provided. This provides appropriate training according to the emotional state of the cleaning worker, facilitating skill improvement.

[0081] The processing flow of the second embodiment will be briefly explained below.

[0082] Step 1: The AR device guides the cleaning worker through the cleaning procedure. For example, the AR device visually shows the areas and procedures to be cleaned to the cleaning worker wearing the device. The AR device also displays the details of the cleaning procedure in real time, helping the cleaning worker to follow the standardized procedure. Step 2: The AI ​​evaluation unit evaluates the cleaning results. For example, a camera or sensor is used to photograph the condition of the cleaning area, and the image data is analyzed by the AI. The AI ​​determines the degree of completion and cleanliness of the cleaning and makes an appropriate evaluation. Step 3: The feedback provider provides feedback to the cleaner. For example, it specifically indicates areas where cleaning was inadequate and areas where improvement is needed. This allows the cleaner to improve their skills.

[0083] 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.

[0084] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (Internet Search<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.

[0085] 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.

[0086] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0087] 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.

[0088] 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.

[0089] 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.

[0090] 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.

[0091] 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).

[0092] 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.

[0093] 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.

[0094] 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.

[0095] 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 the user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

[0096] 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. Note that the smart glasses 214 may have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59.

[0097] 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.

[0098] 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.

[0099] 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.

[0100] 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.

[0101] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0102] 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.

[0103] 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.

[0104] 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.

[0105] 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.

[0106] 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).

[0107] 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.

[0108] 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.

[0109] 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.

[0110] 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 the user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

[0111] 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 may also have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59.

[0112] 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.

[0113] 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.

[0114] 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.

[0115] 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.

[0116] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0117] 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.

[0118] 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.

[0119] 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.

[0120] 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.

[0121] 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).

[0122] 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.

[0123] 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.

[0124] 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.

[0125] 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.

[0126] 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 the user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

[0127] In the robot 414, the processor 46 performs the identification process. 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. The robot 414 may have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59.

[0128] 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.

[0129] 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.

[0130] 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.

[0131] 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.

[0132] 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.

[0133] 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.

[0134] 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.

[0135] 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).

[0136] 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.

[0137] 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."

[0138] 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.

[0139] 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.

[0140] 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.

[0141] 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.

[0142] 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.

[0143] 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.

[0144] 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.

[0145] 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.

[0146] 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.

[0147] 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.

[0148] 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.

[0149] 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. [Explanation of symbols]

[0150] 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. AR devices and AI Evaluation Department and a feedback providing unit, The AR device includes: Guide cleaning workers through cleaning procedures, The AI ​​evaluation unit Evaluate the cleaning results and The feedback providing unit: Provide feedback to cleaners A system characterized by:

2. The AR device includes: It has a customization function based on the cleaning worker's past work history, Providing the cleaning procedure optimized for each individual worker 2. The system of claim 1.

3. The AR device includes: Adapting the cleaning procedure guidelines into different languages; Making it available to foreign workers 2. The system of claim 1.

4. The AI ​​evaluation unit Adding motion analysis of cleaning workers, Include efficient operation in the evaluation criteria 2. The system of claim 1.

5. The feedback providing unit: Include personalized advice based on cleaners' past performance data 2. The system of claim 1.

6. The AR device includes: Equipped with emotion estimation function, Monitors the stress levels of cleaners in real time and provides relaxation advice when stress levels rise 2. The system of claim 1.

7. The AI ​​evaluation unit The emotional state of the cleaner is reflected in the evaluation, and bonus points are awarded for highly positive emotions.

2. The system of claim 1.

8. The feedback providing unit: Consider the emotional state of the cleaner when giving feedback. Use language that evokes positive emotions 2. The system of claim 1.

Images