System

Abstract

To provide a system for realizing smooth handover at the time of an employee shift change.SOLUTION: The system according to an embodiment includes a recording unit, a summarizing unit, and a providing unit. The recording unit records multiple events while an employee is working. The summarizing unit summarizes the multiple events recorded by the recording unit. The providing unit provides the information summarized by the summarizing unit to a next employee at the time of a shift change.SELECTED DRAWING: Figure 1

Description

[Technical field]

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

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

[0003] [Patent Document 1] JP 2022-180282 A Summary of the Invention [Problem to be solved by the invention]

[0004] With conventional technology, handovers when employees change jobs are not smooth, which can lead to information leaks and misunderstandings.

[0005] The system according to the embodiment aims to ensure smooth handover when employees are replaced. [Means for solving the problem]

[0006] The system according to the embodiment includes a recording unit, a summarizing unit, and a providing unit. The recording unit records a plurality of events during the work of an employee. The summarizing unit summarizes the plurality of events recorded by the recording unit. When an employee is replaced, the providing unit provides the information summarized by the summarizing unit to the next employee. Effect of the Invention

[0007] The system according to the embodiment can smoothly carry out handover when employees are replaced. [Brief description of the drawings]

[0008] [Figure 1] 1 is a conceptual diagram showing an example of the configuration of a data processing system according to a first embodiment. [Diagram 2] 1 is a conceptual diagram showing an example of main functions of a data processing device and a smart device according to a first embodiment. FIG. [Diagram 3] FIG. 11 is a conceptual diagram showing an example of the configuration of a data processing system according to a second embodiment. [Figure 4] FIG. 11 is a conceptual diagram showing an example of main functions of a data processing device and smart glasses according to a second embodiment. [Diagram 5] FIG. 13 is a conceptual diagram showing an example of the configuration of a data processing system according to a third embodiment. [Figure 6] FIG. 11 is a conceptual diagram showing an example of main functions of a data processing device and a headset-type terminal according to a third embodiment. [Figure 7] FIG. 13 is a conceptual diagram showing an example of the configuration of a data processing system according to a fourth embodiment. [Figure 8] FIG. 13 is a conceptual diagram showing an example of main functions of a data processing device and a robot according to a fourth embodiment. [Figure 9] 1 shows an emotion map onto which multiple emotions are mapped. [Figure 10] 1 shows an emotion map onto which multiple emotions are mapped. DETAILED DESCRIPTION OF THE PREFERRED EMBODIMENTS

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

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

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

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

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

[0014] In the following embodiments, a communication I / F (Interface) with a code is an interface including a communication processor and an antenna. The communication I / F controls communication between multiple computers. An example of a communication standard applied to the communication I / F is a wireless communication standard including 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." In other words, "A and / or B" means that it may be only A, only B, or a combination of A and B. In addition, in this specification, the same idea as "A and / or B" is also applied when three or more things are expressed by connecting them with "and / or."

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

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

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

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

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

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

[0022] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for transmitting and receiving various types of information between the processor 46 and the processor 28 via the network 54.

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

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

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

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

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

[0028] (Example 1) The AI ​​agent system according to the embodiment of the present invention is a system for smoothly transferring work from the first shift to the second shift in a factory. In this system, the AI ​​agent uses a camera and voice to record multiple events occurring during the work of the first shift employees, including the progress of the work, important events, and malfunctions. The recorded data is summarized by the AI ​​agent and handed over to the next second shift employee. The summary selects important events and malfunctions based on specific keywords and event triggers, and evaluates the importance of each event to determine the priority. Furthermore, the content of the summary is optimized based on the behavior patterns of the first and second shift employees. This allows for a smooth transfer and improves work efficiency. For example, the AI ​​agent uses a camera to record the work of the first shift employees in real time and simultaneously collects voice data. This allows for a detailed record of the progress of the work, important events, and malfunctions. Next, the AI ​​agent analyzes the recorded data and selects important events and malfunctions based on specific keywords and event triggers. For example, data containing keywords such as "error" and "problem" is preferentially extracted. Furthermore, the AI ​​agent evaluates the importance of each event and prioritizes summaries based on the importance. For example, the importance is evaluated based on the magnitude of impact and frequency of occurrence. Finally, the AI ​​agent optimizes the content of the summary based on the behavior patterns of the first and second shift employees. For example, the summary is created by taking into account the tasks that the first shift employees frequently perform and the matters that the second shift employees should pay special attention to. This allows for a smooth handover and improves work efficiency. As a result, the AI ​​agent system can smoothly carry out the handover when switching from the first shift to the second shift in a factory.

[0029] The AI ​​agent system according to the embodiment includes a recording unit, a summarizing unit, and a providing unit. The recording unit records a plurality of events occurring during the work of the first shift employee. The plurality of events occurring during the work of the first shift employee include, for example, the progress of the work, the occurrence of a problem, the contents of communication, and the like, but are not limited to such examples. The recording unit, for example, uses a camera to record the progress of the work in real time. The recording unit can also collect voice data and record conversations and instructions during the work. Furthermore, the recording unit can also collect data of the work environment using a sensor. For example, the recording unit uses a camera to record the progress of the work in high resolution and simultaneously collect voice data. The voice data is used to record conversations and instructions during the work in detail. The sensor is used to collect data such as temperature, humidity, and vibration of the work environment, and to grasp the progress of the work in detail. The summarizing unit summarizes the plurality of events recorded by the recording unit. The summarizing is performed, for example, based on the length of the sentence and the importance of the information to be summarized, but are not limited to such examples. For example, the summarizing unit uses a generation AI to analyze the recorded data and extract important events and defects. The generation AI uses a text generation AI (e.g., LLM) to concisely summarize the recorded data. The summarization unit can also use a multimodal generation AI to analyze image and audio data and generate summaries. For example, the summarization unit uses the generation AI to extract important events and defects from the recorded data based on specific keywords and event triggers. The generation AI uses keyword extraction technology to pick up particularly important information from the recorded data and summarizes it based on that. The provision unit provides the information summarized by the summarization unit to the second shift employee when the first shift employee is replaced by the second shift employee. The provision unit, for example, displays the summarized information in text format. The provision unit can also provide information in audio format. Furthermore, the provision unit can provide information in a visual format. For example, the provision unit displays the summarized information in text format so that employees can easily understand it. Providing information in audio format is used to allow employees to check information without using their hands while working.Providing information in a visual format visually displays information using graphs and charts, allowing employees to intuitively understand the information. As a result, the AI ​​agent system according to the embodiment can smoothly perform handover by recording, summarizing, and providing multiple events occurring during the work of first-shift employees. Some or all of the above-mentioned processing in the providing unit may be performed, for example, using AI, or may be performed without using AI. For example, the providing unit can provide information using an AI model that receives the summary generated by the summarizing unit as input and determines the method of providing information.

[0030] The recording unit records a number of events occurring during the work of the first shift employee. The number of events occurring during the work of the first shift employee includes, but is not limited to, the progress of the work, the occurrence of a problem, and the contents of communication. For example, the recording unit uses a camera to record the progress of the work in real time. Specifically, the camera can capture high-resolution images and record the details of the work in detail. This makes it possible to accurately grasp the progress of the work and the location of the problem. The recording unit can also collect voice data and record conversations and instructions during the work. The voice data is collected at high quality using a microphone and records the contents of communication during the work in detail. This makes it possible to accurately grasp the transmission status of instructions and the contents of problem reports. Furthermore, the recording unit can also collect data on the work environment using a sensor. For example, a temperature sensor, a humidity sensor, a vibration sensor, etc. are used to monitor changes in the work environment in real time. This makes it possible to grasp in detail the impact of changes in the work environment on the work. The recording unit can centrally manage these data and link with other systems and departments as necessary. For example, the collected data is stored in a cloud server and made accessible to the analysis and summarization units. In addition, the frequency and accuracy of data collection can be adjusted to accommodate specific situations and conditions. This allows the recording unit to collect data efficiently and effectively, improving the performance of the entire system.

[0031] The summarizing unit summarizes the multiple events recorded by the recording unit. The summarization is performed, for example, based on the length of the sentences and the importance of the information to be summarized, but is not limited to such examples. Specifically, the summarizing unit uses a generation AI to analyze the recorded data and extract important events and defects. The generation AI uses a text generation AI (for example, LLM) to concisely summarize the recorded data. For example, the generation AI uses natural language processing technology to analyze the recorded text data and extract important keywords and phrases. This makes it possible to summarize long text data in a short time. The summarizing unit can also use a multimodal generation AI to analyze image and audio data and generate a summary. For example, image recognition technology is used to extract important scenes from the recorded video data, and a summary is generated based on the extracted scenes. Voice data can also be converted into text using voice recognition technology, and important conversations and instruction contents can be extracted. Furthermore, the summarizing unit uses a generation AI to extract important events and defects from the recorded data based on specific keywords and event triggers. The generation AI uses keyword extraction technology to pick out particularly important information from the recorded data and use it to create a summary, which allows the summarization section to efficiently analyze the recorded data and quickly extract important information.

[0032] The providing unit provides the information summarized by the summarizing unit to the second shift employee when the first shift employee is replaced by the second shift employee. The providing unit displays the summarized information in text format, for example. Specifically, the providing unit displays the summarized information on a display so that the employee can easily understand it. The providing unit can also provide the information in audio format. Providing information in audio format is used to enable the employee to check the information without using his / her hands while working. For example, the summarized information can be read out aloud using a speaker. Furthermore, the providing unit can also provide the information in visual format. Providing information in visual format visually displays the information using graphs and charts so that the employee can intuitively understand the information. For example, the progress of the work and the location of the problem can be displayed in a graph to make it easier to grasp visually. This allows the providing unit to quickly provide appropriate action instructions to each user and minimize the risk of disaster. Furthermore, the providing unit can collect user feedback and continuously improve the accuracy and effectiveness of the instructions. For example, the providing unit reviews the evacuation route and improves the instructions based on feedback from users who have received evacuation instructions. In addition, the information providing unit can reliably transmit information using multiple communication methods. For example, important information can be reliably delivered by using not only smartphone notifications but also voice calls, SMS, emails, etc. This allows the information providing unit to quickly and reliably provide instructions to users on what to do and minimize the risk of damage.

[0033] The recording unit can record a plurality of events by images and sounds. The images and sounds include, for example, photos taken during work and recordings of conversations, but are not limited to such examples. For example, the recording unit takes photos taken during work using a camera and saves them as image data. The recording unit can also record conversations during work using a microphone and save them as audio data. For example, the recording unit takes photos taken during work at high resolution using a camera and saves them as image data. The image data is used to visually record the progress of work and important events. The audio data is used to record conversations and instructions during work in detail. This allows more detailed information to be obtained by recording events using images and sounds. Some or all of the above-mentioned processing in the recording unit may be performed using, for example, AI, or may be performed without using AI. For example, the recording unit can input image data taken by a camera to the generation AI and have the generation AI perform analysis of the image data.

[0034] The recording unit can select an important event or malfunction based on a specific keyword or a specific event trigger. The specific keyword includes, but is not limited to, for example, an error, a problem, or completion. The specific event trigger includes, but is not limited to, for example, a time, a location, or a specific action. The recording unit preferentially extracts data including keywords such as an error or a problem. The recording unit can also select an event that occurred at a specific time or location. For example, the recording unit preferentially extracts data including keywords such as an error or a problem, and selects an important event or malfunction. An event that occurred at a specific time or location is selected based on a specific event trigger. This allows important information to be efficiently recorded by selecting an important event or malfunction based on a specific keyword or event trigger. A part or all of the above-mentioned processing in the recording unit may be performed using, for example, AI, or may be performed without using AI. For example, the recording unit can select an important event or malfunction using an AI model that inputs a specific keyword or event trigger.

[0035] The summarizing unit can evaluate the importance of each of the multiple events recorded in the recording unit and determine the priority of the summarization based on the importance. The importance includes, for example, the magnitude of the impact and the frequency of occurrence, but is not limited to such examples. The summarizing unit can evaluate the importance of each event based on, for example, the magnitude of the impact and the frequency of occurrence. The summarizing unit can also determine the priority of the summarization based on the importance. For example, the summarizing unit evaluates the importance of each event based on the magnitude of the impact and the frequency of occurrence, and determines the priority of the summarization based on the importance. In this way, important information can be preferentially summarized by evaluating the importance of the events and determining the priority. A part or all of the above-mentioned processing in the summarizing unit may be performed using, for example, AI, or may be performed without using AI. For example, the summarizing unit can perform the evaluation using an AI model that inputs the magnitude of the impact and the frequency of occurrence and evaluates the importance.

[0036] The summarizing unit can adjust the content of the summary based on the behavioral patterns of the employees. The behavioral patterns include, for example, the order, frequency, and time period of the work, but are not limited to such examples. The summarizing unit creates the summary, for example, taking into consideration the work frequently performed by the first shift employees and the matters that the second shift employees should pay particular attention to. The summarizing unit can also optimize the content of the summary based on the behavioral patterns of the employees. For example, the summarizing unit creates a summary and optimizes the content of the summary, taking into consideration the work frequently performed by the first shift employees and the matters that the second shift employees should pay particular attention to. In this way, a more appropriate summary can be provided by optimizing the content of the summary based on the behavioral patterns of the employees. A part or all of the above-mentioned processing in the summarizing unit may be performed, for example, using AI, or may be performed without using AI. For example, the summarizing unit can make adjustments using an AI model that uses the behavioral patterns of the employees as input and adjusts the content of the summary.

[0037] The recording unit can analyze the employee's past work history and select an appropriate recording method. For example, the recording unit preferentially records work that the employee frequently performed in the past. The recording unit can also automatically record specific work procedures from the employee's past work history. The recording unit can also analyze the employee's past work history and preferentially record important work contents. For example, the recording unit preferentially records work that the employee frequently performed in the past and records important work contents in detail. The specific work procedures are automatically recorded based on the employee's past work history. This makes it possible to select an optimal recording method by analyzing the employee's past work history. A part or all of the above-mentioned processing in the recording unit may be performed, for example, using AI or may be performed without using AI. For example, the recording unit can make the selection using an AI model that inputs the employee's past work history and selects the optimal recording method.

[0038] The recording unit can perform filtering based on the employee's current work situation and field of interest. The recording unit, for example, records only relevant information based on the work content currently being performed by the employee. The recording unit can also preferentially record important information based on the employee's field of interest. Furthermore, the recording unit can analyze the employee's current work situation and filter and record unnecessary information. For example, the recording unit records only relevant information and preferentially records important information based on the work content currently being performed by the employee. The employee's field of interest is identified based on past remarks and selected options. This makes it possible to record only relevant information by filtering based on the employee's current work situation and field of interest. Part or all of the above-mentioned processing in the recording unit may be performed, for example, using AI or may be performed without using AI. For example, the recording unit can perform filtering using an AI model that uses the employee's current work situation and field of interest as input.

[0039] The recording unit can preferentially record events with high relevance based on the geographical location information of the employee. For example, when the employee is working in a specific area, the recording unit preferentially records events related to the area. The recording unit can also select and record important events based on the geographical location information of the employee. Furthermore, when the employee is moving, the recording unit can preferentially record events related to the area to which the employee is moving. For example, when the employee is working in a specific area, the recording unit preferentially records events related to the area and records important events in detail. The geographical location information of the employee is acquired using GPS data, beacons, etc. As a result, by taking the geographical location information of the employee into consideration, events with high relevance can be preferentially recorded. A part or all of the above-mentioned processing in the recording unit may be performed, for example, using AI, or may be performed without using AI. For example, the recording unit can select events with high relevance using an AI model that inputs the geographical location information of the employee.

[0040] The recording unit can analyze the employee's social media activity and record related events. The recording unit, for example, records related events based on information shared by the employee on social media. The recording unit can also analyze the employee's social media activity and select and record important events. Furthermore, the recording unit can preferentially record events mentioned by the employee on social media. For example, the recording unit records related events based on information shared by the employee on social media and records important events in detail. The employee's social media activity is analyzed based on the content of posts, the number of likes, comments, etc. In this way, related events can be recorded by analyzing the employee's social media activity. Part or all of the above-mentioned processing in the recording unit may be performed, for example, using AI or may be performed without using AI. For example, the recording unit can make the selection using an AI model that inputs the employee's social media activity and selects related events.

[0041] The summarization unit can adjust the level of detail of the summary based on the importance of the event. For example, the summarization unit provides a detailed summary for an event with high importance. The summarization unit can also provide a concise summary for an event with low importance. Furthermore, the summarization unit can adjust the length of the summary according to the importance. For example, the summarization unit evaluates the importance of each event based on the magnitude of the impact or the frequency of occurrence, and adjusts the level of detail of the summary based on the importance. In this way, important events can be summarized in detail by adjusting the level of detail of the summary based on the importance of the event. Part or all of the above-mentioned processing in the summarization unit may be performed, for example, using AI, or may be performed without using AI. For example, the summarization unit can make adjustments using an AI model that uses the magnitude of the impact or the frequency of occurrence as input and adjusts the level of detail of the summary.

[0042] The summarization unit can apply different summarization algorithms depending on the category of the event. For example, for events related to work content, the summarization unit provides a summary including detailed procedures. In addition, for events related to important events, the summarization unit can also provide a summary including the scope of impact. Furthermore, for events related to malfunctions, the summarization unit can also provide a summary including causes and measures. For example, for events related to work content, the summarization unit provides a summary including detailed procedures, and for events related to important events, the summarization unit provides a summary including the scope of impact. For events related to malfunctions, the summarization unit provides a summary including causes and measures. In this way, by applying different summarization algorithms depending on the category of the event, more appropriate summaries can be provided. Part or all of the above-mentioned processing in the summarization unit may be performed, for example, using AI, or may be performed without using AI. For example, the summarization unit can perform summarization using an AI model that inputs the category of the event and applies different summarization algorithms.

[0043] The summarization unit can determine the priority of summarization based on the time of occurrence of an event. For example, the summarization unit prioritizes summarization of recently occurring events. The summarization unit can also provide a concise summary of events that occurred in the past. Furthermore, the summarization unit can adjust the level of detail of the summarization according to the time of occurrence. For example, the summarization unit prioritizes summarization of recently occurring events and provides a concise summary of events that occurred in the past. By adjusting the level of detail of the summarization according to the time of occurrence, the latest information can be prioritized. A part or all of the above-mentioned processing in the summarization unit may be performed, for example, using AI or may be performed without using AI. For example, the summarization unit can determine the priority using an AI model that inputs the time of occurrence of an event and determines the priority of summarization.

[0044] The summarization unit can adjust the order of summaries based on the relevance of events. For example, the summarization unit prioritizes summarization of highly relevant events. The summarization unit can also provide a concise summary for events with low relevance. Furthermore, the summarization unit can adjust the order of summaries according to the relevance. For example, the summarization unit prioritizes summarization of highly relevant events and provides a concise summary for events with low relevance. By adjusting the order of summaries according to the relevance, events with high relevance can be summarized preferentially. A part or all of the above-mentioned processing in the summarization unit may be performed, for example, using AI or may be performed without using AI. For example, the summarization unit can adjust the order using an AI model that uses the relevance of events as an input and adjusts the order of summaries.

[0045] The providing unit can select the optimal information providing method by referring to the employee's past work history. The providing unit provides related information based on, for example, work that the employee frequently performed in the past. The providing unit can also select the optimal information providing method by referring to the employee's past work history. Furthermore, the providing unit can analyze the employee's past work history and provide important information preferentially. For example, the providing unit provides related information based on work that the employee frequently performed in the past, and provides important information in detail. The employee's past work history is analyzed based on the type of work, time, result, etc. As a result, the optimal information providing method can be selected by referring to the employee's past work history. Part or all of the above-mentioned processing in the providing unit may be performed, for example, using AI, or may be performed without using AI. For example, the providing unit can make the selection using an AI model that inputs the employee's past work history and selects the optimal information providing method.

[0046] The providing unit can customize the content to be provided based on the employee's current work situation. The providing unit provides relevant information based on the work content currently being performed by the employee, for example. The providing unit can also analyze the employee's current work situation and select the optimal information providing method. Furthermore, the providing unit can provide important information preferentially based on the employee's current work situation. For example, the providing unit provides relevant information and provides important information in detail based on the work content currently being performed by the employee. The employee's current work situation is analyzed based on the progress of the work, the tools being used, and the like. As a result, the content to be provided can be customized based on the employee's current work situation, thereby providing more appropriate information. Part or all of the above-mentioned processing in the providing unit may be performed, for example, using AI, or may be performed without using AI. For example, the providing unit can perform customization using an AI model that uses the employee's current work situation as an input and customizes the content to be provided.

[0047] The providing unit can select the optimal providing method in consideration of the geographical location information of the employee. For example, when the employee is working in a specific area, the providing unit provides information related to the area. The providing unit can also select and provide important information based on the geographical location information of the employee. Furthermore, when the employee is moving, the providing unit can also provide information related to the area to which the employee is moving. For example, when the employee is working in a specific area, the providing unit provides information related to the area and provides important information in detail. The geographical location information of the employee is acquired using GPS data, beacons, etc. As a result, highly relevant information can be provided by considering the geographical location information of the employee. Part or all of the above-mentioned processing in the providing unit may be performed, for example, using AI, or may be performed without using AI. For example, the providing unit can make the selection using an AI model that inputs the geographical location information of the employee and selects the optimal providing method.

[0048] The provision unit can adjust the content provided by analyzing the employee's social media activity. The provision unit can provide relevant information based on the information shared by the employee on social media, for example. The provision unit can also analyze the employee's social media activity and select and provide important information. Furthermore, the provision unit can preferentially provide information mentioned by the employee on social media. For example, the provision unit provides relevant information based on the information shared by the employee on social media, and provides important information in detail. The employee's social media activity is analyzed based on the posted content, the number of likes, comments, etc. In this way, the relevant information can be provided by analyzing the employee's social media activity. A part or all of the above-mentioned processing in the provision unit can be performed, for example, using AI, or can be performed without using AI. For example, the provision unit can make adjustments using an AI model that uses the employee's social media activity as an input and adjusts the content provided.

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

[0050] The recording unit can analyze the audio data of the employee's work environment and issue an alert if the noise level exceeds a certain standard. For example, if the noise level in the work environment is high, the recording unit issues an alert to encourage the employee to use earplugs. The recording unit can also analyze the audio data of the work environment and issue an alert if it detects an abnormal sound (e.g., an abnormal machine sound or an alarm sound). Furthermore, the recording unit can analyze the audio data of the work environment, evaluate the quality of communication between employees, and make improvement suggestions as necessary. This makes it possible to utilize the audio data of the work environment to improve employee safety and the quality of communication.

[0051] The recording department can monitor the work efficiency of employees in real time, and make suggestions for improvement if the efficiency declines. For example, if the work efficiency falls below a certain standard, the recording department can suggest reviewing the work procedures or changing the tools. The recording department can also monitor the work efficiency of employees, and if the efficiency is high, share the method with other employees. Furthermore, the recording department can monitor the work efficiency of employees, identify the cause of the decline in efficiency, and propose specific measures to address the cause. This makes it possible to monitor the work efficiency of employees in real time and improve efficiency.

[0052] The summarization unit can adjust the content of the summary based on the employee's skill level. For example, a summary including detailed technical information is provided to an employee with a high skill level, and a summary focusing on basic information is provided to an employee with a low skill level. The summarization unit can also adjust the difficulty of the summary based on the employee's skill level. Furthermore, the summarization unit can select the format of the summary (e.g., text, diagram, video, etc.) based on the employee's skill level. This makes it possible to provide an optimal summary according to the employee's skill level.

[0053] The recording unit can monitor employees' posture while working and suggest improvements if the posture is poor. For example, if posture becomes poor after sitting at work for a long time, the recording unit will issue an alert to encourage stretching and a break. The recording unit can also monitor posture while working and suggest maintaining posture if it has improved. Furthermore, the recording unit can analyze employees' posture data and suggest a long-term posture improvement plan. This can help maintain employees' health and improve work efficiency.

[0054] The recording unit can monitor the movements of employees while they are working and recommend efficient movements. For example, it can detect unnecessary movements while working and suggest efficient movement methods. The recording unit can also analyze the movement data of employees and suggest optimal work procedures. Furthermore, the recording unit can make suggestions for improving the work environment based on the movement data of employees. This makes it possible to optimize the movements of employees and improve work efficiency.

[0055] The recording unit can monitor the energy consumption of employees while they are working and suggest energy-efficient work methods. For example, if energy consumption is high, the recording unit can suggest energy-efficient work procedures. The recording unit can also monitor the energy consumption of employees, and if energy consumption is low, share the method with other employees. Furthermore, the recording unit can monitor the energy consumption of employees, identify the cause of high energy consumption, and suggest specific measures to address the cause. This makes it possible to monitor the energy consumption of employees in real time and improve energy efficiency.

[0056] The process flow of the first embodiment will be briefly described below.

[0057] Step 1: The recording unit records multiple events occurring during the work of the first shift employees. These include the progress of work, occurrence of problems, and content of communication. The recording unit uses cameras to record the progress of work in real time, and collects audio data to record conversations and instructions during work. It can also use sensors to collect data on the work environment (temperature, humidity, vibration, etc.). Step 2: The summarization unit summarizes the multiple events recorded by the recording unit. The summarization is based on the length of the text and the importance of the information. The summarization unit uses a generation AI to analyze the recorded data and extract important events and defects. The generation AI uses a text generation AI (e.g., LLM) and a multimodal generation AI to concisely summarize the text, image, and audio data. Step 3: The providing unit provides the information summarized by the summarizing unit to the second shift employee at the time of handover from the first shift employee to the second shift employee. The providing unit can provide the summarized information in a text format, an audio format, or a visual format. For example, the providing unit displays the information in a text format, provides the information in an audio format, and visually displays the information using graphs and charts in a visual format.

[0058] (Example 2) The AI ​​agent system according to the embodiment of the present invention is a system for smoothly transferring work from the first shift to the second shift in a factory. In this system, the AI ​​agent uses a camera and voice to record multiple events occurring during the work of the first shift employees, including the progress of the work, important events, and malfunctions. The recorded data is summarized by the AI ​​agent and handed over to the next second shift employee. The summary selects important events and malfunctions based on specific keywords and event triggers, and evaluates the importance of each event to determine the priority. Furthermore, the content of the summary is optimized based on the behavior patterns of the first and second shift employees. This allows for a smooth transfer and improves work efficiency. For example, the AI ​​agent uses a camera to record the work of the first shift employees in real time and simultaneously collects voice data. This allows for a detailed record of the progress of the work, important events, and malfunctions. Next, the AI ​​agent analyzes the recorded data and selects important events and malfunctions based on specific keywords and event triggers. For example, data containing keywords such as "error" and "problem" is preferentially extracted. Furthermore, the AI ​​agent evaluates the importance of each event and prioritizes summaries based on the importance. For example, the importance is evaluated based on the magnitude of impact and frequency of occurrence. Finally, the AI ​​agent optimizes the content of the summary based on the behavior patterns of the first and second shift employees. For example, the summary is created by taking into account the tasks that the first shift employees frequently perform and the matters that the second shift employees should pay special attention to. This allows for a smooth handover and improves work efficiency. As a result, the AI ​​agent system can smoothly carry out the handover when switching from the first shift to the second shift in a factory.

[0059] The AI ​​agent system according to the embodiment includes a recording unit, a summarizing unit, and a providing unit. The recording unit records a plurality of events occurring during the work of the first shift employee. The plurality of events occurring during the work of the first shift employee include, for example, the progress of the work, the occurrence of a problem, the contents of communication, and the like, but are not limited to such examples. The recording unit, for example, uses a camera to record the progress of the work in real time. The recording unit can also collect voice data and record conversations and instructions during the work. Furthermore, the recording unit can also collect data of the work environment using a sensor. For example, the recording unit uses a camera to record the progress of the work in high resolution and simultaneously collect voice data. The voice data is used to record conversations and instructions during the work in detail. The sensor is used to collect data such as temperature, humidity, and vibration of the work environment, and to grasp the progress of the work in detail. The summarizing unit summarizes the plurality of events recorded by the recording unit. The summarizing is performed, for example, based on the length of the sentence and the importance of the information to be summarized, but are not limited to such examples. For example, the summarizing unit uses a generation AI to analyze the recorded data and extract important events and defects. The generation AI uses a text generation AI (e.g., LLM) to concisely summarize the recorded data. The summarization unit can also use a multimodal generation AI to analyze image and audio data and generate summaries. For example, the summarization unit uses the generation AI to extract important events and defects from the recorded data based on specific keywords and event triggers. The generation AI uses keyword extraction technology to pick up particularly important information from the recorded data and summarizes it based on that. The provision unit provides the information summarized by the summarization unit to the second shift employee when the first shift employee is replaced by the second shift employee. The provision unit, for example, displays the summarized information in text format. The provision unit can also provide information in audio format. Furthermore, the provision unit can provide information in a visual format. For example, the provision unit displays the summarized information in text format so that employees can easily understand it. Providing information in audio format is used to allow employees to check information without using their hands while working.Providing information in a visual format visually displays information using graphs and charts, allowing employees to intuitively understand the information. As a result, the AI ​​agent system according to the embodiment can smoothly perform handover by recording, summarizing, and providing multiple events occurring during the work of first-shift employees. Some or all of the above-mentioned processing in the providing unit may be performed, for example, using AI, or may be performed without using AI. For example, the providing unit can provide information using an AI model that receives the summary generated by the summarizing unit as input and determines the method of providing information.

[0060] The recording unit records a number of events occurring during the work of the first shift employee. The number of events occurring during the work of the first shift employee includes, but is not limited to, the progress of the work, the occurrence of a problem, and the contents of communication. For example, the recording unit uses a camera to record the progress of the work in real time. Specifically, the camera can capture high-resolution images and record the details of the work in detail. This makes it possible to accurately grasp the progress of the work and the location of the problem. The recording unit can also collect voice data and record conversations and instructions during the work. The voice data is collected at high quality using a microphone and records the contents of communication during the work in detail. This makes it possible to accurately grasp the transmission status of instructions and the contents of problem reports. Furthermore, the recording unit can also collect data on the work environment using a sensor. For example, a temperature sensor, a humidity sensor, a vibration sensor, etc. are used to monitor changes in the work environment in real time. This makes it possible to grasp in detail the impact of changes in the work environment on the work. The recording unit can centrally manage these data and link with other systems and departments as necessary. For example, the collected data is stored in a cloud server and made accessible to the analysis and summarization units. In addition, the frequency and accuracy of data collection can be adjusted to accommodate specific situations and conditions. This allows the recording unit to collect data efficiently and effectively, improving the performance of the entire system.

[0061] The summarizing unit summarizes the multiple events recorded by the recording unit. The summarization is performed, for example, based on the length of the sentences and the importance of the information to be summarized, but is not limited to such examples. Specifically, the summarizing unit uses a generation AI to analyze the recorded data and extract important events and defects. The generation AI uses a text generation AI (for example, LLM) to concisely summarize the recorded data. For example, the generation AI uses natural language processing technology to analyze the recorded text data and extract important keywords and phrases. This makes it possible to summarize long text data in a short time. The summarizing unit can also use a multimodal generation AI to analyze image and audio data and generate a summary. For example, image recognition technology is used to extract important scenes from the recorded video data, and a summary is generated based on the extracted scenes. Voice data can also be converted into text using voice recognition technology, and important conversations and instruction contents can be extracted. Furthermore, the summarizing unit uses a generation AI to extract important events and defects from the recorded data based on specific keywords and event triggers. The generation AI uses keyword extraction technology to pick out particularly important information from the recorded data and use it to create a summary, which allows the summarization section to efficiently analyze the recorded data and quickly extract important information.

[0062] The providing unit provides the information summarized by the summarizing unit to the second shift employee when the first shift employee is replaced by the second shift employee. The providing unit displays the summarized information in text format, for example. Specifically, the providing unit displays the summarized information on a display so that the employee can easily understand it. The providing unit can also provide the information in audio format. Providing information in audio format is used to enable the employee to check the information without using his / her hands while working. For example, the summarized information can be read out aloud using a speaker. Furthermore, the providing unit can also provide the information in visual format. Providing information in visual format visually displays the information using graphs and charts so that the employee can intuitively understand the information. For example, the progress of the work and the location of the problem can be displayed in a graph to make it easier to grasp visually. This allows the providing unit to quickly provide appropriate action instructions to each user and minimize the risk of disaster. Furthermore, the providing unit can collect user feedback and continuously improve the accuracy and effectiveness of the instructions. For example, the providing unit reviews the evacuation route and improves the instructions based on feedback from users who have received evacuation instructions. In addition, the information providing unit can reliably transmit information using multiple communication methods. For example, important information can be reliably delivered by using not only smartphone notifications but also voice calls, SMS, emails, etc. This allows the information providing unit to quickly and reliably provide instructions to users on what to do and minimize the risk of damage.

[0063] The recording unit can record a plurality of events by images and sounds. The images and sounds include, for example, photos taken during work and recordings of conversations, but are not limited to such examples. For example, the recording unit takes photos taken during work using a camera and saves them as image data. The recording unit can also record conversations during work using a microphone and save them as audio data. For example, the recording unit takes photos taken during work at high resolution using a camera and saves them as image data. The image data is used to visually record the progress of work and important events. The audio data is used to record conversations and instructions during work in detail. This allows more detailed information to be obtained by recording events using images and sounds. Some or all of the above-mentioned processing in the recording unit may be performed using, for example, AI, or may be performed without using AI. For example, the recording unit can input image data taken by a camera to the generation AI and have the generation AI perform analysis of the image data.

[0064] The recording unit can select an important event or malfunction based on a specific keyword or a specific event trigger. The specific keyword includes, but is not limited to, for example, an error, a problem, or completion. The specific event trigger includes, but is not limited to, for example, a time, a location, or a specific action. The recording unit preferentially extracts data including keywords such as an error or a problem. The recording unit can also select an event that occurred at a specific time or location. For example, the recording unit preferentially extracts data including keywords such as an error or a problem, and selects an important event or malfunction. An event that occurred at a specific time or location is selected based on a specific event trigger. This allows important information to be efficiently recorded by selecting an important event or malfunction based on a specific keyword or event trigger. A part or all of the above-mentioned processing in the recording unit may be performed using, for example, AI, or may be performed without using AI. For example, the recording unit can select an important event or malfunction using an AI model that inputs a specific keyword or event trigger.

[0065] The summarizing unit can evaluate the importance of each of the multiple events recorded in the recording unit and determine the priority of the summarization based on the importance. The importance includes, for example, the magnitude of the impact and the frequency of occurrence, but is not limited to such examples. The summarizing unit can evaluate the importance of each event based on, for example, the magnitude of the impact and the frequency of occurrence. The summarizing unit can also determine the priority of the summarization based on the importance. For example, the summarizing unit evaluates the importance of each event based on the magnitude of the impact and the frequency of occurrence, and determines the priority of the summarization based on the importance. In this way, important information can be preferentially summarized by evaluating the importance of the events and determining the priority. A part or all of the above-mentioned processing in the summarizing unit may be performed using, for example, AI, or may be performed without using AI. For example, the summarizing unit can perform the evaluation using an AI model that inputs the magnitude of the impact and the frequency of occurrence and evaluates the importance.

[0066] The summarizing unit can adjust the content of the summary based on the behavioral patterns of the employees. The behavioral patterns include, for example, the order, frequency, and time period of the work, but are not limited to such examples. The summarizing unit creates the summary, for example, taking into consideration the work frequently performed by the first shift employees and the matters that the second shift employees should pay particular attention to. The summarizing unit can also optimize the content of the summary based on the behavioral patterns of the employees. For example, the summarizing unit creates a summary and optimizes the content of the summary, taking into consideration the work frequently performed by the first shift employees and the matters that the second shift employees should pay particular attention to. In this way, a more appropriate summary can be provided by optimizing the content of the summary based on the behavioral patterns of the employees. A part or all of the above-mentioned processing in the summarizing unit may be performed, for example, using AI, or may be performed without using AI. For example, the summarizing unit can make adjustments using an AI model that uses the behavioral patterns of the employees as input and adjusts the content of the summary.

[0067] The recording unit can estimate the employee's emotions and adjust the timing of recording based on the estimated employee's emotions. For example, when the employee is feeling stressed, the AI ​​detects the emotions and pauses recording until the stress is reduced. In addition, when the employee is relaxed, the AI ​​can detect the emotions and record the work content in the relaxed state in detail. In addition, when the employee is impatient, the AI ​​can detect the emotions and refrain from recording until the impatience subsides. For example, the recording unit captures the employee's facial expression with a camera and estimates the emotion using an emotion estimation algorithm. The emotion estimation algorithm calculates an emotion score based on the change in facial expression and adjusts the timing of recording. In addition, the recording unit can record the employee's voice and estimate the emotion using voice analysis technology. For example, the tone and speed of the voice are analyzed, an emotion score is calculated, and the timing of recording is adjusted. In addition, the recording unit can collect the employee's biometric data (heart rate and electrodermal activity) with a sensor and estimate the emotion using an emotion estimation algorithm. For example, an emotion score is calculated based on the fluctuation of the heart rate, and the timing of recording is adjusted. This allows recording to be performed at a more appropriate timing by adjusting the timing of recording based on the employee's emotion. The emotion estimation is realized using an emotion estimation function, for example, using an emotion engine or a generation AI. The generation AI is a text generation AI (for example, LLM) or a multimodal generation AI, but is not limited to such examples. A part or all of the above-mentioned processing in the recording unit may be performed, for example, using an AI, or may be performed without using an AI. For example, the recording unit can input image data of the employee taken by a camera to the generation AI, and cause the generation AI to estimate the employee's emotion.

[0068] The recording unit can analyze the employee's past work history and select an appropriate recording method. For example, the recording unit preferentially records work that the employee frequently performed in the past. The recording unit can also automatically record specific work procedures from the employee's past work history. The recording unit can also analyze the employee's past work history and preferentially record important work contents. For example, the recording unit preferentially records work that the employee frequently performed in the past and records important work contents in detail. The specific work procedures are automatically recorded based on the employee's past work history. This makes it possible to select an optimal recording method by analyzing the employee's past work history. A part or all of the above-mentioned processing in the recording unit may be performed, for example, using AI or may be performed without using AI. For example, the recording unit can make the selection using an AI model that inputs the employee's past work history and selects the optimal recording method.

[0069] The recording unit can perform filtering based on the employee's current work situation and field of interest. The recording unit, for example, records only relevant information based on the work content currently being performed by the employee. The recording unit can also preferentially record important information based on the employee's field of interest. Furthermore, the recording unit can analyze the employee's current work situation and filter and record unnecessary information. For example, the recording unit records only relevant information and preferentially records important information based on the work content currently being performed by the employee. The employee's field of interest is identified based on past remarks and selected options. This makes it possible to record only relevant information by filtering based on the employee's current work situation and field of interest. Part or all of the above-mentioned processing in the recording unit may be performed, for example, using AI or may be performed without using AI. For example, the recording unit can perform filtering using an AI model that uses the employee's current work situation and field of interest as input.

[0070] The recording unit can estimate the emotion of the employee and determine the priority of events to be recorded based on the estimated emotion of the employee. For example, when the employee is feeling stressed, the recording unit preferentially records important events. In addition, when the employee is relaxed, the recording unit can also preferentially record detailed events. Furthermore, when the employee is in a hurry, the recording unit can also preferentially record events with high urgency. For example, the recording unit captures the facial expression of the employee with a camera and estimates the emotion using an emotion estimation algorithm. The emotion estimation algorithm calculates an emotion score based on the change in facial expression and determines the priority of events to be recorded. In addition, the recording unit can record the voice of the employee and estimate the emotion using a voice analysis technology. For example, the tone and speed of the voice are analyzed, an emotion score is calculated, and the priority of events to be recorded is determined. Furthermore, the recording unit can collect the employee's biometric data (heart rate and electrodermal activity) with a sensor and estimate the emotion using an emotion estimation algorithm. For example, an emotion score is calculated based on the fluctuation of the heart rate and the priority of events to be recorded is determined. This allows important events to be recorded preferentially by determining the priority of events to be recorded based on the employee's emotions. The emotion estimation is realized by using an emotion estimation function using, for example, an emotion engine or a generation AI. The generation AI is, for example, a text generation AI (for example, LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above-mentioned processing in the recording unit may be performed, for example, using AI or may be performed without using AI. For example, the recording unit can input image data of the employee taken by a camera to the generation AI and cause the generation AI to estimate the employee's emotions.

[0071] The recording unit can preferentially record events with high relevance based on the geographical location information of the employee. For example, when the employee is working in a specific area, the recording unit preferentially records events related to the area. The recording unit can also select and record important events based on the geographical location information of the employee. Furthermore, when the employee is moving, the recording unit can preferentially record events related to the area to which the employee is moving. For example, when the employee is working in a specific area, the recording unit preferentially records events related to the area and records important events in detail. The geographical location information of the employee is acquired using GPS data, beacons, etc. As a result, by taking the geographical location information of the employee into consideration, events with high relevance can be preferentially recorded. A part or all of the above-mentioned processing in the recording unit may be performed, for example, using AI, or may be performed without using AI. For example, the recording unit can select events with high relevance using an AI model that inputs the geographical location information of the employee.

[0072] The recording unit can analyze the employee's social media activity and record related events. The recording unit, for example, records related events based on information shared by the employee on social media. The recording unit can also analyze the employee's social media activity and select and record important events. Furthermore, the recording unit can preferentially record events mentioned by the employee on social media. For example, the recording unit records related events based on information shared by the employee on social media and records important events in detail. The employee's social media activity is analyzed based on the content of posts, the number of likes, comments, etc. In this way, related events can be recorded by analyzing the employee's social media activity. Part or all of the above-mentioned processing in the recording unit may be performed, for example, using AI or may be performed without using AI. For example, the recording unit can make the selection using an AI model that inputs the employee's social media activity and selects related events.

[0073] The summarizing unit can estimate the employee's emotion and adjust the expression method of the summary based on the estimated emotion of the employee. For example, when the employee is stressed, the summarizing unit provides a concise and easy-to-understand summary. In addition, when the employee is relaxed, the summarizing unit can also provide a detailed summary. Furthermore, when the employee is impatient, the summarizing unit can provide a summary that emphasizes important points. For example, the summarizing unit captures the employee's facial expression with a camera and estimates the emotion using an emotion estimation algorithm. The emotion estimation algorithm calculates an emotion score based on the change in facial expression and adjusts the expression method of the summary. In addition, the summarizing unit can record the employee's voice and estimate the emotion using a voice analysis technology. For example, the tone and speed of the voice are analyzed, an emotion score is calculated, and the expression method of the summary is adjusted. Furthermore, the summarizing unit can collect the employee's biometric data (heart rate and electrodermal activity) with a sensor and estimate the emotion using an emotion estimation algorithm. For example, an emotion score is calculated based on the fluctuation of the heart rate, and the expression method of the summary is adjusted. In this way, a more appropriate summary can be provided by adjusting the expression method of the summary based on the employee's emotion. The estimation of emotions is realized using an emotion estimation function, for example, using an emotion engine or a generation AI. The generation AI may be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-mentioned processing in the summarization unit may be performed, for example, using an AI or may be performed without using an AI. For example, the summarization unit may input the employee's emotion data to the generation AI and cause the generation AI to adjust the way the summary is expressed.

[0074] The summarization unit can adjust the level of detail of the summary based on the importance of the event. For example, the summarization unit provides a detailed summary for an event with high importance. The summarization unit can also provide a concise summary for an event with low importance. Furthermore, the summarization unit can adjust the length of the summary according to the importance. For example, the summarization unit evaluates the importance of each event based on the magnitude of the impact or the frequency of occurrence, and adjusts the level of detail of the summary based on the importance. In this way, important events can be summarized in detail by adjusting the level of detail of the summary based on the importance of the event. Part or all of the above-mentioned processing in the summarization unit may be performed, for example, using AI, or may be performed without using AI. For example, the summarization unit can make adjustments using an AI model that uses the magnitude of the impact or the frequency of occurrence as input and adjusts the level of detail of the summary.

[0075] The summarization unit can apply different summarization algorithms depending on the category of the event. For example, for events related to work content, the summarization unit provides a summary including detailed procedures. In addition, for events related to important events, the summarization unit can also provide a summary including the scope of impact. Furthermore, for events related to malfunctions, the summarization unit can also provide a summary including causes and measures. For example, for events related to work content, the summarization unit provides a summary including detailed procedures, and for events related to important events, the summarization unit provides a summary including the scope of impact. For events related to malfunctions, the summarization unit provides a summary including causes and measures. In this way, by applying different summarization algorithms depending on the category of the event, more appropriate summaries can be provided. Part or all of the above-mentioned processing in the summarization unit may be performed, for example, using AI, or may be performed without using AI. For example, the summarization unit can perform summarization using an AI model that inputs the category of the event and applies different summarization algorithms.

[0076] The summarizing unit can estimate the employee's emotion and adjust the length of the summary based on the estimated emotion of the employee. For example, if the employee is stressed, the summarizing unit provides a short summary that focuses on the main points. Also, if the employee is relaxed, the summarizing unit can provide a detailed summary. Furthermore, if the employee is impatient, the summarizing unit can provide a short summary that emphasizes important points. For example, the summarizing unit captures the employee's facial expression with a camera and estimates the emotion using an emotion estimation algorithm. The emotion estimation algorithm calculates an emotion score based on the change in facial expression and adjusts the length of the summary. Also, the summarizing unit can record the employee's voice and estimate the emotion using a voice analysis technology. For example, the tone and speed of the voice are analyzed, an emotion score is calculated, and the length of the summary is adjusted. Furthermore, the summarizing unit can collect the employee's biometric data (heart rate and electrodermal activity) with a sensor and estimate the emotion using an emotion estimation algorithm. For example, an emotion score is calculated based on the fluctuation of the heart rate, and the length of the summary is adjusted. In this way, a more appropriate summary can be provided by adjusting the length of the summary based on the employee's emotion. The estimation of emotions is realized using an emotion estimation function, for example, using an emotion engine or a generation AI. The generation AI may be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-mentioned processing in the summarization unit may be performed, for example, using an AI or without using an AI. For example, the summarization unit may input employee emotion data to the generation AI and cause the generation AI to adjust the length of the summary.

[0077] The summarization unit can determine the priority of summarization based on the time of occurrence of an event. For example, the summarization unit prioritizes summarization of recently occurring events. The summarization unit can also provide a concise summary of events that occurred in the past. Furthermore, the summarization unit can adjust the level of detail of the summarization according to the time of occurrence. For example, the summarization unit prioritizes summarization of recently occurring events and provides a concise summary of events that occurred in the past. By adjusting the level of detail of the summarization according to the time of occurrence, the latest information can be prioritized. A part or all of the above-mentioned processing in the summarization unit may be performed, for example, using AI or may be performed without using AI. For example, the summarization unit can determine the priority using an AI model that inputs the time of occurrence of an event and determines the priority of summarization.

[0078] The summarization unit can adjust the order of summaries based on the relevance of events. For example, the summarization unit prioritizes summarization of highly relevant events. The summarization unit can also provide a concise summary for events with low relevance. Furthermore, the summarization unit can adjust the order of summaries according to the relevance. For example, the summarization unit prioritizes summarization of highly relevant events and provides a concise summary for events with low relevance. By adjusting the order of summaries according to the relevance, events with high relevance can be summarized preferentially. A part or all of the above-mentioned processing in the summarization unit may be performed, for example, using AI or may be performed without using AI. For example, the summarization unit can adjust the order using an AI model that uses the relevance of events as an input and adjusts the order of summaries.

[0079] The providing unit can estimate the employee's emotion and adjust the method of providing information based on the estimated employee's emotion. For example, when the employee is feeling stressed, the providing unit provides concise and easy-to-understand information. Furthermore, when the employee is relaxed, the providing unit can provide detailed information. Furthermore, when the employee is impatient, the providing unit can provide information emphasizing important points. For example, the providing unit captures the employee's facial expression with a camera and estimates the emotion using an emotion estimation algorithm. The emotion estimation algorithm calculates an emotion score based on the change in facial expression and adjusts the method of providing information. Furthermore, the providing unit can record the employee's voice and estimate the emotion using a voice analysis technology. For example, the tone and speed of the voice are analyzed, an emotion score is calculated, and the method of providing information is adjusted. Furthermore, the providing unit can collect the employee's biometric data (heart rate and electrodermal activity) with a sensor and estimate the emotion using an emotion estimation algorithm. For example, an emotion score is calculated based on the fluctuation of the heart rate, and the method of providing information is adjusted. In this way, by adjusting the method of providing information based on the employee's emotion, more appropriate information can be provided. The estimation of emotions is realized, for example, by using an emotion estimation function using an emotion engine or a generation AI. The generation AI may be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-mentioned processing in the providing unit may be performed, for example, using an AI or may be performed without using an AI. For example, the providing unit may input employee emotion data to the generation AI and cause the generation AI to adjust the method of providing information.

[0080] The providing unit can select the optimal information providing method by referring to the employee's past work history. The providing unit provides related information based on, for example, work that the employee frequently performed in the past. The providing unit can also select the optimal information providing method by referring to the employee's past work history. Furthermore, the providing unit can analyze the employee's past work history and provide important information preferentially. For example, the providing unit provides related information based on work that the employee frequently performed in the past, and provides important information in detail. The employee's past work history is analyzed based on the type of work, time, result, etc. As a result, the optimal information providing method can be selected by referring to the employee's past work history. Part or all of the above-mentioned processing in the providing unit may be performed, for example, using AI, or may be performed without using AI. For example, the providing unit can make the selection using an AI model that inputs the employee's past work history and selects the optimal information providing method.

[0081] The providing unit can customize the content to be provided based on the employee's current work situation. The providing unit provides relevant information based on the work content currently being performed by the employee, for example. The providing unit can also analyze the employee's current work situation and select the optimal information providing method. Furthermore, the providing unit can provide important information preferentially based on the employee's current work situation. For example, the providing unit provides relevant information and provides important information in detail based on the work content currently being performed by the employee. The employee's current work situation is analyzed based on the progress of the work, the tools being used, and the like. As a result, the content to be provided can be customized based on the employee's current work situation, thereby providing more appropriate information. Part or all of the above-mentioned processing in the providing unit may be performed, for example, using AI, or may be performed without using AI. For example, the providing unit can perform customization using an AI model that uses the employee's current work situation as an input and customizes the content to be provided.

[0082] The providing unit can estimate the emotion of the employee and determine the priority of information provision based on the estimated emotion of the employee. For example, when the employee is feeling stressed, the providing unit can provide important information with priority. Furthermore, when the employee is relaxed, the providing unit can also provide detailed information with priority. Furthermore, when the employee is in a hurry, the providing unit can also provide information with high urgency with priority. For example, the providing unit captures the facial expression of the employee with a camera and estimates the emotion using an emotion estimation algorithm. The emotion estimation algorithm calculates an emotion score based on the change in the facial expression and determines the priority of information provision. Furthermore, the providing unit can record the voice of the employee and estimate the emotion using a voice analysis technology. For example, the tone and speed of the voice are analyzed, an emotion score is calculated, and the priority of information provision is determined. Furthermore, the providing unit can collect the employee's biometric data (heart rate and electrodermal activity) with a sensor and estimate the emotion using an emotion estimation algorithm. For example, an emotion score is calculated based on the fluctuation of the heart rate and the priority of information provision is determined. In this way, important information can be provided with priority by determining the priority of information provision based on the emotion of the employee. The estimation of emotions is realized, for example, by using an emotion estimation function using an emotion engine or a generation AI. The generation AI may be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-mentioned processing in the providing unit may be performed, for example, using an AI or may be performed without using an AI. For example, the providing unit may input employee emotion data to the generation AI and cause the generation AI to determine the priority of information provision.

[0083] The providing unit can select the optimal providing method in consideration of the geographical location information of the employee. For example, when the employee is working in a specific area, the providing unit provides information related to the area. The providing unit can also select and provide important information based on the geographical location information of the employee. Furthermore, when the employee is moving, the providing unit can also provide information related to the area to which the employee is moving. For example, when the employee is working in a specific area, the providing unit provides information related to the area and provides important information in detail. The geographical location information of the employee is acquired using GPS data, beacons, etc. As a result, highly relevant information can be provided by considering the geographical location information of the employee. Part or all of the above-mentioned processing in the providing unit may be performed, for example, using AI, or may be performed without using AI. For example, the providing unit can make the selection using an AI model that inputs the geographical location information of the employee and selects the optimal providing method.

[0084] The provision unit can adjust the content provided by analyzing the employee's social media activity. The provision unit can provide relevant information based on the information shared by the employee on social media, for example. The provision unit can also analyze the employee's social media activity and select and provide important information. Furthermore, the provision unit can preferentially provide information mentioned by the employee on social media. For example, the provision unit provides relevant information based on the information shared by the employee on social media, and provides important information in detail. The employee's social media activity is analyzed based on the posted content, the number of likes, comments, etc. In this way, the relevant information can be provided by analyzing the employee's social media activity. A part or all of the above-mentioned processing in the provision unit can be performed, for example, using AI, or can be performed without using AI. For example, the provision unit can make adjustments using an AI model that uses the employee's social media activity as an input and adjusts the content provided.

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

[0086] The recording unit can monitor the employee's biometric data in real time and issue an alert if an abnormality is detected. For example, if a sudden change in heart rate or blood pressure is detected, the recording unit immediately issues an alert to ensure the safety of the employee. The recording unit can also estimate the employee's fatigue level and issue an alert encouraging the employee to take a break if a certain level of fatigue is exceeded. Furthermore, the recording unit can monitor the employee's stress level and issue an alert suggesting ways to relax if stress increases. This makes it possible to monitor the employee's health condition in real time and respond quickly when an abnormality occurs.

[0087] The recording unit can analyze the audio data of the employee's work environment and issue an alert if the noise level exceeds a certain standard. For example, if the noise level in the work environment is high, the recording unit issues an alert to encourage the employee to use earplugs. The recording unit can also analyze the audio data of the work environment and issue an alert if it detects an abnormal sound (e.g., an abnormal machine sound or an alarm sound). Furthermore, the recording unit can analyze the audio data of the work environment, evaluate the quality of communication between employees, and make improvement suggestions as necessary. This makes it possible to utilize the audio data of the work environment to improve employee safety and the quality of communication.

[0088] The recording department can monitor the work efficiency of employees in real time, and make suggestions for improvement if the efficiency declines. For example, if the work efficiency falls below a certain standard, the recording department can suggest reviewing the work procedures or changing the tools. The recording department can also monitor the work efficiency of employees, and if the efficiency is high, share the method with other employees. Furthermore, the recording department can monitor the work efficiency of employees, identify the cause of the decline in efficiency, and propose specific measures to address the cause. This makes it possible to monitor the work efficiency of employees in real time and improve efficiency.

[0089] The summarization unit can adjust the content of the summary based on the employee's skill level. For example, a summary including detailed technical information is provided to an employee with a high skill level, and a summary focusing on basic information is provided to an employee with a low skill level. The summarization unit can also adjust the difficulty of the summary based on the employee's skill level. Furthermore, the summarization unit can select the format of the summary (e.g., text, diagram, video, etc.) based on the employee's skill level. This makes it possible to provide an optimal summary according to the employee's skill level.

[0090] The summarization unit can estimate the employee's emotions and adjust the tone of the summary based on the estimated employee's emotions. For example, if the employee is feeling stressed, the summary can be provided in a positive tone to ease the employee's feelings. Also, if the employee is relaxed, the summary can be provided in a neutral tone. Furthermore, if the employee is impatient, the summary can be provided in a calm and collected tone to reduce the employee's impatience. This enables more effective information communication by providing a summary in a tone that corresponds to the employee's emotions.

[0091] The recording unit can monitor employees' posture while working and suggest improvements if the posture is poor. For example, if posture becomes poor after sitting at work for a long time, the recording unit will issue an alert to encourage stretching and a break. The recording unit can also monitor posture while working and suggest maintaining posture if it has improved. Furthermore, the recording unit can analyze employees' posture data and suggest a long-term posture improvement plan. This can help maintain employees' health and improve work efficiency.

[0092] The recording unit can estimate the employee's emotions and adjust the level of detail of the data to be recorded based on the estimated emotions. For example, if the employee is feeling stressed, detailed data can be recorded to provide information for later analysis. If the employee is relaxed, only basic data can be recorded. Furthermore, if the employee is in a hurry, data with high urgency can be recorded with priority. This allows for more appropriate data collection by adjusting the level of detail of the data to be recorded based on the employee's emotions.

[0093] The recording unit can monitor the movements of employees while they are working and recommend efficient movements. For example, it can detect unnecessary movements while working and suggest efficient movement methods. The recording unit can also analyze the movement data of employees and suggest optimal work procedures. Furthermore, the recording unit can make suggestions for improving the work environment based on the movement data of employees. This makes it possible to optimize the movements of employees and improve work efficiency.

[0094] The recording unit can estimate the employee's emotions and select the type of data to record based on the estimated emotions. For example, if the employee is feeling stressed, data that causes stress can be recorded with priority. Also, if the employee is relaxed, work data in a relaxed state can be recorded in detail. Furthermore, if the employee is impatient, data that causes impatience can be recorded with priority. This makes it possible to collect more appropriate data by selecting the type of data to record based on the employee's emotions.

[0095] The recording unit can monitor the energy consumption of employees while they are working and suggest energy-efficient work methods. For example, if energy consumption is high, the recording unit can suggest energy-efficient work procedures. The recording unit can also monitor the energy consumption of employees, and if energy consumption is low, share the method with other employees. Furthermore, the recording unit can monitor the energy consumption of employees, identify the cause of high energy consumption, and suggest specific measures to address the cause. This makes it possible to monitor the energy consumption of employees in real time and improve energy efficiency.

[0096] The process flow of the second embodiment will be briefly described below.

[0097] Step 1: The recording unit records multiple events occurring during the work of the first shift employees. These include the progress of work, occurrence of problems, and content of communication. The recording unit uses cameras to record the progress of work in real time, and collects audio data to record conversations and instructions during work. It can also use sensors to collect data on the work environment (temperature, humidity, vibration, etc.). Step 2: The summarization unit summarizes the multiple events recorded by the recording unit. The summarization is based on the length of the text and the importance of the information. The summarization unit uses a generation AI to analyze the recorded data and extract important events and defects. The generation AI uses a text generation AI (e.g., LLM) and a multimodal generation AI to concisely summarize the text, image, and audio data. Step 3: The providing unit provides the information summarized by the summarizing unit to the second shift employee at the time of handover from the first shift employee to the second shift employee. The providing unit can provide the summarized information in a text format, an audio format, or a visual format. For example, the providing unit displays the information in a text format, provides the information in an audio format, and visually displays the information using graphs and charts in a visual format.

[0098] The specific processing unit 290 transmits the result of the specific processing to the smart device 14. In the smart device 14, the control unit 46A causes the output device 40 to output the result of the specific processing. The microphone 38B acquires a voice indicating a user input for the result of the specific processing. The control unit 46A transmits the voice data indicating the user input acquired by the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the voice data.

[0099] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (registered trademark) (Internet search<URL: https: / / openai.com / blog / chatgpt> ) and other generation AIs. The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating a voice, text data indicating a text, and image data indicating an image (e.g., still image data or video data) are input. The data generation model 58 infers the input inference data according to the instruction indicated by the prompt, and outputs the inference result in one or more data formats of voice data, text data, and image data. The data generation model 58 includes, for example, a text generation AI, an image generation AI, and a multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specification processing unit 290 performs the above-mentioned specification processing while using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction, and in this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. In the data processing device 12 and the like, the data generation model 58 includes a plurality of types, and the data generation model 58 includes an AI other than the generation AI. The AI ​​other than the generation AI is, for example, a linear regression, a logistic regression, a decision tree, a random forest, a support vector machine (SVM), a k-means clustering, a convolutional neural network (CNN), a recurrent neural network (RNN), a generative adversarial network (GAN), or a naive Bayes, and can perform various processes, but is not limited to such examples. The AI ​​may also be an AI agent. In addition, when the processes of each of the above-mentioned parts are performed by an AI, the processes are performed in part or in whole by the AI, but are not limited to such examples.In addition, processing performed by AI including the generation AI may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by AI including the generation AI.

[0100] Furthermore, the above-described processing by the data processing system 10 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart device 14, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart device 14. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the smart device 14 or an external device, and the smart device 14 acquires or collects information required for processing from the data processing device 12 or an external device.

[0101] For example, the recording unit is realized by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart device 14. For example, the camera 42 and the microphone 38B are used to record the work of the first shift employees in real time, and audio data is also collected at the same time. The summarizing unit is realized by the specific processing unit 290 of the data processing device 12, for example, and analyzes the recorded data and extracts important events and defects based on specific keywords and event triggers. The providing unit is realized by the control unit 46A of the smart device 14 or the specific processing unit 290 of the data processing device 12, for example, and provides summarized information to the second shift employees. The correspondence between each unit and the device or control unit is not limited to the above-mentioned example, and various changes are possible.

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

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

[0104] 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. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

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

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

[0107] Camera 42 is a small digital camera equipped with an optical system including a lens, an aperture, and a shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the user's surroundings (e.g., an imaging range defined by an angle of view equivalent to the width of the field of vision of an average healthy person).

[0108] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is performed in a secure state.

[0109] Fig. 4 shows an example of main functions of the data processing device 12 and the smart glasses 214. As shown in Fig. 4, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0110] The processor 28 reads the specific processing program 56 from the storage 32, and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0111] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the emotion of the user using the emotion identification model 59 and perform identification processing using the user's emotion. In the emotion estimation function (emotion identification function) using the emotion identification model 59, various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, are performed, but are not limited to such examples. In addition, the estimation and prediction of the emotion also include, for example, analysis of the emotion.

[0112] In the smart glasses 214, the 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 operating as the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. Note that the smart glasses 214 have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can also perform processing similar to that of the specific processing unit 290 using these models.

[0113] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 obtains a processing result (such as a prediction result) using the data generation model 58 by communicating with the server device having the data generation model 58. In addition, the data processing device 12 may be a server device, or may be a terminal device (such as a mobile phone, a robot, or a home appliance) owned by a user.

[0114] The specific processing unit 290 transmits the result of the specific processing to the smart glasses 214. In the smart glasses 214, the control unit 46A causes the speaker 240 to output the result of the specific processing. The microphone 238 acquires a voice indicating a user input for the result of the specific processing. The control unit 46A transmits the voice data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the voice data.

[0115] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating a voice, text data indicating a text, and image data indicating an image (e.g., still image data or video data) is input. The data generation model 58 infers the input inference data according to the instruction indicated by the prompt, and outputs the inference result in one or more data formats of voice data, text data, and image data. The data generation model 58 includes, for example, a text generation AI, an image generation AI, and a multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specification processing unit 290 performs the above-mentioned specification processing while using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction, and in this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. In the data processing device 12 and the like, the data generation model 58 includes a plurality of types, and the data generation model 58 includes an AI other than the generation AI. The AI ​​other than the generation AI is, for example, a linear regression, a logistic regression, a decision tree, a random forest, a support vector machine (SVM), a k-means clustering, a convolutional neural network (CNN), a recurrent neural network (RNN), a generative adversarial network (GAN), or a naive Bayes, and can perform various processes, but is not limited to such examples. The AI ​​may be an AI agent. In addition, when the processes of each part described above are performed by an AI, the processes are performed in part or in whole by the AI, but are not limited to such examples. In addition, the processes performed by the AI ​​including the generation AI may be replaced with rule-based processes, and the rule-based processes may be replaced with processes performed by the AI ​​including the generation AI.

[0116] The data processing system 210 according to the second embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 210 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart glasses 214, but may be executed 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 required for processing from the smart glasses 214 or an external device, and the smart glasses 214 acquires or collects information required for processing from the data processing device 12 or an external device.

[0117] For example, the recording unit is realized by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart glasses 214. For example, the camera 42 and the microphone 238 are used to record the work of the first shift employees in real time, and audio data is also collected at the same time. The summarizing unit is realized by the specific processing unit 290 of the data processing device 12, for example, and analyzes the recorded data and extracts important events and defects based on specific keywords and event triggers. The providing unit is realized by the control unit 46A of the smart glasses 214 or the specific processing unit 290 of the data processing device 12, for example, and provides summarized information to the second shift employees. The correspondence between each unit and the device or control unit is not limited to the above-mentioned example, and various changes are possible.

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

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

[0120] 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. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

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

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

[0123] Camera 42 is a small digital camera equipped with an optical system including a lens, an aperture, and a shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the user's surroundings (e.g., an imaging range defined by an angle of view equivalent to the width of the field of vision of an average healthy person).

[0124] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is performed in a secure state.

[0125] Fig. 6 shows an example of the main functions of the data processing device 12 and the headset type terminal 314. As shown in Fig. 6, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0126] The processor 28 reads the specific processing program 56 from the storage 32, and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0127] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the emotion of the user using the emotion identification model 59 and perform identification processing using the user's emotion. In the emotion estimation function (emotion identification function) using the emotion identification model 59, various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, are performed, but are not limited to such examples. In addition, the estimation and prediction of the emotion also include, for example, analysis of the emotion.

[0128] In the headset type terminal 314, the 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 operating as the control unit 46A in accordance with the specific program 60 executed on the RAM 48. Note that the headset type terminal 314 has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can also perform processing similar to that of the identification processing unit 290 using these models.

[0129] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 obtains a processing result (such as a prediction result) using the data generation model 58 by communicating with the server device having the data generation model 58. In addition, the data processing device 12 may be a server device, or may be a terminal device (such as a mobile phone, a robot, or a home appliance) owned by a user.

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

[0131] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating a voice, text data indicating a text, and image data indicating an image (e.g., still image data or video data) is input. The data generation model 58 infers the input inference data according to the instruction indicated by the prompt, and outputs the inference result in one or more data formats of voice data, text data, and image data. The data generation model 58 includes, for example, a text generation AI, an image generation AI, and a multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specification processing unit 290 performs the above-mentioned specification processing while using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction, and in this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. In the data processing device 12 and the like, the data generation model 58 includes a plurality of types, and the data generation model 58 includes an AI other than the generation AI. The AI ​​other than the generation AI is, for example, a linear regression, a logistic regression, a decision tree, a random forest, a support vector machine (SVM), a k-means clustering, a convolutional neural network (CNN), a recurrent neural network (RNN), a generative adversarial network (GAN), or a naive Bayes, and can perform various processes, but is not limited to such examples. The AI ​​may be an AI agent. In addition, when the processes of each part described above are performed by an AI, the processes are performed in part or in whole by the AI, but are not limited to such examples. In addition, the processes performed by the AI ​​including the generation AI may be replaced with rule-based processes, and the rule-based processes may be replaced with processes performed by the AI ​​including the generation AI.

[0132] The data processing system 310 according to the third embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 310 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the headset type terminal 314, but may be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the headset type terminal 314. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the headset type terminal 314 or an external device, and the headset type terminal 314 acquires or collects information required for processing from the data processing device 12 or an external device.

[0133] For example, the recording unit is realized by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the headset terminal 314. For example, the camera 42 and the microphone 238 are used to record the work of the first shift employees in real time, and audio data is also collected at the same time. The summarizing unit is realized by the specific processing unit 290 of the data processing device 12, for example, and analyzes the recorded data to extract important events and defects based on specific keywords and event triggers. The providing unit is realized by the control unit 46A of the headset terminal 314 or the specific processing unit 290 of the data processing device 12, for example, and provides summarized information to the second shift employees. The correspondence between each unit and the device or control unit is not limited to the above-mentioned example, and various changes are possible.

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

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

[0136] 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. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

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

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

[0139] Camera 42 is a small digital camera equipped with an optical system including a lens, an aperture, and a shutter, and an imaging element such as a CMOS image sensor or a CCD image sensor, and captures images of the user's surroundings (e.g., an imaging range defined by an angle of view equivalent to the width of the field of vision of a typical healthy person).

[0140] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is performed in a secure state.

[0141] The control object 443 includes a display device, LEDs in the eyes, and motors for driving the arms, hands, and legs. The posture and behavior of the robot 414 are controlled by controlling the motors of the arms, hands, and legs. Some of the emotions of the robot 414 can be expressed by controlling these motors. In addition, the facial expressions of the robot 414 can also be expressed by controlling the light emission state of the LEDs in the eyes of the robot 414.

[0142] Fig. 8 shows an example of the main functions of the data processing device 12 and the robot 414. As shown in Fig. 8, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0143] The processor 28 reads the specific processing program 56 from the storage 32, and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0144] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the emotion of the user using the emotion identification model 59 and perform identification processing using the user's emotion. In the emotion estimation function (emotion identification function) using the emotion identification model 59, various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, are performed, but are not limited to such examples. In addition, the estimation and prediction of the emotion also include, for example, analysis of the emotion.

[0145] In the robot 414, the specific processing is performed by the processor 46. The storage 50 stores a specific program 60. The processor 46 reads out the specific program 60 from the storage 50, and executes the specific program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as the control unit 46A in accordance with the specific program 60 executed on the RAM 48. The robot 414 has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can also perform processing similar to that of the specific processing unit 290 using these models.

[0146] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 obtains a processing result (such as a prediction result) using the data generation model 58 by communicating with the server device having the data generation model 58. In addition, the data processing device 12 may be a server device, or may be a terminal device (such as a mobile phone, a robot, or a home appliance) owned by a user.

[0147] The specific processing unit 290 transmits the result of the specific processing to the robot 414. In the robot 414, the control unit 46A causes the speaker 240 and the control target 443 to output the result of the specific processing. The microphone 238 acquires a voice indicating a user input for the result of the specific processing. The control unit 46A transmits voice data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the voice data.

[0148] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating a voice, text data indicating a text, and image data indicating an image (e.g., still image data or video data) is input. The data generation model 58 infers the input inference data according to the instruction indicated by the prompt, and outputs the inference result in one or more data formats of voice data, text data, and image data. The data generation model 58 includes, for example, a text generation AI, an image generation AI, and a multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specification processing unit 290 performs the above-mentioned specification processing while using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction, and in this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. In the data processing device 12 and the like, the data generation model 58 includes a plurality of types, and the data generation model 58 includes an AI other than the generation AI. The AI ​​other than the generation AI is, for example, a linear regression, a logistic regression, a decision tree, a random forest, a support vector machine (SVM), a k-means clustering, a convolutional neural network (CNN), a recurrent neural network (RNN), a generative adversarial network (GAN), or a naive Bayes, and can perform various processes, but is not limited to such examples. The AI ​​may be an AI agent. In addition, when the processes of each part described above are performed by an AI, the processes are performed in part or in whole by the AI, but are not limited to such examples. In addition, the processes performed by the AI ​​including the generation AI may be replaced with rule-based processes, and the rule-based processes may be replaced with processes performed by the AI ​​including the generation AI.

[0149] The data processing system 410 according to the fourth embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 410 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the robot 414, but may be executed 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 required for processing from the robot 414 or an external device, etc., and the robot 414 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0150] For example, the recording unit is realized by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the robot 414. For example, the camera 42 and the microphone 238 are used to record the work of the first shift employees in real time, and audio data is also collected at the same time. The summarizing unit is realized by the specific processing unit 290 of the data processing device 12, for example, and analyzes the recorded data to extract important events and defects based on specific keywords and event triggers. The providing unit is realized by the control unit 46A of the robot 414 or the specific processing unit 290 of the data processing device 12, for example, and provides summarized information to the second shift employees. The correspondence between each unit and the device or control unit is not limited to the above-mentioned example, and various changes are possible.

[0151] The emotion identification model 59 as an emotion engine may determine the emotion of the user according to a specific mapping. Specifically, the emotion identification model 59 may determine the emotion of the user according to an emotion map (see FIG. 9), which is a specific mapping. Similarly, the emotion identification model 59 may determine the emotion of the robot, and the identification processing unit 290 may perform identification processing using the emotion of the robot.

[0152] FIG. 9 is a diagram showing an emotion map 400 on which multiple emotions are mapped. In the emotion map 400, emotions are arranged in concentric circles radiating from the center. The closer to the center of the concentric circles, the more primitive emotions are arranged. The more outside the concentric circles, the more emotions that represent states and actions that arise from a state of mind are arranged. Emotions are a concept that includes emotions and mental states. On the left side of the concentric circles, emotions that are generally generated from reactions that occur in the brain are arranged. On the right side of the concentric circles, emotions that are generally induced by situational judgment are arranged. On the upper and lower sides of the concentric circles, emotions that are generally generated from reactions that occur in the brain and are induced by situational judgment are arranged. In addition, on the upper side of the concentric circles, emotions of "pleasure" are arranged, and on the lower side, emotions of "discomfort" are arranged. In this way, in the emotion map 400, multiple emotions are mapped based on the structure in which emotions are generated, and emotions that tend to occur simultaneously are mapped close to each other.

[0153] These emotions are distributed in the three o'clock direction of emotion map 400 and usually fluctuate between relief and anxiety. In the right half of emotion map 400, situational awareness dominates over internal sensations, resulting in a sense of calm.

[0154] The inside of emotion map 400 represents what is going on inside one's mind, and the outside of emotion map 400 represents behavior, so the further out you go on emotion map 400, the more visible the emotions become (the more they are expressed in behavior).

[0155] Here, human emotions are based on various balances such as posture and blood sugar level, and when these balances are far from the ideal, it indicates an unpleasant state, and when they are close to the ideal, it indicates a pleasant state. Emotions can also be created for robots, cars, motorcycles, etc., based on various balances such as posture and battery level, so that when these balances are far from the ideal, it indicates an unpleasant state, and when they are close to the ideal, it indicates a pleasant state. The emotion map may be generated, for example, based on the emotion map of Dr. Mitsuyoshi (Research on speech emotion recognition and emotion brain physiological signal analysis system, Tokushima University, doctoral dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). On the left half of the emotion map, emotions belonging to an area called "reaction" where sensation is dominant are lined up. On the right half of the emotion map, emotions belonging to an area called "situation" where situation recognition is dominant are lined up.

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

[0157] The emotion identification model 59 inputs the user input to a pre-trained neural network, obtains emotion values ​​indicating each emotion shown in the emotion map 400, and determines the emotion of the user. This neural network is pre-trained based on multiple learning data that are combinations of the user input and emotion values ​​indicating each emotion shown in the emotion map 400. Furthermore, this neural network is trained so that emotions that are located close to each other have similar values, as in the emotion map 900 shown in Fig. 10. Fig. 10 shows an example in which multiple emotions, "relief," "calm," and "encouraging," have similar emotion values.

[0158] In the above embodiment, an example was given in which a specific process is performed by one computer 22, but the technology disclosed herein is not limited to this, and distributed processing of the specific process may be performed by multiple computers, including computer 22.

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

[0160] In addition, 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 upon request from the data processing device 12.

[0161] It is not necessary to store all of the specific processing program 56 in a storage device such as a server connected to the data processing device 12 via the network 54, or to store all of the specific processing program 56 in the storage 32; only a portion of the specific processing program 56 may be stored.

[0162] As the hardware resource for executing the specific process, various processors as shown below can be used. An example of the processor is a CPU, which is a general-purpose processor that functions as a hardware resource for executing the specific process by executing software, i.e., a program. Another example of the processor is a dedicated electric circuit, which is a processor having a circuit configuration designed exclusively for executing a specific process, such as a Field-Programmable Gate Array (FPGA), a Programmable Logic Device (PLD), or an Application Specific Integrated Circuit (ASIC). Each processor has a built-in or connected memory, and each processor executes the specific process by using the memory.

[0163] The hardware resource that executes the specific process may be one of these various processors, or may be a combination of two or more processors of the same or different types (for example, a combination of multiple FPGAs, or a combination of a CPU and an FPGA). Also, the hardware resource that executes the specific process may be a single processor.

[0164] As an example of a configuration using one processor, first, one processor is configured by combining one or more CPUs and software, and this processor functions as a hardware resource that executes a specific process. Second, there is a configuration using a processor that realizes the functions of the entire system including multiple hardware resources that execute a specific process on a single IC chip, as typified by SoC (System-on-a-chip). In this way, a specific process is realized using one or more of the above-mentioned various processors as hardware resources.

[0165] Furthermore, more specifically, the hardware structure of these various processors can be an electric circuit that combines circuit elements such as semiconductor elements. The specific processes described above are merely examples. It goes without saying that unnecessary steps may be deleted, new steps may be added, or the order of processes may be changed without departing from the spirit of the invention.

[0166] In the above example, the first to fourth embodiments are described separately, but some or all of these embodiments may be combined. The smart device 14, the smart glasses 214, the headset terminal 314, and the robot 414 are only examples, and they may be combined with each other, or may be other devices. In the above example, the first and second embodiments are described separately, but they may be combined.

[0167] The above description and illustrations are detailed descriptions of the parts related to the technology of the present disclosure, and are merely an example of the technology of the present disclosure. For example, the above description of the configuration, function, action, and effect is an example of the configuration, function, action, and effect of the parts related to the technology of the present disclosure. Therefore, it goes without saying that unnecessary parts may be deleted, new elements may be added, or replaced with respect to the description and illustrations shown above, within the scope of the gist of the technology of the present disclosure. In addition, in order to avoid confusion and to facilitate understanding of the parts related to the technology of the present disclosure, the description and illustrations shown above omit explanations of technical common sense that do not require particular explanation in enabling the implementation of the technology of the present disclosure.

[0168] All publications, patent applications, and standards mentioned in this specification are herein incorporated by reference to the same extent as if each individual publication, patent application, and standard was specifically and individually indicated to be incorporated by reference.

[0169] (Appendix 1) A recording unit that records a number of events occurring during the employee's work; a summary unit that summarizes the plurality of events recorded by the recording unit; and a providing unit that provides the information summarized by the summarizing unit to a next employee when an employee is replaced. A system characterized by: (Appendix 2) The recording unit is Recording said events visually and audio-visually 2. The system of claim 1, (Appendix 3) The system of claim 1, wherein the recording unit selects important events or malfunctions based on specific keywords or specific event triggers. (Appendix 4) The summary portion comprises: Evaluating the importance of each of the plurality of events recorded in the recorder and prioritizing summaries of each of the plurality of events based on the importance. 2. The system of claim 1, (Appendix 5) The system of claim 1, wherein the summarizing unit adjusts the content of the summary based on employee behavior patterns. (Appendix 6) The recording unit is Estimate employee sentiment and adjust recording timing based on estimated employee sentiment 2. The system of claim 1, (Appendix 7) The system described in Appendix 1, wherein the recording unit analyzes the employee's past work history and selects an appropriate recording method. (Appendix 8) The recording unit is Filter based on employees' current work activities and interests 2. The system of claim 1, (Appendix 9) The recording unit is Estimate employee sentiment and prioritize events to record based on the estimated employee sentiment 2. The system of claim 1, (Appendix 10) The system described in Appendix 1, wherein the recording unit prioritizes recording of highly relevant events based on the employee's geographic location information. (Appendix 11) The recording unit is Analyze employees' social media activity and record related events 2. The system of claim 1, (Appendix 12) The summary portion comprises: Estimate employee sentiment and adjust summary presentation based on estimated employee sentiment 2. The system of claim 1, (Appendix 13) The summary portion comprises: Adjust the level of detail in your summary based on the importance of the event 2. The system of claim 1, (Appendix 14) The summary portion comprises: Apply different summarization algorithms depending on the category of events 2. The system of claim 1, (Appendix 15) The summary portion comprises: Estimate employee sentiment and adjust the length of the summary based on the estimated employee sentiment 2. The system of claim 1, (Appendix 16) The summary portion comprises: Prioritize summaries based on when events occurred 2. The system of claim 1, (Appendix 17) The summary portion comprises: Adjust the order of summaries based on the relevance of events 2. The system of claim 1, (Appendix 18) The providing unit is Estimate employee sentiment and adjust information delivery methods based on the estimated employee sentiment 2. The system of claim 1, (Appendix 19) The providing unit is Select the best delivery method by referencing the employee's past work history 2. The system of claim 1, (Appendix 20) The providing unit is Customize offerings based on employees' current work situations 2. The system of claim 1, (Appendix 21) The providing unit is Estimate employee sentiment and prioritize information provision based on the estimated employee sentiment 2. The system of claim 1, (Appendix 22) The providing unit is Choose the best delivery method based on employee geographic location 2. The system of claim 1, (Appendix 23) The providing unit is Analyze employee social media activity to tailor your offerings 2. The system of claim 1, [Explanation of symbols]

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

Claims

1. A recording unit that records a number of events occurring during the employee's work; a summary unit that summarizes the plurality of events recorded by the recording unit; and a providing unit that provides the information summarized by the summarizing unit to a next employee when an employee is replaced. A system characterized by:

2. The recording unit is Recording said events visually and audio-visually 2. The system of claim 1 .

3. The recording unit is Identifying important events or issues based on specific keywords or specific event triggers 2. The system of claim 1 .

4. The summary portion comprises: Evaluating the importance of each of the plurality of events recorded in the recorder and prioritizing summaries of each of the plurality of events based on the importance.

2. The system of claim 1 .

5. The summary portion comprises: Tailor summary content based on employee behavior patterns 2. The system of claim 1 .

6. The recording unit is Estimate employee sentiment and adjust recording timing based on estimated employee sentiment 2. The system of claim 1 .

7. 2. The system according to claim 1, wherein the recording unit analyzes the past work history of employees and selects an appropriate recording method.

8. The recording unit is Filter based on employees' current work activities and interests 2. The system of claim 1 .

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