system

Abstract

The system according to the embodiment aims to accurately grasp the emotions of people who cannot speak. [Solution] A system according to an embodiment includes an image acquisition unit, an analysis unit, an estimation unit, and a display unit or output unit. The image acquisition unit acquires an image of a subject's face. The analysis unit analyzes the image acquired by the image acquisition unit and detects changes in facial expression. The estimation unit estimates an emotion based on the changes in expression detected by the analysis unit. The display unit or output unit displays or outputs as sound the emotion estimated by the estimation unit.

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 an instruction sentence related to a description of 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] Japanese Patent Publication No. 2022-180282 Summary of the Invention [Problem to be solved by the invention]

[0004] Conventional technology has had the problem of making it difficult to accurately grasp the emotions of people who cannot speak.

[0005] The system according to the embodiment aims to accurately grasp the emotions of people who cannot speak. [Means for solving the problem]

[0006] The system according to the embodiment includes an image acquisition unit, an analysis unit, an estimation unit, and a display unit or an output unit. The image acquisition unit acquires an image of the subject's face. The analysis unit analyzes the image acquired by the image acquisition unit and detects changes in facial expression. The estimation unit estimates an emotion based on the changes in expression detected by the analysis unit. The display unit or the output unit displays or outputs as sound the emotion estimated by the estimation unit. [Effects of the Invention]

[0007] The system according to the embodiment can accurately grasp the emotions of people who cannot speak. [Brief explanation 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. [Figure 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. [Figure 3] FIG. 10 is a conceptual diagram showing an example of the configuration of a data processing system according to a second embodiment. [Figure 4] FIG. 10 is a conceptual diagram showing an example of main functions of a data processing device and smart glasses according to a second embodiment. [Figure 5] FIG. 10 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. 10 is a conceptual diagram showing an example of the configuration of a data processing system according to a fourth embodiment. [Figure 8] FIG. 10 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 INVENTION

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

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

[0011] In the following embodiments, a coded processor (hereinafter simply referred to as a "processor") may be a single arithmetic device or a combination of multiple arithmetic devices. Furthermore, the processor may be a single 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 coded RAM (Random Access Memory) is a memory in which information is temporarily stored and is used as a working memory by a processor.

[0013] In the following embodiments, the coded storage is one or more non-volatile storage devices that store various programs, 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 symbol is an interface including a communication processor, an antenna, etc. The communication I / F controls communication between multiple computers. Examples of communication standards applied to the communication I / F include wireless communication standards including 5G (5th Generation Mobile Communication System), Wi-Fi (registered trademark), and 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. Furthermore, in this specification, the same concept as "A and / or B" is also applied when three or more things are expressed connected by "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, RAM 30, and 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 (for example, 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., audio and / or text). Display 40A displays visible information such as text and images in accordance with instructions from processor 46. Speaker 40B outputs audio in accordance with 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 CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor.

[0022] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 control the exchange of various information between the processor 46 and the processor 28 via the network 54.

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

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

[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 a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[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 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 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. Furthermore, the data processing device 12 may be a server device, or may be a terminal device owned by a user (e.g., a mobile phone, a robot, a home appliance, etc.). Next, an example of processing by the data processing system 10 according to the first embodiment will be described.

[0028] (Example 1) An emotion reading system according to an embodiment of the present invention is a system that reads emotions from people who, for some reason, cannot speak well, such as people with disabilities or the elderly, by observing changes in facial expressions. This emotion reading system acquires an image of the subject's face, analyzes the image to detect changes in facial expression, estimates emotions based on the detected changes in facial expression, and displays or outputs the estimated emotions as audio. For example, an image acquisition unit acquires an image of the subject's face. At this time, the position and angle of the camera are adjusted to capture the subject's entire face. For example, the camera is placed in front of the subject to capture facial expressions in detail. Next, an analysis unit analyzes the acquired image. The analysis unit detects facial feature points in the image and analyzes changes in facial expression. For example, by detecting feature points such as eye and mouth movements and the raising and lowering of eyebrows and analyzing these changes, changes in facial expression can be captured. Furthermore, an estimation unit estimates emotions based on the detected changes in facial expression. The estimation unit estimates emotions such as joy, sadness, and anger based on the changes in facial expression detected by the analysis unit. For example, emotions are estimated from changes in facial expression, such as joy when the corners of the mouth are raised and sadness when the eyebrows are lowered. Finally, the display unit or output unit displays or outputs the estimated emotion as sound. The display unit displays the estimated emotion on a screen, allowing people nearby to understand the emotion visually. The output unit outputs the estimated emotion as sound, allowing people nearby to understand the emotion auditorily. For example, outputting the emotion as sound, such as "I'm happy" or "I'm sad," makes it easier for people nearby to understand the emotion. In this way, the emotion reading system makes it easier for people nearby to understand the emotions of people who cannot speak, enabling smooth communication.

[0029] An emotion reading system according to an embodiment includes an image acquisition unit, an analysis unit, an estimation unit, and a display unit or output unit. The image acquisition unit acquires an image of the subject's face. For example, the image acquisition unit acquires an image of the subject's face using a camera. The position and angle of the camera are adjusted to capture the subject's entire face. For example, the camera is placed in front of the subject to capture facial expressions in detail. The analysis unit analyzes the image acquired by the image acquisition unit to detect changes in facial expression. For example, the analysis unit detects facial feature points in the image and analyzes changes in facial expression. By detecting feature points such as eye and mouth movements and the raising and lowering of eyebrows and analyzing these changes, changes in facial expression can be captured. The estimation unit estimates emotions based on changes in facial expression detected by the analysis unit. For example, the estimation unit estimates emotions such as joy, sadness, and anger based on changes in facial expression detected by the analysis unit. Emotions are estimated from changes in facial expression, such as joy when the corners of the mouth are raised and sadness when the eyebrows are lowered. The display unit or the output unit displays or outputs the emotion estimated by the estimation unit as sound. For example, the display unit displays the estimated emotion on a screen so that people nearby can visually understand the emotion. The output unit outputs the estimated emotion as sound so that people nearby can auditorily understand the emotion. For example, outputting the emotion as sound such as "I'm happy" or "I'm sad" makes it easier for people nearby to understand the emotion. In this way, the emotion reading system according to the embodiment makes it easier for people nearby to understand the emotion of a person who cannot speak, enabling smooth communication.

[0030] The image acquisition unit can acquire an image of the subject's face using a camera. The image acquisition unit can acquire an image of the subject's face using, for example, a camera. The type and specifications of the camera include resolution, frame rate, and the like. For example, the image acquisition unit can acquire a detailed image of the subject's face using a high-resolution camera. Furthermore, the image acquisition unit can capture smooth changes in the subject's facial expression using a camera with a high frame rate. Furthermore, the image acquisition unit can acquire an image of the subject's face even in a dark environment using an infrared camera. As a result, the image of the subject's face can be accurately acquired by using the camera.

[0031] The analysis unit can detect facial feature points in the image and analyze changes in facial expression. The analysis unit, for example, detects facial feature points in the image and analyzes changes in facial expression. Facial feature points include the positions of the eyes, nose, mouth, etc. For example, the analysis unit detects the positions of the eyes and analyzes eye movements. The analysis unit can also detect the position of the mouth and analyze mouth movements. Furthermore, the analysis unit can detect the position of the eyebrows and analyze eyebrow movements. In this way, by detecting facial feature points and analyzing changes in facial expression, the accuracy of emotion estimation is improved.

[0032] The estimation unit can estimate emotions based on changes in facial expressions detected by the analysis unit. The estimation unit estimates emotions based on changes in facial expressions detected by the analysis unit, for example. Changes in facial expressions include happiness when the corners of the mouth are raised and sadness when the eyebrows are lowered. For example, the estimation unit estimates the emotion of happiness when the corners of the mouth are raised. The estimation unit can also estimate sadness when the eyebrows are lowered. Furthermore, the estimation unit can estimate emotions by combining changes in multiple feature points, such as eye movement and mouth movement. This improves the accuracy of emotion estimation by estimating emotions based on changes in facial expressions.

[0033] The display unit can display the estimated emotion on a screen. The display unit displays the estimated emotion on a screen, for example. The display screen includes a monitor screen, a smartphone screen, and the like. For example, the display unit displays the estimated emotion on a monitor so that people around can visually understand the emotion. The display unit can also display the estimated emotion on a smartphone screen. Furthermore, the display unit can visually emphasize the emotion using a graphical display method. In this way, by displaying the estimated emotion on a screen, people around can visually understand the emotion.

[0034] The output unit can output the estimated emotion as sound. The output unit outputs the estimated emotion as sound, for example. The sound output includes a speaker, headphones, and the like. For example, the output unit outputs the estimated emotion as sound using a speaker, so that people around can understand the emotion auditorily. The output unit can also output the estimated emotion as sound using headphones. Furthermore, the output unit can output the emotion in a natural voice using voice synthesis technology. In this way, by outputting the estimated emotion as sound, people around can understand the emotion auditorily.

[0035] The image acquisition unit can select the optimal acquisition method by referring to the subject's past facial expression data when acquiring an image. For example, the image acquisition unit selects the optimal acquisition method by referring to the subject's past facial expression data when acquiring an image. Past facial expression data includes databases and historical data. For example, the image acquisition unit sets a camera angle to capture the most natural expression based on the subject's past facial expression data. The image acquisition unit can also analyze the subject's past facial expression data and acquire images at times when a particular facial expression is likely to appear. Furthermore, the image acquisition unit can also acquire images by referencing the subject's past facial expression data and recreating environmental conditions that make a particular facial expression likely to appear. In this way, the optimal image acquisition method can be selected by referring to the past facial expression data.

[0036] The image acquisition unit can automatically adjust camera settings based on the subject's current environment and situation when acquiring an image. For example, the image acquisition unit automatically adjusts camera settings based on the subject's current environment and situation when acquiring an image. The current environment and situation include changes in lighting conditions and background. For example, if the subject is indoors, the image acquisition unit automatically adjusts the camera's exposure to match the lighting conditions. In addition, if the subject is outdoors, the image acquisition unit can automatically adjust the camera's white balance according to the intensity of natural light. Furthermore, if the subject is moving, the image acquisition unit can increase the camera's shutter speed to prevent blur. This allows optimal images to be acquired by automatically adjusting camera settings based on the environment and situation.

[0037] The image acquisition unit can prioritize acquiring highly relevant images in consideration of the geographical location information of the subject when acquiring images. For example, the image acquisition unit prioritizes acquiring highly relevant images in consideration of the geographical location information of the subject when acquiring images. Geographical location information includes GPS data, location sensor data, and the like. For example, if the subject is at home, the image acquisition unit prioritizes acquiring images with a relaxed expression. Furthermore, if the subject is at work, the image acquisition unit can also prioritize acquiring images with a concentrated expression. Furthermore, if the subject is in a park, the image acquisition unit can prioritize acquiring images with a enjoying expression. In this way, highly relevant images can be prioritized by considering the geographical location information.

[0038] The image acquisition unit can analyze the social media activity of the subject at the time of image acquisition and acquire related images. For example, the image acquisition unit analyzes the social media activity of the subject at the time of image acquisition and acquires related images. Social media activity includes the content of posts and the number of likes. For example, the image acquisition unit acquires images of similar facial expressions based on the facial expressions in photos recently posted by the subject. The image acquisition unit can also acquire images of related facial expressions based on emotions shared by the subject on social media. Furthermore, the image acquisition unit can acquire images of related facial expressions based on events in which the subject is tagged on social media. In this way, related images can be acquired by analyzing social media activity.

[0039] The analysis unit can adjust the level of detail of the analysis based on the importance of facial feature points during analysis. For example, the analysis unit adjusts the level of detail of the analysis based on the importance of facial feature points during analysis. The importance of facial feature points includes eye movement, mouth movement, and the like. For example, if eye movement is important, the analysis unit analyzes eye feature points in detail. Furthermore, if mouth movement is important, the analysis unit can also analyze mouth feature points in detail. Furthermore, if eyebrow movement is important, the analysis unit can also analyze eyebrow feature points in detail. In this way, by adjusting the level of detail of the analysis based on the importance of feature points, the analysis accuracy is improved.

[0040] The analysis unit can apply different analysis algorithms depending on the category of the facial feature points during analysis. For example, the analysis unit applies different analysis algorithms depending on the category of the facial feature points during analysis. Feature point categories include eyes, nose, mouth, etc. For example, the analysis unit applies an algorithm that analyzes eye movement to eye feature points. The analysis unit can also apply an algorithm that analyzes mouth movement to mouth feature points. Furthermore, the analysis unit can apply an algorithm that analyzes eyebrow movement to eyebrow feature points. This improves the accuracy of analysis by applying different analysis algorithms depending on the category.

[0041] The analysis unit can determine the analysis priority based on the detection time of facial feature points during analysis. For example, the analysis unit determines the analysis priority based on the detection time of facial feature points during analysis. The detection time includes real-time data and past data. For example, if the eye feature points are detected first, the analysis unit may prioritize the analysis of the eyes. Furthermore, if the mouth feature points are detected first, the analysis unit may prioritize the analysis of the mouth. Furthermore, if the eyebrow feature points are detected first, the analysis unit may prioritize the analysis of the eyebrows. In this way, by determining the analysis priority based on the detection time, analysis efficiency is improved.

[0042] The analysis unit can adjust the order of analysis based on the relevance of facial feature points during analysis. For example, the analysis unit adjusts the order of analysis based on the relevance of facial feature points during analysis. The relevance of feature points includes the relevance of eye and mouth movements. For example, if the feature points of the eyes and mouth are related, the analysis unit can analyze the eyes first. Also, if the feature points of the mouth and eyebrows are related, the analysis unit can analyze the mouth first. Furthermore, if the feature points of the eyes and eyebrows are related, the analysis unit can analyze the eyes first. In this way, by adjusting the order of analysis based on the relevance, the analysis accuracy is improved.

[0043] The estimation unit can improve the accuracy of estimation by taking into account the interrelationships between changes in facial expressions during estimation. For example, the estimation unit improves the accuracy of estimation by taking into account the interrelationships between changes in facial expressions during estimation. The interrelationships between changes in facial expressions include the correlation between eye and mouth movements. For example, the estimation unit estimates emotions by taking into account the interrelationships between eyebrow and mouth movements. The estimation unit can also estimate emotions by taking into account the interrelationships between eyebrow and mouth movements. In addition, the estimation unit can also estimate emotions by taking into account the interrelationships between eyebrow and eyebrow movements. In this way, the estimation accuracy is improved by taking into account the interrelationships between changes in facial expressions.

[0044] The estimation unit can make estimations taking into account attribute information of the subject when making estimations. For example, the estimation unit makes estimations taking into account attribute information of the subject when making estimations. Attribute information includes age, gender, cultural background, and the like. For example, the estimation unit estimates emotions taking into account the age of the subject. The estimation unit can also estimate emotions taking into account the gender of the subject. Furthermore, the estimation unit can estimate emotions taking into account the cultural background of the subject. In this way, by taking attribute information into account, estimation accuracy is improved.

[0045] The estimation unit can perform estimation by taking into account the geographical distribution of facial expression changes. For example, the estimation unit performs estimation by taking into account the geographical distribution of facial expression changes. The geographical distribution includes the distribution of emotions by region. For example, when the subject is in a specific region, the estimation unit estimates the emotion by taking into account the cultural background of that region. Furthermore, when the subject is traveling, the estimation unit can also estimate the emotion by taking into account the cultural background of the destination. Furthermore, when the subject is in a different region, the estimation unit can also estimate the emotion by taking into account the facial expression characteristics of that region. In this way, by taking into account the geographical distribution, estimation accuracy is improved.

[0046] The estimation unit can improve the accuracy of the estimation by referring to literature related to changes in facial expressions during estimation. For example, the estimation unit improves the accuracy of the estimation by referring to literature related to changes in facial expressions during estimation. Related literature includes academic papers, technical reports, and the like. For example, the estimation unit estimates emotions by referring to literature on the latest facial expression recognition technology. The estimation unit can also estimate emotions by referring to past research data. Furthermore, the estimation unit can estimate emotions by referring to the results of other researchers. In this way, by referring to related literature, the estimation accuracy is improved.

[0047] The display unit can optimize the current display by referring to past display data when displaying. For example, the display unit optimizes the current display by referring to past display data when displaying. Past display data includes history data, log data, etc. For example, the display unit selects the optimal display method based on information displayed in the past. The display unit can also analyze past display data and select a display method that is easy for the user to understand. Furthermore, the display unit can refer to past display data and select a display method that suits the user's preferences. In this way, the optimal display method can be selected by referring to past display data.

[0048] The display unit can apply different display methods to each emotion category during display. For example, the display unit applies different display methods to each emotion category during display. Emotion categories include joy, sadness, anger, etc. For example, the display unit applies a bright color display method to the emotion of joy. The display unit can also apply a subdued color display method to the emotion of sadness. Furthermore, the display unit can also apply an emphasized color display method to the emotion of anger. In this way, by applying an appropriate display method to each emotion category, the displayed content becomes easier to understand.

[0049] The display unit can analyze changes in the display based on the timing of emotion display during display. For example, the display unit analyzes changes in the display based on the timing of emotion display during display. The display timing includes real-time data and past data. For example, the display unit records the timing of emotion changes and analyzes the changes in the display. In addition, if emotion changes occur frequently, the display unit can adjust the display based on the timing. Furthermore, if emotion changes are infrequent, the display unit can minimize changes in the display. In this way, by analyzing changes in the display based on the timing, the display content can be provided more appropriately.

[0050] The display unit may analyze the display by referring to market data related to the emotion when displaying the content. For example, the display unit may analyze the display by referring to market data related to the emotion when displaying the content. The related market data includes market research data, consumer behavior data, and the like. For example, the display unit may optimize the emotion display method based on the market data. The display unit may also select a display method preferred by a user by referring to the market data. Furthermore, the display unit may analyze the market data and improve the emotion display method. In this way, the display content is provided more appropriately by referring to the related market data.

[0051] The output unit can optimize the current output by referring to past output data at the time of output. For example, the output unit optimizes the current output by referring to past output data at the time of output. Past output data includes history data, log data, etc. For example, the output unit selects the optimal output method based on information output in the past. The output unit can also analyze past output data and select an output method that is easy for the user to understand. Furthermore, the output unit can select an output method that suits the user's preferences by referring to past output data. In this way, the optimal output method can be selected by referring to past output data.

[0052] The output unit can apply different output methods to each emotion category during output. For example, the output unit applies different output methods to each emotion category during output. Emotion categories include joy, sadness, anger, etc. For example, the output unit applies a bright voice output method to the emotion of joy. The output unit can also apply a calm voice output method to the emotion of sadness. Furthermore, the output unit can apply an emphasized voice output method to the emotion of anger. In this way, by applying an appropriate output method to each emotion category, the output content becomes easier to understand.

[0053] The output unit can analyze changes in the output based on the timing of emotion output at the time of output. For example, the output unit analyzes changes in the output based on the timing of emotion output at the time of output. The output timing includes real-time data and past data. For example, the output unit records the timing at which emotions change and analyzes changes in the output. In addition, the output unit can adjust the output based on the timing when emotions change frequently. Furthermore, the output unit can minimize changes in the output when emotions change little. In this way, by analyzing changes in the output based on the output timing, more appropriate output content can be provided.

[0054] The output unit may analyze the output by referring to market data related to the emotion at the time of output. For example, the output unit may analyze the output by referring to market data related to the emotion at the time of output. The related market data includes market research data, consumer behavior data, etc. For example, the output unit may optimize the emotion output method based on the market data. The output unit may also select an output method preferred by a user by referring to the market data. Furthermore, the output unit may analyze the market data and improve the emotion output method. In this way, by referring to the related market data, the output content is provided more appropriately.

[0055] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0056] The emotion reading system may further include a user setting unit. The user setting unit may acquire individual settings of the subject and estimate the emotion based on the settings. For example, the user setting unit may set in advance the emotion that the subject will exhibit when showing a specific facial expression and estimate the emotion based on the settings. The user setting unit may also set in advance the emotion that the subject will exhibit under specific environmental conditions and estimate the emotion based on the settings. Furthermore, the user setting unit may also set in advance the emotion that the subject will exhibit when showing a specific behavior and estimate the emotion based on the settings. In this way, by using the user setting unit, the emotion of the subject can be more individually estimated.

[0057] The emotion reading system can further include a data sharing unit. The data sharing unit can share data between multiple emotion reading systems and estimate emotions based on that data. For example, the data sharing unit can share data of a subject between emotion reading systems installed in different locations and estimate emotions comprehensively. The data sharing unit can also share data acquired at different time periods and estimate changes in emotions over time. Furthermore, the data sharing unit can share data of different subjects and estimate emotions based on similar emotion patterns. As a result, using the data sharing unit improves the accuracy of emotion estimation.

[0058] The emotion reading system can further include a feedback unit. The feedback unit can feed back the emotion estimation result to the subject and adjust the emotion based on the feedback. For example, the feedback unit can display a message such as "Relax" to the subject to encourage a relaxed facial expression. The feedback unit can also display a message such as "Take a deep breath" to the subject to relieve tension. The feedback unit can also display a message such as "Smile" to the subject to elicit positive emotions. In this way, the feedback unit can be used to guide the subject's emotions in a better direction.

[0059] The emotion reading system can further include a learning unit. The learning unit can learn the emotion data of the subject and estimate the emotion based on the learning results. For example, the learning unit can learn the subject's past emotion data and estimate the subject's current emotion more accurately. The learning unit can also learn the emotion data of different subjects and estimate the emotion based on common emotion patterns. Furthermore, the learning unit can continuously learn new emotion data and improve the emotion estimation accuracy. In this way, using the learning unit improves the emotion estimation accuracy.

[0060] The processing flow of the first embodiment will be briefly explained below.

[0061] Step 1: The image acquisition unit acquires an image of the subject's face. For example, the image acquisition unit acquires an image of the subject's face using a camera. The position and angle of the camera are adjusted so that the subject's entire face is captured. For example, the camera is placed in front of the subject so that facial expressions can be captured in detail. Step 2: The analysis unit analyzes the images acquired by the image acquisition unit and detects changes in facial expression. For example, the analysis unit detects facial feature points in the image and analyzes changes in facial expression. By detecting feature points such as eye and mouth movements and the raising and lowering of eyebrows and analyzing these changes, changes in facial expression can be captured. Step 3: The estimation unit estimates emotions based on the changes in facial expressions detected by the analysis unit. For example, the estimation unit estimates emotions such as joy, sadness, and anger based on the changes in facial expressions detected by the analysis unit. Emotions are estimated from changes in facial expressions, such as joy if the corners of the mouth are raised and sadness if the eyebrows are lowered. Step 4: The display unit or output unit displays or outputs the emotion estimated by the estimation unit as sound. For example, the display unit displays the estimated emotion on a screen so that people around can understand the emotion visually. The output unit outputs the estimated emotion as sound so that people around can understand the emotion auditorily. For example, outputting the emotion as sound such as "I'm happy" or "I'm sad" makes it easier for people around to understand the emotion.

[0062] (Example 2) An emotion reading system according to an embodiment of the present invention is a system that reads emotions from people who, for some reason, cannot speak well, such as people with disabilities or the elderly, by observing changes in facial expressions. This emotion reading system acquires an image of the subject's face, analyzes the image to detect changes in facial expression, estimates emotions based on the detected changes in facial expression, and displays or outputs the estimated emotions as audio. For example, an image acquisition unit acquires an image of the subject's face. At this time, the position and angle of the camera are adjusted to capture the subject's entire face. For example, the camera is placed in front of the subject to capture facial expressions in detail. Next, an analysis unit analyzes the acquired image. The analysis unit detects facial feature points in the image and analyzes changes in facial expression. For example, by detecting feature points such as eye and mouth movements and the raising and lowering of eyebrows and analyzing these changes, changes in facial expression can be captured. Furthermore, an estimation unit estimates emotions based on the detected changes in facial expression. The estimation unit estimates emotions such as joy, sadness, and anger based on the changes in facial expression detected by the analysis unit. For example, emotions are estimated from changes in facial expression, such as joy when the corners of the mouth are raised and sadness when the eyebrows are lowered. Finally, the display unit or output unit displays or outputs the estimated emotion as sound. The display unit displays the estimated emotion on a screen, allowing people nearby to understand the emotion visually. The output unit outputs the estimated emotion as sound, allowing people nearby to understand the emotion auditorily. For example, outputting the emotion as sound, such as "I'm happy" or "I'm sad," makes it easier for people nearby to understand the emotion. In this way, the emotion reading system makes it easier for people nearby to understand the emotions of people who cannot speak, enabling smooth communication.

[0063] An emotion reading system according to an embodiment includes an image acquisition unit, an analysis unit, an estimation unit, and a display unit or output unit. The image acquisition unit acquires an image of the subject's face. For example, the image acquisition unit acquires an image of the subject's face using a camera. The position and angle of the camera are adjusted to capture the subject's entire face. For example, the camera is placed in front of the subject to capture facial expressions in detail. The analysis unit analyzes the image acquired by the image acquisition unit to detect changes in facial expression. For example, the analysis unit detects facial feature points in the image and analyzes changes in facial expression. By detecting feature points such as eye and mouth movements and the raising and lowering of eyebrows and analyzing these changes, changes in facial expression can be captured. The estimation unit estimates emotions based on changes in facial expression detected by the analysis unit. For example, the estimation unit estimates emotions such as joy, sadness, and anger based on changes in facial expression detected by the analysis unit. Emotions are estimated from changes in facial expression, such as joy when the corners of the mouth are raised and sadness when the eyebrows are lowered. The display unit or the output unit displays or outputs the emotion estimated by the estimation unit as sound. For example, the display unit displays the estimated emotion on a screen so that people nearby can visually understand the emotion. The output unit outputs the estimated emotion as sound so that people nearby can auditorily understand the emotion. For example, outputting the emotion as sound such as "I'm happy" or "I'm sad" makes it easier for people nearby to understand the emotion. In this way, the emotion reading system according to the embodiment makes it easier for people nearby to understand the emotion of a person who cannot speak, enabling smooth communication.

[0064] The image acquisition unit can acquire an image of the subject's face using a camera. The image acquisition unit can acquire an image of the subject's face using, for example, a camera. The type and specifications of the camera include resolution, frame rate, and the like. For example, the image acquisition unit can acquire a detailed image of the subject's face using a high-resolution camera. Furthermore, the image acquisition unit can capture smooth changes in the subject's facial expression using a camera with a high frame rate. Furthermore, the image acquisition unit can acquire an image of the subject's face even in a dark environment using an infrared camera. As a result, the image of the subject's face can be accurately acquired by using the camera.

[0065] The analysis unit can detect facial feature points in the image and analyze changes in facial expression. The analysis unit, for example, detects facial feature points in the image and analyzes changes in facial expression. Facial feature points include the positions of the eyes, nose, mouth, etc. For example, the analysis unit detects the positions of the eyes and analyzes eye movements. The analysis unit can also detect the position of the mouth and analyze mouth movements. Furthermore, the analysis unit can detect the position of the eyebrows and analyze eyebrow movements. In this way, by detecting facial feature points and analyzing changes in facial expression, the accuracy of emotion estimation is improved.

[0066] The estimation unit can estimate emotions based on changes in facial expressions detected by the analysis unit. The estimation unit estimates emotions based on changes in facial expressions detected by the analysis unit, for example. Changes in facial expressions include happiness when the corners of the mouth are raised and sadness when the eyebrows are lowered. For example, the estimation unit estimates the emotion of happiness when the corners of the mouth are raised. The estimation unit can also estimate sadness when the eyebrows are lowered. Furthermore, the estimation unit can estimate emotions by combining changes in multiple feature points, such as eye movement and mouth movement. This improves the accuracy of emotion estimation by estimating emotions based on changes in facial expressions.

[0067] The display unit can display the estimated emotion on a screen. The display unit displays the estimated emotion on a screen, for example. The display screen includes a monitor screen, a smartphone screen, and the like. For example, the display unit displays the estimated emotion on a monitor so that people around can visually understand the emotion. The display unit can also display the estimated emotion on a smartphone screen. Furthermore, the display unit can visually emphasize the emotion using a graphical display method. In this way, by displaying the estimated emotion on a screen, people around can visually understand the emotion.

[0068] The output unit can output the estimated emotion as sound. The output unit outputs the estimated emotion as sound, for example. The sound output includes a speaker, headphones, and the like. For example, the output unit outputs the estimated emotion as sound using a speaker, so that people around can understand the emotion auditorily. The output unit can also output the estimated emotion as sound using headphones. Furthermore, the output unit can output the emotion in a natural voice using voice synthesis technology. In this way, by outputting the estimated emotion as sound, people around can understand the emotion auditorily.

[0069] The image acquisition unit can estimate the emotion of the subject and adjust the timing of image acquisition based on the estimated emotion. The image acquisition unit, for example, estimates the emotion of the subject and adjusts the timing of image acquisition based on the estimated emotion. The timing of image acquisition includes at regular intervals or when a change in emotion is detected. For example, when the subject is relaxed, the image acquisition unit periodically acquires images to capture a natural facial expression. When the subject is nervous, the image acquisition unit frequently acquires images to capture subtle changes in facial expression. Furthermore, when the subject is excited, the image acquisition unit shortens the interval between image acquisitions to capture sudden changes in facial expression. As a result, by adjusting the timing of image acquisition based on emotion, more natural facial expressions can be captured.

[0070] The image acquisition unit can select the optimal acquisition method by referring to the subject's past facial expression data when acquiring an image. For example, the image acquisition unit selects the optimal acquisition method by referring to the subject's past facial expression data when acquiring an image. Past facial expression data includes databases and historical data. For example, the image acquisition unit sets a camera angle to capture the most natural expression based on the subject's past facial expression data. The image acquisition unit can also analyze the subject's past facial expression data and acquire images at times when a particular facial expression is likely to appear. Furthermore, the image acquisition unit can also acquire images by referencing the subject's past facial expression data and recreating environmental conditions that make a particular facial expression likely to appear. In this way, the optimal image acquisition method can be selected by referring to the past facial expression data.

[0071] The image acquisition unit can automatically adjust camera settings based on the subject's current environment and situation when acquiring an image. For example, the image acquisition unit automatically adjusts camera settings based on the subject's current environment and situation when acquiring an image. The current environment and situation include changes in lighting conditions and background. For example, if the subject is indoors, the image acquisition unit automatically adjusts the camera's exposure to match the lighting conditions. In addition, if the subject is outdoors, the image acquisition unit can automatically adjust the camera's white balance according to the intensity of natural light. Furthermore, if the subject is moving, the image acquisition unit can increase the camera's shutter speed to prevent blur. This allows optimal images to be acquired by automatically adjusting camera settings based on the environment and situation.

[0072] The image acquisition unit can estimate the emotion of the subject and determine the priority of images to be acquired based on the estimated emotion. The image acquisition unit, for example, estimates the emotion of the subject and determines the priority of images to be acquired based on the estimated emotion. The priority of images includes the intensity and importance of the emotion. For example, if the subject is happy, the image acquisition unit can preferentially acquire images of the subject smiling. Also, if the subject is sad, the image acquisition unit can preferentially acquire images of the subject shedding tears. Furthermore, if the subject is angry, the image acquisition unit can preferentially acquire images of the subject furrowing their brows. In this way, by determining the priority of images based on emotion, important facial expressions can be preferentially acquired.

[0073] The image acquisition unit can prioritize acquiring highly relevant images in consideration of the geographical location information of the subject when acquiring images. For example, the image acquisition unit prioritizes acquiring highly relevant images in consideration of the geographical location information of the subject when acquiring images. Geographical location information includes GPS data, location sensor data, and the like. For example, if the subject is at home, the image acquisition unit prioritizes acquiring images with a relaxed expression. Furthermore, if the subject is at work, the image acquisition unit can also prioritize acquiring images with a concentrated expression. Furthermore, if the subject is in a park, the image acquisition unit can prioritize acquiring images with a enjoying expression. In this way, highly relevant images can be prioritized by considering the geographical location information.

[0074] The image acquisition unit can analyze the social media activity of the subject at the time of image acquisition and acquire related images. For example, the image acquisition unit analyzes the social media activity of the subject at the time of image acquisition and acquires related images. Social media activity includes the content of posts and the number of likes. For example, the image acquisition unit acquires images of similar facial expressions based on the facial expressions in photos recently posted by the subject. The image acquisition unit can also acquire images of related facial expressions based on emotions shared by the subject on social media. Furthermore, the image acquisition unit can acquire images of related facial expressions based on events in which the subject is tagged on social media. In this way, related images can be acquired by analyzing social media activity.

[0075] The analysis unit can estimate the subject's emotions and adjust the way the analysis is presented based on the estimated emotions. The analysis unit, for example, estimates the subject's emotions and adjusts the way the analysis is presented based on the estimated emotions. The way the analysis is presented includes graph display, text display, and the like. For example, if the subject is relaxed, the analysis unit can display the analysis results in detail. Also, if the subject is nervous, the analysis unit can display the analysis results in a concise manner. Furthermore, if the subject is excited, the analysis unit can display the analysis results in a visually emphasized manner. In this way, by adjusting the way the analysis is presented based on emotions, the analysis results can be more easily understood.

[0076] The analysis unit can adjust the level of detail of the analysis based on the importance of facial feature points during analysis. For example, the analysis unit adjusts the level of detail of the analysis based on the importance of facial feature points during analysis. The importance of facial feature points includes eye movement, mouth movement, and the like. For example, if eye movement is important, the analysis unit analyzes eye feature points in detail. Furthermore, if mouth movement is important, the analysis unit can also analyze mouth feature points in detail. Furthermore, if eyebrow movement is important, the analysis unit can also analyze eyebrow feature points in detail. In this way, by adjusting the level of detail of the analysis based on the importance of feature points, the analysis accuracy is improved.

[0077] The analysis unit can apply different analysis algorithms depending on the category of the facial feature points during analysis. For example, the analysis unit applies different analysis algorithms depending on the category of the facial feature points during analysis. Feature point categories include eyes, nose, mouth, etc. For example, the analysis unit applies an algorithm that analyzes eye movement to eye feature points. The analysis unit can also apply an algorithm that analyzes mouth movement to mouth feature points. Furthermore, the analysis unit can apply an algorithm that analyzes eyebrow movement to eyebrow feature points. This improves the accuracy of analysis by applying different analysis algorithms depending on the category.

[0078] The analysis unit can estimate the subject's emotion and adjust the length of the analysis based on the estimated emotion. The analysis unit, for example, estimates the subject's emotion and adjusts the length of the analysis based on the estimated emotion. The length of the analysis includes the analysis time and the level of detail of the analysis. For example, if the subject is relaxed, the analysis unit can perform a detailed analysis for a long time. Also, if the subject is nervous, the analysis unit can perform a brief analysis and finish the analysis in a short time. Furthermore, if the subject is excited, the analysis unit can perform the analysis quickly and display the results in a short time. In this way, by adjusting the length of the analysis based on the emotion, the analysis results can be provided more appropriately.

[0079] The analysis unit can determine the analysis priority based on the detection time of facial feature points during analysis. For example, the analysis unit determines the analysis priority based on the detection time of facial feature points during analysis. The detection time includes real-time data and past data. For example, if the eye feature points are detected first, the analysis unit may prioritize the analysis of the eyes. Furthermore, if the mouth feature points are detected first, the analysis unit may prioritize the analysis of the mouth. Furthermore, if the eyebrow feature points are detected first, the analysis unit may prioritize the analysis of the eyebrows. In this way, by determining the analysis priority based on the detection time, analysis efficiency is improved.

[0080] The analysis unit can adjust the order of analysis based on the relevance of facial feature points during analysis. For example, the analysis unit adjusts the order of analysis based on the relevance of facial feature points during analysis. The relevance of feature points includes the relevance of eye and mouth movements. For example, if the feature points of the eyes and mouth are related, the analysis unit can analyze the eyes first. Also, if the feature points of the mouth and eyebrows are related, the analysis unit can analyze the mouth first. Furthermore, if the feature points of the eyes and eyebrows are related, the analysis unit can analyze the eyes first. In this way, by adjusting the order of analysis based on the relevance, the analysis accuracy is improved.

[0081] The estimation unit can estimate the emotion of the subject and adjust the estimation criteria based on the estimated emotion. The estimation unit, for example, estimates the emotion of the subject and adjusts the estimation criteria based on the estimated emotion. The estimation criteria include the intensity and frequency of the emotion. For example, the estimation unit can set the emotion estimation criteria to be gentle when the subject is relaxed. Also, the estimation unit can set the emotion estimation criteria to be strict when the subject is nervous. Furthermore, the estimation unit can dynamically set the emotion estimation criteria when the subject is excited. In this way, the estimation accuracy is improved by adjusting the estimation criteria based on the emotion.

[0082] The estimation unit can improve the accuracy of estimation by taking into account the interrelationships between changes in facial expressions during estimation. For example, the estimation unit improves the accuracy of estimation by taking into account the interrelationships between changes in facial expressions during estimation. The interrelationships between changes in facial expressions include the correlation between eye and mouth movements. For example, the estimation unit estimates emotions by taking into account the interrelationships between eyebrow and mouth movements. The estimation unit can also estimate emotions by taking into account the interrelationships between eyebrow and mouth movements. In addition, the estimation unit can also estimate emotions by taking into account the interrelationships between eyebrow and eyebrow movements. In this way, the estimation accuracy is improved by taking into account the interrelationships between changes in facial expressions.

[0083] The estimation unit can make estimations taking into account attribute information of the subject when making estimations. For example, the estimation unit makes estimations taking into account attribute information of the subject when making estimations. Attribute information includes age, gender, cultural background, and the like. For example, the estimation unit estimates emotions taking into account the age of the subject. The estimation unit can also estimate emotions taking into account the gender of the subject. Furthermore, the estimation unit can estimate emotions taking into account the cultural background of the subject. In this way, by taking attribute information into account, estimation accuracy is improved.

[0084] The estimation unit can estimate the emotion of the subject and adjust the order in which the estimation results are displayed based on the estimated emotion. The estimation unit, for example, estimates the emotion of the subject and adjusts the order in which the estimation results are displayed based on the estimated emotion. The display order includes the intensity and importance of the emotion. For example, if the subject is relaxed, the estimation unit can display detailed results first. Also, if the subject is nervous, the estimation unit can display concise results first. Furthermore, if the subject is excited, the estimation unit can display visually emphasized results first. In this way, adjusting the display order based on emotion makes the results easier to understand.

[0085] The estimation unit can perform estimation by taking into account the geographical distribution of facial expression changes. For example, the estimation unit performs estimation by taking into account the geographical distribution of facial expression changes. The geographical distribution includes the distribution of emotions by region. For example, when the subject is in a specific region, the estimation unit estimates the emotion by taking into account the cultural background of that region. Furthermore, when the subject is traveling, the estimation unit can also estimate the emotion by taking into account the cultural background of the destination. Furthermore, when the subject is in a different region, the estimation unit can also estimate the emotion by taking into account the facial expression characteristics of that region. In this way, by taking into account the geographical distribution, estimation accuracy is improved.

[0086] The estimation unit can improve the accuracy of the estimation by referring to literature related to changes in facial expressions during estimation. For example, the estimation unit improves the accuracy of the estimation by referring to literature related to changes in facial expressions during estimation. Related literature includes academic papers, technical reports, and the like. For example, the estimation unit estimates emotions by referring to literature on the latest facial expression recognition technology. The estimation unit can also estimate emotions by referring to past research data. Furthermore, the estimation unit can estimate emotions by referring to the results of other researchers. In this way, by referring to related literature, the estimation accuracy is improved.

[0087] The display unit can estimate the emotion of the subject and adjust the display method based on the estimated emotion. For example, the display unit can estimate the emotion of the subject and adjust the display method based on the estimated emotion. Display methods include text display, graphical display, and the like. For example, the display unit can display detailed information when the subject is relaxed. Also, the display unit can display concise information when the subject is nervous. Furthermore, the display unit can display visually emphasized information when the subject is excited. In this way, adjusting the display method based on the emotion makes the displayed content easier to understand.

[0088] The display unit can optimize the current display by referring to past display data when displaying. For example, the display unit optimizes the current display by referring to past display data when displaying. Past display data includes history data, log data, etc. For example, the display unit selects the optimal display method based on information displayed in the past. The display unit can also analyze past display data and select a display method that is easy for the user to understand. Furthermore, the display unit can refer to past display data and select a display method that suits the user's preferences. In this way, the optimal display method can be selected by referring to past display data.

[0089] The display unit can apply different display methods to each emotion category during display. For example, the display unit applies different display methods to each emotion category during display. Emotion categories include joy, sadness, anger, etc. For example, the display unit applies a bright color display method to the emotion of joy. The display unit can also apply a subdued color display method to the emotion of sadness. Furthermore, the display unit can also apply an emphasized color display method to the emotion of anger. In this way, by applying an appropriate display method to each emotion category, the displayed content becomes easier to understand.

[0090] The display unit can estimate the emotion of the subject and determine a display priority based on the estimated emotion. The display unit, for example, estimates the emotion of the subject and determines a display priority based on the estimated emotion. The display priority includes the intensity and importance of the emotion. For example, if the subject is happy, the display unit can preferentially display the emotion of joy. Also, if the subject is sad, the display unit can preferentially display the emotion of sadness. Furthermore, if the subject is angry, the display unit can preferentially display the emotion of anger. In this way, by determining the display priority based on the emotion, important information can be preferentially displayed.

[0091] The display unit can analyze changes in the display based on the timing of emotion display during display. For example, the display unit analyzes changes in the display based on the timing of emotion display during display. The display timing includes real-time data and past data. For example, the display unit records the timing of emotion changes and analyzes the changes in the display. In addition, if emotion changes occur frequently, the display unit can adjust the display based on the timing. Furthermore, if emotion changes are infrequent, the display unit can minimize changes in the display. In this way, by analyzing changes in the display based on the timing, the display content can be provided more appropriately.

[0092] The display unit may analyze the display by referring to market data related to the emotion when displaying the content. For example, the display unit may analyze the display by referring to market data related to the emotion when displaying the content. The related market data includes market research data, consumer behavior data, and the like. For example, the display unit may optimize the emotion display method based on the market data. The display unit may also select a display method preferred by a user by referring to the market data. Furthermore, the display unit may analyze the market data and improve the emotion display method. In this way, the display content is provided more appropriately by referring to the related market data.

[0093] The output unit can estimate the emotion of the subject and adjust the output method based on the estimated emotion. For example, the output unit can estimate the emotion of the subject and adjust the output method based on the estimated emotion. Output methods include audio output, text output, and the like. For example, the output unit can provide detailed audio output when the subject is relaxed. Also, the output unit can provide concise audio output when the subject is nervous. Furthermore, the output unit can provide visually emphasized audio output when the subject is excited. In this way, adjusting the output method based on the emotion makes the output content easier to understand.

[0094] The output unit can optimize the current output by referring to past output data at the time of output. For example, the output unit optimizes the current output by referring to past output data at the time of output. Past output data includes history data, log data, etc. For example, the output unit selects the optimal output method based on information output in the past. The output unit can also analyze past output data and select an output method that is easy for the user to understand. Furthermore, the output unit can select an output method that suits the user's preferences by referring to past output data. In this way, the optimal output method can be selected by referring to past output data.

[0095] The output unit can apply different output methods to each emotion category during output. For example, the output unit applies different output methods to each emotion category during output. Emotion categories include joy, sadness, anger, etc. For example, the output unit applies a bright voice output method to the emotion of joy. The output unit can also apply a calm voice output method to the emotion of sadness. Furthermore, the output unit can apply an emphasized voice output method to the emotion of anger. In this way, by applying an appropriate output method to each emotion category, the output content becomes easier to understand.

[0096] The output unit can estimate the emotion of the subject and determine the priority of output based on the estimated emotion. The output unit, for example, estimates the emotion of the subject and determines the priority of output based on the estimated emotion. The priority of output includes the intensity and importance of the emotion. For example, if the subject is happy, the output unit can preferentially output the emotion of joy. Also, if the subject is sad, the output unit can preferentially output the emotion of sadness. Furthermore, if the subject is angry, the output unit can preferentially output the emotion of anger. In this way, by determining the priority of output based on emotion, important information can be preferentially output.

[0097] The output unit can analyze changes in the output based on the timing of emotion output at the time of output. For example, the output unit analyzes changes in the output based on the timing of emotion output at the time of output. The output timing includes real-time data and past data. For example, the output unit records the timing at which emotions change and analyzes changes in the output. In addition, the output unit can adjust the output based on the timing when emotions change frequently. Furthermore, the output unit can minimize changes in the output when emotions change little. In this way, by analyzing changes in the output based on the output timing, more appropriate output content can be provided.

[0098] The output unit may analyze the output by referring to market data related to the emotion at the time of output. For example, the output unit may analyze the output by referring to market data related to the emotion at the time of output. The related market data includes market research data, consumer behavior data, etc. For example, the output unit may optimize the emotion output method based on the market data. The output unit may also select an output method preferred by a user by referring to the market data. Furthermore, the output unit may analyze the market data and improve the emotion output method. In this way, by referring to the related market data, the output content is provided more appropriately. === Hard Collateral 1-1 === Each of the multiple elements, including the image acquisition unit, analysis unit, estimation unit, display unit, and output unit, described above, is realized, for example, by at least one of the smart device 14 and the data processing device 12. For example, the image acquisition unit is realized by the camera 42 of the smart device 14 and acquires an image of the subject's face. The analysis unit is realized by the specific processing unit 290 of the data processing device 12 and analyzes the acquired image to detect changes in facial expression. The estimation unit is realized by the specific processing unit 290 of the data processing device 12 and estimates emotions based on the changes in facial expression detected by the analysis unit. The display unit is realized by the display 40A of the smart device 14 and displays the estimated emotions on the screen. The output unit is realized by the speaker 40B of the smart device 14 and outputs the estimated emotions as audio. === Hard Collateral 1-2 === Each of the multiple elements including the image acquisition unit, analysis unit, estimation unit, display unit, and output unit described above is realized, for example, by at least one of the smart glasses 214 and the data processing device 12. For example, the image acquisition unit is realized by the camera 42 of the smart glasses 214 and acquires an image of the subject's face. The analysis unit is realized by the specific processing unit 290 of the data processing device 12 and analyzes the acquired image to detect changes in facial expression. The estimation unit is realized by the specific processing unit 290 of the data processing device 12 and estimates emotions based on the changes in facial expression detected by the analysis unit. The display unit is realized by the display of the smart glasses 214 and displays the estimated emotions on the screen. The output unit is realized by the speaker 240 of the smart glasses 214 and outputs the estimated emotions as audio. === Hard Collateral 1-3 === Each of the multiple elements including the image acquisition unit, analysis unit, estimation unit, display unit, and output unit described above is realized, for example, by at least one of the headset-type terminal 314 and the data processing device 12. For example, the image acquisition unit is realized by the camera 42 of the headset-type terminal 314 and acquires an image of the subject's face. The analysis unit is realized by the specific processing unit 290 of the data processing device 12 and analyzes the acquired image to detect changes in facial expression. The estimation unit is realized by the specific processing unit 290 of the data processing device 12 and estimates an emotion based on the changes in facial expression detected by the analysis unit. The display unit is realized by the display 343 of the headset-type terminal 314 and displays the estimated emotion on the screen. The output unit is realized by the speaker 240 of the headset-type terminal 314 and outputs the estimated emotion as audio. === Hard Collateral 1-4 === Each of the multiple elements including the image acquisition unit, analysis unit, estimation unit, display unit, and output unit described above is realized, for example, by at least one of the robot 414 and the data processing device 12. For example, the image acquisition unit is realized by the camera 42 of the robot 414 and acquires an image of the subject's face. The analysis unit is realized by the specific processing unit 290 of the data processing device 12 and analyzes the acquired image to detect changes in facial expression. The estimation unit is realized by the specific processing unit 290 of the data processing device 12 and estimates emotions based on the changes in facial expression detected by the analysis unit. The display unit is realized by the display device of the robot 414 and displays the estimated emotions on a screen. The output unit is realized by the speaker 240 of the robot 414 and outputs the estimated emotions as audio.

[0099] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0100] The emotion reading system can further include a voice recognition unit. The voice recognition unit can acquire the voice uttered by the subject and analyze the voice to infer the emotion. For example, if the subject laughs, the voice recognition unit can analyze the voice and infer the emotion of joy. If the subject cries, the voice recognition unit can also analyze the voice and infer the emotion of sadness. Furthermore, if the subject yells, the voice recognition unit can analyze the voice and infer the emotion of anger. Thus, by using the voice recognition unit, the emotion of the subject can be inferred from a wider range of perspectives.

[0101] The emotion reading system may further include an environmental sensor unit. The environmental sensor unit acquires environmental information about the subject's surroundings and can estimate the subject's emotions based on that information. For example, the environmental sensor unit may use a temperature sensor to measure the temperature around the subject and estimate discomfort if the subject is in a cold environment. The environmental sensor unit may also use an illuminance sensor to measure the brightness of the surroundings and estimate anxiety if the subject is in a dark environment. The environmental sensor unit may also use a noise sensor to measure the noise level of the surroundings and estimate stress if the subject is in a noisy environment. As a result, the environmental sensor unit can be used to more accurately estimate the subject's emotions.

[0102] The emotion reading system may further include a biometric information acquisition unit. The biometric information acquisition unit acquires biometric information of the subject and can infer emotions based on that information. For example, the biometric information acquisition unit may measure the subject's heart rate using a heart rate sensor, and infer an excited state if the heart rate is elevated. The biometric information acquisition unit may also measure the subject's electrical activity of the skin using an electrodermal activity sensor, and infer a stress state. Furthermore, the biometric information acquisition unit may measure the subject's body temperature using a body temperature sensor, and infer a nervous state if the body temperature is elevated. In this way, the biometric information acquisition unit can infer the subject's emotions in more detail.

[0103] The emotion reading system can further include a behavior analysis unit. The behavior analysis unit can analyze the behavior of the subject and infer emotions based on that behavior. For example, the behavior analysis unit can analyze the walking pattern of the subject and infer emotions of impatience if the subject walks quickly. The behavior analysis unit can also analyze the movement of the subject's hands and infer emotions of tension if the hands are shaking. Furthermore, the behavior analysis unit can analyze the subject's posture and infer emotions of fatigue if the posture is poor. In this way, by using the behavior analysis unit, it is possible to infer emotions of the subject from more diverse perspectives.

[0104] The emotion reading system can further include a history data analysis unit. The history data analysis unit can analyze the subject's past emotion data and estimate the subject's current emotion based on that data. For example, the history data analysis unit references data of when the subject previously expressed the emotion of happiness, and if the subject's current facial expression is similar, estimates the emotion of happiness. The history data analysis unit can also reference data of when the subject previously expressed the emotion of sadness, and if the subject's current facial expression is similar, estimates the emotion of sadness. Furthermore, the history data analysis unit can reference data of when the subject previously expressed the emotion of anger, and if the subject's current facial expression is similar, estimates the emotion of anger. In this way, by using the history data analysis unit, the subject's emotions can be estimated more accurately.

[0105] The emotion reading system may further include a user setting unit. The user setting unit may acquire individual settings of the subject and estimate the emotion based on the settings. For example, the user setting unit may set in advance the emotion that the subject will exhibit when showing a specific facial expression and estimate the emotion based on the settings. The user setting unit may also set in advance the emotion that the subject will exhibit under specific environmental conditions and estimate the emotion based on the settings. Furthermore, the user setting unit may also set in advance the emotion that the subject will exhibit when showing a specific behavior and estimate the emotion based on the settings. In this way, by using the user setting unit, the emotion of the subject can be more individually estimated.

[0106] The emotion reading system can further include a data sharing unit. The data sharing unit can share data between multiple emotion reading systems and estimate emotions based on that data. For example, the data sharing unit can share data of a subject between emotion reading systems installed in different locations and estimate emotions comprehensively. The data sharing unit can also share data acquired at different time periods and estimate changes in emotions over time. Furthermore, the data sharing unit can share data of different subjects and estimate emotions based on similar emotion patterns. As a result, using the data sharing unit improves the accuracy of emotion estimation.

[0107] The emotion reading system can further include a feedback unit. The feedback unit can feed back the emotion estimation result to the subject and adjust the emotion based on the feedback. For example, the feedback unit can display a message such as "Relax" to the subject to encourage a relaxed facial expression. The feedback unit can also display a message such as "Take a deep breath" to the subject to relieve tension. The feedback unit can also display a message such as "Smile" to the subject to elicit positive emotions. In this way, the feedback unit can be used to guide the subject's emotions in a better direction.

[0108] The emotion reading system can further include a learning unit. The learning unit can learn the emotion data of the subject and estimate the emotion based on the learning results. For example, the learning unit can learn the subject's past emotion data and estimate the subject's current emotion more accurately. The learning unit can also learn the emotion data of different subjects and estimate the emotion based on common emotion patterns. Furthermore, the learning unit can continuously learn new emotion data and improve the emotion estimation accuracy. In this way, using the learning unit improves the emotion estimation accuracy.

[0109] The emotion reading system may further include a notification unit. The notification unit can estimate the emotion of the subject and notify a specific person of the estimation result. For example, if the subject is sad, the notification unit can notify family members of the emotion. If the subject is angry, the notification unit can also notify medical staff of the emotion. Furthermore, if the subject is happy, the notification unit can also notify friends of the emotion. In this way, the notification unit can be used to quickly communicate the emotion of the subject to people around them.

[0110] The processing flow of the second embodiment will be briefly explained below.

[0111] Step 1: The image acquisition unit acquires an image of the subject's face. For example, the image acquisition unit acquires an image of the subject's face using a camera. The position and angle of the camera are adjusted so that the subject's entire face is captured. For example, the camera is placed in front of the subject so that facial expressions can be captured in detail. Step 2: The analysis unit analyzes the images acquired by the image acquisition unit and detects changes in facial expression. For example, the analysis unit detects facial feature points in the image and analyzes changes in facial expression. By detecting feature points such as eye and mouth movements and the raising and lowering of eyebrows and analyzing these changes, changes in facial expression can be captured. Step 3: The estimation unit estimates emotions based on the changes in facial expressions detected by the analysis unit. For example, the estimation unit estimates emotions such as joy, sadness, and anger based on the changes in facial expressions detected by the analysis unit. Emotions are estimated from changes in facial expressions, such as joy if the corners of the mouth are raised and sadness if the eyebrows are lowered. Step 4: The display unit or output unit displays or outputs the emotion estimated by the estimation unit as sound. For example, the display unit displays the estimated emotion on a screen so that people around can understand the emotion visually. The output unit outputs the estimated emotion as sound so that people around can understand the emotion auditorily. For example, outputting the emotion as sound such as "I'm happy" or "I'm sad" makes it easier for people around to understand the emotion.

[0112] The specific processing unit 290 transmits the result of the specific processing to the smart device 14. In the smart device 14, the control unit 46A causes the output device 40 to output the result of the specific processing. The microphone 38B acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio 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 audio data.

[0113] 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 engine).<URL: https: / / openai.com / blog / chatgpt> Examples of the generative AI include a neural network (NN) and a neural network (NN). The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image (e.g., still image data or video data). The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt, and outputs the inference result in one or more data formats of voice data, text data, image data, etc. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specification processing unit 290 performs the above-mentioned specification processing 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. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and may perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processing of each of the above-mentioned parts is performed by an AI, the processing may be performed in part or entirely by the AI, but is not limited to these examples. The processing performed by an AI including the generative AI may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by an AI including the generative AI.

[0114] Furthermore, the processing by the data processing system 10 described above 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 necessary for processing from the smart device 14 or an external device, and the smart device 14 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0115] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0116] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0117] 3, the data processing system 210 includes a data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0118] 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, RAM 30, and 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.

[0119] 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.

[0120] 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 audio according to instructions from the processor 46.

[0121] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an 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 (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0122] 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 carried out in a secure state.

[0123] Fig. 4 shows an example of the 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.

[0124] 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.

[0125] 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 a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0126] In the smart glasses 214, the specific processing is performed by the processor 46. A specific processing program 60 is stored in the storage 50. 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. The smart glasses 214 also 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 perform processing similar to that of the specific processing unit 290 using these models.

[0127] 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 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0128] The specific processing unit 290 transmits the result of the specific processing to the smart glasses 214. In the smart glasses 214, the control unit 46A causes the speaker 240 to output the result of the specific processing. The microphone 238 acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio 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 audio data.

[0129] 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. The data generation model 58 receives a prompt including an instruction, as well as inference data such as audio data indicating speech, text data indicating text, and image data indicating an image (e.g., still image data or video data). The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt, and outputs the inference result in one or more data formats, such as audio data, text data, and image data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing 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. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation models 58 include AIs other than the generation AI. Examples of AIs other than the generation AI include, but are not limited to, linear regression, logistic regression, decision trees, random forests, support vector machines (SVMs), k-means clustering, convolutional neural networks (CNNs), recurrent neural networks (RNNs), generative adversarial networks (GANs), and naive Bayes. These AIs can perform various types of processing, but are not limited to these examples. The AI ​​may also be an AI agent. When the processing of each of the above-described parts is performed by an AI, the processing may be performed in part or entirely by the AI, but is not limited to these examples. Processing performed by an AI, including the generation AI, may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by an AI, including the generation AI.

[0130] 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 also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart glasses 214. Furthermore, 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, etc., and the smart glasses 214 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0131] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0132] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0133] 5, the data processing system 310 includes the data processing device 12 and a headset terminal 314. An example of the data processing device 12 is a server.

[0134] 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, RAM 30, and 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.

[0135] 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.

[0136] 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 audio according to instructions from the processor 46.

[0137] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an 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 (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0138] 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 carried out in a secure state.

[0139] 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.

[0140] 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.

[0141] 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 a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0142] In the headset type terminal 314, the identification process is performed by the processor 46. A identification program 60 is stored in the storage 50. The processor 46 reads the identification program 60 from the storage 50 and executes the read identification program 60 on the RAM 48. The identification process is realized by the processor 46 operating as a control unit 46A in accordance with the identification 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.

[0143] 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 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0144] 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 audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio 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 audio data.

[0145] 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. The data generation model 58 receives a prompt including an instruction, as well as inference data such as audio data indicating speech, text data indicating text, and image data indicating an image (e.g., still image data or video data). The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt, and outputs the inference result in one or more data formats, such as audio data, text data, and image data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing 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. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation models 58 include AIs other than the generation AI. Examples of AIs other than the generation AI include, but are not limited to, linear regression, logistic regression, decision trees, random forests, support vector machines (SVMs), k-means clustering, convolutional neural networks (CNNs), recurrent neural networks (RNNs), generative adversarial networks (GANs), and naive Bayes. These AIs can perform various types of processing, but are not limited to these examples. The AI ​​may also be an AI agent. When the processing of each of the above-described parts is performed by an AI, the processing may be performed in part or entirely by the AI, but is not limited to these examples. Processing performed by an AI, including the generation AI, may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by an AI, including the generation AI.

[0146] 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 also 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. Furthermore, 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, etc., and the headset type terminal 314 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0147] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0148] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0149] 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.

[0150] 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, RAM 30, and 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.

[0151] 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.

[0152] 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 audio according to instructions from the processor 46.

[0153] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS image sensor or a CCD image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0154] 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 carried out in a secure state.

[0155] The control object 443 includes a display device, LEDs in the eyes, and motors that drive the arms, hands, and feet. The posture and gestures of the robot 414 are controlled by controlling the motors of the arms, hands, and feet. Some of the 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.

[0156] 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.

[0157] 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.

[0158] 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 a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0159] In the robot 414, the processor 46 performs the identification process. The storage 50 stores the identification program 60. The processor 46 reads the identification program 60 from the storage 50 and executes the read identification program 60 on the RAM 48. The identification process is realized by the processor 46 operating as the control unit 46A in accordance with the identification program 60 executed on the RAM 48. The robot 414 also 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 perform the same process as the identification processing unit 290 using these models.

[0160] 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 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0161] 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 voice indicating a user input regarding 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.

[0162] 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. The data generation model 58 receives a prompt including an instruction, as well as inference data such as audio data indicating speech, text data indicating text, and image data indicating an image (e.g., still image data or video data). The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt, and outputs the inference result in one or more data formats, such as audio data, text data, and image data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing 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. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation models 58 include AIs other than the generation AI. Examples of AIs other than the generation AI include, but are not limited to, linear regression, logistic regression, decision trees, random forests, support vector machines (SVMs), k-means clustering, convolutional neural networks (CNNs), recurrent neural networks (RNNs), generative adversarial networks (GANs), and naive Bayes. These AIs can perform various types of processing, but are not limited to these examples. The AI ​​may also be an AI agent. When the processing of each of the above-described parts is performed by an AI, the processing may be performed in part or entirely by the AI, but is not limited to these examples. Processing performed by an AI, including the generation AI, may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by an AI, including the generation AI.

[0163] 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 also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the robot 414. Furthermore, 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.

[0164] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0165] The emotion identification model 59 as an emotion engine may determine the user's emotion according to a specific mapping. Specifically, the emotion identification model 59 may determine the user's emotion according to an emotion map (see FIG. 9), which is a specific mapping. Similarly, the emotion identification model 59 may determine the robot's emotion, and the identification processing unit 290 may perform identification processing using the robot's emotion.

[0166] FIG. 9 illustrates 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. Emotions closer to the center of the concentric circles are more primitive. Emotions representing states and behaviors arising from a state of mind are arranged on the outer edges of the concentric circles. The concept of emotion encompasses both emotions and mental states. Emotions generally generated from reactions occurring in the brain are arranged on the left side of the concentric circles. Emotions generally induced by situational judgment are arranged on the right side of the concentric circles. Emotions generally generated from reactions occurring in the brain and induced by situational judgment are arranged on the upper and lower sides of the concentric circles. Furthermore, the emotion of "pleasure" is arranged on the upper side of the concentric circles, and the emotion of "discomfort" is arranged on the lower side. In this way, in the emotion map 400, multiple emotions are mapped based on the structure by which emotions are generated, and emotions that tend to occur simultaneously are mapped close to each other.

[0167] These emotions are distributed in the 3 o'clock direction on emotion map 400, and typically 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.

[0168] The inside of emotion map 400 represents what is going on in the mind, and the outside of emotion map 400 represents behavior, so the further you go outside emotion map 400, the more visible the emotions become (the more they are expressed in behavior).

[0169] Human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, a state of discomfort is expressed, and when they approach the ideal, a state of pleasure is expressed. Emotions can also be created for robots, cars, and motorcycles, based on various balances, such as posture and remaining battery life. When these balances deviate from the ideal, a state of discomfort is expressed, and when they approach the ideal, a state of pleasure is expressed. An emotion map can be generated, for example, based on Dr. Mitsuyoshi's emotion map (Research on speech emotion recognition and brain physiological signal analysis systems for emotions, Tokushima University, doctoral dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map lists emotions belonging to the area called "reaction," where sensation is dominant. The right half of the emotion map lists emotions belonging to the area called "situation," where situational awareness is dominant.

[0170] The emotion map defines two emotions that promote learning. One is a negative emotion on the situation side, around the middle of "repentance" or "reflection." In other words, this occurs 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 a positive emotion on the response side, around "desire." In other words, this occurs when the robot experiences positive feelings such as "I want more" or "I want to know more."

[0171] The emotion identification model 59 inputs user input into a pre-trained neural network, obtains emotion values ​​indicating each emotion shown in the emotion map 400, and determines the user's emotion. This neural network is pre-trained based on multiple pieces of training data that are combinations of 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, "relieved," "calm," and "reassuring," have similar emotion values.

[0172] 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.

[0173] 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 USB (Universal Serial Bus) 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.

[0174] Alternatively, the specific processing program 56 may be stored in a storage device such as a server connected to the data processing device 12 via the network 54, and the specific processing program 56 may be downloaded and installed on the computer 22 in response to a request from the data processing device 12.

[0175] 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.

[0176] The hardware resource for executing a specific process can be any of the following types of processors: A processor, for example, is a CPU, which is a general-purpose processor that functions as a hardware resource for executing a specific process by executing software, i.e., a program. A processor also includes a dedicated electrical circuit, such as an FPGA (Field-Programmable Gate Array), a PLD (Programmable Logic Device), or an ASIC (Application Specific Integrated Circuit), which is a processor with a circuit configuration designed specifically for executing a specific process. Each processor has built-in or connected memory, and each processor uses the memory to execute the specific process.

[0177] The hardware resource that executes the specific process may be configured with one of these various processors, or may be configured with 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.

[0178] As an example of a system configured with a single 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 system that uses a processor that realizes the functions of an 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.

[0179] Furthermore, the hardware structure of these various processors can be, more specifically, an electric circuit that combines circuit elements such as semiconductor devices. The specific processing described above is merely an example. Therefore, it goes without saying that unnecessary steps may be deleted, new steps may be added, or the processing order may be rearranged, without departing from the spirit of the invention.

[0180] In the above example, the first to fourth embodiments have been described separately, but some or all of these embodiments may be combined. The smart device 14, smart glasses 214, headset terminal 314, and robot 414 are merely examples, and they may be combined, or other devices may be used. In the above example, the first and second embodiments have been described separately, but they may be combined.

[0181] The above-described description and illustrations are a detailed explanation 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, functions, actions, and effects is an explanation of an example of the configuration, functions, actions, and effects 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 replacements may be made to the above-described description and illustrations within the scope of the gist of the technology of the present disclosure. Furthermore, to avoid confusion and facilitate understanding of the parts related to the technology of the present disclosure, the above-described description and illustrations omit explanations of common technical knowledge that do not require particular explanation to enable the implementation of the technology of the present disclosure.

[0182] All publications, patent applications, and technical standards mentioned in this specification are herein incorporated by reference to the same extent as if each individual publication, patent application, or technical standard was specifically and individually indicated to be incorporated by reference.

[0183] [Explanation of symbols]

[0184] 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 image acquisition unit that acquires an image of the subject's face; an analysis unit that analyzes the image acquired by the image acquisition unit and detects changes in facial expressions; an estimation unit that estimates an emotion based on the change in facial expression detected by the analysis unit; a display unit or output unit that displays the emotion estimated by the estimation unit; Equipped with A system characterized by:

2. The image acquisition unit Capture an image of the subject's face using a camera 2. The system of claim 1.

3. The analysis unit Detect facial features in images and analyze changes in facial expressions 2. The system of claim 1.

4. The estimation unit The emotion is estimated based on the change in facial expression detected by the analysis unit.

2. The system of claim 1.

5. The display unit Display estimated emotions on the screen 2. The system of claim 1.

6. The output unit Output the estimated emotion as voice 2. The system of claim 1.

7. The image acquisition unit Estimate the subject's emotion and determine how to adjust the timing of image acquisition based on the estimated emotion.

2. The system of claim 1.

8. The image acquisition unit When acquiring images, the appropriate acquisition method is selected by referring to the subject's past facial expression data.

2. The system of claim 1.

9. The image acquisition unit Automatically adjusts camera settings based on the subject's current environment and situation when capturing images 2. The system of claim 1.

10. The image acquisition unit Determine a method to estimate the subject's emotions and prioritize images to be acquired based on the estimated emotions.

2. The system of claim 1.

Images