system

Abstract

The system according to this embodiment aims to provide appropriate work procedures and methods to individuals with disabilities. [Solution] The system according to the embodiment comprises a collection unit, a storage unit, a shooting unit, a generation unit, an audio unit, and a provision unit. The collection unit collects individual information. The storage unit stores the information collected by the collection unit in a database. The shooting unit shoots and saves basic videos of the work. The generation unit generates individual videos based on the information stored in the storage unit. The audio unit adds explanatory audio and commentary text to the videos generated by the generation unit. The provision unit provides the videos generated by the generation unit to the person concerned.

Description

Technical Field

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

Background Art

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

Prior Art Documents

Patent Documents

[0003]

Patent Document 1

Summary of the Invention

Problems to be Solved by the Invention

[0004] In the conventional technology, it is difficult to teach appropriate working procedures and methods to individual people with disabilities, and there is room for improvement.

[0005] The system according to the embodiment aims to provide appropriate working procedures and methods for individual people with disabilities.

Means for Solving the Problems

[0006] The system according to this embodiment comprises a collection unit, a storage unit, a shooting unit, a generation unit, an audio unit, and a provision unit. The collection unit collects individual information. The storage unit stores the information collected by the collection unit in a database. The shooting unit shoots and saves basic videos of the work. The generation unit generates individual videos based on the information stored in the storage unit. The audio unit adds explanatory audio and commentary text to the videos generated by the generation unit. The provision unit provides the videos generated by the generation unit to the user. [Effects of the Invention]

[0007] The system according to this embodiment can provide appropriate work procedures and methods to individuals with disabilities. [Brief explanation of the drawing]

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

[0028] (Example of form 1) The Agent Job Coach System according to an embodiment of the present invention is a system that utilizes generative AI to provide personalized work procedures in video format. This system collects general information about each person with a disability and stores their actions, challenges, events, and words in a database. This database is updated as needed to reflect the latest information. Next, a basic video of the work is filmed and saved. This basic video is designed so that a non-disabled person can understand the work method and procedure in one viewing. If there are new tasks, changes, or revisions, the video is edited, modified, revised, and saved. Next, a video tailored to each individual is generated using the database. The generative AI adds explanatory audio and commentary text, automatically generating process divisions, flow changes, and content additions. This allows for the creation of videos of work procedures tailored to individual aptitudes, and enables the explanation of procedures and methods to the individual through video. For example, for individuals with developmental disabilities or intellectual disabilities, individual work procedures are created as videos, and audio and comments are added to support work acquisition. It can also be used for spot work staff and general workers (non-disabled individuals). By utilizing generative AI, both the database of collected individual information and the database for forming videos are generated simultaneously, efficiently providing work procedures. This system standardizes work instruction for people with disabilities, solves the problem of inconsistent work speed, and provides appropriate support to people with disabilities who have difficulty learning through photos and videos alone. As a result, the Agent Job Coach system can provide personalized work procedures via video for each person with a disability.

[0029] The agent job coaching system according to the embodiment comprises a collection unit, a storage unit, a shooting unit, a generation unit, an audio unit, and a provision unit. The collection unit collects individual information. Individual information includes, but is not limited to, personal profile information, behavioral history, and health status. The collection unit can collect information by methods such as data collection using sensors or surveys. The storage unit stores the collected information in a database. The database includes, but is not limited to, relational databases and NoSQL databases. The storage unit can update the collected information as needed. For example, the information can be updated by methods such as real-time updates or periodic batch processing. The shooting unit shoots and saves basic videos of the work. Basic videos include, but are not limited to, details of the work procedure and shooting viewpoints. The shooting unit can, for example, shoot and save basic videos of the work using a high-resolution camera. The generation unit generates individual videos using generation AI. Generation AI includes, but is not limited to, deep learning models and generation algorithms. The generation unit can, for example, generate individual videos using generation AI and add explanatory audio and commentary text. The audio unit adds explanatory audio and commentary text to the generated videos. Explanatory audio and commentary text include, but are not limited to, speech synthesis technology and text generation technology. By adding explanatory audio and commentary text to the generated videos, the audio unit can provide work procedures tailored to each individual with a disability. The delivery unit provides the generated videos to the individuals. The delivery unit can, for example, adjust the video distribution method and delivery timing. As a result, the agent Job Coach system according to this embodiment can provide work procedures tailored to each individual with a disability in video format.

[0030] The data collection unit collects individual information. This individual information includes, but is not limited to, personal profile information, behavioral history, and health status. The data collection unit can collect information through methods such as data collection using sensors and surveys. Specifically, as personal profile information, it collects basic information such as name, age, gender, address, occupation, and educational background. As behavioral history, it collects daily activity patterns, past work history, and data on behavior during work. As health status, it collects physical and mental health status, past medical history, and current health management status. This information can be collected in real time using sensors. For example, wearable devices can be used to collect data such as heart rate, steps taken, and sleep status. It can also collect information such as an individual's awareness and feelings, as well as opinions and requests regarding work, through surveys. The collected information can be used to provide work procedures tailored to individual needs and circumstances.

[0031] The storage unit stores the collected information in a database. The database includes, but is not limited to, relational databases and NoSQL databases. Relational databases manage data in a tabular format, and data manipulation and retrieval can be performed using SQL. NoSQL databases, on the other hand, employ a schema-less data model and have a flexible data structure, allowing for high-speed processing of large amounts of data. The storage unit can update the collected information as needed. For example, information can be updated through real-time updates or periodic batch processing. Real-time updates instantly reflect collected data in the database, ensuring that the latest information is always available. Periodic batch processing distributes the system load by updating data in batches at regular time intervals. This allows the storage unit to efficiently manage the collected information and make it quickly accessible when needed.

[0032] The filming unit films and saves basic videos of the work. These basic videos include, but are not limited to, detailed work procedures and filming perspectives. The filming unit can, for example, use a high-resolution camera to film and save basic videos of the work. High-resolution cameras can capture details clearly and accurately record the details of the work procedures. Filming perspectives can include the worker's perspective and a third-party perspective, allowing for a multifaceted understanding of the work procedures. The filmed basic videos are later used as foundational data when the generation unit generates individual videos. The basic videos include the workflow, important points, and precautions, serving as a reference for workers to perform their tasks accurately.

[0033] The generation unit generates individual videos using generative AI. Generative AI includes, but is not limited to, deep learning models and generative algorithms. For example, the generation unit can generate individual videos using generative AI and add explanatory audio and commentary text. Deep learning models possess advanced pattern recognition and generation capabilities by learning from large amounts of data. Generative algorithms can generate data based on specific rules and conditions. Based on individual information collected and stored in the collection and storage units, the generation unit generates videos tailored to individual needs and situations. For example, it can provide work procedures tailored to each individual with a disability by highlighting specific work procedures or adding individual points of caution. The generated videos can include explanatory audio and commentary text, providing information both visually and aurally.

[0034] The audio unit adds explanatory audio and commentary text to the generated video. This includes, but is not limited to, examples of technologies such as speech synthesis and text generation. Speech synthesis technology converts text data into speech, enabling the generation of natural-sounding and natural-sounding speech. Text generation technology generates text data based on specific conditions and contexts, automatically generating appropriate comments and explanations. By adding explanatory audio and commentary text to the generated video, the audio unit can provide work procedures tailored to each individual with a disability. For example, it can support workers in performing tasks accurately by providing detailed explanations and points to note for each step of the work in audio or text. This ensures that even those with visual or hearing impairments can obtain appropriate information.

[0035] The service provider will provide the generated video to the individual. The service provider can adjust, for example, the method and timing of video distribution. Possible video distribution methods include streaming via the internet and download distribution. Streaming allows real-time video viewing and is available anywhere with an internet connection. Download distribution allows offline viewing once the video has been downloaded. In terms of timing, videos can be provided at appropriate times, such as during the preparation phase before work, support during work, and review after work. This allows the agent Job Coach system according to this embodiment to provide work procedures tailored to each individual with a disability through video. The service provider can also collect user feedback and use it to improve the method and content of delivery. For example, they can analyze video viewing history and feedback to consider more effective delivery methods. This allows the service provider to provide optimal support to users and improve the efficiency and quality of their work.

[0036] The data collection unit can collect preliminary information about each person with a disability and store it in a database, including their movements, challenges, events, and verbal and physical actions. For example, the data collection unit can collect basic profile information and past behavioral history for each person with a disability. The data collection unit can also collect information such as movements, challenges, events, and verbal and physical actions. For example, the data collection unit can collect information such as daily living activities and responses to specific challenges. By collecting preliminary information about each person with a disability and storing it in a database, basic information can be obtained to provide individualized work procedures. Some or all of the above processing in the data collection unit may be performed using AI, for example, or not. For example, the data collection unit can collect movement data of persons with disabilities using sensors and analyze the data using AI.

[0037] The storage unit can store the collected information in a database and update it as needed. For example, the storage unit can store the collected information in a relational database or a NoSQL database. The storage unit can also update the collected information in real time. For example, the storage unit can update the information using periodic batch processing. This allows for the provision of individual work procedures that reflect the latest information by storing the collected information in a database and updating it as needed. Some or all of the above-described processes in the storage unit may be performed using AI, for example, or without AI. For example, the storage unit can analyze the collected information using AI and store it in the database.

[0038] The filming unit can film and save basic videos of the work. For example, the filming unit can film basic videos of the work using a high-resolution camera. The filming unit can also save the filmed videos. For example, the filming unit can save the filmed videos to cloud storage. By filming and saving basic videos of the work, it is possible to provide basic videos that allow able-bodied individuals to understand the work methods and procedures in one viewing. Some or all of the above processing in the filming unit may be performed using AI, for example, or not using AI. For example, the filming unit can use AI to edit and modify the filmed videos.

[0039] The generation unit can generate individual videos using a generation AI. The generation unit can, for example, generate individual videos using a generation AI. The generation AI includes, but is not limited to, deep learning models and generation algorithms. The generation unit can generate individual videos using a generation AI and add explanatory audio and commentary text. This makes it possible to provide work procedures tailored to each individual with a disability by generating individual videos using a generation AI. Some or all of the above-described processes in the generation unit may be performed using a generation AI, for example, or without a generation AI. For example, the generation unit can generate individual videos using a generation AI and add explanatory audio and commentary text.

[0040] The audio unit can add explanatory audio and commentary text to the generated video. For example, the audio unit can add explanatory audio to the generated video using speech synthesis technology. The audio unit can also add commentary text to the generated video using text generation technology. By adding explanatory audio and commentary text to the generated video, it is possible to provide work procedures tailored to each individual with a disability. Some or all of the above processing in the audio unit may be performed using AI, for example, or without AI. For example, the audio unit can add explanatory audio and commentary text to a video generated using AI.

[0041] The service provider can provide the generated video to the individual. The service provider can, for example, adjust the video distribution method. For example, the service provider can stream the video. The service provider can also adjust the timing of video delivery. For example, the service provider can deliver the video in real time. By providing the generated video to the individual, it is possible to provide work procedures tailored to each person with a disability. Some or all of the above processing in the service provider may be performed using AI, for example, or not using AI. For example, the service provider can use AI to adjust the video distribution method and timing.

[0042] The data collection unit can analyze the past behavioral history of persons with disabilities and select the optimal information collection method. For example, the data collection unit can identify the time of day when the user can provide information most efficiently based on past behavioral history. The data collection unit can also prioritize information collection methods (such as voice and text) that the user has previously preferred. Furthermore, the data collection unit can analyze the user's behavioral patterns and suggest the optimal information collection method. This allows for efficient information collection by analyzing the past behavioral history of persons with disabilities and selecting the optimal information collection method. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input past behavioral history data into AI and have the AI ​​select the optimal information collection method.

[0043] The data collection unit can filter information based on the current health status and living situation of persons with disabilities. For example, if a user is unwell, the data collection unit can temporarily stop collecting information and resume it after recovery. The data collection unit can also select an appropriate method of information collection based on the user's living situation (work, home, etc.). Furthermore, the data collection unit can adjust the frequency and content of information collection according to the user's health status. This allows for the collection of appropriate information by filtering based on the current health status and living situation of persons with disabilities. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input the user's health status data into AI and have the AI ​​perform the filtering.

[0044] The data collection unit can prioritize the collection of highly relevant information by considering the geographical location of persons with disabilities during information gathering. For example, if a user is in a specific area, the data collection unit will prioritize the collection of information related to that area. Furthermore, if a user is on the move, the data collection unit can also collect the most relevant information based on their current location. Additionally, if a user is staying in a specific location, the data collection unit can prioritize the collection of information related to that location. This allows for efficient information collection by prioritizing the collection of highly relevant information while considering the geographical location of persons with disabilities. Some or all of the above-described processes in the data collection unit may be performed using AI, or not. For example, the data collection unit can input the user's geographical location information into the AI ​​and have the AI ​​collect highly relevant information.

[0045] The data collection unit can analyze the social media activities of persons with disabilities and collect relevant information during the information gathering process. For example, the data collection unit can collect relevant information based on information shared by users on social media. The data collection unit can also analyze the user's interests from their social media activities and collect the most relevant information. Furthermore, the data collection unit can collect relevant information based on information from accounts that users follow on social media. This allows for efficient information gathering by analyzing the social media activities of persons with disabilities and collecting relevant information. Some or all of the above-described processes in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input the user's social media data into AI and have the AI ​​collect relevant information.

[0046] The storage unit can periodically cleanse the data to maintain the integrity of the stored data. For example, the storage unit can periodically remove duplicate data in the database to maintain integrity. The storage unit can also detect data inconsistencies and perform a cleansing process to correct them. Furthermore, the storage unit can archive old data and retain only the latest data. This ensures that data quality is maintained by periodically cleansing the data to maintain its integrity. Some or all of the above processes in the storage unit may be performed using AI, for example, or not. For example, the storage unit can input data from the database into an AI and have the AI ​​perform the data cleansing.

[0047] The storage unit can classify and efficiently manage the stored data based on the attribute information of persons with disabilities. For example, the storage unit classifies the data based on the attribute information of persons with disabilities (age, gender, type of disability, etc.). The storage unit can also streamline data retrieval and filtering based on the attribute information. Furthermore, the storage unit can optimize the data management method based on the attribute information. This allows for efficient data retrieval and filtering by classifying and efficiently managing the stored data based on the attribute information of persons with disabilities. Some or all of the above processing in the storage unit may be performed using AI, for example, or without AI. For example, the storage unit can input the attribute information of persons with disabilities into AI and have the AI ​​perform data classification and management.

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

[0049] The data collection unit can analyze the past behavioral history of individuals with disabilities and select the most suitable information collection method. For example, it can identify the time of day when a user can provide information most efficiently based on their past behavioral history. The data collection unit can also prioritize information collection methods (such as voice or text) that users have previously preferred. Furthermore, the data collection unit can analyze user behavioral patterns and suggest the most suitable information collection method. This allows for efficient information collection by analyzing the past behavioral history of individuals with disabilities and selecting the most suitable information collection method.

[0050] The storage unit can periodically cleanse the stored data to maintain its integrity. For example, it can periodically remove duplicate data from the database to maintain consistency. The storage unit can also detect and correct data inconsistencies by executing a cleansing process. Furthermore, the storage unit can archive older data, retaining only the most recent data. This allows for the maintenance of data quality by periodically cleansing the stored data to ensure its integrity.

[0051] The filming department can film and save basic work videos. For example, they can use a high-resolution camera to film basic work videos. The filming department can also save the filmed videos to cloud storage. This allows them to provide basic work videos that are sufficient for able-bodied individuals to understand the work methods and procedures in a single viewing.

[0052] The generation unit can generate individual videos using generation AI. For example, it can generate individual videos using generation AI and add explanatory audio and commentary text. This makes it possible to provide work procedures tailored to each individual with a disability by generating individual videos using generation AI.

[0053] The audio unit can add explanatory audio and commentary text to the generated video. For example, explanatory audio can be added to the generated video using speech synthesis technology. Commentary text can also be added to the generated video using text generation technology. This allows for the provision of work procedures tailored to each individual with a disability by adding explanatory audio and commentary text to the generated video.

[0054] The following briefly describes the processing flow for example form 1.

[0055] Step 1: The collection unit collects individual information. This individual information includes, for example, personal profile information, behavioral history, and health status. The collection unit can collect this information through methods such as data collection using sensors or surveys. Step 2: The storage unit stores the collected information in a database. The database can include relational databases, NoSQL databases, etc. The storage unit can update the collected information as needed, and the information can be updated using methods such as real-time updates or periodic batch processing. Step 3: The filming team films and saves basic videos of the work. These basic videos include details of the work procedure and filming angles. The filming team can use a high-resolution camera to film and save these basic videos of the work. Step 4: The generation unit generates individual videos based on the information stored in the storage unit. The generation unit can generate individual videos using generation AI and add explanatory audio and commentary text. Generation AI includes deep learning models and generation algorithms. Step 5: The audio unit adds explanatory audio and commentary text to the generated video. This includes speech synthesis and text generation technologies. By adding explanatory audio and commentary text to the generated video, the audio unit can provide work procedures tailored to each individual with a disability. Step 6: The service provider provides the generated video to the individual. The service provider can adjust the video distribution method and timing. This allows for the provision of personalized work procedures via video for each person with a disability.

[0056] (Example of form 2) The Agent Job Coach System according to an embodiment of the present invention is a system that utilizes generative AI to provide personalized work procedures in video format. This system collects general information about each person with a disability and stores their actions, challenges, events, and words in a database. This database is updated as needed to reflect the latest information. Next, a basic video of the work is filmed and saved. This basic video is designed so that a non-disabled person can understand the work method and procedure in one viewing. If there are new tasks, changes, or revisions, the video is edited, modified, revised, and saved. Next, a video tailored to each individual is generated using the database. The generative AI adds explanatory audio and commentary text, automatically generating process divisions, flow changes, and content additions. This allows for the creation of videos of work procedures tailored to individual aptitudes, and enables the explanation of procedures and methods to the individual through video. For example, for individuals with developmental disabilities or intellectual disabilities, individual work procedures are created as videos, and audio and comments are added to support work acquisition. It can also be used for spot work staff and general workers (non-disabled individuals). By utilizing generative AI, both the database of collected individual information and the database for forming videos are generated simultaneously, efficiently providing work procedures. This system standardizes work instruction for people with disabilities, solves the problem of inconsistent work speed, and provides appropriate support to people with disabilities who have difficulty learning through photos and videos alone. As a result, the Agent Job Coach system can provide personalized work procedures via video for each person with a disability.

[0057] The agent job coaching system according to the embodiment comprises a collection unit, a storage unit, a shooting unit, a generation unit, an audio unit, and a provision unit. The collection unit collects individual information. Individual information includes, but is not limited to, personal profile information, behavioral history, and health status. The collection unit can collect information by methods such as data collection using sensors or surveys. The storage unit stores the collected information in a database. The database includes, but is not limited to, relational databases and NoSQL databases. The storage unit can update the collected information as needed. For example, the information can be updated by methods such as real-time updates or periodic batch processing. The shooting unit shoots and saves basic videos of the work. Basic videos include, but are not limited to, details of the work procedure and shooting viewpoints. The shooting unit can, for example, shoot and save basic videos of the work using a high-resolution camera. The generation unit generates individual videos using generation AI. Generation AI includes, but is not limited to, deep learning models and generation algorithms. The generation unit can, for example, generate individual videos using generation AI and add explanatory audio and commentary text. The audio unit adds explanatory audio and commentary text to the generated videos. Explanatory audio and commentary text include, but are not limited to, speech synthesis technology and text generation technology. By adding explanatory audio and commentary text to the generated videos, the audio unit can provide work procedures tailored to each individual with a disability. The delivery unit provides the generated videos to the individuals. The delivery unit can, for example, adjust the video distribution method and delivery timing. As a result, the agent Job Coach system according to this embodiment can provide work procedures tailored to each individual with a disability in video format.

[0058] The data collection unit collects individual information. This individual information includes, but is not limited to, personal profile information, behavioral history, and health status. The data collection unit can collect information through methods such as data collection using sensors and surveys. Specifically, as personal profile information, it collects basic information such as name, age, gender, address, occupation, and educational background. As behavioral history, it collects daily activity patterns, past work history, and data on behavior during work. As health status, it collects physical and mental health status, past medical history, and current health management status. This information can be collected in real time using sensors. For example, wearable devices can be used to collect data such as heart rate, steps taken, and sleep status. It can also collect information such as an individual's awareness and feelings, as well as opinions and requests regarding work, through surveys. The collected information can be used to provide work procedures tailored to individual needs and circumstances.

[0059] The storage unit stores the collected information in a database. The database includes, but is not limited to, relational databases and NoSQL databases. Relational databases manage data in a tabular format, and data manipulation and retrieval can be performed using SQL. NoSQL databases, on the other hand, employ a schema-less data model and have a flexible data structure, allowing for high-speed processing of large amounts of data. The storage unit can update the collected information as needed. For example, information can be updated through real-time updates or periodic batch processing. Real-time updates instantly reflect collected data in the database, ensuring that the latest information is always available. Periodic batch processing distributes the system load by updating data in batches at regular time intervals. This allows the storage unit to efficiently manage the collected information and make it quickly accessible when needed.

[0060] The filming unit films and saves basic videos of the work. These basic videos include, but are not limited to, detailed work procedures and filming perspectives. The filming unit can, for example, use a high-resolution camera to film and save basic videos of the work. High-resolution cameras can capture details clearly and accurately record the details of the work procedures. Filming perspectives can include the worker's perspective and a third-party perspective, allowing for a multifaceted understanding of the work procedures. The filmed basic videos are later used as foundational data when the generation unit generates individual videos. The basic videos include the workflow, important points, and precautions, serving as a reference for workers to perform their tasks accurately.

[0061] The generation unit generates individual videos using generative AI. Generative AI includes, but is not limited to, deep learning models and generative algorithms. For example, the generation unit can generate individual videos using generative AI and add explanatory audio and commentary text. Deep learning models possess advanced pattern recognition and generation capabilities by learning from large amounts of data. Generative algorithms can generate data based on specific rules and conditions. Based on individual information collected and stored in the collection and storage units, the generation unit generates videos tailored to individual needs and situations. For example, it can provide work procedures tailored to each individual with a disability by highlighting specific work procedures or adding individual points of caution. The generated videos can include explanatory audio and commentary text, providing information both visually and aurally.

[0062] The audio unit adds explanatory audio and commentary text to the generated video. This includes, but is not limited to, examples of technologies such as speech synthesis and text generation. Speech synthesis technology converts text data into speech, enabling the generation of natural-sounding and natural-sounding speech. Text generation technology generates text data based on specific conditions and contexts, automatically generating appropriate comments and explanations. By adding explanatory audio and commentary text to the generated video, the audio unit can provide work procedures tailored to each individual with a disability. For example, it can support workers in performing tasks accurately by providing detailed explanations and points to note for each step of the work in audio or text. This ensures that even those with visual or hearing impairments can obtain appropriate information.

[0063] The service provider will provide the generated video to the individual. The service provider can adjust, for example, the method and timing of video distribution. Possible video distribution methods include streaming via the internet and download distribution. Streaming allows real-time video viewing and is available anywhere with an internet connection. Download distribution allows offline viewing once the video has been downloaded. In terms of timing, videos can be provided at appropriate times, such as during the preparation phase before work, support during work, and review after work. This allows the agent Job Coach system according to this embodiment to provide work procedures tailored to each individual with a disability through video. The service provider can also collect user feedback and use it to improve the method and content of delivery. For example, they can analyze video viewing history and feedback to consider more effective delivery methods. This allows the service provider to provide optimal support to users and improve the efficiency and quality of their work.

[0064] The data collection unit can collect preliminary information about each person with a disability and store it in a database, including their movements, challenges, events, and verbal and physical actions. For example, the data collection unit can collect basic profile information and past behavioral history for each person with a disability. The data collection unit can also collect information such as movements, challenges, events, and verbal and physical actions. For example, the data collection unit can collect information such as daily living activities and responses to specific challenges. By collecting preliminary information about each person with a disability and storing it in a database, basic information can be obtained to provide individualized work procedures. Some or all of the above processing in the data collection unit may be performed using AI, for example, or not. For example, the data collection unit can collect movement data of persons with disabilities using sensors and analyze the data using AI.

[0065] The storage unit can store the collected information in a database and update it as needed. For example, the storage unit can store the collected information in a relational database or a NoSQL database. The storage unit can also update the collected information in real time. For example, the storage unit can update the information using periodic batch processing. This allows for the provision of individual work procedures that reflect the latest information by storing the collected information in a database and updating it as needed. Some or all of the above-described processes in the storage unit may be performed using AI, for example, or without AI. For example, the storage unit can analyze the collected information using AI and store it in the database.

[0066] The filming unit can film and save basic videos of the work. For example, the filming unit can film basic videos of the work using a high-resolution camera. The filming unit can also save the filmed videos. For example, the filming unit can save the filmed videos to cloud storage. By filming and saving basic videos of the work, it is possible to provide basic videos that allow able-bodied individuals to understand the work methods and procedures in one viewing. Some or all of the above processing in the filming unit may be performed using AI, for example, or not using AI. For example, the filming unit can use AI to edit and modify the filmed videos.

[0067] The generation unit can generate individual videos using a generation AI. The generation unit can, for example, generate individual videos using a generation AI. The generation AI includes, but is not limited to, deep learning models and generation algorithms. The generation unit can generate individual videos using a generation AI and add explanatory audio and commentary text. This makes it possible to provide work procedures tailored to each individual with a disability by generating individual videos using a generation AI. Some or all of the above-described processes in the generation unit may be performed using a generation AI, for example, or without a generation AI. For example, the generation unit can generate individual videos using a generation AI and add explanatory audio and commentary text.

[0068] The audio unit can add explanatory audio and commentary text to the generated video. For example, the audio unit can add explanatory audio to the generated video using speech synthesis technology. The audio unit can also add commentary text to the generated video using text generation technology. By adding explanatory audio and commentary text to the generated video, it is possible to provide work procedures tailored to each individual with a disability. Some or all of the above processing in the audio unit may be performed using AI, for example, or without AI. For example, the audio unit can add explanatory audio and commentary text to a video generated using AI.

[0069] The service provider can provide the generated video to the individual. The service provider can, for example, adjust the video distribution method. For example, the service provider can stream the video. The service provider can also adjust the timing of video delivery. For example, the service provider can deliver the video in real time. By providing the generated video to the individual, it is possible to provide work procedures tailored to each person with a disability. Some or all of the above processing in the service provider may be performed using AI, for example, or not using AI. For example, the service provider can use AI to adjust the video distribution method and timing.

[0070] The data collection unit can estimate the user's emotions and adjust the timing of information collection based on the estimated emotions. For example, if the user is stressed, the data collection unit can reduce the frequency of information collection and collect information when the user is relaxed. The data collection unit can also adjust the timing of information collection if the user is concentrating, so as not to interrupt their work. Furthermore, if the user is tired, the data collection unit can temporarily stop information collection and resume it after they have rested. By adjusting the timing of information collection based on the user's emotions, the burden on the user can be reduced and information can be collected efficiently. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the data collection unit may be performed using AI, for example, or not using AI. For example, the data collection unit can input the user's emotion data into a generative AI and have the generative AI perform emotion estimation.

[0071] The data collection unit can analyze the past behavioral history of persons with disabilities and select the optimal information collection method. For example, the data collection unit can identify the time of day when the user can provide information most efficiently based on past behavioral history. The data collection unit can also prioritize information collection methods (such as voice and text) that the user has previously preferred. Furthermore, the data collection unit can analyze the user's behavioral patterns and suggest the optimal information collection method. This allows for efficient information collection by analyzing the past behavioral history of persons with disabilities and selecting the optimal information collection method. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input past behavioral history data into AI and have the AI ​​select the optimal information collection method.

[0072] The data collection unit can filter information based on the current health status and living situation of persons with disabilities. For example, if a user is unwell, the data collection unit can temporarily stop collecting information and resume it after recovery. The data collection unit can also select an appropriate method of information collection based on the user's living situation (work, home, etc.). Furthermore, the data collection unit can adjust the frequency and content of information collection according to the user's health status. This allows for the collection of appropriate information by filtering based on the current health status and living situation of persons with disabilities. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input the user's health status data into AI and have the AI ​​perform the filtering.

[0073] The data collection unit can estimate the user's emotions and determine the priority of information to collect based on the estimated emotions. For example, if the user is stressed, the data collection unit will prioritize collecting important information and postpone less important information. If the user is relaxed, the data collection unit can also prioritize collecting detailed information. Furthermore, if the user is in a hurry, the data collection unit can prioritize collecting information that can be quickly gathered. This allows for efficient information collection by prioritizing information based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI may be, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the data collection unit may be performed using AI, or not. For example, the data collection unit can input user emotion data into a generative AI and have the generative AI determine the priority of information.

[0074] The data collection unit can prioritize the collection of highly relevant information by considering the geographical location of persons with disabilities during information gathering. For example, if a user is in a specific area, the data collection unit will prioritize the collection of information related to that area. Furthermore, if a user is on the move, the data collection unit can also collect the most relevant information based on their current location. Additionally, if a user is staying in a specific location, the data collection unit can prioritize the collection of information related to that location. This allows for efficient information collection by prioritizing the collection of highly relevant information while considering the geographical location of persons with disabilities. Some or all of the above-described processes in the data collection unit may be performed using AI, or not. For example, the data collection unit can input the user's geographical location information into the AI ​​and have the AI ​​collect highly relevant information.

[0075] The data collection unit can analyze the social media activities of persons with disabilities and collect relevant information during the information gathering process. For example, the data collection unit can collect relevant information based on information shared by users on social media. The data collection unit can also analyze the user's interests from their social media activities and collect the most relevant information. Furthermore, the data collection unit can collect relevant information based on information from accounts that users follow on social media. This allows for efficient information gathering by analyzing the social media activities of persons with disabilities and collecting relevant information. Some or all of the above-described processes in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input the user's social media data into AI and have the AI ​​collect relevant information.

[0076] The storage unit can estimate the user's emotions and adjust the data update frequency based on the estimated emotions. For example, if the user is stressed, the storage unit can reduce the data update frequency and update the data when the user is relaxed. The storage unit can also adjust the data update frequency when the user is concentrating to avoid interrupting their work. Furthermore, if the user is tired, the storage unit can temporarily stop data updates and resume them after rest. This allows for efficient data updates by adjusting the data update frequency based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI may be, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the storage unit may be performed using AI or not. For example, the storage unit can input user emotion data into a generative AI and have the generative AI adjust the data update frequency.

[0077] The storage unit can periodically cleanse the data to maintain the integrity of the stored data. For example, the storage unit can periodically remove duplicate data in the database to maintain integrity. The storage unit can also detect data inconsistencies and perform a cleansing process to correct them. Furthermore, the storage unit can archive old data and retain only the latest data. This ensures that data quality is maintained by periodically cleansing the data to maintain its integrity. Some or all of the above processes in the storage unit may be performed using AI, for example, or not. For example, the storage unit can input data from the database into an AI and have the AI ​​perform the data cleansing.

[0078] The data storage unit can estimate the user's emotions and prioritize data based on those emotions. For example, if the user is stressed, the storage unit will prioritize storing important data and postpone less important data. If the user is relaxed, the storage unit can also prioritize storing detailed data. Furthermore, if the user is in a hurry, the storage unit can prioritize storing data that can be quickly processed. This allows for efficient data storage by prioritizing data based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI may be, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the storage unit may be performed using AI or not. For example, the storage unit can input user emotion data into a generative AI and have the generative AI determine data prioritization.

[0079] The storage unit can classify and efficiently manage the stored data based on the attribute information of persons with disabilities. For example, the storage unit classifies the data based on the attribute information of persons with disabilities (age, gender, type of disability, etc.). The storage unit can also streamline data retrieval and filtering based on the attribute information. Furthermore, the storage unit can optimize the data management method based on the attribute information. This allows for efficient data retrieval and filtering by classifying and efficiently managing the stored data based on the attribute information of persons with disabilities. Some or all of the above processing in the storage unit may be performed using AI, for example, or without AI. For example, the storage unit can input the attribute information of persons with disabilities into AI and have the AI ​​perform data classification and management.

[0080] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0081] The data collection unit can estimate the user's emotions and adjust its information collection methods based on those estimates. For example, if the user is stressed, the unit will reduce the frequency of information collection and collect information when the user is relaxed. If the user is concentrating, the unit can also adjust the timing of information collection to avoid interrupting their work. Furthermore, if the user is tired, the unit can temporarily stop information collection and resume it after they have rested. By adjusting the information collection method based on the user's emotions, the system can reduce the user's burden and collect information efficiently.

[0082] The data storage unit can analyze the collected information and adjust the data update frequency based on the user's emotions. For example, if the user is stressed, the storage unit will reduce the data update frequency and update when the user is relaxed. Similarly, if the user is concentrating, the storage unit can adjust the data update frequency to avoid interrupting their work. Furthermore, if the user is tired, the storage unit can temporarily stop data updates and resume them after a rest. This allows for efficient data updates by adjusting the update frequency based on the user's emotions.

[0083] The camera crew can estimate the user's emotions and adjust the timing and method of shooting based on those estimates. For example, if the user is stressed, the crew will reduce the frequency of shooting and shoot when the user is relaxed. If the user is concentrating, the crew can adjust the timing of shooting to avoid interrupting their work. Furthermore, if the user is tired, the crew can temporarily stop shooting and resume after they have rested. By adjusting the timing and method of shooting based on the user's emotions, the crew can reduce the user's burden and conduct shooting more efficiently.

[0084] The generation unit can estimate the user's emotions and adjust the video content and presentation based on those emotions. For example, if the user is stressed, the generation unit will make the video content concise, and provide detailed explanations when the user is relaxed. If the user is focused, the generation unit can delve deeper into the video content to facilitate understanding of the task. Furthermore, if the user is tired, the generation unit can lighten the video content and resume it after a break. In this way, by adjusting the video content and presentation based on the user's emotions, the burden on the user is reduced and videos can be generated efficiently.

[0085] The voice unit can estimate the user's emotions and adjust the content and tone of explanatory voice and commentary text based on those emotions. For example, if the user is stressed, the voice unit will soften the tone of the explanatory voice and commentary text, and provide more detailed explanations when the user is relaxed. If the user is focused, the voice unit can deepen the content of the explanatory voice and commentary text to facilitate understanding of the task. Furthermore, if the user is tired, the voice unit can reduce the content of the explanatory voice and commentary text, and resume after a break. This allows for more efficient explanations and reduces the user's burden by adjusting the content and tone of explanatory voice and commentary text based on the user's emotions.

[0086] The data collection unit can analyze the past behavioral history of individuals with disabilities and select the most suitable information collection method. For example, it can identify the time of day when a user can provide information most efficiently based on their past behavioral history. The data collection unit can also prioritize information collection methods (such as voice or text) that users have previously preferred. Furthermore, the data collection unit can analyze user behavioral patterns and suggest the most suitable information collection method. This allows for efficient information collection by analyzing the past behavioral history of individuals with disabilities and selecting the most suitable information collection method.

[0087] The storage unit can periodically cleanse the stored data to maintain its integrity. For example, it can periodically remove duplicate data from the database to maintain consistency. The storage unit can also detect and correct data inconsistencies by executing a cleansing process. Furthermore, the storage unit can archive older data, retaining only the most recent data. This allows for the maintenance of data quality by periodically cleansing the stored data to ensure its integrity.

[0088] The filming department can film and save basic work videos. For example, they can use a high-resolution camera to film basic work videos. The filming department can also save the filmed videos to cloud storage. This allows them to provide basic work videos that are sufficient for able-bodied individuals to understand the work methods and procedures in a single viewing.

[0089] The generation unit can generate individual videos using generation AI. For example, it can generate individual videos using generation AI and add explanatory audio and commentary text. This makes it possible to provide work procedures tailored to each individual with a disability by generating individual videos using generation AI.

[0090] The audio unit can add explanatory audio and commentary text to the generated video. For example, explanatory audio can be added to the generated video using speech synthesis technology. Commentary text can also be added to the generated video using text generation technology. This allows for the provision of work procedures tailored to each individual with a disability by adding explanatory audio and commentary text to the generated video.

[0091] The following briefly describes the processing flow for example form 2.

[0092] Step 1: The collection unit collects individual information. This individual information includes, for example, personal profile information, behavioral history, and health status. The collection unit can collect this information through methods such as data collection using sensors or surveys. Step 2: The storage unit stores the collected information in a database. The database can include relational databases, NoSQL databases, etc. The storage unit can update the collected information as needed, and the information can be updated using methods such as real-time updates or periodic batch processing. Step 3: The filming team films and saves basic videos of the work. These basic videos include details of the work procedure and filming angles. The filming team can use a high-resolution camera to film and save these basic videos of the work. Step 4: The generation unit generates individual videos based on the information stored in the storage unit. The generation unit can generate individual videos using generation AI and add explanatory audio and commentary text. Generation AI includes deep learning models and generation algorithms. Step 5: The audio unit adds explanatory audio and commentary text to the generated video. This includes speech synthesis and text generation technologies. By adding explanatory audio and commentary text to the generated video, the audio unit can provide work procedures tailored to each individual with a disability. Step 6: The service provider provides the generated video to the individual. The service provider can adjust the video distribution method and timing. This allows for the provision of personalized work procedures via video for each person with a disability.

[0093] The specific processing unit 290 transmits the result of the specific processing to the smart device 14. In the smart device 14, the control unit 46A causes the output device 40 to output the result of the specific processing. The microphone 38B acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0094] Data generation model 58 is a form of so-called generative AI (Artificial Intelligence). An example of data generation model 58 is ChatGPT (registered trademark) (Internet search).<URL: https: / / openai.com / blog / chatgpt> Examples of generative AI include text generation AI, image generation AI, and multimodal generation AI. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and with inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats from audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVMs), k-means clustering, convolutional neural networks (CNNs), recurrent neural networks (RNNs), generative adversarial networks (GANs), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each of the above parts is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example.Furthermore, processing performed by AI, including generative AI, may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by AI, including generative AI.

[0095] Furthermore, the processing performed by the data processing system 10 described above is carried out by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart device 14, but it may also be carried out by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart device 14. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the smart device 14 or an external device, and the smart device 14 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0096] Each of the multiple elements described above, including the collection unit, storage unit, shooting unit, generation unit, audio unit, and provision unit, is implemented in at least one of the smart device 14 and the data processing unit 12. For example, the collection unit collects individual information using the sensors and questionnaire functions of the smart device 14. The storage unit stores the collected information in the database 24 of the data processing unit 12 and updates it as needed. The shooting unit shoots and saves a basic video of the work using the high-resolution camera 42 of the smart device 14. The generation unit generates individual videos using generation AI via the specific processing unit 290 of the data processing unit 12. The audio unit adds explanatory audio and commentary text to the video generated by the specific processing unit 290 of the data processing unit 12. The provision unit provides the video generated by the control unit 46A of the smart device 14 to the user. The correspondence between each unit and the device or control unit is not limited to the example described above and can be modified in various ways.

[0097] [Second Embodiment] Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment.

[0098] As shown in Figure 3, the data processing system 210 includes a data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0099] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN and / or LAN.

[0100] The smart glasses 214 include a computer 36, a microphone 238, a speaker 240, a camera 42, and a communication interface 44. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, and camera 42 are also connected to the bus 52.

[0101] The microphone 238 receives voice signals from the user and accepts instructions from the user. The microphone 238 captures the voice signals from the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0102] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, which captures images of the area around the user (for example, an imaging range defined by a field of view equivalent to the field of vision of a typical healthy person).

[0103] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0104] Figure 4 shows an example of the main functions of the data processing device 12 and the smart glasses 214. As shown in Figure 4, the data processing device 12 performs specific processing by the processor 28. The storage 32 stores the specific processing program 56.

[0105] The processor 28 reads a specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 acting as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0106] Storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0107] In the smart glasses 214, specific processing is performed by the processor 46. The storage 50 stores a specific processing program 60. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 acting as a control unit 46A according to the specific processing program 60 executed on the RAM 48. The smart glasses 214 also have a data generation model 58 and an emotion identification model 59, similar to the data generation model and emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0108] Furthermore, other devices besides the data processing device 12 may also have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 obtains processing results (such as prediction results) using the data generation model 58 by communicating with the server device that has the data generation model 58. Also, the data processing device 12 may be a server device or a terminal device owned by the user (for example, a mobile phone, robot, home appliance, etc.).

[0109] The specific processing unit 290 transmits the result of the specific processing to the smart glasses 214. In the smart glasses 214, the control unit 46A causes the speaker 240 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0110] The data generation model 58 is a so-called generative AI. An example of a data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats such as audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVM), k-means clustering, convolutional neural networks (CNN), recurrent neural networks (RNN), generative adversarial networks (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each part described above is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example. Also, processing performed by an AI including a generative AI may be replaced by rule-based processing, and rule-based processing may be replaced by processing performed by an AI including a generative AI.

[0111] The data processing system 210 according to the second embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 210 is performed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart glasses 214, but it may also be performed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart glasses 214. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the smart glasses 214 or an external device, and the smart glasses 214 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0112] Each of the multiple elements described above, including the collection unit, storage unit, shooting unit, generation unit, audio unit, and provision unit, is implemented in at least one of the smart glasses 214 and the data processing unit 12. For example, the collection unit collects individual information using the sensors and questionnaire functions of the smart glasses 214. The storage unit stores the collected information in the database 24 of the data processing unit 12 and updates it as needed. The shooting unit shoots and saves a basic video of the work using the high-resolution camera 42 of the smart glasses 214. The generation unit generates individual videos using generation AI via the specific processing unit 290 of the data processing unit 12. The audio unit adds explanatory audio and commentary text to the video generated by the specific processing unit 290 of the data processing unit 12. The provision unit provides the video generated by the control unit 46A of the smart glasses 214 to the user. The correspondence between each unit and the device or control unit is not limited to the example described above and can be changed in various ways.

[0113] [Third Embodiment] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[0114] As shown in Figure 5, the data processing system 310 includes a data processing device 12 and a headset terminal 314. An example of the data processing device 12 is a server.

[0115] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN and / or LAN.

[0116] The headset terminal 314 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication interface 44, and a display 343. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, camera 42, and display 343 are also connected to the bus 52.

[0117] The microphone 238 receives voice signals from the user and accepts instructions from the user. The microphone 238 captures the voice signals from the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0118] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, which captures images of the area around the user (for example, an imaging range defined by a field of view equivalent to the field of vision of a typical healthy person).

[0119] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0120] Figure 6 shows an example of the main functions of the data processing device 12 and the headset terminal 314. As shown in Figure 6, the data processing device 12 performs specific processing using the processor 28. The storage 32 stores the specific processing program 56.

[0121] The processor 28 reads a specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 acting as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0122] Storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0123] In the headset terminal 314, specific processing is performed by the processor 46. The storage 50 stores a specific program 60. The processor 46 reads the specific program 60 from the storage 50 and executes the read specific program 60 on the RAM 48. The specific processing is realized by the processor 46 acting as a control unit 46A according to the specific program 60 executed on the RAM 48. The headset terminal 314 also has a data generation model 58 and an emotion identification model 59, similar to the data generation model and emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0124] Furthermore, other devices besides the data processing device 12 may also have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 obtains processing results (such as prediction results) using the data generation model 58 by communicating with the server device that has the data generation model 58. Also, the data processing device 12 may be a server device or a terminal device owned by the user (for example, a mobile phone, robot, home appliance, etc.).

[0125] The specific processing unit 290 transmits the result of the specific processing to the headset terminal 314. In the headset terminal 314, the control unit 46A causes the speaker 240 and display 343 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0126] The data generation model 58 is a so-called generative AI. An example of a data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats such as audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVM), k-means clustering, convolutional neural networks (CNN), recurrent neural networks (RNN), generative adversarial networks (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each part described above is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example. Also, processing performed by an AI including a generative AI may be replaced by rule-based processing, and rule-based processing may be replaced by processing performed by an AI including a generative AI.

[0127] The data processing system 310 according to the third embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 310 is performed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the headset terminal 314, but may also be performed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the headset terminal 314. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the headset terminal 314 or an external device, and the headset terminal 314 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0128] Each of the multiple elements described above, including the collection unit, storage unit, shooting unit, generation unit, audio unit, and provision unit, is implemented, for example, by at least one of the headset terminal 314 and the data processing unit 12. For example, the collection unit collects individual information using the sensors and questionnaire functions of the headset terminal 314. The storage unit stores the collected information in the database 24 of the data processing unit 12 and updates it as needed. The shooting unit shoots and saves a basic video of the work using the high-resolution camera 42 of the headset terminal 314. The generation unit generates individual videos using generation AI by the specific processing unit 290 of the data processing unit 12. The audio unit adds explanatory audio and commentary text to the video generated by the specific processing unit 290 of the data processing unit 12. The provision unit provides the video generated by the control unit 46A of the headset terminal 314 to the person. The correspondence between each unit and the device or control unit is not limited to the example described above and can be changed in various ways.

[0129] [Fourth Embodiment] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[0130] As shown in Figure 7, the data processing system 410 includes a data processing device 12 and a robot 414. An example of the data processing device 12 is a server.

[0131] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN and / or LAN.

[0132] The robot 414 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication interface 44, and a controlled object 443. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, camera 42, and controlled object 443 are also connected to the bus 52.

[0133] The microphone 238 receives voice signals from the user and accepts instructions from the user. The microphone 238 captures the voice signals from the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0134] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS image sensor or CCD image sensor, which captures images of the area around the user (for example, an imaging range defined by a field of view equivalent to the field of vision of a typical healthy person).

[0135] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0136] The controlled object 443 includes a display device, LEDs in the eyes, and motors that drive the arms, hands, and feet. The posture and gestures of the robot 414 are controlled by controlling the motors of the arms, hands, and feet. Some of the robot 414's emotions can be expressed by controlling these motors. The robot 414's facial expressions can also be expressed by controlling the illumination state of the LEDs in its eyes.

[0137] Figure 8 shows an example of the main functions of the data processing device 12 and the robot 414. As shown in Figure 8, the data processing device 12 performs specific processing using the processor 28. The storage 32 stores the specific processing program 56.

[0138] The processor 28 reads a specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 acting as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0139] Storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0140] In robot 414, specific processing is performed by processor 46. A specific program 60 is stored in storage 50. Processor 46 reads the specific program 60 from storage 50 and executes it on RAM 48. The specific processing is achieved by processor 46 acting as a control unit 46A according to the specific program 60 executed on RAM 48. Robot 414 also has data generation model 58 and emotion identification model 59, similar to those of the robot, and can perform processing similar to that of the specific processing unit 290 using these models.

[0141] Furthermore, other devices besides the data processing device 12 may also have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 obtains processing results (such as prediction results) using the data generation model 58 by communicating with the server device that has the data generation model 58. Also, the data processing device 12 may be a server device or a terminal device owned by the user (for example, a mobile phone, robot, home appliance, etc.).

[0142] The specific processing unit 290 transmits the result of the specific processing to the robot 414. In the robot 414, the control unit 46A causes the speaker 240 and the controlled object 443 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0143] The data generation model 58 is a so-called generative AI. An example of a data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats such as audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVM), k-means clustering, convolutional neural networks (CNN), recurrent neural networks (RNN), generative adversarial networks (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each part described above is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example. Also, processing performed by an AI including a generative AI may be replaced by rule-based processing, and rule-based processing may be replaced by processing performed by an AI including a generative AI.

[0144] The data processing system 410 according to the fourth embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 410 is performed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the robot 414, but it may also be performed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the robot 414. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the robot 414 or an external device, and the robot 414 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0145] Each of the multiple elements described above, including the collection unit, storage unit, shooting unit, generation unit, audio unit, and provision unit, is implemented in at least one of the following: the robot 414 and the data processing unit 12. For example, the collection unit collects individual information using the robot 414's sensors and questionnaire function. The storage unit stores the collected information in the database 24 of the data processing unit 12 and updates it as needed. The shooting unit shoots and saves basic videos of the work using the robot 414's high-resolution camera 42. The generation unit generates individual videos using generation AI via the specific processing unit 290 of the data processing unit 12. The audio unit adds explanatory audio and commentary text to the videos generated by the specific processing unit 290 of the data processing unit 12. The provision unit provides the videos generated by the control unit 46A of the robot 414 to the user. The correspondence between each unit and the device or control unit is not limited to the example described above and can be modified in various ways.

[0146] Furthermore, the emotion identification model 59, acting as an emotion engine, may determine the user's emotion according to a specific mapping. Specifically, the emotion identification model 59 may determine the user's emotion according to a specific mapping, which is an emotion map (see Figure 9). Similarly, the emotion identification model 59 may also determine the robot's emotion, and the identification processing unit 290 may perform identification processing using the robot's emotion.

[0147] Figure 9 shows the emotion map 400, in which multiple emotions are mapped. In the emotion map 400, emotions are arranged in concentric circles radiating from the center. The closer to the center of the concentric circles, the more primitive the emotions are located. Further out of the concentric circles, emotions representing states and actions arising from mental states are located. Emotion is a concept that includes feelings and mental states. On the left side of the concentric circles, emotions that are generally generated from reactions occurring in the brain are located. On the right side of the concentric circles, emotions that are generally induced by situational judgment are located. Above and below the concentric circles, emotions that are generally generated from reactions occurring in the brain and induced by situational judgment are located. In addition, the emotion of "pleasure" is located on the upper side of the concentric circles, and the emotion of "displeasure" is located on the lower side. Thus, in the emotion map 400, multiple emotions are mapped based on the structure in which emotions arise, and emotions that are likely to occur simultaneously are mapped close together.

[0148] These emotions are distributed at the 3 o'clock position on the Emotion Map 400, and usually fluctuate between feelings of security and anxiety. In the right half of the Emotion Map 400, situational awareness takes precedence over internal feelings, resulting in a calm impression.

[0149] The inside of the Emotion Map 400 represents inner thoughts, while the outside represents actions. Therefore, the further you go from the outside of the Emotion Map 400, the more visible (expressed in actions) your emotions become.

[0150] Here, human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, it results in discomfort, and when they approach the ideal, it results in pleasure. Similarly, in robots, cars, and motorcycles, emotions can be created based on various balances, such as posture and battery level. When these balances deviate from the ideal, it results in discomfort, and when they approach the ideal, it results in pleasure. The emotion map can be generated based, for example, on Dr. Mitsuyoshi's emotion map (Research on a system for analyzing brain physiological signals of speech emotion recognition and emotion, Tokushima University, doctoral dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map contains emotions belonging to a region called "response," where sensation is dominant. The right half of the emotion map contains emotions belonging to a region called "situation," where situational awareness is dominant.

[0151] The emotion map defines two emotions that promote learning. One is the emotion around the middle of the negative "repentance" and "reflection" on the situation side. In other words, it is when the robot experiences negative emotions such as "I never want to feel this way again" or "I don't want to be scolded again." The other is the emotion around the positive "desire" on the reaction side. In other words, it is when the robot has positive feelings such as "I want more" or "I want to know more."

[0152] The emotion identification model 59 inputs user input into a pre-trained neural network, obtains emotion values ​​representing each emotion shown in the emotion map 400, and determines the user's emotion. This neural network is pre-trained based on multiple training data sets, which are combinations of user input and emotion values ​​representing each emotion shown in the emotion map 400. Furthermore, this neural network is trained so that emotions located close together have similar values, as shown in the emotion map 900 in Figure 10. Figure 10 shows an example where multiple emotions such as "reassured," "calm," and "confident" have similar emotion values.

[0153] In the above embodiment, an example was given in which a specific process is performed by a single computer 22. However, the technology of this disclosure is not limited thereto, and a distributed processing method for the specific process may be used, which includes computer 22 and multiple other computers.

[0154] In the above embodiment, an example was given in which the specific processing program 56 is stored in the storage 32, but the technology of this disclosure is not limited thereto. For example, the specific processing program 56 may be stored in a portable, computer-readable, non-temporary storage medium such as a USB (Universal Serial Bus) memory. The specific processing program 56 stored in the non-temporary storage medium is installed in the computer 22 of the data processing device 12. The processor 28 executes specific processing according to the specific processing program 56.

[0155] Alternatively, the specific processing program 56 may be stored in a storage device such as a server connected to the data processing device 12 via the network 54, and the specific processing program 56 may be downloaded and installed on the computer 22 in response to a request from the data processing device 12.

[0156] Furthermore, it is not necessary to store the entirety of the specific processing program 56 in a storage device such as a server connected to the data processing device 12 via the network 54, or to store the entirety of the specific processing program 56 in the storage 32; it is acceptable to store only a portion of the specific processing program 56.

[0157] The following types of processors can be used as hardware resources to perform specific processing. Examples of processors include a CPU, a general-purpose processor that functions as a hardware resource to perform specific processing by executing software, i.e., a program. Other examples of processors include dedicated electrical circuits, such as FPGAs (Field-Programmable Gate Arrays), PLDs (Programmable Logic Devices), or ASICs (Application Specific Integrated Circuits), which have circuit configurations specifically designed to perform specific processing. All of these processors have built-in or connected memory, and all of them perform specific processing by using memory.

[0158] The hardware resource that performs a specific process may consist of one of these various processors, or it may consist of a combination of two or more processors of the same or different types (for example, a combination of multiple FPGAs, or a combination of a CPU and an FPGA). Alternatively, the hardware resource that performs a specific process may consist of a single processor.

[0159] Examples of configurations using a single processor include, firstly, a configuration in which one or more CPUs and software are combined to form a single processor, and this processor functions as a hardware resource that performs a specific process. Secondly, there is a configuration using a processor that realizes the functions of the entire system, including multiple hardware resources that perform a specific process, on a single IC chip, as exemplified by SoCs (System-on-a-chip). In this way, a specific process is realized using one or more of the above types of processors as hardware resources.

[0160] Furthermore, the hardware structure of these various processors can more specifically utilize electrical circuits that combine circuit elements such as semiconductor devices. Also, the specific processing described above is merely an example. Therefore, it goes without saying that unnecessary steps can be deleted, new steps added, or the processing order rearranged, as long as it does not deviate from the main purpose.

[0161] Furthermore, although the above-described examples were divided into four embodiments, some or all of these embodiments may be combined. Also, the smart device 14, smart glasses 214, headset terminal 314, and robot 414 are just examples, and they may be combined, or other devices may be used. Also, although the above-described examples were divided into two embodiments, Embodiment 1 and Embodiment 2, these may be combined.

[0162] The descriptions and illustrations presented above are detailed explanations of the technical aspects of this disclosure and are merely examples of the technical aspects. For example, the above descriptions of the structure, function, operation, and effect are examples of the structure, function, operation, and effect of the technical aspects of this disclosure. Therefore, it goes without saying that you may delete unnecessary parts, add new elements, or replace elements in the descriptions and illustrations presented above, as long as you do not deviate from the essence of the technical aspects of this disclosure. Furthermore, in order to avoid confusion and facilitate understanding of the technical aspects of this disclosure, explanations of common technical knowledge and other things that do not require special explanation to enable the implementation of the technical aspects of this disclosure have been omitted from the descriptions and illustrations presented above.

[0163] All documents, patent applications, and technical standards described herein are incorporated by reference to the same extent as if each individual document, patent application, and technical standard were specifically and individually noted to be incorporated by reference.

[0164] (Note 1) A collection unit that collects individual information, A storage unit that stores the information collected by the collection unit in a database, The filming department is responsible for recording and saving basic work videos, A generation unit that generates individual videos based on the information stored in the storage unit, An audio unit that adds explanatory audio and commentary text to the video generated by the generation unit, The system includes a provisioning unit that provides the video generated by the generation unit to the person concerned. A system characterized by the following features. (Note 2) The aforementioned collection unit is We collect general information about each person with a disability and store it in a database, including their movements, challenges, events, and actions. The system described in Appendix 1, characterized by the features described herein. (Note 3) The storage unit is The collected information is stored in a database and updated as needed. The system described in Appendix 1, characterized by the features described herein. (Note 4) The aforementioned imaging unit is Record and save a basic video of the work process. The system described in Appendix 1, characterized by the features described herein. (Note 5) The generating unit is Generate individual videos using generation AI. The system described in Appendix 1, characterized by the features described herein. (Note 6) The aforementioned audio unit is Add explanatory audio and commentary to the generated video. The system described in Appendix 1, characterized by the features described herein. (Note 7) The aforementioned supply unit is, Provide the generated video to the person concerned. The system described in Appendix 1, characterized by the features described herein. (Note 8) The aforementioned collection unit is It estimates the user's emotions and adjusts the timing of information collection based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 9) The aforementioned collection unit is Analyze the past behavioral history of people with disabilities and select the most suitable information gathering method. The system described in Appendix 1, characterized by the features described herein. (Note 10) The aforementioned collection unit is When collecting information, filtering is performed based on the current health status and living situation of people with disabilities. The system described in Appendix 1, characterized by the features described herein. (Note 11) The aforementioned collection unit is It estimates the user's emotions and prioritizes the information to collect based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 12) The aforementioned collection unit is When gathering information, prioritize collecting highly relevant information by considering the geographical location of persons with disabilities. The system described in Appendix 1, characterized by the features described herein. (Note 13) The aforementioned collection unit is When gathering information, analyze the social media activities of people with disabilities and collect relevant information. The system described in Appendix 1, characterized by the features described herein. (Note 14) The storage unit is It estimates the user's emotions and adjusts the data update frequency based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 15) The storage unit is To maintain the integrity of accumulated data, data cleansing is performed regularly. The system described in Appendix 1, characterized by the features described herein. (Note 16) The storage unit is It estimates user sentiment and prioritizes data based on the estimated user sentiment. The system described in Appendix 1, characterized by the features described herein. (Note 17) The storage unit is The accumulated data is classified and efficiently managed based on the attribute information of people with disabilities. The system described in Appendix 1, characterized by the features described herein. [Explanation of symbols]

[0165] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Devices 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robots

Claims

1. A collection unit that collects individual information, A storage unit that stores the information collected by the collection unit in a database, The filming department is responsible for recording and saving basic work videos, A generation unit that generates individual videos based on the information stored in the storage unit, An audio unit that adds explanatory audio and commentary text to the video generated by the generation unit, The system includes a provisioning unit that provides the video generated by the generation unit to the person concerned. A system characterized by the following features.

2. The aforementioned collection unit is We collect general information about each person with a disability and store it in a database, including their movements, challenges, events, and actions. The system according to feature 1.

3. The storage unit is The collected information is stored in a database and updated as needed. The system according to feature 1.

4. The aforementioned imaging unit is Record and save a basic video of the work process. The system according to feature 1.

5. The generating unit is Generate individual videos using generation AI. The system according to feature 1.

6. The aforementioned audio unit is Add explanatory audio and commentary to the generated video. The system according to feature 1.

7. The aforementioned supply unit is, Provide the generated video to the person concerned. The system according to feature 1.

8. The aforementioned collection unit is It estimates the user's emotions and adjusts the timing of information collection based on the estimated user emotions. The system according to feature 1.

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