System

Abstract

To provide a system that accurately estimates the emotions of a viewer of a video work, and evaluates the work and proposes points to be improved on the basis of the estimation.SOLUTION: A system according to an embodiment comprises an acquisition unit, an estimation unit, an evaluation unit, and a proposal unit. The acquisition unit acquires emotion estimation information for estimating the emotions of a viewer of a video work. The estimation unit estimates the emotions of the viewer on the basis of the emotion estimation information acquired by the acquisition unit. The evaluation unit evaluates the video work on the basis of an estimation result of the estimation unit. The proposal unit proposes points to be improved regarding the video work on the basis of an evaluation result of the evaluation unit.SELECTED DRAWING: Figure 1

Description

[Technical field]

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

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

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

[0004] Conventional technology does not adequately grasp the emotions of viewers of a video work accurately and, based on that, evaluates the work or suggests areas for improvement, so there is room for improvement.

[0005] The system according to the embodiment aims to accurately estimate the emotions of viewers of a video work and, based on that, evaluate the work and suggest areas for improvement. [Means for solving the problem]

[0006] The system according to the embodiment includes an acquisition unit, an estimation unit, an evaluation unit, and a suggestion unit. The acquisition unit acquires emotion estimation information for estimating the emotion of a viewer of a video work. The estimation unit estimates the emotion of the viewer based on the emotion estimation information acquired by the acquisition unit. The evaluation unit evaluates the video work based on the estimation result by the estimation unit. The suggestion unit suggests improvements to the video work based on the evaluation result by the evaluation unit. Effect of the Invention

[0007] The system according to the embodiment can accurately estimate the emotions of viewers of a video work and, based on that, can evaluate the work and suggest areas for improvement. [Brief description of the drawings]

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

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

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

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

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

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

[0014] In the following embodiments, a communication I / F (Interface) with a code is an interface including a communication processor and an antenna. The communication I / F controls communication between multiple computers. An example of a communication standard applied to the communication I / F is a wireless communication standard including 5G (5th Generation Mobile Communication System), Wi-Fi (registered trademark), or Bluetooth (registered trademark).

[0015] In the following embodiments, "A and / or B" is synonymous with "at least one of A and B." In other words, "A and / or B" means that it may be only A, only B, or a combination of A and B. In addition, in this specification, the same idea as "A and / or B" is also applied when three or more things are expressed by connecting them with "and / or."

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

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

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

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

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

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

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

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

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

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

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

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

[0028] (Example 1) A system according to an embodiment of the present invention is a system that reads the viewer's emotions in real time and provides an evaluation and improvement points for a video work. The system acquires information for estimating the viewer's emotions, estimates the viewer's emotions based on the information, evaluates the video work, and proposes improvements. For example, the system acquires information such as the viewer's facial expressions, voice, movements, heart rate, blood pressure, etc., and estimates the viewer's emotions based on this information. Next, the system evaluates the video work based on the estimated emotions, and compares the transition of the viewer's emotions with the target transition of emotions in the video work. Furthermore, the system proposes improvements to the video work based on the evaluation results, and proposes scene changes to evoke a specific emotional response in the viewer. Finally, the system modifies the video work based on the proposed improvements.

[0029] The evaluation support system according to the embodiment includes an acquisition unit, an estimation unit, an evaluation unit, and a proposal unit. The acquisition unit acquires information for estimating the viewer's emotion. The information for estimating the viewer's emotion includes, but is not limited to, the viewer's facial expression, voice, movement, heart rate, blood pressure, and the like. The acquisition unit, for example, photographs the viewer's facial expression using a camera and records the viewer's voice using a microphone. The acquisition unit can also measure the viewer's heart rate and blood pressure using a sensor. For example, the acquisition unit photographs the viewer's facial expression with a camera and estimates the emotion using a facial expression recognition technique. The acquisition unit can also record the viewer's voice with a microphone and estimate the emotion using a voice analysis technique. Furthermore, the acquisition unit can measure the viewer's heart rate and blood pressure with a sensor and estimate the emotion based on these data. The estimation unit estimates the viewer's emotion based on the information acquired by the acquisition unit. The estimation unit analyzes, for example, the viewer's facial expression, voice, movement, heart rate, and the like, and estimates what emotion the viewer is feeling. The estimation unit, for example, analyzes the viewer's facial expression and estimates whether the viewer is happy, sad, surprised, or the like. The estimation unit can also analyze the viewer's voice and estimate the viewer's emotions from the tone and rhythm of the viewer's voice. The estimation unit can also analyze the viewer's movements and heart rate to estimate the viewer's emotions. The evaluation unit evaluates the video work based on the estimation result by the estimation unit. The evaluation unit compares the viewer's emotional transition with the emotional transition target in the video work and evaluates what emotions the video work evoked in the viewer. The evaluation unit, for example, graphs the viewer's emotional transition and compares it with the emotional transition target for each scene in the video work. The evaluation unit can also quantify the viewer's emotional transition and calculate an evaluation score for the video work. The suggestion unit suggests improvements to the video work based on the evaluation result by the evaluation unit. The suggestion unit suggests scene changes to evoke a specific emotional response in the viewer. The suggestion unit suggests, for example, adding a scene that moves the viewer or deleting a scene that excites the viewer. The suggestion unit can also suggest changing music to help the viewer relax, or adjusting visual effects to help the viewer concentrate.As a result, the evaluation support system according to the embodiment can read the emotions of the viewer in real time and provide an evaluation of the video work and suggestions for improvement.

[0030] The acquisition unit acquires information for estimating the viewer's emotions. The information for estimating the viewer's emotions includes, but is not limited to, the viewer's facial expression, voice, movement, heart rate, blood pressure, and the like. For example, the acquisition unit uses a camera to capture the viewer's facial expression and a microphone to record the viewer's voice. The acquisition unit can also measure the viewer's heart rate and blood pressure using a sensor. Specifically, the camera captures the viewer's face with high resolution and detects subtle changes in facial expression using facial expression recognition technology. This makes it possible to grasp changes in emotions in real time, such as the moment the viewer smiles or frowns. The microphone captures changes in the tone, rhythm, and volume of the viewer's voice and estimates the emotion using voice analysis technology. For example, a higher voice indicates excitement, and a lower voice indicates calmness. Furthermore, a sensor that measures heart rate and blood pressure captures the viewer's physical reaction. Information is acquired, such as an increase in heart rate indicating tension, and a decrease in blood pressure indicating relaxation. These data are transmitted to a central database in real time and used for subsequent processing. The acquisition unit integrates this diverse data and provides basic information for estimating viewer emotions with high accuracy.

[0031] The estimation unit estimates the viewer's emotions based on the information acquired by the acquisition unit. The estimation unit, for example, analyzes the viewer's facial expressions, voice, movements, heart rate, etc., and estimates what emotions the viewer is feeling. Specifically, image recognition technology using deep learning is used to analyze the viewer's facial expressions. This makes it possible to capture subtle changes in the viewer's facial expressions and estimate emotions such as joy, sadness, surprise, and anger with high accuracy. In addition, using voice analysis technology, changes in the tone, rhythm, and volume of the viewer's voice are analyzed to estimate emotions. For example, patterns such as excited when the voice becomes higher and calm when the voice becomes lower are analyzed. Furthermore, motion capture technology can be used to analyze the viewer's movements. Changes in the viewer's body movements and posture are captured to estimate changes in emotions. For example, information such as interest is acquired when the body leans forward, and boredom is acquired when the body leans backward is acquired. Heart rate and blood pressure data are also analyzed, and emotions are estimated based on the viewer's physical reactions. This allows the estimation unit to integrate various data on the viewer and estimate the viewer's emotions with high accuracy.

[0032] The evaluation unit evaluates the video work based on the estimation result by the estimation unit. The evaluation unit compares the transition of the viewer's emotions with the target transition of emotions in the video work, and evaluates what emotions the video work evoked in the viewer. Specifically, the evaluation unit graphs the transition of the viewer's emotions and compares it with the target transition of emotions for each scene in the video work. For example, in a scene that moves the viewer, it checks whether the viewer's emotions are elevated and evaluates the effectiveness of the scene. It is also possible to quantify the transition of the viewer's emotions and calculate an evaluation score for the video work. The evaluation unit statistically analyzes the viewer's emotion data and quantitatively evaluates the impact that each scene in the video work has on the viewer. For example, it identifies the scene in which the viewer's emotions were the most elevated and, conversely, the scene in which the emotions were the most depressed, and clarifies the strengths and areas for improvement of the video work. Furthermore, the evaluation unit evaluates the flow of emotions in the entire video work based on the viewer's emotion data, and checks whether the change in the viewer's emotions is progressing as intended. In this way, the evaluation unit can evaluate the emotional impact of the video work with high accuracy and contribute to improving the quality of the video work.

[0033] The suggestion unit proposes improvements to the video work based on the evaluation results by the evaluation unit. The suggestion unit proposes scene changes to induce a particular emotional response in the viewer. Specifically, the suggestion unit proposes adding a scene that moves the viewer or deleting a scene that excites the viewer. The suggestion unit can also propose changing music to relax the viewer or adjusting video effects to help the viewer concentrate. For example, based on the viewer's emotional data, if the viewer's emotions are lowered in a particular scene, the suggestion unit makes a proposal to change the content of the scene. Specifically, in order to enhance the production of the scene, the suggestion unit makes proposals such as adding music or sound effects, changing the character's lines, or re-editing the video cuts. In addition, in a scene in which the viewer's emotions are elevated, the suggestion unit makes a proposal to further enhance the effect. For example, in a scene in which the viewer is moved, the suggestion unit makes proposals such as adding music to enhance the emotion or strengthening the visual effects. Furthermore, the suggestion unit can also propose improvements to the overall composition and storytelling of the video work based on the viewer's feedback. In this way, the suggestion unit can utilize the viewer's emotional data to provide specific improvements to improve the quality of the video work and maximize the appeal of the video work.

[0034] The suggestion unit may include a correction unit that corrects the video work based on the proposed improvements. The correction unit corrects the video work based on the improvements proposed by the suggestion unit. The correction unit may, for example, edit the video or adjust the audio. The correction unit may, for example, cut a specific scene from the video or add a new scene. The correction unit may also adjust the volume of the audio or change the music. For example, the correction unit may shoot and edit a new video to add a scene that moves the viewer. The correction unit may also cut an existing video to delete a scene that excites the viewer. Furthermore, the correction unit may select and insert new music to change the music that relaxes the viewer. In this way, the video work may be corrected based on the proposed improvements, thereby further improving the viewer's satisfaction.

[0035] The estimation unit can estimate the emotion based on the viewer's facial expression, voice, movement, heart rate, or a combination of these. For example, the estimation unit analyzes the viewer's facial expression and estimates whether the viewer is happy, sad, surprised, or the like. For example, the estimation unit captures the viewer's facial expression with a camera and estimates the emotion using a facial expression recognition technology. The estimation unit can also analyze the viewer's voice and estimate the emotion from the tone and rhythm of the viewer's voice. For example, the estimation unit records the viewer's voice with a microphone and estimates the emotion using a voice analysis technology. Furthermore, the estimation unit can analyze the viewer's movement and heart rate and estimate the viewer's emotion. For example, the estimation unit captures the viewer's movement with a camera and estimates the emotion using a movement analysis technology. The estimation unit can also measure the viewer's heart rate with a sensor and estimate the emotion from the fluctuation of the heart rate. This makes it possible to accurately estimate the emotion based on various information about the viewer.

[0036] The evaluation unit can evaluate the video work based on the transition of the viewer's emotions and the target transition of emotions in the video work. The evaluation unit compares the transition of the viewer's emotions with the target transition of emotions in the video work and evaluates what emotions the video work evoked in the viewer. For example, the evaluation unit graphs the transition of the viewer's emotions and compares it with the target transition of emotions for each scene of the video work. The evaluation unit can also quantify the transition of the viewer's emotions and calculate an evaluation score for the video work. For example, the evaluation unit tracks the transition of the viewer's emotions in real time and immediately grasps changes in emotions. Furthermore, the evaluation unit can accumulate the viewer's emotion data and analyze long-term trends in emotions. For example, the evaluation unit analyzes the viewer's emotion data in chronological order and identifies patterns of change in emotions. This allows the video work to be accurately evaluated by comparing the transition of the viewer's emotions with the target. The estimation of emotions is realized using an emotion estimation function using, for example, an emotion engine or a generation AI. The generation AI is, for example, a text generation AI (for example, LLM) or a multimodal generation AI, but is not limited to such examples.

[0037] The evaluation unit may have a transition goal for each genre of the video work. The evaluation unit sets a different emotion transition goal for each genre of the video work and performs evaluation. The evaluation unit sets different emotion transition goals according to genres such as drama, comedy, and horror. For example, the goal for drama is to cause emotion and empathy, and the goal for comedy is to cause laughter and enjoyment. Also, the goal for horror is to cause fear and tension. The evaluation unit compares the transition of the viewer's emotion with the goal for each genre and evaluates how well the video work matches the goal. For example, the evaluation unit compares the transition of the viewer's emotion with the emotion goal of the drama and evaluates whether emotion and empathy are caused. The evaluation unit can also compare the transition of the viewer's emotion with the emotion goal of the comedy and evaluate whether laughter and enjoyment are caused. Furthermore, the evaluation unit can compare the transition of the viewer's emotion with the emotion goal of the horror and evaluate whether fear and tension are caused. This allows for more appropriate evaluation by having different evaluation criteria for each genre.

[0038] The evaluation unit can aggregate emotion estimation results of multiple viewers and evaluate the video work. For example, the evaluation unit collects emotion data of multiple viewers and calculates an average value or a median value. The evaluation unit can also aggregate the emotion data of viewers and analyze the distribution and tendency of emotions. For example, the evaluation unit can aggregate the emotion data of multiple viewers and calculate an average emotion value. The evaluation unit can also aggregate the emotion data of viewers and graph the distribution of emotions. The evaluation unit can also aggregate the emotion data of viewers and analyze the tendency of emotions. For example, the evaluation unit analyzes the emotion data of viewers in chronological order and identifies the pattern of change in emotions. In this way, a more objective evaluation can be made by aggregating the emotions of multiple viewers.

[0039] The evaluation unit can classify the evaluations based on the age, gender, and cultural background of the viewer. For example, the evaluation unit classifies the evaluations based on the age of the viewer. For example, the evaluation unit performs evaluations by age, such as children, adolescents, adults, and the elderly. The evaluation unit can also classify the evaluations based on the gender of the viewer. For example, the evaluation unit performs evaluations by male, female, and other genders. Furthermore, the evaluation unit can also classify the evaluations based on the cultural background of the viewer. For example, the evaluation unit performs evaluations by cultural background, such as the nationality, religion, and region of the viewer. This allows for more detailed evaluations by classifying the evaluations based on the attributes of the viewer.

[0040] The suggestion unit can suggest a scene change to induce a particular emotional response in the viewer. For example, the suggestion unit can suggest adding a scene that moves the viewer or deleting a scene that excites the viewer. The suggestion unit can also suggest changing music to relax the viewer or adjusting a video effect to concentrate the viewer. For example, the suggestion unit can suggest shooting and editing a new video to add a scene that moves the viewer. The suggestion unit can also suggest cutting an existing video to delete a scene that excites the viewer. Furthermore, the suggestion unit can suggest selecting and inserting new music to change the music to relax the viewer. This can improve the quality of the video work by suggesting a specific scene change to induce an emotional response in the viewer.

[0041] The acquisition unit can acquire one or more pieces of information of the viewer's facial expression, movement, voice, heart rate, and blood pressure as emotion estimation information. The acquisition unit, for example, photographs the viewer's facial expression using a camera and records the viewer's voice using a microphone. The acquisition unit can also measure the viewer's heart rate and blood pressure using a sensor. For example, the acquisition unit photographs the viewer's facial expression with a camera and estimates the emotion using facial expression recognition technology. The acquisition unit can also record the viewer's voice with a microphone and estimate the emotion using voice analysis technology. Furthermore, the acquisition unit can measure the viewer's heart rate and blood pressure with a sensor and estimate the emotion based on these data. In this way, the accuracy of emotion estimation can be improved by acquiring various information of the viewer.

[0042] The acquisition unit can estimate the emotion of the viewer and adjust the timing of acquiring the emotion estimation information based on the estimated emotion of the viewer. For example, when the viewer is excited, the acquisition unit can increase the frequency of acquiring the emotion estimation information. Also, when the viewer is relaxed, the acquisition unit can reduce the frequency of acquiring the emotion estimation information. Furthermore, when the viewer is concentrating, the acquisition unit can keep the timing of acquiring the emotion estimation information constant. For example, when the viewer is excited, the acquisition unit can focus on acquiring information on the heart rate and blood pressure. Also, when the viewer is relaxed, the acquisition unit can focus on acquiring information on facial expressions and voice. Furthermore, when the viewer is concentrating, the acquisition unit can focus on acquiring information on movements and postures. This allows for more accurate emotion estimation by adjusting the timing of acquiring information according to the emotion of the viewer.

[0043] The acquisition unit can analyze the viewer's past emotion data and select the optimal acquisition method. For example, the acquisition unit acquires emotion estimation information for similar scenes based on a scene in which the viewer showed a high emotional response in the past. The acquisition unit can also analyze that emotions are likely to fluctuate in a specific time period from the viewer's past emotion data and concentrate acquisition on that time period. The acquisition unit can also select the most effective acquisition method in a specific emotional state based on the viewer's past emotion data. For example, the acquisition unit acquires emotion estimation information for similar scenes based on a scene in which the viewer showed a high emotional response in the past. The acquisition unit can also analyze that emotions are likely to fluctuate in a specific time period from the viewer's past emotion data and concentrate acquisition on that time period. The acquisition unit can also select the most effective acquisition method in a specific emotional state based on the viewer's past emotion data. Thereby, the accuracy of emotion estimation can be improved by selecting the optimal acquisition method based on past emotion data.

[0044] The acquisition unit can perform filtering based on the viewer's current psychological state and environment when acquiring emotion estimation information. For example, when the viewer is stressed, the acquisition unit can focus on acquiring information on heart rate and blood pressure. Also, when the viewer is relaxed, the acquisition unit can focus on acquiring information on facial expressions and voice. Furthermore, when the viewer is concentrating, the acquisition unit can focus on acquiring information on movement and posture. For example, when the viewer is stressed, the acquisition unit can focus on acquiring information on heart rate and blood pressure. Also, when the viewer is relaxed, the acquisition unit can focus on acquiring information on facial expressions and voice. Furthermore, when the viewer is concentrating, the acquisition unit can focus on acquiring information on movement and posture. In this way, by filtering information based on the viewer's psychological state and environment, the accuracy of emotion estimation can be improved.

[0045] The acquisition unit can estimate the emotion of the viewer and determine the priority of emotion estimation information to be acquired based on the estimated emotion of the viewer. For example, when the viewer is excited, the acquisition unit preferentially acquires information on heart rate and blood pressure. Also, when the viewer is relaxed, the acquisition unit can preferentially acquire information on facial expressions and voice. Furthermore, when the viewer is concentrating, the acquisition unit can preferentially acquire information on movement and posture. For example, when the viewer is excited, the acquisition unit preferentially acquires information on heart rate and blood pressure. Also, when the viewer is relaxed, the acquisition unit can preferentially acquire information on facial expressions and voice. Furthermore, when the viewer is concentrating, the acquisition unit can preferentially acquire information on movement and posture. In this way, by determining the priority of information based on the emotion of the viewer, the accuracy of emotion estimation can be improved.

[0046] When acquiring emotion estimation information, the acquisition unit can preferentially acquire highly relevant information in consideration of the geographical location information of the viewer. For example, when the viewer is at home, the acquisition unit preferentially acquires information based on a relaxed environment. Also, when the viewer is in a public place, the acquisition unit can preferentially acquire information on surrounding sounds and actions. Furthermore, when the viewer is on the move, the acquisition unit can preferentially acquire information on heart rate and actions. For example, when the viewer is at home, the acquisition unit preferentially acquires information based on a relaxed environment. Also, when the viewer is in a public place, the acquisition unit can preferentially acquire information on surrounding sounds and actions. Furthermore, when the viewer is on the move, the acquisition unit can preferentially acquire information on heart rate and actions. In this way, highly relevant information can be preferentially acquired by considering the geographical location information of the viewer.

[0047] The acquisition unit can analyze the viewer's social media activity when acquiring the emotion estimation information and acquire related information. For example, when the viewer posts an emotional message on social media, the acquisition unit acquires emotion estimation information based on the content of the message. Also, when the viewer mentions a specific video work on social media, the acquisition unit can acquire information related to the work. Furthermore, when the viewer has an emotional exchange with other users on social media, the acquisition unit can acquire emotion estimation information based on the exchange. For example, when the viewer posts an emotional message on social media, the acquisition unit acquires emotion estimation information based on the content of the message. Also, when the viewer mentions a specific video work on social media, the acquisition unit can acquire information related to the work. Furthermore, when the viewer has an emotional exchange with other users on social media, the acquisition unit can acquire emotion estimation information based on the exchange. In this way, related information can be acquired by analyzing the viewer's social media activity.

[0048] The estimation unit can estimate the emotion of the viewer and adjust the emotion estimation algorithm based on the estimated emotion of the viewer. For example, the estimation unit can increase the sensitivity of the emotion estimation algorithm when the viewer is excited. Also, the estimation unit can decrease the sensitivity of the emotion estimation algorithm when the viewer is relaxed. Furthermore, the estimation unit can keep the sensitivity of the emotion estimation algorithm constant when the viewer is focused. For example, the estimation unit can increase the sensitivity of the emotion estimation algorithm when the viewer is excited. Also, the estimation unit can decrease the sensitivity of the emotion estimation algorithm when the viewer is relaxed. Furthermore, the estimation unit can keep the sensitivity of the emotion estimation algorithm constant when the viewer is focused. In this way, the accuracy of emotion estimation can be improved by adjusting the algorithm based on the emotion of the viewer.

[0049] The estimation unit can track changes in the viewer's emotions in real time during estimation, thereby improving the estimation accuracy. The estimation unit can track changes in, for example, the viewer's heart rate and blood pressure in real time, thereby improving the accuracy of emotion estimation. The estimation unit can also track changes in the viewer's facial expression and voice in real time, thereby improving the accuracy of emotion estimation. The estimation unit can also track changes in the viewer's movements and posture in real time, thereby improving the accuracy of emotion estimation. For example, the estimation unit can track changes in the viewer's heart rate and blood pressure in real time, thereby improving the accuracy of emotion estimation. The estimation unit can also track changes in the viewer's facial expression and voice in real time, thereby improving the accuracy of emotion estimation. The estimation unit can also track changes in the viewer's movements and posture in real time, thereby improving the accuracy of emotion estimation. Thereby, by tracking changes in the viewer's emotions in real time, the estimation accuracy can be improved.

[0050] The estimation unit can correct the estimation result by referring to the viewer's past emotion data at the time of estimation. For example, the estimation unit corrects the estimation result by referring to emotion data shown by the viewer in a similar scene in the past. The estimation unit can also correct the estimation result in a specific emotional state from the viewer's past emotion data. Furthermore, the estimation unit can improve the accuracy of the estimation result based on the viewer's past emotion data. For example, the estimation unit corrects the estimation result by referring to emotion data shown by the viewer in a similar scene in the past. The estimation unit can also correct the estimation result in a specific emotional state from the viewer's past emotion data. Furthermore, the estimation unit can improve the accuracy of the estimation result based on the viewer's past emotion data. Thereby, the accuracy of the estimation result can be improved by referring to the past emotion data.

[0051] The estimation unit can estimate the viewer's emotion and adjust the display method of the estimation result based on the estimated viewer's emotion. For example, when the viewer is excited, the estimation unit provides a visually stimulating display method. Also, when the viewer is relaxed, the estimation unit can provide a calm display method. Furthermore, when the viewer is concentrated, the estimation unit can provide a simple and highly visible display method. For example, when the viewer is excited, the estimation unit provides a visually stimulating display method. Also, when the viewer is relaxed, the estimation unit can provide a calm display method. Furthermore, when the viewer is concentrated, the estimation unit can provide a simple and highly visible display method. This allows for more appropriate display by adjusting the display method based on the viewer's emotion.

[0052] The estimation unit can customize the emotion estimation algorithm based on the age and gender of the viewer at the time of estimation. For example, when the viewer is a young person, the estimation unit customizes the emotion estimation algorithm for young people. Also, when the viewer is an elderly person, the estimation unit can customize the emotion estimation algorithm for elderly people. Furthermore, the emotion estimation algorithm can be customized based on the gender of the viewer. For example, when the viewer is a young person, the estimation unit customizes the emotion estimation algorithm for young people. Also, when the viewer is an elderly person, the estimation unit can customize the emotion estimation algorithm for elderly people. Furthermore, the emotion estimation algorithm can be customized based on the gender of the viewer. Thereby, by customizing the algorithm based on the age and gender of the viewer, the accuracy of emotion estimation can be improved.

[0053] The estimation unit can optimize the emotion estimation algorithm in consideration of the cultural background of the viewer at the time of estimation. For example, when the viewer belongs to a specific cultural sphere, the estimation unit optimizes the emotion estimation algorithm based on the culture. The emotion estimation algorithm can also be customized based on the cultural background of the viewer. Furthermore, the accuracy of the emotion estimation algorithm can be improved in consideration of the cultural background of the viewer. For example, when the viewer belongs to a specific cultural sphere, the estimation unit optimizes the emotion estimation algorithm based on the culture. The estimation unit can also customize the emotion estimation algorithm based on the cultural background of the viewer. Furthermore, the estimation unit can improve the accuracy of the emotion estimation algorithm in consideration of the cultural background of the viewer. Thereby, the accuracy of emotion estimation can be improved by considering the cultural background of the viewer.

[0054] The evaluation unit can estimate the viewer's emotions and adjust the evaluation criteria based on the estimated viewer's emotions. For example, the evaluation unit can set stricter evaluation criteria when the viewer is excited. Also, the evaluation unit can loosen the evaluation criteria when the viewer is relaxed. Furthermore, the evaluation unit can keep the evaluation criteria constant when the viewer is focused. For example, the evaluation unit can set stricter evaluation criteria when the viewer is excited. Also, the evaluation unit can loosen the evaluation criteria when the viewer is relaxed. Furthermore, the evaluation unit can keep the evaluation criteria constant when the viewer is focused. In this way, by adjusting the evaluation criteria based on the viewer's emotions, more appropriate evaluation is possible.

[0055] At the time of evaluation, the evaluation unit can compare the transition of the viewer's emotions with the emotional goal for each scene of the video work to make the evaluation. For example, the evaluation unit tracks the transition of the viewer's emotions in real time and makes the evaluation by comparing it with the emotional goal for each scene. The evaluation unit can also analyze the transition of the viewer's emotions and make the evaluation based on the emotional goal for each scene. Furthermore, the evaluation unit can also aggregate the transition of the viewer's emotions and make the evaluation by comparing it with the emotional goal for each scene. For example, the evaluation unit tracks the transition of the viewer's emotions in real time and makes the evaluation by comparing it with the emotional goal for each scene. The evaluation unit can also analyze the transition of the viewer's emotions and make the evaluation based on the emotional goal for each scene. Furthermore, the evaluation unit can also aggregate the transition of the viewer's emotions and make the evaluation by comparing it with the emotional goal for each scene. This makes it possible to make a more accurate evaluation by comparing the transition of the viewer's emotions with the emotional goal for each scene.

[0056] The evaluation unit can compile the viewer's emotion data in real time at the time of evaluation and update the evaluation result. The evaluation unit, for example, compiles the viewer's emotion data in real time and updates the evaluation result accordingly. The evaluation unit can also analyze the viewer's emotion data in real time and reflect the evaluation result. Furthermore, the evaluation unit can process the viewer's emotion data in real time and keep the evaluation result up to date. For example, the evaluation unit compiles the viewer's emotion data in real time and updates the evaluation result accordingly. The evaluation unit can also analyze the viewer's emotion data in real time and reflect the evaluation result. Furthermore, the evaluation unit can process the viewer's emotion data in real time and keep the evaluation result up to date. In this way, the latest evaluation result can be provided by compiling the viewer's emotion data in real time.

[0057] The evaluation unit can estimate the viewer's emotions and adjust the display method of the evaluation result based on the estimated viewer's emotions. For example, when the viewer is excited, the evaluation unit provides a visually stimulating display method. Also, when the viewer is relaxed, the evaluation unit can provide a calm display method. Furthermore, when the viewer is concentrated, the evaluation unit can provide a simple and highly visible display method. For example, when the viewer is excited, the evaluation unit provides a visually stimulating display method. Also, when the viewer is relaxed, the evaluation unit can provide a calm display method. Furthermore, when the viewer is concentrated, the evaluation unit can provide a simple and highly visible display method. In this way, by adjusting the display method based on the viewer's emotions, a more appropriate evaluation result can be provided.

[0058] At the time of evaluation, the evaluation unit can classify the evaluation results based on the age, gender, and cultural background of the viewer. The evaluation unit classifies the evaluation results, for example, based on the age of the viewer. The evaluation unit can also classify the evaluation results based on the gender of the viewer. Furthermore, the evaluation unit can classify the evaluation results based on the cultural background of the viewer. For example, the evaluation unit classifies the evaluation results based on the age of the viewer. The evaluation unit can also classify the evaluation results based on the gender of the viewer. Furthermore, the evaluation unit can classify the evaluation results based on the cultural background of the viewer. This enables more detailed evaluation by classifying the evaluation results based on the attributes of the viewer.

[0059] The evaluation unit can analyze the evaluation results taking into account the geographical distribution of viewers at the time of evaluation. The evaluation unit analyzes the evaluation results based on, for example, the geographical distribution of viewers. The evaluation unit can also collect emotion data for each region of viewers and analyze the evaluation results. Furthermore, the evaluation unit can optimize the evaluation results taking into account the geographical distribution of viewers. For example, the evaluation unit analyzes the evaluation results based on the geographical distribution of viewers. The evaluation unit can also collect emotion data for each region of viewers and analyze the evaluation results. Furthermore, the evaluation unit can optimize the evaluation results taking into account the geographical distribution of viewers. In this way, a more appropriate evaluation result can be provided by taking into account the geographical distribution of viewers.

[0060] The suggestion unit can estimate the viewer's emotion and adjust the proposed content based on the estimated viewer's emotion. For example, when the viewer is excited, the suggestion unit can suggest a scene change that calms the viewer's emotion. Also, when the viewer is relaxed, the suggestion unit can suggest a scene change that uplifts the viewer's emotion. Furthermore, when the viewer is concentrated, the suggestion unit can suggest a scene change that maintains the viewer's emotion. For example, when the viewer is excited, the suggestion unit can suggest a scene change that calms the viewer's emotion. Also, when the viewer is relaxed, the suggestion unit can suggest a scene change that uplifts the viewer's emotion. Furthermore, when the viewer is concentrated, the suggestion unit can suggest a scene change that maintains the viewer's emotion. This allows for more appropriate suggestions by adjusting the proposed content based on the viewer's emotion.

[0061] The suggestion unit can track changes in the viewer's emotions in real time when making a suggestion, and update the proposed content. For example, if the viewer's emotions change suddenly, the suggestion unit can instantly update the proposed content. Also, if the viewer's emotions change gradually, the suggestion unit can update the proposed content step by step. Furthermore, if the viewer's emotions are stable, the suggestion unit can maintain the proposed content. For example, if the viewer's emotions change suddenly, the suggestion unit can instantly update the proposed content. Also, if the viewer's emotions change gradually, the suggestion unit can update the proposed content step by step. Furthermore, if the viewer's emotions are stable, the suggestion unit can maintain the proposed content. In this way, the latest proposed content can be provided by tracking changes in the viewer's emotions in real time.

[0062] The suggestion unit can correct the proposal content by referring to the viewer's past emotion data when making a suggestion. For example, the suggestion unit corrects the proposal content by referring to emotion data shown by the viewer in a similar scene in the past. The suggestion unit can also correct the proposal content in a specific emotional state from the viewer's past emotion data. Furthermore, the suggestion unit can improve the accuracy of the proposal content based on the viewer's past emotion data. For example, the suggestion unit corrects the proposal content by referring to emotion data shown by the viewer in a similar scene in the past. The suggestion unit can also correct the proposal content in a specific emotional state from the viewer's past emotion data. Furthermore, the suggestion unit can improve the accuracy of the proposal content based on the viewer's past emotion data. Thereby, the accuracy of the proposal content can be improved by referring to the past emotion data.

[0063] The suggestion unit can estimate the viewer's emotion and adjust the display method of the proposed result based on the estimated viewer's emotion. For example, when the viewer is excited, the suggestion unit provides a visually stimulating display method. Also, when the viewer is relaxed, the suggestion unit can provide a calm display method. Furthermore, when the viewer is concentrated, the suggestion unit can provide a simple and highly visible display method. For example, when the viewer is excited, the suggestion unit provides a visually stimulating display method. Also, when the viewer is relaxed, the suggestion unit can provide a calm display method. Furthermore, when the viewer is concentrated, the suggestion unit can provide a simple and highly visible display method. In this way, by adjusting the display method based on the viewer's emotion, a more appropriate proposed result can be provided.

[0064] At the time of proposal, the suggestion unit can customize the proposal content based on the age and gender of the viewer. For example, if the viewer is a young person, the suggestion unit provides proposal content for young people. Also, if the viewer is an elderly person, the suggestion unit can provide proposal content for elderly people. Furthermore, the suggestion unit can customize the proposal content based on the gender of the viewer. For example, if the viewer is a young person, the suggestion unit provides proposal content for young people. Also, if the viewer is an elderly person, the suggestion unit can provide proposal content for elderly people. Furthermore, the suggestion unit can customize the proposal content based on the gender of the viewer. This enables more appropriate proposals by customizing the proposal content based on the age and gender of the viewer.

[0065] The suggestion unit can optimize the proposed content by taking into account the cultural background of the viewer when making a suggestion. For example, when the viewer belongs to a specific cultural sphere, the suggestion unit optimizes the proposed content based on the culture. The suggestion unit can also customize the proposed content based on the cultural background of the viewer. Furthermore, the accuracy of the proposed content can be improved by taking into account the cultural background of the viewer. For example, when the viewer belongs to a specific cultural sphere, the suggestion unit optimizes the proposed content based on the culture. The suggestion unit can also customize the proposed content based on the cultural background of the viewer. Furthermore, the suggestion unit can improve the accuracy of the proposed content by taking into account the cultural background of the viewer. Thereby, the accuracy of the proposed content can be improved by taking into account the cultural background of the viewer.

[0066] The correction unit can estimate the viewer's emotion and adjust the correction content based on the estimated viewer's emotion. For example, when the viewer is excited, the correction unit can suggest correction content that calms the viewer's emotion. Also, when the viewer is relaxed, the correction unit can suggest correction content that uplifts the viewer's emotion. Furthermore, when the viewer is focused, the correction unit can suggest correction content that maintains the viewer's emotion. For example, when the viewer is excited, the correction unit can suggest correction content that calms the viewer's emotion. Also, when the viewer is relaxed, the correction unit can suggest correction content that uplifts the viewer's emotion. Furthermore, when the viewer is focused, the correction unit can suggest correction content that maintains the viewer's emotion. This allows for more appropriate correction by adjusting the correction content based on the viewer's emotion.

[0067] The correction unit can track changes in the viewer's emotions in real time during correction and update the correction content. For example, if the viewer's emotions change suddenly, the correction unit immediately updates the correction content. Also, if the viewer's emotions change gradually, the correction unit can update the correction content step by step. Furthermore, if the viewer's emotions are stable, the correction content can be maintained. For example, the correction unit immediately updates the correction content if the viewer's emotions change suddenly. Also, if the viewer's emotions change gradually, the correction unit can update the correction content step by step. Furthermore, if the viewer's emotions are stable, the correction unit can maintain the correction content. In this way, the latest correction content can be provided by tracking changes in the viewer's emotions in real time.

[0068] At the time of correction, the correction unit can correct the correction contents by referring to the viewer's past emotion data. The correction unit, for example, refers to emotion data shown by the viewer in a similar scene in the past and corrects the correction contents. The correction unit can also correct the correction contents in a specific emotional state from the viewer's past emotion data. Furthermore, the correction unit can improve the accuracy of the correction contents based on the viewer's past emotion data. For example, the correction unit refers to emotion data shown by the viewer in a similar scene in the past and corrects the correction contents. The correction unit can also correct the correction contents in a specific emotional state from the viewer's past emotion data. Furthermore, the correction unit can improve the accuracy of the correction contents based on the viewer's past emotion data. Thereby, the accuracy of the correction contents can be improved by referring to the past emotion data.

[0069] The correction unit can estimate the viewer's emotion and adjust the display method of the correction result based on the estimated viewer's emotion. For example, when the viewer is excited, the correction unit provides a visually stimulating display method. Also, when the viewer is relaxed, the correction unit can provide a calm display method. Furthermore, when the viewer is concentrated, the correction unit can provide a simple and highly visible display method. For example, when the viewer is excited, the correction unit provides a visually stimulating display method. Also, when the viewer is relaxed, the correction unit can provide a calm display method. Furthermore, when the viewer is concentrated, the correction unit can provide a simple and highly visible display method. In this way, by adjusting the display method based on the viewer's emotion, a more appropriate correction result can be provided.

[0070] At the time of correction, the correction unit can customize the correction content based on the age and gender of the viewer. For example, if the viewer is a young person, the correction unit can provide correction content for young people. Also, if the viewer is an elderly person, the correction unit can provide correction content for elderly people. Furthermore, the correction content can be customized based on the gender of the viewer. For example, if the viewer is a young person, the correction unit can provide correction content for young people. Also, if the viewer is an elderly person, the correction unit can provide correction content for elderly people. Furthermore, the correction unit can customize the correction content based on the gender of the viewer. This allows for more appropriate correction by customizing the correction content based on the age and gender of the viewer.

[0071] The correction unit can optimize the correction content by taking into account the cultural background of the viewer during correction. For example, when the viewer belongs to a specific cultural sphere, the correction unit optimizes the correction content based on the culture. The correction unit can also customize the correction content based on the cultural background of the viewer. Furthermore, the correction unit can improve the accuracy of the correction content by taking into account the cultural background of the viewer. For example, when the viewer belongs to a specific cultural sphere, the correction unit optimizes the correction content based on the culture. The correction unit can also customize the correction content based on the cultural background of the viewer. Furthermore, the correction unit can improve the accuracy of the correction content by taking into account the cultural background of the viewer. This makes it possible to improve the accuracy of the correction content by taking into account the cultural background of the viewer.

[0072] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0073] The acquisition unit acquires the gaze movement of the viewer, and the estimation unit can also analyze the gaze movement to estimate the viewer's interest. For example, if the viewer focuses his / her gaze on a particular scene, it can be estimated that the viewer is interested in that scene. Also, if the viewer frequently moves his / her gaze, it can be estimated that the viewer is not interested in that scene. Furthermore, it is possible to analyze the pattern of the viewer's gaze movement and grasp the tendency of the viewer's interest. In this way, the accuracy of interest estimation can be improved by taking the viewer's gaze movement into consideration.

[0074] The evaluation unit can also evaluate the viewer's interest in each scene of a video work based on the viewer's line of sight. For example, if the viewer focuses his or her eyes on a particular scene, the evaluation unit can give that scene a higher evaluation. Also, if the viewer frequently moves his or her eyes, the evaluation unit can give that scene a lower evaluation. Furthermore, the evaluation unit can graph the viewer's line of sight movement patterns and evaluate the interest level of the entire video work. This allows the evaluation of a video work to be performed in more detail by taking the viewer's line of sight movement into consideration.

[0075] The suggestion unit can also suggest changes to the camera angle and framing for each scene of a video work based on the viewer's line of sight. For example, if the viewer focuses his or her gaze on a particular scene, it can suggest changing the camera angle or adjusting the framing for that scene. Also, if the viewer frequently moves his or her gaze, it can suggest fixing the camera angle or simplifying the framing for that scene. Furthermore, it can analyze the viewer's line of sight movement patterns and suggest optimal camera angles and framing. In this way, the quality of a video work can be improved by taking the viewer's line of sight movement into consideration.

[0076] The correction unit can also suggest changes to edits or cuts for each scene of a video work based on the viewer's gaze movements. For example, if the viewer focuses his or her gaze on a particular scene, it can suggest strengthening the editing of that scene or adding a cut. Also, if the viewer frequently moves his or her gaze, it can suggest simplifying the editing of that scene or reducing cuts. Furthermore, it can analyze the viewer's gaze movement patterns and suggest optimal edits and cuts. This makes it possible to improve the quality of a video work by taking into account the viewer's gaze movements.

[0077] The evaluation unit can also evaluate the viewer's visual interest level for each scene of a video work based on the viewer's gaze movement. For example, if the viewer focuses his / her gaze on a particular scene, the visual interest level of that scene can be evaluated as high. Also, if the viewer frequently moves his / her gaze, the visual interest level of that scene can be evaluated as low. Furthermore, the pattern of the viewer's gaze movement can be graphed to evaluate the visual interest level of the entire video work. In this way, the visual interest level of a video work can be evaluated in more detail by taking the viewer's gaze movement into consideration.

[0078] The process flow of the first embodiment will be briefly described below.

[0079] Step 1: The acquisition unit acquires information for estimating the viewer's emotions. The information for estimating the viewer's emotions includes, for example, the viewer's facial expression, voice, movement, heart rate, blood pressure, etc. The acquisition unit photographs the viewer's facial expression using a camera and records the viewer's voice using a microphone. The acquisition unit can also measure the viewer's heart rate and blood pressure using a sensor. Step 2: The estimation unit estimates the viewer's emotions based on the information acquired by the acquisition unit. The estimation unit analyzes the viewer's facial expressions, voice, movements, heart rate, etc., to estimate what emotions the viewer is feeling. For example, the estimation unit analyzes the viewer's facial expressions to estimate whether the viewer is happy, sad, surprised, etc. It can also analyze the viewer's voice and estimate emotions from the tone and rhythm of the viewer's voice. It can also analyze the viewer's movements and heart rate to estimate the viewer's emotions. Step 3: The evaluation unit evaluates the video work based on the estimation result by the estimation unit. The evaluation unit compares the viewer's emotional transition with the target emotional transition in the video work, and evaluates what emotions the video work evoked in the viewer. For example, the evaluation unit may graph the viewer's emotional transition and compare it with the target emotional transition for each scene in the video work. It may also quantify the viewer's emotional transition and calculate an evaluation score for the video work. Step 4: The suggestion unit suggests improvements to the video work based on the evaluation results by the evaluation unit. The suggestion unit suggests scene changes to evoke a particular emotional response in the viewer. For example, the suggestion unit suggests adding a scene that moves the viewer or deleting a scene that excites the viewer. It can also suggest changing music to relax the viewer or adjusting visual effects to help the viewer concentrate.

[0080] (Example 2) A system according to an embodiment of the present invention is a system that reads the viewer's emotions in real time and provides an evaluation and improvement points for a video work. The system acquires information for estimating the viewer's emotions, estimates the viewer's emotions based on the information, evaluates the video work, and proposes improvements. For example, the system acquires information such as the viewer's facial expressions, voice, movements, heart rate, blood pressure, etc., and estimates the viewer's emotions based on this information. Next, the system evaluates the video work based on the estimated emotions, and compares the transition of the viewer's emotions with the target transition of emotions in the video work. Furthermore, the system proposes improvements to the video work based on the evaluation results, and proposes scene changes to evoke a specific emotional response in the viewer. Finally, the system modifies the video work based on the proposed improvements.

[0081] The evaluation support system according to the embodiment includes an acquisition unit, an estimation unit, an evaluation unit, and a proposal unit. The acquisition unit acquires information for estimating the viewer's emotion. The information for estimating the viewer's emotion includes, but is not limited to, the viewer's facial expression, voice, movement, heart rate, blood pressure, and the like. The acquisition unit, for example, photographs the viewer's facial expression using a camera and records the viewer's voice using a microphone. The acquisition unit can also measure the viewer's heart rate and blood pressure using a sensor. For example, the acquisition unit photographs the viewer's facial expression with a camera and estimates the emotion using a facial expression recognition technique. The acquisition unit can also record the viewer's voice with a microphone and estimate the emotion using a voice analysis technique. Furthermore, the acquisition unit can measure the viewer's heart rate and blood pressure with a sensor and estimate the emotion based on these data. The estimation unit estimates the viewer's emotion based on the information acquired by the acquisition unit. The estimation unit analyzes, for example, the viewer's facial expression, voice, movement, heart rate, and the like, and estimates what emotion the viewer is feeling. The estimation unit, for example, analyzes the viewer's facial expression and estimates whether the viewer is happy, sad, surprised, or the like. The estimation unit can also analyze the viewer's voice and estimate the viewer's emotions from the tone and rhythm of the viewer's voice. The estimation unit can also analyze the viewer's movements and heart rate to estimate the viewer's emotions. The evaluation unit evaluates the video work based on the estimation result by the estimation unit. The evaluation unit compares the viewer's emotional transition with the emotional transition target in the video work and evaluates what emotions the video work evoked in the viewer. The evaluation unit, for example, graphs the viewer's emotional transition and compares it with the emotional transition target for each scene in the video work. The evaluation unit can also quantify the viewer's emotional transition and calculate an evaluation score for the video work. The suggestion unit suggests improvements to the video work based on the evaluation result by the evaluation unit. The suggestion unit suggests scene changes to evoke a specific emotional response in the viewer. The suggestion unit suggests, for example, adding a scene that moves the viewer or deleting a scene that excites the viewer. The suggestion unit can also suggest changing music to help the viewer relax, or adjusting visual effects to help the viewer concentrate.As a result, the evaluation support system according to the embodiment can read the emotions of the viewer in real time and provide an evaluation of the video work and suggestions for improvement.

[0082] The acquisition unit acquires information for estimating the viewer's emotions. The information for estimating the viewer's emotions includes, but is not limited to, the viewer's facial expression, voice, movement, heart rate, blood pressure, and the like. For example, the acquisition unit uses a camera to capture the viewer's facial expression and a microphone to record the viewer's voice. The acquisition unit can also measure the viewer's heart rate and blood pressure using a sensor. Specifically, the camera captures the viewer's face with high resolution and detects subtle changes in facial expression using facial expression recognition technology. This makes it possible to grasp changes in emotions in real time, such as the moment the viewer smiles or frowns. The microphone captures changes in the tone, rhythm, and volume of the viewer's voice and estimates the emotion using voice analysis technology. For example, a higher voice indicates excitement, and a lower voice indicates calmness. Furthermore, a sensor that measures heart rate and blood pressure captures the viewer's physical reaction. Information is acquired, such as an increase in heart rate indicating tension, and a decrease in blood pressure indicating relaxation. These data are transmitted to a central database in real time and used for subsequent processing. The acquisition unit integrates this diverse data and provides basic information for estimating viewer emotions with high accuracy.

[0083] The estimation unit estimates the viewer's emotions based on the information acquired by the acquisition unit. The estimation unit, for example, analyzes the viewer's facial expressions, voice, movements, heart rate, etc., and estimates what emotions the viewer is feeling. Specifically, image recognition technology using deep learning is used to analyze the viewer's facial expressions. This makes it possible to capture subtle changes in the viewer's facial expressions and estimate emotions such as joy, sadness, surprise, and anger with high accuracy. In addition, using voice analysis technology, changes in the tone, rhythm, and volume of the viewer's voice are analyzed to estimate emotions. For example, patterns such as excited when the voice becomes higher and calm when the voice becomes lower are analyzed. Furthermore, motion capture technology can be used to analyze the viewer's movements. Changes in the viewer's body movements and posture are captured to estimate changes in emotions. For example, information such as interest is acquired when the body leans forward, and boredom is acquired when the body leans backward is acquired. Heart rate and blood pressure data are also analyzed, and emotions are estimated based on the viewer's physical reactions. This allows the estimation unit to integrate various data on the viewer and estimate the viewer's emotions with high accuracy.

[0084] The evaluation unit evaluates the video work based on the estimation result by the estimation unit. The evaluation unit compares the transition of the viewer's emotions with the target transition of emotions in the video work, and evaluates what emotions the video work evoked in the viewer. Specifically, the evaluation unit graphs the transition of the viewer's emotions and compares it with the target transition of emotions for each scene in the video work. For example, in a scene that moves the viewer, it checks whether the viewer's emotions are elevated and evaluates the effectiveness of the scene. It is also possible to quantify the transition of the viewer's emotions and calculate an evaluation score for the video work. The evaluation unit statistically analyzes the viewer's emotion data and quantitatively evaluates the impact that each scene in the video work has on the viewer. For example, it identifies the scene in which the viewer's emotions were the most elevated and, conversely, the scene in which the emotions were the most depressed, and clarifies the strengths and areas for improvement of the video work. Furthermore, the evaluation unit evaluates the flow of emotions in the entire video work based on the viewer's emotion data, and checks whether the change in the viewer's emotions is progressing as intended. In this way, the evaluation unit can evaluate the emotional impact of the video work with high accuracy and contribute to improving the quality of the video work.

[0085] The suggestion unit proposes improvements to the video work based on the evaluation results by the evaluation unit. The suggestion unit proposes scene changes to induce a particular emotional response in the viewer. Specifically, the suggestion unit proposes adding a scene that moves the viewer or deleting a scene that excites the viewer. The suggestion unit can also propose changing music to relax the viewer or adjusting video effects to help the viewer concentrate. For example, based on the viewer's emotional data, if the viewer's emotions are lowered in a particular scene, the suggestion unit makes a proposal to change the content of the scene. Specifically, in order to enhance the production of the scene, the suggestion unit makes proposals such as adding music or sound effects, changing the character's lines, or re-editing the video cuts. In addition, in a scene in which the viewer's emotions are elevated, the suggestion unit makes a proposal to further enhance the effect. For example, in a scene in which the viewer is moved, the suggestion unit makes proposals such as adding music to enhance the emotion or strengthening the visual effects. Furthermore, the suggestion unit can also propose improvements to the overall composition and storytelling of the video work based on the viewer's feedback. In this way, the suggestion unit can utilize the viewer's emotional data to provide specific improvements to improve the quality of the video work and maximize the appeal of the video work.

[0086] The suggestion unit may include a correction unit that corrects the video work based on the proposed improvements. The correction unit corrects the video work based on the improvements proposed by the suggestion unit. The correction unit may, for example, edit the video or adjust the audio. The correction unit may, for example, cut a specific scene from the video or add a new scene. The correction unit may also adjust the volume of the audio or change the music. For example, the correction unit may shoot and edit a new video to add a scene that moves the viewer. The correction unit may also cut an existing video to delete a scene that excites the viewer. Furthermore, the correction unit may select and insert new music to change the music that relaxes the viewer. In this way, the video work may be corrected based on the proposed improvements, thereby further improving the viewer's satisfaction.

[0087] The estimation unit can estimate the emotion based on the viewer's facial expression, voice, movement, heart rate, or a combination of these. For example, the estimation unit analyzes the viewer's facial expression and estimates whether the viewer is happy, sad, surprised, or the like. For example, the estimation unit captures the viewer's facial expression with a camera and estimates the emotion using a facial expression recognition technology. The estimation unit can also analyze the viewer's voice and estimate the emotion from the tone and rhythm of the viewer's voice. For example, the estimation unit records the viewer's voice with a microphone and estimates the emotion using a voice analysis technology. Furthermore, the estimation unit can analyze the viewer's movement and heart rate and estimate the viewer's emotion. For example, the estimation unit captures the viewer's movement with a camera and estimates the emotion using a movement analysis technology. The estimation unit can also measure the viewer's heart rate with a sensor and estimate the emotion from the fluctuation of the heart rate. This makes it possible to accurately estimate the emotion based on various information about the viewer.

[0088] The evaluation unit can evaluate the video work based on the transition of the viewer's emotions and the target transition of emotions in the video work. The evaluation unit compares the transition of the viewer's emotions with the target transition of emotions in the video work and evaluates what emotions the video work evoked in the viewer. For example, the evaluation unit graphs the transition of the viewer's emotions and compares it with the target transition of emotions for each scene of the video work. The evaluation unit can also quantify the transition of the viewer's emotions and calculate an evaluation score for the video work. For example, the evaluation unit tracks the transition of the viewer's emotions in real time and immediately grasps changes in emotions. Furthermore, the evaluation unit can accumulate the viewer's emotion data and analyze long-term trends in emotions. For example, the evaluation unit analyzes the viewer's emotion data in chronological order and identifies patterns of change in emotions. This allows the video work to be accurately evaluated by comparing the transition of the viewer's emotions with the target. The estimation of emotions is realized using an emotion estimation function using, for example, an emotion engine or a generation AI. The generation AI is, for example, a text generation AI (for example, LLM) or a multimodal generation AI, but is not limited to such examples.

[0089] The evaluation unit may have a transition goal for each genre of the video work. The evaluation unit sets a different emotion transition goal for each genre of the video work and performs evaluation. The evaluation unit sets different emotion transition goals according to genres such as drama, comedy, and horror. For example, the goal for drama is to cause emotion and empathy, and the goal for comedy is to cause laughter and enjoyment. Also, the goal for horror is to cause fear and tension. The evaluation unit compares the transition of the viewer's emotion with the goal for each genre and evaluates how well the video work matches the goal. For example, the evaluation unit compares the transition of the viewer's emotion with the emotion goal of the drama and evaluates whether emotion and empathy are caused. The evaluation unit can also compare the transition of the viewer's emotion with the emotion goal of the comedy and evaluate whether laughter and enjoyment are caused. Furthermore, the evaluation unit can compare the transition of the viewer's emotion with the emotion goal of the horror and evaluate whether fear and tension are caused. This allows for more appropriate evaluation by having different evaluation criteria for each genre.

[0090] The evaluation unit can aggregate emotion estimation results of multiple viewers and evaluate the video work. For example, the evaluation unit collects emotion data of multiple viewers and calculates an average value or a median value. The evaluation unit can also aggregate the emotion data of viewers and analyze the distribution and tendency of emotions. For example, the evaluation unit can aggregate the emotion data of multiple viewers and calculate an average emotion value. The evaluation unit can also aggregate the emotion data of viewers and graph the distribution of emotions. The evaluation unit can also aggregate the emotion data of viewers and analyze the tendency of emotions. For example, the evaluation unit analyzes the emotion data of viewers in chronological order and identifies the pattern of change in emotions. In this way, a more objective evaluation can be made by aggregating the emotions of multiple viewers.

[0091] The evaluation unit can classify the evaluations based on the age, gender, and cultural background of the viewer. For example, the evaluation unit classifies the evaluations based on the age of the viewer. For example, the evaluation unit performs evaluations by age, such as children, adolescents, adults, and the elderly. The evaluation unit can also classify the evaluations based on the gender of the viewer. For example, the evaluation unit performs evaluations by male, female, and other genders. Furthermore, the evaluation unit can also classify the evaluations based on the cultural background of the viewer. For example, the evaluation unit performs evaluations by cultural background, such as the nationality, religion, and region of the viewer. This allows for more detailed evaluations by classifying the evaluations based on the attributes of the viewer.

[0092] The suggestion unit can suggest a scene change to induce a particular emotional response in the viewer. For example, the suggestion unit can suggest adding a scene that moves the viewer or deleting a scene that excites the viewer. The suggestion unit can also suggest changing music to relax the viewer or adjusting a video effect to concentrate the viewer. For example, the suggestion unit can suggest shooting and editing a new video to add a scene that moves the viewer. The suggestion unit can also suggest cutting an existing video to delete a scene that excites the viewer. Furthermore, the suggestion unit can suggest selecting and inserting new music to change the music to relax the viewer. This can improve the quality of the video work by suggesting a specific scene change to induce an emotional response in the viewer.

[0093] The acquisition unit can acquire one or more pieces of information of the viewer's facial expression, movement, voice, heart rate, and blood pressure as emotion estimation information. The acquisition unit, for example, photographs the viewer's facial expression using a camera and records the viewer's voice using a microphone. The acquisition unit can also measure the viewer's heart rate and blood pressure using a sensor. For example, the acquisition unit photographs the viewer's facial expression with a camera and estimates the emotion using facial expression recognition technology. The acquisition unit can also record the viewer's voice with a microphone and estimate the emotion using voice analysis technology. Furthermore, the acquisition unit can measure the viewer's heart rate and blood pressure with a sensor and estimate the emotion based on these data. In this way, the accuracy of emotion estimation can be improved by acquiring various information of the viewer.

[0094] The acquisition unit can estimate the emotion of the viewer and adjust the timing of acquiring the emotion estimation information based on the estimated emotion of the viewer. For example, when the viewer is excited, the acquisition unit can increase the frequency of acquiring the emotion estimation information. Also, when the viewer is relaxed, the acquisition unit can reduce the frequency of acquiring the emotion estimation information. Furthermore, when the viewer is concentrating, the acquisition unit can keep the timing of acquiring the emotion estimation information constant. For example, when the viewer is excited, the acquisition unit can focus on acquiring information on the heart rate and blood pressure. Also, when the viewer is relaxed, the acquisition unit can focus on acquiring information on facial expressions and voice. Furthermore, when the viewer is concentrating, the acquisition unit can focus on acquiring information on movements and postures. This allows for more accurate emotion estimation by adjusting the timing of acquiring information according to the emotion of the viewer.

[0095] The acquisition unit can analyze the viewer's past emotion data and select the optimal acquisition method. For example, the acquisition unit acquires emotion estimation information for similar scenes based on a scene in which the viewer showed a high emotional response in the past. The acquisition unit can also analyze that emotions are likely to fluctuate in a specific time period from the viewer's past emotion data and concentrate acquisition on that time period. The acquisition unit can also select the most effective acquisition method in a specific emotional state based on the viewer's past emotion data. For example, the acquisition unit acquires emotion estimation information for similar scenes based on a scene in which the viewer showed a high emotional response in the past. The acquisition unit can also analyze that emotions are likely to fluctuate in a specific time period from the viewer's past emotion data and concentrate acquisition on that time period. The acquisition unit can also select the most effective acquisition method in a specific emotional state based on the viewer's past emotion data. Thereby, the accuracy of emotion estimation can be improved by selecting the optimal acquisition method based on past emotion data.

[0096] The acquisition unit can perform filtering based on the viewer's current psychological state and environment when acquiring emotion estimation information. For example, when the viewer is stressed, the acquisition unit can focus on acquiring information on heart rate and blood pressure. Also, when the viewer is relaxed, the acquisition unit can focus on acquiring information on facial expressions and voice. Furthermore, when the viewer is concentrating, the acquisition unit can focus on acquiring information on movement and posture. For example, when the viewer is stressed, the acquisition unit can focus on acquiring information on heart rate and blood pressure. Also, when the viewer is relaxed, the acquisition unit can focus on acquiring information on facial expressions and voice. Furthermore, when the viewer is concentrating, the acquisition unit can focus on acquiring information on movement and posture. In this way, by filtering information based on the viewer's psychological state and environment, the accuracy of emotion estimation can be improved.

[0097] The acquisition unit can estimate the emotion of the viewer and determine the priority of emotion estimation information to be acquired based on the estimated emotion of the viewer. For example, when the viewer is excited, the acquisition unit preferentially acquires information on heart rate and blood pressure. Also, when the viewer is relaxed, the acquisition unit can preferentially acquire information on facial expressions and voice. Furthermore, when the viewer is concentrating, the acquisition unit can preferentially acquire information on movement and posture. For example, when the viewer is excited, the acquisition unit preferentially acquires information on heart rate and blood pressure. Also, when the viewer is relaxed, the acquisition unit can preferentially acquire information on facial expressions and voice. Furthermore, when the viewer is concentrating, the acquisition unit can preferentially acquire information on movement and posture. In this way, by determining the priority of information based on the emotion of the viewer, the accuracy of emotion estimation can be improved.

[0098] When acquiring emotion estimation information, the acquisition unit can preferentially acquire highly relevant information in consideration of the geographical location information of the viewer. For example, when the viewer is at home, the acquisition unit preferentially acquires information based on a relaxed environment. Also, when the viewer is in a public place, the acquisition unit can preferentially acquire information on surrounding sounds and actions. Furthermore, when the viewer is on the move, the acquisition unit can preferentially acquire information on heart rate and actions. For example, when the viewer is at home, the acquisition unit preferentially acquires information based on a relaxed environment. Also, when the viewer is in a public place, the acquisition unit can preferentially acquire information on surrounding sounds and actions. Furthermore, when the viewer is on the move, the acquisition unit can preferentially acquire information on heart rate and actions. In this way, highly relevant information can be preferentially acquired by considering the geographical location information of the viewer.

[0099] The acquisition unit can analyze the viewer's social media activity when acquiring the emotion estimation information and acquire related information. For example, when the viewer posts an emotional message on social media, the acquisition unit acquires emotion estimation information based on the content of the message. Also, when the viewer mentions a specific video work on social media, the acquisition unit can acquire information related to the work. Furthermore, when the viewer has an emotional exchange with other users on social media, the acquisition unit can acquire emotion estimation information based on the exchange. For example, when the viewer posts an emotional message on social media, the acquisition unit acquires emotion estimation information based on the content of the message. Also, when the viewer mentions a specific video work on social media, the acquisition unit can acquire information related to the work. Furthermore, when the viewer has an emotional exchange with other users on social media, the acquisition unit can acquire emotion estimation information based on the exchange. In this way, related information can be acquired by analyzing the viewer's social media activity.

[0100] The estimation unit can estimate the emotion of the viewer and adjust the emotion estimation algorithm based on the estimated emotion of the viewer. For example, the estimation unit can increase the sensitivity of the emotion estimation algorithm when the viewer is excited. Also, the estimation unit can decrease the sensitivity of the emotion estimation algorithm when the viewer is relaxed. Furthermore, the estimation unit can keep the sensitivity of the emotion estimation algorithm constant when the viewer is focused. For example, the estimation unit can increase the sensitivity of the emotion estimation algorithm when the viewer is excited. Also, the estimation unit can decrease the sensitivity of the emotion estimation algorithm when the viewer is relaxed. Furthermore, the estimation unit can keep the sensitivity of the emotion estimation algorithm constant when the viewer is focused. In this way, the accuracy of emotion estimation can be improved by adjusting the algorithm based on the emotion of the viewer.

[0101] The estimation unit can track changes in the viewer's emotions in real time during estimation, thereby improving the estimation accuracy. The estimation unit can track changes in, for example, the viewer's heart rate and blood pressure in real time, thereby improving the accuracy of emotion estimation. The estimation unit can also track changes in the viewer's facial expression and voice in real time, thereby improving the accuracy of emotion estimation. The estimation unit can also track changes in the viewer's movements and posture in real time, thereby improving the accuracy of emotion estimation. For example, the estimation unit can track changes in the viewer's heart rate and blood pressure in real time, thereby improving the accuracy of emotion estimation. The estimation unit can also track changes in the viewer's facial expression and voice in real time, thereby improving the accuracy of emotion estimation. The estimation unit can also track changes in the viewer's movements and posture in real time, thereby improving the accuracy of emotion estimation. Thereby, by tracking changes in the viewer's emotions in real time, the estimation accuracy can be improved.

[0102] The estimation unit can correct the estimation result by referring to the viewer's past emotion data at the time of estimation. For example, the estimation unit corrects the estimation result by referring to emotion data shown by the viewer in a similar scene in the past. The estimation unit can also correct the estimation result in a specific emotional state from the viewer's past emotion data. Furthermore, the estimation unit can improve the accuracy of the estimation result based on the viewer's past emotion data. For example, the estimation unit corrects the estimation result by referring to emotion data shown by the viewer in a similar scene in the past. The estimation unit can also correct the estimation result in a specific emotional state from the viewer's past emotion data. Furthermore, the estimation unit can improve the accuracy of the estimation result based on the viewer's past emotion data. Thereby, the accuracy of the estimation result can be improved by referring to the past emotion data.

[0103] The estimation unit can estimate the viewer's emotion and adjust the display method of the estimation result based on the estimated viewer's emotion. For example, when the viewer is excited, the estimation unit provides a visually stimulating display method. Also, when the viewer is relaxed, the estimation unit can provide a calm display method. Furthermore, when the viewer is concentrated, the estimation unit can provide a simple and highly visible display method. For example, when the viewer is excited, the estimation unit provides a visually stimulating display method. Also, when the viewer is relaxed, the estimation unit can provide a calm display method. Furthermore, when the viewer is concentrated, the estimation unit can provide a simple and highly visible display method. This allows for more appropriate display by adjusting the display method based on the viewer's emotion.

[0104] The estimation unit can customize the emotion estimation algorithm based on the age and gender of the viewer at the time of estimation. For example, when the viewer is a young person, the estimation unit customizes the emotion estimation algorithm for young people. Also, when the viewer is an elderly person, the estimation unit can customize the emotion estimation algorithm for elderly people. Furthermore, the emotion estimation algorithm can be customized based on the gender of the viewer. For example, when the viewer is a young person, the estimation unit customizes the emotion estimation algorithm for young people. Also, when the viewer is an elderly person, the estimation unit can customize the emotion estimation algorithm for elderly people. Furthermore, the emotion estimation algorithm can be customized based on the gender of the viewer. Thereby, by customizing the algorithm based on the age and gender of the viewer, the accuracy of emotion estimation can be improved.

[0105] The estimation unit can optimize the emotion estimation algorithm in consideration of the cultural background of the viewer at the time of estimation. For example, when the viewer belongs to a specific cultural sphere, the estimation unit optimizes the emotion estimation algorithm based on the culture. The emotion estimation algorithm can also be customized based on the cultural background of the viewer. Furthermore, the accuracy of the emotion estimation algorithm can be improved in consideration of the cultural background of the viewer. For example, when the viewer belongs to a specific cultural sphere, the estimation unit optimizes the emotion estimation algorithm based on the culture. The estimation unit can also customize the emotion estimation algorithm based on the cultural background of the viewer. Furthermore, the estimation unit can improve the accuracy of the emotion estimation algorithm in consideration of the cultural background of the viewer. Thereby, the accuracy of emotion estimation can be improved by considering the cultural background of the viewer.

[0106] The evaluation unit can estimate the viewer's emotions and adjust the evaluation criteria based on the estimated viewer's emotions. For example, the evaluation unit can set stricter evaluation criteria when the viewer is excited. Also, the evaluation unit can loosen the evaluation criteria when the viewer is relaxed. Furthermore, the evaluation unit can keep the evaluation criteria constant when the viewer is focused. For example, the evaluation unit can set stricter evaluation criteria when the viewer is excited. Also, the evaluation unit can loosen the evaluation criteria when the viewer is relaxed. Furthermore, the evaluation unit can keep the evaluation criteria constant when the viewer is focused. In this way, by adjusting the evaluation criteria based on the viewer's emotions, more appropriate evaluation is possible.

[0107] At the time of evaluation, the evaluation unit can compare the transition of the viewer's emotions with the emotional goal for each scene of the video work to make the evaluation. For example, the evaluation unit tracks the transition of the viewer's emotions in real time and makes the evaluation by comparing it with the emotional goal for each scene. The evaluation unit can also analyze the transition of the viewer's emotions and make the evaluation based on the emotional goal for each scene. Furthermore, the evaluation unit can also aggregate the transition of the viewer's emotions and make the evaluation by comparing it with the emotional goal for each scene. For example, the evaluation unit tracks the transition of the viewer's emotions in real time and makes the evaluation by comparing it with the emotional goal for each scene. The evaluation unit can also analyze the transition of the viewer's emotions and make the evaluation based on the emotional goal for each scene. Furthermore, the evaluation unit can also aggregate the transition of the viewer's emotions and make the evaluation by comparing it with the emotional goal for each scene. This makes it possible to make a more accurate evaluation by comparing the transition of the viewer's emotions with the emotional goal for each scene.

[0108] The evaluation unit can compile the viewer's emotion data in real time at the time of evaluation and update the evaluation result. The evaluation unit, for example, compiles the viewer's emotion data in real time and updates the evaluation result accordingly. The evaluation unit can also analyze the viewer's emotion data in real time and reflect the evaluation result. Furthermore, the evaluation unit can process the viewer's emotion data in real time and keep the evaluation result up to date. For example, the evaluation unit compiles the viewer's emotion data in real time and updates the evaluation result accordingly. The evaluation unit can also analyze the viewer's emotion data in real time and reflect the evaluation result. Furthermore, the evaluation unit can process the viewer's emotion data in real time and keep the evaluation result up to date. In this way, the latest evaluation result can be provided by compiling the viewer's emotion data in real time.

[0109] The evaluation unit can estimate the viewer's emotions and adjust the display method of the evaluation result based on the estimated viewer's emotions. For example, when the viewer is excited, the evaluation unit provides a visually stimulating display method. Also, when the viewer is relaxed, the evaluation unit can provide a calm display method. Furthermore, when the viewer is concentrated, the evaluation unit can provide a simple and highly visible display method. For example, when the viewer is excited, the evaluation unit provides a visually stimulating display method. Also, when the viewer is relaxed, the evaluation unit can provide a calm display method. Furthermore, when the viewer is concentrated, the evaluation unit can provide a simple and highly visible display method. In this way, by adjusting the display method based on the viewer's emotions, a more appropriate evaluation result can be provided.

[0110] At the time of evaluation, the evaluation unit can classify the evaluation results based on the age, gender, and cultural background of the viewer. The evaluation unit classifies the evaluation results, for example, based on the age of the viewer. The evaluation unit can also classify the evaluation results based on the gender of the viewer. Furthermore, the evaluation unit can classify the evaluation results based on the cultural background of the viewer. For example, the evaluation unit classifies the evaluation results based on the age of the viewer. The evaluation unit can also classify the evaluation results based on the gender of the viewer. Furthermore, the evaluation unit can classify the evaluation results based on the cultural background of the viewer. This enables more detailed evaluation by classifying the evaluation results based on the attributes of the viewer.

[0111] The evaluation unit can analyze the evaluation results taking into account the geographical distribution of viewers at the time of evaluation. The evaluation unit analyzes the evaluation results based on, for example, the geographical distribution of viewers. The evaluation unit can also collect emotion data for each region of viewers and analyze the evaluation results. Furthermore, the evaluation unit can optimize the evaluation results taking into account the geographical distribution of viewers. For example, the evaluation unit analyzes the evaluation results based on the geographical distribution of viewers. The evaluation unit can also collect emotion data for each region of viewers and analyze the evaluation results. Furthermore, the evaluation unit can optimize the evaluation results taking into account the geographical distribution of viewers. In this way, a more appropriate evaluation result can be provided by taking into account the geographical distribution of viewers.

[0112] The suggestion unit can estimate the viewer's emotion and adjust the proposed content based on the estimated viewer's emotion. For example, when the viewer is excited, the suggestion unit can suggest a scene change that calms the viewer's emotion. Also, when the viewer is relaxed, the suggestion unit can suggest a scene change that uplifts the viewer's emotion. Furthermore, when the viewer is concentrated, the suggestion unit can suggest a scene change that maintains the viewer's emotion. For example, when the viewer is excited, the suggestion unit can suggest a scene change that calms the viewer's emotion. Also, when the viewer is relaxed, the suggestion unit can suggest a scene change that uplifts the viewer's emotion. Furthermore, when the viewer is concentrated, the suggestion unit can suggest a scene change that maintains the viewer's emotion. This allows for more appropriate suggestions by adjusting the proposed content based on the viewer's emotion.

[0113] The suggestion unit can track changes in the viewer's emotions in real time when making a suggestion, and update the proposed content. For example, if the viewer's emotions change suddenly, the suggestion unit can instantly update the proposed content. Also, if the viewer's emotions change gradually, the suggestion unit can update the proposed content step by step. Furthermore, if the viewer's emotions are stable, the suggestion unit can maintain the proposed content. For example, if the viewer's emotions change suddenly, the suggestion unit can instantly update the proposed content. Also, if the viewer's emotions change gradually, the suggestion unit can update the proposed content step by step. Furthermore, if the viewer's emotions are stable, the suggestion unit can maintain the proposed content. In this way, the latest proposed content can be provided by tracking changes in the viewer's emotions in real time.

[0114] The suggestion unit can correct the proposal content by referring to the viewer's past emotion data when making a suggestion. For example, the suggestion unit corrects the proposal content by referring to emotion data shown by the viewer in a similar scene in the past. The suggestion unit can also correct the proposal content in a specific emotional state from the viewer's past emotion data. Furthermore, the suggestion unit can improve the accuracy of the proposal content based on the viewer's past emotion data. For example, the suggestion unit corrects the proposal content by referring to emotion data shown by the viewer in a similar scene in the past. The suggestion unit can also correct the proposal content in a specific emotional state from the viewer's past emotion data. Furthermore, the suggestion unit can improve the accuracy of the proposal content based on the viewer's past emotion data. Thereby, the accuracy of the proposal content can be improved by referring to the past emotion data.

[0115] The suggestion unit can estimate the viewer's emotion and adjust the display method of the proposed result based on the estimated viewer's emotion. For example, when the viewer is excited, the suggestion unit provides a visually stimulating display method. Also, when the viewer is relaxed, the suggestion unit can provide a calm display method. Furthermore, when the viewer is concentrated, the suggestion unit can provide a simple and highly visible display method. For example, when the viewer is excited, the suggestion unit provides a visually stimulating display method. Also, when the viewer is relaxed, the suggestion unit can provide a calm display method. Furthermore, when the viewer is concentrated, the suggestion unit can provide a simple and highly visible display method. In this way, by adjusting the display method based on the viewer's emotion, a more appropriate proposed result can be provided.

[0116] At the time of proposal, the suggestion unit can customize the proposal content based on the age and gender of the viewer. For example, if the viewer is a young person, the suggestion unit provides proposal content for young people. Also, if the viewer is an elderly person, the suggestion unit can provide proposal content for elderly people. Furthermore, the suggestion unit can customize the proposal content based on the gender of the viewer. For example, if the viewer is a young person, the suggestion unit provides proposal content for young people. Also, if the viewer is an elderly person, the suggestion unit can provide proposal content for elderly people. Furthermore, the suggestion unit can customize the proposal content based on the gender of the viewer. This enables more appropriate proposals by customizing the proposal content based on the age and gender of the viewer.

[0117] The suggestion unit can optimize the proposed content by taking into account the cultural background of the viewer when making a suggestion. For example, when the viewer belongs to a specific cultural sphere, the suggestion unit optimizes the proposed content based on the culture. The suggestion unit can also customize the proposed content based on the cultural background of the viewer. Furthermore, the accuracy of the proposed content can be improved by taking into account the cultural background of the viewer. For example, when the viewer belongs to a specific cultural sphere, the suggestion unit optimizes the proposed content based on the culture. The suggestion unit can also customize the proposed content based on the cultural background of the viewer. Furthermore, the suggestion unit can improve the accuracy of the proposed content by taking into account the cultural background of the viewer. Thereby, the accuracy of the proposed content can be improved by taking into account the cultural background of the viewer.

[0118] The correction unit can estimate the viewer's emotion and adjust the correction content based on the estimated viewer's emotion. For example, when the viewer is excited, the correction unit can suggest correction content that calms the viewer's emotion. Also, when the viewer is relaxed, the correction unit can suggest correction content that uplifts the viewer's emotion. Furthermore, when the viewer is focused, the correction unit can suggest correction content that maintains the viewer's emotion. For example, when the viewer is excited, the correction unit can suggest correction content that calms the viewer's emotion. Also, when the viewer is relaxed, the correction unit can suggest correction content that uplifts the viewer's emotion. Furthermore, when the viewer is focused, the correction unit can suggest correction content that maintains the viewer's emotion. This allows for more appropriate correction by adjusting the correction content based on the viewer's emotion.

[0119] The correction unit can track changes in the viewer's emotions in real time during correction and update the correction content. For example, if the viewer's emotions change suddenly, the correction unit immediately updates the correction content. Also, if the viewer's emotions change gradually, the correction unit can update the correction content step by step. Furthermore, if the viewer's emotions are stable, the correction content can be maintained. For example, the correction unit immediately updates the correction content if the viewer's emotions change suddenly. Also, if the viewer's emotions change gradually, the correction unit can update the correction content step by step. Furthermore, if the viewer's emotions are stable, the correction unit can maintain the correction content. In this way, the latest correction content can be provided by tracking changes in the viewer's emotions in real time.

[0120] At the time of correction, the correction unit can correct the correction contents by referring to the viewer's past emotion data. The correction unit, for example, refers to emotion data shown by the viewer in a similar scene in the past and corrects the correction contents. The correction unit can also correct the correction contents in a specific emotional state from the viewer's past emotion data. Furthermore, the correction unit can improve the accuracy of the correction contents based on the viewer's past emotion data. For example, the correction unit refers to emotion data shown by the viewer in a similar scene in the past and corrects the correction contents. The correction unit can also correct the correction contents in a specific emotional state from the viewer's past emotion data. Furthermore, the correction unit can improve the accuracy of the correction contents based on the viewer's past emotion data. Thereby, the accuracy of the correction contents can be improved by referring to the past emotion data.

[0121] The correction unit can estimate the viewer's emotion and adjust the display method of the correction result based on the estimated viewer's emotion. For example, when the viewer is excited, the correction unit provides a visually stimulating display method. Also, when the viewer is relaxed, the correction unit can provide a calm display method. Furthermore, when the viewer is concentrated, the correction unit can provide a simple and highly visible display method. For example, when the viewer is excited, the correction unit provides a visually stimulating display method. Also, when the viewer is relaxed, the correction unit can provide a calm display method. Furthermore, when the viewer is concentrated, the correction unit can provide a simple and highly visible display method. In this way, by adjusting the display method based on the viewer's emotion, a more appropriate correction result can be provided.

[0122] At the time of correction, the correction unit can customize the correction content based on the age and gender of the viewer. For example, if the viewer is a young person, the correction unit can provide correction content for young people. Also, if the viewer is an elderly person, the correction unit can provide correction content for elderly people. Furthermore, the correction content can be customized based on the gender of the viewer. For example, if the viewer is a young person, the correction unit can provide correction content for young people. Also, if the viewer is an elderly person, the correction unit can provide correction content for elderly people. Furthermore, the correction unit can customize the correction content based on the gender of the viewer. This allows for more appropriate correction by customizing the correction content based on the age and gender of the viewer.

[0123] The correction unit can optimize the correction content by taking into account the cultural background of the viewer during correction. For example, when the viewer belongs to a specific cultural sphere, the correction unit optimizes the correction content based on the culture. The correction unit can also customize the correction content based on the cultural background of the viewer. Furthermore, the correction unit can improve the accuracy of the correction content by taking into account the cultural background of the viewer. For example, when the viewer belongs to a specific cultural sphere, the correction unit optimizes the correction content based on the culture. The correction unit can also customize the correction content based on the cultural background of the viewer. Furthermore, the correction unit can improve the accuracy of the correction content by taking into account the cultural background of the viewer. This makes it possible to improve the accuracy of the correction content by taking into account the cultural background of the viewer.

[0124] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0125] The acquisition unit acquires the viewer's environmental sound, and the estimation unit can also analyze the environmental sound to estimate the viewer's emotion. For example, if the viewer is in a quiet environment, it can be estimated that the viewer is relaxed. Also, if the viewer is in a noisy environment, it can be estimated that the viewer is stressed. Furthermore, if the viewer is in an environment where natural sounds can be heard, it can be estimated that the viewer is relaxed. In this way, by taking into account the viewer's environmental sound, it is possible to improve the accuracy of emotion estimation.

[0126] The evaluation unit can also evaluate the viewer's concentration level for each scene of a video work based on the viewer's emotion estimation result. For example, if the viewer is concentrating on a particular scene, the evaluation of that scene can be increased. Also, if the viewer is not concentrating on a particular scene, the evaluation of that scene can be decreased. Furthermore, the transition of the viewer's concentration level can be graphed to evaluate the concentration level of the entire video work. In this way, the video work can be evaluated in more detail by taking the viewer's concentration level into consideration.

[0127] The suggestion unit can also suggest changes to the volume and music for each scene of a video work based on the viewer's emotion estimation result. For example, if the viewer is relaxed, it can suggest lowering the volume or adding calm music. Also, if the viewer is excited, it can suggest increasing the volume or adding intense music. Furthermore, if the viewer is concentrating, it can suggest keeping the volume constant or adding music that is easy to concentrate on. In this way, the quality of the video work can be improved by adjusting the volume and music based on the viewer's emotions.

[0128] The correction unit can also suggest changes to the color tone and brightness for each scene of the video work based on the viewer's emotion estimation result. For example, if the viewer is relaxed, it can suggest changing the color tone to a warmer color tone or lowering the brightness. Also, if the viewer is excited, it can suggest making the color tone vivid or increasing the brightness. Furthermore, if the viewer is concentrating, it can suggest keeping the color tone neutral and keeping the brightness constant. In this way, the quality of the video work can be improved by adjusting the color tone and brightness based on the viewer's emotion.

[0129] The evaluation unit can also evaluate the intensity of the viewer's emotion for each scene of the video work based on the viewer's emotion estimation result. For example, if the viewer shows strong emotion in a particular scene, the evaluation of that scene can be increased. Also, if the viewer shows weak emotion in a particular scene, the evaluation of that scene can be decreased. Furthermore, the transition of the viewer's emotion intensity can be graphed to evaluate the emotion intensity of the entire video work. This allows the video work to be evaluated in more detail by taking into account the intensity of the viewer's emotion.

[0130] The acquisition unit acquires the gaze movement of the viewer, and the estimation unit can also analyze the gaze movement to estimate the viewer's interest. For example, if the viewer focuses his / her gaze on a particular scene, it can be estimated that the viewer is interested in that scene. Also, if the viewer frequently moves his / her gaze, it can be estimated that the viewer is not interested in that scene. Furthermore, it is possible to analyze the pattern of the viewer's gaze movement and grasp the tendency of the viewer's interest. In this way, the accuracy of interest estimation can be improved by taking the viewer's gaze movement into consideration.

[0131] The evaluation unit can also evaluate the viewer's interest in each scene of a video work based on the viewer's line of sight. For example, if the viewer focuses his or her eyes on a particular scene, the evaluation unit can give that scene a higher evaluation. Also, if the viewer frequently moves his or her eyes, the evaluation unit can give that scene a lower evaluation. Furthermore, the evaluation unit can graph the viewer's line of sight movement patterns and evaluate the interest level of the entire video work. This allows the evaluation of a video work to be performed in more detail by taking the viewer's line of sight movement into consideration.

[0132] The suggestion unit can also suggest changes to the camera angle and framing for each scene of a video work based on the viewer's line of sight. For example, if the viewer focuses his or her gaze on a particular scene, it can suggest changing the camera angle or adjusting the framing for that scene. Also, if the viewer frequently moves his or her gaze, it can suggest fixing the camera angle or simplifying the framing for that scene. Furthermore, it can analyze the viewer's line of sight movement patterns and suggest optimal camera angles and framing. In this way, the quality of a video work can be improved by taking the viewer's line of sight movement into consideration.

[0133] The correction unit can also suggest changes to edits or cuts for each scene of a video work based on the viewer's gaze movements. For example, if the viewer focuses his or her gaze on a particular scene, it can suggest strengthening the editing of that scene or adding a cut. Also, if the viewer frequently moves his or her gaze, it can suggest simplifying the editing of that scene or reducing cuts. Furthermore, it can analyze the viewer's gaze movement patterns and suggest optimal edits and cuts. This makes it possible to improve the quality of a video work by taking into account the viewer's gaze movements.

[0134] The evaluation unit can also evaluate the viewer's visual interest level for each scene of a video work based on the viewer's gaze movement. For example, if the viewer focuses his / her gaze on a particular scene, the visual interest level of that scene can be evaluated as high. Also, if the viewer frequently moves his / her gaze, the visual interest level of that scene can be evaluated as low. Furthermore, the pattern of the viewer's gaze movement can be graphed to evaluate the visual interest level of the entire video work. In this way, the visual interest level of a video work can be evaluated in more detail by taking the viewer's gaze movement into consideration.

[0135] The process flow of the second embodiment will be briefly described below.

[0136] Step 1: The acquisition unit acquires information for estimating the viewer's emotions. The information for estimating the viewer's emotions includes, for example, the viewer's facial expression, voice, movement, heart rate, blood pressure, etc. The acquisition unit photographs the viewer's facial expression using a camera and records the viewer's voice using a microphone. The acquisition unit can also measure the viewer's heart rate and blood pressure using a sensor. Step 2: The estimation unit estimates the viewer's emotions based on the information acquired by the acquisition unit. The estimation unit analyzes the viewer's facial expressions, voice, movements, heart rate, etc., to estimate what emotions the viewer is feeling. For example, the estimation unit analyzes the viewer's facial expressions to estimate whether the viewer is happy, sad, surprised, etc. It can also analyze the viewer's voice and estimate emotions from the tone and rhythm of the viewer's voice. It can also analyze the viewer's movements and heart rate to estimate the viewer's emotions. Step 3: The evaluation unit evaluates the video work based on the estimation result by the estimation unit. The evaluation unit compares the viewer's emotional transition with the target emotional transition in the video work, and evaluates what emotions the video work evoked in the viewer. For example, the evaluation unit may graph the viewer's emotional transition and compare it with the target emotional transition for each scene in the video work. It may also quantify the viewer's emotional transition and calculate an evaluation score for the video work. Step 4: The suggestion unit suggests improvements to the video work based on the evaluation results by the evaluation unit. The suggestion unit suggests scene changes to evoke a particular emotional response in the viewer. For example, the suggestion unit suggests adding a scene that moves the viewer or deleting a scene that excites the viewer. It can also suggest changing music to relax the viewer or adjusting visual effects to help the viewer concentrate.

[0137] The specific processing unit 290 transmits the result of the specific processing to the smart device 14. In the smart device 14, the control unit 46A causes the output device 40 to output the result of the specific processing. The microphone 38B acquires a voice indicating a user input for the result of the specific processing. The control unit 46A transmits the voice data indicating the user input acquired by the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the voice data.

[0138] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (registered trademark) (Internet search<URL: https: / / openai.com / blog / chatgpt> ) and other generation AIs. The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating a voice, text data indicating a text, and image data indicating an image (e.g., still image data or video data) are input. The data generation model 58 infers the input inference data according to the instruction indicated by the prompt, and outputs the inference result in one or more data formats of voice data, text data, and image data. The data generation model 58 includes, for example, a text generation AI, an image generation AI, and a multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specification processing unit 290 performs the above-mentioned specification processing while using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction, and in this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. In the data processing device 12 and the like, the data generation model 58 includes a plurality of types, and the data generation model 58 includes an AI other than the generation AI. The AI ​​other than the generation AI is, for example, a linear regression, a logistic regression, a decision tree, a random forest, a support vector machine (SVM), a k-means clustering, a convolutional neural network (CNN), a recurrent neural network (RNN), a generative adversarial network (GAN), or a naive Bayes, and can perform various processes, but is not limited to such examples. The AI ​​may also be an AI agent. In addition, when the processes of each of the above-mentioned parts are performed by an AI, the processes are performed in part or in whole by the AI, but are not limited to such examples.In addition, processing performed by AI including the generation AI may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by AI including the generation AI.

[0139] Furthermore, the above-described processing by the data processing system 10 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart device 14, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart device 14. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the smart device 14 or an external device, and the smart device 14 acquires or collects information required for processing from the data processing device 12 or an external device.

[0140] For example, the acquisition unit can acquire the facial expression and voice of the viewer using the camera 42 and microphone 38B of the smart device 14. The estimation unit is realized by the specific processing unit 290 of the data processing device 12, and estimates the viewer's emotions by analyzing the information obtained from the acquisition unit. The evaluation unit is realized by the specific processing unit 290 of the data processing device 12, and evaluates the video work based on the estimated emotions. The suggestion unit is realized by the specific processing unit 290 of the data processing device 12, and suggests improvements to the video work based on the evaluation results. The correspondence between each unit and the device or control unit is not limited to the above-mentioned example, and various changes are possible.

[0141] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0142] 3, the data processing system 210 includes the data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0143] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, the RAM 30, and the storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0144] The smart glasses 214 include a computer 36, a microphone 238, a speaker 240, a camera 42, and a communication I / F 44. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, and the camera 42 are also connected to the bus 52.

[0145] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs the voice according to instructions from the processor 46.

[0146] Camera 42 is a small digital camera equipped with an optical system including a lens, an aperture, and a shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the user's surroundings (e.g., an imaging range defined by an angle of view equivalent to the width of the field of vision of an average healthy person).

[0147] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is performed in a secure state.

[0148] Fig. 4 shows an example of main functions of the data processing device 12 and the smart glasses 214. As shown in Fig. 4, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0149] The processor 28 reads the specific processing program 56 from the storage 32, and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0150] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the emotion of the user using the emotion identification model 59 and perform identification processing using the user's emotion. In the emotion estimation function (emotion identification function) using the emotion identification model 59, various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, are performed, but are not limited to such examples. In addition, the estimation and prediction of the emotion also include, for example, analysis of the emotion.

[0151] In the smart glasses 214, the specific processing is performed by the processor 46. The storage 50 stores a specific processing program 60. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. Note that the smart glasses 214 have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can also perform processing similar to that of the specific processing unit 290 using these models.

[0152] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 obtains a processing result (such as a prediction result) using the data generation model 58 by communicating with the server device having the data generation model 58. In addition, the data processing device 12 may be a server device, or may be a terminal device (such as a mobile phone, a robot, or a home appliance) owned by a user.

[0153] The specific processing unit 290 transmits the result of the specific processing to the smart glasses 214. In the smart glasses 214, the control unit 46A causes the speaker 240 to output the result of the specific processing. The microphone 238 acquires a voice indicating a user input for the result of the specific processing. The control unit 46A transmits the voice data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the voice data.

[0154] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating a voice, text data indicating a text, and image data indicating an image (e.g., still image data or video data) is input. The data generation model 58 infers the input inference data according to the instruction indicated by the prompt, and outputs the inference result in one or more data formats of voice data, text data, and image data. The data generation model 58 includes, for example, a text generation AI, an image generation AI, and a multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specification processing unit 290 performs the above-mentioned specification processing while using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction, and in this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. In the data processing device 12 and the like, the data generation model 58 includes a plurality of types, and the data generation model 58 includes an AI other than the generation AI. The AI ​​other than the generation AI is, for example, a linear regression, a logistic regression, a decision tree, a random forest, a support vector machine (SVM), a k-means clustering, a convolutional neural network (CNN), a recurrent neural network (RNN), a generative adversarial network (GAN), or a naive Bayes, and can perform various processes, but is not limited to such examples. The AI ​​may be an AI agent. In addition, when the processes of each part described above are performed by an AI, the processes are performed in part or in whole by the AI, but are not limited to such examples. In addition, the processes performed by the AI ​​including the generation AI may be replaced with rule-based processes, and the rule-based processes may be replaced with processes performed by the AI ​​including the generation AI.

[0155] The data processing system 210 according to the second embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 210 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart glasses 214, but may be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart glasses 214. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the smart glasses 214 or an external device, and the smart glasses 214 acquires or collects information required for processing from the data processing device 12 or an external device.

[0156] For example, the acquisition unit can acquire the facial expression and voice of the viewer using the camera 42 and microphone 238 of the smart glasses 214. The estimation unit is realized by the specific processing unit 290 of the data processing device 12, and estimates the viewer's emotions by analyzing the information obtained from the acquisition unit. The evaluation unit is realized by the specific processing unit 290 of the data processing device 12, and evaluates the video work based on the estimated emotions. The suggestion unit is realized by the specific processing unit 290 of the data processing device 12, and suggests improvements to the video work based on the evaluation results. The correspondence between each unit and the device or control unit is not limited to the above-mentioned example, and various changes are possible.

[0157] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0158] 5, the data processing system 310 includes the data processing device 12 and a headset type terminal 314. An example of the data processing device 12 is a server.

[0159] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, the RAM 30, and the storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0160] The headset type terminal 314 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication I / F 44, and a display 343. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, the camera 42, and the display 343 are also connected to the bus 52.

[0161] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs the voice according to instructions from the processor 46.

[0162] Camera 42 is a small digital camera equipped with an optical system including a lens, an aperture, and a shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the user's surroundings (e.g., an imaging range defined by an angle of view equivalent to the width of the field of vision of an average healthy person).

[0163] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is performed in a secure state.

[0164] Fig. 6 shows an example of the main functions of the data processing device 12 and the headset type terminal 314. As shown in Fig. 6, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0165] The processor 28 reads the specific processing program 56 from the storage 32, and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0166] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the emotion of the user using the emotion identification model 59 and perform identification processing using the user's emotion. In the emotion estimation function (emotion identification function) using the emotion identification model 59, various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, are performed, but are not limited to such examples. In addition, the estimation and prediction of the emotion also include, for example, analysis of the emotion.

[0167] In the headset type terminal 314, the specific processing is performed by the processor 46. The storage 50 stores a specific program 60. The processor 46 reads the specific program 60 from the storage 50, and executes the read specific program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as the control unit 46A in accordance with the specific program 60 executed on the RAM 48. Note that the headset type terminal 314 has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can also perform processing similar to that of the identification processing unit 290 using these models.

[0168] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 obtains a processing result (such as a prediction result) using the data generation model 58 by communicating with the server device having the data generation model 58. In addition, the data processing device 12 may be a server device, or may be a terminal device (such as a mobile phone, a robot, or a home appliance) owned by a user.

[0169] The specific processing unit 290 transmits the result of the specific processing to the headset type terminal 314. In the headset type terminal 314, the control unit 46A causes the speaker 240 and the display 343 to output the result of the specific processing. The microphone 238 acquires voice indicating a user input for the result of the specific processing. The control unit 46A transmits voice data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the voice data.

[0170] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating a voice, text data indicating a text, and image data indicating an image (e.g., still image data or video data) is input. The data generation model 58 infers the input inference data according to the instruction indicated by the prompt, and outputs the inference result in one or more data formats of voice data, text data, and image data. The data generation model 58 includes, for example, a text generation AI, an image generation AI, and a multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specification processing unit 290 performs the above-mentioned specification processing while using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction, and in this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. In the data processing device 12 and the like, the data generation model 58 includes a plurality of types, and the data generation model 58 includes an AI other than the generation AI. The AI ​​other than the generation AI is, for example, a linear regression, a logistic regression, a decision tree, a random forest, a support vector machine (SVM), a k-means clustering, a convolutional neural network (CNN), a recurrent neural network (RNN), a generative adversarial network (GAN), or a naive Bayes, and can perform various processes, but is not limited to such examples. The AI ​​may be an AI agent. In addition, when the processes of each part described above are performed by an AI, the processes are performed in part or in whole by the AI, but are not limited to such examples. In addition, the processes performed by the AI ​​including the generation AI may be replaced with rule-based processes, and the rule-based processes may be replaced with processes performed by the AI ​​including the generation AI.

[0171] The data processing system 310 according to the third embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 310 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the headset type terminal 314, but may be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the headset type terminal 314. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the headset type terminal 314 or an external device, and the headset type terminal 314 acquires or collects information required for processing from the data processing device 12 or an external device.

[0172] For example, the acquisition unit can acquire the facial expressions and voice of the viewer using the camera 42 and microphone 238 of the headset type terminal 314. The estimation unit is realized by the specific processing unit 290 of the data processing device 12, and estimates the viewer's emotions by analyzing information obtained from the acquisition unit. The evaluation unit is realized by the specific processing unit 290 of the data processing device 12, and evaluates the video work based on the estimated emotions. The suggestion unit is realized by the specific processing unit 290 of the data processing device 12, and suggests improvements to the video work based on the evaluation results. The correspondence between each unit and the device or control unit is not limited to the above-mentioned example, and various changes are possible.

[0173] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0174] 7, a data processing system 410 includes a data processing device 12 and a robot 414. An example of the data processing device 12 is a server.

[0175] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, the RAM 30, and the storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0176] The robot 414 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication I / F 44, and a control target 443. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, the camera 42, and the control target 443 are also connected to the bus 52.

[0177] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs the voice according to instructions from the processor 46.

[0178] Camera 42 is a small digital camera equipped with an optical system including a lens, an aperture, and a shutter, and an imaging element such as a CMOS image sensor or a CCD image sensor, and captures images of the user's surroundings (e.g., an imaging range defined by an angle of view equivalent to the width of the field of vision of a typical healthy person).

[0179] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is performed in a secure state.

[0180] The control object 443 includes a display device, LEDs in the eyes, and motors for driving the arms, hands, and legs. The posture and behavior of the robot 414 are controlled by controlling the motors of the arms, hands, and legs. Some of the emotions of the robot 414 can be expressed by controlling these motors. In addition, the facial expressions of the robot 414 can also be expressed by controlling the light emission state of the LEDs in the eyes of the robot 414.

[0181] Fig. 8 shows an example of the main functions of the data processing device 12 and the robot 414. As shown in Fig. 8, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0182] The processor 28 reads the specific processing program 56 from the storage 32, and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0183] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the emotion of the user using the emotion identification model 59 and perform identification processing using the user's emotion. In the emotion estimation function (emotion identification function) using the emotion identification model 59, various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, are performed, but are not limited to such examples. In addition, the estimation and prediction of the emotion also include, for example, analysis of the emotion.

[0184] In the robot 414, the specific processing is performed by the processor 46. The storage 50 stores a specific program 60. The processor 46 reads out the specific program 60 from the storage 50, and executes the specific program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as the control unit 46A in accordance with the specific program 60 executed on the RAM 48. The robot 414 has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can also perform processing similar to that of the specific processing unit 290 using these models.

[0185] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 obtains a processing result (such as a prediction result) using the data generation model 58 by communicating with the server device having the data generation model 58. In addition, the data processing device 12 may be a server device, or may be a terminal device (such as a mobile phone, a robot, or a home appliance) owned by a user.

[0186] The specific processing unit 290 transmits the result of the specific processing to the robot 414. In the robot 414, the control unit 46A causes the speaker 240 and the control target 443 to output the result of the specific processing. The microphone 238 acquires a voice indicating a user input for the result of the specific processing. The control unit 46A transmits voice data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the voice data.

[0187] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating a voice, text data indicating a text, and image data indicating an image (e.g., still image data or video data) is input. The data generation model 58 infers the input inference data according to the instruction indicated by the prompt, and outputs the inference result in one or more data formats of voice data, text data, and image data. The data generation model 58 includes, for example, a text generation AI, an image generation AI, and a multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specification processing unit 290 performs the above-mentioned specification processing while using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction, and in this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. In the data processing device 12 and the like, the data generation model 58 includes a plurality of types, and the data generation model 58 includes an AI other than the generation AI. The AI ​​other than the generation AI is, for example, a linear regression, a logistic regression, a decision tree, a random forest, a support vector machine (SVM), a k-means clustering, a convolutional neural network (CNN), a recurrent neural network (RNN), a generative adversarial network (GAN), or a naive Bayes, and can perform various processes, but is not limited to such examples. The AI ​​may be an AI agent. In addition, when the processes of each part described above are performed by an AI, the processes are performed in part or in whole by the AI, but are not limited to such examples. In addition, the processes performed by the AI ​​including the generation AI may be replaced with rule-based processes, and the rule-based processes may be replaced with processes performed by the AI ​​including the generation AI.

[0188] The data processing system 410 according to the fourth embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 410 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the robot 414, but may be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the robot 414. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the robot 414 or an external device, etc., and the robot 414 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0189] For example, the acquisition unit can acquire the facial expressions and voices of the viewer using the camera 42 and microphone 238 of the robot 414. The estimation unit is realized by the specific processing unit 290 of the data processing device 12, and estimates the viewer's emotions by analyzing the information acquired from the acquisition unit. The evaluation unit is realized by the specific processing unit 290 of the data processing device 12, and evaluates the video work based on the estimated emotions. The suggestion unit is realized by the specific processing unit 290 of the data processing device 12, and suggests improvements to the video work based on the evaluation results. The correspondence between each unit and the device or control unit is not limited to the above-mentioned example, and various changes are possible.

[0190] The emotion identification model 59 as an emotion engine may determine the emotion of the user according to a specific mapping. Specifically, the emotion identification model 59 may determine the emotion of the user according to an emotion map (see FIG. 9), which is a specific mapping. Similarly, the emotion identification model 59 may determine the emotion of the robot, and the identification processing unit 290 may perform identification processing using the emotion of the robot.

[0191] FIG. 9 is a diagram showing an emotion map 400 on which multiple emotions are mapped. In the emotion map 400, emotions are arranged in concentric circles radiating from the center. The closer to the center of the concentric circles, the more primitive emotions are arranged. The more outside the concentric circles, the more emotions that represent states and actions that arise from a state of mind are arranged. Emotions are a concept that includes emotions and mental states. On the left side of the concentric circles, emotions that are generally generated from reactions that occur in the brain are arranged. On the right side of the concentric circles, emotions that are generally induced by situational judgment are arranged. On the upper and lower sides of the concentric circles, emotions that are generally generated from reactions that occur in the brain and are induced by situational judgment are arranged. In addition, on the upper side of the concentric circles, emotions of "pleasure" are arranged, and on the lower side, emotions of "discomfort" are arranged. In this way, in the emotion map 400, multiple emotions are mapped based on the structure in which emotions are generated, and emotions that tend to occur simultaneously are mapped close to each other.

[0192] These emotions are distributed in the three o'clock direction of emotion map 400 and usually fluctuate between relief and anxiety. In the right half of emotion map 400, situational awareness dominates over internal sensations, resulting in a sense of calm.

[0193] The inside of emotion map 400 represents what is going on inside one's mind, and the outside of emotion map 400 represents behavior, so the further out on emotion map 400 you go, the more visible the emotions become (the more they are expressed in behavior).

[0194] Here, human emotions are based on various balances such as posture and blood sugar level, and when these balances are far from the ideal, it indicates an unpleasant state, and when they are close to the ideal, it indicates a pleasant state. Emotions can also be created for robots, cars, motorcycles, etc., based on various balances such as posture and battery level, so that when these balances are far from the ideal, it indicates an unpleasant state, and when they are close to the ideal, it indicates a pleasant state. The emotion map may be generated, for example, based on the emotion map of Dr. Mitsuyoshi (Research on speech emotion recognition and emotion brain physiological signal analysis system, Tokushima University, doctoral dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). On the left half of the emotion map, emotions belonging to an area called "reaction" where sensation is dominant are lined up. On the right half of the emotion map, emotions belonging to an area called "situation" where situation recognition is dominant are lined up.

[0195] The emotion map defines two emotions that promote learning. The first is the negative emotion around the middle of "repentance" or "remorse" on the situation side. In other words, this is when the robot experiences negative emotions such as "I never want to feel this way again" or "I don't want to be scolded again." The other is the positive emotion around "desire" on the response side. In other words, this is when the robot has positive feelings such as "I want more" or "I want to know more."

[0196] The emotion identification model 59 inputs the user input to a pre-trained neural network, obtains emotion values ​​indicating each emotion shown in the emotion map 400, and determines the emotion of the user. This neural network is pre-trained based on multiple learning data that are combinations of the user input and emotion values ​​indicating each emotion shown in the emotion map 400. Furthermore, this neural network is trained so that emotions that are located close to each other have similar values, as in the emotion map 900 shown in Fig. 10. Fig. 10 shows an example in which multiple emotions, "relief," "calm," and "encouraging," have similar emotion values.

[0197] In the above embodiment, an example was given in which a specific process is performed by one computer 22, but the technology disclosed herein is not limited to this, and distributed processing of the specific process may be performed by multiple computers, including computer 22.

[0198] In the above embodiment, an example in which the specific processing program 56 is stored in the storage 32 has been described, but the technology of the present disclosure is not limited to this. For example, the specific processing program 56 may be stored in a portable computer-readable non-transitory storage medium such as a Universal Serial Bus (USB) memory. The specific processing program 56 stored in the non-transitory storage medium is installed in the computer 22 of the data processing device 12. The processor 28 executes the specific processing in accordance with the specific processing program 56.

[0199] In addition, the specific processing program 56 may be stored in a storage device such as a server connected to the data processing device 12 via the network 54, and the specific processing program 56 may be downloaded and installed on the computer 22 upon request from the data processing device 12.

[0200] It is not necessary to store all of the specific processing program 56 in a storage device such as a server connected to the data processing device 12 via the network 54, or to store all of the specific processing program 56 in the storage 32; only a portion of the specific processing program 56 may be stored.

[0201] As the hardware resource for executing the specific process, the following various processors can be used. An example of the processor is a CPU, which is a general-purpose processor that functions as a hardware resource for executing the specific process by executing software, i.e., a program. Another example of the processor is a dedicated electric circuit, which is a processor having a circuit configuration designed exclusively for executing a specific process, such as a field-programmable gate array (FPGA), a programmable logic device (PLD), or an application specific integrated circuit (ASIC). Each processor has a built-in or connected memory, and each processor executes the specific process by using the memory.

[0202] The hardware resource that executes the specific process may be one of these various processors, or may be a combination of two or more processors of the same or different types (for example, a combination of multiple FPGAs, or a combination of a CPU and an FPGA). Also, the hardware resource that executes the specific process may be a single processor.

[0203] As an example of a configuration using one processor, first, one processor is configured by combining one or more CPUs and software, and this processor functions as a hardware resource that executes a specific process. Second, there is a configuration in which a processor is used that realizes the functions of the entire system including multiple hardware resources that execute a specific process on a single IC chip, as typified by SoC (System-on-a-chip). In this way, a specific process is realized using one or more of the above-mentioned various processors as hardware resources.

[0204] Furthermore, more specifically, the hardware structure of these various processors can be an electric circuit that combines circuit elements such as semiconductor elements. The specific processes described above are merely examples. It goes without saying that unnecessary steps may be deleted, new steps may be added, or the order of processes may be changed without departing from the spirit of the invention.

[0205] In the above example, the first to fourth embodiments are described separately, but some or all of these embodiments may be combined. The smart device 14, the smart glasses 214, the headset terminal 314, and the robot 414 are only examples, and they may be combined with each other, or may be other devices. In the above example, the first and second embodiments are described separately, but they may be combined.

[0206] The above description and illustrations are detailed descriptions of the parts related to the technology of the present disclosure, and are merely an example of the technology of the present disclosure. For example, the above description of the configuration, function, action, and effect is an example of the configuration, function, action, and effect of the parts related to the technology of the present disclosure. Therefore, it goes without saying that unnecessary parts may be deleted, new elements may be added, or replaced with respect to the description and illustrations shown above, within the scope of the gist of the technology of the present disclosure. In addition, in order to avoid confusion and to facilitate understanding of the parts related to the technology of the present disclosure, the description and illustrations shown above omit explanations of technical common sense that do not require particular explanation in enabling the implementation of the technology of the present disclosure.

[0207] All publications, patent applications, and standards mentioned in this specification are herein incorporated by reference to the same extent as if each individual publication, patent application, and standard was specifically and individually indicated to be incorporated by reference.

[0208] (Appendix 1) An acquisition unit that acquires emotion estimation information for estimating an emotion of a viewer of a video work; an estimation unit that estimates an emotion of the viewer based on the emotion estimation information acquired by the acquisition unit; an evaluation unit that evaluates the video work based on an estimation result by the estimation unit; a suggestion unit that suggests improvements to the video work based on the evaluation result by the evaluation unit. A system characterized by: (Appendix 2) a correction unit that corrects the video work based on the improvement points proposed by the suggestion unit; 2. The system of claim 1, (Appendix 3) The estimation unit is Inferring the emotion based on the viewer's facial expression, voice, movement, heart rate, or a combination thereof. 2. The system of claim 1, (Appendix 4) The evaluation unit is The video work is evaluated based on the transition of the viewer's emotion estimated by the estimation unit and a target transition of the emotion in the video work. 2. The system of claim 1, (Appendix 5) The evaluation unit is The transition goal is set for each genre of the video work. 2. The system of claim 1, (Appendix 6) The evaluation unit is The emotion estimation results of multiple viewers are collected and the video work is evaluated. 2. The system of claim 1, (Appendix 7) The evaluation unit is Classifying the ratings of said video works based on the age, gender, and cultural background of said viewers. 2. The system of claim 1, (Appendix 8) The suggestion unit, Suggesting scene changes to elicit particular emotional responses in the viewer based on the evaluation of the video work. 2. The system of claim 1, (Appendix 9) The acquisition unit is One or more pieces of information on the viewer's facial expression, movement, voice, heart rate, and blood pressure are acquired as the emotion estimation information. 2. The system of claim 1, (Appendix 10) The acquisition unit is To estimate a viewer's emotion and adjust timing of acquiring emotion estimation information based on the estimated viewer's emotion. 2. The system of claim 1, (Appendix 11) The acquisition unit is Analyze viewers' past emotional data and select the optimal acquisition method 2. The system of claim 1, (Appendix 12) The acquisition unit is When acquiring emotion estimation information, filtering is performed based on the viewer's current psychological state and environment. 2. The system of claim 1, (Appendix 13) The acquisition unit is To estimate a viewer's emotion and determine a priority order of emotion estimation information to be acquired based on the estimated viewer's emotion. 2. The system of claim 1, (Appendix 14) The acquisition unit is When acquiring information for emotion estimation, the system prioritizes acquiring information that is highly relevant by taking into account the viewer's geographic location information. 2. The system of claim 1, (Appendix 15) The acquisition unit is When obtaining information for emotion estimation, analyze the viewer's social media activity to obtain related information. 2. The system of claim 1, (Appendix 16) The estimation unit is Estimating viewer sentiment and adjusting sentiment estimation algorithms based on the estimated viewer sentiment 2. The system of claim 1, (Appendix 17) The estimation unit is During estimation, we track changes in viewer sentiment in real time to improve estimation accuracy. 2. The system of claim 1, (Appendix 18) The estimation unit is When making predictions, the prediction results are corrected by referring to the viewer's past emotion data. 2. The system of claim 1, (Appendix 19) The estimation unit is Estimate viewer sentiment and adjust how the estimation results are displayed based on the estimated viewer sentiment 2. The system of claim 1, (Appendix 20) The estimation unit is At the time of estimation, customize the emotion estimation algorithm based on the age and gender of the viewer 2. The system of claim 1, (Appendix 21) The estimation unit is Optimize emotion estimation algorithms by considering the cultural background of viewers during estimation. 2. The system of claim 1, (Appendix 22) The evaluation unit is Estimating viewer sentiment and adjusting evaluation criteria based on the estimated viewer sentiment 2. The system of claim 1, (Appendix 23) The evaluation unit is During evaluation, the viewer's emotional transition is compared with the emotional target for each scene of the video work to make an evaluation. 2. The system of claim 1, (Appendix 24) The evaluation unit is During evaluation, viewer sentiment data is collected in real time and the evaluation results are updated. 2. The system of claim 1, (Appendix 25) The evaluation unit is Estimating viewer sentiment and adjusting how ratings are displayed based on the estimated viewer sentiment 2. The system of claim 1, (Appendix 26) The evaluation unit is During evaluation, categorize the results based on the age, gender, and cultural background of the viewers. 2. The system of claim 1, (Appendix 27) The evaluation unit is When evaluating, the geographical distribution of viewers is taken into account in analyzing the evaluation results. 2. The system of claim 1, (Appendix 28) The suggestion unit, Estimating viewer sentiment and tailoring suggestions based on the estimated viewer sentiment 2. The system of claim 1, (Appendix 29) The suggestion unit, Track changes in viewer sentiment in real time as you make suggestions and update your suggestions accordingly 2. The system of claim 1, (Appendix 30) The suggestion unit, When making a proposal, the proposal content is corrected by referring to the viewer's past emotional data. 2. The system of claim 1, (Appendix 31) The suggestion unit, Estimate the viewer's emotions and adjust the way the proposed results are displayed based on the estimated viewer's emotions 2. The system of claim 1, (Appendix 32) The suggestion unit, When making suggestions, tailor the suggestions based on the viewer's age and gender 2. The system of claim 1, (Appendix 33) The suggestion unit, When making suggestions, optimize the content of the suggestions by taking into account the cultural background of the viewer. 2. The system of claim 1, (Appendix 34) The correction unit is Estimate viewer sentiment and adjust modifications based on the estimated viewer sentiment 3. The system of claim 2, (Appendix 35) The correction unit is As you make edits, track changes in audience sentiment in real time and update your edits accordingly 3. The system of claim 2, (Appendix 36) The correction unit is When making edits, the editor refers to the viewer's past emotional data to correct the edits. 3. The system of claim 2, (Appendix 37) The correction unit is Inferring viewer sentiment and adjusting the presentation of correction results based on the estimated viewer sentiment 3. The system of claim 2, (Appendix 38) The correction unit is When editing, customize your edits based on the age and gender of your audience 3. The system of claim 2, (Appendix 39) The correction unit is When editing, optimize the editing content by considering the cultural background of the audience. 3. The system of claim 2, [Explanation of symbols]

[0209] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Device 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robot

Claims

1. An acquisition unit that acquires emotion estimation information for estimating an emotion of a viewer of a video work; an estimation unit that estimates an emotion of the viewer based on the emotion estimation information acquired by the acquisition unit; an evaluation unit that evaluates the video work based on an estimation result by the estimation unit; a suggestion unit that suggests improvements to the video work based on the evaluation result by the evaluation unit. A system characterized by:

2. a correction unit that corrects the video work based on the improvement points proposed by the suggestion unit; 2. The system of claim 1 .

3. The estimation unit is Inferring the emotion based on the viewer's facial expression, voice, movement, heart rate, or a combination thereof.

2. The system of claim 1 .

4. The evaluation unit is The video work is evaluated based on the transition of the viewer's emotion estimated by the estimation unit and a target transition of the emotion in the video work.

2. The system of claim 1 .

5. The evaluation unit is The transition goal is set for each genre of the video work.

5. The system of claim 4.

6. The evaluation unit is The emotion estimation results of multiple viewers are collected and the video work is evaluated.

2. The system of claim 1 .

7. The evaluation unit is Classifying the ratings of said video works based on the age, gender, and cultural background of said viewers.

2. The system of claim 1 .

8. The suggestion unit, Suggesting scene changes to elicit particular emotional responses in the viewer based on the evaluation of the video work.

2. The system of claim 1 .

9. The acquisition unit is One or more pieces of information on the viewer's facial expression, movement, voice, heart rate, and blood pressure are acquired as the emotion estimation information.

2. The system of claim 1 .

10. The acquisition unit is To estimate a viewer's emotion and adjust timing of acquiring emotion estimation information based on the estimated viewer's emotion.

2. The system of claim 1 .

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