system

Abstract

The system according to this embodiment aims to provide training to accurately understand the questioner's intent and respond appropriately. [Solution] The system according to the embodiment comprises a reception unit, a generation unit, and a provision unit. The reception unit receives requests to view videos. The generation unit generates advice based on the information received by the reception unit. The provision unit provides videos containing the advice generated by the generation unit.

Description

Technical Field

[0004] ,

[0006] , , , ,

[0005] , , , ,

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

Background Art

[0002] Patent Document 1 discloses a method for controlling a persona chatbot, which is performed by at least one processor, and includes 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, sufficient training for accurately understanding the intention of the questioner and appropriately responding thereto has not been carried out, and there is room for improvement.

[0005] The system according to the embodiment aims to provide training for accurately understanding the intention of the questioner and appropriately responding thereto.

Means for Solving the Problems

[0006] The system according to the embodiment includes a reception unit, a generation unit, and a provision unit. The reception unit performs reception for viewing a video. The generation unit generates advice based on the information received by the reception unit. The provision unit provides a video including the advice generated by the generation unit. [Effects of the Invention]

[0007] The system according to this embodiment can provide training to accurately understand the questioner's intent and respond appropriately. [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 signed communication interface (I / F) is an interface that includes a communication processor and an antenna. The communication interface manages communication between multiple computers. Examples of communication standards applicable to the communication interface 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 includes a computer 36, a reception device 38, an output device 40, a camera 42, and a communication I / F 44. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. Also, the reception device 38, the output device 40, and the camera 42 are 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 training system according to an embodiment of the present invention is a system for accurately understanding the intent of the questioner and responding appropriately in interviews and communication. This training system allows the user to view a video of themselves and generates a video that includes advice comments generated by AI. Furthermore, the AI ​​recommends helpful videos and encourages the learner to watch them. The recommended videos can also be generated with the faces and voices of the people in them replaced with those of the learner themselves, allowing for a realistic understanding of the educational content. This system simulates realistic situations and trains the learner in understanding and responding to questions through its unique question generation and evaluation mechanism. Feedback is provided in both text and video, allowing the learner to objectively understand their own areas for improvement by watching a video of themselves. This enables the learner to hone their skills in a way that is relevant to the real world and deepen their understanding of questions through daily training. For example, the system allows the user to view a video of themselves. For example, an interview scene is recorded, and the AI ​​analyzes the video to generate advice comments. Next, the user is provided with a video that includes the generated advice comments. This allows the user to objectively understand their own areas for improvement. Furthermore, the AI ​​recommends helpful videos and encourages the learner to watch them. For example, the AI ​​selects and provides videos to improve communication skills to learners. The recommended videos can even be generated with the learner's own face and voice, allowing for a more realistic understanding of the educational content. This system enables users to train their understanding and response to questions while simulating real-world situations. For instance, a user can answer a question generated by the AI ​​and receive video feedback on their response. This allows users to hone their skills in a real-world context and deepen their understanding of questions through daily training. The training system allows users to train their understanding and response to questions by providing videos that show them their own image and include AI-generated advice.

[0029] The training system according to this embodiment comprises a reception unit, a generation unit, and a provision unit. The reception unit receives information for viewing a video. The reception unit can, for example, receive user input. The reception unit can also receive sensor information. For example, the reception unit receives information for viewing a video based on information entered by the user. The generation unit generates advice based on the information received by the reception unit. The generation unit can, for example, generate advice using an AI algorithm. The generation unit can also generate advice by referring to a database. For example, the generation unit analyzes the user's input information using an AI algorithm and generates appropriate advice. The provision unit provides a video containing the advice generated by the generation unit. The provision unit can, for example, provide the video using streaming technology. The provision unit can also provide the video in download format. For example, the provision unit streams the video containing the generated advice to the user. As a result, the training system allows the user to train in understanding and responding to questions by viewing a video of themselves and being provided with a video containing advice generated by AI.

[0030] The reception desk receives information for viewing videos. Specifically, when a user accesses the training system and enters information about the video they want to watch, the reception desk receives that information. For example, a user can select specific training content or themes and send that selection information to the reception desk. The reception desk can also receive sensor information. For example, if a user is wearing a wearable device, it can receive data such as heart rate and exercise volume from that device. This allows the reception desk to collect information for video viewing that is tailored to the user's current state and needs. Furthermore, the reception desk can refer to the user's past viewing and training history to receive more personalized information. For example, it can suggest the most suitable videos to the user based on the videos and training content they have watched in the past. This allows the reception desk to efficiently receive information that meets the user's needs and enhance the overall effectiveness of the training system.

[0031] The generation unit generates advice based on the information received by the reception unit. Specifically, it uses an AI algorithm to analyze the user's input information and generate appropriate advice. For example, based on the training goals and current status entered by the user, the AI ​​suggests the optimal training method and points to note. The generation unit can also generate advice by referring to a database. The database stores past training data and expert advice, and uses this to generate specific advice for the user. For example, it can provide detailed explanations of specific training methods or points to avoid common mistakes. Furthermore, the generation unit can analyze the user's sensor information in real time and generate appropriate advice on the spot. For example, if the user's heart rate exceeds a certain range, it will generate advice prompting them to take a break. In this way, the generation unit can provide highly accurate advice tailored to the user's needs and condition, maximizing the effectiveness of training.

[0032] The service provider provides videos containing advice generated by the generation unit. Specifically, videos can be provided using streaming technology. For example, when a user accesses the training system, videos containing generated advice are streamed in real time. The service provider can also provide videos in downloadable format. Users can download videos as needed and watch them offline. Furthermore, the service provider can collect video viewing history and feedback to improve future training. For example, it can record how users reacted to specific advice and adjust the advice for the next session based on that information. This allows the service provider to always provide users with the latest and most optimal training videos, thereby enhancing the effectiveness of their training. In addition, the service provider supports multiple devices and platforms, allowing users to watch videos on various devices such as smartphones, tablets, and PCs. This improves user convenience and promotes the use of the training system.

[0033] The training system includes a replacement unit that replaces the faces and voices of the characters. The replacement unit can, for example, replace the characters' faces using deepfake technology. It can also replace the characters' voices using speech synthesis technology. For instance, the replacement unit can use deepfake technology to replace the characters' faces with the user's own face. This allows for a more realistic understanding of the educational content by replacing the characters' faces and voices with those of the user.

[0034] The training system includes a question generation unit that generates questions. The question generation unit can generate questions using, for example, natural language processing techniques. It can also generate questions by referring to a database. For instance, the question generation unit analyzes user input using natural language processing techniques and generates appropriate questions. This allows for training in question understanding and response through a unique question generation mechanism.

[0035] The training system includes a feedback unit that provides feedback. The feedback unit can provide feedback in, for example, text format. It can also provide feedback in video format. For example, the feedback unit provides feedback to the user in text format. This allows users to objectively understand their own areas for improvement by providing feedback in both text and video formats.

[0036] The generation unit can record the interview scene, analyze the video, and generate advice comments. For example, the generation unit records the interview scene using a recording device. The generation unit then analyzes the recorded video using an analysis algorithm and generates advice comments. For example, the generation unit records the interview scene using a recording device, analyzes the video, and generates advice comments. This allows for the provision of specific advice by analyzing the interview scene.

[0037] The service provider can provide users with videos containing generated advice comments. The service provider can provide videos using, for example, a distribution method. Alternatively, the service provider can provide videos through a user interface. For example, the service provider can distribute videos containing generated advice comments to users. By providing videos containing generated advice comments, users can more easily understand their areas for improvement.

[0038] The reception desk can analyze a user's past viewing history and automatically select the most suitable video. For example, the reception desk can use an analysis algorithm to analyze a user's past viewing history. It can also select the most suitable video using selection criteria. For instance, the reception desk can analyze the content of videos the user has watched in the past and suggest highly relevant videos. This allows the system to provide users with the most suitable videos by analyzing their past viewing history.

[0039] The reception desk can filter videos based on the user's current learning progress. For example, the reception desk can track the user's current learning progress using a progress tracking method. It can also filter videos using filtering criteria. For instance, the reception desk can analyze the user's current learning progress and prioritize suggesting unwatched videos. This allows for the provision of appropriate videos by filtering based on learning progress.

[0040] The reception desk can prioritize providing highly relevant videos to users when they are watching videos, taking into account their geographical location. For example, the reception desk can obtain the user's geographical location using a location information acquisition method. Furthermore, the reception desk can provide highly relevant videos using relevance evaluation criteria. For instance, if a user is in a specific region, the reception desk will prioritize suggesting videos related to that region. This allows for the provision of highly relevant videos by considering geographical location.

[0041] The reception desk can analyze a user's social media activity while they are watching a video and provide them with relevant videos. For example, the reception desk can use an analysis algorithm to analyze a user's social media activity. It can also provide relevant videos using relevance evaluation criteria. For instance, the reception desk can suggest relevant videos based on what the user has shared on social media. In this way, relevant videos can be provided by analyzing social media activity.

[0042] The generation unit can generate optimal advice by referring to the user's past response history when generating advice. For example, the generation unit can refer to the user's past response history using a history referencing method. Alternatively, the generation unit can generate optimal advice using an advice generation algorithm. For example, the generation unit can generate relevant advice based on advice the user has received in the past. This allows the system to provide optimal advice by referring to past response history.

[0043] The generation unit can adjust the level of detail of advice based on the user's current learning progress when generating advice. For example, the generation unit can determine the user's current learning progress using a progress tracking method. The generation unit can also adjust the level of detail of advice using a level of detail adjustment criterion. For example, if the user's learning progress is advanced, the generation unit will generate detailed advice. This allows for the provision of appropriate advice by adjusting the level of detail based on learning progress.

[0044] The generation unit can determine the priority of advice based on the user's submission timing when generating advice. For example, the generation unit can determine the user's submission timing using a submission timing tracking method. The generation unit can also determine the priority of advice using priority determination criteria. For example, if a user submits early, the generation unit will prioritize generating detailed advice. This allows for timely advice delivery by prioritizing advice based on submission timing.

[0045] The generation unit can adjust the order of advice based on the user's relevance when generating advice. For example, the generation unit can evaluate the user's relevance using relevance evaluation criteria. It can also adjust the order of advice using an ordering method. For example, the generation unit can prioritize generating advice that is most relevant to the user's current challenges. This allows for the provision of effective advice by adjusting the order of advice based on relevance.

[0046] The service provider can select the optimal delivery method when providing videos by referring to the user's past viewing history. For example, the service provider can refer to the user's past viewing history using a history referencing method. Alternatively, the service provider can select the optimal delivery method using delivery method selection criteria. For example, the service provider can provide videos in a similar format to videos the user has previously viewed. This allows the service provider to select the optimal delivery method by referring to past viewing history.

[0047] The service provider can customize the delivery method based on the user's current learning progress when providing videos. For example, the service provider can use a progress tracking method to understand the user's current learning progress. Furthermore, the service provider can customize the delivery method using customization criteria. For example, if the user's learning progress is advanced, the service provider can provide a more detailed video. This allows for the provision of appropriate videos by customizing the delivery method based on learning progress.

[0048] The service provider can select the optimal delivery method when providing videos, taking into account the user's geographical location information. For example, the service provider can obtain the user's geographical location information using a location information acquisition method. Alternatively, the service provider can select the optimal delivery method using delivery method selection criteria. For example, if the user is in a specific region, the service provider can prioritize providing videos related to that region. This allows for the selection of the optimal delivery method by considering geographical location information.

[0049] The service provider can analyze users' social media activity and propose delivery methods when providing videos. For example, the service provider can analyze users' social media activity using an analysis algorithm. Alternatively, the service provider can propose delivery methods using suggested criteria. For example, the service provider can provide relevant videos based on content shared by users on social media. This allows for the provision of relevant videos by analyzing social media activity.

[0050] The replacement unit can select the optimal replacement method when replacing the faces and voices of characters by referring to the user's past viewing history. For example, the replacement unit can refer to the user's past viewing history using a history referencing method. Alternatively, the replacement unit can select the optimal replacement method using replacement method selection criteria. For example, the replacement unit can select the optimal replacement method based on the characteristics of characters the user has previously enjoyed watching. This allows the optimal replacement method to be selected by referring to past viewing history.

[0051] The replacement unit can select the optimal replacement method when replacing the faces and voices of characters, taking into account the user's geographical location information. For example, the replacement unit can acquire the user's geographical location information using a location information acquisition method. The replacement unit can also select the optimal replacement method using replacement method selection criteria. For example, if the user is in a specific region, the replacement unit will prioritize replacing characters with faces and voices associated with that region. In this way, the optimal replacement method can be selected by taking geographical location information into consideration.

[0052] The question generation unit can generate the most suitable question by referring to the user's past answer history during question generation. For example, the question generation unit can refer to the user's past answer history using a history referencing method. Alternatively, the question generation unit can generate the most suitable question using a question generation algorithm. For example, the question generation unit can generate related questions based on questions the user has answered in the past. This allows the system to generate the most suitable question by referring to the user's past answer history.

[0053] The question generation unit can adjust the level of detail of questions based on the user's current learning progress when generating questions. For example, the question generation unit can grasp the user's current learning progress using a progress tracking method. Furthermore, the question generation unit can adjust the level of detail of questions using a level of detail adjustment criterion. For example, if the user's learning progress is advanced, the question generation unit will generate more detailed questions. This allows for the generation of appropriate questions by adjusting the level of detail based on learning progress.

[0054] The question generation unit can generate optimal questions by considering the user's geographical location information during question generation. For example, the question generation unit can obtain the user's geographical location information using a location information acquisition method. Furthermore, the question generation unit can generate optimal questions using question generation criteria. For example, if the user is in a specific region, the question generation unit will prioritize generating questions related to that region. This allows for the generation of optimal questions by considering geographical location information.

[0055] The feedback unit can provide optimal feedback by referring to the user's past response history when providing feedback. For example, the feedback unit can refer to the user's past response history using a history referencing method. Alternatively, the feedback unit can provide optimal feedback using a feedback provision algorithm. For example, the feedback unit can provide relevant feedback based on feedback the user has received in the past. This allows the system to provide optimal feedback by referring to the past response history.

[0056] The feedback unit can adjust the level of detail of the feedback provided based on the user's current learning progress. For example, the feedback unit can use a progress tracking method to understand the user's current learning progress. It can also adjust the level of detail of the feedback using a detail adjustment criterion. For instance, if the user's learning progress is advanced, the feedback unit will provide more detailed feedback. This allows for the provision of appropriate feedback by adjusting the level of detail based on learning progress.

[0057] The feedback unit can provide optimal feedback by considering the user's geographical location information when providing feedback. For example, the feedback unit can acquire the user's geographical location information using a location information acquisition method. Furthermore, the feedback unit can provide optimal feedback using criteria for selecting the delivery method. For example, if the user is in a specific region, the feedback unit will prioritize providing feedback related to that region. This allows for the provision of optimal feedback by considering geographical location information.

[0058] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0059] The training system may include a learning style analysis unit that analyzes the user's learning style and proposes the optimal training method. For example, the learning style analysis unit analyzes the user's past learning data to identify learning styles such as visual, auditory, and tactile. For instance, visual learners can be provided with videos that heavily utilize diagrams and graphs. Auditory learners can be provided with videos featuring comprehensive audio commentary. Tactile learners can be provided with videos that include interactive elements. This allows for effective learning by providing the optimal training method tailored to the user's learning style.

[0060] Training systems can incorporate gamification elements to maintain user motivation. For example, users can earn badges or points when they achieve specific training goals. Furthermore, ranking systems that allow users to compete with others can be implemented. For instance, rankings can be updated weekly based on training performance, with rewards offered to top-ranking users. Virtual rewards or titles can also be earned based on training progress. This can increase user motivation and encourage continuous learning.

[0061] The training system can monitor the user's health status and provide training tailored to that status. For example, it can acquire the user's heart rate and sleep data to assess their health. If the user is fatigued, it can suggest lighter training. Conversely, if the user is energetic, it can offer more challenging training. Furthermore, if the user sets a specific health goal, the system can create a training plan tailored to that goal. This allows for effective learning by providing optimal training content based on the user's health status.

[0062] The training system can create individualized learning plans based on the user's learning history. For example, it can analyze past training data to identify the user's strengths and weaknesses. It can then suggest training to further develop their strengths and provide training to overcome their weaknesses. Furthermore, it can adjust the frequency and content of training according to the user's learning pace. This enables effective learning by providing an optimal learning plan based on the user's learning history.

[0063] The training system can include an environment adjustment unit to optimize the user's learning environment. For example, it can detect the noise level around the user and suggest a quiet learning environment. It can also adjust the brightness of the lighting to provide an eye-friendly environment. Furthermore, it can monitor temperature and humidity to maintain a comfortable learning environment. By optimizing the user's learning environment in this way, effective learning becomes possible.

[0064] The training system can provide customizable training modules tailored to the user's learning goals. For example, if a user wants to acquire a specific skill, a training module focused on that skill can be provided. If a user wants to achieve results in a short period, an intensive training module can be offered. Furthermore, if a user wants to create a long-term learning plan, a progressively advancing training module can be provided. This allows for effective learning by providing the optimal training module for each user's learning objectives.

[0065] The following briefly describes the processing flow for example form 1.

[0066] Step 1: The reception unit receives information for viewing the video. The reception unit can, for example, receive user input. It can also receive sensor information. Specifically, it receives information for viewing the video based on the information entered by the user. Step 2: The generation unit generates advice based on the information received by the reception unit. The generation unit can generate advice using, for example, an AI algorithm. It can also generate advice by referring to a database. Specifically, it analyzes the user's input information using an AI algorithm and generates appropriate advice. Step 3: The provider unit provides a video containing the advice generated by the generator unit. The provider unit can provide the video using, for example, streaming technology. It can also provide the video in download format. Specifically, it streams the video containing the generated advice to the user.

[0067] (Example of form 2) The training system according to an embodiment of the present invention is a system for accurately understanding the intent of the questioner and responding appropriately in interviews and communication. This training system allows the user to view a video of themselves and generates a video that includes advice comments generated by AI. Furthermore, the AI ​​recommends helpful videos and encourages the learner to watch them. The recommended videos can also be generated with the faces and voices of the people in them replaced with those of the learner themselves, allowing for a realistic understanding of the educational content. This system simulates realistic situations and trains the learner in understanding and responding to questions through its unique question generation and evaluation mechanism. Feedback is provided in both text and video, allowing the learner to objectively understand their own areas for improvement by watching a video of themselves. This enables the learner to hone their skills in a way that is relevant to the real world and deepen their understanding of questions through daily training. For example, the system allows the user to view a video of themselves. For example, an interview scene is recorded, and the AI ​​analyzes the video to generate advice comments. Next, the user is provided with a video that includes the generated advice comments. This allows the user to objectively understand their own areas for improvement. Furthermore, the AI ​​recommends helpful videos and encourages the learner to watch them. For example, the AI ​​selects and provides videos to improve communication skills to learners. The recommended videos can even be generated with the learner's own face and voice, allowing for a more realistic understanding of the educational content. This system enables users to train their understanding and response to questions while simulating real-world situations. For instance, a user can answer a question generated by the AI ​​and receive video feedback on their response. This allows users to hone their skills in a real-world context and deepen their understanding of questions through daily training. The training system allows users to train their understanding and response to questions by providing videos that show them their own image and include AI-generated advice.

[0068] The training system according to this embodiment comprises a reception unit, a generation unit, and a provision unit. The reception unit receives information for viewing a video. The reception unit can, for example, receive user input. The reception unit can also receive sensor information. For example, the reception unit receives information for viewing a video based on information entered by the user. The generation unit generates advice based on the information received by the reception unit. The generation unit can, for example, generate advice using an AI algorithm. The generation unit can also generate advice by referring to a database. For example, the generation unit analyzes the user's input information using an AI algorithm and generates appropriate advice. The provision unit provides a video containing the advice generated by the generation unit. The provision unit can, for example, provide the video using streaming technology. The provision unit can also provide the video in download format. For example, the provision unit streams the video containing the generated advice to the user. As a result, the training system allows the user to train in understanding and responding to questions by viewing a video of themselves and being provided with a video containing advice generated by AI.

[0069] The reception desk receives information for viewing videos. Specifically, when a user accesses the training system and enters information about the video they want to watch, the reception desk receives that information. For example, a user can select specific training content or themes and send that selection information to the reception desk. The reception desk can also receive sensor information. For example, if a user is wearing a wearable device, it can receive data such as heart rate and exercise volume from that device. This allows the reception desk to collect information for video viewing that is tailored to the user's current state and needs. Furthermore, the reception desk can refer to the user's past viewing and training history to receive more personalized information. For example, it can suggest the most suitable videos to the user based on the videos and training content they have watched in the past. This allows the reception desk to efficiently receive information that meets the user's needs and enhance the overall effectiveness of the training system.

[0070] The generation unit generates advice based on the information received by the reception unit. Specifically, it uses an AI algorithm to analyze the user's input information and generate appropriate advice. For example, based on the training goals and current status entered by the user, the AI ​​suggests the optimal training method and points to note. The generation unit can also generate advice by referring to a database. The database stores past training data and expert advice, and uses this to generate specific advice for the user. For example, it can provide detailed explanations of specific training methods or points to avoid common mistakes. Furthermore, the generation unit can analyze the user's sensor information in real time and generate appropriate advice on the spot. For example, if the user's heart rate exceeds a certain range, it will generate advice prompting them to take a break. In this way, the generation unit can provide highly accurate advice tailored to the user's needs and condition, maximizing the effectiveness of training.

[0071] The service provider provides videos containing advice generated by the generation unit. Specifically, videos can be provided using streaming technology. For example, when a user accesses the training system, videos containing generated advice are streamed in real time. The service provider can also provide videos in downloadable format. Users can download videos as needed and watch them offline. Furthermore, the service provider can collect video viewing history and feedback to improve future training. For example, it can record how users reacted to specific advice and adjust the advice for the next session based on that information. This allows the service provider to always provide users with the latest and most optimal training videos, thereby enhancing the effectiveness of their training. In addition, the service provider supports multiple devices and platforms, allowing users to watch videos on various devices such as smartphones, tablets, and PCs. This improves user convenience and promotes the use of the training system.

[0072] The training system includes a replacement unit that replaces the faces and voices of the characters. The replacement unit can, for example, replace the characters' faces using deepfake technology. It can also replace the characters' voices using speech synthesis technology. For instance, the replacement unit can use deepfake technology to replace the characters' faces with the user's own face. This allows for a more realistic understanding of the educational content by replacing the characters' faces and voices with those of the user.

[0073] The training system includes a question generation unit that generates questions. The question generation unit can generate questions using, for example, natural language processing techniques. It can also generate questions by referring to a database. For instance, the question generation unit analyzes user input using natural language processing techniques and generates appropriate questions. This allows for training in question understanding and response through a unique question generation mechanism.

[0074] The training system includes a feedback unit that provides feedback. The feedback unit can provide feedback in, for example, text format. It can also provide feedback in video format. For example, the feedback unit provides feedback to the user in text format. This allows users to objectively understand their own areas for improvement by providing feedback in both text and video formats.

[0075] The generation unit can record the interview scene, analyze the video, and generate advice comments. For example, the generation unit records the interview scene using a recording device. The generation unit then analyzes the recorded video using an analysis algorithm and generates advice comments. For example, the generation unit records the interview scene using a recording device, analyzes the video, and generates advice comments. This allows for the provision of specific advice by analyzing the interview scene.

[0076] The service provider can provide users with videos containing generated advice comments. The service provider can provide videos using, for example, a distribution method. Alternatively, the service provider can provide videos through a user interface. For example, the service provider can distribute videos containing generated advice comments to users. By providing videos containing generated advice comments, users can more easily understand their areas for improvement.

[0077] The reception desk can estimate the user's emotions and adjust the timing of video viewing based on the estimated emotions. For example, the reception desk might use an emotion estimation algorithm to estimate the user's emotions. It can also adjust the timing of video viewing using a timing adjustment method. For instance, if the reception desk is feeling stressed, it might suggest watching the video during a time when the user can relax. This allows for more effective learning by adjusting the timing of video viewing according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI may include, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI.

[0078] The reception desk can analyze a user's past viewing history and automatically select the most suitable video. For example, the reception desk can use an analysis algorithm to analyze a user's past viewing history. It can also select the most suitable video using selection criteria. For instance, the reception desk can analyze the content of videos the user has watched in the past and suggest highly relevant videos. This allows the system to provide users with the most suitable videos by analyzing their past viewing history.

[0079] The reception desk can filter videos based on the user's current learning progress. For example, the reception desk can track the user's current learning progress using a progress tracking method. It can also filter videos using filtering criteria. For instance, the reception desk can analyze the user's current learning progress and prioritize suggesting unwatched videos. This allows for the provision of appropriate videos by filtering based on learning progress.

[0080] The reception desk can estimate the user's emotions and determine the priority of videos to watch based on those estimated emotions. For example, the reception desk might use an emotion estimation algorithm to estimate the user's emotions. It can also use priority criteria to determine the priority of videos to watch. For instance, if the user is relaxed, the reception desk will prioritize suggesting videos with relaxing content. This allows for effective learning by prioritizing videos based on the user's emotions. Emotion estimation is achieved using emotion estimation functions, such as an emotion engine or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI.

[0081] The reception desk can prioritize providing highly relevant videos to users when they are watching videos, taking into account their geographical location. For example, the reception desk can obtain the user's geographical location using a location information acquisition method. Furthermore, the reception desk can provide highly relevant videos using relevance evaluation criteria. For instance, if a user is in a specific region, the reception desk will prioritize suggesting videos related to that region. This allows for the provision of highly relevant videos by considering geographical location.

[0082] The reception desk can analyze a user's social media activity while they are watching a video and provide them with relevant videos. For example, the reception desk can use an analysis algorithm to analyze a user's social media activity. It can also provide relevant videos using relevance evaluation criteria. For instance, the reception desk can suggest relevant videos based on what the user has shared on social media. In this way, relevant videos can be provided by analyzing social media activity.

[0083] The generation unit can estimate the user's emotions and adjust the way advice is expressed based on those emotions. For example, the generation unit might use an emotion estimation algorithm to estimate the user's emotions. It can also adjust the way advice is expressed using expression adjustment criteria. For instance, if the user is relaxed, the generation unit will generate advice in a calm tone. This allows for the provision of effective advice by adjusting its expression according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or a generation AI. The generation AI could be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI.

[0084] The generation unit can generate optimal advice by referring to the user's past response history when generating advice. For example, the generation unit can refer to the user's past response history using a history referencing method. Alternatively, the generation unit can generate optimal advice using an advice generation algorithm. For example, the generation unit can generate relevant advice based on advice the user has received in the past. This allows the system to provide optimal advice by referring to past response history.

[0085] The generation unit can adjust the level of detail of advice based on the user's current learning progress when generating advice. For example, the generation unit can determine the user's current learning progress using a progress tracking method. The generation unit can also adjust the level of detail of advice using a level of detail adjustment criterion. For example, if the user's learning progress is advanced, the generation unit will generate detailed advice. This allows for the provision of appropriate advice by adjusting the level of detail based on learning progress.

[0086] The generation unit can estimate the user's emotions and adjust the length of the advice based on the estimated emotions. For example, the generation unit might use an emotion estimation algorithm to estimate the user's emotions. It can also adjust the length of the advice using length adjustment criteria. For instance, if the user is relaxed, the generation unit might generate detailed advice. This allows for the provision of effective advice by adjusting the length according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or a generation AI. The generation AI could be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI.

[0087] The generation unit can determine the priority of advice based on the user's submission timing when generating advice. For example, the generation unit can determine the user's submission timing using a submission timing tracking method. The generation unit can also determine the priority of advice using priority determination criteria. For example, if a user submits early, the generation unit will prioritize generating detailed advice. This allows for timely advice delivery by prioritizing advice based on submission timing.

[0088] The generation unit can adjust the order of advice based on the user's relevance when generating advice. For example, the generation unit can evaluate the user's relevance using relevance evaluation criteria. It can also adjust the order of advice using an ordering method. For example, the generation unit can prioritize generating advice that is most relevant to the user's current challenges. This allows for the provision of effective advice by adjusting the order of advice based on relevance.

[0089] The service provider can estimate the user's emotions and adjust the video delivery method based on the estimated emotions. For example, the service provider might use an emotion estimation algorithm to estimate the user's emotions. It can also adjust the video delivery method using delivery method adjustment criteria. For example, if the user is relaxed, the service provider might deliver the video with calming music. This allows for effective learning by adjusting the video delivery method according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI.

[0090] The service provider can select the optimal delivery method when providing videos by referring to the user's past viewing history. For example, the service provider can refer to the user's past viewing history using a history referencing method. Alternatively, the service provider can select the optimal delivery method using delivery method selection criteria. For example, the service provider can provide videos in a similar format to videos the user has previously viewed. This allows the service provider to select the optimal delivery method by referring to past viewing history.

[0091] The service provider can customize the delivery method based on the user's current learning progress when providing videos. For example, the service provider can use a progress tracking method to understand the user's current learning progress. Furthermore, the service provider can customize the delivery method using customization criteria. For example, if the user's learning progress is advanced, the service provider can provide a more detailed video. This allows for the provision of appropriate videos by customizing the delivery method based on learning progress.

[0092] The service provider can estimate the user's emotions and determine the priority of video delivery based on the estimated emotions. For example, the service provider can estimate the user's emotions using an emotion estimation algorithm. The service provider can also determine the priority of video delivery using priority criteria. For example, if the user is relaxed, the service provider will prioritize providing videos with relaxing content. This allows for effective learning by prioritizing video delivery according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI.

[0093] The service provider can select the optimal delivery method when providing videos, taking into account the user's geographical location information. For example, the service provider can obtain the user's geographical location information using a location information acquisition method. Alternatively, the service provider can select the optimal delivery method using delivery method selection criteria. For example, if the user is in a specific region, the service provider can prioritize providing videos related to that region. This allows for the selection of the optimal delivery method by considering geographical location information.

[0094] The service provider can analyze users' social media activity and propose delivery methods when providing videos. For example, the service provider can analyze users' social media activity using an analysis algorithm. Alternatively, the service provider can propose delivery methods using suggested criteria. For example, the service provider can provide relevant videos based on content shared by users on social media. This allows for the provision of relevant videos by analyzing social media activity.

[0095] The substitution unit can estimate the user's emotions and adjust the method of substituting the characters' faces and voices based on the estimated user emotions. For example, the substitution unit estimates the user's emotions using an emotion estimation algorithm. It can also adjust the method of substituting the characters' faces and voices using substitution method adjustment criteria. For example, if the user is relaxed, the substitution unit will substitute the character with one that has a calm expression and voice. This allows for effective learning by adjusting the method of substituting the characters' faces and voices according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI.

[0096] The replacement unit can select the optimal replacement method when replacing the faces and voices of characters by referring to the user's past viewing history. For example, the replacement unit can refer to the user's past viewing history using a history referencing method. Alternatively, the replacement unit can select the optimal replacement method using replacement method selection criteria. For example, the replacement unit can select the optimal replacement method based on the characteristics of characters the user has previously enjoyed watching. This allows the optimal replacement method to be selected by referring to past viewing history.

[0097] The replacement unit can estimate the user's emotions and determine the priority of replacing characters' faces and voices based on the estimated user emotions. For example, the replacement unit estimates the user's emotions using an emotion estimation algorithm. It can also determine the priority of replacing characters' faces and voices using priority criteria. For example, if the user is relaxed, the replacement unit will prioritize replacing the user with characters whose faces and voices evoke a sense of relaxation. This allows for effective learning by determining the priority of character face and voice replacements according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI.

[0098] The replacement unit can select the optimal replacement method when replacing the faces and voices of characters, taking into account the user's geographical location information. For example, the replacement unit can acquire the user's geographical location information using a location information acquisition method. The replacement unit can also select the optimal replacement method using replacement method selection criteria. For example, if the user is in a specific region, the replacement unit will prioritize replacing characters with faces and voices associated with that region. In this way, the optimal replacement method can be selected by taking geographical location information into consideration.

[0099] The question generation unit can estimate the user's emotions and adjust the question generation method based on the estimated emotions. For example, the question generation unit might use an emotion estimation algorithm to estimate the user's emotions. It can also adjust the question generation method using generation method adjustment criteria. For instance, if the user is relaxed, the question generation unit might generate questions in a calm tone. This allows for effective learning by adjusting the question generation method according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or a generative AI. Generative AIs include, but are not limited to, text generation AIs (e.g., LLMs) or multimodal generation AIs.

[0100] The question generation unit can generate the most suitable question by referring to the user's past answer history during question generation. For example, the question generation unit can refer to the user's past answer history using a history referencing method. Alternatively, the question generation unit can generate the most suitable question using a question generation algorithm. For example, the question generation unit can generate related questions based on questions the user has answered in the past. This allows the system to generate the most suitable question by referring to the user's past answer history.

[0101] The question generation unit can adjust the level of detail of questions based on the user's current learning progress when generating questions. For example, the question generation unit can grasp the user's current learning progress using a progress tracking method. Furthermore, the question generation unit can adjust the level of detail of questions using a level of detail adjustment criterion. For example, if the user's learning progress is advanced, the question generation unit will generate more detailed questions. This allows for the generation of appropriate questions by adjusting the level of detail based on learning progress.

[0102] The question generation unit can estimate the user's emotions and determine the priority of questions based on the estimated emotions. For example, the question generation unit can estimate the user's emotions using an emotion estimation algorithm. It can also determine the priority of questions using priority criteria. For example, if the user is relaxed, the question generation unit will prioritize generating questions that promote relaxation. This allows for effective learning by prioritizing questions according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI may include, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI.

[0103] The question generation unit can generate optimal questions by considering the user's geographical location information during question generation. For example, the question generation unit can obtain the user's geographical location information using a location information acquisition method. Furthermore, the question generation unit can generate optimal questions using question generation criteria. For example, if the user is in a specific region, the question generation unit will prioritize generating questions related to that region. This allows for the generation of optimal questions by considering geographical location information.

[0104] The feedback unit can estimate the user's emotions and adjust the way feedback is delivered based on the estimated emotions. For example, the feedback unit might use an emotion estimation algorithm to estimate the user's emotions. It can also adjust the way feedback is delivered using delivery method adjustment criteria. For example, if the user is relaxed, the feedback unit might provide feedback in a calm tone. This allows for effective learning by adjusting the feedback delivery method according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI.

[0105] The feedback unit can provide optimal feedback by referring to the user's past response history when providing feedback. For example, the feedback unit can refer to the user's past response history using a history referencing method. Alternatively, the feedback unit can provide optimal feedback using a feedback provision algorithm. For example, the feedback unit can provide relevant feedback based on feedback the user has received in the past. This allows the system to provide optimal feedback by referring to the past response history.

[0106] The feedback unit can adjust the level of detail of the feedback provided based on the user's current learning progress. For example, the feedback unit can use a progress tracking method to understand the user's current learning progress. It can also adjust the level of detail of the feedback using a detail adjustment criterion. For instance, if the user's learning progress is advanced, the feedback unit will provide more detailed feedback. This allows for the provision of appropriate feedback by adjusting the level of detail based on learning progress.

[0107] The feedback unit can estimate the user's emotions and determine the priority of feedback based on the estimated emotions. For example, the feedback unit might use an emotion estimation algorithm to estimate the user's emotions. It can also determine the priority of feedback using priority criteria. For instance, if the user is relaxed, the feedback unit will prioritize providing relaxing feedback. This allows for effective learning by prioritizing feedback according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI.

[0108] The feedback unit can provide optimal feedback by considering the user's geographical location information when providing feedback. For example, the feedback unit can acquire the user's geographical location information using a location information acquisition method. Furthermore, the feedback unit can provide optimal feedback using criteria for selecting the delivery method. For example, if the user is in a specific region, the feedback unit will prioritize providing feedback related to that region. This allows for the provision of optimal feedback by considering geographical location information.

[0109] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0110] The training system may include a learning style analysis unit that analyzes the user's learning style and proposes the optimal training method. For example, the learning style analysis unit analyzes the user's past learning data to identify learning styles such as visual, auditory, and tactile. For instance, visual learners can be provided with videos that heavily utilize diagrams and graphs. Auditory learners can be provided with videos featuring comprehensive audio commentary. Tactile learners can be provided with videos that include interactive elements. This allows for effective learning by providing the optimal training method tailored to the user's learning style.

[0111] Training systems can incorporate gamification elements to maintain user motivation. For example, users can earn badges or points when they achieve specific training goals. Furthermore, ranking systems that allow users to compete with others can be implemented. For instance, rankings can be updated weekly based on training performance, with rewards offered to top-ranking users. Virtual rewards or titles can also be earned based on training progress. This can increase user motivation and encourage continuous learning.

[0112] The training system can estimate the user's emotions and customize the training content based on those emotions. For example, if the user is stressed, it can provide training that promotes relaxation. Conversely, if the user is focused, it can provide more challenging training. Furthermore, if the user is tired, it can suggest short, effective training sessions. This allows for more effective learning by providing optimal training content tailored to the user's emotions.

[0113] The training system can monitor the user's health status and provide training tailored to that status. For example, it can acquire the user's heart rate and sleep data to assess their health. If the user is fatigued, it can suggest lighter training. Conversely, if the user is energetic, it can offer more challenging training. Furthermore, if the user sets a specific health goal, the system can create a training plan tailored to that goal. This allows for effective learning by providing optimal training content based on the user's health status.

[0114] The training system can estimate the user's emotions and adjust the content of the feedback based on those emotions. For example, if the user is feeling down, it can provide feedback that includes words of encouragement. Conversely, if the user is confident, it can provide feedback that points out specific areas for improvement. Furthermore, if the user is feeling anxious, it can provide feedback that includes advice to help them calm down. This allows for effective learning by providing optimal feedback tailored to the user's emotions.

[0115] The training system can create individualized learning plans based on the user's learning history. For example, it can analyze past training data to identify the user's strengths and weaknesses. It can then suggest training to further develop their strengths and provide training to overcome their weaknesses. Furthermore, it can adjust the frequency and content of training according to the user's learning pace. This enables effective learning by providing an optimal learning plan based on the user's learning history.

[0116] The training system can estimate the user's emotions and adjust the training pace based on those emotions. For example, if the user is anxious, the training pace can be slowed down. Conversely, if the user is relaxed, the training pace can be increased. Furthermore, if the user is focused, the training pace can be optimized. This allows for effective learning by providing an optimal training pace tailored to the user's emotions.

[0117] The training system can include an environment adjustment unit to optimize the user's learning environment. For example, it can detect the noise level around the user and suggest a quiet learning environment. It can also adjust the brightness of the lighting to provide an eye-friendly environment. Furthermore, it can monitor temperature and humidity to maintain a comfortable learning environment. By optimizing the user's learning environment in this way, effective learning becomes possible.

[0118] The training system can estimate the user's emotions and adjust the training difficulty based on those emotions. For example, if the user is stressed, the training difficulty can be lowered. Conversely, if the user is relaxed, the training difficulty can be increased. Furthermore, if the user is focused, the training difficulty can be optimized. This allows for effective learning by providing the optimal training difficulty level according to the user's emotions.

[0119] The training system can provide customizable training modules tailored to the user's learning goals. For example, if a user wants to acquire a specific skill, a training module focused on that skill can be provided. If a user wants to achieve results in a short period, an intensive training module can be offered. Furthermore, if a user wants to create a long-term learning plan, a progressively advancing training module can be provided. This allows for effective learning by providing the optimal training module for each user's learning objectives.

[0120] The following briefly describes the processing flow for example form 2.

[0121] Step 1: The reception unit receives information for viewing the video. The reception unit can, for example, receive user input. It can also receive sensor information. Specifically, it receives information for viewing the video based on the information entered by the user. Step 2: The generation unit generates advice based on the information received by the reception unit. The generation unit can generate advice using, for example, an AI algorithm. It can also generate advice by referring to a database. Specifically, it analyzes the user's input information using an AI algorithm and generates appropriate advice. Step 3: The provider unit provides a video containing the advice generated by the generator unit. The provider unit can provide the video using, for example, streaming technology. It can also provide the video in download format. Specifically, it streams the video containing the generated advice to the user.

[0122] 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.

[0123] 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.

[0124] 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.

[0125] Each of the multiple elements described above, including the reception unit, generation unit, provision unit, replacement unit, question generation unit, and feedback unit, is implemented in at least one of the smart device 14 and the data processing device 12. For example, the reception unit is implemented by the control unit 46A of the smart device 14 and receives user input information. The generation unit is implemented by the specific processing unit 290 of the data processing device 12 and generates advice using an AI algorithm. The provision unit is implemented by the control unit 46A of the smart device 14 and streams a video containing the generated advice. The replacement unit is implemented by the specific processing unit 290 of the data processing device 12 and replaces the faces of characters with the user's own face using deepfake technology. The question generation unit is implemented by the specific processing unit 290 of the data processing device 12 and generates questions using natural language processing technology. The feedback unit is implemented by the control unit 46A of the smart device 14 and provides feedback in text and video formats. 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.

[0126] [Second Embodiment] Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment.

[0127] 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.

[0128] 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.

[0129] 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.

[0130] 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.

[0131] 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).

[0132] 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.

[0133] 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.

[0134] 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.

[0135] 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.

[0136] 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.

[0137] 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.).

[0138] 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.

[0139] 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.

[0140] 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.

[0141] Each of the multiple elements described above, including the reception unit, generation unit, provision unit, replacement unit, question generation unit, and feedback unit, is implemented in at least one of the smart glasses 214 and the data processing unit 12. For example, the reception unit is implemented by the control unit 46A of the smart glasses 214 and receives user input information. The generation unit is implemented by the specific processing unit 290 of the data processing unit 12 and generates advice using an AI algorithm. The provision unit is implemented by the control unit 46A of the smart glasses 214 and streams a video containing the generated advice. The replacement unit is implemented by the specific processing unit 290 of the data processing unit 12 and replaces the faces of characters with the user's own face using deepfake technology. The question generation unit is implemented by the specific processing unit 290 of the data processing unit 12 and generates questions using natural language processing technology. The feedback unit is implemented by the control unit 46A of the smart glasses 214 and provides feedback in text and video formats. The correspondence between each part and the device or control unit is not limited to the examples described above, and various modifications are possible.

[0142] [Third Embodiment] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[0143] 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.

[0144] 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.

[0145] 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.

[0146] 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.

[0147] 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).

[0148] 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.

[0149] 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.

[0150] 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.

[0151] 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.

[0152] 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.

[0153] 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.).

[0154] 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.

[0155] 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.

[0156] 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.

[0157] Each of the multiple elements described above, including the reception unit, generation unit, provision unit, replacement unit, question generation unit, and feedback unit, is implemented in at least one of the headset terminal 314 and the data processing unit 12. For example, the reception unit is implemented by the control unit 46A of the headset terminal 314 and receives user input information. The generation unit is implemented by the specific processing unit 290 of the data processing unit 12 and generates advice using an AI algorithm. The provision unit is implemented by the control unit 46A of the headset terminal 314 and streams a video containing the generated advice. The replacement unit is implemented by the specific processing unit 290 of the data processing unit 12 and replaces the faces of characters with the user's own face using deepfake technology. The question generation unit is implemented by the specific processing unit 290 of the data processing unit 12 and generates questions using natural language processing technology. The feedback unit is implemented by the control unit 46A of the headset terminal 314 and provides feedback in text and video formats. The correspondence between each part and the device or control unit is not limited to the examples described above, and various modifications are possible.

[0158] [Fourth Embodiment] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[0159] 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.

[0160] 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.

[0161] 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.

[0162] 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.

[0163] 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).

[0164] 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.

[0165] 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.

[0166] 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.

[0167] 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.

[0168] 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.

[0169] 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.

[0170] 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.).

[0171] 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.

[0172] 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.

[0173] 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.

[0174] Each of the multiple elements described above, including the reception unit, generation unit, provision unit, replacement unit, question generation unit, and feedback unit, is implemented in at least one of the robot 414 and the data processing unit 12. For example, the reception unit is implemented by the control unit 46A of the robot 414 and receives user input information. The generation unit is implemented by the specific processing unit 290 of the data processing unit 12 and generates advice using an AI algorithm. The provision unit is implemented by the control unit 46A of the robot 414 and streams a video containing the generated advice. The replacement unit is implemented by the specific processing unit 290 of the data processing unit 12 and replaces the faces of characters with the user's own face using deepfake technology. The question generation unit is implemented by the specific processing unit 290 of the data processing unit 12 and generates questions using natural language processing technology. The feedback unit is implemented by the control unit 46A of the robot 414 and provides feedback in text and video formats. The correspondence between each unit and the device or control unit is not limited to the examples described above and can be modified in various ways.

[0175] 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.

[0176] 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.

[0177] 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.

[0178] 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.

[0179] 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.

[0180] 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."

[0181] 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.

[0182] 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.

[0183] 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.

[0184] 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.

[0185] 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.

[0186] 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.

[0187] 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.

[0188] 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.

[0189] 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.

[0190] 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.

[0191] 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.

[0192] 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.

[0193] (Note 1) A reception area for viewing videos, A generation unit that generates advice based on the information received by the reception unit, The system comprises a providing unit that provides a video including advice generated by the generation unit. A system characterized by the following features. (Note 2) It includes a replacement function that replaces the faces and voices of the characters. The system described in Appendix 1, characterized by the features described herein. (Note 3) It includes a question generation unit that generates questions. The system described in Appendix 1, characterized by the features described herein. (Note 4) Equipped with a feedback unit that provides feedback. The system described in Appendix 1, characterized by the features described herein. (Note 5) The generating unit is The interview scene is recorded, and the video is analyzed to generate advice comments. The system described in Appendix 1, characterized by the features described herein. (Note 6) The aforementioned supply unit is, Provide users with a video containing generated advice comments. The system described in Appendix 1, characterized by the features described herein. (Note 7) The aforementioned reception unit is It estimates the user's emotions and adjusts the timing of video viewing based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 8) The aforementioned reception unit is It analyzes the user's past viewing history and automatically selects the most suitable videos. The system described in Appendix 1, characterized by the features described herein. (Note 9) The aforementioned reception unit is When viewing videos, filtering is performed based on the user's current learning progress. The system described in Appendix 1, characterized by the features described herein. (Note 10) The aforementioned reception unit is It estimates the user's emotions and prioritizes videos to watch based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 11) The aforementioned reception unit is When users watch videos, the system prioritizes providing relevant videos by considering their geographical location. The system described in Appendix 1, characterized by the features described herein. (Note 12) The aforementioned reception unit is When users watch videos, the system analyzes their social media activity and provides relevant videos. The system described in Appendix 1, characterized by the features described herein. (Note 13) The generating unit is It estimates the user's emotions and adjusts the way advice is presented based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 14) The generating unit is When generating advice, the system refers to the user's past response history to generate the most suitable advice. The system described in Appendix 1, characterized by the features described herein. (Note 15) The generating unit is When generating advice, adjust the level of detail of the advice based on the user's current learning progress. The system described in Appendix 1, characterized by the features described herein. (Note 16) The generating unit is It estimates the user's emotions and adjusts the length of the advice based on the estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 17) The generating unit is When generating advice, the system prioritizes the advice based on when the user submitted it. The system described in Appendix 1, characterized by the features described herein. (Note 18) The generating unit is When generating advice, the order of advice is adjusted based on the relevance of the user. The system described in Appendix 1, characterized by the features described herein. (Note 19) The aforementioned supply unit is, We estimate the user's emotions and adjust the way we deliver videos based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 20) The aforementioned supply unit is, When providing videos, the system selects the optimal delivery method by referring to the user's past viewing history. The system described in Appendix 1, characterized by the features described herein. (Note 21) The aforementioned supply unit is, When providing videos, customize the delivery method based on the user's current learning progress. The system described in Appendix 1, characterized by the features described herein. (Note 22) The aforementioned supply unit is, The system estimates user sentiment and prioritizes video content based on that estimated sentiment. The system described in Appendix 1, characterized by the features described herein. (Note 23) The aforementioned supply unit is, When providing videos, the optimal delivery method is selected considering the user's geographical location information. The system described in Appendix 1, characterized by the features described herein. (Note 24) The aforementioned supply unit is, When providing videos, we analyze users' social media activity and suggest methods for distribution. The system described in Appendix 1, characterized by the features described herein. (Note 25) The aforementioned replacement part is The system estimates the user's emotions and adjusts how the characters' faces and voices are replaced based on those estimated emotions. The system described in Appendix 2, characterized by the features described herein. (Note 26) The aforementioned replacement part is When replacing the faces and voices of characters, the system selects the optimal replacement method by referring to the user's past viewing history. The system described in Appendix 2, characterized by the features described herein. (Note 27) The aforementioned replacement part is The system estimates the user's emotions and determines the priority for replacing characters' faces and voices based on those estimated emotions. The system described in Appendix 2, characterized by the features described herein. (Note 28) The aforementioned replacement part is When replacing the faces and voices of characters, the system selects the optimal replacement method while considering the user's geographical location information. The system described in Appendix 2, characterized by the features described herein. (Note 29) The aforementioned question generation unit, We estimate the user's emotions and adjust how questions are generated based on those estimated emotions. The system described in Appendix 3, characterized by the features described herein. (Note 30) The aforementioned question generation unit, When generating questions, the system refers to the user's past answer history to generate the most suitable questions. The system described in Appendix 3, characterized by the features described herein. (Note 31) The aforementioned question generation unit, When generating questions, adjust the level of detail based on the user's current learning progress. The system described in Appendix 3, characterized by the features described herein. (Note 32) The aforementioned question generation unit, The system estimates the user's emotions and prioritizes questions based on those estimated emotions. The system described in Appendix 3, characterized by the features described herein. (Note 33) The aforementioned question generation unit, When generating questions, the system takes the user's geographical location into consideration to generate the most appropriate questions. The system described in Appendix 3, characterized by the features described herein. (Note 34) The aforementioned feedback unit is It estimates the user's emotions and adjusts how feedback is delivered based on those estimated emotions. The system described in Appendix 4, characterized by the features described herein. (Note 35) The aforementioned feedback unit is When providing feedback, we refer to the user's past response history to provide the most appropriate feedback. The system described in Appendix 4, characterized by the features described herein. (Note 36) The aforementioned feedback unit is When providing feedback, adjust the level of detail in the feedback based on the user's current learning progress. The system described in Appendix 4, characterized by the features described herein. (Note 37) The aforementioned feedback unit is It estimates the user's emotions and prioritizes feedback based on those estimated emotions. The system described in Appendix 4, characterized by the features described herein. (Note 38) The aforementioned feedback unit is When providing feedback, we take the user's geographical location into consideration to provide the most appropriate feedback. The system described in Appendix 4, characterized by the features described herein. [Explanation of Symbols]

[0194] 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 reception area for viewing videos, A generation unit that generates advice based on the information received by the reception unit, The system comprises a providing unit that provides a video including advice generated by the generation unit. A system characterized by the following features.

2. It includes a replacement function that replaces the faces and voices of the characters. The system according to feature 1.

3. It includes a question generation unit that generates questions. The system according to feature 1.

4. Equipped with a feedback unit that provides feedback. The system according to feature 1.

5. The generating unit is The interview scene is recorded, and the video is analyzed to generate advice comments. The system according to feature 1.

6. The aforementioned supply unit is, Provide users with a video containing generated advice comments. The system according to feature 1.

7. The aforementioned reception unit is It estimates the user's emotions and adjusts the timing of video viewing based on those estimated emotions. The system according to feature 1.

8. The aforementioned reception unit is It analyzes the user's past viewing history and automatically selects the most suitable videos. The system according to feature 1.

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