system

Abstract

The system according to this embodiment aims to provide appropriate feedback to user statements. [Solution] The system according to the embodiment comprises an acquisition unit, an analysis unit, a generation unit, and a display unit. The acquisition unit acquires sound. The analysis unit analyzes the sound acquired by the acquisition unit. The generation unit generates feedback based on the results of the analysis by the analysis unit. The display unit displays the feedback generated by the generation unit.

Description

Technical Field

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

Background Art

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

Prior Art Documents

Patent Documents

[0003]

Patent Document 1

Summary of the Invention

Problems to be Solved by the Invention

[0004] In the prior art, there is a problem that it is difficult to notice whether one's own speech is correct.

[0005] The system according to the embodiment aims to provide appropriate feedback for a user's utterance.

Means for Solving the Problems

[0006] The system according to the embodiment includes an acquisition unit, an analysis unit, a generation unit, and a display unit. The acquisition unit acquires voice. The analysis unit analyzes the voice acquired by the acquisition unit. The generation unit generates feedback based on the result analyzed by the analysis unit. The display unit displays the feedback generated by the generation unit.

Effects of the Invention

[0007] The system according to this embodiment can provide appropriate feedback to user statements. [Brief explanation of the drawing]

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

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

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

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

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

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

[0014] In the following embodiments, the signed communication interface (I / F) is an interface that includes a communication processor and an antenna. The communication interface manages communication between multiple computers. Examples of communication standards applicable to the communication interface include wireless communication standards such as 5G (5th Generation Mobile Communication System), Wi-Fi (registered trademark), or Bluetooth (registered trademark).

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

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

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

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

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

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

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

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

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

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

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

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

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

[0028] (Example of form 1) The self-improvement promotion system according to an embodiment of the present invention is a system that provides feedback on the user's actions and statements to encourage self-improvement. The self-improvement promotion system acquires the user's spoken voice via a smartphone, and a multimodal generation AI analyzes it. Based on the analyzed voice, it compares it with past literature and makes an evaluation such as "That's wrong." Based on the result, it generates somewhat harsh words and displays them on the screen. At this time, it increases the influence on the user by displaying a face that the user will find easy to listen to (for example, parents or close friends). It also presents paid content for self-improvement. For example, the self-improvement promotion system acquires the user's spoken voice via a smartphone. For example, the acquired voice is analyzed by a multimodal generation AI and evaluated by comparing it with past literature. For example, it makes an evaluation such as "That's wrong." Based on the result, it generates somewhat harsh words and displays them on the screen. For example, it generates words such as "That's not right." At this time, it increases the influence on the user by displaying a face that the user will find easy to listen to (for example, parents or close friends). It also presents paid content for self-improvement. For example, it presents content such as self-help books or online courses. This allows the self-improvement promotion system to provide feedback on user behavior and statements, thereby encouraging self-improvement.

[0029] The self-improvement promotion system according to this embodiment comprises an acquisition unit, an analysis unit, a generation unit, and a display unit. The acquisition unit acquires the voice spoken by the user. The acquisition unit can acquire the voice using, for example, the microphone of a smartphone. The acquisition unit can also acquire the voice spoken by the user in real time. Furthermore, the acquisition unit can convert the voice into text data using speech recognition technology. For example, the acquisition unit acquires the voice using the microphone of a smartphone and converts it into text data using speech recognition technology. The analysis unit analyzes the voice acquired by the acquisition unit. The analysis unit can analyze the voice using, for example, a multimodal generation AI. Furthermore, the analysis unit can analyze the content of the voice and estimate emotions and intentions. Furthermore, the analysis unit can also perform evaluation by comparing it with past literature. For example, the analysis unit analyzes the voice using a multimodal generation AI and performs evaluation by comparing it with past literature. The generation unit generates feedback based on the results analyzed by the analysis unit. The generation unit can generate feedback using, for example, a generation AI. Furthermore, the generation unit can also generate somewhat harsh words. Furthermore, the generation unit can also generate feedback to display a face that makes it easier for the user to listen. For example, the generation unit uses a generation AI to generate feedback, and generates somewhat harsh words. The display unit displays the feedback generated by the generation unit. The display unit can, for example, display the feedback on a smartphone screen. The display unit can also display a face that makes it easier for the user to listen. Furthermore, the display unit can also present paid content for self-improvement. For example, the display unit displays feedback on a smartphone screen and displays a face that makes it easier for the user to listen. In this way, the self-improvement promotion system according to the embodiment can provide feedback on the user's actions and statements and encourage self-improvement.

[0030] The acquisition unit captures the voice spoken by the user. For example, the acquisition unit can acquire voice using the microphone of a smartphone. Specifically, it uses the smartphone's built-in microphone or an externally connected microphone to record the user's speech with high accuracy. The acquisition unit employs noise cancellation technology to maintain voice clarity and remove ambient noise. Furthermore, the acquisition unit can acquire the user's voice in real time. This allows for the immediate collection of voice data while the user is speaking, enabling rapid transfer to subsequent processing. In addition, the acquisition unit can convert the voice into text data using speech recognition technology. For example, the acquisition unit acquires voice using the smartphone's microphone and converts it into text data using speech recognition technology. The speech recognition technology utilizes a deep learning model, achieving high-precision text conversion by considering differences in pronunciation and accent. This allows the acquisition unit to accurately save the user's speech as text data, which can then be used for detailed analysis by the analysis unit.

[0031] The analysis unit analyzes the audio acquired by the acquisition unit. For example, the analysis unit can analyze the audio using a multimodal generation AI. Specifically, it simultaneously analyzes audio and text data to comprehensively evaluate the user's utterances, emotions, and intentions. The analysis unit analyzes acoustic features such as tone, pitch, and speed to estimate the user's emotional state. For example, an exhilarating tone may indicate excitement or joy, while a low, subdued tone may indicate sadness or fatigue. The analysis unit can also analyze the content of the audio to estimate emotions and intentions. This involves analyzing text data using natural language processing techniques to understand the context and meaning of the utterances. Furthermore, the analysis unit can evaluate the utterances in comparison to past literature. For example, the analysis unit can analyze the audio using a multimodal generation AI and evaluate it in comparison to past literature. This allows for evaluation of how closely the user's utterances match past successes and failures, enabling the provision of more specific feedback. Based on these analysis results, the analysis unit extracts information useful for the user's self-improvement and passes it to the generation unit.

[0032] The generation unit generates feedback based on the results analyzed by the analysis unit. The generation unit can generate feedback using, for example, a generation AI. Specifically, it automatically generates appropriate feedback according to the user's statements and emotional state. The generation AI learns from past data and literature and has an algorithm to provide the most effective feedback for the user. The generation unit can also generate somewhat harsh language. For example, if a user is seeking harsh criticism for self-improvement, the generation unit will provide feedback that meets that request. Furthermore, the generation unit can generate feedback that displays a face that makes it easier for the user to listen. For example, the generation unit uses a generation AI to generate feedback and generates somewhat harsh language. This makes it easier for the user to understand specific areas for improvement regarding their statements and actions. The generation unit can continuously improve its accuracy by collecting user reactions and feedback and reflecting them in subsequent feedback generation.

[0033] The display unit displays the feedback generated by the generation unit. The display unit can, for example, display the feedback on a smartphone screen. Specifically, it displays feedback on what the user has said as text or graphics, providing it in a visually easy-to-understand format. The display unit can also display a face that makes it easier for the user to listen. For example, it can use an avatar or character to provide feedback in a friendly way to the user. Furthermore, the display unit can also present paid content for self-improvement. For example, the display unit displays feedback on a smartphone screen and displays a face that makes it easier for the user to listen. As a result, the self-improvement promotion system according to the embodiment can provide feedback on the user's actions and statements and encourage self-improvement. The display unit can monitor the user's reactions in real time and adjust the feedback content as needed. As a result, the user can always improve themselves based on the latest information.

[0034] The display unit includes a face display unit that displays a face that the user finds easy to listen to. The face display unit can, for example, display a face that the user finds easy to listen to. The face display unit can, for example, display an avatar. The face display unit can also display a real face. Furthermore, the face display unit can display an illustrated face. For example, the face display unit can display an avatar and a face that the user finds easy to listen to. This can increase the influence on the user. Some or all of the above processing in the face display unit may be performed using AI, for example, or without AI. For example, the face display unit can estimate the user's emotions and select a face to display based on the estimated emotions.

[0035] The generation unit can perform evaluations by comparing them with past literature. For example, the generation unit can refer to past literature and evaluate the user's statements. For example, the generation unit can refer to academic papers and evaluate the user's statements. The generation unit can also refer to technical reports and evaluate the user's statements. Furthermore, the generation unit can refer to news articles and evaluate the user's statements. For example, the generation unit can refer to academic papers and evaluate the user's statements. This allows for appropriate evaluation of the user's statements. Some or all of the above processing in the generation unit may be performed using a generation AI, for example, or without a generation AI. For example, the generation unit can input past literature into a generation AI, and the generation AI can perform the evaluation.

[0036] The generation unit can generate harsh words. For example, the generation unit can generate somewhat harsh words. For example, the generation unit can generate the phrase, "That's wrong." The generation unit can also generate the phrase, "No, that's not right." Furthermore, the generation unit can generate the phrase, "You should think about it more." For example, the generation unit generates the phrase, "That's wrong." This allows for providing harsh feedback to the user. Some or all of the above processing in the generation unit may be performed using a generation AI, for example, or without a generation AI. For example, the generation unit can input the user's statement into a generation AI, and the generation AI can generate harsh words.

[0037] The display unit includes a content display unit that presents paid content for self-improvement. The content display unit can, for example, present paid content for self-improvement. The content display unit can, for example, present self-help books. The content display unit can also present online courses. Furthermore, the content display unit can also present video content. For example, the content display unit can present self-help books and provide the user with content for self-improvement. This allows the user to be provided with content for self-improvement. Some or all of the above processing in the content display unit may be performed using AI, for example, or without AI. For example, the content display unit can select the most suitable content based on the user's interests and preferences.

[0038] The acquisition unit can analyze the user's past speech history and select the optimal acquisition method. For example, the acquisition unit can prioritize the acquisition of phrases that the user has frequently used in the past. The acquisition unit can also analyze the user's past speech patterns and increase the acquisition frequency during certain time periods if there are many speeches during those times. Furthermore, the acquisition unit can prioritize the acquisition of speeches related to specific topics from the user's past speech history. For example, the acquisition unit prioritizes the acquisition of phrases that the user has frequently used in the past. This enables efficient speech acquisition by selecting the optimal acquisition method based on the user's past speech history. Some or all of the above processing in the acquisition unit may be performed using AI, for example, or without AI. For example, the acquisition unit can input the user's past speech history into a generating AI, which can then select the optimal acquisition method.

[0039] The acquisition unit can filter audio based on the user's current situation and environment when acquiring audio. For example, the acquisition unit can increase the audio acquisition sensitivity when the user is in a quiet environment. Furthermore, the acquisition unit can apply noise cancellation when the user is in a noisy environment. Additionally, the acquisition unit can filter background noise when the user is moving. For example, the acquisition unit increases the audio acquisition sensitivity when the user is in a quiet environment. This allows for the acquisition of less noisy audio by filtering the audio according to the user's situation and environment. Some or all of the above processing in the acquisition unit may be performed using AI, or without AI. For example, the acquisition unit can input user environment data into a generating AI, which can then perform audio filtering.

[0040] The acquisition unit can prioritize the acquisition of relevant audio by considering the user's geographical location information when acquiring audio. For example, if the user is in a specific location, the acquisition unit can prioritize the acquisition of statements related to that location. Also, if the user is traveling, the acquisition unit can prioritize the acquisition of statements related to the travel destination. Furthermore, if the user is at home, the acquisition unit can prioritize the acquisition of statements related to home. For example, if the acquisition unit is in a specific location, it prioritizes the acquisition of statements related to that location. This allows for the acquisition of more relevant audio based on the user's geographical location information, thereby providing more appropriate feedback. Some or all of the above processing in the acquisition unit may be performed using AI, for example, or without AI. For example, the acquisition unit can input the user's geographical location information into a generating AI, which can then prioritize the acquisition of relevant audio.

[0041] The acquisition unit can analyze the user's social media activity and acquire relevant audio when acquiring audio. For example, the acquisition unit can prioritize acquiring phrases that the user frequently uses on social media. The acquisition unit can also analyze the content of the user's social media posts and acquire relevant audio. Furthermore, the acquisition unit can analyze the user's interactions with their social media followers and acquire relevant audio. For example, the acquisition unit prioritizes acquiring phrases that the user frequently uses on social media. This allows for more appropriate feedback to be provided by acquiring relevant audio based on the user's social media activity. Some or all of the above processing in the acquisition unit may be performed using AI, for example, or without AI. For example, the acquisition unit can input the user's social media activity data into a generating AI, which can then acquire relevant audio.

[0042] The analysis unit can adjust the level of detail of the analysis based on the importance of the audio during analysis. For example, the analysis unit can perform a detailed analysis on important statements. It can also perform a concise analysis on general statements. Furthermore, it can perform a concise analysis on repeated statements. For example, the analysis unit performs a detailed analysis on important statements. By adjusting the level of detail of the analysis based on the importance of the audio, efficient analysis becomes possible. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input audio data into a generating AI, which can then adjust the level of detail of the analysis.

[0043] The analysis unit can apply different analysis algorithms depending on the category of the audio during analysis. For example, the analysis unit can apply an emotion analysis algorithm to statements related to emotions. It can also apply a fact-checking algorithm to statements related to facts. Furthermore, it can apply an opinion analysis algorithm to statements related to opinions. For example, the analysis unit applies an emotion analysis algorithm to statements related to emotions. This allows for highly accurate analysis by applying the appropriate analysis algorithm according to the category of the audio. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input audio data into a generating AI, which can then apply an appropriate analysis algorithm.

[0044] The analysis unit can determine the priority of analysis based on the timing of audio submission. For example, the analysis unit can prioritize the analysis of the most recent utterances. It can also prioritize the analysis of utterances submitted within a specific time period. Furthermore, it can prioritize the analysis of utterances submitted within a time period specified by the user. For example, the analysis unit prioritizes the analysis of the most recent utterances. This enables efficient analysis by determining the priority of analysis based on the timing of audio submission. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input audio data into a generating AI, which can then determine the priority of analysis.

[0045] The analysis unit can adjust the order of analysis based on the relevance of the audio during analysis. For example, the analysis unit can prioritize the analysis of statements related to important topics. It can also prioritize the analysis of statements related to topics of high user interest. Furthermore, the analysis unit can prioritize the analysis of statements related to repeated statements. For example, the analysis unit prioritizes the analysis of statements related to important topics. This allows for efficient analysis by adjusting the order of analysis based on the relevance of the audio. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input audio data into a generating AI, which can then adjust the order of analysis.

[0046] The generation unit can adjust the level of detail of the feedback based on the importance of the audio when generating feedback. For example, the generation unit can provide detailed feedback for important statements. It can also provide concise feedback for general statements. Furthermore, it can provide concise feedback for repeated statements. For example, the generation unit provides detailed feedback for important statements. This allows for efficient feedback by adjusting the level of detail of the feedback based on the importance of the audio. Some or all of the above processing in the generation unit may be performed using AI, for example, or without AI. For example, the generation unit can input audio data into a generation AI, which can then adjust the level of detail of the feedback.

[0047] The generation unit can apply different feedback generation algorithms depending on the category of the audio when generating feedback. For example, the generation unit can apply an emotion analysis algorithm to statements related to emotions. It can also apply a fact-checking algorithm to statements related to facts. Furthermore, it can apply an opinion analysis algorithm to statements related to opinions. For example, the generation unit applies an emotion analysis algorithm to statements related to emotions. By applying an appropriate feedback generation algorithm according to the category of the audio, highly accurate feedback becomes possible. Some or all of the above processing in the generation unit may be performed using AI, for example, or without AI. For example, the generation unit can input audio data into a generation AI, and the generation AI can apply an appropriate feedback generation algorithm.

[0048] The generation unit can determine the priority of feedback based on the timing of audio submission when generating feedback. For example, the generation unit can prioritize feedback for the most recent utterance. It can also prioritize feedback for utterances submitted within a specific time period. Furthermore, it can prioritize feedback for utterances submitted within a time period specified by the user. For example, the generation unit prioritizes feedback for the most recent utterance. This enables efficient feedback by determining the priority of feedback based on the timing of audio submission. Some or all of the above processing in the generation unit may be performed using AI, for example, or without AI. For example, the generation unit can input audio data into a generation AI, which can then determine the priority of feedback.

[0049] The generation unit can adjust the order of feedback based on the relevance of the audio when generating feedback. For example, the generation unit can prioritize providing feedback to statements related to important topics. It can also prioritize providing feedback to statements related to topics of high user interest. Furthermore, the generation unit can prioritize providing feedback to statements related to repeated statements. For example, the generation unit prioritizes providing feedback to statements related to important topics. This allows for efficient feedback by adjusting the order of feedback based on the relevance of the audio. Some or all of the above processing in the generation unit may be performed using AI, for example, or without AI. For example, the generation unit can input audio data into a generation AI, which can then adjust the order of feedback.

[0050] The display unit can select an appropriate display method by referring to the user's past operation history when displaying information. For example, the display unit can prioritize providing display methods that the user has preferred to use in the past. Furthermore, the display unit can suggest a specific display method based on the user's past operation history. In addition, the display unit can analyze the user's past operation history and select the optimal display method. For example, the display unit prioritizes providing display methods that the user has preferred to use in the past. This enables efficient display by selecting the optimal display method based on the user's past operation history. Some or all of the above processing in the display unit may be performed using AI, for example, or without AI. For example, the display unit can input user operation history data into a generating AI, which can then select the optimal display method.

[0051] The display unit can adjust its display method based on the user's current situation and environment when displaying information. For example, if the user is in a dark place, the display unit can display bright text on a dark background. Conversely, if the user is in a bright place, the display unit can display dark text on a bright background. Furthermore, if the user is on the move, the display unit can provide a concise and highly visible display method. For example, if the user is in a dark place, the display unit can display bright text on a dark background. This allows for more appropriate display by adjusting the display method according to the user's situation and environment. Some or all of the above processing in the display unit may be performed using AI, for example, or without AI. For example, the display unit can input user environment data into a generating AI, which can then adjust the display method.

[0052] The display unit can select the optimal display method when displaying information, taking into account the user's device information. For example, if the user is using a smartphone, the display unit can provide a display method that matches the screen size. Furthermore, if the user is using a tablet, the display unit can provide a display method optimized for a larger screen. Additionally, if the user is using a smartwatch, the display unit can provide a concise and highly visible display method. For example, if the user is using a smartphone, the display unit provides a display method that matches the screen size. This enables efficient display by selecting the optimal display method based on the user's device information. Some or all of the above processing in the display unit may be performed using AI, or without AI. For example, the display unit can input the user's device information into a generating AI, which can then select the optimal display method.

[0053] The display unit can provide a multilingual display according to the user's language settings when displaying information. For example, the display unit can automatically set the display language based on the language settings of the user's device. The display unit can also provide a language switching function if the user uses multiple languages. Furthermore, if the user selects a specific language, the display unit can provide the display in that language. For example, the display unit automatically sets the display language based on the language settings of the user's device. This allows the display to support a wider range of users by providing a multilingual display based on the user's language settings. Some or all of the above processing in the display unit may be performed using AI, for example, or without AI. For example, the display unit can input the user's language setting data into a generating AI, and the generating AI can provide a multilingual display.

[0054] The face display unit can select the optimal face by referring to the user's past reaction history when displaying faces. For example, the face display unit can prioritize displaying faces that the user has previously reacted to favorably. The face display unit can also suggest faces with specific expressions based on the user's past reaction history. Furthermore, the face display unit can analyze the user's past reaction history and select the optimal face. For example, the face display unit prioritizes displaying faces that the user has previously reacted to favorably. This enables efficient feedback by selecting the optimal face based on the user's past reaction history. Some or all of the above processing in the face display unit may be performed using AI, for example, or without AI. For example, the face display unit can input user reaction history data into a generating AI, which can then select the optimal face.

[0055] The face display unit can adjust the face displayed based on the user's current situation and environment when displaying a face. For example, if the user is in a dark place, the face display unit can display a face with a bright expression. Also, if the user is in a bright place, the face display unit can display a face with a calm expression. Furthermore, if the user is moving, the face display unit can display a concise and highly visible face. For example, if the user is in a dark place, the face display unit will display a face with a bright expression. By adjusting the face displayed according to the user's situation and environment, more appropriate feedback can be provided. Some or all of the above processing in the face display unit may be performed using AI, for example, or without AI. For example, the face display unit can input user environment data into a generating AI, and the generating AI can adjust the face it displays.

[0056] The face display unit can select the optimal face when displaying a face, taking into account the user's geographical location information. For example, if the user is in a specific location, the face display unit can display a face related to that location. Also, if the user is traveling, the face display unit can display a face related to the travel destination. Furthermore, if the user is at home, the face display unit can display a face related to the home. For example, if the user is in a specific location, the face display unit will display a face related to that location. This enables efficient feedback by selecting the optimal face based on the user's geographical location information. Some or all of the above processing in the face display unit may be performed using AI, for example, or without AI. For example, the face display unit can input the user's geographical location information into a generating AI, which can then select the optimal face.

[0057] The face display unit can analyze the user's social media activity and display relevant faces when displaying faces. For example, the face display unit can display faces with expressions that the user frequently uses on social media. The face display unit can also analyze the content of the user's social media posts and display relevant faces. Furthermore, the face display unit can analyze the user's interactions with their social media followers and display relevant faces. For example, the face display unit can display faces with expressions that the user frequently uses on social media. This allows for more appropriate feedback by displaying relevant faces based on the user's social media activity. Some or all of the above processing in the face display unit may be performed using AI, for example, or without AI. For example, the face display unit can input the user's social media activity data into a generating AI, which can then display relevant faces.

[0058] The content presentation unit can select the most suitable content by referring to the user's past purchase history when presenting content. For example, the content presentation unit can present relevant content based on the content the user has previously purchased. Furthermore, the content presentation unit can suggest content of a specific genre based on the user's past purchase history. In addition, the content presentation unit can analyze the user's past purchase history and select the most suitable content. For example, the content presentation unit presents relevant content based on the content the user has previously purchased. This enables efficient content presentation by selecting the most suitable content based on the user's past purchase history. Some or all of the above processing in the content presentation unit may be performed using AI, for example, or without AI. For example, the content presentation unit can input the user's purchase history data into a generating AI, which can then select the most suitable content.

[0059] The content presentation unit can adjust the content it presents based on the user's current situation and environment. For example, if the user is in a dark place, the content presentation unit can present highly visible content. If the user is in a bright place, the content presentation unit can present content with a calm tone. Furthermore, if the user is on the move, the content presentation unit can present concise and highly visible content. For example, if the content presentation unit is in a dark place, it will present highly visible content. By adjusting the content presented according to the user's situation and environment, more appropriate content can be provided. Some or all of the above processing in the content presentation unit may be performed using AI, for example, or without AI. For example, the content presentation unit can input user environment data into a generating AI and adjust the content presented by the generating AI.

[0060] The content presentation unit can select the most suitable content when presenting content, taking into account the user's device information. For example, if the user is using a smartphone, the content presentation unit can provide content that matches the screen size. Furthermore, if the user is using a tablet, the content presentation unit can provide content optimized for a larger screen. Additionally, if the user is using a smartwatch, the content presentation unit can provide concise and highly visible content. For example, if the user is using a smartphone, the content presentation unit provides content that matches the screen size. This enables efficient content presentation by selecting the most suitable content based on the user's device information. Some or all of the above processing in the content presentation unit may be performed using AI, for example, or without AI. For example, the content presentation unit can input the user's device information into a generating AI, which can then select the most suitable content.

[0061] The content presentation unit can provide multilingual content according to the user's language settings when presenting content. For example, the content presentation unit can automatically set the language of the content based on the language settings of the user's device. The content presentation unit can also provide a language switching function if the user uses multiple languages. Furthermore, if the content presentation unit selects a specific language, it can provide content in that language. For example, the content presentation unit automatically sets the language of the content based on the language settings of the user's device. This allows for support of a larger number of users by providing multilingual content based on the user's language settings. Some or all of the above processing in the content presentation unit may be performed using AI, for example, or without AI. For example, the content presentation unit can input the user's language setting data into a generating AI, and the generating AI can provide multilingual content.

[0062] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0063] The analysis unit can estimate the user's interests and concerns based on the content of their statements and adjust the level of detail of the analysis based on these estimated interests. For example, if a user shows a strong interest in a particular topic, the analysis unit can perform a detailed analysis of their statements on that topic. Conversely, if a user shows general interest, the analysis unit can perform a concise analysis. Furthermore, the analysis unit can perform a concise analysis of topics that the user repeatedly mentions. By adjusting the level of detail of the analysis based on the user's interests, efficient analysis becomes possible.

[0064] The generation unit can estimate the user's learning style based on the user's utterances and adjust the way feedback is presented based on the estimated learning style. For example, if the user has a visual learning style, the generation unit can provide visually stimulating feedback. If the user has an auditory learning style, the generation unit can provide audio feedback. Furthermore, if the user has a tactile learning style, the generation unit can provide interactive feedback. By adjusting the way feedback is presented according to the user's learning style, more effective feedback can be provided.

[0065] The acquisition unit evaluates the reliability of user statements based on their content and prioritizes acquiring highly reliable statements. For example, if a user has previously provided accurate information, the acquisition unit can prioritize acquiring those statements. Furthermore, if a user has previously provided incorrect information, the acquisition unit can filter out those statements. In addition, the acquisition unit can analyze the content of user statements and make evaluations based on reliable sources. This allows for the acquisition of more accurate information by adjusting audio acquisition based on the reliability of user statements.

[0066] The generation unit can estimate the user's cultural background based on their statements and adjust the content of the feedback based on that estimated cultural background. For example, if the user belongs to a specific culture, the generation unit can provide feedback appropriate to that culture. Furthermore, if the user has a multicultural background, the generation unit can provide feedback that takes multiple cultures into consideration. In addition, the generation unit can select appropriate wording and expressions based on the user's cultural background. This allows for the provision of more appropriate feedback by adjusting the content of the feedback according to the user's cultural background.

[0067] The acquisition unit evaluates the importance of user statements based on their content and can prioritize the acquisition of important statements. For example, if a user is speaking about an important topic, the acquisition unit can prioritize the acquisition of that statement. Furthermore, if a user is speaking about a general topic, the acquisition unit can filter out that statement. In addition, the acquisition unit can analyze the content of user statements and determine the acquisition priority based on their importance. This allows for the acquisition of more important information by adjusting audio acquisition based on the importance of user statements.

[0068] The following briefly describes the processing flow for example form 1.

[0069] Step 1: The acquisition unit acquires the voice spoken by the user. The acquisition unit can acquire voice using, for example, the microphone of a smartphone. The acquisition unit can also acquire the voice spoken by the user in real time. Furthermore, the acquisition unit can convert the voice into text data using speech recognition technology. For example, the acquisition unit acquires voice using the microphone of a smartphone and converts it into text data using speech recognition technology. Step 2: The analysis unit analyzes the audio acquired by the acquisition unit. The analysis unit can analyze the audio using, for example, a multimodal generation AI. The analysis unit can also analyze the content of the audio and estimate emotions and intentions. Furthermore, the analysis unit can perform evaluations by comparing it with past literature. For example, the analysis unit can analyze the audio using a multimodal generation AI and perform evaluations by comparing it with past literature. Step 3: The generation unit generates feedback based on the results analyzed by the analysis unit. The generation unit can generate feedback using, for example, a generation AI. The generation unit can also generate somewhat harsh language. Furthermore, the generation unit can generate feedback to display a face that makes it easier for the user to listen. For example, the generation unit generates feedback using a generation AI and generates somewhat harsh language. Step 4: The display unit displays the feedback generated by the generation unit. The display unit can, for example, display the feedback on a smartphone screen. The display unit can also display a face that makes it easier for the user to listen. Furthermore, the display unit can present paid content for self-improvement. For example, the display unit displays feedback on a smartphone screen and displays a face that makes it easier for the user to listen.

[0070] (Example of form 2) The self-improvement promotion system according to an embodiment of the present invention is a system that provides feedback on the user's actions and statements to encourage self-improvement. The self-improvement promotion system acquires the user's spoken voice via a smartphone, and a multimodal generation AI analyzes it. Based on the analyzed voice, it compares it with past literature and makes an evaluation such as "That's wrong." Based on the result, it generates somewhat harsh words and displays them on the screen. At this time, it increases the influence on the user by displaying a face that the user will find easy to listen to (for example, parents or close friends). It also presents paid content for self-improvement. For example, the self-improvement promotion system acquires the user's spoken voice via a smartphone. For example, the acquired voice is analyzed by a multimodal generation AI and evaluated by comparing it with past literature. For example, it makes an evaluation such as "That's wrong." Based on the result, it generates somewhat harsh words and displays them on the screen. For example, it generates words such as "That's not right." At this time, it increases the influence on the user by displaying a face that the user will find easy to listen to (for example, parents or close friends). It also presents paid content for self-improvement. For example, it presents content such as self-help books or online courses. This allows the self-improvement promotion system to provide feedback on user behavior and statements, thereby encouraging self-improvement.

[0071] The self-improvement promotion system according to this embodiment comprises an acquisition unit, an analysis unit, a generation unit, and a display unit. The acquisition unit acquires the voice spoken by the user. The acquisition unit can acquire the voice using, for example, the microphone of a smartphone. The acquisition unit can also acquire the voice spoken by the user in real time. Furthermore, the acquisition unit can convert the voice into text data using speech recognition technology. For example, the acquisition unit acquires the voice using the microphone of a smartphone and converts it into text data using speech recognition technology. The analysis unit analyzes the voice acquired by the acquisition unit. The analysis unit can analyze the voice using, for example, a multimodal generation AI. Furthermore, the analysis unit can analyze the content of the voice and estimate emotions and intentions. Furthermore, the analysis unit can also perform evaluation by comparing it with past literature. For example, the analysis unit analyzes the voice using a multimodal generation AI and performs evaluation by comparing it with past literature. The generation unit generates feedback based on the results analyzed by the analysis unit. The generation unit can generate feedback using, for example, a generation AI. Furthermore, the generation unit can also generate somewhat harsh words. Furthermore, the generation unit can also generate feedback to display a face that makes it easier for the user to listen. For example, the generation unit uses a generation AI to generate feedback, and generates somewhat harsh words. The display unit displays the feedback generated by the generation unit. The display unit can, for example, display the feedback on a smartphone screen. The display unit can also display a face that makes it easier for the user to listen. Furthermore, the display unit can also present paid content for self-improvement. For example, the display unit displays feedback on a smartphone screen and displays a face that makes it easier for the user to listen. In this way, the self-improvement promotion system according to the embodiment can provide feedback on the user's actions and statements and encourage self-improvement.

[0072] The acquisition unit captures the voice spoken by the user. For example, the acquisition unit can acquire voice using the microphone of a smartphone. Specifically, it uses the smartphone's built-in microphone or an externally connected microphone to record the user's speech with high accuracy. The acquisition unit employs noise cancellation technology to maintain voice clarity and remove ambient noise. Furthermore, the acquisition unit can acquire the user's voice in real time. This allows for the immediate collection of voice data while the user is speaking, enabling rapid transfer to subsequent processing. In addition, the acquisition unit can convert the voice into text data using speech recognition technology. For example, the acquisition unit acquires voice using the smartphone's microphone and converts it into text data using speech recognition technology. The speech recognition technology utilizes a deep learning model, achieving high-precision text conversion by considering differences in pronunciation and accent. This allows the acquisition unit to accurately save the user's speech as text data, which can then be used for detailed analysis by the analysis unit.

[0073] The analysis unit analyzes the audio acquired by the acquisition unit. For example, the analysis unit can analyze the audio using a multimodal generation AI. Specifically, it simultaneously analyzes audio and text data to comprehensively evaluate the user's utterances, emotions, and intentions. The analysis unit analyzes acoustic features such as tone, pitch, and speed to estimate the user's emotional state. For example, an exhilarating tone may indicate excitement or joy, while a low, subdued tone may indicate sadness or fatigue. The analysis unit can also analyze the content of the audio to estimate emotions and intentions. This involves analyzing text data using natural language processing techniques to understand the context and meaning of the utterances. Furthermore, the analysis unit can evaluate the utterances in comparison to past literature. For example, the analysis unit can analyze the audio using a multimodal generation AI and evaluate it in comparison to past literature. This allows for evaluation of how closely the user's utterances match past successes and failures, enabling the provision of more specific feedback. Based on these analysis results, the analysis unit extracts information useful for the user's self-improvement and passes it to the generation unit.

[0074] The generation unit generates feedback based on the results analyzed by the analysis unit. The generation unit can generate feedback using, for example, a generation AI. Specifically, it automatically generates appropriate feedback according to the user's statements and emotional state. The generation AI learns from past data and literature and has an algorithm to provide the most effective feedback for the user. The generation unit can also generate somewhat harsh language. For example, if a user is seeking harsh criticism for self-improvement, the generation unit will provide feedback that meets that request. Furthermore, the generation unit can generate feedback that displays a face that makes it easier for the user to listen. For example, the generation unit uses a generation AI to generate feedback and generates somewhat harsh language. This makes it easier for the user to understand specific areas for improvement regarding their statements and actions. The generation unit can continuously improve its accuracy by collecting user reactions and feedback and reflecting them in subsequent feedback generation.

[0075] The display unit displays the feedback generated by the generation unit. The display unit can, for example, display the feedback on a smartphone screen. Specifically, it displays feedback on what the user has said as text or graphics, providing it in a visually easy-to-understand format. The display unit can also display a face that makes it easier for the user to listen. For example, it can use an avatar or character to provide feedback in a friendly way to the user. Furthermore, the display unit can also present paid content for self-improvement. For example, the display unit displays feedback on a smartphone screen and displays a face that makes it easier for the user to listen. As a result, the self-improvement promotion system according to the embodiment can provide feedback on the user's actions and statements and encourage self-improvement. The display unit can monitor the user's reactions in real time and adjust the feedback content as needed. As a result, the user can always improve themselves based on the latest information.

[0076] The display unit includes a face display unit that displays a face that the user finds easy to listen to. The face display unit can, for example, display a face that the user finds easy to listen to. The face display unit can, for example, display an avatar. The face display unit can also display a real face. Furthermore, the face display unit can display an illustrated face. For example, the face display unit can display an avatar and a face that the user finds easy to listen to. This can increase the influence on the user. Some or all of the above processing in the face display unit may be performed using AI, for example, or without AI. For example, the face display unit can estimate the user's emotions and select a face to display based on the estimated emotions.

[0077] The generation unit can perform evaluations by comparing them with past literature. For example, the generation unit can refer to past literature and evaluate the user's statements. For example, the generation unit can refer to academic papers and evaluate the user's statements. The generation unit can also refer to technical reports and evaluate the user's statements. Furthermore, the generation unit can refer to news articles and evaluate the user's statements. For example, the generation unit can refer to academic papers and evaluate the user's statements. This allows for appropriate evaluation of the user's statements. Some or all of the above processing in the generation unit may be performed using a generation AI, for example, or without a generation AI. For example, the generation unit can input past literature into a generation AI, and the generation AI can perform the evaluation.

[0078] The generation unit can generate harsh words. For example, the generation unit can generate somewhat harsh words. For example, the generation unit can generate the phrase, "That's wrong." The generation unit can also generate the phrase, "No, that's not right." Furthermore, the generation unit can generate the phrase, "You should think about it more." For example, the generation unit generates the phrase, "That's wrong." This allows for providing harsh feedback to the user. Some or all of the above processing in the generation unit may be performed using a generation AI, for example, or without a generation AI. For example, the generation unit can input the user's statement into a generation AI, and the generation AI can generate harsh words.

[0079] The display unit includes a content display unit that presents paid content for self-improvement. The content display unit can, for example, present paid content for self-improvement. The content display unit can, for example, present self-help books. The content display unit can also present online courses. Furthermore, the content display unit can also present video content. For example, the content display unit can present self-help books and provide the user with content for self-improvement. This allows the user to be provided with content for self-improvement. Some or all of the above processing in the content display unit may be performed using AI, for example, or without AI. For example, the content display unit can select the most suitable content based on the user's interests and preferences.

[0080] The acquisition unit can estimate the user's emotions and adjust the timing of voice acquisition based on the estimated emotions. For example, if the user is relaxed, the acquisition unit can set a low frequency of voice acquisition to maintain a natural flow of conversation. Conversely, if the user is stressed, the acquisition unit can set a high frequency of voice acquisition to provide immediate feedback. Furthermore, if the user is concentrating, the acquisition unit can adjust the timing of voice acquisition to avoid disrupting their concentration. For example, if the acquisition unit is relaxed, it can set a low frequency of voice acquisition to maintain a natural flow of conversation. This allows for more appropriate feedback to be provided by adjusting the timing of voice acquisition according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the acquisition unit may be performed using AI, or not using AI. For example, the acquisition unit can input user emotion data into the generative AI, which can then adjust the timing of voice acquisition.

[0081] The acquisition unit can analyze the user's past speech history and select the optimal acquisition method. For example, the acquisition unit can prioritize the acquisition of phrases that the user has frequently used in the past. The acquisition unit can also analyze the user's past speech patterns and increase the acquisition frequency during certain time periods if there are many speeches during those times. Furthermore, the acquisition unit can prioritize the acquisition of speeches related to specific topics from the user's past speech history. For example, the acquisition unit prioritizes the acquisition of phrases that the user has frequently used in the past. This enables efficient speech acquisition by selecting the optimal acquisition method based on the user's past speech history. Some or all of the above processing in the acquisition unit may be performed using AI, for example, or without AI. For example, the acquisition unit can input the user's past speech history into a generating AI, which can then select the optimal acquisition method.

[0082] The acquisition unit can filter audio based on the user's current situation and environment when acquiring audio. For example, the acquisition unit can increase the audio acquisition sensitivity when the user is in a quiet environment. Furthermore, the acquisition unit can apply noise cancellation when the user is in a noisy environment. Additionally, the acquisition unit can filter background noise when the user is moving. For example, the acquisition unit increases the audio acquisition sensitivity when the user is in a quiet environment. This allows for the acquisition of less noisy audio by filtering the audio according to the user's situation and environment. Some or all of the above processing in the acquisition unit may be performed using AI, or without AI. For example, the acquisition unit can input user environment data into a generating AI, which can then perform audio filtering.

[0083] The acquisition unit can estimate the user's emotions and determine the priority of the audio to acquire based on the estimated user emotions. For example, if the user is angry, the acquisition unit can prioritize acquiring statements related to anger. Similarly, if the user is sad, the acquisition unit can prioritize acquiring statements containing words of comfort. Furthermore, if the user is happy, the acquisition unit can prioritize acquiring statements related to happiness. For example, if the user is angry, the acquisition unit prioritizes acquiring statements related to anger. This allows for the priority acquisition of important audio by determining the priority of audio according to the user's emotions. Emotion estimation is implemented using an emotion estimation function, for example, using an emotion engine or generative AI. The generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the acquisition unit may be performed using AI, for example, or without AI. For example, the acquisition unit can input the user's emotion data into the generative AI, which can then determine the priority of the audio.

[0084] The acquisition unit can prioritize the acquisition of relevant audio by considering the user's geographical location information when acquiring audio. For example, if the user is in a specific location, the acquisition unit can prioritize the acquisition of statements related to that location. Also, if the user is traveling, the acquisition unit can prioritize the acquisition of statements related to the travel destination. Furthermore, if the user is at home, the acquisition unit can prioritize the acquisition of statements related to home. For example, if the acquisition unit is in a specific location, it prioritizes the acquisition of statements related to that location. This allows for the acquisition of more relevant audio based on the user's geographical location information, thereby providing more appropriate feedback. Some or all of the above processing in the acquisition unit may be performed using AI, for example, or without AI. For example, the acquisition unit can input the user's geographical location information into a generating AI, which can then prioritize the acquisition of relevant audio.

[0085] The acquisition unit can analyze the user's social media activity and acquire relevant audio when acquiring audio. For example, the acquisition unit can prioritize acquiring phrases that the user frequently uses on social media. The acquisition unit can also analyze the content of the user's social media posts and acquire relevant audio. Furthermore, the acquisition unit can analyze the user's interactions with their social media followers and acquire relevant audio. For example, the acquisition unit prioritizes acquiring phrases that the user frequently uses on social media. This allows for more appropriate feedback to be provided by acquiring relevant audio based on the user's social media activity. Some or all of the above processing in the acquisition unit may be performed using AI, for example, or without AI. For example, the acquisition unit can input the user's social media activity data into a generating AI, which can then acquire relevant audio.

[0086] The analysis unit can estimate the user's emotions and adjust the presentation of the analysis based on the estimated emotions. For example, if the user is relaxed, the analysis unit can provide the analysis results in a calm expression. If the user is stressed, the analysis unit can provide the analysis results in a concise and clear expression. Furthermore, if the user is excited, the analysis unit can provide the analysis results in a visually stimulating expression. For example, if the user is relaxed, the analysis unit provides the analysis results in a calm expression. By adjusting the presentation of the analysis according to the user's emotions, more appropriate analysis results can be provided. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or a generative AI. The generative AI is, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input the user's emotion data into the generative AI, which can then adjust the presentation of the analysis.

[0087] The analysis unit can adjust the level of detail of the analysis based on the importance of the audio during analysis. For example, the analysis unit can perform a detailed analysis on important statements. It can also perform a concise analysis on general statements. Furthermore, it can perform a concise analysis on repeated statements. For example, the analysis unit performs a detailed analysis on important statements. By adjusting the level of detail of the analysis based on the importance of the audio, efficient analysis becomes possible. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input audio data into a generating AI, which can then adjust the level of detail of the analysis.

[0088] The analysis unit can apply different analysis algorithms depending on the category of the audio during analysis. For example, the analysis unit can apply an emotion analysis algorithm to statements related to emotions. It can also apply a fact-checking algorithm to statements related to facts. Furthermore, it can apply an opinion analysis algorithm to statements related to opinions. For example, the analysis unit applies an emotion analysis algorithm to statements related to emotions. This allows for highly accurate analysis by applying the appropriate analysis algorithm according to the category of the audio. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input audio data into a generating AI, which can then apply an appropriate analysis algorithm.

[0089] The analysis unit can estimate the user's emotions and adjust the length of the analysis based on the estimated emotions. For example, if the user is in a hurry, the analysis unit can provide a short, concise analysis. If the user is relaxed, the analysis unit can provide a detailed analysis. Furthermore, if the user is excited, the analysis unit can provide a visually stimulating analysis. For example, if the user is in a hurry, the analysis unit provides a short, concise analysis. By adjusting the length of the analysis according to the user's emotions, more appropriate analysis results can be provided. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input user emotion data into the generative AI, which can then adjust the length of the analysis.

[0090] The analysis unit can determine the priority of analysis based on the timing of audio submission. For example, the analysis unit can prioritize the analysis of the most recent utterances. It can also prioritize the analysis of utterances submitted within a specific time period. Furthermore, it can prioritize the analysis of utterances submitted within a time period specified by the user. For example, the analysis unit prioritizes the analysis of the most recent utterances. This enables efficient analysis by determining the priority of analysis based on the timing of audio submission. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input audio data into a generating AI, which can then determine the priority of analysis.

[0091] The analysis unit can adjust the order of analysis based on the relevance of the audio during analysis. For example, the analysis unit can prioritize the analysis of statements related to important topics. It can also prioritize the analysis of statements related to topics of high user interest. Furthermore, the analysis unit can prioritize the analysis of statements related to repeated statements. For example, the analysis unit prioritizes the analysis of statements related to important topics. This allows for efficient analysis by adjusting the order of analysis based on the relevance of the audio. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input audio data into a generating AI, which can then adjust the order of analysis.

[0092] The generation unit can estimate the user's emotions and adjust the way feedback is expressed based on the estimated emotions. For example, if the user is relaxed, the generation unit can provide feedback in a gentle manner. If the user is stressed, the generation unit can provide feedback in a concise and clear manner. Furthermore, if the user is excited, the generation unit can provide feedback in a visually stimulating manner. For example, if the user is relaxed, the generation unit provides feedback in a gentle manner. This allows for more appropriate feedback to be provided by adjusting the way feedback is expressed according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or a generation AI. The generation AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the generation unit may be performed using AI, for example, or without AI. For example, the generation unit can input user emotion data into the generation AI, which can then adjust the way feedback is expressed.

[0093] The generation unit can adjust the level of detail of the feedback based on the importance of the audio when generating feedback. For example, the generation unit can provide detailed feedback for important statements. It can also provide concise feedback for general statements. Furthermore, it can provide concise feedback for repeated statements. For example, the generation unit provides detailed feedback for important statements. This allows for efficient feedback by adjusting the level of detail of the feedback based on the importance of the audio. Some or all of the above processing in the generation unit may be performed using AI, for example, or without AI. For example, the generation unit can input audio data into a generation AI, which can then adjust the level of detail of the feedback.

[0094] The generation unit can apply different feedback generation algorithms depending on the category of the audio when generating feedback. For example, the generation unit can apply an emotion analysis algorithm to statements related to emotions. It can also apply a fact-checking algorithm to statements related to facts. Furthermore, it can apply an opinion analysis algorithm to statements related to opinions. For example, the generation unit applies an emotion analysis algorithm to statements related to emotions. By applying an appropriate feedback generation algorithm according to the category of the audio, highly accurate feedback becomes possible. Some or all of the above processing in the generation unit may be performed using AI, for example, or without AI. For example, the generation unit can input audio data into a generation AI, and the generation AI can apply an appropriate feedback generation algorithm.

[0095] The generation unit can estimate the user's emotions and adjust the length of the feedback based on the estimated emotions. For example, if the user is in a hurry, the generation unit can provide short, concise feedback. If the user is relaxed, the generation unit can provide detailed feedback. Furthermore, if the user is excited, the generation unit can provide visually stimulating feedback. For example, if the user is in a hurry, the generation unit provides short, concise feedback. By adjusting the length of the feedback according to the user's emotions, more appropriate feedback can be provided. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or a generation AI. The generation AI is, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above processing in the generation unit may be performed using AI, for example, or not using AI. For example, the generation unit can input user emotion data into the generation AI, which can then adjust the length of the feedback.

[0096] The generation unit can determine the priority of feedback based on the timing of audio submission when generating feedback. For example, the generation unit can prioritize feedback for the most recent utterance. It can also prioritize feedback for utterances submitted within a specific time period. Furthermore, it can prioritize feedback for utterances submitted within a time period specified by the user. For example, the generation unit prioritizes feedback for the most recent utterance. This enables efficient feedback by determining the priority of feedback based on the timing of audio submission. Some or all of the above processing in the generation unit may be performed using AI, for example, or without AI. For example, the generation unit can input audio data into a generation AI, which can then determine the priority of feedback.

[0097] The generation unit can adjust the order of feedback based on the relevance of the audio when generating feedback. For example, the generation unit can prioritize providing feedback to statements related to important topics. It can also prioritize providing feedback to statements related to topics of high user interest. Furthermore, the generation unit can prioritize providing feedback to statements related to repeated statements. For example, the generation unit prioritizes providing feedback to statements related to important topics. This allows for efficient feedback by adjusting the order of feedback based on the relevance of the audio. Some or all of the above processing in the generation unit may be performed using AI, for example, or without AI. For example, the generation unit can input audio data into a generation AI, which can then adjust the order of feedback.

[0098] The display unit can estimate the user's emotions and adjust the display method based on the estimated emotions. For example, if the user is relaxed, the display unit can display in calm colors. If the user is stressed, the display unit can provide a concise and clear display method. Furthermore, if the user is excited, the display unit can provide a visually stimulating display method. For example, if the user is relaxed, the display unit displays in calm colors. By adjusting the display method according to the user's emotions, a more appropriate display becomes possible. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or a generative AI. The generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the display unit may be performed using AI, for example, or without AI. For example, the display unit can input user emotion data into the generative AI, and the generative AI can adjust the display method.

[0099] The display unit can select an appropriate display method by referring to the user's past operation history when displaying information. For example, the display unit can prioritize providing display methods that the user has preferred to use in the past. Furthermore, the display unit can suggest a specific display method based on the user's past operation history. In addition, the display unit can analyze the user's past operation history and select the optimal display method. For example, the display unit prioritizes providing display methods that the user has preferred to use in the past. This enables efficient display by selecting the optimal display method based on the user's past operation history. Some or all of the above processing in the display unit may be performed using AI, for example, or without AI. For example, the display unit can input user operation history data into a generating AI, which can then select the optimal display method.

[0100] The display unit can adjust its display method based on the user's current situation and environment when displaying information. For example, if the user is in a dark place, the display unit can display bright text on a dark background. Conversely, if the user is in a bright place, the display unit can display dark text on a bright background. Furthermore, if the user is on the move, the display unit can provide a concise and highly visible display method. For example, if the user is in a dark place, the display unit can display bright text on a dark background. This allows for more appropriate display by adjusting the display method according to the user's situation and environment. Some or all of the above processing in the display unit may be performed using AI, for example, or without AI. For example, the display unit can input user environment data into a generating AI, which can then adjust the display method.

[0101] The display unit can estimate the user's emotions and determine the display priority based on the estimated emotions. For example, if the user is angry, the display unit can prioritize displaying anger-related feedback. Similarly, if the user is sad, the display unit can prioritize displaying comforting feedback. Furthermore, if the user is happy, the display unit can prioritize displaying joy-related feedback. For example, if the user is angry, the display unit will prioritize displaying anger-related feedback. This allows important information to be displayed preferentially by determining the display priority according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generative AI. The generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the display unit may be performed using AI, for example, or without AI. For example, the display unit can input user emotion data into a generative AI, which can then determine the display priority.

[0102] The display unit can select the optimal display method when displaying information, taking into account the user's device information. For example, if the user is using a smartphone, the display unit can provide a display method that matches the screen size. Furthermore, if the user is using a tablet, the display unit can provide a display method optimized for a larger screen. Additionally, if the user is using a smartwatch, the display unit can provide a concise and highly visible display method. For example, if the user is using a smartphone, the display unit provides a display method that matches the screen size. This enables efficient display by selecting the optimal display method based on the user's device information. Some or all of the above processing in the display unit may be performed using AI, or without AI. For example, the display unit can input the user's device information into a generating AI, which can then select the optimal display method.

[0103] The display unit can provide a multilingual display according to the user's language settings when displaying information. For example, the display unit can automatically set the display language based on the language settings of the user's device. The display unit can also provide a language switching function if the user uses multiple languages. Furthermore, if the user selects a specific language, the display unit can provide the display in that language. For example, the display unit automatically sets the display language based on the language settings of the user's device. This allows the display to support a wider range of users by providing a multilingual display based on the user's language settings. Some or all of the above processing in the display unit may be performed using AI, for example, or without AI. For example, the display unit can input the user's language setting data into a generating AI, and the generating AI can provide a multilingual display.

[0104] The face display unit can estimate the user's emotions and adjust the facial expression displayed based on the estimated emotions. For example, if the user is relaxed, the face display unit can display a calm facial expression. If the user is stressed, the face display unit can display an encouraging facial expression. Furthermore, if the user is excited, the face display unit can display a joyful facial expression. For example, if the user is relaxed, the face display unit displays a calm facial expression. This allows for more appropriate feedback by adjusting the facial expression displayed according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the face display unit may be performed using AI, for example, or without AI. For example, the face display unit can input user emotion data into the generative AI and adjust the facial expression displayed by the generative AI.

[0105] The face display unit can select the optimal face by referring to the user's past reaction history when displaying faces. For example, the face display unit can prioritize displaying faces that the user has previously reacted to favorably. The face display unit can also suggest faces with specific expressions based on the user's past reaction history. Furthermore, the face display unit can analyze the user's past reaction history and select the optimal face. For example, the face display unit prioritizes displaying faces that the user has previously reacted to favorably. This enables efficient feedback by selecting the optimal face based on the user's past reaction history. Some or all of the above processing in the face display unit may be performed using AI, for example, or without AI. For example, the face display unit can input user reaction history data into a generating AI, which can then select the optimal face.

[0106] The face display unit can adjust the face displayed based on the user's current situation and environment when displaying a face. For example, if the user is in a dark place, the face display unit can display a face with a bright expression. Also, if the user is in a bright place, the face display unit can display a face with a calm expression. Furthermore, if the user is moving, the face display unit can display a concise and highly visible face. For example, if the user is in a dark place, the face display unit will display a face with a bright expression. By adjusting the face displayed according to the user's situation and environment, more appropriate feedback can be provided. Some or all of the above processing in the face display unit may be performed using AI, for example, or without AI. For example, the face display unit can input user environment data into a generating AI, and the generating AI can adjust the face it displays.

[0107] The face display unit can estimate the user's emotions and adjust the order in which faces are displayed based on the estimated emotions. For example, if the user is angry, the face display unit can prioritize displaying faces with expressions related to anger. Similarly, if the user is sad, the face display unit can prioritize displaying faces with comforting expressions. Furthermore, if the user is happy, the face display unit can prioritize displaying faces with expressions related to happiness. For example, if the user is angry, the face display unit will prioritize displaying faces with expressions related to anger. By adjusting the order in which faces are displayed according to the user's emotions, important information can be prioritized. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above-described processing in the face display unit may be performed using AI, or not using AI. For example, the face display unit can input user emotion data into the generative AI, and the generative AI can adjust the order in which faces are displayed.

[0108] The face display unit can select the optimal face when displaying a face, taking into account the user's geographical location information. For example, if the user is in a specific location, the face display unit can display a face related to that location. Also, if the user is traveling, the face display unit can display a face related to the travel destination. Furthermore, if the user is at home, the face display unit can display a face related to the home. For example, if the user is in a specific location, the face display unit will display a face related to that location. This enables efficient feedback by selecting the optimal face based on the user's geographical location information. Some or all of the above processing in the face display unit may be performed using AI, for example, or without AI. For example, the face display unit can input the user's geographical location information into a generating AI, which can then select the optimal face.

[0109] The face display unit can analyze the user's social media activity and display relevant faces when displaying faces. For example, the face display unit can display faces with expressions that the user frequently uses on social media. The face display unit can also analyze the content of the user's social media posts and display relevant faces. Furthermore, the face display unit can analyze the user's interactions with their social media followers and display relevant faces. For example, the face display unit can display faces with expressions that the user frequently uses on social media. This allows for more appropriate feedback by displaying relevant faces based on the user's social media activity. Some or all of the above processing in the face display unit may be performed using AI, for example, or without AI. For example, the face display unit can input the user's social media activity data into a generating AI, which can then display relevant faces.

[0110] The content presentation unit can estimate the user's emotions and adjust the type of content presented based on the estimated emotions. For example, if the user is relaxed, the content presentation unit can present calming content. If the user is stressed, the content presentation unit can present relaxing content. Furthermore, if the user is excited, the content presentation unit can present visually stimulating content. For example, if the content presentation unit is relaxed, it presents calming content. By adjusting the type of content presented according to the user's emotions, more appropriate content can be provided. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the content presentation unit may be performed using AI, for example, or without AI. For example, the content presentation unit can input user emotion data into the generative AI and adjust the type of content presented by the generative AI.

[0111] The content presentation unit can select the most suitable content by referring to the user's past purchase history when presenting content. For example, the content presentation unit can present relevant content based on the content the user has previously purchased. Furthermore, the content presentation unit can suggest content of a specific genre based on the user's past purchase history. In addition, the content presentation unit can analyze the user's past purchase history and select the most suitable content. For example, the content presentation unit presents relevant content based on the content the user has previously purchased. This enables efficient content presentation by selecting the most suitable content based on the user's past purchase history. Some or all of the above processing in the content presentation unit may be performed using AI, for example, or without AI. For example, the content presentation unit can input the user's purchase history data into a generating AI, which can then select the most suitable content.

[0112] The content presentation unit can adjust the content it presents based on the user's current situation and environment. For example, if the user is in a dark place, the content presentation unit can present highly visible content. If the user is in a bright place, the content presentation unit can present content with a calm tone. Furthermore, if the user is on the move, the content presentation unit can present concise and highly visible content. For example, if the content presentation unit is in a dark place, it will present highly visible content. By adjusting the content presented according to the user's situation and environment, more appropriate content can be provided. Some or all of the above processing in the content presentation unit may be performed using AI, for example, or without AI. For example, the content presentation unit can input user environment data into a generating AI and adjust the content presented by the generating AI.

[0113] The content presentation unit can estimate the user's emotions and determine the priority of the content to present based on the estimated emotions. For example, if the user is angry, the content presentation unit can prioritize presenting content that alleviates anger. Similarly, if the user is sad, the content presentation unit can prioritize presenting comforting content. Furthermore, if the user is happy, the content presentation unit can prioritize presenting content that amplifies that happiness. For example, if the user is angry, the content presentation unit prioritizes presenting content that alleviates anger. By determining the priority of the content presented according to the user's emotions, important content can be provided preferentially. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the content presentation unit may be performed using AI, for example, or without AI. For example, the content presentation unit can input user emotion data into a generative AI and determine the priority of the content presented by the generative AI.

[0114] The content presentation unit can select the most suitable content when presenting content, taking into account the user's device information. For example, if the user is using a smartphone, the content presentation unit can provide content that matches the screen size. Furthermore, if the user is using a tablet, the content presentation unit can provide content optimized for a larger screen. Additionally, if the user is using a smartwatch, the content presentation unit can provide concise and highly visible content. For example, if the user is using a smartphone, the content presentation unit provides content that matches the screen size. This enables efficient content presentation by selecting the most suitable content based on the user's device information. Some or all of the above processing in the content presentation unit may be performed using AI, for example, or without AI. For example, the content presentation unit can input the user's device information into a generating AI, which can then select the most suitable content.

[0115] The content presentation unit can provide multilingual content according to the user's language settings when presenting content. For example, the content presentation unit can automatically set the language of the content based on the language settings of the user's device. The content presentation unit can also provide a language switching function if the user uses multiple languages. Furthermore, if the content presentation unit selects a specific language, it can provide content in that language. For example, the content presentation unit automatically sets the language of the content based on the language settings of the user's device. This allows for support of a larger number of users by providing multilingual content based on the user's language settings. Some or all of the above processing in the content presentation unit may be performed using AI, for example, or without AI. For example, the content presentation unit can input the user's language setting data into a generating AI, and the generating AI can provide multilingual content.

[0116] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0117] The acquisition unit can acquire the user's biometric information and adjust the timing of voice acquisition based on that information. For example, the acquisition unit can monitor the user's heart rate and skin electrical activity, and set the voice acquisition frequency lower when the user is relaxed and higher when the user is stressed. The acquisition unit can also analyze the user's biometric information and adjust the voice acquisition timing when the user is concentrating to avoid disrupting their concentration. By adjusting the voice acquisition timing based on the user's biometric information, more appropriate feedback can be provided.

[0118] The analysis unit can estimate the user's interests and concerns based on the content of their statements and adjust the level of detail of the analysis based on these estimated interests. For example, if a user shows a strong interest in a particular topic, the analysis unit can perform a detailed analysis of their statements on that topic. Conversely, if a user shows general interest, the analysis unit can perform a concise analysis. Furthermore, the analysis unit can perform a concise analysis of topics that the user repeatedly mentions. By adjusting the level of detail of the analysis based on the user's interests, efficient analysis becomes possible.

[0119] The generation unit can estimate the user's learning style based on the user's utterances and adjust the way feedback is presented based on the estimated learning style. For example, if the user has a visual learning style, the generation unit can provide visually stimulating feedback. If the user has an auditory learning style, the generation unit can provide audio feedback. Furthermore, if the user has a tactile learning style, the generation unit can provide interactive feedback. By adjusting the way feedback is presented according to the user's learning style, more effective feedback can be provided.

[0120] The display unit can estimate the user's emotions and adjust the order of feedback displayed based on those emotions. For example, if the user is angry, the display unit can prioritize displaying feedback that soothes the anger. Similarly, if the user is sad, the display unit can prioritize displaying feedback that includes words of comfort. Furthermore, if the user is happy, the display unit can prioritize displaying feedback that amplifies their happiness. By adjusting the order of feedback displayed according to the user's emotions, important information can be prioritized.

[0121] The acquisition unit evaluates the reliability of user statements based on their content and prioritizes acquiring highly reliable statements. For example, if a user has previously provided accurate information, the acquisition unit can prioritize acquiring those statements. Furthermore, if a user has previously provided incorrect information, the acquisition unit can filter out those statements. In addition, the acquisition unit can analyze the content of user statements and make evaluations based on reliable sources. This allows for the acquisition of more accurate information by adjusting audio acquisition based on the reliability of user statements.

[0122] The analysis unit can estimate the user's emotions and determine the priority of analysis based on the estimated emotions. For example, if the user is angry, the analysis unit can prioritize analyzing statements related to anger. Similarly, if the user is sad, the analysis unit can prioritize analyzing statements containing words of comfort. Furthermore, if the user is happy, the analysis unit can prioritize analyzing statements related to happiness. By determining the priority of analysis according to the user's emotions, important statements can be analyzed preferentially.

[0123] The generation unit can estimate the user's cultural background based on their statements and adjust the content of the feedback based on that estimated cultural background. For example, if the user belongs to a specific culture, the generation unit can provide feedback appropriate to that culture. Furthermore, if the user has a multicultural background, the generation unit can provide feedback that takes multiple cultures into consideration. In addition, the generation unit can select appropriate wording and expressions based on the user's cultural background. This allows for the provision of more appropriate feedback by adjusting the content of the feedback according to the user's cultural background.

[0124] The display unit can estimate the user's emotions and adjust the level of detail in the feedback displayed based on those emotions. For example, if the user is relaxed, the display unit can provide detailed feedback. If the user is stressed, the display unit can provide concise and clear feedback. Furthermore, if the user is excited, the display unit can provide visually stimulating feedback. By adjusting the level of detail in the feedback displayed according to the user's emotions, more appropriate feedback can be provided.

[0125] The acquisition unit evaluates the importance of user statements based on their content and can prioritize the acquisition of important statements. For example, if a user is speaking about an important topic, the acquisition unit can prioritize the acquisition of that statement. Furthermore, if a user is speaking about a general topic, the acquisition unit can filter out that statement. In addition, the acquisition unit can analyze the content of user statements and determine the acquisition priority based on their importance. This allows for the acquisition of more important information by adjusting audio acquisition based on the importance of user statements.

[0126] The analysis unit can estimate the user's emotions and adjust the presentation of the analysis based on those emotions. For example, if the user is relaxed, the analysis unit can provide the results in a calm and gentle manner. If the user is stressed, the analysis unit can provide the results in a concise and clear manner. Furthermore, if the user is excited, the analysis unit can provide the results in a visually stimulating manner. By adjusting the presentation of the analysis according to the user's emotions, more appropriate analysis results can be provided.

[0127] The following briefly describes the processing flow for example form 2.

[0128] Step 1: The acquisition unit acquires the voice spoken by the user. The acquisition unit can acquire voice using, for example, the microphone of a smartphone. The acquisition unit can also acquire the voice spoken by the user in real time. Furthermore, the acquisition unit can convert the voice into text data using speech recognition technology. For example, the acquisition unit acquires voice using the microphone of a smartphone and converts it into text data using speech recognition technology. Step 2: The analysis unit analyzes the audio acquired by the acquisition unit. The analysis unit can analyze the audio using, for example, a multimodal generation AI. The analysis unit can also analyze the content of the audio and estimate emotions and intentions. Furthermore, the analysis unit can perform evaluations by comparing it with past literature. For example, the analysis unit can analyze the audio using a multimodal generation AI and perform evaluations by comparing it with past literature. Step 3: The generation unit generates feedback based on the results analyzed by the analysis unit. The generation unit can generate feedback using, for example, a generation AI. The generation unit can also generate somewhat harsh language. Furthermore, the generation unit can generate feedback to display a face that makes it easier for the user to listen. For example, the generation unit generates feedback using a generation AI and generates somewhat harsh language. Step 4: The display unit displays the feedback generated by the generation unit. The display unit can, for example, display the feedback on a smartphone screen. The display unit can also display a face that makes it easier for the user to listen. Furthermore, the display unit can present paid content for self-improvement. For example, the display unit displays feedback on a smartphone screen and displays a face that makes it easier for the user to listen.

[0129] The specific processing unit 290 transmits the result of the specific processing to the smart device 14. In the smart device 14, the control unit 46A causes the output device 40 to output the result of the specific processing. The microphone 38B acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0130] Data generation model 58 is a form of so-called generative AI (Artificial Intelligence). An example of data generation model 58 is ChatGPT (registered trademark) (Internet search).<URL: https: / / openai.com / blog / chatgpt> Examples of generative AI include text generation AI, image generation AI, and multimodal generation AI. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and with inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats from audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVMs), k-means clustering, convolutional neural networks (CNNs), recurrent neural networks (RNNs), generative adversarial networks (GANs), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each of the above parts is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example.Furthermore, processing performed by AI, including generative AI, may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by AI, including generative AI.

[0131] Furthermore, the processing performed by the data processing system 10 described above is carried out by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart device 14, but it may also be carried out by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart device 14. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the smart device 14 or an external device, and the smart device 14 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0132] For example, the acquisition unit can acquire the user's spoken voice using the microphone 38B of the smart device 14. For example, the analysis unit is implemented by the specific processing unit 290 of the data processing device 12, which analyzes the acquired voice and evaluates it by comparing it with past literature. For example, the generation unit is implemented by the specific processing unit 290 of the data processing device 12, which generates somewhat harsh words based on the analysis results. For example, the display unit displays the generated feedback using the display 40A of the smart device 14, displaying a face that makes it easier for the user to listen. The correspondence between each unit and the device and control unit is not limited to the examples described above, and various changes are possible.

[0133] [Second Embodiment] Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment.

[0134] As shown in Figure 3, the data processing system 210 includes a data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0135] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN and / or LAN.

[0136] The smart glasses 214 include a computer 36, a microphone 238, a speaker 240, a camera 42, and a communication interface 44. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, and camera 42 are also connected to the bus 52.

[0137] The microphone 238 receives voice signals from the user and accepts instructions from the user. The microphone 238 captures the voice signals from the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0138] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, which captures images of the area around the user (for example, an imaging range defined by a field of view equivalent to the field of vision of a typical healthy person).

[0139] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0140] Figure 4 shows an example of the main functions of the data processing device 12 and the smart glasses 214. As shown in Figure 4, the data processing device 12 performs specific processing by the processor 28. The storage 32 stores the specific processing program 56.

[0141] The processor 28 reads a specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 acting as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0142] Storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0143] In the smart glasses 214, specific processing is performed by the processor 46. The storage 50 stores a specific processing program 60. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 acting as a control unit 46A according to the specific processing program 60 executed on the RAM 48. The smart glasses 214 also have a data generation model 58 and an emotion identification model 59, similar to the data generation model and emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0144] Furthermore, other devices besides the data processing device 12 may also have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 obtains processing results (such as prediction results) using the data generation model 58 by communicating with the server device that has the data generation model 58. Also, the data processing device 12 may be a server device or a terminal device owned by the user (for example, a mobile phone, robot, home appliance, etc.).

[0145] The specific processing unit 290 transmits the result of the specific processing to the smart glasses 214. In the smart glasses 214, the control unit 46A causes the speaker 240 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0146] The data generation model 58 is a so-called generative AI. An example of a data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats such as audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVM), k-means clustering, convolutional neural networks (CNN), recurrent neural networks (RNN), generative adversarial networks (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each part described above is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example. Also, processing performed by an AI including a generative AI may be replaced by rule-based processing, and rule-based processing may be replaced by processing performed by an AI including a generative AI.

[0147] The data processing system 210 according to the second embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 210 is performed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart glasses 214, but it may also be performed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart glasses 214. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the smart glasses 214 or an external device, and the smart glasses 214 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0148] For example, the acquisition unit can acquire the user's spoken voice using the microphone 238 of the smart glasses 214. For example, the analysis unit is implemented by the specific processing unit 290 of the data processing device 12, which analyzes the acquired voice and evaluates it by comparing it with past literature. For example, the generation unit is implemented by the specific processing unit 290 of the data processing device 12, which generates somewhat harsh words based on the analysis results. For example, the display unit displays the generated feedback using the display of the smart glasses 214, displaying a face that makes it easier for the user to listen. The correspondence between each unit and the device and control unit is not limited to the examples described above, and various changes are possible.

[0149] [Third Embodiment] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[0150] As shown in Figure 5, the data processing system 310 includes a data processing device 12 and a headset terminal 314. An example of the data processing device 12 is a server.

[0151] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN and / or LAN.

[0152] The headset terminal 314 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication interface 44, and a display 343. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, camera 42, and display 343 are also connected to the bus 52.

[0153] The microphone 238 receives voice signals from the user and accepts instructions from the user. The microphone 238 captures the voice signals from the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0154] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, which captures images of the area around the user (for example, an imaging range defined by a field of view equivalent to the field of vision of a typical healthy person).

[0155] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0156] Figure 6 shows an example of the main functions of the data processing device 12 and the headset terminal 314. As shown in Figure 6, the data processing device 12 performs specific processing using the processor 28. The storage 32 stores the specific processing program 56.

[0157] The processor 28 reads a specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 acting as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0158] Storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0159] In the headset terminal 314, specific processing is performed by the processor 46. The storage 50 stores a specific program 60. The processor 46 reads the specific program 60 from the storage 50 and executes the read specific program 60 on the RAM 48. The specific processing is realized by the processor 46 acting as a control unit 46A according to the specific program 60 executed on the RAM 48. The headset terminal 314 also has a data generation model 58 and an emotion identification model 59, similar to the data generation model and emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0160] Furthermore, other devices besides the data processing device 12 may also have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 obtains processing results (such as prediction results) using the data generation model 58 by communicating with the server device that has the data generation model 58. Also, the data processing device 12 may be a server device or a terminal device owned by the user (for example, a mobile phone, robot, home appliance, etc.).

[0161] The specific processing unit 290 transmits the result of the specific processing to the headset terminal 314. In the headset terminal 314, the control unit 46A causes the speaker 240 and display 343 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0162] The data generation model 58 is a so-called generative AI. An example of a data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats such as audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVM), k-means clustering, convolutional neural networks (CNN), recurrent neural networks (RNN), generative adversarial networks (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each part described above is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example. Also, processing performed by an AI including a generative AI may be replaced by rule-based processing, and rule-based processing may be replaced by processing performed by an AI including a generative AI.

[0163] The data processing system 310 according to the third embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 310 is performed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the headset terminal 314, but may also be performed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the headset terminal 314. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the headset terminal 314 or an external device, and the headset terminal 314 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0164] For example, the acquisition unit can acquire the user's spoken voice using the microphone 238 of the headset terminal 314. For example, the analysis unit is implemented by the specific processing unit 290 of the data processing device 12, which analyzes the acquired voice and evaluates it by comparing it with past literature. For example, the generation unit is implemented by the specific processing unit 290 of the data processing device 12, which generates somewhat harsh words based on the analysis results. For example, the display unit displays the generated feedback using the display 343 of the headset terminal 314, displaying a face that makes it easier for the user to listen. The correspondence between each unit and the device or control unit is not limited to the examples described above, and various changes are possible.

[0165] [Fourth Embodiment] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[0166] As shown in Figure 7, the data processing system 410 includes a data processing device 12 and a robot 414. An example of the data processing device 12 is a server.

[0167] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN and / or LAN.

[0168] The robot 414 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication interface 44, and a controlled object 443. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, camera 42, and controlled object 443 are also connected to the bus 52.

[0169] The microphone 238 receives voice signals from the user and accepts instructions from the user. The microphone 238 captures the voice signals from the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0170] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS image sensor or CCD image sensor, which captures images of the area around the user (for example, an imaging range defined by a field of view equivalent to the field of vision of a typical healthy person).

[0171] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0172] The controlled object 443 includes a display device, LEDs in the eyes, and motors that drive the arms, hands, and feet. The posture and gestures of the robot 414 are controlled by controlling the motors of the arms, hands, and feet. Some of the robot 414's emotions can be expressed by controlling these motors. The robot 414's facial expressions can also be expressed by controlling the illumination state of the LEDs in its eyes.

[0173] Figure 8 shows an example of the main functions of the data processing device 12 and the robot 414. As shown in Figure 8, the data processing device 12 performs specific processing using the processor 28. The storage 32 stores the specific processing program 56.

[0174] The processor 28 reads a specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 acting as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0175] Storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0176] In robot 414, specific processing is performed by processor 46. A specific program 60 is stored in storage 50. Processor 46 reads the specific program 60 from storage 50 and executes it on RAM 48. The specific processing is achieved by processor 46 acting as a control unit 46A according to the specific program 60 executed on RAM 48. Robot 414 also has data generation model 58 and emotion identification model 59, similar to those of the robot, and can perform processing similar to that of the specific processing unit 290 using these models.

[0177] Furthermore, other devices besides the data processing device 12 may also have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 obtains processing results (such as prediction results) using the data generation model 58 by communicating with the server device that has the data generation model 58. Also, the data processing device 12 may be a server device or a terminal device owned by the user (for example, a mobile phone, robot, home appliance, etc.).

[0178] The specific processing unit 290 transmits the result of the specific processing to the robot 414. In the robot 414, the control unit 46A causes the speaker 240 and the controlled object 443 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0179] The data generation model 58 is a so-called generative AI. An example of a data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats such as audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVM), k-means clustering, convolutional neural networks (CNN), recurrent neural networks (RNN), generative adversarial networks (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each part described above is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example. Also, processing performed by an AI including a generative AI may be replaced by rule-based processing, and rule-based processing may be replaced by processing performed by an AI including a generative AI.

[0180] The data processing system 410 according to the fourth embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 410 is performed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the robot 414, but it may also be performed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the robot 414. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the robot 414 or an external device, and the robot 414 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0181] For example, the acquisition unit can acquire the user's spoken voice using the microphone 238 of the robot 414. For example, the analysis unit is implemented by the specific processing unit 290 of the data processing device 12, which analyzes the acquired voice and evaluates it by comparing it with past literature. For example, the generation unit is implemented by the specific processing unit 290 of the data processing device 12, which generates somewhat harsh words based on the analysis results. For example, the display unit displays the generated feedback using the display of the robot 414, and displays a face that makes it easier for the user to listen. The correspondence between each unit and the device or control unit is not limited to the examples described above, and various changes are possible.

[0182] Furthermore, the emotion identification model 59, acting as an emotion engine, may determine the user's emotion according to a specific mapping. Specifically, the emotion identification model 59 may determine the user's emotion according to a specific mapping, which is an emotion map (see Figure 9). Similarly, the emotion identification model 59 may also determine the robot's emotion, and the identification processing unit 290 may perform identification processing using the robot's emotion.

[0183] Figure 9 shows the emotion map 400, in which multiple emotions are mapped. In the emotion map 400, emotions are arranged in concentric circles radiating from the center. The closer to the center of the concentric circles, the more primitive the emotions are located. Further out of the concentric circles, emotions representing states and actions arising from mental states are located. Emotion is a concept that includes feelings and mental states. On the left side of the concentric circles, emotions that are generally generated from reactions occurring in the brain are located. On the right side of the concentric circles, emotions that are generally induced by situational judgment are located. Above and below the concentric circles, emotions that are generally generated from reactions occurring in the brain and induced by situational judgment are located. In addition, the emotion of "pleasure" is located on the upper side of the concentric circles, and the emotion of "displeasure" is located on the lower side. Thus, in the emotion map 400, multiple emotions are mapped based on the structure in which emotions arise, and emotions that are likely to occur simultaneously are mapped close together.

[0184] These emotions are distributed at the 3 o'clock position on the Emotion Map 400, and usually fluctuate between feelings of security and anxiety. In the right half of the Emotion Map 400, situational awareness takes precedence over internal feelings, resulting in a calm impression.

[0185] The inside of the Emotion Map 400 represents inner thoughts, while the outside represents actions. Therefore, the further you go from the outside of the Emotion Map 400, the more visible (expressed in actions) your emotions become.

[0186] Here, human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, it results in discomfort, and when they approach the ideal, it results in pleasure. Similarly, in robots, cars, and motorcycles, emotions can be created based on various balances, such as posture and battery level. When these balances deviate from the ideal, it results in discomfort, and when they approach the ideal, it results in pleasure. The emotion map can be generated based, for example, on Dr. Mitsuyoshi's emotion map (Research on a system for analyzing brain physiological signals of speech emotion recognition and emotion, Tokushima University, doctoral dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map contains emotions belonging to a region called "response," where sensation is dominant. The right half of the emotion map contains emotions belonging to a region called "situation," where situational awareness is dominant.

[0187] The emotion map defines two emotions that promote learning. One is the emotion around the middle of the negative "repentance" and "reflection" on the situation side. In other words, it is when the robot experiences negative emotions such as "I never want to feel this way again" or "I don't want to be scolded again." The other is the emotion around the positive "desire" on the reaction side. In other words, it is when the robot has positive feelings such as "I want more" or "I want to know more."

[0188] The emotion identification model 59 inputs user input into a pre-trained neural network, obtains emotion values ​​representing each emotion shown in the emotion map 400, and determines the user's emotion. This neural network is pre-trained based on multiple training data sets, which are combinations of user input and emotion values ​​representing each emotion shown in the emotion map 400. Furthermore, this neural network is trained so that emotions located close together have similar values, as shown in the emotion map 900 in Figure 10. Figure 10 shows an example where multiple emotions such as "reassured," "calm," and "confident" have similar emotion values.

[0189] In the above embodiment, an example was given in which a specific process is performed by a single computer 22. However, the technology of this disclosure is not limited thereto, and a distributed processing method for the specific process may be used, which includes computer 22 and multiple other computers.

[0190] In the above embodiment, an example was given in which the specific processing program 56 is stored in the storage 32, but the technology of this disclosure is not limited thereto. For example, the specific processing program 56 may be stored in a portable, computer-readable, non-temporary storage medium such as a USB (Universal Serial Bus) memory. The specific processing program 56 stored in the non-temporary storage medium is installed in the computer 22 of the data processing device 12. The processor 28 executes specific processing according to the specific processing program 56.

[0191] 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.

[0192] Furthermore, it is not necessary to store the entirety of the specific processing program 56 in a storage device such as a server connected to the data processing device 12 via the network 54, or to store the entirety of the specific processing program 56 in the storage 32; it is acceptable to store only a portion of the specific processing program 56.

[0193] The following types of processors can be used as hardware resources to perform specific processing. Examples of processors include a CPU, a general-purpose processor that functions as a hardware resource to perform specific processing by executing software, i.e., a program. Other examples of processors include dedicated electrical circuits, such as FPGAs (Field-Programmable Gate Arrays), PLDs (Programmable Logic Devices), or ASICs (Application Specific Integrated Circuits), which have circuit configurations specifically designed to perform specific processing. All of these processors have built-in or connected memory, and all of them perform specific processing by using memory.

[0194] The hardware resource that performs a specific process may consist of one of these various processors, or it may consist of a combination of two or more processors of the same or different types (for example, a combination of multiple FPGAs, or a combination of a CPU and an FPGA). Alternatively, the hardware resource that performs a specific process may consist of a single processor.

[0195] Examples of configurations using a single processor include, firstly, a configuration in which one or more CPUs and software are combined to form a single processor, and this processor functions as a hardware resource that performs a specific process. Secondly, there is a configuration using a processor that realizes the functions of the entire system, including multiple hardware resources that perform a specific process, on a single IC chip, as exemplified by SoCs (System-on-a-chip). In this way, a specific process is realized using one or more of the above types of processors as hardware resources.

[0196] Furthermore, the hardware structure of these various processors can more specifically utilize electrical circuits that combine circuit elements such as semiconductor devices. Also, the specific processing described above is merely an example. Therefore, it goes without saying that unnecessary steps can be deleted, new steps added, or the processing order rearranged, as long as it does not deviate from the main purpose.

[0197] Furthermore, although the above-described examples were divided into four embodiments, some or all of these embodiments may be combined. Also, the smart device 14, smart glasses 214, headset terminal 314, and robot 414 are just examples, and they may be combined, or other devices may be used. Also, although the above-described examples were divided into two embodiments, Embodiment 1 and Embodiment 2, these may be combined.

[0198] The descriptions and illustrations presented above are detailed explanations of the technical aspects of this disclosure and are merely examples of the technical aspects. For example, the above descriptions of the structure, function, operation, and effect are examples of the structure, function, operation, and effect of the technical aspects of this disclosure. Therefore, it goes without saying that you may delete unnecessary parts, add new elements, or replace elements in the descriptions and illustrations presented above, as long as you do not deviate from the essence of the technical aspects of this disclosure. Furthermore, in order to avoid confusion and facilitate understanding of the technical aspects of this disclosure, explanations of common technical knowledge and other things that do not require special explanation to enable the implementation of the technical aspects of this disclosure have been omitted from the descriptions and illustrations presented above.

[0199] All documents, patent applications, and technical standards described herein are incorporated by reference to the same extent as if each individual document, patent application, and technical standard were specifically and individually noted to be incorporated by reference.

[0200] (Note 1) The unit that acquires sound, An analysis unit analyzes the audio acquired by the acquisition unit, A generation unit that generates feedback based on the results of analysis performed by the analysis unit, The system includes a display unit that displays the feedback generated by the generation unit. A system characterized by the following features. (Note 2) The aforementioned display unit is It features a face display unit that shows a face that is easy for the user to listen to. The system described in Appendix 1, characterized by the features described herein. (Note 3) The generating unit is The evaluation will be conducted by comparing it with past literature. The system described in Appendix 1, characterized by the features described herein. (Note 4) The generating unit is Generates harsh words The system described in Appendix 1, characterized by the features described herein. (Note 5) The aforementioned display unit is It features a content presentation section that offers paid content for self-improvement. The system described in Appendix 1, characterized by the features described herein. (Note 6) The acquisition unit is, The system estimates the user's emotions and adjusts the timing of voice acquisition based on the estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 7) The acquisition unit is, Analyze the user's past statement history and select the appropriate method for retrieving it. The system described in Appendix 1, characterized by the features described herein. (Note 8) The acquisition unit is, When acquiring audio, filtering is performed based on the user's current situation and environment. The system described in Appendix 1, characterized by the features described herein. (Note 9) The acquisition unit is, It estimates the user's emotions and determines the priority of audio to acquire based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 10) The acquisition unit is, When acquiring audio, the system prioritizes acquiring relevant audio by considering the user's geographical location. The system described in Appendix 1, characterized by the features described herein. (Note 11) The acquisition unit is, When acquiring audio, the system analyzes the user's social media activity and retrieves relevant audio. The system described in Appendix 1, characterized by the features described herein. (Note 12) The aforementioned analysis unit, The system estimates the user's emotions and adjusts the representation of the analysis based on the estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 13) The aforementioned analysis unit, During analysis, the level of detail is adjusted based on the importance of the audio. The system described in Appendix 1, characterized by the features described herein. (Note 14) The aforementioned analysis unit, During analysis, different analysis algorithms are applied depending on the audio category. The system described in Appendix 1, characterized by the features described herein. (Note 15) The aforementioned analysis unit, It estimates the user's emotions and adjusts the length of the analysis based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 16) The aforementioned analysis unit, During analysis, the priority of analysis is determined based on when the audio was submitted. The system described in Appendix 1, characterized by the features described herein. (Note 17) The aforementioned analysis unit, During analysis, the order of analysis is adjusted based on the relevance of the audio. The system described in Appendix 1, characterized by the features described herein. (Note 18) The generating unit is It estimates the user's emotions and adjusts how feedback is expressed based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 19) The generating unit is When generating feedback, adjust the level of detail in the feedback based on the importance of the audio. The system described in Appendix 1, characterized by the features described herein. (Note 20) The generating unit is When generating feedback, different feedback generation algorithms are applied depending on the audio category. The system described in Appendix 1, characterized by the features described herein. (Note 21) The generating unit is It estimates the user's emotions and adjusts the length of the feedback based on the estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 22) The generating unit is When generating feedback, the feedback priority is determined based on when the audio was submitted. The system described in Appendix 1, characterized by the features described herein. (Note 23) The generating unit is When generating feedback, adjust the order of feedback based on the relevance of the audio. The system described in Appendix 1, characterized by the features described herein. (Note 24) The aforementioned display unit is It estimates the user's emotions and adjusts the display method based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 25) The aforementioned display unit is When displaying information, the system selects the appropriate display method by referring to the user's past operation history. The system described in Appendix 1, characterized by the features described herein. (Note 26) The aforementioned display unit is When displaying content, the display method is adjusted based on the user's current situation and environment. The system described in Appendix 1, characterized by the features described herein. (Note 27) The aforementioned display unit is It estimates the user's emotions and determines the display priority based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 28) The aforementioned display unit is When displaying content, the system selects the optimal display method by considering the user's device information. The system described in Appendix 1, characterized by the features described herein. (Note 29) The aforementioned display unit is When displayed, the system provides multilingual support according to the user's language settings. The system described in Appendix 1, characterized by the features described herein. (Note 30) The aforementioned face display unit is It estimates the user's emotions and adjusts the facial expressions displayed based on those estimated emotions. The system described in Appendix 2, characterized by the features described herein. (Note 31) The aforementioned face display unit is When displaying faces, the system selects the most suitable face by referring to the user's past reaction history. The system described in Appendix 2, characterized by the features described herein. (Note 32) The aforementioned face display unit is When displaying faces, the displayed faces are adjusted based on the user's current situation and environment. The system described in Appendix 2, characterized by the features described herein. (Note 33) The aforementioned face display unit is It estimates the user's emotions and adjusts the order in which faces are displayed based on those estimated emotions. The system described in Appendix 2, characterized by the features described herein. (Note 34) The aforementioned face display unit is When displaying faces, the system selects the most suitable face by considering the user's geographical location. The system described in Appendix 2, characterized by the features described herein. (Note 35) The aforementioned face display unit is When displaying faces, the system analyzes the user's social media activity and displays relevant faces. The system described in Appendix 2, characterized by the features described herein. (Note 36) The aforementioned content display unit is It estimates the user's emotions and adjusts the type of content presented based on those estimated emotions. The system described in Appendix 5, characterized by the features described herein. (Note 37) The aforementioned content display unit is When presenting content, the system selects the most suitable content by referring to the user's past purchase history. The system described in Appendix 5, characterized by the features described herein. (Note 38) The aforementioned content display unit is When presenting content, adjust the content displayed based on the user's current situation and environment. The system described in Appendix 5, characterized by the features described herein. (Note 39) The aforementioned content display unit is It estimates the user's emotions and determines the priority of the content presented based on those estimated emotions. The system described in Appendix 5, characterized by the features described herein. (Note 40) The aforementioned content display unit is When presenting content, the system selects the most suitable content by considering the user's device information. The system described in Appendix 5, characterized by the features described herein. (Note 41) The aforementioned content display unit is When presenting content, provide multilingual content according to the user's language settings. The system described in Appendix 5, characterized by the features described herein. [Explanation of symbols]

[0201] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Devices 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robots

Claims

1. The unit that acquires sound, An analysis unit analyzes the audio acquired by the acquisition unit, A generation unit that generates feedback based on the results of analysis performed by the analysis unit, The system includes a display unit that displays the feedback generated by the generation unit. A system characterized by the following features.

2. The aforementioned display unit is It features a face display unit that shows a face that is easy for the user to listen to. The system according to feature 1.

3. The generating unit is The evaluation will be conducted by comparing it with past literature. The system according to feature 1.

4. The generating unit is Generates harsh words The system according to feature 1.

5. The aforementioned display unit is It features a content presentation section that offers paid content for self-improvement. The system according to feature 1.

6. The acquisition unit is, The system estimates the user's emotions and adjusts the timing of voice acquisition based on the estimated emotions. The system according to feature 1.

7. The acquisition unit is, Analyze the user's past statement history and select the appropriate method for retrieving it. The system according to feature 1.

8. The acquisition unit is, When acquiring audio, filtering is performed based on the user's current situation and environment. The system according to feature 1.

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