system

Abstract

The system according to this embodiment aims to help people with autism spectrum disorder (ASD) gain confidence in their daily communication. [Solution] The system according to the embodiment comprises a voice analysis unit, a language analysis unit, a non-verbal analysis unit, and a scenario provision unit. The voice analysis unit analyzes voice. The language analysis unit generates an appropriate response based on the voice analyzed by the voice analysis unit. The non-verbal analysis unit analyzes facial expressions and gestures based on the response generated by the language analysis unit. The scenario provision unit provides a virtual scenario based on the information analyzed by the non-verbal analysis unit.

Description

Technical Field

[0006] , , ,

[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 character of the chatbot, encoding the prompt, and inputting the encoded prompt into a language model to generate a chatbot utterance as a 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 was a problem that it was difficult for people with autism spectrum disorder (ASD) to have confidence in daily communication.

[0005] The system according to the embodiment aims to enable people with autism spectrum disorder (ASD) to have confidence in daily communication.

Means for Solving the Problems

[0006] The system according to this embodiment comprises a voice analysis unit, a language analysis unit, a non-verbal analysis unit, and a scenario provision unit. The voice analysis unit analyzes voice. The language analysis unit generates an appropriate response based on the voice analyzed by the voice analysis unit. The non-verbal analysis unit analyzes facial expressions and gestures based on the response generated by the language analysis unit. The scenario provision unit provides a virtual scenario based on the information analyzed by the non-verbal analysis unit. [Effects of the Invention]

[0007] The system according to this embodiment enables people with autism spectrum disorder (ASD) to gain confidence in their daily communication. [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 labeled communication I / F (Interface) is an interface including a communication processor, an antenna, and the like. The communication I / F manages communication between a plurality of computers. Examples of communication standards applied to the communication I / F 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 1 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 comprises a computer 36, a receiving device 38, an output device 40, a camera 42, and a communication interface 44. The computer 36 comprises a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The receiving device 38, output device 40, and camera 42 are also 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) Spectrum Navi: Autism Communication Support, according to an embodiment of the present invention, is an AI-based tool that supports communication for people with autism spectrum disorder (ASD). This system uses speech recognition technology to analyze the user's conversation in real time and language analysis technology to generate appropriate responses. Furthermore, it supports nonverbal communication by analyzing facial expressions and gestures. It also provides social skills training using virtual scenarios to help users adapt to social situations. For example, when a user starts a conversation, speech recognition technology analyzes the content in real time. Next, language analysis technology understands the analyzed content and generates an appropriate response. For example, if the user says "Hello," the agent generates a response such as "Hello, how are you today?" Furthermore, the agent analyzes the user's facial expressions and gestures. For example, if the user smiles, the agent generates a response such as "You seem happy." In this way, the user is also supported in nonverbal communication. Social skills training using virtual scenarios is also provided. For example, a user can experience a scenario in which they participate in group activities at school. In this scenario, the agent reconstructs the teacher's instructions in an easy-to-understand format and displays them to the user. This allows users to independently understand the content of their activities and respond appropriately. In this way, SpectrumNavi supports people with ASD in gaining confidence in their daily communication and leading richer social lives.

[0029] The spectrum navigation system according to this embodiment comprises a voice analysis unit, a language analysis unit, a non-verbal analysis unit, and a scenario provision unit. The voice analysis unit analyzes voice. The voice analysis unit analyzes the user's utterances in real time, for example, using speech recognition technology. The voice analysis unit can also detect specific voice patterns, for example. The voice analysis unit analyzes the rhythm, tone, pitch, etc., of the voice. The language analysis unit generates an appropriate response based on the voice analyzed by the voice analysis unit. The language analysis unit understands the user's utterances and generates an appropriate response, for example, using natural language processing technology. The language analysis unit can also emphasize specific keywords, for example. The language analysis unit generates a response by emphasizing important words or phrases, for example. The non-verbal analysis unit analyzes facial expressions and gestures based on the response generated by the language analysis unit. The non-verbal analysis unit analyzes the user's facial expressions, for example, using facial expression recognition technology. The non-verbal analysis unit can also recognize specific facial expressions, for example. The non-verbal analysis unit recognizes and analyzes facial expressions such as smiles and anger, for example. The scenario provider unit provides a virtual scenario based on information analyzed by the nonverbal analysis unit. For example, the scenario provider unit provides a scenario in which a user participates in a group activity at school. The scenario provider unit can also customize specific scenarios. For example, the scenario provider unit can customize and provide the theme and settings of a scenario. In this way, the spectrum navigation according to this embodiment can support communication for people with ASD.

[0030] The voice analysis unit analyzes speech. For example, it uses speech recognition technology to analyze the user's speech in real time. Specifically, speech recognition technology converts the user's speech into text data and analyzes its content. The voice analysis unit can also detect specific speech patterns. This involves a process of extracting features such as speech rhythm, tone, and pitch and matching them to specific patterns. For example, it can identify speech patterns when a user is excited or calm. The voice analysis unit analyzes speech rhythm, tone, and pitch, allowing for a more accurate understanding of the user's emotional state and intentions. The voice analysis unit provides these analysis results to other departments in real time, playing a role in quickly and appropriately adjusting the overall system response. Furthermore, the voice analysis unit can use noise reduction technology to remove ambient and background noise, enabling a clearer analysis of the user's speech. This improves the accuracy of speech analysis and increases system reliability. The voice analysis unit can also accumulate the user's speech history and perform personalized analysis based on past data. This enables advanced voice analysis that takes into account the individual characteristics and tendencies of each user.

[0031] The language analysis unit generates appropriate responses based on the speech analyzed by the speech analysis unit. The language analysis unit understands the user's utterances using, for example, natural language processing techniques and generates appropriate responses. Specifically, natural language processing techniques include analyzing text data provided by the speech analysis unit to understand context and meaning. The language analysis unit can also, for example, emphasize specific keywords. This involves extracting important words and phrases from the user's utterances and generating responses based on them. For example, if a user says "help," the language analysis unit will emphasize this keyword and generate a response that provides quick and appropriate assistance. The language analysis unit generates responses by emphasizing important words and phrases, for example. This enables responses that accurately reflect the user's intentions and emotions. Furthermore, the language analysis unit can generate personalized responses based on the user's speech history and past dialogue data. This enables sophisticated dialogue tailored to the user's individual needs and preferences. The language analysis unit can also support multiple languages ​​and dialects, providing consistent, high-quality responses to different users. This allows the language analysis unit to accurately understand the user's speech and generate appropriate and effective responses.

[0032] The nonverbal analysis unit analyzes facial expressions and gestures based on the responses generated by the verbal analysis unit. For example, the nonverbal analysis unit analyzes the user's facial expressions using facial recognition technology. Specifically, facial recognition technology includes a process of detecting the user's facial features and analyzing changes in facial expressions in real time. The nonverbal analysis unit can also recognize specific facial expressions, such as smiles, anger, and sadness. For example, if a user smiles, the nonverbal analysis unit recognizes this expression and adjusts the system's response accordingly. The nonverbal analysis unit recognizes and analyzes expressions such as smiles and anger, enabling accurate understanding of the user's emotional state and providing appropriate responses. Furthermore, the nonverbal analysis unit can analyze the user's hand and body movements using gesture recognition technology. This allows for a more detailed understanding of the user's intentions and actions. For example, if a user raises their hand, the nonverbal analysis unit recognizes this gesture and provides an appropriate response. Additionally, the nonverbal analysis unit can accumulate the user's past facial and gesture data to perform personalized analysis. This enables advanced nonverbal analysis that takes into account the user's individual characteristics and tendencies.

[0033] The Scenario Provider provides virtual scenarios based on information analyzed by the Nonverbal Analysis Unit. For example, the Scenario Provider might provide a scenario in which a user participates in a group activity at school. Specifically, the Scenario Provider generates virtual scenarios tailored to the user's needs and goals, helping users improve their skills through experiences that closely resemble real-world situations. The Scenario Provider can also customize specific scenarios. This involves adjusting the scenario's theme and settings to match the user's preferences and needs. For example, if a user wants to improve their communication skills in a specific situation, the Scenario Provider will provide a scenario suited to that situation. The Scenario Provider can customize the scenario's theme and settings, for example. This allows users to effectively improve their skills through scenarios that are right for them. Furthermore, the Scenario Provider can continuously improve scenarios based on the user's progress and feedback. This enables flexible responses to the user's growth and changes. The Scenario Provider can also combine multiple scenarios to provide users with diverse experiences. This allows users to comprehensively improve their skills in various situations.

[0034] The voice analysis unit can detect specific voice patterns. For example, the voice analysis unit detects specific voice patterns by analyzing the rhythm, tone, pitch, etc. The voice analysis unit can improve the accuracy of voice analysis by detecting specific voice patterns. For example, the voice analysis unit can use speech recognition technology to detect specific voice patterns. This improves the accuracy of voice analysis by detecting specific voice patterns.

[0035] The language analysis unit can emphasize specific keywords. For example, the language analysis unit generates responses by emphasizing important words or phrases. The language analysis unit can improve the accuracy of language analysis by emphasizing specific keywords. For example, the language analysis unit can emphasize specific keywords using natural language processing techniques. This improves the accuracy of language analysis by emphasizing specific keywords.

[0036] The nonverbal analysis unit can recognize specific facial expressions. For example, the nonverbal analysis unit recognizes specific facial expressions using facial expression recognition technology. For example, the nonverbal analysis unit recognizes and analyzes facial expressions such as smiles and anger. The nonverbal analysis unit can improve the accuracy of nonverbal analysis by recognizing specific facial expressions. Thus, recognizing specific facial expressions improves the accuracy of nonverbal analysis.

[0037] The scenario provider can customize specific scenarios. For example, the scenario provider can customize the theme and settings of a scenario before providing it. By customizing specific scenarios, the scenario provider can improve the accuracy of its scenario provision. This means that customizing specific scenarios improves the accuracy of scenario provision.

[0038] The scenario provider can provide scenarios in which users participate in group activities at school. For example, the scenario provider can provide scenarios in which users participate in group activities at school. By providing scenarios in which users participate in group activities at school, the scenario provider can improve users' social skills. This improves users' social skills by providing scenarios in which they participate in group activities at school.

[0039] The voice analysis unit can optimize its analysis algorithm by referring to the user's past speech patterns during voice analysis. For example, the voice analysis unit may prioritize the analysis of specific phrases the user has used in the past. For example, the voice analysis unit may adjust its analysis algorithm by referring to the user's past speech speed and tone. For example, the voice analysis unit may emphasize specific keywords based on the user's past speech content. In this way, the analysis algorithm can be optimized by referring to the user's past speech patterns.

[0040] The voice analysis unit can adjust the level of detail in its analysis based on the user's speaking speed and tone. For example, if the user speaks quickly, the voice analysis unit increases the level of detail to accurately capture all information. For example, if the user speaks slowly, the voice analysis unit maintains the level of detail at a normal level to facilitate natural conversation. For example, if the user's tone changes, the voice analysis unit adjusts the level of detail accordingly. This improves the accuracy of the analysis by adjusting the level of detail based on the user's speaking speed and tone.

[0041] The voice analysis unit can prioritize the analysis of highly relevant voice patterns by considering the user's geographical location during voice analysis. For example, if the user is in a specific region, the voice analysis unit will prioritize the analysis of voice patterns specific to that region. For example, if the user is traveling, the voice analysis unit will consider the language and dialect of the travel destination during the analysis. For example, if the user is participating in a specific event, the voice analysis unit will prioritize the analysis of voice patterns related to that event. In this way, by considering the user's geographical location, the voice analysis unit can prioritize the analysis of highly relevant voice patterns.

[0042] The voice analysis unit can analyze the user's social media activity and identify relevant voice patterns during voice analysis. For example, the voice analysis unit prioritizes analyzing phrases that the user frequently uses on social media. For example, the voice analysis unit analyzes voice patterns related to topics of interest from the user's social media activity. For example, the voice analysis unit adjusts the accuracy of voice analysis by referring to the user's emotional expressions on social media. This allows for the analysis of relevant voice patterns by analyzing the user's social media activity.

[0043] The language analysis unit can adjust the level of detail of its analysis based on the importance of the utterances during language analysis. For example, the language analysis unit performs a detailed analysis on important utterances. For example, the language analysis unit performs a normal analysis on general utterances. For example, the language analysis unit performs a simplified analysis on repeatedly uttered content. By adjusting the level of detail of the analysis based on the importance of the utterances, important utterances can be analyzed in detail.

[0044] The language analysis unit can apply different analysis algorithms depending on the category of the utterance during language analysis. For example, it can apply an analysis algorithm that takes technical terms into account to technical content. For example, it can apply a general analysis algorithm to everyday conversation. For example, it can apply an algorithm specialized in emotion analysis to emotional expressions. By applying different analysis algorithms depending on the category of the utterance, the accuracy of the analysis is improved.

[0045] The language analysis unit can determine the priority of analysis based on the timing of utterance submission during language analysis. For example, the language analysis unit prioritizes analysis of urgent utterances. For example, the language analysis unit analyzes regular utterances with normal priority. For example, the language analysis unit analyzes past utterances with lower priority. In this way, by determining the priority of analysis based on the timing of utterance submission, urgent utterances can be analyzed preferentially.

[0046] The language analysis unit can adjust the order of analysis based on the relevance of the utterances during language analysis. For example, the language analysis unit prioritizes the analysis of highly relevant utterances. For example, the language analysis unit postpones the analysis of less relevant utterances. For example, the language analysis unit evaluates the relevance of utterances in real time and dynamically adjusts the order of analysis. This allows for prioritizing the analysis of highly relevant utterances by adjusting the order of analysis based on the relevance of the utterances.

[0047] The nonverbal analysis unit can perform nonverbal analysis while considering the user's attribute information. For example, the nonverbal analysis unit analyzes facial expressions and gestures while considering the user's age and gender. For example, the nonverbal analysis unit analyzes nonverbal communication while considering the user's cultural background. For example, the nonverbal analysis unit improves the accuracy of the analysis by considering the user's personal characteristics. In this way, the accuracy of the analysis is improved by considering the user's attribute information.

[0048] The nonverbal analysis unit can perform nonverbal analysis while considering the user's geographical distribution. For example, if the user is in a specific region, the nonverbal analysis unit will consider the nonverbal communication specific to that region. For example, if the user is traveling, the nonverbal analysis unit will consider the cultural background of the travel destination. For example, if the user is participating in a specific event, the nonverbal analysis unit will consider the nonverbal communication related to that event. This allows for accurate analysis of region-specific nonverbal communication by considering the user's geographical distribution.

[0049] The nonverbal analysis unit can improve the accuracy of its analysis by referring to relevant literature during nonverbal analysis. For example, the nonverbal analysis unit can improve its nonverbal analysis algorithm by referring to the latest research findings. For example, the nonverbal analysis unit can improve the accuracy of its nonverbal analysis by referring to relevant academic papers. For example, the nonverbal analysis unit can verify the results of its nonverbal analysis by referring to expert opinions. This improves the accuracy of nonverbal analysis by referring to relevant literature.

[0050] The scenario provider can optimize the current scenario by referring to past scenario data when providing a scenario. For example, the scenario provider optimizes the current scenario based on scenarios the user has experienced in the past. For example, the scenario provider provides a scenario tailored to the user's preferences from past scenario data. For example, the scenario provider analyzes past scenario data and provides the most effective scenario. In this way, the current scenario can be optimized by referring to past scenario data.

[0051] The scenario provider can apply different delivery methods depending on the scenario category. For example, the scenario provider might use visual teaching materials for educational scenarios, provide dialogue-based scenarios for social scenarios, or provide scenarios with interactive elements for entertainment scenarios. By applying different delivery methods to each scenario category, the optimal scenario delivery becomes possible.

[0052] The scenario provider can analyze scenario changes based on the submission timing when providing scenarios. For example, the scenario provider can provide scenarios related to seasons or events based on the submission timing. For example, the scenario provider can provide scenarios tailored to the user's schedule based on the submission timing. For example, the scenario provider can analyze past scenario data based on the submission timing to provide the optimal scenario. In this way, by analyzing scenario changes based on the submission timing, the optimal scenario can be provided.

[0053] The scenario provider can analyze scenarios by referring to relevant market data when providing them. For example, the scenario provider can provide scenarios tailored to user interests by referring to relevant market data. For example, the scenario provider can provide scenarios aligned with trends by referring to relevant market data. For example, the scenario provider can provide scenarios tailored to user needs by referring to relevant market data. This allows the provider to provide scenarios tailored to user interests by referring to relevant market data.

[0054] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0055] Spectrum Navi can optimize how scenarios are presented by referencing the user's past behavior data. For example, it can customize the current scenario based on scenarios the user has previously succeeded with. It can also reduce user stress by avoiding scenarios that the user has found difficult in the past. Furthermore, it can analyze the user's past behavior data and provide the most effective scenario. This allows for the optimization of the current scenario by referencing the user's past behavior data.

[0056] Spectrum Navi can customize scenario content by taking into account the user's geographical location. For example, if the user is in a specific region, it can provide scenarios that reflect the unique culture and customs of that region. If the user is traveling, it can provide scenarios that take into account the cultural background of their destination. Furthermore, if the user is attending a specific event, it can provide scenarios related to that event. This allows for the provision of more relevant scenarios by considering the user's geographical location.

[0057] Spectrum Navi can analyze a user's social media activity and provide relevant scenarios. For example, it can customize scenarios based on phrases and topics that a user frequently uses on social media. It can also provide scenarios related to topics of interest based on the user's social media activity. Furthermore, it can adjust the content of scenarios based on the user's emotional expressions on social media. In this way, it can provide relevant scenarios by analyzing a user's social media activity.

[0058] Spectrum Navi can customize scenario content by considering user attribute information. For example, it can provide appropriate scenarios considering the user's age and gender. It can also analyze nonverbal communication considering the user's cultural background. Furthermore, it can adjust scenario content considering the user's personal characteristics. This allows for the provision of more relevant scenarios by considering user attribute information.

[0059] Spectrum Navi can optimize the current scenario by referencing the user's past scenario data. For example, it can customize the current scenario based on scenarios the user has experienced in the past. It can also reduce user stress by avoiding scenarios that the user found difficult in the past. Furthermore, it can analyze the user's past scenario data and provide the most effective scenario. In this way, the current scenario can be optimized by referring to the user's past scenario data.

[0060] The following briefly describes the processing flow for example form 1.

[0061] Step 1: The voice analysis unit analyzes the speech. The voice analysis unit analyzes the user's speech in real time, for example, using speech recognition technology. The voice analysis unit can also detect specific speech patterns and analyze the rhythm, tone, pitch, etc. of the speech. Step 2: The language analysis unit generates an appropriate response based on the speech analyzed by the speech analysis unit. The language analysis unit understands the user's utterance using, for example, natural language processing techniques and generates an appropriate response. The language analysis unit can also generate responses that emphasize specific keywords and highlight important words and phrases. Step 3: The nonverbal analysis unit analyzes facial expressions and gestures based on the responses generated by the verbal analysis unit. The nonverbal analysis unit can, for example, use facial recognition technology to analyze the user's facial expressions and recognize specific expressions. The nonverbal analysis unit recognizes and analyzes expressions such as smiles and anger. Step 4: The scenario provider provides a virtual scenario based on the information analyzed by the nonverbal analysis provider. For example, the scenario provider can provide a scenario in which a user participates in a group activity at school, and can also customize specific scenarios. The scenario provider provides customized scenario themes and settings.

[0062] (Example of form 2) Spectrum Navi: Autism Communication Support, according to an embodiment of the present invention, is an AI-based tool that supports communication for people with autism spectrum disorder (ASD). This system uses speech recognition technology to analyze the user's conversation in real time and language analysis technology to generate appropriate responses. Furthermore, it supports nonverbal communication by analyzing facial expressions and gestures. It also provides social skills training using virtual scenarios to help users adapt to social situations. For example, when a user starts a conversation, speech recognition technology analyzes the content in real time. Next, language analysis technology understands the analyzed content and generates an appropriate response. For example, if the user says "Hello," the agent generates a response such as "Hello, how are you today?" Furthermore, the agent analyzes the user's facial expressions and gestures. For example, if the user smiles, the agent generates a response such as "You seem happy." In this way, the user is also supported in nonverbal communication. Social skills training using virtual scenarios is also provided. For example, a user can experience a scenario in which they participate in group activities at school. In this scenario, the agent reconstructs the teacher's instructions in an easy-to-understand format and displays them to the user. This allows users to independently understand the content of their activities and respond appropriately. In this way, SpectrumNavi supports people with ASD in gaining confidence in their daily communication and leading richer social lives.

[0063] The spectrum navigation system according to this embodiment comprises a voice analysis unit, a language analysis unit, a non-verbal analysis unit, and a scenario provision unit. The voice analysis unit analyzes voice. The voice analysis unit analyzes the user's utterances in real time, for example, using speech recognition technology. The voice analysis unit can also detect specific voice patterns, for example. The voice analysis unit analyzes the rhythm, tone, pitch, etc., of the voice. The language analysis unit generates an appropriate response based on the voice analyzed by the voice analysis unit. The language analysis unit understands the user's utterances and generates an appropriate response, for example, using natural language processing technology. The language analysis unit can also emphasize specific keywords, for example. The language analysis unit generates a response by emphasizing important words or phrases, for example. The non-verbal analysis unit analyzes facial expressions and gestures based on the response generated by the language analysis unit. The non-verbal analysis unit analyzes the user's facial expressions, for example, using facial expression recognition technology. The non-verbal analysis unit can also recognize specific facial expressions, for example. The non-verbal analysis unit recognizes and analyzes facial expressions such as smiles and anger, for example. The scenario provider unit provides a virtual scenario based on information analyzed by the nonverbal analysis unit. For example, the scenario provider unit provides a scenario in which a user participates in a group activity at school. The scenario provider unit can also customize specific scenarios. For example, the scenario provider unit can customize and provide the theme and settings of a scenario. In this way, the spectrum navigation according to this embodiment can support communication for people with ASD.

[0064] The voice analysis unit analyzes speech. For example, it uses speech recognition technology to analyze the user's speech in real time. Specifically, speech recognition technology converts the user's speech into text data and analyzes its content. The voice analysis unit can also detect specific speech patterns. This involves a process of extracting features such as speech rhythm, tone, and pitch and matching them to specific patterns. For example, it can identify speech patterns when a user is excited or calm. The voice analysis unit analyzes speech rhythm, tone, and pitch, allowing for a more accurate understanding of the user's emotional state and intentions. The voice analysis unit provides these analysis results to other departments in real time, playing a role in quickly and appropriately adjusting the overall system response. Furthermore, the voice analysis unit can use noise reduction technology to remove ambient and background noise, enabling a clearer analysis of the user's speech. This improves the accuracy of speech analysis and increases system reliability. The voice analysis unit can also accumulate the user's speech history and perform personalized analysis based on past data. This enables advanced voice analysis that takes into account the individual characteristics and tendencies of each user.

[0065] The language analysis unit generates appropriate responses based on the speech analyzed by the speech analysis unit. The language analysis unit understands the user's utterances using, for example, natural language processing techniques and generates appropriate responses. Specifically, natural language processing techniques include analyzing text data provided by the speech analysis unit to understand context and meaning. The language analysis unit can also, for example, emphasize specific keywords. This involves extracting important words and phrases from the user's utterances and generating responses based on them. For example, if a user says "help," the language analysis unit will emphasize this keyword and generate a response that provides quick and appropriate assistance. The language analysis unit generates responses by emphasizing important words and phrases, for example. This enables responses that accurately reflect the user's intentions and emotions. Furthermore, the language analysis unit can generate personalized responses based on the user's speech history and past dialogue data. This enables sophisticated dialogue tailored to the user's individual needs and preferences. The language analysis unit can also support multiple languages ​​and dialects, providing consistent, high-quality responses to different users. This allows the language analysis unit to accurately understand the user's speech and generate appropriate and effective responses.

[0066] The nonverbal analysis unit analyzes facial expressions and gestures based on the responses generated by the verbal analysis unit. For example, the nonverbal analysis unit analyzes the user's facial expressions using facial recognition technology. Specifically, facial recognition technology includes a process of detecting the user's facial features and analyzing changes in facial expressions in real time. The nonverbal analysis unit can also recognize specific facial expressions, such as smiles, anger, and sadness. For example, if a user smiles, the nonverbal analysis unit recognizes this expression and adjusts the system's response accordingly. The nonverbal analysis unit recognizes and analyzes expressions such as smiles and anger, enabling accurate understanding of the user's emotional state and providing appropriate responses. Furthermore, the nonverbal analysis unit can analyze the user's hand and body movements using gesture recognition technology. This allows for a more detailed understanding of the user's intentions and actions. For example, if a user raises their hand, the nonverbal analysis unit recognizes this gesture and provides an appropriate response. Additionally, the nonverbal analysis unit can accumulate the user's past facial and gesture data to perform personalized analysis. This enables advanced nonverbal analysis that takes into account the user's individual characteristics and tendencies.

[0067] The Scenario Provider provides virtual scenarios based on information analyzed by the Nonverbal Analysis Unit. For example, the Scenario Provider might provide a scenario in which a user participates in a group activity at school. Specifically, the Scenario Provider generates virtual scenarios tailored to the user's needs and goals, helping users improve their skills through experiences that closely resemble real-world situations. The Scenario Provider can also customize specific scenarios. This involves adjusting the scenario's theme and settings to match the user's preferences and needs. For example, if a user wants to improve their communication skills in a specific situation, the Scenario Provider will provide a scenario suited to that situation. The Scenario Provider can customize the scenario's theme and settings, for example. This allows users to effectively improve their skills through scenarios that are right for them. Furthermore, the Scenario Provider can continuously improve scenarios based on the user's progress and feedback. This enables flexible responses to the user's growth and changes. The Scenario Provider can also combine multiple scenarios to provide users with diverse experiences. This allows users to comprehensively improve their skills in various situations.

[0068] The voice analysis unit can detect specific voice patterns. For example, the voice analysis unit detects specific voice patterns by analyzing the rhythm, tone, pitch, etc. The voice analysis unit can improve the accuracy of voice analysis by detecting specific voice patterns. For example, the voice analysis unit can use speech recognition technology to detect specific voice patterns. This improves the accuracy of voice analysis by detecting specific voice patterns.

[0069] The language analysis unit can emphasize specific keywords. For example, the language analysis unit generates responses by emphasizing important words or phrases. The language analysis unit can improve the accuracy of language analysis by emphasizing specific keywords. For example, the language analysis unit can emphasize specific keywords using natural language processing techniques. This improves the accuracy of language analysis by emphasizing specific keywords.

[0070] The nonverbal analysis unit can recognize specific facial expressions. For example, the nonverbal analysis unit recognizes specific facial expressions using facial expression recognition technology. For example, the nonverbal analysis unit recognizes and analyzes facial expressions such as smiles and anger. The nonverbal analysis unit can improve the accuracy of nonverbal analysis by recognizing specific facial expressions. Thus, recognizing specific facial expressions improves the accuracy of nonverbal analysis.

[0071] The scenario provider can customize specific scenarios. For example, the scenario provider can customize the theme and settings of a scenario before providing it. By customizing specific scenarios, the scenario provider can improve the accuracy of its scenario provision. This means that customizing specific scenarios improves the accuracy of scenario provision.

[0072] The scenario provider can provide scenarios in which users participate in group activities at school. For example, the scenario provider can provide scenarios in which users participate in group activities at school. By providing scenarios in which users participate in group activities at school, the scenario provider can improve users' social skills. This improves users' social skills by providing scenarios in which they participate in group activities at school.

[0073] The voice analysis unit can estimate the user's emotions and adjust the accuracy of the voice analysis based on the estimated emotions. For example, if the user is nervous, the voice analysis unit can increase the accuracy of the voice analysis to detect even subtle changes in voice. For example, if the user is relaxed, the voice analysis unit can maintain the accuracy of the voice analysis at a normal level to facilitate natural conversation. For example, if the user is excited, the voice analysis unit can adjust the accuracy of the voice analysis to quickly detect changes in emotion. This improves the accuracy of the analysis by adjusting the accuracy of the voice analysis based on 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.

[0074] The voice analysis unit can optimize its analysis algorithm by referring to the user's past speech patterns during voice analysis. For example, the voice analysis unit may prioritize the analysis of specific phrases the user has used in the past. For example, the voice analysis unit may adjust its analysis algorithm by referring to the user's past speech speed and tone. For example, the voice analysis unit may emphasize specific keywords based on the user's past speech content. In this way, the analysis algorithm can be optimized by referring to the user's past speech patterns.

[0075] The voice analysis unit can adjust the level of detail in its analysis based on the user's speaking speed and tone. For example, if the user speaks quickly, the voice analysis unit increases the level of detail to accurately capture all information. For example, if the user speaks slowly, the voice analysis unit maintains the level of detail at a normal level to facilitate natural conversation. For example, if the user's tone changes, the voice analysis unit adjusts the level of detail accordingly. This improves the accuracy of the analysis by adjusting the level of detail based on the user's speaking speed and tone.

[0076] The voice analysis unit can estimate the user's emotions and determine the priority of voice analysis based on the estimated emotions. For example, if the user is nervous, the voice analysis unit will prioritize analyzing important utterances. If the user is relaxed, the voice analysis unit will analyze the overall conversation content evenly. If the user is excited, the voice analysis unit will prioritize analyzing utterances related to changes in emotion. This allows for the prioritization of important utterances by determining the priority of voice analysis based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI.

[0077] The voice analysis unit can prioritize the analysis of highly relevant voice patterns by considering the user's geographical location during voice analysis. For example, if the user is in a specific region, the voice analysis unit will prioritize the analysis of voice patterns specific to that region. For example, if the user is traveling, the voice analysis unit will consider the language and dialect of the travel destination during the analysis. For example, if the user is participating in a specific event, the voice analysis unit will prioritize the analysis of voice patterns related to that event. In this way, by considering the user's geographical location, the voice analysis unit can prioritize the analysis of highly relevant voice patterns.

[0078] The voice analysis unit can analyze the user's social media activity and identify relevant voice patterns during voice analysis. For example, the voice analysis unit prioritizes analyzing phrases that the user frequently uses on social media. For example, the voice analysis unit analyzes voice patterns related to topics of interest from the user's social media activity. For example, the voice analysis unit adjusts the accuracy of voice analysis by referring to the user's emotional expressions on social media. This allows for the analysis of relevant voice patterns by analyzing the user's social media activity.

[0079] The language analysis unit can estimate the user's emotions and adjust the expression of the language analysis based on the estimated user emotions. For example, if the user is nervous, the language analysis unit will use a simple and clear expression. For example, if the user is relaxed, the language analysis unit will use a detailed and rich expression. For example, if the user is excited, the language analysis unit will use an expression that emphasizes the emotion. In this way, by adjusting the expression of the language analysis based on the user's emotions, an appropriate expression 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.

[0080] The language analysis unit can adjust the level of detail of its analysis based on the importance of the utterances during language analysis. For example, the language analysis unit performs a detailed analysis on important utterances. For example, the language analysis unit performs a normal analysis on general utterances. For example, the language analysis unit performs a simplified analysis on repeatedly uttered content. By adjusting the level of detail of the analysis based on the importance of the utterances, important utterances can be analyzed in detail.

[0081] The language analysis unit can apply different analysis algorithms depending on the category of the utterance during language analysis. For example, it can apply an analysis algorithm that takes technical terms into account to technical content. For example, it can apply a general analysis algorithm to everyday conversation. For example, it can apply an algorithm specialized in emotion analysis to emotional expressions. By applying different analysis algorithms depending on the category of the utterance, the accuracy of the analysis is improved.

[0082] The language 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 language analysis unit will perform a short, concise analysis. If the user is relaxed, the language analysis unit will perform a detailed analysis. If the user is excited, the language analysis unit will perform an emotionally emphasized analysis. By adjusting the length of the analysis based on the user's emotions, appropriate analysis results can be provided. Emotion estimation is achieved using an emotion estimation function, for example, with an emotion engine or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI.

[0083] The language analysis unit can determine the priority of analysis based on the timing of utterance submission during language analysis. For example, the language analysis unit prioritizes analysis of urgent utterances. For example, the language analysis unit analyzes regular utterances with normal priority. For example, the language analysis unit analyzes past utterances with lower priority. In this way, by determining the priority of analysis based on the timing of utterance submission, urgent utterances can be analyzed preferentially.

[0084] The language analysis unit can adjust the order of analysis based on the relevance of the utterances during language analysis. For example, the language analysis unit prioritizes the analysis of highly relevant utterances. For example, the language analysis unit postpones the analysis of less relevant utterances. For example, the language analysis unit evaluates the relevance of utterances in real time and dynamically adjusts the order of analysis. This allows for prioritizing the analysis of highly relevant utterances by adjusting the order of analysis based on the relevance of the utterances.

[0085] The nonverbal analysis unit can estimate the user's emotions and adjust the nonverbal analysis criteria based on the estimated user emotions. For example, if the user is tense, the nonverbal analysis unit can detect even subtle changes in facial expression. For example, if the user is relaxed, the nonverbal analysis unit can detect normal changes in facial expression. For example, if the user is excited, the nonverbal analysis unit can quickly detect changes in emotion. This improves the accuracy of the analysis by adjusting the nonverbal analysis criteria based on 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.

[0086] The nonverbal analysis unit can perform nonverbal analysis while considering the user's attribute information. For example, the nonverbal analysis unit analyzes facial expressions and gestures while considering the user's age and gender. For example, the nonverbal analysis unit analyzes nonverbal communication while considering the user's cultural background. For example, the nonverbal analysis unit improves the accuracy of the analysis by considering the user's personal characteristics. In this way, the accuracy of the analysis is improved by considering the user's attribute information.

[0087] The nonverbal analysis unit can estimate the user's emotions and adjust the order in which the nonverbal analysis results are displayed based on the estimated user emotions. For example, if the user is tense, the nonverbal analysis unit will prioritize displaying important nonverbal information. For example, if the user is relaxed, the nonverbal analysis unit will display all nonverbal information evenly. For example, if the user is excited, the nonverbal analysis unit will prioritize displaying nonverbal information related to changes in emotion. This allows for the prioritization of important nonverbal information by adjusting the order in which the nonverbal analysis results are displayed based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI.

[0088] The nonverbal analysis unit can perform nonverbal analysis while considering the user's geographical distribution. For example, if the user is in a specific region, the nonverbal analysis unit will consider the nonverbal communication specific to that region. For example, if the user is traveling, the nonverbal analysis unit will consider the cultural background of the travel destination. For example, if the user is participating in a specific event, the nonverbal analysis unit will consider the nonverbal communication related to that event. This allows for accurate analysis of region-specific nonverbal communication by considering the user's geographical distribution.

[0089] The nonverbal analysis unit can improve the accuracy of its analysis by referring to relevant literature during nonverbal analysis. For example, the nonverbal analysis unit can improve its nonverbal analysis algorithm by referring to the latest research findings. For example, the nonverbal analysis unit can improve the accuracy of its nonverbal analysis by referring to relevant academic papers. For example, the nonverbal analysis unit can verify the results of its nonverbal analysis by referring to expert opinions. This improves the accuracy of nonverbal analysis by referring to relevant literature.

[0090] The scenario provider can estimate the user's emotions and adjust the way the scenario is displayed based on those emotions. For example, if the user is nervous, the scenario provider provides a simple and highly visible display method. If the user is relaxed, the scenario provider provides a display method that includes detailed information. If the user is excited, the scenario provider provides a display method that emphasizes emotions. By adjusting the scenario display method based on the user's emotions, the system can provide the optimal display method for the user. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI may include, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI.

[0091] The scenario provider can optimize the current scenario by referring to past scenario data when providing a scenario. For example, the scenario provider optimizes the current scenario based on scenarios the user has experienced in the past. For example, the scenario provider provides a scenario tailored to the user's preferences from past scenario data. For example, the scenario provider analyzes past scenario data and provides the most effective scenario. In this way, the current scenario can be optimized by referring to past scenario data.

[0092] The scenario provider can apply different delivery methods depending on the scenario category. For example, the scenario provider might use visual teaching materials for educational scenarios, provide dialogue-based scenarios for social scenarios, or provide scenarios with interactive elements for entertainment scenarios. By applying different delivery methods to each scenario category, the optimal scenario delivery becomes possible.

[0093] The scenario provider can estimate the user's emotions and adjust the importance of scenarios based on the estimated emotions. For example, if the user is tense, the scenario provider will prioritize providing important scenarios. If the user is relaxed, the scenario provider will provide all scenarios equally. If the user is excited, the scenario provider will prioritize providing scenarios related to changes in emotions. This allows for the prioritization of important scenarios by adjusting their importance based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI.

[0094] The scenario provider can analyze scenario changes based on the submission timing when providing scenarios. For example, the scenario provider can provide scenarios related to seasons or events based on the submission timing. For example, the scenario provider can provide scenarios tailored to the user's schedule based on the submission timing. For example, the scenario provider can analyze past scenario data based on the submission timing to provide the optimal scenario. In this way, by analyzing scenario changes based on the submission timing, the optimal scenario can be provided.

[0095] The scenario provider can analyze scenarios by referring to relevant market data when providing them. For example, the scenario provider can provide scenarios tailored to user interests by referring to relevant market data. For example, the scenario provider can provide scenarios aligned with trends by referring to relevant market data. For example, the scenario provider can provide scenarios tailored to user needs by referring to relevant market data. This allows the provider to provide scenarios tailored to user interests by referring to relevant market data.

[0096] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0097] Spectrum Navi can estimate the user's emotions and adjust the difficulty of the scenario based on those emotions. For example, if the user is nervous, the scenario provider can offer a simple scenario; if the user is relaxed, it can offer a more complex scenario. Furthermore, if the user is excited, the scenario provider can offer an emotionally emphasized scenario. This allows for the provision of the optimal scenario tailored to the user's emotions. Emotion estimation is achieved using an emotion engine or generative AI.

[0098] Spectrum Navi can optimize how scenarios are presented by referencing the user's past behavior data. For example, it can customize the current scenario based on scenarios the user has previously succeeded with. It can also reduce user stress by avoiding scenarios that the user has found difficult in the past. Furthermore, it can analyze the user's past behavior data and provide the most effective scenario. This allows for the optimization of the current scenario by referencing the user's past behavior data.

[0099] Spectrum Navi can estimate a user's emotions and adjust the content of the feedback based on those emotions. For example, if the user is nervous, the feedback can be simple and positive. If the user is relaxed, detailed feedback can be provided. If the user is excited, emotionally emphasized feedback can be provided. This allows for the provision of optimal feedback tailored to the user's emotions. Emotion estimation is achieved using an emotion engine or generative AI, among other methods.

[0100] Spectrum Navi can customize scenario content by taking into account the user's geographical location. For example, if the user is in a specific region, it can provide scenarios that reflect the unique culture and customs of that region. If the user is traveling, it can provide scenarios that take into account the cultural background of their destination. Furthermore, if the user is attending a specific event, it can provide scenarios related to that event. This allows for the provision of more relevant scenarios by considering the user's geographical location.

[0101] Spectrum Navi can estimate the user's emotions and adjust the scenario's pace based on those emotions. For example, if the user is nervous, the scenario's pace can be slowed down to make it easier for the user to understand. If the user is relaxed, the scenario can be presented at a normal pace. If the user is excited, the scenario's pace can be sped up to accommodate the change in emotion. This allows for the provision of an optimal scenario pace tailored to the user's emotions. Emotion estimation is achieved using an emotion engine or generative AI, among other methods.

[0102] Spectrum Navi can analyze a user's social media activity and provide relevant scenarios. For example, it can customize scenarios based on phrases and topics that a user frequently uses on social media. It can also provide scenarios related to topics of interest based on the user's social media activity. Furthermore, it can adjust the content of scenarios based on the user's emotional expressions on social media. In this way, it can provide relevant scenarios by analyzing a user's social media activity.

[0103] Spectrum Navi can estimate a user's emotions and adjust the interactive elements of a scenario based on those emotions. For example, if the user is nervous, the interactive elements can be reduced to provide a simpler scenario. If the user is relaxed, the interactive elements can be increased to provide a richer scenario. Furthermore, if the user is excited, emotionally emphasized interactive elements can be provided. This allows for the provision of optimal interactive elements tailored to the user's emotions. Emotion estimation is achieved using an emotion engine or generative AI, among other methods.

[0104] Spectrum Navi can customize scenario content by considering user attribute information. For example, it can provide appropriate scenarios considering the user's age and gender. It can also analyze nonverbal communication considering the user's cultural background. Furthermore, it can adjust scenario content considering the user's personal characteristics. This allows for the provision of more relevant scenarios by considering user attribute information.

[0105] Spectrum Navi can estimate a user's emotions and adjust the scenario's feedback method based on those emotions. For example, if the user is nervous, the feedback can be simple and positive. If the user is relaxed, detailed feedback can be provided. If the user is excited, emotionally emphasized feedback can be provided. This allows for the provision of the most appropriate feedback method according to the user's emotions. Emotion estimation is achieved using an emotion engine or generative AI, among other methods.

[0106] Spectrum Navi can optimize the current scenario by referencing the user's past scenario data. For example, it can customize the current scenario based on scenarios the user has experienced in the past. It can also reduce user stress by avoiding scenarios that the user found difficult in the past. Furthermore, it can analyze the user's past scenario data and provide the most effective scenario. In this way, the current scenario can be optimized by referring to the user's past scenario data.

[0107] The following briefly describes the processing flow for example form 2.

[0108] Step 1: The voice analysis unit analyzes the speech. The voice analysis unit analyzes the user's speech in real time, for example, using speech recognition technology. The voice analysis unit can also detect specific speech patterns and analyze the rhythm, tone, pitch, etc. of the speech. Step 2: The language analysis unit generates an appropriate response based on the speech analyzed by the speech analysis unit. The language analysis unit understands the user's utterance using, for example, natural language processing techniques and generates an appropriate response. The language analysis unit can also generate responses that emphasize specific keywords and highlight important words and phrases. Step 3: The nonverbal analysis unit analyzes facial expressions and gestures based on the responses generated by the verbal analysis unit. The nonverbal analysis unit can, for example, use facial recognition technology to analyze the user's facial expressions and recognize specific expressions. The nonverbal analysis unit recognizes and analyzes expressions such as smiles and anger. Step 4: The scenario provider provides a virtual scenario based on the information analyzed by the nonverbal analysis provider. For example, the scenario provider can provide a scenario in which a user participates in a group activity at school, and can also customize specific scenarios. The scenario provider provides customized scenario themes and settings.

[0109] 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.

[0110] 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.

[0111] 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.

[0112] Each of the multiple elements described above, including the voice analysis unit, language analysis unit, non-verbal analysis unit, and scenario provision unit, is implemented in at least one of the smart device 14 and the data processing unit 12. For example, the voice analysis unit is implemented by the microphone 38B and control unit 46A of the smart device 14 and analyzes the user's speech in real time. The language analysis unit is implemented by the specific processing unit 290 of the data processing unit 12 and generates an appropriate response using natural language processing technology. The non-verbal analysis unit is implemented by the camera 42 and control unit 46A of the smart device 14 and analyzes the user's facial expressions and gestures. The scenario provision unit is implemented by the specific processing unit 290 of the data processing unit 12 and provides a virtual scenario. The correspondence between each unit and the device or control unit is not limited to the example described above and can be modified in various ways.

[0113] [Second Embodiment] Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment.

[0114] 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.

[0115] 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.

[0116] 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.

[0117] 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.

[0118] 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).

[0119] 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.

[0120] 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.

[0121] 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.

[0122] 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.

[0123] 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.

[0124] 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.).

[0125] 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.

[0126] 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.

[0127] 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.

[0128] Each of the multiple elements described above, including the voice analysis unit, language analysis unit, non-verbal analysis unit, and scenario provision unit, is implemented in at least one of the smart glasses 214 and the data processing unit 12. For example, the voice analysis unit is implemented by the microphone 238 and control unit 46A of the smart glasses 214 and analyzes the user's speech in real time. The language analysis unit is implemented by the specific processing unit 290 of the data processing unit 12 and generates an appropriate response using natural language processing technology. The non-verbal analysis unit is implemented by the camera 42 and control unit 46A of the smart glasses 214 and analyzes the user's facial expressions and gestures. The scenario provision unit is implemented by the specific processing unit 290 of the data processing unit 12 and provides a virtual scenario. The correspondence between each unit and the device or control unit is not limited to the example described above and can be modified in various ways.

[0129] [Third Embodiment] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[0130] 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.

[0131] 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.

[0132] 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.

[0133] 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.

[0134] 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).

[0135] 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.

[0136] 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.

[0137] 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.

[0138] 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.

[0139] 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.

[0140] 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.).

[0141] 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.

[0142] 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.

[0143] 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.

[0144] Each of the multiple elements described above, including the voice analysis unit, language analysis unit, non-verbal analysis unit, and scenario provision unit, is implemented in at least one of the headset terminal 314 and the data processing unit 12. For example, the voice analysis unit is implemented by the microphone 238 and control unit 46A of the headset terminal 314 and analyzes the user's speech in real time. The language analysis unit is implemented by the specific processing unit 290 of the data processing unit 12 and generates an appropriate response using natural language processing technology. The non-verbal analysis unit is implemented by the camera 42 and control unit 46A of the headset terminal 314 and analyzes the user's facial expressions and gestures. The scenario provision unit is implemented by the specific processing unit 290 of the data processing unit 12 and provides a virtual scenario. The correspondence between each unit and the device or control unit is not limited to the example described above and can be modified in various ways.

[0145] [Fourth Embodiment] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[0146] 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.

[0147] 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.

[0148] 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.

[0149] 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.

[0150] 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).

[0151] 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.

[0152] 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.

[0153] 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.

[0154] 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.

[0155] 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.

[0156] 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.

[0157] 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.).

[0158] 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.

[0159] 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.

[0160] 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.

[0161] Each of the multiple elements described above, including the voice analysis unit, language analysis unit, non-verbal analysis unit, and scenario provision unit, is implemented in at least one of the following: the robot 414 and the data processing unit 12. For example, the voice analysis unit is implemented by the microphone 238 and control unit 46A of the robot 414 and analyzes the user's speech in real time. The language analysis unit is implemented by the specific processing unit 290 of the data processing unit 12 and generates an appropriate response using natural language processing technology. The non-verbal analysis unit is implemented by the camera 42 and control unit 46A of the robot 414 and analyzes the user's facial expressions and gestures. The scenario provision unit is implemented by the specific processing unit 290 of the data processing unit 12 and provides a virtual scenario. The correspondence between each unit and the device or control unit is not limited to the example described above and can be modified in various ways.

[0162] 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.

[0163] 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.

[0164] 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.

[0165] 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.

[0166] 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.

[0167] 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."

[0168] 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.

[0169] 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.

[0170] 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.

[0171] 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.

[0172] 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.

[0173] 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.

[0174] 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.

[0175] 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.

[0176] 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.

[0177] 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.

[0178] 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.

[0179] 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.

[0180] (Note 1) A voice analysis unit that analyzes the sound, A language analysis unit that generates an appropriate response based on the speech analyzed by the aforementioned speech analysis unit, A nonverbal analysis unit analyzes facial expressions and gestures based on the responses generated by the verbal analysis unit, The system includes a scenario providing unit that provides a virtual scenario based on information analyzed by the non-verbal analysis unit. A system characterized by the following features. (Note 2) The aforementioned voice analysis unit, Detecting specific voice patterns The system described in Appendix 1, characterized by the features described herein. (Note 3) The language analysis unit described above is Emphasize specific keywords The system described in Appendix 1, characterized by the features described herein. (Note 4) The aforementioned nonverbal analysis unit, Recognizing specific facial expressions The system described in Appendix 1, characterized by the features described herein. (Note 5) The aforementioned scenario provision unit, Customize specific scenarios The system described in Appendix 1, characterized by the features described herein. (Note 6) The aforementioned scenario provision unit, Provides a scenario in which users participate in group activities at school. The system described in Appendix 1, characterized by the features described herein. (Note 7) The aforementioned voice analysis unit, It estimates the user's emotions and adjusts the accuracy of the voice analysis based on the estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 8) The aforementioned voice analysis unit, During speech analysis, the analysis algorithm is optimized by referring to the user's past speech patterns. The system described in Appendix 1, characterized by the features described herein. (Note 9) The aforementioned voice analysis unit, During speech analysis, the level of detail is adjusted based on the user's speaking speed and tone. The system described in Appendix 1, characterized by the features described herein. (Note 10) The aforementioned voice analysis unit, It estimates the user's emotions and determines the priority of voice analysis based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 11) The aforementioned voice analysis unit, During voice analysis, the system prioritizes analyzing highly relevant voice patterns by considering the user's geographical location. The system described in Appendix 1, characterized by the features described herein. (Note 12) The aforementioned voice analysis unit, During voice analysis, the system analyzes the user's social media activity and identifies related voice patterns. The system described in Appendix 1, characterized by the features described herein. (Note 13) The language analysis unit described above is It estimates the user's emotions and adjusts the language analysis representation based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 14) The language analysis unit described above is During language analysis, the level of detail is adjusted based on the importance of the spoken content. The system described in Appendix 1, characterized by the features described herein. (Note 15) The language analysis unit described above is During language analysis, different analysis algorithms are applied depending on the category of the utterance. The system described in Appendix 1, characterized by the features described herein. (Note 16) The language analysis unit described above is It estimates the user's emotions and adjusts the length of the language analysis based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 17) The language analysis unit described above is During language analysis, the priority of analysis is determined based on the timing of the submission of the utterance. The system described in Appendix 1, characterized by the features described herein. (Note 18) The language analysis unit described above is During language analysis, the order of analysis is adjusted based on the relevance of the utterances. The system described in Appendix 1, characterized by the features described herein. (Note 19) The aforementioned nonverbal analysis unit, We estimate the user's emotions and adjust the nonverbal analysis criteria based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 20) The aforementioned nonverbal analysis unit, During nonverbal analysis, the analysis is performed while taking into account the user's attribute information. The system described in Appendix 1, characterized by the features described herein. (Note 21) The aforementioned nonverbal analysis unit, It estimates the user's emotions and adjusts the order in which nonverbal analysis results are displayed based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 22) The aforementioned nonverbal analysis unit, During nonverbal analysis, the analysis takes into account the geographical distribution of users. The system described in Appendix 1, characterized by the features described herein. (Note 23) The aforementioned nonverbal analysis unit, When performing nonverbal analysis, referencing relevant literature improves the accuracy of the analysis. The system described in Appendix 1, characterized by the features described herein. (Note 24) The aforementioned scenario provision unit, It estimates the user's emotions and adjusts how the scenario is displayed based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 25) The aforementioned scenario provision unit, When providing a scenario, the current scenario is optimized by referencing past scenario data. The system described in Appendix 1, characterized by the features described herein. (Note 26) The aforementioned scenario provision unit, When providing scenarios, different delivery methods will be applied depending on the scenario category. The system described in Appendix 1, characterized by the features described herein. (Note 27) The aforementioned scenario provision unit, It estimates the user's emotions and adjusts the importance of scenarios based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 28) The aforementioned scenario provision unit, When providing a scenario, analyze how the scenario changes based on when it was submitted. The system described in Appendix 1, characterized by the features described herein. (Note 29) The aforementioned scenario provision unit, When providing a scenario, we analyze the scenario by referring to relevant market data. The system described in Appendix 1, characterized by the features described herein. [Explanation of symbols]

[0181] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Devices 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robots

Claims

1. A voice analysis unit that analyzes the sound, A language analysis unit that generates an appropriate response based on the speech analyzed by the aforementioned speech analysis unit, A nonverbal analysis unit analyzes facial expressions and gestures based on the responses generated by the language analysis unit, The system includes a scenario providing unit that provides a virtual scenario based on information analyzed by the non-verbal analysis unit. A system characterized by the following features.

2. The aforementioned voice analysis unit, Detecting specific voice patterns The system according to feature 1.

3. The language analysis unit described above, Emphasize specific keywords The system according to feature 1.

4. The aforementioned nonverbal analysis unit, Recognizing specific facial expressions The system according to feature 1.

5. The aforementioned scenario provision unit, Customize specific scenarios The system according to feature 1.

6. The aforementioned scenario provision unit, Provides a scenario in which users participate in group activities at school. The system according to feature 1.

7. The aforementioned voice analysis unit, It estimates the user's emotions and adjusts the accuracy of the voice analysis based on the estimated emotions. The system according to feature 1.

8. The aforementioned voice analysis unit, During speech analysis, the analysis algorithm is optimized by referring to the user's past speech patterns. The system according to feature 1.

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