system

Abstract

The system according to this embodiment aims to provide real-time, specific instructions, support multiple languages, and evaluate and provide feedback on operations in convenience store operations. [Solution] The system according to this embodiment comprises an instruction provision unit, a multilingual support unit, and an evaluation feedback unit. The instruction provision unit provides specific instructions in real time to staff wearing smart glasses with a camera. The multilingual support unit provides multilingual support based on the instructions provided by the instruction provision unit. The evaluation feedback unit provides evaluation and feedback on work based on the instructions provided by the multilingual support unit.

Description

Technical Field

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

Background Art

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

Patent Document 1

Summary of the Invention

Problems to be Solved by the Invention

[0004] In the prior art, in convenience store operations, with the increase of part-time workers and foreign staff, training and business support have not been fully carried out, and there is room for improvement.

[0005] [[ID=?]] The system according to the embodiment aims to provide specific instructions in real time, support multiple languages, and conduct business evaluation and feedback in convenience store operations.

Means for Solving the Problems

[0006] It should be noted that there are some tags with "?" in the original text which seem to be incorrect or incomplete tags. I have translated them as they are while maintaining the consistency of the translation format. If these are actual important tags that need to be corrected, please provide the correct information for a more accurate translation.The system according to this embodiment comprises an instruction provision unit, a multilingual support unit, and an evaluation and feedback unit. The instruction provision unit provides specific instructions in real time to staff wearing smart glasses with a camera. The multilingual support unit provides multilingual support based on the instructions provided by the instruction provision unit. The evaluation and feedback unit provides evaluation and feedback on work based on the instructions provided by the multilingual support unit. [Effects of the Invention]

[0007] The system according to this embodiment can provide real-time, specific instructions, support multiple languages, and perform work evaluation and feedback in convenience store operations. [Brief explanation of the drawing]

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

[0028] (Example of form 1) The business support system according to an embodiment of the present invention is a system that utilizes smart glasses with a camera to address the challenges posed by the increasing number of part-time workers and foreign staff in modern retail stores, which are becoming more sophisticated. This system provides specific instructions in real time to staff wearing smart glasses with a camera. For example, it can provide information necessary for work, such as how to display products and how to operate the cash register, in voice, text, and image formats. This allows staff to carry out their work smoothly. Furthermore, this system provides support that overcomes language barriers through multilingual support. In modern retail stores, where the number of foreign staff is increasing, language barriers are a major challenge. This system can provide instructions in the staff's native language, thus overcoming language barriers and improving work efficiency. In addition, this system performs work evaluation and feedback using AI. The AI ​​analyzes the images captured by the smart glasses with a camera and evaluates the staff's work. For example, it evaluates whether products are displayed accurately and whether cash register operations are performed appropriately, and provides feedback. This allows staff to understand areas for improvement in their work and shorten training periods. This system improves the operational efficiency of retail stores and shortens training periods. Furthermore, it enables the provision of appropriate instructions to foreign staff, overcoming language barriers. This solves the challenges of modern retail operations and improves staff efficiency.

[0029] The business support system according to this embodiment comprises an instruction provision unit, a multilingual support unit, and an evaluation feedback unit. The instruction provision unit provides specific instructions in real time to staff wearing smart glasses with a camera. The instruction provision unit can provide information necessary for the work, such as how to display products or the procedure for operating the cash register, in the form of voice, text, and images. For example, the instruction provision unit can give a voice instruction such as, "Please display this product on this shelf." The instruction provision unit can also display text such as, "The procedure for operating the cash register is as follows." Furthermore, the instruction provision unit can show the product display method with an image. The multilingual support unit performs multilingual support based on the instructions provided by the instruction provision unit. For example, the multilingual support unit can provide instructions in the staff's native language. For example, the multilingual support unit can provide instructions in Japanese to staff whose native language is Japanese, and instructions in English to staff whose native language is English. For example, the multilingual support unit can automatically recognize the staff's native language and provide instructions in that language. The evaluation and feedback unit evaluates and provides feedback on work based on instructions provided by the multilingual support unit. The evaluation and feedback unit can, for example, analyze images captured by smart glasses with a camera to evaluate the staff's work. The evaluation and feedback unit can, for example, evaluate whether products are displayed correctly or whether the cash register is operated properly. The evaluation and feedback unit can, for example, provide feedback on areas for improvement if products are not displayed correctly. The evaluation and feedback unit can, for example, provide feedback on areas for improvement if the cash register is operated properly. In this way, the business support system can evaluate the staff's work and provide feedback. Some or all of the above processing in the evaluation and feedback unit may be performed using AI, for example, or without AI. For example, the evaluation and feedback unit can input images captured by smart glasses with a camera into the AI ​​and have the AI ​​perform the work evaluation.

[0030] The instruction unit provides real-time, specific instructions to staff wearing smart glasses equipped with cameras. The instruction unit can provide necessary information for tasks, such as product display methods and register operation procedures, via voice, text, and images. Specifically, the instruction unit can display images showing product display methods so that staff can visually confirm them through the smart glasses. For example, it can display an image showing how to place a specific product on a specific shelf, enabling staff to display the products accordingly. Voice instructions allow staff to receive instructions without using their hands while working, enabling efficient work. For example, it can provide specific instructions via voice, such as "Please display this product on this shelf." Furthermore, text instructions allow staff to visually confirm detailed procedures and points to note, enabling accurate work. For example, it can display detailed procedures such as "The register operation procedure is as follows," enabling staff to operate the register accordingly. In this way, the instruction unit can provide real-time, specific instructions to enable staff to perform their tasks efficiently and accurately.

[0031] The multilingual support unit provides multilingual support based on instructions provided by the instruction provision unit. For example, the multilingual support unit can provide instructions in the staff member's native language. Specifically, it can automatically recognize the staff member's native language and provide instructions in that language. For example, it can provide instructions in Japanese to a staff member whose native language is Japanese, and in English to a staff member whose native language is English. This allows staff members to receive instructions in a language they understand easily, improving work efficiency and accuracy. The multilingual support unit can accurately recognize the staff member's native language and provide instructions in the appropriate language using speech recognition and natural language processing technologies. For example, when a staff member wears smart glasses, speech recognition technology can be used to analyze the staff member's speech and identify their language. Furthermore, the multilingual support unit can translate instructions provided by the instruction provision unit in real time and provide them to the staff. This enables the multilingual support unit to ensure that staff members who speak different languages ​​understand and can execute the same work instructions.

[0032] The Performance Evaluation and Feedback Department evaluates and provides feedback on work based on instructions provided by the Multilingual Support Department. For example, the Performance Evaluation and Feedback Department analyzes video footage captured by smart glasses with cameras to evaluate staff performance. Specifically, it can evaluate whether products are displayed correctly and whether cash register operations are performed appropriately. For instance, if products are not displayed correctly, it can provide feedback on areas for improvement. The Performance Evaluation and Feedback Department can use AI to analyze video footage captured by smart glasses with cameras and evaluate work performance. The AI ​​uses image recognition technology to analyze the product display situation and determine if products are displayed correctly. It can also analyze video footage of cash register operations and evaluate whether they are performed appropriately. For example, if cash register operations are not performed appropriately, it can provide feedback on areas for improvement. Furthermore, the Performance Evaluation and Feedback Department can continuously monitor staff performance and provide regular feedback. This allows staff to understand areas for improvement in their work and enhance efficiency and accuracy. By using AI, the Performance Evaluation and Feedback Department can quickly and accurately evaluate work and provide appropriate feedback to staff.

[0033] The instruction-providing unit can provide instructions via voice, text, and images. For example, the instruction-providing unit can give a voice instruction saying, "Please display this product on this shelf." For example, the instruction-providing unit can display a text message saying, "The procedure for operating the register is as follows." For example, the instruction-providing unit can show how to display products using images. This allows staff to carry out their work smoothly by providing instructions via voice, text, and images. Some or all of the above-described processes in the instruction-providing unit may be performed using AI, for example, or without AI. For example, the instruction-providing unit can have a generating AI perform the generation of voice instructions.

[0034] The evaluation feedback unit can analyze the video captured by the smart glasses with a camera and evaluate the staff's work. For example, the evaluation feedback unit can analyze the video captured by the smart glasses with a camera using image recognition technology to evaluate whether the products are displayed correctly. For example, the evaluation feedback unit can analyze the video captured by the smart glasses with a camera and evaluate whether the cash register operations are performed appropriately. For example, if the products are not displayed correctly, the evaluation feedback unit can provide feedback on areas for improvement. For example, if the cash register operations are not performed correctly, the evaluation feedback unit can provide feedback on areas for improvement. In this way, the staff's work can be accurately evaluated by analyzing the video captured by the smart glasses with a camera. Some or all of the above processing in the evaluation feedback unit may be performed using AI, for example, or without AI. For example, the evaluation feedback unit can input the video captured by the smart glasses with a camera into the AI ​​and have the AI ​​perform the work evaluation.

[0035] The multilingual support unit can provide instructions in the staff's native language. For example, the multilingual support unit can provide instructions in Japanese to staff whose native language is Japanese, and in English to staff whose native language is English. The multilingual support unit can, for example, automatically recognize the staff's native language and provide instructions in that language. This overcomes language barriers and improves work efficiency by providing instructions in the staff's native language. Some or all of the above processing in the multilingual support unit may be performed using AI, for example, or without AI. For example, the multilingual support unit can input the staff's native language into the AI ​​and have the AI ​​translate the instructions.

[0036] The evaluation feedback unit can evaluate whether products are displayed correctly and whether the cash register is operated appropriately. For example, the evaluation feedback unit can evaluate whether products are displayed correctly. For example, if products are not displayed correctly, the evaluation feedback unit can provide feedback on areas for improvement. For example, if cash register operations are not performed appropriately, the evaluation feedback unit can provide feedback on areas for improvement. This improves the accuracy of operations by evaluating product displays and cash register operations. Some or all of the above processing in the evaluation feedback unit may be performed using AI, for example, or without AI. For example, the evaluation feedback unit can have AI perform the evaluation of product displays and cash register operations.

[0037] The performance feedback unit can provide feedback on areas for improvement in staff work. For example, if product displays are not done correctly, the performance feedback unit can provide feedback on areas for improvement. For example, if cash register operations are not performed properly, the performance feedback unit can provide feedback on areas for improvement. For example, the performance feedback unit can specifically point out areas for improvement in staff work. By providing feedback on areas for improvement in staff work, training periods can be shortened and work efficiency can be improved. Some or all of the above processes in the performance feedback unit may be performed using AI, for example, or not using AI. For example, the performance feedback unit can have AI perform the feedback on areas for improvement in staff work.

[0038] The instruction-providing unit can select the most appropriate instruction by referring to the staff member's past work history when providing instructions. For example, the instruction-providing unit can provide instructions for similar tasks based on the staff member's past work experience. For example, the instruction-providing unit can analyze the staff member's strengths and weaknesses in specific tasks from their past work history and provide appropriate instructions. For example, the instruction-providing unit can refer to the staff member's past work history and provide instructions according to the progress of the task. In this way, the instruction-providing unit can provide the most appropriate instructions by referring to the staff member's past work history. Some or all of the above processes in the instruction-providing unit may be performed using AI, for example, or not using AI. For example, the instruction-providing unit can input the staff member's past work history into AI and have AI select the most appropriate instructions.

[0039] The instruction-providing unit can adjust the priority of instructions based on the staff's current work status when providing instructions. For example, the instruction-providing unit can grasp the progress of the staff's current work in real time and provide high-priority instructions. For example, the instruction-providing unit can prioritize providing instructions of high urgency according to the staff's current work status. For example, the instruction-providing unit can analyze the staff's current work status and provide instructions for efficient work execution. This makes efficient work execution possible by adjusting the priority of instructions based on the staff's current work status. Some or all of the above processes in the instruction-providing unit may be performed using AI, for example, or not using AI. For example, the instruction-providing unit can input the staff's current work status into AI and have AI perform the adjustment of instruction priorities.

[0040] The instruction-providing unit can provide highly relevant instructions by considering the staff's geographical location information when providing instructions. For example, if a staff member is in a specific area, the instruction-providing unit can provide instructions for tasks related to that area. For example, the instruction-providing unit can provide optimal task instructions based on the staff member's geographical location information. For example, if a staff member is on the move, the instruction-providing unit can provide instructions for the next task area. This allows the instruction-providing unit to provide highly relevant instructions by considering the staff member's geographical location information. Some or all of the above processing in the instruction-providing unit may be performed using AI, for example, or without AI. For example, the instruction-providing unit can input the staff member's geographical location information into the AI ​​and have the AI ​​perform the task of providing highly relevant instructions.

[0041] The instruction-providing unit can analyze staff members' social media activity and provide relevant instructions when providing instructions. For example, the instruction-providing unit can provide work instructions based on staff members' interests and preferences from their social media activity. For example, the instruction-providing unit can analyze staff members' social media activity and provide instructions at the appropriate time. For example, the instruction-providing unit can provide work-related information based on staff members' social media activity. In this way, relevant instructions can be provided by analyzing staff members' social media activity. Some or all of the above processes in the instruction-providing unit may be performed using AI, for example, or not using AI. For example, the instruction-providing unit can input staff members' social media activity into AI and have the AI ​​provide relevant instructions.

[0042] The multilingual support unit can adjust the level of detail in translations based on the staff's proficiency in their native languages ​​when handling multilingual tasks. For example, if the staff's proficiency in their native languages ​​is high, the multilingual support unit can provide a detailed translation. For example, if the staff's proficiency in their native languages ​​is low, the multilingual support unit can provide a concise and clear translation. For example, the multilingual support unit can provide an appropriate level of detail in translations according to the staff's proficiency in their native languages. This allows for the provision of appropriate translations by adjusting the level of detail in translations based on the staff's proficiency in their native languages. Some or all of the above processing in the multilingual support unit may be performed using AI, for example, or without AI. For example, the multilingual support unit can input the staff's proficiency in their native languages ​​into the AI ​​and have the AI ​​perform the adjustment of the level of detail in translations.

[0043] The multilingual support unit can select the optimal translation method when providing multilingual support, taking into account the cultural background of the staff. For example, the multilingual support unit can provide an appropriate translation method based on the staff's cultural background. For example, the multilingual support unit can use culturally appropriate expressions, taking into account the staff's cultural background. For example, the multilingual support unit can select the optimal translation method according to the staff's cultural background. This allows the unit to provide the optimal translation method by considering the staff's cultural background. Some or all of the above-described processes in the multilingual support unit may be performed using AI, or not. For example, the multilingual support unit can input the staff's cultural background into AI and have AI select the optimal translation method.

[0044] The performance feedback unit can select the most appropriate feedback content by referring to the staff member's past performance history when providing performance feedback. For example, the performance feedback unit can specifically point out areas for improvement based on the staff member's past performance history. For example, the performance feedback unit can provide feedback that highlights the staff member's strengths based on their past performance history. For example, the performance feedback unit can refer to the staff member's past performance history and provide feedback that is appropriate to the progress of their work. In this way, the performance feedback unit can provide the most appropriate feedback content by referring to the staff member's past performance history. Some or all of the above processes in the performance feedback unit may be performed using AI, for example, or not using AI. For example, the performance feedback unit can input the staff member's past performance history into AI and have the AI ​​select the most appropriate feedback content.

[0045] The performance feedback unit can adjust the priority of feedback based on the staff member's current work status during the performance feedback process. For example, the performance feedback unit can grasp the progress of the staff member's current work in real time and provide high-priority feedback. For example, the performance feedback unit can prioritize providing urgent feedback according to the staff member's current work status. For example, the performance feedback unit can analyze the staff member's current work status and provide feedback for efficient work execution. This makes efficient work execution possible by adjusting the priority of feedback based on the staff member's current work status. Some or all of the above processes in the performance feedback unit may be performed using AI, for example, or not using AI. For example, the performance feedback unit can input the staff member's current work status into AI and have AI perform the adjustment of feedback priorities.

[0046] The performance feedback unit can provide highly relevant feedback by considering the staff's geographical location information during performance feedback. For example, if a staff member is in a specific area, the performance feedback unit can provide feedback on tasks related to that area. For example, the performance feedback unit can provide optimal task feedback based on the staff member's geographical location information. For example, if a staff member is on the move, the performance feedback unit can provide feedback on the next task area. This allows for the provision of highly relevant feedback by considering the staff member's geographical location information. Some or all of the above processing in the performance feedback unit may be performed using AI, for example, or without AI. For example, the performance feedback unit can input the staff member's geographical location information into AI and have the AI ​​perform the task of providing highly relevant feedback.

[0047] The performance feedback department can analyze staff members' social media activities and provide relevant feedback during performance feedback. For example, the performance feedback department can provide work-related feedback based on staff members' interests and preferences from their social media activities. For example, the performance feedback department can analyze staff members' social media activities and provide feedback at the appropriate time. For example, the performance feedback department can provide work-related information based on staff members' social media activities. In this way, relevant feedback can be provided by analyzing staff members' social media activities. Some or all of the above processes in the performance feedback department may be performed using AI, for example, or not using AI. For example, the performance feedback department can input staff members' social media activities into AI and have the AI ​​provide relevant feedback.

[0048] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0049] The instruction provision unit can monitor the health status of staff and provide instructions tailored to their health condition. For example, if a staff member is feeling fatigued, it can provide instructions encouraging them to take a break. It can also provide instructions to reduce the workload if a staff member reports feeling unwell. Furthermore, it can monitor staff health status in real time and provide instructions for immediate action if any abnormalities are detected. This allows for improved work efficiency by providing appropriate instructions based on staff health conditions.

[0050] The instruction provision department can evaluate staff skill levels and provide instructions tailored to those skill levels. For example, it can provide detailed instructions to novice staff and concise instructions to experienced staff. It can also provide training instructions to staff lacking specific skills to address those shortcomings. Furthermore, it can continuously evaluate staff skill levels and adjust instructions as skills improve. This allows for improved work efficiency by providing appropriate instructions based on staff skill levels.

[0051] The instruction provision department can refer to staff members' past work history and provide instructions to prevent past mistakes. For example, it can provide instructions to staff members who have made mistakes in a particular task in the past, including points to be careful about in that task. It can also analyze the causes of past mistakes and provide instructions to prevent similar mistakes. Furthermore, based on past work history, it can identify staff members' strengths and weaknesses and provide appropriate instructions. In this way, by providing appropriate instructions to prevent past mistakes, the efficiency of work can be improved.

[0052] The instruction provision unit can grasp the current work status of staff in real time and provide instructions according to the progress of their work. For example, when a staff member completes a specific task, instructions for the next task can be immediately provided. Furthermore, it can assign high-priority tasks according to the progress of the work. In addition, it can monitor the progress of work in real time and adjust instructions as needed. This allows for improved work efficiency by providing appropriate instructions according to the progress of the work.

[0053] The instruction provision unit can provide optimal instructions by taking into account the geographical location of staff. For example, if a staff member is in a specific area, it can provide instructions for tasks related to that area. It can also provide optimal task instructions based on the staff member's geographical location. Furthermore, if a staff member is on the move, it can provide instructions for the next task area. In this way, by considering the staff member's geographical location, it can provide highly relevant instructions.

[0054] The instruction provision department can analyze staff members' social media activity and provide work instructions based on their interests and preferences. For example, it can identify staff members' interests in specific tasks from their social media activity and provide instructions for those tasks. It can also analyze staff members' social media activity and provide instructions at the appropriate time. Furthermore, it can provide work-related information based on staff members' social media activity. In this way, by analyzing staff members' social media activity, it is possible to provide relevant instructions.

[0055] The following briefly describes the processing flow for example form 1.

[0056] Step 1: The instruction unit provides real-time, specific instructions to staff wearing smart glasses with cameras. For example, it can provide information necessary for the job, such as how to display products or how to operate the cash register, via voice, text, and images. The instruction unit can give voice instructions such as "Please display this product on this shelf," display text such as "The procedure for operating the cash register is as follows," or show product display methods with images. Step 2: The multilingual support unit provides multilingual support based on the instructions provided by the instruction provision unit. For example, it can provide instructions in the staff's native language, in Japanese for staff whose native language is Japanese, and in English for staff whose native language is English. The multilingual support unit can automatically recognize the staff's native language and provide instructions in that language. Step 3: The evaluation and feedback unit evaluates and provides feedback on the work based on the instructions provided by the multilingual support unit. For example, it can analyze images captured by smart glasses with a camera to evaluate whether products are displayed correctly or whether the cash register is operated properly. If products are not displayed correctly or the cash register is not operated properly, the evaluation and feedback unit can provide feedback on areas for improvement. In this way, the business support system can evaluate the work of staff and provide feedback. The processing in the evaluation and feedback unit may be performed using AI or not. For example, images captured by smart glasses with a camera can be input into the AI, and the AI ​​can perform the evaluation of the work.

[0057] (Example of form 2) The business support system according to an embodiment of the present invention is a system that utilizes smart glasses with a camera to address the challenges posed by the increasing number of part-time workers and foreign staff in modern retail stores, which are becoming more sophisticated. This system provides specific instructions in real time to staff wearing smart glasses with a camera. For example, it can provide information necessary for work, such as how to display products and how to operate the cash register, in voice, text, and image formats. This allows staff to carry out their work smoothly. Furthermore, this system provides support that overcomes language barriers through multilingual support. In modern retail stores, where the number of foreign staff is increasing, language barriers are a major challenge. This system can provide instructions in the staff's native language, thus overcoming language barriers and improving work efficiency. In addition, this system performs work evaluation and feedback using AI. The AI ​​analyzes the images captured by the smart glasses with a camera and evaluates the staff's work. For example, it evaluates whether products are displayed accurately and whether cash register operations are performed appropriately, and provides feedback. This allows staff to understand areas for improvement in their work and shorten training periods. This system improves the operational efficiency of retail stores and shortens training periods. Furthermore, it enables the provision of appropriate instructions to foreign staff, overcoming language barriers. This solves the challenges of modern retail operations and improves staff efficiency.

[0058] The business support system according to this embodiment comprises an instruction provision unit, a multilingual support unit, and an evaluation feedback unit. The instruction provision unit provides specific instructions in real time to staff wearing smart glasses with a camera. The instruction provision unit can provide information necessary for the work, such as how to display products or the procedure for operating the cash register, in the form of voice, text, and images. For example, the instruction provision unit can give a voice instruction such as, "Please display this product on this shelf." The instruction provision unit can also display text such as, "The procedure for operating the cash register is as follows." Furthermore, the instruction provision unit can show the product display method with an image. The multilingual support unit performs multilingual support based on the instructions provided by the instruction provision unit. For example, the multilingual support unit can provide instructions in the staff's native language. For example, the multilingual support unit can provide instructions in Japanese to staff whose native language is Japanese, and instructions in English to staff whose native language is English. For example, the multilingual support unit can automatically recognize the staff's native language and provide instructions in that language. The evaluation and feedback unit evaluates and provides feedback on work based on instructions provided by the multilingual support unit. The evaluation and feedback unit can, for example, analyze images captured by smart glasses with a camera to evaluate the staff's work. The evaluation and feedback unit can, for example, evaluate whether products are displayed correctly or whether the cash register is operated properly. The evaluation and feedback unit can, for example, provide feedback on areas for improvement if products are not displayed correctly. The evaluation and feedback unit can, for example, provide feedback on areas for improvement if the cash register is operated properly. In this way, the business support system can evaluate the staff's work and provide feedback. Some or all of the above processing in the evaluation and feedback unit may be performed using AI, for example, or without AI. For example, the evaluation and feedback unit can input images captured by smart glasses with a camera into the AI ​​and have the AI ​​perform the work evaluation.

[0059] The instruction unit provides real-time, specific instructions to staff wearing smart glasses equipped with cameras. The instruction unit can provide necessary information for tasks, such as product display methods and register operation procedures, via voice, text, and images. Specifically, the instruction unit can display images showing product display methods so that staff can visually confirm them through the smart glasses. For example, it can display an image showing how to place a specific product on a specific shelf, enabling staff to display the products accordingly. Voice instructions allow staff to receive instructions without using their hands while working, enabling efficient work. For example, it can provide specific instructions via voice, such as "Please display this product on this shelf." Furthermore, text instructions allow staff to visually confirm detailed procedures and points to note, enabling accurate work. For example, it can display detailed procedures such as "The register operation procedure is as follows," enabling staff to operate the register accordingly. In this way, the instruction unit can provide real-time, specific instructions to enable staff to perform their tasks efficiently and accurately.

[0060] The multilingual support unit provides multilingual support based on instructions provided by the instruction provision unit. For example, the multilingual support unit can provide instructions in the staff member's native language. Specifically, it can automatically recognize the staff member's native language and provide instructions in that language. For example, it can provide instructions in Japanese to a staff member whose native language is Japanese, and in English to a staff member whose native language is English. This allows staff members to receive instructions in a language they understand easily, improving work efficiency and accuracy. The multilingual support unit can accurately recognize the staff member's native language and provide instructions in the appropriate language using speech recognition and natural language processing technologies. For example, when a staff member wears smart glasses, speech recognition technology can be used to analyze the staff member's speech and identify their language. Furthermore, the multilingual support unit can translate instructions provided by the instruction provision unit in real time and provide them to the staff. This enables the multilingual support unit to ensure that staff members who speak different languages ​​understand and can execute the same work instructions.

[0061] The Performance Evaluation and Feedback Department evaluates and provides feedback on work based on instructions provided by the Multilingual Support Department. For example, the Performance Evaluation and Feedback Department analyzes video footage captured by smart glasses with cameras to evaluate staff performance. Specifically, it can evaluate whether products are displayed correctly and whether cash register operations are performed appropriately. For instance, if products are not displayed correctly, it can provide feedback on areas for improvement. The Performance Evaluation and Feedback Department can use AI to analyze video footage captured by smart glasses with cameras and evaluate work performance. The AI ​​uses image recognition technology to analyze the product display situation and determine if products are displayed correctly. It can also analyze video footage of cash register operations and evaluate whether they are performed appropriately. For example, if cash register operations are not performed appropriately, it can provide feedback on areas for improvement. Furthermore, the Performance Evaluation and Feedback Department can continuously monitor staff performance and provide regular feedback. This allows staff to understand areas for improvement in their work and enhance efficiency and accuracy. By using AI, the Performance Evaluation and Feedback Department can quickly and accurately evaluate work and provide appropriate feedback to staff.

[0062] The instruction-providing unit can provide instructions via voice, text, and images. For example, the instruction-providing unit can give a voice instruction saying, "Please display this product on this shelf." For example, the instruction-providing unit can display a text message saying, "The procedure for operating the register is as follows." For example, the instruction-providing unit can show how to display products using images. This allows staff to carry out their work smoothly by providing instructions via voice, text, and images. Some or all of the above-described processes in the instruction-providing unit may be performed using AI, for example, or without AI. For example, the instruction-providing unit can have a generating AI perform the generation of voice instructions.

[0063] The evaluation feedback unit can analyze the video captured by the smart glasses with a camera and evaluate the staff's work. For example, the evaluation feedback unit can analyze the video captured by the smart glasses with a camera using image recognition technology to evaluate whether the products are displayed correctly. For example, the evaluation feedback unit can analyze the video captured by the smart glasses with a camera and evaluate whether the cash register operations are performed appropriately. For example, if the products are not displayed correctly, the evaluation feedback unit can provide feedback on areas for improvement. For example, if the cash register operations are not performed correctly, the evaluation feedback unit can provide feedback on areas for improvement. In this way, the staff's work can be accurately evaluated by analyzing the video captured by the smart glasses with a camera. Some or all of the above processing in the evaluation feedback unit may be performed using AI, for example, or without AI. For example, the evaluation feedback unit can input the video captured by the smart glasses with a camera into the AI ​​and have the AI ​​perform the work evaluation.

[0064] The multilingual support unit can provide instructions in the staff's native language. For example, the multilingual support unit can provide instructions in Japanese to staff whose native language is Japanese, and in English to staff whose native language is English. The multilingual support unit can, for example, automatically recognize the staff's native language and provide instructions in that language. This overcomes language barriers and improves work efficiency by providing instructions in the staff's native language. Some or all of the above processing in the multilingual support unit may be performed using AI, for example, or without AI. For example, the multilingual support unit can input the staff's native language into the AI ​​and have the AI ​​translate the instructions.

[0065] The evaluation feedback unit can evaluate whether products are displayed correctly and whether the cash register is operated appropriately. For example, the evaluation feedback unit can evaluate whether products are displayed correctly. For example, if products are not displayed correctly, the evaluation feedback unit can provide feedback on areas for improvement. For example, if cash register operations are not performed appropriately, the evaluation feedback unit can provide feedback on areas for improvement. This improves the accuracy of operations by evaluating product displays and cash register operations. Some or all of the above processing in the evaluation feedback unit may be performed using AI, for example, or without AI. For example, the evaluation feedback unit can have AI perform the evaluation of product displays and cash register operations.

[0066] The performance feedback unit can provide feedback on areas for improvement in staff work. For example, if product displays are not done correctly, the performance feedback unit can provide feedback on areas for improvement. For example, if cash register operations are not performed properly, the performance feedback unit can provide feedback on areas for improvement. For example, the performance feedback unit can specifically point out areas for improvement in staff work. By providing feedback on areas for improvement in staff work, training periods can be shortened and work efficiency can be improved. Some or all of the above processes in the performance feedback unit may be performed using AI, for example, or not using AI. For example, the performance feedback unit can have AI perform the feedback on areas for improvement in staff work.

[0067] The instruction provider can estimate the user's emotions and adjust the way instructions are expressed based on the estimated emotions. For example, if the user is stressed, the instruction provider can provide simple and clear instructions. For example, if the user is relaxed, the instruction provider can provide instructions that include detailed explanations. For example, if the user is in a hurry, the instruction provider can provide quick and concise instructions. This allows for the provision of more appropriate instructions by adjusting the way instructions are expressed according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or a generative AI. The generative AI is, for example, a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above processing in the instruction provider may be performed using AI, or not using AI. For example, the instruction provider can input user emotion data into the generative AI and have the generative AI adjust the way instructions are expressed.

[0068] The instruction-providing unit can select the most appropriate instruction by referring to the staff member's past work history when providing instructions. For example, the instruction-providing unit can provide instructions for similar tasks based on the staff member's past work experience. For example, the instruction-providing unit can analyze the staff member's strengths and weaknesses in specific tasks from their past work history and provide appropriate instructions. For example, the instruction-providing unit can refer to the staff member's past work history and provide instructions according to the progress of the task. In this way, the instruction-providing unit can provide the most appropriate instructions by referring to the staff member's past work history. Some or all of the above processes in the instruction-providing unit may be performed using AI, for example, or not using AI. For example, the instruction-providing unit can input the staff member's past work history into AI and have AI select the most appropriate instructions.

[0069] The instruction-providing unit can adjust the priority of instructions based on the staff's current work status when providing instructions. For example, the instruction-providing unit can grasp the progress of the staff's current work in real time and provide high-priority instructions. For example, the instruction-providing unit can prioritize providing instructions of high urgency according to the staff's current work status. For example, the instruction-providing unit can analyze the staff's current work status and provide instructions for efficient work execution. This makes efficient work execution possible by adjusting the priority of instructions based on the staff's current work status. Some or all of the above processes in the instruction-providing unit may be performed using AI, for example, or not using AI. For example, the instruction-providing unit can input the staff's current work status into AI and have AI perform the adjustment of instruction priorities.

[0070] The instruction provider can estimate the user's emotions and adjust the timing of instructions based on the estimated emotions. For example, if the user is stressed, the instruction provider can delay the timing of instructions. For example, if the user is relaxed, the instruction provider can adjust the timing of instructions as appropriate. For example, if the user is in a hurry, the instruction provider can provide instructions quickly. By adjusting the timing of instructions according to the user's emotions, instructions can be provided at a more appropriate time. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or a generative AI. The generative AI is, for example, a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above processing in the instruction provider may be performed using AI, for example, or without AI. For example, the instruction provider can input user emotion data into the generative AI and have the generative AI adjust the timing of instructions.

[0071] The instruction-providing unit can provide highly relevant instructions by considering the staff's geographical location information when providing instructions. For example, if a staff member is in a specific area, the instruction-providing unit can provide instructions for tasks related to that area. For example, the instruction-providing unit can provide optimal task instructions based on the staff member's geographical location information. For example, if a staff member is on the move, the instruction-providing unit can provide instructions for the next task area. This allows the instruction-providing unit to provide highly relevant instructions by considering the staff member's geographical location information. Some or all of the above processing in the instruction-providing unit may be performed using AI, for example, or without AI. For example, the instruction-providing unit can input the staff member's geographical location information into the AI ​​and have the AI ​​perform the task of providing highly relevant instructions.

[0072] The instruction-providing unit can analyze staff members' social media activity and provide relevant instructions when providing instructions. For example, the instruction-providing unit can provide work instructions based on staff members' interests and preferences from their social media activity. For example, the instruction-providing unit can analyze staff members' social media activity and provide instructions at the appropriate time. For example, the instruction-providing unit can provide work-related information based on staff members' social media activity. In this way, relevant instructions can be provided by analyzing staff members' social media activity. Some or all of the above processes in the instruction-providing unit may be performed using AI, for example, or not using AI. For example, the instruction-providing unit can input staff members' social media activity into AI and have the AI ​​provide relevant instructions.

[0073] The multilingual support unit can estimate the user's emotions and adjust the translation method of instructions based on the estimated user emotions. For example, if the user is stressed, the multilingual support unit can provide a concise and clear translation. For example, if the user is relaxed, the multilingual support unit can provide a translation that includes detailed explanations. For example, if the user is in a hurry, the multilingual support unit can provide a quick and concise translation. This allows for more appropriate translations by adjusting the translation method of instructions according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, for example, a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above processing in the multilingual support unit may be performed using AI, for example, or without AI. For example, the multilingual support unit can input user emotion data into a generative AI and have the generative AI adjust the translation method of instructions.

[0074] The multilingual support unit can adjust the level of detail in translations based on the staff's proficiency in their native languages ​​when handling multilingual tasks. For example, if the staff's proficiency in their native languages ​​is high, the multilingual support unit can provide a detailed translation. For example, if the staff's proficiency in their native languages ​​is low, the multilingual support unit can provide a concise and clear translation. For example, the multilingual support unit can provide an appropriate level of detail in translations according to the staff's proficiency in their native languages. This allows for the provision of appropriate translations by adjusting the level of detail in translations based on the staff's proficiency in their native languages. Some or all of the above processing in the multilingual support unit may be performed using AI, for example, or without AI. For example, the multilingual support unit can input the staff's proficiency in their native languages ​​into the AI ​​and have the AI ​​perform the adjustment of the level of detail in translations.

[0075] The multilingual support unit can select the optimal translation method when providing multilingual support, taking into account the cultural background of the staff. For example, the multilingual support unit can provide an appropriate translation method based on the staff's cultural background. For example, the multilingual support unit can use culturally appropriate expressions, taking into account the staff's cultural background. For example, the multilingual support unit can select the optimal translation method according to the staff's cultural background. This allows the unit to provide the optimal translation method by considering the staff's cultural background. Some or all of the above-described processes in the multilingual support unit may be performed using AI, or not. For example, the multilingual support unit can input the staff's cultural background into AI and have AI select the optimal translation method.

[0076] The multilingual support unit can estimate the user's emotions and determine translation priorities based on the estimated emotions. For example, if the user is stressed, the multilingual support unit will prioritize translating important instructions. If the user is relaxed, the multilingual support unit can translate overall instructions in a balanced manner. If the user is in a hurry, the multilingual support unit can quickly translate necessary instructions. This allows for prioritizing the translation of important instructions by determining translation priorities according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the multilingual support unit may be performed using AI or not. For example, the multilingual support unit can input user emotion data into a generative AI and have the generative AI determine translation priorities.

[0077] The evaluation feedback unit can estimate the user's emotions and adjust the way feedback is expressed based on the estimated emotions. For example, if the user is stressed, the evaluation feedback unit will prioritize positive feedback. For example, if the user is relaxed, the evaluation feedback unit can provide detailed feedback. For example, if the user is in a hurry, the evaluation feedback unit can provide quick and concise feedback. This allows for more appropriate feedback to be provided by adjusting the way feedback is expressed according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI is, for example, a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above processing in the evaluation feedback unit may be performed using AI, or not using AI. For example, the evaluation feedback unit can input user emotion data into a generative AI and have the generative AI adjust the way feedback is expressed.

[0078] The performance feedback unit can select the most appropriate feedback content by referring to the staff member's past performance history when providing performance feedback. For example, the performance feedback unit can specifically point out areas for improvement based on the staff member's past performance history. For example, the performance feedback unit can provide feedback that highlights the staff member's strengths based on their past performance history. For example, the performance feedback unit can refer to the staff member's past performance history and provide feedback that is appropriate to the progress of their work. In this way, the performance feedback unit can provide the most appropriate feedback content by referring to the staff member's past performance history. Some or all of the above processes in the performance feedback unit may be performed using AI, for example, or not using AI. For example, the performance feedback unit can input the staff member's past performance history into AI and have the AI ​​select the most appropriate feedback content.

[0079] The performance feedback unit can adjust the priority of feedback based on the staff member's current work status during the performance feedback process. For example, the performance feedback unit can grasp the progress of the staff member's current work in real time and provide high-priority feedback. For example, the performance feedback unit can prioritize providing urgent feedback according to the staff member's current work status. For example, the performance feedback unit can analyze the staff member's current work status and provide feedback for efficient work execution. This makes efficient work execution possible by adjusting the priority of feedback based on the staff member's current work status. Some or all of the above processes in the performance feedback unit may be performed using AI, for example, or not using AI. For example, the performance feedback unit can input the staff member's current work status into AI and have AI perform the adjustment of feedback priorities.

[0080] The evaluation feedback unit can estimate the user's emotions and adjust the timing of feedback based on the estimated emotions. For example, if the user is stressed, the evaluation feedback unit can delay the timing of feedback. For example, if the user is relaxed, the evaluation feedback unit can adjust the timing of feedback as appropriate. For example, if the user is in a hurry, the evaluation feedback unit can provide feedback quickly. In this way, by adjusting the timing of feedback according to the user's emotions, feedback can be provided at a more appropriate time. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or a generative AI. The generative AI is, for example, a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above processing in the evaluation feedback unit may be performed using AI, for example, or without using AI. For example, the evaluation feedback unit can input user emotion data into a generative AI and have the generative AI perform the adjustment of the timing of feedback.

[0081] The performance feedback unit can provide highly relevant feedback by considering the staff's geographical location information during performance feedback. For example, if a staff member is in a specific area, the performance feedback unit can provide feedback on tasks related to that area. For example, the performance feedback unit can provide optimal task feedback based on the staff member's geographical location information. For example, if a staff member is on the move, the performance feedback unit can provide feedback on the next task area. This allows for the provision of highly relevant feedback by considering the staff member's geographical location information. Some or all of the above processing in the performance feedback unit may be performed using AI, for example, or without AI. For example, the performance feedback unit can input the staff member's geographical location information into AI and have the AI ​​perform the task of providing highly relevant feedback.

[0082] The performance feedback department can analyze staff members' social media activities and provide relevant feedback during performance feedback. For example, the performance feedback department can provide work-related feedback based on staff members' interests and preferences from their social media activities. For example, the performance feedback department can analyze staff members' social media activities and provide feedback at the appropriate time. For example, the performance feedback department can provide work-related information based on staff members' social media activities. In this way, relevant feedback can be provided by analyzing staff members' social media activities. Some or all of the above processes in the performance feedback department may be performed using AI, for example, or not using AI. For example, the performance feedback department can input staff members' social media activities into AI and have the AI ​​provide relevant feedback.

[0083] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0084] The instruction provision unit can monitor the health status of staff and provide instructions tailored to their health condition. For example, if a staff member is feeling fatigued, it can provide instructions encouraging them to take a break. It can also provide instructions to reduce the workload if a staff member reports feeling unwell. Furthermore, it can monitor staff health status in real time and provide instructions for immediate action if any abnormalities are detected. This allows for improved work efficiency by providing appropriate instructions based on staff health conditions.

[0085] The instruction provision department can evaluate staff skill levels and provide instructions tailored to those skill levels. For example, it can provide detailed instructions to novice staff and concise instructions to experienced staff. It can also provide training instructions to staff lacking specific skills to address those shortcomings. Furthermore, it can continuously evaluate staff skill levels and adjust instructions as skills improve. This allows for improved work efficiency by providing appropriate instructions based on staff skill levels.

[0086] The performance feedback department can estimate staff emotions and adjust the content of feedback based on those estimates. For example, if a staff member is stressed, positive feedback can be prioritized. If a staff member is relaxed, detailed feedback can be provided. Furthermore, if a staff member is in a hurry, quick and concise feedback can be provided. This allows for improved work efficiency by providing appropriate feedback tailored to staff emotions.

[0087] The instruction provision department can refer to staff members' past work history and provide instructions to prevent past mistakes. For example, it can provide instructions to staff members who have made mistakes in a particular task in the past, including points to be careful about in that task. It can also analyze the causes of past mistakes and provide instructions to prevent similar mistakes. Furthermore, based on past work history, it can identify staff members' strengths and weaknesses and provide appropriate instructions. In this way, by providing appropriate instructions to prevent past mistakes, the efficiency of work can be improved.

[0088] The performance feedback department can estimate staff emotions and adjust the timing of feedback based on those estimates. For example, if a staff member is stressed, the feedback can be delayed. Conversely, if a staff member is relaxed, the timing of feedback can be adjusted accordingly. Furthermore, if a staff member is in a hurry, feedback can be provided quickly. This allows for improved work efficiency by providing feedback at the appropriate time according to the staff member's emotions.

[0089] The instruction provision unit can grasp the current work status of staff in real time and provide instructions according to the progress of their work. For example, when a staff member completes a specific task, instructions for the next task can be immediately provided. Furthermore, it can assign high-priority tasks according to the progress of the work. In addition, it can monitor the progress of work in real time and adjust instructions as needed. This allows for improved work efficiency by providing appropriate instructions according to the progress of the work.

[0090] The performance feedback department can estimate staff emotions and adjust the content of feedback based on those estimates. For example, if a staff member is stressed, positive feedback can be prioritized. If a staff member is relaxed, detailed feedback can be provided. Furthermore, if a staff member is in a hurry, quick and concise feedback can be provided. This allows for improved work efficiency by providing appropriate feedback tailored to staff emotions.

[0091] The instruction provision unit can provide optimal instructions by taking into account the geographical location of staff. For example, if a staff member is in a specific area, it can provide instructions for tasks related to that area. It can also provide optimal task instructions based on the staff member's geographical location. Furthermore, if a staff member is on the move, it can provide instructions for the next task area. In this way, by considering the staff member's geographical location, it can provide highly relevant instructions.

[0092] The performance feedback department can estimate staff emotions and adjust the content of feedback based on those estimates. For example, if a staff member is stressed, positive feedback can be prioritized. If a staff member is relaxed, detailed feedback can be provided. Furthermore, if a staff member is in a hurry, quick and concise feedback can be provided. This allows for improved work efficiency by providing appropriate feedback tailored to staff emotions.

[0093] The instruction provision department can analyze staff members' social media activity and provide work instructions based on their interests and preferences. For example, it can identify staff members' interests in specific tasks from their social media activity and provide instructions for those tasks. It can also analyze staff members' social media activity and provide instructions at the appropriate time. Furthermore, it can provide work-related information based on staff members' social media activity. In this way, by analyzing staff members' social media activity, it is possible to provide relevant instructions.

[0094] The following briefly describes the processing flow for example form 2.

[0095] Step 1: The instruction unit provides real-time, specific instructions to staff wearing smart glasses with cameras. For example, it can provide information necessary for the job, such as how to display products or how to operate the cash register, via voice, text, and images. The instruction unit can give voice instructions such as "Please display this product on this shelf," display text such as "The procedure for operating the cash register is as follows," or show product display methods with images. Step 2: The multilingual support unit provides multilingual support based on the instructions provided by the instruction provision unit. For example, it can provide instructions in the staff's native language, in Japanese for staff whose native language is Japanese, and in English for staff whose native language is English. The multilingual support unit can automatically recognize the staff's native language and provide instructions in that language. Step 3: The evaluation and feedback unit evaluates and provides feedback on the work based on the instructions provided by the multilingual support unit. For example, it can analyze images captured by smart glasses with a camera to evaluate whether products are displayed correctly or whether the cash register is operated properly. If products are not displayed correctly or the cash register is not operated properly, the evaluation and feedback unit can provide feedback on areas for improvement. In this way, the business support system can evaluate the work of staff and provide feedback. The processing in the evaluation and feedback unit may be performed using AI or not. For example, images captured by smart glasses with a camera can be input into the AI, and the AI ​​can perform the evaluation of the work.

[0096] The specific processing unit 290 transmits the result of the specific processing to the smart device 14. In the smart device 14, the control unit 46A causes the output device 40 to output the result of the specific processing. The microphone 38B acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0097] Data generation model 58 is a form of so-called generative AI (Artificial Intelligence). An example of data generation model 58 is ChatGPT (registered trademark) (Internet search).<URL: https: / / openai.com / blog / chatgpt> Examples of generative AI include text generation AI, image generation AI, and multimodal generation AI. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and with inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats from audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVMs), k-means clustering, convolutional neural networks (CNNs), recurrent neural networks (RNNs), generative adversarial networks (GANs), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each of the above parts is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example.Furthermore, processing performed by AI, including generative AI, may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by AI, including generative AI.

[0098] Furthermore, the processing performed by the data processing system 10 described above is carried out by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart device 14, but it may also be carried out by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart device 14. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the smart device 14 or an external device, and the smart device 14 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0099] Each of the multiple elements described above, including the instruction provision unit, the multilingual support unit, and the evaluation feedback unit, is implemented in at least one of the smart device 14 and the data processing unit 12. For example, the instruction provision unit is implemented by the control unit 46A of the smart device 14 and provides instructions to staff using the smart device 14's display 40A and speaker 40B. The multilingual support unit is implemented by the specific processing unit 290 of the data processing unit 12 and recognizes the staff's native language and provides instructions in that language. The evaluation feedback unit is implemented by the specific processing unit 290 of the data processing unit 12 and analyzes the video captured by the camera 42, evaluates the staff's work, and provides feedback. The correspondence between each unit and the device or control unit is not limited to the example described above and can be modified in various ways.

[0100] [Second Embodiment] Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment.

[0101] As shown in Figure 3, the data processing system 210 includes a data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0102] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN and / or LAN.

[0103] The smart glasses 214 include a computer 36, a microphone 238, a speaker 240, a camera 42, and a communication interface 44. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, and camera 42 are also connected to the bus 52.

[0104] The microphone 238 receives voice signals from the user and accepts instructions from the user. The microphone 238 captures the voice signals from the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0105] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, which captures images of the area around the user (for example, an imaging range defined by a field of view equivalent to the field of vision of a typical healthy person).

[0106] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0107] Figure 4 shows an example of the main functions of the data processing device 12 and the smart glasses 214. As shown in Figure 4, the data processing device 12 performs specific processing by the processor 28. The storage 32 stores the specific processing program 56.

[0108] The processor 28 reads a specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 acting as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0109] Storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0110] In the smart glasses 214, specific processing is performed by the processor 46. The storage 50 stores a specific processing program 60. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 acting as a control unit 46A according to the specific processing program 60 executed on the RAM 48. The smart glasses 214 also have a data generation model 58 and an emotion identification model 59, similar to the data generation model and emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0111] Furthermore, other devices besides the data processing device 12 may also have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 obtains processing results (such as prediction results) using the data generation model 58 by communicating with the server device that has the data generation model 58. Also, the data processing device 12 may be a server device or a terminal device owned by the user (for example, a mobile phone, robot, home appliance, etc.).

[0112] The specific processing unit 290 transmits the result of the specific processing to the smart glasses 214. In the smart glasses 214, the control unit 46A causes the speaker 240 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0113] The data generation model 58 is a so-called generative AI. An example of a data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats such as audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVM), k-means clustering, convolutional neural networks (CNN), recurrent neural networks (RNN), generative adversarial networks (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each part described above is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example. Also, processing performed by an AI including a generative AI may be replaced by rule-based processing, and rule-based processing may be replaced by processing performed by an AI including a generative AI.

[0114] The data processing system 210 according to the second embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 210 is performed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart glasses 214, but it may also be performed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart glasses 214. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the smart glasses 214 or an external device, and the smart glasses 214 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0115] Each of the multiple elements described above, including the instruction provision unit, the multilingual support unit, and the evaluation feedback unit, is implemented in at least one of the smart glasses 214 and the data processing unit 12. For example, the instruction provision unit is implemented by the control unit 46A of the smart glasses 214 and provides instructions to staff using the display and speaker of the smart glasses 214. The multilingual support unit is implemented by the specific processing unit 290 of the data processing unit 12 and recognizes the staff's native language and provides instructions in that language. The evaluation feedback unit is implemented by the specific processing unit 290 of the data processing unit 12 and analyzes the video captured by the camera 42, evaluates the staff's work, and provides feedback. The correspondence between each unit and the device or control unit is not limited to the example described above and can be modified in various ways.

[0116] [Third Embodiment] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[0117] As shown in Figure 5, the data processing system 310 includes a data processing device 12 and a headset terminal 314. An example of the data processing device 12 is a server.

[0118] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN and / or LAN.

[0119] The headset terminal 314 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication interface 44, and a display 343. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, camera 42, and display 343 are also connected to the bus 52.

[0120] The microphone 238 receives voice signals from the user and accepts instructions from the user. The microphone 238 captures the voice signals from the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0121] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, which captures images of the area around the user (for example, an imaging range defined by a field of view equivalent to the field of vision of a typical healthy person).

[0122] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0123] Figure 6 shows an example of the main functions of the data processing device 12 and the headset terminal 314. As shown in Figure 6, the data processing device 12 performs specific processing using the processor 28. The storage 32 stores the specific processing program 56.

[0124] The processor 28 reads a specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 acting as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0125] Storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0126] In the headset terminal 314, specific processing is performed by the processor 46. The storage 50 stores a specific program 60. The processor 46 reads the specific program 60 from the storage 50 and executes the read specific program 60 on the RAM 48. The specific processing is realized by the processor 46 acting as a control unit 46A according to the specific program 60 executed on the RAM 48. The headset terminal 314 also has a data generation model 58 and an emotion identification model 59, similar to the data generation model and emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0127] Furthermore, other devices besides the data processing device 12 may also have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 obtains processing results (such as prediction results) using the data generation model 58 by communicating with the server device that has the data generation model 58. Also, the data processing device 12 may be a server device or a terminal device owned by the user (for example, a mobile phone, robot, home appliance, etc.).

[0128] The specific processing unit 290 transmits the result of the specific processing to the headset terminal 314. In the headset terminal 314, the control unit 46A causes the speaker 240 and display 343 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0129] The data generation model 58 is a so-called generative AI. An example of a data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats such as audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVM), k-means clustering, convolutional neural networks (CNN), recurrent neural networks (RNN), generative adversarial networks (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each part described above is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example. Also, processing performed by an AI including a generative AI may be replaced by rule-based processing, and rule-based processing may be replaced by processing performed by an AI including a generative AI.

[0130] The data processing system 310 according to the third embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 310 is performed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the headset terminal 314, but may also be performed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the headset terminal 314. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the headset terminal 314 or an external device, and the headset terminal 314 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0131] Each of the multiple elements described above, including the instruction provision unit, the multilingual support unit, and the evaluation feedback unit, is implemented in at least one of the headset terminal 314 and the data processing unit 12. For example, the instruction provision unit is implemented by the control unit 46A of the headset terminal 314 and provides instructions to staff using the display 343 and speaker 240 of the headset terminal 314. The multilingual support unit is implemented by the specific processing unit 290 of the data processing unit 12 and recognizes the staff's native language and provides instructions in that language. The evaluation feedback unit is implemented by the specific processing unit 290 of the data processing unit 12 and analyzes the video captured by the camera 42, evaluates the staff's work, and provides feedback. The correspondence between each unit and the device or control unit is not limited to the example described above and can be modified in various ways.

[0132] [Fourth Embodiment] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[0133] As shown in Figure 7, the data processing system 410 includes a data processing device 12 and a robot 414. An example of the data processing device 12 is a server.

[0134] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN and / or LAN.

[0135] The robot 414 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication interface 44, and a controlled object 443. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, camera 42, and controlled object 443 are also connected to the bus 52.

[0136] The microphone 238 receives voice signals from the user and accepts instructions from the user. The microphone 238 captures the voice signals from the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0137] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS image sensor or CCD image sensor, which captures images of the area around the user (for example, an imaging range defined by a field of view equivalent to the field of vision of a typical healthy person).

[0138] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0139] The controlled object 443 includes a display device, LEDs in the eyes, and motors that drive the arms, hands, and feet. The posture and gestures of the robot 414 are controlled by controlling the motors of the arms, hands, and feet. Some of the robot 414's emotions can be expressed by controlling these motors. The robot 414's facial expressions can also be expressed by controlling the illumination state of the LEDs in its eyes.

[0140] Figure 8 shows an example of the main functions of the data processing device 12 and the robot 414. As shown in Figure 8, the data processing device 12 performs specific processing using the processor 28. The storage 32 stores the specific processing program 56.

[0141] The processor 28 reads a specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 acting as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0142] Storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0143] In robot 414, specific processing is performed by processor 46. A specific program 60 is stored in storage 50. Processor 46 reads the specific program 60 from storage 50 and executes it on RAM 48. The specific processing is achieved by processor 46 acting as a control unit 46A according to the specific program 60 executed on RAM 48. Robot 414 also has data generation model 58 and emotion identification model 59, similar to those of the robot, and can perform processing similar to that of the specific processing unit 290 using these models.

[0144] Furthermore, other devices besides the data processing device 12 may also have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 obtains processing results (such as prediction results) using the data generation model 58 by communicating with the server device that has the data generation model 58. Also, the data processing device 12 may be a server device or a terminal device owned by the user (for example, a mobile phone, robot, home appliance, etc.).

[0145] The specific processing unit 290 transmits the result of the specific processing to the robot 414. In the robot 414, the control unit 46A causes the speaker 240 and the controlled object 443 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0146] The data generation model 58 is a so-called generative AI. An example of a data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats such as audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVM), k-means clustering, convolutional neural networks (CNN), recurrent neural networks (RNN), generative adversarial networks (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each part described above is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example. Also, processing performed by an AI including a generative AI may be replaced by rule-based processing, and rule-based processing may be replaced by processing performed by an AI including a generative AI.

[0147] The data processing system 410 according to the fourth embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 410 is performed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the robot 414, but it may also be performed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the robot 414. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the robot 414 or an external device, and the robot 414 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0148] Each of the multiple elements described above, including the instruction provision unit, the multilingual support unit, and the evaluation feedback unit, is implemented in at least one of the robot 414 and the data processing unit 12. For example, the instruction provision unit is implemented by the control unit 46A of the robot 414 and provides instructions to the staff using the robot 414's display and speaker 240. The multilingual support unit is implemented by the specific processing unit 290 of the data processing unit 12 and recognizes the staff's native language and provides instructions in that language. The evaluation feedback unit is implemented by the specific processing unit 290 of the data processing unit 12 and analyzes the video captured by the camera 42, evaluates the staff's work, and provides feedback. The correspondence between each unit and the device or control unit is not limited to the example described above and can be modified in various ways.

[0149] Furthermore, the emotion identification model 59, acting as an emotion engine, may determine the user's emotion according to a specific mapping. Specifically, the emotion identification model 59 may determine the user's emotion according to a specific mapping, which is an emotion map (see Figure 9). Similarly, the emotion identification model 59 may also determine the robot's emotion, and the identification processing unit 290 may perform identification processing using the robot's emotion.

[0150] Figure 9 shows the emotion map 400, in which multiple emotions are mapped. In the emotion map 400, emotions are arranged in concentric circles radiating from the center. The closer to the center of the concentric circles, the more primitive the emotions are located. Further out of the concentric circles, emotions representing states and actions arising from mental states are located. Emotion is a concept that includes feelings and mental states. On the left side of the concentric circles, emotions that are generally generated from reactions occurring in the brain are located. On the right side of the concentric circles, emotions that are generally induced by situational judgment are located. Above and below the concentric circles, emotions that are generally generated from reactions occurring in the brain and induced by situational judgment are located. In addition, the emotion of "pleasure" is located on the upper side of the concentric circles, and the emotion of "displeasure" is located on the lower side. Thus, in the emotion map 400, multiple emotions are mapped based on the structure in which emotions arise, and emotions that are likely to occur simultaneously are mapped close together.

[0151] These emotions are distributed at the 3 o'clock position on the Emotion Map 400, and usually fluctuate between feelings of security and anxiety. In the right half of the Emotion Map 400, situational awareness takes precedence over internal feelings, resulting in a calm impression.

[0152] The inside of the Emotion Map 400 represents inner thoughts, while the outside represents actions. Therefore, the further you go from the outside of the Emotion Map 400, the more visible (expressed in actions) your emotions become.

[0153] Here, human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, it results in discomfort, and when they approach the ideal, it results in pleasure. Similarly, in robots, cars, and motorcycles, emotions can be created based on various balances, such as posture and battery level. When these balances deviate from the ideal, it results in discomfort, and when they approach the ideal, it results in pleasure. The emotion map can be generated based, for example, on Dr. Mitsuyoshi's emotion map (Research on a system for analyzing brain physiological signals of speech emotion recognition and emotion, Tokushima University, doctoral dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map contains emotions belonging to a region called "response," where sensation is dominant. The right half of the emotion map contains emotions belonging to a region called "situation," where situational awareness is dominant.

[0154] The emotion map defines two emotions that promote learning. One is the emotion around the middle of the negative "repentance" and "reflection" on the situation side. In other words, it is when the robot experiences negative emotions such as "I never want to feel this way again" or "I don't want to be scolded again." The other is the emotion around the positive "desire" on the reaction side. In other words, it is when the robot has positive feelings such as "I want more" or "I want to know more."

[0155] The emotion identification model 59 inputs user input into a pre-trained neural network, obtains emotion values ​​representing each emotion shown in the emotion map 400, and determines the user's emotion. This neural network is pre-trained based on multiple training data sets, which are combinations of user input and emotion values ​​representing each emotion shown in the emotion map 400. Furthermore, this neural network is trained so that emotions located close together have similar values, as shown in the emotion map 900 in Figure 10. Figure 10 shows an example where multiple emotions such as "reassured," "calm," and "confident" have similar emotion values.

[0156] In the above embodiment, an example was given in which a specific process is performed by a single computer 22. However, the technology of this disclosure is not limited thereto, and a distributed processing method for the specific process may be used, which includes computer 22 and multiple other computers.

[0157] In the above embodiment, an example was given in which the specific processing program 56 is stored in the storage 32, but the technology of this disclosure is not limited thereto. For example, the specific processing program 56 may be stored in a portable, computer-readable, non-temporary storage medium such as a USB (Universal Serial Bus) memory. The specific processing program 56 stored in the non-temporary storage medium is installed in the computer 22 of the data processing device 12. The processor 28 executes specific processing according to the specific processing program 56.

[0158] 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.

[0159] Furthermore, it is not necessary to store the entirety of the specific processing program 56 in a storage device such as a server connected to the data processing device 12 via the network 54, or to store the entirety of the specific processing program 56 in the storage 32; it is acceptable to store only a portion of the specific processing program 56.

[0160] The following types of processors can be used as hardware resources to perform specific processing. Examples of processors include a CPU, a general-purpose processor that functions as a hardware resource to perform specific processing by executing software, i.e., a program. Other examples of processors include dedicated electrical circuits, such as FPGAs (Field-Programmable Gate Arrays), PLDs (Programmable Logic Devices), or ASICs (Application Specific Integrated Circuits), which have circuit configurations specifically designed to perform specific processing. All of these processors have built-in or connected memory, and all of them perform specific processing by using memory.

[0161] The hardware resource that performs a specific process may consist of one of these various processors, or it may consist of a combination of two or more processors of the same or different types (for example, a combination of multiple FPGAs, or a combination of a CPU and an FPGA). Alternatively, the hardware resource that performs a specific process may consist of a single processor.

[0162] Examples of configurations using a single processor include, firstly, a configuration in which one or more CPUs and software are combined to form a single processor, and this processor functions as a hardware resource that performs a specific process. Secondly, there is a configuration using a processor that realizes the functions of the entire system, including multiple hardware resources that perform a specific process, on a single IC chip, as exemplified by SoCs (System-on-a-chip). In this way, a specific process is realized using one or more of the above types of processors as hardware resources.

[0163] Furthermore, the hardware structure of these various processors can more specifically utilize electrical circuits that combine circuit elements such as semiconductor devices. Also, the specific processing described above is merely an example. Therefore, it goes without saying that unnecessary steps can be deleted, new steps added, or the processing order rearranged, as long as it does not deviate from the main purpose.

[0164] Furthermore, although the above-described examples were divided into four embodiments, some or all of these embodiments may be combined. Also, the smart device 14, smart glasses 214, headset terminal 314, and robot 414 are just examples, and they may be combined, or other devices may be used. Also, although the above-described examples were divided into two embodiments, Embodiment 1 and Embodiment 2, these may be combined.

[0165] The descriptions and illustrations presented above are detailed explanations of the technical aspects of this disclosure and are merely examples of the technical aspects. For example, the above descriptions of the structure, function, operation, and effect are examples of the structure, function, operation, and effect of the technical aspects of this disclosure. Therefore, it goes without saying that you may delete unnecessary parts, add new elements, or replace elements in the descriptions and illustrations presented above, as long as you do not deviate from the essence of the technical aspects of this disclosure. Furthermore, in order to avoid confusion and facilitate understanding of the technical aspects of this disclosure, explanations of common technical knowledge and other things that do not require special explanation to enable the implementation of the technical aspects of this disclosure have been omitted from the descriptions and illustrations presented above.

[0166] All documents, patent applications, and technical standards described herein are incorporated by reference to the same extent as if each individual document, patent application, and technical standard were specifically and individually noted to be incorporated by reference.

[0167] (Note 1) The instruction provision unit provides real-time, specific instructions to staff wearing smart glasses with cameras, A multilingual support unit that performs multilingual support based on instructions provided by the instruction provision unit, The system includes an evaluation and feedback unit that performs evaluation and feedback on work based on instructions provided by the multilingual support unit. A system characterized by the following features. (Note 2) The instruction providing unit is, Provide instructions using audio, text, and images. The system described in Appendix 1, characterized by the features described herein. (Note 3) The aforementioned evaluation feedback unit is Smart glasses with cameras analyze images captured by the system to evaluate staff performance. The system described in Appendix 1, characterized by the features described herein. (Note 4) The aforementioned multilingual support unit is Provide instructions in the staff's native language. The system described in Appendix 1, characterized by the features described herein. (Note 5) The aforementioned evaluation feedback unit is We evaluate whether the products are displayed correctly and whether the cash register is operated properly. The system described in Appendix 1, characterized by the features described herein. (Note 6) The aforementioned evaluation feedback unit is Provide feedback on areas for improvement in staff work. The system described in Appendix 1, characterized by the features described herein. (Note 7) The instruction providing unit is, It estimates the user's emotions and adjusts the way instructions are expressed based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 8) The instruction providing unit is, When providing instructions, refer to the staff member's past work history to select the most appropriate instructions. The system described in Appendix 1, characterized by the features described herein. (Note 9) The instruction providing unit is, When providing instructions, adjust the priority of those instructions based on the staff's current workload. The system described in Appendix 1, characterized by the features described herein. (Note 10) The instruction providing unit is, It estimates the user's emotions and adjusts the timing of instructions based on those emotions. The system described in Appendix 1, characterized by the features described herein. (Note 11) The instruction providing unit is, When providing instructions, consider the staff's geographical location to provide more relevant instructions. The system described in Appendix 1, characterized by the features described herein. (Note 12) The instruction providing unit is, When providing instructions, analyze staff social media activity and provide relevant instructions. The system described in Appendix 1, characterized by the features described herein. (Note 13) The aforementioned multilingual support unit is It estimates the user's emotions and adjusts how instructions are translated based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 14) The aforementioned multilingual support unit is When providing multilingual support, the level of detail in translation is adjusted based on the staff's proficiency in their native language. The system described in Appendix 1, characterized by the features described herein. (Note 15) The aforementioned multilingual support unit is When providing multilingual support, we select the most appropriate translation method by considering the cultural background of the staff. The system described in Appendix 1, characterized by the features described herein. (Note 16) The aforementioned multilingual support unit is It estimates the user's emotions and determines translation priorities based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 17) The aforementioned evaluation feedback unit is It estimates the user's emotions and adjusts how feedback is expressed based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 18) The aforementioned evaluation feedback unit is When providing performance feedback, refer to the staff member's past performance history to select the most appropriate feedback content. The system described in Appendix 1, characterized by the features described herein. (Note 19) The aforementioned evaluation feedback unit is When providing performance feedback, we adjust the priority of feedback based on the staff member's current work situation. The system described in Appendix 1, characterized by the features described herein. (Note 20) The aforementioned evaluation feedback unit is It estimates the user's emotions and adjusts the timing of feedback based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 21) The aforementioned evaluation feedback unit is When providing evaluation feedback, consider the staff's geographical location to provide more relevant feedback. The system described in Appendix 1, characterized by the features described herein. (Note 22) The aforementioned evaluation feedback unit is During performance feedback, we analyze staff social media activity and provide relevant feedback. The system described in Appendix 1, characterized by the features described herein. [Explanation of Symbols]

[0168] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Devices 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robots

Claims

1. The instruction provision unit provides real-time, specific instructions to staff wearing smart glasses with cameras, A multilingual support unit that performs multilingual support based on instructions provided by the instruction provision unit, The system includes an evaluation and feedback unit that evaluates and provides feedback on work based on instructions provided by the multilingual support unit. A system characterized by the following features.

2. The instruction providing unit is, Provide instructions using audio, text, and images. The system according to feature 1.

3. The aforementioned evaluation feedback unit is Smart glasses with cameras analyze images captured by the system to evaluate staff performance. The system according to feature 1.

4. The aforementioned multilingual support unit is Provide instructions in the staff's native language. The system according to feature 1.

5. The aforementioned evaluation feedback unit is We evaluate whether the products are displayed correctly and whether the cash register is operated properly. The system according to feature 1.

6. The aforementioned evaluation feedback unit is Provide feedback on areas for improvement in staff work. The system according to feature 1.

7. The instruction providing unit is, It estimates the user's emotions and adjusts the way instructions are expressed based on those estimated emotions. The system according to feature 1.

8. The instruction providing unit is, When providing instructions, refer to the staff member's past work history to select the most appropriate instructions. The system according to feature 1.

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