system

Abstract

The system according to the embodiment aims to accurately grasp the emotions of a subject and respond to the care needs of each individual user. [Solution] The system according to the embodiment includes a collection unit, an analysis unit, a provision unit, and a learning unit. The collection unit collects facial expressions and tone of voice of the subject. The analysis unit analyzes the information collected by the collection unit to grasp the subject's emotions. The provision unit provides the analysis results obtained by the analysis unit in real time. The learning unit self-learns based on the information provided by the provision unit to address the care needs of each individual user.

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] With conventional technology, it is difficult for caregivers and child welfare workers to accurately grasp the emotions of the person in question and respond appropriately, and there is room for improvement.

[0005] The system according to the embodiment aims to accurately grasp the emotions of a subject and respond to the care needs of each individual user. [Means for solving the problem]

[0006] The system according to the embodiment includes a collection unit, an analysis unit, a provision unit, and a learning unit. The collection unit collects facial expressions and tone of voice of the subject. The analysis unit analyzes the information collected by the collection unit to understand the subject's emotions. The provision unit provides the analysis results obtained by the analysis unit in real time. The learning unit self-learns based on the information provided by the provision unit to address the care needs of each individual user. [Effects of the Invention]

[0007] The system according to the embodiment can accurately grasp the emotions of a subject and respond to the care needs of each individual user. [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 recognition support system according to an embodiment of the present invention collects a subject's facial expressions and tone of voice, and an AI analyzes this information to understand their emotions and provide the results in real time. The emotion recognition support system collects a subject's facial expressions and tone of voice, and an AI analyzes this information to understand their emotions. The analysis results are provided in real time through an earphone-type device and suggest appropriate countermeasures. Furthermore, the AI ​​self-learns to address the care needs of each user and suggests the optimal approach. For example, the emotion recognition support system uses a microphone and camera-type device to collect a subject's facial expressions and tone of voice. For example, if a subject is smiling while speaking, the system collects their facial expressions and tone of voice. This information is input into the AI. The emotion recognition support system then uses the AI ​​to analyze the collected information and understand the subject's emotions. For example, the AI ​​analyzes a smile and a cheerful tone of voice and determines that the subject is happy. The analysis results are provided in real time through an earphone-type device. Furthermore, the emotion recognition support system self-learns to address the care needs of each user. For example, the AI ​​learns what emotions a specific subject shows in a specific situation and suggests the optimal approach based on that information. As a result, the emotion understanding support system can reduce the burden on caregivers and child welfare workers and improve the quality of welfare services. As a result, the emotion understanding support system can reduce the burden on caregivers and child welfare workers and improve the quality of welfare services. For example, by understanding the emotions of the recipient in real time, appropriate responses can be made quickly, reducing the stress of caregivers and child welfare workers. In addition, by responding based on the recipient's emotions, the recipient's satisfaction level increases. Furthermore, by using AI to analyze emotions and suggest countermeasures, the work of caregivers and child welfare workers becomes more efficient, allowing high-quality welfare services to be provided even with fewer staff.

[0029] An emotion understanding support system according to an embodiment includes a collection unit, an analysis unit, a provision unit, and a learning unit. The collection unit collects facial expressions and tone of voice of a subject. The facial expressions of the subject include, but are not limited to, smiling, anger, sadness, etc. The collection unit collects the facial expressions and tone of voice of the subject using, for example, a microphone and camera-type device. The collection unit can also collect the tone of voice of the subject. For example, the collection unit collects the high and low pitches and intonation of the subject's voice. The analysis unit analyzes the information collected by the collection unit to understand the subject's emotions. The understanding of emotions is performed, for example, but is not limited to, using an emotion recognition algorithm. For example, the analysis unit analyzes the collected facial expression data to determine whether the subject is smiling. The analysis unit can also analyze the collected tone of voice to determine whether the subject is angry. The analysis unit can also combine and analyze the collected facial expression data and tone of voice to comprehensively understand the subject's emotions. The provision unit provides the analysis results obtained by the analysis unit in real time. The information is provided, for example, through an earphone-type device, but is not limited to this example. For example, the providing unit provides the analysis results to a caregiver or child welfare worker through the earphone-type device. The providing unit can also display the analysis results on a display. The providing unit can also notify the analysis results by voice. The learning unit self-learns to address the care needs of each user based on the information provided by the providing unit. The self-learning is performed, for example, using a machine learning algorithm, but is not limited to this example. For example, the learning unit learns what emotions a specific subject shows in a specific situation. The learning unit can also learn what kind of response a specific subject prefers in a specific situation. The learning unit can also present an optimal approach based on the learned information. As a result, the emotion understanding support system according to the embodiment can reduce the burden on caregivers and child welfare workers and improve the quality of welfare services.

[0030] The emotion understanding support system includes a collection unit that collects the facial expressions or tone of voice of a target person using a microphone / camera type device. The collection unit collects the facial expressions or tone of voice of a target person using the microphone / camera type device. Examples of the microphone / camera type device include, but are not limited to, high-resolution cameras and high-sensitivity microphones. For example, the collection unit collects the facial expressions of a target person in detail using a high-resolution camera. The collection unit can also accurately collect the tone of voice of a target person using a high-sensitivity microphone. The collection unit can also collect the natural facial expressions and tone of voice of a target person by devising a method for installing the microphone / camera type device. For example, the collection unit installs the camera at eye level of the target person to collect natural facial expressions. The collection unit can also install the microphone close to the target person's mouth to accurately collect the tone of voice. In this way, the microphone / camera type device can accurately collect the facial expressions and tone of voice of a target person.

[0031] The analysis unit can analyze the collected information to grasp the emotions of the subject. The analysis unit analyzes the collected information to grasp the emotions of the subject. The analysis is performed, for example, using a data analysis algorithm, but is not limited to such an example. For example, the analysis unit can analyze the collected facial expression data to determine whether the subject is smiling. The analysis unit can also analyze the collected tone of voice to determine whether the subject is angry. The analysis unit can also analyze the collected facial expression data and tone of voice in combination to comprehensively grasp the emotions of the subject. For example, the analysis unit can analyze the facial expression of the subject using a facial expression recognition algorithm. The analysis unit can also analyze the tone of voice of the subject using a voice analysis algorithm. The analysis unit can also analyze the facial expression data and tone of voice in combination to grasp the emotions of the subject using an emotion recognition algorithm. In this way, the emotions of the subject can be accurately grasped by analyzing the collected information.

[0032] The providing unit can provide the analysis results in real time through an earphone-type device. The providing unit provides the analysis results in real time through an earphone-type device. Examples of earphone-type devices include, but are not limited to, high-quality earphones and wireless earphones. For example, the providing unit provides the analysis results clearly using high-quality earphones. The providing unit can also provide the analysis results using wireless earphones. The providing unit can also provide the analysis results in real time by having a caregiver or child welfare worker wear the earphone-type device. For example, the providing unit notifies the caregiver or child welfare worker of the analysis results by voice. The providing unit can also notify the caregiver or child welfare worker of the analysis results by text message. The providing unit can also display the analysis results on a display. In this way, by providing the analysis results in real time through the earphone-type device, the caregiver or child welfare worker can respond quickly.

[0033] The learning unit can learn the emotional patterns of a specific subject and present an optimal approach. The learning unit can learn the emotional patterns of a specific subject and present an optimal approach. Learning can be performed, for example, using a machine learning algorithm, but is not limited to such an example. For example, the learning unit can learn what emotions a specific subject shows in a specific situation. The learning unit can also learn what kind of response a specific subject prefers in a specific situation. The learning unit can also present an optimal approach based on the learned information. For example, if a specific subject is feeling stressed, the learning unit can present an approach that has a relaxing effect. If a specific subject is relaxed, the learning unit can present an approach that provides detailed information. If a specific subject is excited, the learning unit can present a visually stimulating approach. In this way, by learning the emotional patterns of a specific subject, an optimal approach that meets individual care needs can be provided.

[0034] The provision unit can aim to reduce the burden on caregivers and child welfare workers and improve the quality of welfare services. The provision unit aims to reduce the burden on caregivers and child welfare workers and improve the quality of welfare services. Reduction of burden is achieved, for example, by shortening work time and reducing stress, but is not limited to such examples. For example, the provision unit reduces the work time of caregivers and child welfare workers by providing emotion analysis results in real time. The provision unit can also reduce stress on caregivers and child welfare workers by suggesting appropriate countermeasures. The provision unit also aims to improve the quality of welfare services. Improvement of quality is achieved, for example, by setting service evaluation criteria and improvement methods, but is not limited to such examples. For example, the provision unit sets service evaluation criteria based on the emotion analysis results. The provision unit can also suggest service improvement methods based on the emotion analysis results. This reduces the burden on caregivers and child welfare workers and improves the quality of welfare services.

[0035] The emotion understanding support system includes a collection unit that optimizes the collection method by referring to the subject's past emotional data during collection. The collection unit optimizes the collection method by referring to the subject's past emotional data during collection. Optimization is performed, for example, through algorithm adjustment or parameter setting, but is not limited to these examples. For example, the collection unit refers to situations in which the subject felt stressed in the past and focuses on collecting facial expressions and tone of voice in similar situations. The collection unit can also refer to situations in which the subject felt relaxed in the past and adjust the collection method based on the data from those situations. The collection unit can also refer to situations in which the subject felt excited in the past and optimize the collection method based on the data from those situations. By referring to past emotional data, the collection method can be optimized, enabling more accurate data collection. Some or all of the above-described processing in the collection unit may be performed using, for example, AI, or without AI. For example, the collection unit can input past emotional data into an AI model, and the AI ​​can suggest an optimal collection method.

[0036] The emotion understanding support system includes a collection unit that adjusts a collection means based on the subject's current environment during data collection. The collection unit adjusts the collection means based on the subject's current environment (e.g., indoors or outdoors, noise level) during data collection. The adjustment is performed, for example, by selecting a collection means or changing its settings, but is not limited to such examples. For example, the collection unit collects subtle changes in voice tone when the environment is quiet indoors. Furthermore, the collection unit can prioritize collecting changes in facial expressions when the environment is noisy outdoors. Furthermore, the collection unit can collect both facial expressions and voice tone in a balanced manner when the noise level is moderate. This enables more appropriate data collection by adjusting the collection means based on the current environment. Some or all of the above-described processing in the collection unit may be performed using, for example, AI, or without AI. For example, the collection unit can input current environmental data into an AI model, and the AI ​​can suggest the optimal collection means.

[0037] The emotion understanding support system includes a collection unit that corrects collected data during collection, taking into account the subject's physical condition. The collection unit corrects the collected data during collection, taking into account the subject's physical condition (fatigue, health condition, etc.). Correction is performed, for example, through data adjustment or filtering, but is not limited to such examples. For example, if the subject is tired, the collection unit may focus on collecting changes in facial expression and correct changes in voice tone. Furthermore, if the subject is in good health, the collection unit may collect both facial expression and voice tone in a balanced manner. Furthermore, if the subject is in poor health, the collection unit may focus on collecting changes in voice tone and correct changes in facial expression. This allows for more accurate data collection by correcting the collected data taking into account the subject's physical condition. Some or all of the above-described processing in the collection unit may be performed using, for example, AI, or without AI. For example, the collection unit may input physical condition data into an AI model, and the AI ​​may perform the correction of the collected data.

[0038] The emotion understanding support system includes a collection unit that, when collecting data, enhances the relevance of collected data by taking into account the geographical location information of the subject. The collection unit enhances the relevance of collected data by taking into account the geographical location information of the subject. The enhancement of relevance can be achieved, for example, through data selection or filtering, but is not limited to such examples. For example, when the subject is in a park, the collection unit can remove natural sounds to collect facial expressions and tone of voice. When the subject is in an office, the collection unit can also remove background conversation sounds to collect facial expressions and tone of voice. When the subject is at home, the collection unit can also remove household noise to collect facial expressions and tone of voice. This enhances the relevance of collected data by taking into account the geographical location information, enabling more accurate data collection. Some or all of the above-described processing by the collection unit may be performed using, or without, AI. For example, the collection unit can input geographical location information into an AI model, and the AI ​​can perform filtering to enhance the relevance of the collected data.

[0039] The emotion understanding support system includes a collection unit that analyzes the social media activities of a subject and complements related emotional data during collection. The collection unit analyzes the social media activities of the subject and complements related emotional data during collection. Completion is performed, for example, by adding or correcting data, but is not limited to such examples. For example, the collection unit analyzes the content posted by the subject on social media and complements emotional changes. The collection unit can also complement the emotional data by referring to the activities of the subject's friends on social media. The collection unit can also complement the emotional data based on the subject's check-in information on social media. In this way, by analyzing social media activities, related emotional data can be complemented and more accurate emotional analysis can be performed. Some or all of the above-mentioned processing in the collection unit may be performed using, for example, AI, or without AI. For example, the collection unit can input social media activity data into an AI model, and the AI ​​can complement the emotional data.

[0040] The emotion understanding support system includes a collection unit that customizes a collection method by reflecting the subject's past feedback during collection. The collection unit customizes the collection method by reflecting the subject's past feedback during collection. Customization is performed, for example, by selecting a collection means or changing settings, but is not limited to such examples. For example, the collection unit adjusts the collection method based on feedback provided by the subject in the past. The collection unit can also prioritize the use of a collection method that the subject previously preferred. The collection unit can also analyze the subject's past feedback and suggest an optimal collection method. This customizes the collection method by reflecting past feedback, enabling more accurate data collection. Some or all of the above-described processing in the collection unit may be performed using, for example, AI, or may be performed without using AI. For example, the collection unit can input past feedback data into an AI model, and the AI ​​can customize the collection method.

[0041] The emotion understanding support system includes an analysis unit that compares a subject's emotion pattern with past data during analysis to improve accuracy. The analysis unit compares the subject's emotion pattern with past data during analysis to improve accuracy. The improvement in accuracy can be achieved, for example, by improving the accuracy of the data or the algorithm, but is not limited to such examples. For example, the analysis unit compares and analyzes the subject's current emotion pattern based on the subject's past emotion data. The analysis unit can also refer to the subject's past emotion pattern to more accurately understand the subject's current emotion. The analysis unit can also use the subject's past emotion data to improve the accuracy of the analysis algorithm. This allows for improvement in the accuracy of emotion analysis by comparing with past data. Some or all of the above-described processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input past emotion data into an AI model, and the AI ​​can perform analysis to improve accuracy.

[0042] The emotion understanding support system includes an analysis unit that tracks the emotional fluctuations of a subject in real time during analysis and updates the analysis results. The analysis unit tracks the emotional fluctuations of the subject in real time during analysis and updates the analysis results. The tracking is performed, for example, by monitoring the emotional fluctuations in real time and updating the analysis results whenever a fluctuation occurs, but is not limited to this example. For example, the analysis unit updates the analysis results in real time whenever the subject's emotional fluctuations occur. The analysis unit can also track the emotional fluctuations of the subject in real time and provide the latest analysis results. The analysis unit can also immediately update the analysis results if the subject's emotional fluctuations suddenly change. This makes it possible to provide the latest analysis results by tracking emotional fluctuations in real time. Some or all of the above-described processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input real-time emotional data into an AI model, and the AI ​​can update the analysis results.

[0043] The emotion understanding support system includes an analysis unit that considers external factors that affect the subject's emotions during analysis. The analysis unit considers external factors (weather, time of day, etc.) that affect the subject's emotions during analysis. Consideration is performed, for example, by collecting data on external factors and reflecting them in emotion analysis, but is not limited to this example. For example, the analysis unit may consider the impact of bad weather on the subject's emotions during analysis. The analysis unit may also consider fluctuations in emotions over time during analysis. The analysis unit may also consider external factors (noise level, influence of surrounding people, etc.) during analysis. By taking external factors into account, more accurate emotion analysis is possible. Some or all of the above-described processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit may input external factor data into an AI model and reflect it in emotion analysis.

[0044] The emotion understanding support system includes an analysis unit that performs emotion analysis while taking into account the geographical background of the subject. The analysis unit performs emotion analysis while taking into account the geographical background of the subject. Consideration is performed, for example, by collecting data on the geographical background and reflecting it in the emotion analysis, but is not limited to this example. For example, if the subject is in an urban area, the analysis unit may consider stress factors specific to the city when analyzing. Furthermore, if the subject is in a natural environment, the analysis unit may also consider the relaxing effect of the natural environment when analyzing. Furthermore, if the subject is in a specific region, the analysis unit may consider the culture and customs specific to that region when analyzing. This allows for more accurate emotion analysis by taking into account the geographical background. Some or all of the above-described processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit may input geographical background data into an AI model and reflect it in the emotion analysis.

[0045] The emotion understanding support system includes an analysis unit that, during analysis, refers to literature and research data related to the subject to improve the accuracy of the analysis. The analysis unit, during analysis, refers to literature and research data related to the subject to improve the accuracy of the analysis. Referencing is performed, for example, through the collection and analysis of related literature and research data, but is not limited to such examples. For example, the analysis unit refers to the latest research data on the subject's emotions for analysis. The analysis unit can also adjust the analysis algorithm based on literature related to the subject's emotions. The analysis unit can also improve the accuracy of the analysis by referring to past research data on the subject's emotions. In this way, the analysis accuracy can be improved by referring to related literature and research data. Some or all of the above-mentioned processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input related literature and research data into an AI model, and the AI ​​can improve the accuracy of the analysis.

[0046] The emotion understanding support system includes an analysis unit that analyzes emotions taking into account the social background of the subject during analysis. The analysis unit analyzes emotions taking into account the social background (culture, customs, etc.) of the subject during analysis. This consideration is performed, for example, by collecting social background data and reflecting it in emotion analysis, but is not limited to this example. For example, the analysis unit analyzes emotions taking into account the cultural background of the subject. The analysis unit can also analyze emotions taking into account the customs and lifestyle of the subject. The analysis unit can also analyze emotional fluctuations based on the social background of the subject. This allows for more accurate emotion analysis by taking social background into consideration. Some or all of the above-described processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input social background data into an AI model and reflect it in emotion analysis.

[0047] The emotion understanding support system includes a providing unit that optimizes an information provision method by referring to the subject's past responses when providing information. The providing unit optimizes the information provision method by referring to the subject's past responses when providing information. Optimization is performed, for example, by selecting an information provision means or changing settings, but is not limited to such examples. For example, the providing unit preferentially uses an information provision method that the subject has previously preferred. The providing unit can also suggest an optimal information provision method based on the subject's past responses. The providing unit can also analyze the subject's past responses and optimize the information provision method. This optimizes the information provision method by referring to the past responses, enabling more appropriate information to be provided. Some or all of the above-described processing in the providing unit may be performed using, for example, AI, or may be performed without using AI. For example, the providing unit can input past response data into an AI model, and the AI ​​can optimize the information provision method.

[0048] The emotion understanding support system includes a providing unit that adjusts information provision based on the subject's current situation at the time of provision. The providing unit adjusts the information provision based on the subject's current situation (e.g., whether the subject is active or resting). The adjustment is performed, for example, by changing the timing or format of information provision, but is not limited to such examples. For example, the providing unit provides concise and to-the-point information when the subject is active. The providing unit can also provide detailed information when the subject is resting. The providing unit can also suggest an optimal information provision method based on the subject's current situation. This enables more appropriate information provision by adjusting the information provision based on the current situation. Some or all of the above-described processing by the providing unit may be performed using, for example, AI, or may be performed without using AI. For example, the providing unit can input current situation data into an AI model, and the AI ​​can adjust the information provision.

[0049] The emotion understanding support system includes a providing unit that corrects the provided information in consideration of the subject's physical condition when providing the information. The providing unit corrects the provided information in consideration of the subject's physical condition (fatigue, health condition, etc.) when providing the information. The correction is performed, for example, by changing the content or format of the information, but is not limited to such examples. For example, if the subject is tired, the providing unit can provide concise and to-the-point information. Furthermore, if the subject is in good health, the providing unit can provide detailed information. Furthermore, if the subject is not feeling well, the providing unit can provide information with a relaxing effect. In this way, correcting the provided information in consideration of the subject's physical condition enables more appropriate information to be provided. Some or all of the above-described processing by the providing unit may be performed using, for example, AI, or may be performed without using AI. For example, the providing unit can input physical condition data into an AI model, and the AI ​​can correct the provided information.

[0050] The emotion understanding support system includes a providing unit that provides highly relevant information by taking into account the geographical location information of the subject. The providing unit provides highly relevant information by taking into account the geographical location information of the subject. The improvement in relevance is achieved, for example, through information selection or filtering, but is not limited to such examples. For example, if the subject is in a specific area, the providing unit provides information related to that area. Furthermore, if the subject is traveling, the providing unit can also provide information related to the travel destination. Furthermore, if the subject is at home, the providing unit can also provide information about the area around the subject's home. This makes it possible to provide highly relevant information by taking into account the geographical location information. Some or all of the above-described processing by the providing unit may be performed using, for example, AI, or may be performed without using AI. For example, the providing unit can input geographical location information into an AI model to select highly relevant information.

[0051] The emotion understanding support system includes a providing unit that analyzes the social media activities of a subject and provides related information at the time of providing the information. The providing unit analyzes the social media activities of the subject and provides the related information at the time of providing the information. The analysis is performed, for example, through analyzing the content of social media posts and reactions, but is not limited to such an example. For example, the providing unit provides information about locations where the subject checked in on social media. The providing unit can also analyze the content of the subject's social media posts and provide related information. The providing unit can also provide related information by referring to the activities of the subject's friends on social media. In this way, it is possible to provide related information by analyzing social media activities. Some or all of the above-mentioned processing by the providing unit may be performed using, for example, AI, or may be performed without using AI. For example, the providing unit can input social media activity data into an AI model and select related information.

[0052] The emotion understanding support system includes a providing unit that customizes an information provision method by reflecting the subject's past feedback at the time of provision. The providing unit customizes the information provision method by reflecting the subject's past feedback at the time of provision. Customization is performed, for example, by selecting an information provision means or changing settings, but is not limited to such examples. For example, the providing unit preferentially uses an information provision method that the subject has previously preferred. The providing unit can also suggest an optimal information provision method based on the subject's past feedback. The providing unit can also analyze the subject's past feedback and customize the information provision method. This customizes the information provision method by reflecting the past feedback, enabling more appropriate information provision. Some or all of the above-described processing by the providing unit may be performed, for example, using AI or without AI. For example, the providing unit can input past feedback data into an AI model, and the AI ​​can customize the information provision method.

[0053] The emotion understanding support system includes a learning unit that optimizes a learning algorithm by referring to past learning data during learning. The learning unit optimizes the learning algorithm by referring to past learning data during learning. Optimization is performed, for example, through algorithm adjustment or parameter setting, but is not limited to such examples. For example, the learning unit adjusts the current learning algorithm based on past learning data. The learning unit can also find an optimal learning pattern by referring to past learning data. The learning unit can also improve the accuracy of the learning algorithm by using past learning data. In this way, the learning algorithm can be optimized by referring to past learning data, and the learning accuracy can be improved. Some or all of the above-described processing in the learning unit may be performed using, for example, AI, or may be performed without using AI. For example, the learning unit can input past learning data into an AI model, and the AI ​​can optimize the learning algorithm.

[0054] The emotion understanding support system includes a learning unit that analyzes fluctuations in the emotional patterns of a subject during learning and adjusts the update frequency of the learning data. The learning unit analyzes fluctuations in the emotional patterns of the subject during learning and adjusts the update frequency of the learning data. The adjustment is performed, for example, through setting the update frequency or weighting the data, but is not limited to such examples. For example, the learning unit increases the update frequency of the learning data when the emotional patterns of the subject fluctuate frequently. The learning unit can also decrease the update frequency of the learning data when the emotional patterns of the subject are stable. The learning unit can also analyze fluctuations in the emotional patterns of the subject and set an optimal update frequency. In this way, by analyzing fluctuations in emotional patterns, the update frequency of the learning data can be optimized and the accuracy of learning can be improved. Some or all of the above-described processing in the learning unit may be performed using, for example, AI, or may be performed without using AI. For example, the learning unit can input data on fluctuations in emotional patterns into an AI model, and the AI ​​can adjust the update frequency.

[0055] The emotion understanding support system includes a learning unit that updates learning data by reflecting feedback from the subject during learning. The learning unit updates the learning data by reflecting feedback from the subject during learning. The updating is performed, for example, through collecting and analyzing feedback, but is not limited to this example. For example, the learning unit updates the learning data based on feedback provided by the subject. The learning unit can also analyze the feedback from the subject and adjust the learning algorithm. The learning unit can also improve the accuracy of the learning data by reflecting the feedback. In this way, the learning data can be updated by reflecting the feedback, and the accuracy of learning can be improved. Some or all of the above-mentioned processing in the learning unit may be performed using, for example, AI, or may be performed without using AI. For example, the learning unit can input feedback data into an AI model, and the AI ​​can update the learning data.

[0056] The emotion understanding support system includes a learning unit that, during learning, weights the learning data based on the time when the subject submitted the emotion data. The learning unit, during learning, weights the learning data based on the time when the subject submitted the emotion data. Weighting is performed, for example, based on the importance or freshness of the data, but is not limited to such examples. For example, the learning unit weights the learning data based on the time when the subject submitted the emotion data. The learning unit can also adjust the learning algorithm taking into account the time when the subject submitted the emotion data. The learning unit can also set a priority for the learning data based on the time when the subject submitted the emotion data. In this way, weighting the learning data based on the time when the emotion data was submitted can improve the accuracy of learning. Some or all of the above-described processing in the learning unit may be performed using, for example, AI, or may be performed without using AI. For example, the learning unit can input the time when the emotion data was submitted into an AI model, and the AI ​​can perform the weighting.

[0057] The emotion understanding support system includes a learning unit that, during learning, integrates information from different data sources to enrich the learning data. The learning unit, during learning, integrates information from different data sources to enrich the learning data. Integration is performed, for example, through data collection and integration, but is not limited to this example. For example, the learning unit integrates emotion data from different data sources to learn. The learning unit can also enrich the learning data based on information from different data sources. The learning unit can also improve the accuracy of the learning algorithm using data from different data sources. In this way, by integrating information from different data sources, the learning data can be enriched and the accuracy of learning can be improved. Some or all of the above-mentioned processing in the learning unit may be performed using, for example, AI, or may be performed without using AI. For example, the learning unit can input information from different data sources into an AI model, and the AI ​​can perform data integration.

[0058] The emotion understanding support system includes a learning unit that adjusts a learning algorithm during learning, taking into account the social background of the subject. The learning unit adjusts the learning algorithm during learning, taking into account the social background (culture, habits, etc.) of the subject. The adjustment is performed, for example, through parameter setting or weighting of the algorithm, but is not limited to such examples. For example, the learning unit adjusts the learning algorithm taking into account the cultural background of the subject. The learning unit can also adjust the learning algorithm taking into account the habits and lifestyle of the subject. The learning unit can also optimize the learning algorithm based on the social background of the subject. In this way, by taking the social background into account, the learning algorithm can be optimized and the accuracy of learning can be improved. Some or all of the above-mentioned processing in the learning unit may be performed, for example, using AI, or may be performed without using AI. For example, the learning unit can input social background data into an AI model, and the AI ​​can adjust the algorithm.

[0059] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0060] The emotion understanding support system includes a collection unit that optimizes the collection method by referring to the subject's past emotional data during collection. The collection unit optimizes the collection method by referring to the subject's past emotional data during collection. Optimization is performed, for example, through algorithm adjustment or parameter setting, but is not limited to these examples. For example, the collection unit refers to situations in which the subject felt stressed in the past and focuses on collecting facial expressions and tone of voice in similar situations. The collection unit can also refer to situations in which the subject felt relaxed in the past and adjust the collection method based on the data from those situations. The collection unit can also refer to situations in which the subject felt excited in the past and optimize the collection method based on the data from those situations. By referring to past emotional data, the collection method can be optimized, enabling more accurate data collection. Some or all of the above-described processing in the collection unit may be performed using, for example, AI, or without AI. For example, the collection unit can input past emotional data into an AI model, and the AI ​​can suggest an optimal collection method.

[0061] The emotion understanding support system includes a collection unit that adjusts a collection means based on the subject's current environment during data collection. The collection unit adjusts the collection means based on the subject's current environment (e.g., indoors or outdoors, noise level) during data collection. The adjustment is performed, for example, by selecting a collection means or changing its settings, but is not limited to such examples. For example, the collection unit collects subtle changes in voice tone when the environment is quiet indoors. Furthermore, the collection unit can prioritize collecting changes in facial expressions when the environment is noisy outdoors. Furthermore, the collection unit can collect both facial expressions and voice tone in a balanced manner when the noise level is moderate. This enables more appropriate data collection by adjusting the collection means based on the current environment. Some or all of the above-described processing in the collection unit may be performed using, for example, AI, or without AI. For example, the collection unit can input current environmental data into an AI model, and the AI ​​can suggest the optimal collection means.

[0062] The emotion understanding support system includes a collection unit that corrects collected data during collection, taking into account the subject's physical condition. The collection unit corrects the collected data during collection, taking into account the subject's physical condition (fatigue, health condition, etc.). Correction is performed, for example, through data adjustment or filtering, but is not limited to such examples. For example, if the subject is tired, the collection unit may focus on collecting changes in facial expression and correct changes in voice tone. Furthermore, if the subject is in good health, the collection unit may collect both facial expression and voice tone in a balanced manner. Furthermore, if the subject is in poor health, the collection unit may focus on collecting changes in voice tone and correct changes in facial expression. This allows for more accurate data collection by correcting the collected data taking into account the subject's physical condition. Some or all of the above-described processing in the collection unit may be performed using, for example, AI, or without AI. For example, the collection unit may input physical condition data into an AI model, and the AI ​​may perform the correction of the collected data.

[0063] The emotion understanding support system includes a collection unit that, when collecting data, enhances the relevance of collected data by taking into account the geographical location information of the subject. The collection unit enhances the relevance of collected data by taking into account the geographical location information of the subject. The enhancement of relevance can be achieved, for example, through data selection or filtering, but is not limited to such examples. For example, when the subject is in a park, the collection unit can remove natural sounds to collect facial expressions and tone of voice. When the subject is in an office, the collection unit can also remove background conversation sounds to collect facial expressions and tone of voice. When the subject is at home, the collection unit can also remove household noise to collect facial expressions and tone of voice. This enhances the relevance of collected data by taking into account the geographical location information, enabling more accurate data collection. Some or all of the above-described processing by the collection unit may be performed using, or without, AI. For example, the collection unit can input geographical location information into an AI model, and the AI ​​can perform filtering to enhance the relevance of the collected data.

[0064] The emotion understanding support system includes a collection unit that analyzes the social media activities of a subject and complements related emotional data during collection. The collection unit analyzes the social media activities of the subject and complements related emotional data during collection. Completion is performed, for example, by adding or correcting data, but is not limited to such examples. For example, the collection unit analyzes the content posted by the subject on social media and complements emotional changes. The collection unit can also complement the emotional data by referring to the activities of the subject's friends on social media. The collection unit can also complement the emotional data based on the subject's check-in information on social media. In this way, by analyzing social media activities, related emotional data can be complemented and more accurate emotional analysis can be performed. Some or all of the above-mentioned processing in the collection unit may be performed using, for example, AI, or without AI. For example, the collection unit can input social media activity data into an AI model, and the AI ​​can complement the emotional data.

[0065] The emotion understanding support system includes a collection unit that customizes a collection method by reflecting the subject's past feedback during collection. The collection unit customizes the collection method by reflecting the subject's past feedback during collection. Customization is performed, for example, by selecting a collection means or changing settings, but is not limited to such examples. For example, the collection unit adjusts the collection method based on feedback provided by the subject in the past. The collection unit can also prioritize the use of a collection method that the subject previously preferred. The collection unit can also analyze the subject's past feedback and suggest an optimal collection method. This customizes the collection method by reflecting past feedback, enabling more accurate data collection. Some or all of the above-described processing in the collection unit may be performed using, for example, AI, or may be performed without using AI. For example, the collection unit can input past feedback data into an AI model, and the AI ​​can customize the collection method.

[0066] The emotion understanding support system includes an analysis unit that compares a subject's emotion pattern with past data during analysis to improve accuracy. The analysis unit compares the subject's emotion pattern with past data during analysis to improve accuracy. The improvement in accuracy can be achieved, for example, by improving the accuracy of the data or the algorithm, but is not limited to such examples. For example, the analysis unit compares and analyzes the subject's current emotion pattern based on the subject's past emotion data. The analysis unit can also refer to the subject's past emotion pattern to more accurately understand the subject's current emotion. The analysis unit can also use the subject's past emotion data to improve the accuracy of the analysis algorithm. This allows for improvement in the accuracy of emotion analysis by comparing with past data. Some or all of the above-described processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input past emotion data into an AI model, and the AI ​​can perform analysis to improve accuracy.

[0067] The emotion understanding support system includes an analysis unit that tracks the emotional fluctuations of a subject in real time during analysis and updates the analysis results. The analysis unit tracks the emotional fluctuations of the subject in real time during analysis and updates the analysis results. The tracking is performed, for example, by monitoring the emotional fluctuations in real time and updating the analysis results whenever a fluctuation occurs, but is not limited to this example. For example, the analysis unit updates the analysis results in real time whenever the subject's emotional fluctuations occur. The analysis unit can also track the emotional fluctuations of the subject in real time and provide the latest analysis results. The analysis unit can also immediately update the analysis results if the subject's emotional fluctuations suddenly change. This makes it possible to provide the latest analysis results by tracking emotional fluctuations in real time. Some or all of the above-described processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input real-time emotional data into an AI model, and the AI ​​can update the analysis results.

[0068] The emotion understanding support system includes an analysis unit that considers external factors that affect the subject's emotions during analysis. The analysis unit considers external factors (weather, time of day, etc.) that affect the subject's emotions during analysis. Consideration is performed, for example, by collecting data on external factors and reflecting them in emotion analysis, but is not limited to this example. For example, the analysis unit may consider the impact of bad weather on the subject's emotions during analysis. The analysis unit may also consider fluctuations in emotions over time during analysis. The analysis unit may also consider external factors (noise level, influence of surrounding people, etc.) during analysis. By taking external factors into account, more accurate emotion analysis is possible. Some or all of the above-described processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit may input external factor data into an AI model and reflect it in emotion analysis.

[0069] The emotion understanding support system includes an analysis unit that performs emotion analysis while taking into account the geographical background of the subject. The analysis unit performs emotion analysis while taking into account the geographical background of the subject. Consideration is performed, for example, by collecting data on the geographical background and reflecting it in the emotion analysis, but is not limited to this example. For example, if the subject is in an urban area, the analysis unit may consider stress factors specific to the city when analyzing. Furthermore, if the subject is in a natural environment, the analysis unit may also consider the relaxing effect of the natural environment when analyzing. Furthermore, if the subject is in a specific region, the analysis unit may consider the culture and customs specific to that region when analyzing. This allows for more accurate emotion analysis by taking into account the geographical background. Some or all of the above-described processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit may input geographical background data into an AI model and reflect it in the emotion analysis.

[0070] The emotion understanding support system includes an analysis unit that, during analysis, refers to literature and research data related to the subject to improve the accuracy of the analysis. The analysis unit, during analysis, refers to literature and research data related to the subject to improve the accuracy of the analysis. Referencing is performed, for example, through the collection and analysis of related literature and research data, but is not limited to such examples. For example, the analysis unit refers to the latest research data on the subject's emotions for analysis. The analysis unit can also adjust the analysis algorithm based on literature related to the subject's emotions. The analysis unit can also improve the accuracy of the analysis by referring to past research data on the subject's emotions. In this way, the analysis accuracy can be improved by referring to related literature and research data. Some or all of the above-mentioned processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input related literature and research data into an AI model, and the AI ​​can improve the accuracy of the analysis.

[0071] The emotion understanding support system includes an analysis unit that analyzes emotions taking into account the social background of the subject during analysis. The analysis unit analyzes emotions taking into account the social background (culture, customs, etc.) of the subject during analysis. This consideration is performed, for example, by collecting social background data and reflecting it in emotion analysis, but is not limited to this example. For example, the analysis unit analyzes emotions taking into account the cultural background of the subject. The analysis unit can also analyze emotions taking into account the customs and lifestyle of the subject. The analysis unit can also analyze emotional fluctuations based on the social background of the subject. This allows for more accurate emotion analysis by taking social background into consideration. Some or all of the above-described processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input social background data into an AI model and reflect it in emotion analysis.

[0072] The emotion understanding support system includes a providing unit that optimizes an information provision method by referring to the subject's past responses when providing information. The providing unit optimizes the information provision method by referring to the subject's past responses when providing information. Optimization is performed, for example, by selecting an information provision means or changing settings, but is not limited to such examples. For example, the providing unit preferentially uses an information provision method that the subject has previously preferred. The providing unit can also suggest an optimal information provision method based on the subject's past responses. The providing unit can also analyze the subject's past responses and optimize the information provision method. This optimizes the information provision method by referring to the past responses, enabling more appropriate information to be provided. Some or all of the above-described processing in the providing unit may be performed using, for example, AI, or may be performed without using AI. For example, the providing unit can input past response data into an AI model, and the AI ​​can optimize the information provision method.

[0073] The emotion understanding support system includes a providing unit that adjusts information provision based on the subject's current situation at the time of provision. The providing unit adjusts the information provision based on the subject's current situation (e.g., whether the subject is active or resting). The adjustment is performed, for example, by changing the timing or format of information provision, but is not limited to such examples. For example, the providing unit provides concise and to-the-point information when the subject is active. The providing unit can also provide detailed information when the subject is resting. The providing unit can also suggest an optimal information provision method based on the subject's current situation. This enables more appropriate information provision by adjusting the information provision based on the current situation. Some or all of the above-described processing by the providing unit may be performed using, for example, AI, or may be performed without using AI. For example, the providing unit can input current situation data into an AI model, and the AI ​​can adjust the information provision.

[0074] The emotion understanding support system includes a providing unit that corrects the provided information in consideration of the subject's physical condition when providing the information. The providing unit corrects the provided information in consideration of the subject's physical condition (fatigue, health condition, etc.) when providing the information. The correction is performed, for example, by changing the content or format of the information, but is not limited to such examples. For example, if the subject is tired, the providing unit can provide concise and to-the-point information. Furthermore, if the subject is in good health, the providing unit can provide detailed information. Furthermore, if the subject is not feeling well, the providing unit can provide information with a relaxing effect. In this way, correcting the provided information in consideration of the subject's physical condition enables more appropriate information to be provided. Some or all of the above-described processing by the providing unit may be performed using, for example, AI, or may be performed without using AI. For example, the providing unit can input physical condition data into an AI model, and the AI ​​can correct the provided information.

[0075] The emotion understanding support system includes a providing unit that provides highly relevant information by taking into account the geographical location information of the subject. The providing unit provides highly relevant information by taking into account the geographical location information of the subject. The improvement in relevance is achieved, for example, through information selection or filtering, but is not limited to such examples. For example, if the subject is in a specific area, the providing unit provides information related to that area. Furthermore, if the subject is traveling, the providing unit can also provide information related to the travel destination. Furthermore, if the subject is at home, the providing unit can also provide information about the area around the subject's home. This makes it possible to provide highly relevant information by taking into account the geographical location information. Some or all of the above-described processing by the providing unit may be performed using, for example, AI, or may be performed without using AI. For example, the providing unit can input geographical location information into an AI model to select highly relevant information.

[0076] The emotion understanding support system includes a providing unit that analyzes the social media activities of a subject and provides related information at the time of providing the information. The providing unit analyzes the social media activities of the subject and provides the related information at the time of providing the information. The analysis is performed, for example, through analyzing the content of social media posts and reactions, but is not limited to such an example. For example, the providing unit provides information about locations where the subject checked in on social media. The providing unit can also analyze the content of the subject's social media posts and provide related information. The providing unit can also provide related information by referring to the activities of the subject's friends on social media. In this way, it is possible to provide related information by analyzing social media activities. Some or all of the above-mentioned processing by the providing unit may be performed using, for example, AI, or may be performed without using AI. For example, the providing unit can input social media activity data into an AI model and select related information.

[0077] The emotion understanding support system includes a providing unit that customizes an information provision method by reflecting the subject's past feedback at the time of provision. The providing unit customizes the information provision method by reflecting the subject's past feedback at the time of provision. Customization is performed, for example, by selecting an information provision means or changing settings, but is not limited to such examples. For example, the providing unit preferentially uses an information provision method that the subject has previously preferred. The providing unit can also suggest an optimal information provision method based on the subject's past feedback. The providing unit can also analyze the subject's past feedback and customize the information provision method. This customizes the information provision method by reflecting the past feedback, enabling more appropriate information provision. Some or all of the above-described processing by the providing unit may be performed, for example, using AI or without AI. For example, the providing unit can input past feedback data into an AI model, and the AI ​​can customize the information provision method.

[0078] The emotion understanding support system includes a learning unit that optimizes a learning algorithm by referring to past learning data during learning. The learning unit optimizes the learning algorithm by referring to past learning data during learning. Optimization is performed, for example, through algorithm adjustment or parameter setting, but is not limited to such examples. For example, the learning unit adjusts the current learning algorithm based on past learning data. The learning unit can also find an optimal learning pattern by referring to past learning data. The learning unit can also improve the accuracy of the learning algorithm by using past learning data. In this way, the learning algorithm can be optimized by referring to past learning data, and the learning accuracy can be improved. Some or all of the above-described processing in the learning unit may be performed using, for example, AI, or may be performed without using AI. For example, the learning unit can input past learning data into an AI model, and the AI ​​can optimize the learning algorithm.

[0079] The emotion understanding support system includes a learning unit that analyzes fluctuations in the emotional patterns of a subject during learning and adjusts the update frequency of the learning data. The learning unit analyzes fluctuations in the emotional patterns of the subject during learning and adjusts the update frequency of the learning data. The adjustment is performed, for example, through setting the update frequency or weighting the data, but is not limited to such examples. For example, the learning unit increases the update frequency of the learning data when the emotional patterns of the subject fluctuate frequently. The learning unit can also decrease the update frequency of the learning data when the emotional patterns of the subject are stable. The learning unit can also analyze fluctuations in the emotional patterns of the subject and set an optimal update frequency. In this way, by analyzing fluctuations in emotional patterns, the update frequency of the learning data can be optimized and the accuracy of learning can be improved. Some or all of the above-described processing in the learning unit may be performed using, for example, AI, or may be performed without using AI. For example, the learning unit can input data on fluctuations in emotional patterns into an AI model, and the AI ​​can adjust the update frequency.

[0080] The emotion understanding support system includes a learning unit that updates learning data by reflecting feedback from the subject during learning. The learning unit updates the learning data by reflecting feedback from the subject during learning. The updating is performed, for example, through collecting and analyzing feedback, but is not limited to this example. For example, the learning unit updates the learning data based on feedback provided by the subject. The learning unit can also analyze the feedback from the subject and adjust the learning algorithm. The learning unit can also improve the accuracy of the learning data by reflecting the feedback. In this way, the learning data can be updated by reflecting the feedback, and the accuracy of learning can be improved. Some or all of the above-mentioned processing in the learning unit may be performed using, for example, AI, or may be performed without using AI. For example, the learning unit can input feedback data into an AI model, and the AI ​​can update the learning data.

[0081] The emotion understanding support system includes a learning unit that, during learning, weights the learning data based on the time when the subject submitted the emotion data. The learning unit, during learning, weights the learning data based on the time when the subject submitted the emotion data. Weighting is performed, for example, based on the importance or freshness of the data, but is not limited to such examples. For example, the learning unit weights the learning data based on the time when the subject submitted the emotion data. The learning unit can also adjust the learning algorithm taking into account the time when the subject submitted the emotion data. The learning unit can also set a priority for the learning data based on the time when the subject submitted the emotion data. In this way, weighting the learning data based on the time when the emotion data was submitted can improve the accuracy of learning. Some or all of the above-described processing in the learning unit may be performed using, for example, AI, or may be performed without using AI. For example, the learning unit can input the time when the emotion data was submitted into an AI model, and the AI ​​can perform the weighting.

[0082] The emotion understanding support system includes a learning unit that, during learning, integrates information from different data sources to enrich the learning data. The learning unit, during learning, integrates information from different data sources to enrich the learning data. Integration is performed, for example, through data collection and integration, but is not limited to this example. For example, the learning unit integrates emotion data from different data sources to learn. The learning unit can also enrich the learning data based on information from different data sources. The learning unit can also improve the accuracy of the learning algorithm using data from different data sources. In this way, by integrating information from different data sources, the learning data can be enriched and the accuracy of learning can be improved. Some or all of the above-mentioned processing in the learning unit may be performed using, for example, AI, or may be performed without using AI. For example, the learning unit can input information from different data sources into an AI model, and the AI ​​can perform data integration.

[0083] The emotion understanding support system includes a learning unit that adjusts a learning algorithm during learning, taking into account the social background of the subject. The learning unit adjusts the learning algorithm during learning, taking into account the social background (culture, habits, etc.) of the subject. The adjustment is performed, for example, through parameter setting or weighting of the algorithm, but is not limited to such examples. For example, the learning unit adjusts the learning algorithm taking into account the cultural background of the subject. The learning unit can also adjust the learning algorithm taking into account the habits and lifestyle of the subject. The learning unit can also optimize the learning algorithm based on the social background of the subject. In this way, by taking the social background into account, the learning algorithm can be optimized and the accuracy of learning can be improved. Some or all of the above-mentioned processing in the learning unit may be performed, for example, using AI, or may be performed without using AI. For example, the learning unit can input social background data into an AI model, and the AI ​​can adjust the algorithm.

[0084] The processing flow of the first embodiment will be briefly explained below.

[0085] Step 1: The collection unit collects the subject's facial expressions and tone of voice. For example, the collection unit uses a microphone and camera-type device to collect facial expressions such as smiling, anger, and sadness, as well as the pitch, low pitch, and intonation of the voice. Step 2: The analysis unit analyzes the information collected by the collection unit and determines the subject's emotions. For example, it uses emotion recognition algorithms to analyze facial expression data and tone of voice to determine whether the subject is smiling or angry. Step 3: The providing unit provides the analysis results obtained by the analyzing unit in real time. For example, the results can be provided to caregivers or child welfare workers via earphone-type devices, displayed on a display, or notified by voice. Step 4: The learning unit self-learns based on the information provided by the provider to respond to the care needs of each individual user. For example, it uses a machine learning algorithm to learn how a specific subject expresses emotions in specific situations and how they prefer to respond, and then suggests the optimal approach.

[0086] (Example 2) An emotion recognition support system according to an embodiment of the present invention collects a subject's facial expressions and tone of voice, and an AI analyzes this information to understand their emotions and provide the results in real time. The emotion recognition support system collects a subject's facial expressions and tone of voice, and an AI analyzes this information to understand their emotions. The analysis results are provided in real time through an earphone-type device and suggest appropriate countermeasures. Furthermore, the AI ​​self-learns to address the care needs of each user and suggests the optimal approach. For example, the emotion recognition support system uses a microphone and camera-type device to collect a subject's facial expressions and tone of voice. For example, if a subject is smiling while speaking, the system collects their facial expressions and tone of voice. This information is input into the AI. The emotion recognition support system then uses the AI ​​to analyze the collected information and understand the subject's emotions. For example, the AI ​​analyzes a smile and a cheerful tone of voice and determines that the subject is happy. The analysis results are provided in real time through an earphone-type device. Furthermore, the emotion recognition support system self-learns to address the care needs of each user. For example, the AI ​​learns what emotions a specific subject shows in a specific situation and suggests the optimal approach based on that information. As a result, the emotion understanding support system can reduce the burden on caregivers and child welfare workers and improve the quality of welfare services. As a result, the emotion understanding support system can reduce the burden on caregivers and child welfare workers and improve the quality of welfare services. For example, by understanding the emotions of the recipient in real time, appropriate responses can be made quickly, reducing the stress of caregivers and child welfare workers. In addition, by responding based on the recipient's emotions, the recipient's satisfaction level increases. Furthermore, by using AI to analyze emotions and suggest countermeasures, the work of caregivers and child welfare workers becomes more efficient, allowing high-quality welfare services to be provided even with fewer staff.

[0087] An emotion understanding support system according to an embodiment includes a collection unit, an analysis unit, a provision unit, and a learning unit. The collection unit collects facial expressions and tone of voice of a subject. The facial expressions of the subject include, but are not limited to, smiling, anger, sadness, etc. The collection unit collects the facial expressions and tone of voice of the subject using, for example, a microphone and camera-type device. The collection unit can also collect the tone of voice of the subject. For example, the collection unit collects the high and low pitches and intonation of the subject's voice. The analysis unit analyzes the information collected by the collection unit to understand the subject's emotions. The understanding of emotions is performed, for example, but is not limited to, using an emotion recognition algorithm. For example, the analysis unit analyzes the collected facial expression data to determine whether the subject is smiling. The analysis unit can also analyze the collected tone of voice to determine whether the subject is angry. The analysis unit can also combine and analyze the collected facial expression data and tone of voice to comprehensively understand the subject's emotions. The provision unit provides the analysis results obtained by the analysis unit in real time. The information is provided, for example, through an earphone-type device, but is not limited to this example. For example, the providing unit provides the analysis results to a caregiver or child welfare worker through the earphone-type device. The providing unit can also display the analysis results on a display. The providing unit can also notify the analysis results by voice. The learning unit self-learns to address the care needs of each user based on the information provided by the providing unit. The self-learning is performed, for example, using a machine learning algorithm, but is not limited to this example. For example, the learning unit learns what emotions a specific subject shows in a specific situation. The learning unit can also learn what kind of response a specific subject prefers in a specific situation. The learning unit can also present an optimal approach based on the learned information. As a result, the emotion understanding support system according to the embodiment can reduce the burden on caregivers and child welfare workers and improve the quality of welfare services.

[0088] The emotion understanding support system includes a collection unit that collects the facial expressions or tone of voice of a target person using a microphone / camera type device. The collection unit collects the facial expressions or tone of voice of a target person using the microphone / camera type device. Examples of the microphone / camera type device include, but are not limited to, high-resolution cameras and high-sensitivity microphones. For example, the collection unit collects the facial expressions of a target person in detail using a high-resolution camera. The collection unit can also accurately collect the tone of voice of a target person using a high-sensitivity microphone. The collection unit can also collect the natural facial expressions and tone of voice of a target person by devising a method for installing the microphone / camera type device. For example, the collection unit installs the camera at eye level of the target person to collect natural facial expressions. The collection unit can also install the microphone close to the target person's mouth to accurately collect the tone of voice. In this way, the microphone / camera type device can accurately collect the facial expressions and tone of voice of a target person.

[0089] The analysis unit can analyze the collected information to grasp the emotions of the subject. The analysis unit analyzes the collected information to grasp the emotions of the subject. The analysis is performed, for example, using a data analysis algorithm, but is not limited to such an example. For example, the analysis unit can analyze the collected facial expression data to determine whether the subject is smiling. The analysis unit can also analyze the collected tone of voice to determine whether the subject is angry. The analysis unit can also analyze the collected facial expression data and tone of voice in combination to comprehensively grasp the emotions of the subject. For example, the analysis unit can analyze the facial expression of the subject using a facial expression recognition algorithm. The analysis unit can also analyze the tone of voice of the subject using a voice analysis algorithm. The analysis unit can also analyze the facial expression data and tone of voice in combination to grasp the emotions of the subject using an emotion recognition algorithm. In this way, the emotions of the subject can be accurately grasped by analyzing the collected information.

[0090] The providing unit can provide the analysis results in real time through an earphone-type device. The providing unit provides the analysis results in real time through an earphone-type device. Examples of earphone-type devices include, but are not limited to, high-quality earphones and wireless earphones. For example, the providing unit provides the analysis results clearly using high-quality earphones. The providing unit can also provide the analysis results using wireless earphones. The providing unit can also provide the analysis results in real time by having a caregiver or child welfare worker wear the earphone-type device. For example, the providing unit notifies the caregiver or child welfare worker of the analysis results by voice. The providing unit can also notify the caregiver or child welfare worker of the analysis results by text message. The providing unit can also display the analysis results on a display. In this way, by providing the analysis results in real time through the earphone-type device, the caregiver or child welfare worker can respond quickly.

[0091] The learning unit can learn the emotional patterns of a specific subject and present an optimal approach. The learning unit can learn the emotional patterns of a specific subject and present an optimal approach. Learning can be performed, for example, using a machine learning algorithm, but is not limited to such an example. For example, the learning unit can learn what emotions a specific subject shows in a specific situation. The learning unit can also learn what kind of response a specific subject prefers in a specific situation. The learning unit can also present an optimal approach based on the learned information. For example, if a specific subject is feeling stressed, the learning unit can present an approach that has a relaxing effect. If a specific subject is relaxed, the learning unit can present an approach that provides detailed information. If a specific subject is excited, the learning unit can present a visually stimulating approach. In this way, by learning the emotional patterns of a specific subject, an optimal approach that meets individual care needs can be provided.

[0092] The provision unit can aim to reduce the burden on caregivers and child welfare workers and improve the quality of welfare services. The provision unit aims to reduce the burden on caregivers and child welfare workers and improve the quality of welfare services. Reduction of burden is achieved, for example, by shortening work time and reducing stress, but is not limited to such examples. For example, the provision unit reduces the work time of caregivers and child welfare workers by providing emotion analysis results in real time. The provision unit can also reduce stress on caregivers and child welfare workers by suggesting appropriate countermeasures. The provision unit also aims to improve the quality of welfare services. Improvement of quality is achieved, for example, by setting service evaluation criteria and improvement methods, but is not limited to such examples. For example, the provision unit sets service evaluation criteria based on the emotion analysis results. The provision unit can also suggest service improvement methods based on the emotion analysis results. This reduces the burden on caregivers and child welfare workers and improves the quality of welfare services.

[0093] The emotion understanding support system includes a collection unit that estimates a user's emotion and determines the priority of facial expressions or tones of voice to be collected based on the estimated user emotion. The collection unit estimates the user's emotion and determines the priority of facial expressions and tones of voice to be collected based on the estimated user emotion. The priority is determined based on, for example, importance or urgency, but is not limited to, such examples. For example, the collection unit prioritizes collecting subtle changes in facial expressions when the user is stressed. The collection unit can also prioritize collecting changes in voice tone when the user is relaxed. The collection unit can also collect both facial expressions and voice tone in a balanced manner when the user is excited. This enables more accurate emotion analysis by determining the priority of data to be collected based on the user's emotion. Some or all of the above-described processing in the collection unit may be performed using, for example, AI, or without AI. For example, the collection unit can analyze facial expressions and voice tone using an AI model to estimate the user's emotion, and determine the priority of data to be collected based on the results.

[0094] The emotion understanding support system includes a collection unit that optimizes the collection method by referring to the subject's past emotional data during collection. The collection unit optimizes the collection method by referring to the subject's past emotional data during collection. Optimization is performed, for example, through algorithm adjustment or parameter setting, but is not limited to these examples. For example, the collection unit refers to situations in which the subject felt stressed in the past and focuses on collecting facial expressions and tone of voice in similar situations. The collection unit can also refer to situations in which the subject felt relaxed in the past and adjust the collection method based on the data from those situations. The collection unit can also refer to situations in which the subject felt excited in the past and optimize the collection method based on the data from those situations. By referring to past emotional data, the collection method can be optimized, enabling more accurate data collection. Some or all of the above-described processing in the collection unit may be performed using, for example, AI, or without AI. For example, the collection unit can input past emotional data into an AI model, and the AI ​​can suggest an optimal collection method.

[0095] The emotion understanding support system includes a collection unit that adjusts a collection means based on the subject's current environment during data collection. The collection unit adjusts the collection means based on the subject's current environment (e.g., indoors or outdoors, noise level) during data collection. The adjustment is performed, for example, by selecting a collection means or changing its settings, but is not limited to such examples. For example, the collection unit collects subtle changes in voice tone when the environment is quiet indoors. Furthermore, the collection unit can prioritize collecting changes in facial expressions when the environment is noisy outdoors. Furthermore, the collection unit can collect both facial expressions and voice tone in a balanced manner when the noise level is moderate. This enables more appropriate data collection by adjusting the collection means based on the current environment. Some or all of the above-described processing in the collection unit may be performed using, for example, AI, or without AI. For example, the collection unit can input current environmental data into an AI model, and the AI ​​can suggest the optimal collection means.

[0096] The emotion understanding support system includes a collection unit that corrects collected data during collection, taking into account the subject's physical condition. The collection unit corrects the collected data during collection, taking into account the subject's physical condition (fatigue, health condition, etc.). Correction is performed, for example, through data adjustment or filtering, but is not limited to such examples. For example, if the subject is tired, the collection unit may focus on collecting changes in facial expression and correct changes in voice tone. Furthermore, if the subject is in good health, the collection unit may collect both facial expression and voice tone in a balanced manner. Furthermore, if the subject is in poor health, the collection unit may focus on collecting changes in voice tone and correct changes in facial expression. This allows for more accurate data collection by correcting the collected data taking into account the subject's physical condition. Some or all of the above-described processing in the collection unit may be performed using, for example, AI, or without AI. For example, the collection unit may input physical condition data into an AI model, and the AI ​​may perform the correction of the collected data.

[0097] The emotion understanding support system includes a collection unit that estimates a user's emotion and filters the collected data based on the estimated user emotion. The collection unit estimates the user's emotion and filters the collected data based on the estimated user emotion. Filtering can be performed, for example, through data selection and noise removal, but is not limited to these examples. For example, when a user is stressed, the collection unit removes noise to collect subtle changes in facial expression. When a user is relaxed, the collection unit can remove background noise and collect changes in vocal tone. When a user is excited, the collection unit can filter and collect both facial expression and vocal tone. This enables more accurate emotion analysis by filtering data based on the user's emotion. Some or all of the above-described processing in the collection unit may be performed using, for example, AI, or without AI. For example, the collection unit can input the user's emotion data into an AI model, and the AI ​​can perform filtering.

[0098] The emotion understanding support system includes a collection unit that, when collecting data, enhances the relevance of collected data by taking into account the geographical location information of the subject. The collection unit enhances the relevance of collected data by taking into account the geographical location information of the subject. The enhancement of relevance can be achieved, for example, through data selection or filtering, but is not limited to such examples. For example, when the subject is in a park, the collection unit can remove natural sounds to collect facial expressions and tone of voice. When the subject is in an office, the collection unit can also remove background conversation sounds to collect facial expressions and tone of voice. When the subject is at home, the collection unit can also remove household noise to collect facial expressions and tone of voice. This enhances the relevance of collected data by taking into account the geographical location information, enabling more accurate data collection. Some or all of the above-described processing by the collection unit may be performed using, or without, AI. For example, the collection unit can input geographical location information into an AI model, and the AI ​​can perform filtering to enhance the relevance of the collected data.

[0099] The emotion understanding support system includes a collection unit that analyzes the social media activities of a subject and complements related emotional data during collection. The collection unit analyzes the social media activities of the subject and complements related emotional data during collection. Completion is performed, for example, by adding or correcting data, but is not limited to such examples. For example, the collection unit analyzes the content posted by the subject on social media and complements emotional changes. The collection unit can also complement the emotional data by referring to the activities of the subject's friends on social media. The collection unit can also complement the emotional data based on the subject's check-in information on social media. In this way, by analyzing social media activities, related emotional data can be complemented and more accurate emotional analysis can be performed. Some or all of the above-mentioned processing in the collection unit may be performed using, for example, AI, or without AI. For example, the collection unit can input social media activity data into an AI model, and the AI ​​can complement the emotional data.

[0100] The emotion understanding support system includes a collection unit that customizes a collection method by reflecting the subject's past feedback during collection. The collection unit customizes the collection method by reflecting the subject's past feedback during collection. Customization is performed, for example, by selecting a collection means or changing settings, but is not limited to such examples. For example, the collection unit adjusts the collection method based on feedback provided by the subject in the past. The collection unit can also prioritize the use of a collection method that the subject previously preferred. The collection unit can also analyze the subject's past feedback and suggest an optimal collection method. This customizes the collection method by reflecting past feedback, enabling more accurate data collection. Some or all of the above-described processing in the collection unit may be performed using, for example, AI, or may be performed without using AI. For example, the collection unit can input past feedback data into an AI model, and the AI ​​can customize the collection method.

[0101] The emotion understanding support system includes an analysis unit that estimates a user's emotion and adjusts an analysis algorithm based on the estimated user emotion. The analysis unit estimates the user's emotion and adjusts the analysis algorithm based on the estimated user emotion. The adjustment is performed, for example, through parameter setting and weighting of the algorithm, but is not limited to such examples. For example, if the user is stressed, the analysis unit may use an algorithm that emphasizes subtle changes in emotion. Alternatively, if the user is relaxed, the analysis unit may use an algorithm that emphasizes the overall tone of the emotion. Alternatively, if the user is excited, the analysis unit may use an algorithm that emphasizes sudden changes in emotion. This enables more accurate emotion analysis by adjusting the analysis algorithm based on the user's emotion. Some or all of the above-described processing in the analysis unit may be performed using, for example, AI, or without AI. For example, the analysis unit may input the user's emotion data into an AI model, and the AI ​​may adjust the analysis algorithm.

[0102] The emotion understanding support system includes an analysis unit that compares a subject's emotion pattern with past data during analysis to improve accuracy. The analysis unit compares the subject's emotion pattern with past data during analysis to improve accuracy. The improvement in accuracy can be achieved, for example, by improving the accuracy of the data or the algorithm, but is not limited to such examples. For example, the analysis unit compares and analyzes the subject's current emotion pattern based on the subject's past emotion data. The analysis unit can also refer to the subject's past emotion pattern to more accurately understand the subject's current emotion. The analysis unit can also use the subject's past emotion data to improve the accuracy of the analysis algorithm. This allows for improvement in the accuracy of emotion analysis by comparing with past data. Some or all of the above-described processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input past emotion data into an AI model, and the AI ​​can perform analysis to improve accuracy.

[0103] The emotion understanding support system includes an analysis unit that tracks the emotional fluctuations of a subject in real time during analysis and updates the analysis results. The analysis unit tracks the emotional fluctuations of the subject in real time during analysis and updates the analysis results. The tracking is performed, for example, by monitoring the emotional fluctuations in real time and updating the analysis results whenever a fluctuation occurs, but is not limited to this example. For example, the analysis unit updates the analysis results in real time whenever the subject's emotional fluctuations occur. The analysis unit can also track the emotional fluctuations of the subject in real time and provide the latest analysis results. The analysis unit can also immediately update the analysis results if the subject's emotional fluctuations suddenly change. This makes it possible to provide the latest analysis results by tracking emotional fluctuations in real time. Some or all of the above-described processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input real-time emotional data into an AI model, and the AI ​​can update the analysis results.

[0104] The emotion understanding support system includes an analysis unit that considers external factors that affect the subject's emotions during analysis. The analysis unit considers external factors (weather, time of day, etc.) that affect the subject's emotions during analysis. Consideration is performed, for example, by collecting data on external factors and reflecting them in emotion analysis, but is not limited to this example. For example, the analysis unit may consider the impact of bad weather on the subject's emotions during analysis. The analysis unit may also consider fluctuations in emotions over time during analysis. The analysis unit may also consider external factors (noise level, influence of surrounding people, etc.) during analysis. By taking external factors into account, more accurate emotion analysis is possible. Some or all of the above-described processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit may input external factor data into an AI model and reflect it in emotion analysis.

[0105] The emotion understanding support system includes an analysis unit that estimates a user's emotion and adjusts the display method of the analysis results based on the estimated user emotion. The analysis unit estimates the user's emotion and adjusts the display method of the analysis results based on the estimated user emotion. The adjustment is performed, for example, by selecting a display format or changing settings, but is not limited to such examples. For example, the analysis unit provides a simple, highly visible display method when the user is stressed. The analysis unit can also provide a display method that includes detailed information when the user is relaxed. The analysis unit can also provide a visually stimulating display method when the user is excited. This allows for more appropriate information provision by adjusting the display method based on the user's emotion. Some or all of the above-described processing in the analysis unit may be performed using, for example, AI, or without AI. For example, the analysis unit can input the user's emotion data into an AI model, and the AI ​​can adjust the display method.

[0106] The emotion understanding support system includes an analysis unit that performs emotion analysis while taking into account the geographical background of the subject. The analysis unit performs emotion analysis while taking into account the geographical background of the subject. Consideration is performed, for example, by collecting data on the geographical background and reflecting it in the emotion analysis, but is not limited to this example. For example, if the subject is in an urban area, the analysis unit may consider stress factors specific to the city when analyzing. Furthermore, if the subject is in a natural environment, the analysis unit may also consider the relaxing effect of the natural environment when analyzing. Furthermore, if the subject is in a specific region, the analysis unit may consider the culture and customs specific to that region when analyzing. This allows for more accurate emotion analysis by taking into account the geographical background. Some or all of the above-described processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit may input geographical background data into an AI model and reflect it in the emotion analysis.

[0107] The emotion understanding support system includes an analysis unit that, during analysis, refers to literature and research data related to the subject to improve the accuracy of the analysis. The analysis unit, during analysis, refers to literature and research data related to the subject to improve the accuracy of the analysis. Referencing is performed, for example, through the collection and analysis of related literature and research data, but is not limited to such examples. For example, the analysis unit refers to the latest research data on the subject's emotions for analysis. The analysis unit can also adjust the analysis algorithm based on literature related to the subject's emotions. The analysis unit can also improve the accuracy of the analysis by referring to past research data on the subject's emotions. In this way, the analysis accuracy can be improved by referring to related literature and research data. Some or all of the above-mentioned processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input related literature and research data into an AI model, and the AI ​​can improve the accuracy of the analysis.

[0108] The emotion understanding support system includes an analysis unit that analyzes emotions taking into account the social background of the subject during analysis. The analysis unit analyzes emotions taking into account the social background (culture, customs, etc.) of the subject during analysis. This consideration is performed, for example, by collecting social background data and reflecting it in emotion analysis, but is not limited to this example. For example, the analysis unit analyzes emotions taking into account the cultural background of the subject. The analysis unit can also analyze emotions taking into account the customs and lifestyle of the subject. The analysis unit can also analyze emotional fluctuations based on the social background of the subject. This allows for more accurate emotion analysis by taking social background into consideration. Some or all of the above-described processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input social background data into an AI model and reflect it in emotion analysis.

[0109] The emotion understanding assistance system includes a providing unit that estimates a user's emotion and determines the priority of information to be provided based on the estimated user's emotion. The providing unit estimates the user's emotion and determines the priority of information to be provided based on the estimated user's emotion. The priority determination is performed, for example, based on the importance or urgency of the information, but is not limited to such examples. For example, if the user is feeling stressed, the providing unit may prioritize providing information that has a relaxing effect. Furthermore, if the user is relaxed, the providing unit may prioritize providing detailed information. Furthermore, if the user is excited, the providing unit may prioritize providing visually stimulating information. This enables more appropriate information to be provided by determining the priority of information based on the user's emotion. Some or all of the above-described processing by the providing unit may be performed using, for example, AI, or may be performed without using AI. For example, the providing unit may input user emotion data into an AI model, and the AI ​​may determine the priority of information.

[0110] The emotion understanding support system includes a providing unit that optimizes an information provision method by referring to the subject's past responses when providing information. The providing unit optimizes the information provision method by referring to the subject's past responses when providing information. Optimization is performed, for example, by selecting an information provision means or changing settings, but is not limited to such examples. For example, the providing unit preferentially uses an information provision method that the subject has previously preferred. The providing unit can also suggest an optimal information provision method based on the subject's past responses. The providing unit can also analyze the subject's past responses and optimize the information provision method. This optimizes the information provision method by referring to the past responses, enabling more appropriate information to be provided. Some or all of the above-described processing in the providing unit may be performed using, for example, AI, or may be performed without using AI. For example, the providing unit can input past response data into an AI model, and the AI ​​can optimize the information provision method.

[0111] The emotion understanding support system includes a providing unit that adjusts information provision based on the subject's current situation at the time of provision. The providing unit adjusts the information provision based on the subject's current situation (e.g., whether the subject is active or resting). The adjustment is performed, for example, by changing the timing or format of information provision, but is not limited to such examples. For example, the providing unit provides concise and to-the-point information when the subject is active. The providing unit can also provide detailed information when the subject is resting. The providing unit can also suggest an optimal information provision method based on the subject's current situation. This enables more appropriate information provision by adjusting the information provision based on the current situation. Some or all of the above-described processing by the providing unit may be performed using, for example, AI, or may be performed without using AI. For example, the providing unit can input current situation data into an AI model, and the AI ​​can adjust the information provision.

[0112] The emotion understanding support system includes a providing unit that corrects the provided information in consideration of the subject's physical condition when providing the information. The providing unit corrects the provided information in consideration of the subject's physical condition (fatigue, health condition, etc.) when providing the information. The correction is performed, for example, by changing the content or format of the information, but is not limited to such examples. For example, if the subject is tired, the providing unit can provide concise and to-the-point information. Furthermore, if the subject is in good health, the providing unit can provide detailed information. Furthermore, if the subject is not feeling well, the providing unit can provide information with a relaxing effect. In this way, correcting the provided information in consideration of the subject's physical condition enables more appropriate information to be provided. Some or all of the above-described processing by the providing unit may be performed using, for example, AI, or may be performed without using AI. For example, the providing unit can input physical condition data into an AI model, and the AI ​​can correct the provided information.

[0113] The emotion understanding assistance system includes a providing unit that estimates a user's emotion and adjusts the format of information to be provided based on the estimated user's emotion. The providing unit estimates the user's emotion and adjusts the format of information to be provided based on the estimated user's emotion. The adjustment is performed, for example, by changing the display format of the information or the notification method, but is not limited to such examples. For example, if the user is feeling stressed, the providing unit may provide information in a visually simple format. If the user is relaxed, the providing unit may provide information in a format including detailed graphics and diagrams. If the user is excited, the providing unit may provide information in a visually stimulating format. This allows for more appropriate information provision by adjusting the format of information based on the user's emotion. Some or all of the above-described processing by the providing unit may be performed using, for example, AI, or may be performed without using AI. For example, the providing unit may input the user's emotion data into an AI model, and the AI ​​may adjust the format of the information.

[0114] The emotion understanding support system includes a providing unit that provides highly relevant information by taking into account the geographical location information of the subject. The providing unit provides highly relevant information by taking into account the geographical location information of the subject. The improvement in relevance is achieved, for example, through information selection or filtering, but is not limited to such examples. For example, if the subject is in a specific area, the providing unit provides information related to that area. Furthermore, if the subject is traveling, the providing unit can also provide information related to the travel destination. Furthermore, if the subject is at home, the providing unit can also provide information about the area around the subject's home. This makes it possible to provide highly relevant information by taking into account the geographical location information. Some or all of the above-described processing by the providing unit may be performed using, for example, AI, or may be performed without using AI. For example, the providing unit can input geographical location information into an AI model to select highly relevant information.

[0115] The emotion understanding support system includes a providing unit that analyzes the social media activities of a subject and provides related information at the time of providing the information. The providing unit analyzes the social media activities of the subject and provides the related information at the time of providing the information. The analysis is performed, for example, through analyzing the content of social media posts and reactions, but is not limited to such an example. For example, the providing unit provides information about locations where the subject checked in on social media. The providing unit can also analyze the content of the subject's social media posts and provide related information. The providing unit can also provide related information by referring to the activities of the subject's friends on social media. In this way, it is possible to provide related information by analyzing social media activities. Some or all of the above-mentioned processing by the providing unit may be performed using, for example, AI, or may be performed without using AI. For example, the providing unit can input social media activity data into an AI model and select related information.

[0116] The emotion understanding support system includes a providing unit that customizes an information provision method by reflecting the subject's past feedback at the time of provision. The providing unit customizes the information provision method by reflecting the subject's past feedback at the time of provision. Customization is performed, for example, by selecting an information provision means or changing settings, but is not limited to such examples. For example, the providing unit preferentially uses an information provision method that the subject has previously preferred. The providing unit can also suggest an optimal information provision method based on the subject's past feedback. The providing unit can also analyze the subject's past feedback and customize the information provision method. This customizes the information provision method by reflecting the past feedback, enabling more appropriate information provision. Some or all of the above-described processing by the providing unit may be performed, for example, using AI or without AI. For example, the providing unit can input past feedback data into an AI model, and the AI ​​can customize the information provision method.

[0117] The emotion understanding support system includes a learning unit that estimates a user's emotion and selects learning data based on the estimated user's emotion. The learning unit estimates the user's emotion and selects learning data based on the estimated user's emotion. The selection is performed, for example, by determining the type and priority of the learning data, but is not limited to such an example. For example, if the user is feeling stressed, the learning unit may prioritize learning data related to stress reduction. Furthermore, if the user is relaxed, the learning unit may prioritize learning data that has a relaxing effect. Furthermore, if the user is excited, the learning unit may prioritize learning data that calms excitement. This enables more effective learning by selecting learning data based on the user's emotion. Some or all of the above-described processing in the learning unit may be performed using, for example, AI, or may be performed without using AI. For example, the learning unit may input the user's emotion data into an AI model, and the AI ​​may select the learning data.

[0118] The emotion understanding support system includes a learning unit that optimizes a learning algorithm by referring to past learning data during learning. The learning unit optimizes the learning algorithm by referring to past learning data during learning. Optimization is performed, for example, through algorithm adjustment or parameter setting, but is not limited to such examples. For example, the learning unit adjusts the current learning algorithm based on past learning data. The learning unit can also find an optimal learning pattern by referring to past learning data. The learning unit can also improve the accuracy of the learning algorithm by using past learning data. In this way, the learning algorithm can be optimized by referring to past learning data, and the learning accuracy can be improved. Some or all of the above-described processing in the learning unit may be performed using, for example, AI, or may be performed without using AI. For example, the learning unit can input past learning data into an AI model, and the AI ​​can optimize the learning algorithm.

[0119] The emotion understanding support system includes a learning unit that analyzes fluctuations in the emotional patterns of a subject during learning and adjusts the update frequency of the learning data. The learning unit analyzes fluctuations in the emotional patterns of the subject during learning and adjusts the update frequency of the learning data. The adjustment is performed, for example, through setting the update frequency or weighting the data, but is not limited to such examples. For example, the learning unit increases the update frequency of the learning data when the emotional patterns of the subject fluctuate frequently. The learning unit can also decrease the update frequency of the learning data when the emotional patterns of the subject are stable. The learning unit can also analyze fluctuations in the emotional patterns of the subject and set an optimal update frequency. In this way, by analyzing fluctuations in emotional patterns, the update frequency of the learning data can be optimized and the accuracy of learning can be improved. Some or all of the above-described processing in the learning unit may be performed using, for example, AI, or may be performed without using AI. For example, the learning unit can input data on fluctuations in emotional patterns into an AI model, and the AI ​​can adjust the update frequency.

[0120] The emotion understanding support system includes a learning unit that updates learning data by reflecting feedback from the subject during learning. The learning unit updates the learning data by reflecting feedback from the subject during learning. The updating is performed, for example, through collecting and analyzing feedback, but is not limited to this example. For example, the learning unit updates the learning data based on feedback provided by the subject. The learning unit can also analyze the feedback from the subject and adjust the learning algorithm. The learning unit can also improve the accuracy of the learning data by reflecting the feedback. In this way, the learning data can be updated by reflecting the feedback, and the accuracy of learning can be improved. Some or all of the above-mentioned processing in the learning unit may be performed using, for example, AI, or may be performed without using AI. For example, the learning unit can input feedback data into an AI model, and the AI ​​can update the learning data.

[0121] The emotion understanding assistance system includes a learning unit that estimates a user's emotion and adjusts the frequency of learning based on the estimated user emotion. The learning unit estimates the user's emotion and adjusts the frequency of learning based on the estimated user emotion. The adjustment is performed, for example, through setting the timing and frequency of learning, but is not limited to such an example. For example, the learning unit increases the frequency of learning when the user is stressed. The learning unit can also decrease the frequency of learning when the user is relaxed. The learning unit can also adjust the frequency of learning when the user is excited. This enables more effective learning by adjusting the frequency of learning based on the user's emotion. Some or all of the above-described processing in the learning unit may be performed using, for example, AI, or may be performed without using AI. For example, the learning unit can input user emotion data into an AI model, and the AI ​​can adjust the frequency of learning.

[0122] The emotion understanding support system includes a learning unit that, during learning, weights the learning data based on the time when the subject submitted the emotion data. The learning unit, during learning, weights the learning data based on the time when the subject submitted the emotion data. Weighting is performed, for example, based on the importance or freshness of the data, but is not limited to such examples. For example, the learning unit weights the learning data based on the time when the subject submitted the emotion data. The learning unit can also adjust the learning algorithm taking into account the time when the subject submitted the emotion data. The learning unit can also set a priority for the learning data based on the time when the subject submitted the emotion data. In this way, weighting the learning data based on the time when the emotion data was submitted can improve the accuracy of learning. Some or all of the above-described processing in the learning unit may be performed using, for example, AI, or may be performed without using AI. For example, the learning unit can input the time when the emotion data was submitted into an AI model, and the AI ​​can perform the weighting.

[0123] The emotion understanding support system includes a learning unit that, during learning, integrates information from different data sources to enrich the learning data. The learning unit, during learning, integrates information from different data sources to enrich the learning data. Integration is performed, for example, through data collection and integration, but is not limited to this example. For example, the learning unit integrates emotion data from different data sources to learn. The learning unit can also enrich the learning data based on information from different data sources. The learning unit can also improve the accuracy of the learning algorithm using data from different data sources. In this way, by integrating information from different data sources, the learning data can be enriched and the accuracy of learning can be improved. Some or all of the above-mentioned processing in the learning unit may be performed using, for example, AI, or may be performed without using AI. For example, the learning unit can input information from different data sources into an AI model, and the AI ​​can perform data integration.

[0124] The emotion understanding support system includes a learning unit that adjusts a learning algorithm during learning, taking into account the social background of the subject. The learning unit adjusts the learning algorithm during learning, taking into account the social background (culture, habits, etc.) of the subject. The adjustment is performed, for example, through parameter setting or weighting of the algorithm, but is not limited to such examples. For example, the learning unit adjusts the learning algorithm taking into account the cultural background of the subject. The learning unit can also adjust the learning algorithm taking into account the habits and lifestyle of the subject. The learning unit can also optimize the learning algorithm based on the social background of the subject. In this way, by taking the social background into account, the learning algorithm can be optimized and the accuracy of learning can be improved. Some or all of the above-mentioned processing in the learning unit may be performed, for example, using AI, or may be performed without using AI. For example, the learning unit can input social background data into an AI model, and the AI ​​can adjust the algorithm. === Hard Collateral 1-1 === Each of the multiple elements, including the collection unit, analysis unit, provision unit, and learning 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 collection unit collects the subject's facial expressions and tone of voice using the camera 42 and microphone 38B of the smart device 14. The analysis unit is realized, for example, by the specific processing unit 290 of the data processing device 12, and analyzes the collected information to grasp emotions. The provision unit is realized, for example, by the control unit 46A of the smart device 14, and provides analysis results in real time through an earphone-type device. The learning unit is realized, for example, by the specific processing unit 290 of the data processing device 12, and performs self-learning to address the care needs of each individual user. === Hard Collateral 1-2 === Each of the multiple elements, including the collection unit, analysis unit, provision unit, and learning 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 collection unit collects the subject's facial expressions and tone of voice using the camera 42 and microphone 238 of the smart glasses 214. The analysis unit is realized, for example, by the specific processing unit 290 of the data processing device 12, and analyzes the collected information to grasp emotions. The provision unit is realized, for example, by the control unit 46A of the smart glasses 214, and provides analysis results in real time through an earphone-type device. The learning unit is realized, for example, by the specific processing unit 290 of the data processing device 12, and performs self-learning to address the care needs of each individual user. === Hard Collateral 1-3 === Each of the multiple elements including the collection unit, analysis unit, provision unit, and learning 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 collection unit collects the subject's facial expressions and tone of voice using the camera 42 and microphone 238 of the headset-type terminal 314. The analysis unit is realized, for example, by the specific processing unit 290 of the data processing device 12, and analyzes the collected information to grasp emotions. The provision unit is realized, for example, by the control unit 46A of the headset-type terminal 314, and provides analysis results in real time via an earphone-type device. The learning unit is realized, for example, by the specific processing unit 290 of the data processing device 12, and performs self-learning to address the care needs of each individual user. === Hard Collateral 1-4 === Each of the multiple elements including the collection unit, analysis unit, provision unit, and learning unit described above is realized, for example, by at least one of the robot 414 and the data processing device 12. For example, the collection unit collects the facial expressions and tone of voice of the subject using the camera 42 and microphone 238 of the robot 414. The analysis unit is realized, for example, by the specific processing unit 290 of the data processing device 12, and analyzes the collected information to grasp emotions. The provision unit is realized, for example, by the control unit 46A of the robot 414, and provides analysis results in real time through an earphone-type device. The learning unit is realized, for example, by the specific processing unit 290 of the data processing device 12, and performs self-learning to address the care needs of each individual user.

[0125] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0126] The emotion understanding support system includes a collection unit that estimates a user's emotion and determines the priority of facial expressions or tones of voice to be collected based on the estimated user emotion. The collection unit estimates the user's emotion and determines the priority of facial expressions and tones of voice to be collected based on the estimated user emotion. The priority is determined based on, for example, importance or urgency, but is not limited to, such examples. For example, the collection unit prioritizes collecting subtle changes in facial expressions when the user is stressed. The collection unit can also prioritize collecting changes in voice tone when the user is relaxed. The collection unit can also collect both facial expressions and voice tone in a balanced manner when the user is excited. This enables more accurate emotion analysis by determining the priority of data to be collected based on the user's emotion. Some or all of the above-described processing in the collection unit may be performed using, for example, AI, or without AI. For example, the collection unit can analyze facial expressions and voice tone using an AI model to estimate the user's emotion, and determine the priority of data to be collected based on the results.

[0127] The emotion understanding support system includes a collection unit that optimizes the collection method by referring to the subject's past emotional data during collection. The collection unit optimizes the collection method by referring to the subject's past emotional data during collection. Optimization is performed, for example, through algorithm adjustment or parameter setting, but is not limited to these examples. For example, the collection unit refers to situations in which the subject felt stressed in the past and focuses on collecting facial expressions and tone of voice in similar situations. The collection unit can also refer to situations in which the subject felt relaxed in the past and adjust the collection method based on the data from those situations. The collection unit can also refer to situations in which the subject felt excited in the past and optimize the collection method based on the data from those situations. By referring to past emotional data, the collection method can be optimized, enabling more accurate data collection. Some or all of the above-described processing in the collection unit may be performed using, for example, AI, or without AI. For example, the collection unit can input past emotional data into an AI model, and the AI ​​can suggest an optimal collection method.

[0128] The emotion understanding support system includes a collection unit that adjusts a collection means based on the subject's current environment during data collection. The collection unit adjusts the collection means based on the subject's current environment (e.g., indoors or outdoors, noise level) during data collection. The adjustment is performed, for example, by selecting a collection means or changing its settings, but is not limited to such examples. For example, the collection unit collects subtle changes in voice tone when the environment is quiet indoors. Furthermore, the collection unit can prioritize collecting changes in facial expressions when the environment is noisy outdoors. Furthermore, the collection unit can collect both facial expressions and voice tone in a balanced manner when the noise level is moderate. This enables more appropriate data collection by adjusting the collection means based on the current environment. Some or all of the above-described processing in the collection unit may be performed using, for example, AI, or without AI. For example, the collection unit can input current environmental data into an AI model, and the AI ​​can suggest the optimal collection means.

[0129] The emotion understanding support system includes a collection unit that corrects collected data during collection, taking into account the subject's physical condition. The collection unit corrects the collected data during collection, taking into account the subject's physical condition (fatigue, health condition, etc.). Correction is performed, for example, through data adjustment or filtering, but is not limited to such examples. For example, if the subject is tired, the collection unit may focus on collecting changes in facial expression and correct changes in voice tone. Furthermore, if the subject is in good health, the collection unit may collect both facial expression and voice tone in a balanced manner. Furthermore, if the subject is in poor health, the collection unit may focus on collecting changes in voice tone and correct changes in facial expression. This allows for more accurate data collection by correcting the collected data taking into account the subject's physical condition. Some or all of the above-described processing in the collection unit may be performed using, for example, AI, or without AI. For example, the collection unit may input physical condition data into an AI model, and the AI ​​may perform the correction of the collected data.

[0130] The emotion understanding support system includes a collection unit that estimates a user's emotion and filters the collected data based on the estimated user emotion. The collection unit estimates the user's emotion and filters the collected data based on the estimated user emotion. Filtering can be performed, for example, through data selection and noise removal, but is not limited to these examples. For example, when a user is stressed, the collection unit removes noise to collect subtle changes in facial expression. When a user is relaxed, the collection unit can remove background noise and collect changes in vocal tone. When a user is excited, the collection unit can filter and collect both facial expression and vocal tone. This enables more accurate emotion analysis by filtering data based on the user's emotion. Some or all of the above-described processing in the collection unit may be performed using, for example, AI, or without AI. For example, the collection unit can input the user's emotion data into an AI model, and the AI ​​can perform filtering.

[0131] The emotion understanding support system includes a collection unit that, when collecting data, enhances the relevance of collected data by taking into account the geographical location information of the subject. The collection unit enhances the relevance of collected data by taking into account the geographical location information of the subject. The enhancement of relevance can be achieved, for example, through data selection or filtering, but is not limited to such examples. For example, when the subject is in a park, the collection unit can remove natural sounds to collect facial expressions and tone of voice. When the subject is in an office, the collection unit can also remove background conversation sounds to collect facial expressions and tone of voice. When the subject is at home, the collection unit can also remove household noise to collect facial expressions and tone of voice. This enhances the relevance of collected data by taking into account the geographical location information, enabling more accurate data collection. Some or all of the above-described processing by the collection unit may be performed using, or without, AI. For example, the collection unit can input geographical location information into an AI model, and the AI ​​can perform filtering to enhance the relevance of the collected data.

[0132] The emotion understanding support system includes a collection unit that analyzes the social media activities of a subject and complements related emotional data during collection. The collection unit analyzes the social media activities of the subject and complements related emotional data during collection. Completion is performed, for example, by adding or correcting data, but is not limited to such examples. For example, the collection unit analyzes the content posted by the subject on social media and complements emotional changes. The collection unit can also complement the emotional data by referring to the activities of the subject's friends on social media. The collection unit can also complement the emotional data based on the subject's check-in information on social media. In this way, by analyzing social media activities, related emotional data can be complemented and more accurate emotional analysis can be performed. Some or all of the above-mentioned processing in the collection unit may be performed using, for example, AI, or without AI. For example, the collection unit can input social media activity data into an AI model, and the AI ​​can complement the emotional data.

[0133] The emotion understanding support system includes a collection unit that customizes a collection method by reflecting the subject's past feedback during collection. The collection unit customizes the collection method by reflecting the subject's past feedback during collection. Customization is performed, for example, by selecting a collection means or changing settings, but is not limited to such examples. For example, the collection unit adjusts the collection method based on feedback provided by the subject in the past. The collection unit can also prioritize the use of a collection method that the subject previously preferred. The collection unit can also analyze the subject's past feedback and suggest an optimal collection method. This customizes the collection method by reflecting past feedback, enabling more accurate data collection. Some or all of the above-described processing in the collection unit may be performed using, for example, AI, or may be performed without using AI. For example, the collection unit can input past feedback data into an AI model, and the AI ​​can customize the collection method.

[0134] The emotion understanding support system includes an analysis unit that estimates a user's emotion and adjusts an analysis algorithm based on the estimated user emotion. The analysis unit estimates the user's emotion and adjusts the analysis algorithm based on the estimated user emotion. The adjustment is performed, for example, through parameter setting and weighting of the algorithm, but is not limited to such examples. For example, if the user is stressed, the analysis unit may use an algorithm that emphasizes subtle changes in emotion. Alternatively, if the user is relaxed, the analysis unit may use an algorithm that emphasizes the overall tone of the emotion. Alternatively, if the user is excited, the analysis unit may use an algorithm that emphasizes sudden changes in emotion. This enables more accurate emotion analysis by adjusting the analysis algorithm based on the user's emotion. Some or all of the above-described processing in the analysis unit may be performed using, for example, AI, or without AI. For example, the analysis unit may input the user's emotion data into an AI model, and the AI ​​may adjust the analysis algorithm.

[0135] The emotion understanding support system includes an analysis unit that compares a subject's emotion pattern with past data during analysis to improve accuracy. The analysis unit compares the subject's emotion pattern with past data during analysis to improve accuracy. The improvement in accuracy can be achieved, for example, by improving the accuracy of the data or the algorithm, but is not limited to such examples. For example, the analysis unit compares and analyzes the subject's current emotion pattern based on the subject's past emotion data. The analysis unit can also refer to the subject's past emotion pattern to more accurately understand the subject's current emotion. The analysis unit can also use the subject's past emotion data to improve the accuracy of the analysis algorithm. This allows for improvement in the accuracy of emotion analysis by comparing with past data. Some or all of the above-described processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input past emotion data into an AI model, and the AI ​​can perform analysis to improve accuracy.

[0136] The emotion understanding support system includes an analysis unit that tracks the emotional fluctuations of a subject in real time during analysis and updates the analysis results. The analysis unit tracks the emotional fluctuations of the subject in real time during analysis and updates the analysis results. The tracking is performed, for example, by monitoring the emotional fluctuations in real time and updating the analysis results whenever a fluctuation occurs, but is not limited to this example. For example, the analysis unit updates the analysis results in real time whenever the subject's emotional fluctuations occur. The analysis unit can also track the emotional fluctuations of the subject in real time and provide the latest analysis results. The analysis unit can also immediately update the analysis results if the subject's emotional fluctuations suddenly change. This makes it possible to provide the latest analysis results by tracking emotional fluctuations in real time. Some or all of the above-described processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input real-time emotional data into an AI model, and the AI ​​can update the analysis results.

[0137] The emotion understanding support system includes an analysis unit that considers external factors that affect the subject's emotions during analysis. The analysis unit considers external factors (weather, time of day, etc.) that affect the subject's emotions during analysis. Consideration is performed, for example, by collecting data on external factors and reflecting them in emotion analysis, but is not limited to this example. For example, the analysis unit may consider the impact of bad weather on the subject's emotions during analysis. The analysis unit may also consider fluctuations in emotions over time during analysis. The analysis unit may also consider external factors (noise level, influence of surrounding people, etc.) during analysis. By taking external factors into account, more accurate emotion analysis is possible. Some or all of the above-described processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit may input external factor data into an AI model and reflect it in emotion analysis.

[0138] The emotion understanding support system includes an analysis unit that estimates a user's emotion and adjusts the display method of the analysis results based on the estimated user emotion. The analysis unit estimates the user's emotion and adjusts the display method of the analysis results based on the estimated user emotion. The adjustment is performed, for example, by selecting a display format or changing settings, but is not limited to such examples. For example, the analysis unit provides a simple, highly visible display method when the user is stressed. The analysis unit can also provide a display method that includes detailed information when the user is relaxed. The analysis unit can also provide a visually stimulating display method when the user is excited. This allows for more appropriate information provision by adjusting the display method based on the user's emotion. Some or all of the above-described processing in the analysis unit may be performed using, for example, AI, or without AI. For example, the analysis unit can input the user's emotion data into an AI model, and the AI ​​can adjust the display method.

[0139] The emotion understanding support system includes an analysis unit that performs emotion analysis while taking into account the geographical background of the subject. The analysis unit performs emotion analysis while taking into account the geographical background of the subject. Consideration is performed, for example, by collecting data on the geographical background and reflecting it in the emotion analysis, but is not limited to this example. For example, if the subject is in an urban area, the analysis unit may consider stress factors specific to the city when analyzing. Furthermore, if the subject is in a natural environment, the analysis unit may also consider the relaxing effect of the natural environment when analyzing. Furthermore, if the subject is in a specific region, the analysis unit may consider the culture and customs specific to that region when analyzing. This allows for more accurate emotion analysis by taking into account the geographical background. Some or all of the above-described processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit may input geographical background data into an AI model and reflect it in the emotion analysis.

[0140] The emotion understanding support system includes an analysis unit that, during analysis, refers to literature and research data related to the subject to improve the accuracy of the analysis. The analysis unit, during analysis, refers to literature and research data related to the subject to improve the accuracy of the analysis. Referencing is performed, for example, through the collection and analysis of related literature and research data, but is not limited to such examples. For example, the analysis unit refers to the latest research data on the subject's emotions for analysis. The analysis unit can also adjust the analysis algorithm based on literature related to the subject's emotions. The analysis unit can also improve the accuracy of the analysis by referring to past research data on the subject's emotions. In this way, the analysis accuracy can be improved by referring to related literature and research data. Some or all of the above-mentioned processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input related literature and research data into an AI model, and the AI ​​can improve the accuracy of the analysis.

[0141] The emotion understanding support system includes an analysis unit that analyzes emotions taking into account the social background of the subject during analysis. The analysis unit analyzes emotions taking into account the social background (culture, customs, etc.) of the subject during analysis. This consideration is performed, for example, by collecting social background data and reflecting it in emotion analysis, but is not limited to this example. For example, the analysis unit analyzes emotions taking into account the cultural background of the subject. The analysis unit can also analyze emotions taking into account the customs and lifestyle of the subject. The analysis unit can also analyze emotional fluctuations based on the social background of the subject. This allows for more accurate emotion analysis by taking social background into consideration. Some or all of the above-described processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input social background data into an AI model and reflect it in emotion analysis.

[0142] The emotion understanding assistance system includes a providing unit that estimates a user's emotion and determines the priority of information to be provided based on the estimated user's emotion. The providing unit estimates the user's emotion and determines the priority of information to be provided based on the estimated user's emotion. The priority determination is performed, for example, based on the importance or urgency of the information, but is not limited to such examples. For example, if the user is feeling stressed, the providing unit may prioritize providing information that has a relaxing effect. Furthermore, if the user is relaxed, the providing unit may prioritize providing detailed information. Furthermore, if the user is excited, the providing unit may prioritize providing visually stimulating information. This enables more appropriate information to be provided by determining the priority of information based on the user's emotion. Some or all of the above-described processing by the providing unit may be performed using, for example, AI, or may be performed without using AI. For example, the providing unit may input user emotion data into an AI model, and the AI ​​may determine the priority of information.

[0143] The emotion understanding support system includes a providing unit that optimizes an information provision method by referring to the subject's past responses when providing information. The providing unit optimizes the information provision method by referring to the subject's past responses when providing information. Optimization is performed, for example, by selecting an information provision means or changing settings, but is not limited to such examples. For example, the providing unit preferentially uses an information provision method that the subject has previously preferred. The providing unit can also suggest an optimal information provision method based on the subject's past responses. The providing unit can also analyze the subject's past responses and optimize the information provision method. This optimizes the information provision method by referring to the past responses, enabling more appropriate information to be provided. Some or all of the above-described processing in the providing unit may be performed using, for example, AI, or may be performed without using AI. For example, the providing unit can input past response data into an AI model, and the AI ​​can optimize the information provision method.

[0144] The emotion understanding support system includes a providing unit that adjusts information provision based on the subject's current situation at the time of provision. The providing unit adjusts the information provision based on the subject's current situation (e.g., whether the subject is active or resting). The adjustment is performed, for example, by changing the timing or format of information provision, but is not limited to such examples. For example, the providing unit provides concise and to-the-point information when the subject is active. The providing unit can also provide detailed information when the subject is resting. The providing unit can also suggest an optimal information provision method based on the subject's current situation. This enables more appropriate information provision by adjusting the information provision based on the current situation. Some or all of the above-described processing by the providing unit may be performed using, for example, AI, or may be performed without using AI. For example, the providing unit can input current situation data into an AI model, and the AI ​​can adjust the information provision.

[0145] The emotion understanding support system includes a providing unit that corrects the provided information in consideration of the subject's physical condition when providing the information. The providing unit corrects the provided information in consideration of the subject's physical condition (fatigue, health condition, etc.) when providing the information. The correction is performed, for example, by changing the content or format of the information, but is not limited to such examples. For example, if the subject is tired, the providing unit can provide concise and to-the-point information. Furthermore, if the subject is in good health, the providing unit can provide detailed information. Furthermore, if the subject is not feeling well, the providing unit can provide information with a relaxing effect. In this way, correcting the provided information in consideration of the subject's physical condition enables more appropriate information to be provided. Some or all of the above-described processing by the providing unit may be performed using, for example, AI, or may be performed without using AI. For example, the providing unit can input physical condition data into an AI model, and the AI ​​can correct the provided information.

[0146] The emotion understanding assistance system includes a providing unit that estimates a user's emotion and adjusts the format of information to be provided based on the estimated user's emotion. The providing unit estimates the user's emotion and adjusts the format of information to be provided based on the estimated user's emotion. The adjustment is performed, for example, by changing the display format of the information or the notification method, but is not limited to such examples. For example, if the user is feeling stressed, the providing unit may provide information in a visually simple format. If the user is relaxed, the providing unit may provide information in a format including detailed graphics and diagrams. If the user is excited, the providing unit may provide information in a visually stimulating format. This allows for more appropriate information provision by adjusting the format of information based on the user's emotion. Some or all of the above-described processing by the providing unit may be performed using, for example, AI, or may be performed without using AI. For example, the providing unit may input the user's emotion data into an AI model, and the AI ​​may adjust the format of the information.

[0147] The emotion understanding support system includes a providing unit that provides highly relevant information by taking into account the geographical location information of the subject. The providing unit provides highly relevant information by taking into account the geographical location information of the subject. The improvement in relevance is achieved, for example, through information selection or filtering, but is not limited to such examples. For example, if the subject is in a specific area, the providing unit provides information related to that area. Furthermore, if the subject is traveling, the providing unit can also provide information related to the travel destination. Furthermore, if the subject is at home, the providing unit can also provide information about the area around the subject's home. This makes it possible to provide highly relevant information by taking into account the geographical location information. Some or all of the above-described processing by the providing unit may be performed using, for example, AI, or may be performed without using AI. For example, the providing unit can input geographical location information into an AI model to select highly relevant information.

[0148] The emotion understanding support system includes a providing unit that analyzes the social media activities of a subject and provides related information at the time of providing the information. The providing unit analyzes the social media activities of the subject and provides the related information at the time of providing the information. The analysis is performed, for example, through analyzing the content of social media posts and reactions, but is not limited to such an example. For example, the providing unit provides information about locations where the subject checked in on social media. The providing unit can also analyze the content of the subject's social media posts and provide related information. The providing unit can also provide related information by referring to the activities of the subject's friends on social media. In this way, it is possible to provide related information by analyzing social media activities. Some or all of the above-mentioned processing by the providing unit may be performed using, for example, AI, or may be performed without using AI. For example, the providing unit can input social media activity data into an AI model and select related information.

[0149] The emotion understanding support system includes a providing unit that customizes an information provision method by reflecting the subject's past feedback at the time of provision. The providing unit customizes the information provision method by reflecting the subject's past feedback at the time of provision. Customization is performed, for example, by selecting an information provision means or changing settings, but is not limited to such examples. For example, the providing unit preferentially uses an information provision method that the subject has previously preferred. The providing unit can also suggest an optimal information provision method based on the subject's past feedback. The providing unit can also analyze the subject's past feedback and customize the information provision method. This customizes the information provision method by reflecting the past feedback, enabling more appropriate information provision. Some or all of the above-described processing by the providing unit may be performed, for example, using AI or without AI. For example, the providing unit can input past feedback data into an AI model, and the AI ​​can customize the information provision method.

[0150] The emotion understanding support system includes a learning unit that estimates a user's emotion and selects learning data based on the estimated user's emotion. The learning unit estimates the user's emotion and selects learning data based on the estimated user's emotion. The selection is performed, for example, by determining the type and priority of the learning data, but is not limited to such an example. For example, if the user is feeling stressed, the learning unit may prioritize learning data related to stress reduction. Furthermore, if the user is relaxed, the learning unit may prioritize learning data that has a relaxing effect. Furthermore, if the user is excited, the learning unit may prioritize learning data that calms excitement. This enables more effective learning by selecting learning data based on the user's emotion. Some or all of the above-described processing in the learning unit may be performed using, for example, AI, or may be performed without using AI. For example, the learning unit may input the user's emotion data into an AI model, and the AI ​​may select the learning data.

[0151] The emotion understanding support system includes a learning unit that optimizes a learning algorithm by referring to past learning data during learning. The learning unit optimizes the learning algorithm by referring to past learning data during learning. Optimization is performed, for example, through algorithm adjustment or parameter setting, but is not limited to such examples. For example, the learning unit adjusts the current learning algorithm based on past learning data. The learning unit can also find an optimal learning pattern by referring to past learning data. The learning unit can also improve the accuracy of the learning algorithm by using past learning data. In this way, the learning algorithm can be optimized by referring to past learning data, and the learning accuracy can be improved. Some or all of the above-described processing in the learning unit may be performed using, for example, AI, or may be performed without using AI. For example, the learning unit can input past learning data into an AI model, and the AI ​​can optimize the learning algorithm.

[0152] The emotion understanding support system includes a learning unit that analyzes fluctuations in the emotional patterns of a subject during learning and adjusts the update frequency of the learning data. The learning unit analyzes fluctuations in the emotional patterns of the subject during learning and adjusts the update frequency of the learning data. The adjustment is performed, for example, through setting the update frequency or weighting the data, but is not limited to such examples. For example, the learning unit increases the update frequency of the learning data when the emotional patterns of the subject fluctuate frequently. The learning unit can also decrease the update frequency of the learning data when the emotional patterns of the subject are stable. The learning unit can also analyze fluctuations in the emotional patterns of the subject and set an optimal update frequency. In this way, by analyzing fluctuations in emotional patterns, the update frequency of the learning data can be optimized and the accuracy of learning can be improved. Some or all of the above-described processing in the learning unit may be performed using, for example, AI, or may be performed without using AI. For example, the learning unit can input data on fluctuations in emotional patterns into an AI model, and the AI ​​can adjust the update frequency.

[0153] The emotion understanding support system includes a learning unit that updates learning data by reflecting feedback from the subject during learning. The learning unit updates the learning data by reflecting feedback from the subject during learning. The updating is performed, for example, through collecting and analyzing feedback, but is not limited to this example. For example, the learning unit updates the learning data based on feedback provided by the subject. The learning unit can also analyze the feedback from the subject and adjust the learning algorithm. The learning unit can also improve the accuracy of the learning data by reflecting the feedback. In this way, the learning data can be updated by reflecting the feedback, and the accuracy of learning can be improved. Some or all of the above-mentioned processing in the learning unit may be performed using, for example, AI, or may be performed without using AI. For example, the learning unit can input feedback data into an AI model, and the AI ​​can update the learning data.

[0154] The emotion understanding assistance system includes a learning unit that estimates a user's emotion and adjusts the frequency of learning based on the estimated user emotion. The learning unit estimates the user's emotion and adjusts the frequency of learning based on the estimated user emotion. The adjustment is performed, for example, through setting the timing and frequency of learning, but is not limited to such an example. For example, the learning unit increases the frequency of learning when the user is stressed. The learning unit can also decrease the frequency of learning when the user is relaxed. The learning unit can also adjust the frequency of learning when the user is excited. This enables more effective learning by adjusting the frequency of learning based on the user's emotion. Some or all of the above-described processing in the learning unit may be performed using, for example, AI, or may be performed without using AI. For example, the learning unit can input user emotion data into an AI model, and the AI ​​can adjust the frequency of learning.

[0155] The emotion understanding support system includes a learning unit that, during learning, weights the learning data based on the time when the subject submitted the emotion data. The learning unit, during learning, weights the learning data based on the time when the subject submitted the emotion data. Weighting is performed, for example, based on the importance or freshness of the data, but is not limited to such examples. For example, the learning unit weights the learning data based on the time when the subject submitted the emotion data. The learning unit can also adjust the learning algorithm taking into account the time when the subject submitted the emotion data. The learning unit can also set a priority for the learning data based on the time when the subject submitted the emotion data. In this way, weighting the learning data based on the time when the emotion data was submitted can improve the accuracy of learning. Some or all of the above-described processing in the learning unit may be performed using, for example, AI, or may be performed without using AI. For example, the learning unit can input the time when the emotion data was submitted into an AI model, and the AI ​​can perform the weighting.

[0156] The emotion understanding support system includes a learning unit that, during learning, integrates information from different data sources to enrich the learning data. The learning unit, during learning, integrates information from different data sources to enrich the learning data. Integration is performed, for example, through data collection and integration, but is not limited to this example. For example, the learning unit integrates emotion data from different data sources to learn. The learning unit can also enrich the learning data based on information from different data sources. The learning unit can also improve the accuracy of the learning algorithm using data from different data sources. In this way, by integrating information from different data sources, the learning data can be enriched and the accuracy of learning can be improved. Some or all of the above-mentioned processing in the learning unit may be performed using, for example, AI, or may be performed without using AI. For example, the learning unit can input information from different data sources into an AI model, and the AI ​​can perform data integration.

[0157] The emotion understanding support system includes a learning unit that adjusts a learning algorithm during learning, taking into account the social background of the subject. The learning unit adjusts the learning algorithm during learning, taking into account the social background (culture, habits, etc.) of the subject. The adjustment is performed, for example, through parameter setting or weighting of the algorithm, but is not limited to such examples. For example, the learning unit adjusts the learning algorithm taking into account the cultural background of the subject. The learning unit can also adjust the learning algorithm taking into account the habits and lifestyle of the subject. The learning unit can also optimize the learning algorithm based on the social background of the subject. In this way, by taking the social background into account, the learning algorithm can be optimized and the accuracy of learning can be improved. Some or all of the above-mentioned processing in the learning unit may be performed, for example, using AI, or may be performed without using AI. For example, the learning unit can input social background data into an AI model, and the AI ​​can adjust the algorithm.

[0158] The processing flow of the second embodiment will be briefly explained below.

[0159] Step 1: The collection unit collects the subject's facial expressions and tone of voice. For example, the collection unit uses a microphone and camera-type device to collect facial expressions such as smiling, anger, and sadness, as well as the pitch, low pitch, and intonation of the voice. Step 2: The analysis unit analyzes the information collected by the collection unit and determines the subject's emotions. For example, it uses emotion recognition algorithms to analyze facial expression data and tone of voice to determine whether the subject is smiling or angry. Step 3: The providing unit provides the analysis results obtained by the analyzing unit in real time. For example, the results can be provided to caregivers or child welfare workers via earphone-type devices, displayed on a display, or notified by voice. Step 4: The learning unit self-learns based on the information provided by the provider to respond to the care needs of each individual user. For example, it uses a machine learning algorithm to learn how a specific subject expresses emotions in specific situations and how they prefer to respond, and then suggests the optimal approach.

[0160] 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.

[0161] 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 generative AIs include the data generation model 58, such as a neural network model (e.g., a neural network model), and a neural network model (e.g., a neural network model). The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating speech, text data indicating text, and image data indicating an image is also input to the data generation model 58. 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 a data format such as voice data and text data. 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 can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0162] 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.

[0163] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0164] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0165] 3, the data processing system 210 includes the data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0166] 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.

[0167] 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.

[0168] 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.

[0169] 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).

[0170] 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.

[0171] 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.

[0172] 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.

[0173] 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.

[0174] 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.

[0175] 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.

[0176] 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.

[0177] 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 containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. 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 a data format such as voice data and text 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 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 can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0178] 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.

[0179] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0180] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0181] 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.

[0182] 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.

[0183] 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.

[0184] 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.

[0185] 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).

[0186] 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.

[0187] 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.

[0188] 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.

[0189] 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.

[0190] 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.

[0191] 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.

[0192] 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.

[0193] 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 containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. 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 a data format such as voice data and text 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 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 can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0194] 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.

[0195] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0196] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0197] 7, the data processing system 410 includes a data processing device 12 and a robot 414. An example of the data processing device 12 is a server.

[0198] 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.

[0199] 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.

[0200] 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.

[0201] 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).

[0202] 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.

[0203] 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.

[0204] 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.

[0205] 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.

[0206] 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.

[0207] 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.

[0208] 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.

[0209] 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.

[0210] 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 containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. 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 a data format such as voice data and text 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 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 can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0211] 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.

[0212] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0213] 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.

[0214] 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.

[0215] 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.

[0216] 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).

[0217] 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.

[0218] 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."

[0219] 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.

[0220] 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.

[0221] 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.

[0222] 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.

[0223] 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.

[0224] 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.

[0225] 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.

[0226] 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.

[0227] 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.

[0228] 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.

[0229] 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.

[0230] 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.

[0231] [Explanation of symbols]

[0232] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Device 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robot

Claims

1. A collection unit that collects the subject's facial expressions and tone of voice, an analysis unit that analyzes the information collected by the collection unit and grasps the subject's emotions; a providing unit that provides the analysis results obtained by the analysis unit in real time; a learning unit that performs self-learning to meet the care needs of each individual user based on the information provided by the providing unit. system.

2. The collecting unit Use a microphone / camera-type device to collect the subject's facial expressions or tone of voice system.

3. The analysis unit Analyze the collected information and understand the subject's emotions system.

4. The providing unit Provides analysis results in real time through earphone-type devices system.

5. The learning unit Learn the emotional patterns of specific subjects and suggest the best approach system.

6. The providing unit Aiming to reduce the burden on caregivers and child welfare workers and improve the quality of welfare services system.

7. The collecting unit The user's emotions are estimated, and the priority of facial expressions or tones of voice to be collected is determined based on the estimated user's emotions. system.

8. The collecting unit When collecting data, optimize the collection method by referencing the subject's past emotional data. system.

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