System

Abstract

An object of the system according to the embodiment is to enable smooth communication even for a person who is not good at communication in a conversation or a person who has a disability.SOLUTION: A system includes a reception unit, an analysis unit, and an output unit. The reception unit receives an input of a text, a voice, or an image from a user. The analysis unit analyzes the information received by the reception unit and generates a response. The output unit outputs the response generated by the analysis unit by voice or text.SELECTED DRAWING: Figure 1

Description

[Technical Field]

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

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

[0003] [Patent Document 1] Japanese Patent Publication No. 2022-180282 Summary of the Invention [Problem to be solved by the invention]

[0004] Conventional technology has had the problem that it is difficult for people who are not good at conversational communication or who have disabilities to carry out smooth two-way communication.

[0005] The system according to the embodiment aims to enable smooth communication even for people who are not good at conversational communication or who have disabilities. [Means for solving the problem]

[0006] The system according to the embodiment includes a reception unit, an analysis unit, and an output unit. The reception unit receives text, audio, or image input from a user. The analysis unit analyzes the information received by the reception unit and generates a response. The output unit outputs the response generated by the analysis unit in audio or text. [Effects of the Invention]

[0007] The system according to the embodiment allows even people who are not good at conversational communication or who have disabilities to communicate smoothly. [Brief explanation of the drawings]

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

[0028] (Example 1) A communication support system according to an embodiment of the present invention utilizes a generative AI to maintain a friendly, non-adversarial conversation for people who are not good at conversational communication or who have difficulty engaging in smooth two-way communication due to disabilities. The communication support system accepts text, voice, and image input from a user, analyzes it using a generative AI, and generates and outputs an appropriate response. For example, if a user inputs, "How was your day?", the generative AI generates a friendly response to the question. The generative AI then outputs the generated response in voice or text. This allows the user to enjoy a natural conversation. Furthermore, to enhance multimodal support, the generative AI analyzes images and voice and generates an appropriate response. For example, if a user uploads an image, the generative AI analyzes the image and generates a friendly comment related to the image. Similarly, for voice input, the generative AI analyzes the voice and generates an appropriate response. This system enables smooth, friendly communication even for people who are not good at conversational communication or who have disabilities. As a result, the communication support system can provide friendly, smooth communication for people who are not good at conversational communication or who have disabilities. For example, if someone who has difficulty speaking can input text, the AI ​​will output that text as voice, allowing for a smooth conversation. It also supports communication using images and voice, making it easy for people with visual or hearing impairments to use.

[0029] A communication support system according to an embodiment includes a reception unit, an analysis unit, and an output unit. The reception unit receives text, audio, or image input from a user. For example, text formats include plain text, rich text, and HTML. Audio formats include WAV, MP3, and AAC. Image resolutions range from low to high. The analysis unit uses a generation AI to analyze the information received by the reception unit and generate an appropriate response. For example, the generation AI analyzes text using natural language processing technology and generates an appropriate response. The generation AI can also analyze audio using speech recognition technology and generate an appropriate response. The generation AI can also analyze images using image recognition technology and generate a comment related to the image. The output unit outputs the response generated by the analysis unit as audio or text. For example, the output unit outputs a response generated using speech synthesis technology as audio. The output unit can also output a response generated using text generation technology as text. As a result, the communication support system according to an embodiment analyzes user input and generates and outputs an appropriate response, thereby achieving smooth communication.

[0030] The analysis unit can analyze text or speech input by a user and generate a response. The analysis unit can analyze text using, for example, natural language processing technology and generate an appropriate response. For example, the analysis unit can analyze the grammar and meaning of the text and generate an appropriate response. The analysis unit can also analyze speech using speech recognition technology and generate an appropriate response. For example, the analysis unit can analyze speech characteristics, convert speech to text, and generate a response based on that text. In this way, natural conversation can be achieved by generating an appropriate response based on the user's input.

[0031] The analysis unit can analyze images uploaded by users and generate comments related to the images. The analysis unit can analyze images using, for example, image recognition technology and generate comments related to the images. For example, the analysis unit can extract features of the image and generate comments based on the features. The analysis unit can also analyze the content of the image and generate comments based on information related to the image. For example, the analysis unit can recognize objects or scenes included in the image and generate comments related to them. This supports visual communication by generating friendly comments related to the image.

[0032] The output unit can output the generated response as voice or text. For example, the output unit outputs the generated response as voice using speech synthesis technology. For example, the output unit converts the generated text into voice and provides the response to the user as voice. The output unit can also output the generated response as text using text generation technology. For example, the output unit displays the generated text on a screen and provides the response to the user as text. In this way, appropriate feedback is provided to the user by outputting the generated response as voice or text.

[0033] The reception unit can receive voice input from the user. The reception unit receives voice input from the user using, for example, a microphone. For example, the reception unit records what the user says using the microphone and transmits the voice data to the analysis unit. The reception unit can also receive voice input from the user using a voice input device. For example, the reception unit receives voice input using a microphone on a smartphone or tablet. In this way, voice communication is supported by receiving voice input.

[0034] The analysis unit can analyze the voice input and generate an appropriate response. The analysis unit can analyze the voice input using, for example, voice recognition technology and generate an appropriate response. For example, the analysis unit can analyze the characteristics of the voice, convert the voice into text, and generate a response based on the text. The analysis unit can also analyze the emotion of the voice and generate an appropriate response based on the emotion. For example, the analysis unit can analyze the tone and speed of the voice, estimate the user's emotion, and generate a response based on the emotion. In this way, natural voice conversation is realized by analyzing the voice input and generating an appropriate response.

[0035] The reception unit can analyze the user's past input history and select the reception method. The reception unit analyzes the user's past input history using, for example, data mining technology and selects the optimal reception method. For example, the reception unit preferentially suggests input methods (voice, text, etc.) that the user has frequently used in the past. The reception unit can also predict and suggest an input method to be used in a specific time period based on the user's past input history. The reception unit can also suggest a related input method by referring to content that the user has input in the past. In this way, the optimal reception method is provided to the user by analyzing the past input history.

[0036] The reception unit can perform filtering based on the user's current situation and areas of interest when receiving input. The reception unit, for example, identifies the user's current situation using real-time data and preferentially receives information related to that situation. For example, when the user inputs their current situation, the reception unit preferentially receives information related to that situation. The reception unit can also filter and receive related information based on the user's areas of interest. For example, when the user shows interest in a specific topic, the reception unit preferentially receives information related to that topic. In this way, filtering based on the user's situation and areas of interest provides more relevant information.

[0037] When accepting input, the acceptance unit can select an acceptance means according to the user's input method. The acceptance unit selects the optimal acceptance means according to the user's input method, such as voice input, text input, or image input. For example, if the user selects voice input, the acceptance unit accepts the input using voice recognition technology. Also, if the user selects text input, the acceptance unit can accept the input using text analysis technology. Also, if the user uploads an image, the acceptance unit can accept the input using image analysis technology. In this way, smooth input acceptance is achieved by selecting the optimal acceptance means according to the user's input method.

[0038] When receiving input, the reception unit can prioritize receiving highly relevant inputs by taking into account the user's geographical location information. The reception unit, for example, acquires the user's geographical location information using GPS data, and prioritizes receiving highly relevant inputs based on that information. For example, when the user is in a specific area, the reception unit can prioritize receiving information related to that area. Furthermore, when the user is traveling, the reception unit can also prioritize receiving information related to the user's travel destination. Furthermore, when the user is at home, the reception unit can prioritize receiving information about the area around the user's home. In this way, highly relevant information is provided by taking into account the user's geographical location information.

[0039] The reception unit can analyze the user's social media activity and receive related input when receiving input. The reception unit analyzes the user's social media activity using, for example, a social media analysis tool and receives related input. For example, the reception unit preferentially receives information related to places where the user has checked in on social media. The reception unit can also analyze the content of the user's social media posts and preferentially receive related information. The reception unit can also refer to the activities of the user's friends on social media and preferentially receive related information. In this way, highly relevant information can be provided by analyzing social media activity.

[0040] The reception unit can customize the reception method by reflecting the user's past feedback when receiving input. The reception unit, for example, uses a feedback data analysis method to reflect the user's past feedback and customize the optimal reception method. For example, the reception unit suggests the optimal reception method based on feedback provided by the user in the past. The reception unit can also preferentially suggest a specific input method based on the user's past feedback. The reception unit can also analyze the user's past feedback and select the optimal reception means. In this way, the optimal reception method is provided to the user by reflecting the past feedback.

[0041] During analysis, the analysis unit can adjust the level of detail of the analysis based on the importance of the input information. The analysis unit adjusts the level of detail of the analysis based on, for example, the urgency or relevance of the input information. For example, the analysis unit performs a detailed analysis of input information with high importance. The analysis unit can also perform a simplified analysis of input information with low importance. The analysis unit can also perform a quick analysis of input information with high urgency. In this way, by adjusting the level of detail of the analysis based on the importance of the input information, efficient analysis is achieved.

[0042] During analysis, the analysis unit can apply different analysis algorithms depending on the category of input information. The analysis unit applies an appropriate analysis algorithm depending on the category of input information, such as text analysis, image analysis, or voice analysis. For example, the analysis unit applies a natural language processing algorithm to text input. The analysis unit can also apply a voice recognition algorithm to voice input. The analysis unit can also apply an image analysis algorithm to image input. In this way, highly accurate analysis can be achieved by applying an appropriate analysis algorithm depending on the category of input information.

[0043] During analysis, the analysis unit can improve the accuracy of the analysis by referring to the user's past analysis results. The analysis unit, for example, improves the accuracy of the analysis by referring to the user's past analysis results using a method of using past data. For example, the analysis unit performs highly accurate analysis of similar input information based on the user's past analysis results. The analysis unit can also extract specific patterns from the user's past analysis results to improve the accuracy of the analysis. The analysis unit can also analyze the user's past analysis results and optimize the analysis algorithm. In this way, the accuracy of the analysis is improved by referring to the past analysis results.

[0044] During analysis, the analysis unit can determine the analysis priority based on the submission time of the input information. The analysis unit determines the analysis priority based on, for example, the urgency or recency of the submission time. For example, the analysis unit prioritizes analysis of the most recent input information. The analysis unit can also prioritize analysis of input information with high urgency. The analysis unit can also determine the analysis priority based on a deadline specified by the user. In this way, efficient analysis is achieved by determining the analysis priority based on the submission time of the input information.

[0045] During analysis, the analysis unit can adjust the order of analysis based on the relevance of the input information. The analysis unit adjusts the order of analysis based on, for example, the relevance or importance of the information. For example, the analysis unit prioritizes analysis of highly relevant input information. The analysis unit can also postpone analysis of less relevant input information. The analysis unit can also adjust the order of analysis based on the relevance specified by the user. In this way, efficient analysis is achieved by adjusting the order of analysis based on the relevance of the input information.

[0046] During analysis, the analysis unit can adjust the use of technical terms in the analysis according to the user's level of expertise. The analysis unit evaluates the user's level of expertise based on, for example, the user's occupation, educational background, past usage history, etc., and selects appropriate technical terms to provide analysis results. For example, if the user has technical expertise, the analysis unit can provide analysis results that use a lot of technical terms. Also, if the user does not have technical expertise, the analysis unit can provide analysis results in simple language. Also, the analysis unit can select appropriate technical terms to provide analysis results according to the user's level of expertise. In this way, appropriate analysis results can be provided by adjusting the use of technical terms according to the user's level of expertise.

[0047] The output unit can adjust the level of detail of the output based on the importance of the response when outputting. The output unit adjusts the level of detail of the output based on, for example, the urgency or relevance of the response. For example, the output unit outputs a detailed explanation for a response with high importance. The output unit can also output a simplified explanation for a response with low importance. The output unit can also output a quick explanation for a response with high urgency. In this way, adjusting the level of detail of the output based on the importance of the response provides efficient feedback.

[0048] The output unit can apply different output formats depending on the category of the response at the time of output. The output unit applies an appropriate output format depending on the category of the response, such as text output, audio output, or image output. For example, the output unit outputs a text response in text format. The output unit can also output an audio response in audio format. The output unit can also output an image response in image format. In this way, by applying an appropriate output format depending on the category of the response, optimal feedback can be provided to the user.

[0049] The output unit can improve the accuracy of the output by referring to the user's past output results when outputting. For example, the output unit improves the accuracy of the output by referring to the user's past output results using a method of using past data. For example, the output unit provides highly accurate output for similar responses based on the user's past output results. The output unit can also extract specific patterns from the user's past output results to improve the accuracy of the output. The output unit can also analyze the user's past output results and optimize the output algorithm. In this way, the accuracy of the output is improved by referring to the past output results.

[0050] The output unit can determine the output priority based on the submission time of the response at the time of output. The output unit determines the output priority based on, for example, the urgency or recency of the submission time. For example, the output unit prioritizes output of the most recent response. The output unit can also prioritize output of responses with high urgency. The output unit can also determine the output priority based on a deadline specified by the user. In this way, efficient feedback is provided by determining the output priority based on the submission time of the response.

[0051] The output unit can adjust the order of output based on the relevance of responses when outputting. The output unit adjusts the order of output based on, for example, the relevance or importance of responses. For example, the output unit prioritizes output of highly relevant responses. The output unit can also postpone output of less relevant responses. The output unit can also adjust the order of output based on the relevance specified by the user. In this way, efficient feedback can be provided by adjusting the order of output based on the relevance of responses.

[0052] The output unit can adjust the use of technical terms in the output according to the user's level of expertise at the time of output. The output unit evaluates the user's level of expertise based on, for example, the user's occupation, educational background, past usage history, etc., and selects and outputs appropriate technical terms. For example, if the user has technical expertise, the output unit can output using a lot of technical terms. Alternatively, if the user does not have technical expertise, the output unit can output using simple language. Alternatively, the output unit can select and output appropriate technical terms according to the user's level of expertise. In this way, appropriate feedback can be provided by adjusting the use of technical terms according to the user's level of expertise.

[0053] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0054] The reception unit can also infer the user's interests and concerns based on the user's input, and provide related information based on the inferred interests and concerns. For example, if the user inputs a question about travel, the reception unit can infer that the user is interested in travel and provide travel-related information preferentially. Also, if the user inputs a topic about a specific sport, the reception unit can provide the latest news and information related to that sport. Furthermore, if the user inputs a question about cooking, the reception unit can suggest cooking-related recipes and cooking methods. This makes it possible to provide more personalized information based on the user's interests and concerns.

[0055] The output unit can also estimate the user's learning style based on the user's input and adjust the format of the response based on the estimated learning style. For example, if the user is estimated to have a visual learning style, the output unit can generate a response that includes images and diagrams. If the user is estimated to have an auditory learning style, the output unit can generate a response that includes audio explanations. Furthermore, if the user is estimated to have an experiential learning style, the output unit can generate a response that encourages actual experience. This makes it possible to provide appropriate responses according to the user's learning style.

[0056] The reception unit can also estimate the user's cultural background based on the user's input and generate a response based on the estimated cultural background. For example, if the user inputs a topic related to a specific culture, information related to that culture can be provided preferentially. Also, if the user inputs a question about a different culture, accurate information about that culture can be provided. Furthermore, if the user is estimated to have a multicultural background, information about different cultures can be provided in a balanced manner. This makes it possible to provide an appropriate response according to the user's cultural background.

[0057] The reception unit can also infer the user's interests and concerns based on the user's input, and provide related information based on the inferred interests and concerns. For example, if the user inputs a question about travel, the reception unit can infer that the user is interested in travel and provide travel-related information preferentially. Also, if the user inputs a topic about a specific sport, the reception unit can provide the latest news and information related to that sport. Furthermore, if the user inputs a question about cooking, the reception unit can suggest cooking-related recipes and cooking methods. This makes it possible to provide more personalized information based on the user's interests and concerns.

[0058] The output unit can also estimate the user's learning style based on the user's input and adjust the format of the response based on the estimated learning style. For example, if the user is estimated to have a visual learning style, the output unit can generate a response that includes images and diagrams. If the user is estimated to have an auditory learning style, the output unit can generate a response that includes audio explanations. Furthermore, if the user is estimated to have an experiential learning style, the output unit can generate a response that encourages actual experience. This makes it possible to provide appropriate responses according to the user's learning style.

[0059] The processing flow of the first embodiment will be briefly explained below.

[0060] Step 1: The reception unit receives text, audio, and image input from the user. For example, text formats include plain text, rich text, and HTML, and audio formats include WAV, MP3, and AAC. Image resolutions can range from low to high. Step 2: The analysis unit uses the generation AI to analyze the information received by the reception unit and generate an appropriate response. For example, the generation AI can analyze text using natural language processing technology and generate an appropriate response. The generation AI can also analyze speech using speech recognition technology and generate an appropriate response. Furthermore, the generation AI can analyze images using image recognition technology and generate comments related to the images. Step 3: The output unit outputs the response generated by the analysis unit as voice or text. For example, the output unit outputs the response generated by speech synthesis technology as voice. The output unit can also output the response generated by text generation technology as text.

[0061] (Example 2) A communication support system according to an embodiment of the present invention utilizes a generative AI to maintain a friendly, non-adversarial conversation for people who are not good at conversational communication or who have difficulty engaging in smooth two-way communication due to disabilities. The communication support system accepts text, voice, and image input from a user, analyzes it using a generative AI, and generates and outputs an appropriate response. For example, if a user inputs, "How was your day?", the generative AI generates a friendly response to the question. The generative AI then outputs the generated response in voice or text. This allows the user to enjoy a natural conversation. Furthermore, to enhance multimodal support, the generative AI analyzes images and voice and generates an appropriate response. For example, if a user uploads an image, the generative AI analyzes the image and generates a friendly comment related to the image. Similarly, for voice input, the generative AI analyzes the voice and generates an appropriate response. This system enables smooth, friendly communication even for people who are not good at conversational communication or who have disabilities. As a result, the communication support system can provide friendly, smooth communication for people who are not good at conversational communication or who have disabilities. For example, if someone who has difficulty speaking can input text, the AI ​​will output that text as voice, allowing for a smooth conversation. It also supports communication using images and voice, making it easy for people with visual or hearing impairments to use.

[0062] A communication support system according to an embodiment includes a reception unit, an analysis unit, and an output unit. The reception unit receives text, audio, or image input from a user. For example, text formats include plain text, rich text, and HTML. Audio formats include WAV, MP3, and AAC. Image resolutions range from low to high. The analysis unit uses a generation AI to analyze the information received by the reception unit and generate an appropriate response. For example, the generation AI analyzes text using natural language processing technology and generates an appropriate response. The generation AI can also analyze audio using speech recognition technology and generate an appropriate response. The generation AI can also analyze images using image recognition technology and generate a comment related to the image. The output unit outputs the response generated by the analysis unit as audio or text. For example, the output unit outputs a response generated using speech synthesis technology as audio. The output unit can also output a response generated using text generation technology as text. As a result, the communication support system according to an embodiment analyzes user input and generates and outputs an appropriate response, thereby achieving smooth communication.

[0063] The analysis unit can analyze text or speech input by a user and generate a response. The analysis unit can analyze text using, for example, natural language processing technology and generate an appropriate response. For example, the analysis unit can analyze the grammar and meaning of the text and generate an appropriate response. The analysis unit can also analyze speech using speech recognition technology and generate an appropriate response. For example, the analysis unit can analyze speech characteristics, convert speech to text, and generate a response based on that text. In this way, natural conversation can be achieved by generating an appropriate response based on the user's input.

[0064] The analysis unit can analyze images uploaded by users and generate comments related to the images. The analysis unit can analyze images using, for example, image recognition technology and generate comments related to the images. For example, the analysis unit can extract features of the image and generate comments based on the features. The analysis unit can also analyze the content of the image and generate comments based on information related to the image. For example, the analysis unit can recognize objects or scenes included in the image and generate comments related to them. This supports visual communication by generating friendly comments related to the image.

[0065] The output unit can output the generated response as voice or text. For example, the output unit outputs the generated response as voice using speech synthesis technology. For example, the output unit converts the generated text into voice and provides the response to the user as voice. The output unit can also output the generated response as text using text generation technology. For example, the output unit displays the generated text on a screen and provides the response to the user as text. In this way, appropriate feedback is provided to the user by outputting the generated response as voice or text.

[0066] The reception unit can receive voice input from the user. The reception unit receives voice input from the user using, for example, a microphone. For example, the reception unit records what the user says using the microphone and transmits the voice data to the analysis unit. The reception unit can also receive voice input from the user using a voice input device. For example, the reception unit receives voice input using a microphone on a smartphone or tablet. In this way, voice communication is supported by receiving voice input.

[0067] The analysis unit can analyze the voice input and generate an appropriate response. The analysis unit can analyze the voice input using, for example, voice recognition technology and generate an appropriate response. For example, the analysis unit can analyze the characteristics of the voice, convert the voice into text, and generate a response based on the text. The analysis unit can also analyze the emotion of the voice and generate an appropriate response based on the emotion. For example, the analysis unit can analyze the tone and speed of the voice, estimate the user's emotion, and generate a response based on the emotion. In this way, natural voice conversation is realized by analyzing the voice input and generating an appropriate response.

[0068] The reception unit can estimate the user's emotion and adjust the timing of input reception based on the estimated user emotion. The reception unit can estimate the user's emotion using, for example, facial expression recognition technology and adjust the timing of input reception. For example, if the user is nervous, the reception unit can delay the timing of input reception and wait until the user is relaxed. Furthermore, if the user is relaxed, the reception unit can also accelerate the timing of input reception to promote smooth communication. Furthermore, if the user is in a hurry, the reception unit can immediately accept input, enabling a quick response. Thus, by adjusting the timing of input reception according to the user's emotion, more appropriate communication is achieved. The emotion estimation is realized using an emotion estimation function, for example, using an emotion engine or a generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI.

[0069] The reception unit can analyze the user's past input history and select the reception method. The reception unit analyzes the user's past input history using, for example, data mining technology and selects the optimal reception method. For example, the reception unit preferentially suggests input methods (voice, text, etc.) that the user has frequently used in the past. The reception unit can also predict and suggest an input method to be used in a specific time period based on the user's past input history. The reception unit can also suggest a related input method by referring to content that the user has input in the past. In this way, the optimal reception method is provided to the user by analyzing the past input history.

[0070] The reception unit can perform filtering based on the user's current situation and areas of interest when receiving input. The reception unit, for example, identifies the user's current situation using real-time data and preferentially receives information related to that situation. For example, when the user inputs their current situation, the reception unit preferentially receives information related to that situation. The reception unit can also filter and receive related information based on the user's areas of interest. For example, when the user shows interest in a specific topic, the reception unit preferentially receives information related to that topic. In this way, filtering based on the user's situation and areas of interest provides more relevant information.

[0071] When accepting input, the acceptance unit can select an acceptance means according to the user's input method. The acceptance unit selects the optimal acceptance means according to the user's input method, such as voice input, text input, or image input. For example, if the user selects voice input, the acceptance unit accepts the input using voice recognition technology. Also, if the user selects text input, the acceptance unit can accept the input using text analysis technology. Also, if the user uploads an image, the acceptance unit can accept the input using image analysis technology. In this way, smooth input acceptance is achieved by selecting the optimal acceptance means according to the user's input method.

[0072] The reception unit can estimate the user's emotion and determine the priority of inputs to be received based on the estimated user's emotion. The reception unit estimates the user's emotion using, for example, facial expression recognition technology and determines the priority of inputs. For example, if the user is nervous, the reception unit can prioritize receiving inputs with lower importance. Also, if the user is relaxed, the reception unit can prioritize receiving inputs with higher importance. Also, if the user is in a hurry, the reception unit can prioritize receiving inputs with higher urgency. This allows for more appropriate input reception by prioritizing inputs based on the user's emotion. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or a generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI.

[0073] When receiving input, the reception unit can prioritize receiving highly relevant inputs by taking into account the user's geographical location information. The reception unit, for example, acquires the user's geographical location information using GPS data, and prioritizes receiving highly relevant inputs based on that information. For example, when the user is in a specific area, the reception unit can prioritize receiving information related to that area. Furthermore, when the user is traveling, the reception unit can also prioritize receiving information related to the user's travel destination. Furthermore, when the user is at home, the reception unit can prioritize receiving information about the area around the user's home. In this way, highly relevant information is provided by taking into account the user's geographical location information.

[0074] The reception unit can analyze the user's social media activity and receive related input when receiving input. The reception unit analyzes the user's social media activity using, for example, a social media analysis tool and receives related input. For example, the reception unit preferentially receives information related to places where the user has checked in on social media. The reception unit can also analyze the content of the user's social media posts and preferentially receive related information. The reception unit can also refer to the activities of the user's friends on social media and preferentially receive related information. In this way, highly relevant information can be provided by analyzing social media activity.

[0075] The reception unit can customize the reception method by reflecting the user's past feedback when receiving input. The reception unit, for example, uses a feedback data analysis method to reflect the user's past feedback and customize the optimal reception method. For example, the reception unit suggests the optimal reception method based on feedback provided by the user in the past. The reception unit can also preferentially suggest a specific input method based on the user's past feedback. The reception unit can also analyze the user's past feedback and select the optimal reception means. In this way, the optimal reception method is provided to the user by reflecting the past feedback.

[0076] The analysis unit can estimate the user's emotions and adjust the way a response is expressed based on the estimated user's emotions. The analysis unit can estimate the user's emotions using, for example, facial expression recognition technology and adjust the way a response is expressed. For example, if the user is nervous, the analysis unit can generate a response using gentle language. If the user is relaxed, the analysis unit can also generate a response using casual language. If the user is in a hurry, the analysis unit can also generate a concise and quick response. This allows for a more appropriate response by adjusting the way a response is expressed based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI.

[0077] During analysis, the analysis unit can adjust the level of detail of the analysis based on the importance of the input information. The analysis unit adjusts the level of detail of the analysis based on, for example, the urgency or relevance of the input information. For example, the analysis unit performs a detailed analysis of input information with high importance. The analysis unit can also perform a simplified analysis of input information with low importance. The analysis unit can also perform a quick analysis of input information with high urgency. In this way, by adjusting the level of detail of the analysis based on the importance of the input information, efficient analysis is achieved.

[0078] During analysis, the analysis unit can apply different analysis algorithms depending on the category of input information. The analysis unit applies an appropriate analysis algorithm depending on the category of input information, such as text analysis, image analysis, or voice analysis. For example, the analysis unit applies a natural language processing algorithm to text input. The analysis unit can also apply a voice recognition algorithm to voice input. The analysis unit can also apply an image analysis algorithm to image input. In this way, highly accurate analysis can be achieved by applying an appropriate analysis algorithm depending on the category of input information.

[0079] During analysis, the analysis unit can improve the accuracy of the analysis by referring to the user's past analysis results. The analysis unit, for example, improves the accuracy of the analysis by referring to the user's past analysis results using a method of using past data. For example, the analysis unit performs highly accurate analysis of similar input information based on the user's past analysis results. The analysis unit can also extract specific patterns from the user's past analysis results to improve the accuracy of the analysis. The analysis unit can also analyze the user's past analysis results and optimize the analysis algorithm. In this way, the accuracy of the analysis is improved by referring to the past analysis results.

[0080] The analysis unit can estimate the user's emotions and adjust the length of the response based on the estimated user emotions. The analysis unit can estimate the user's emotions using, for example, facial expression recognition technology and adjust the length of the response. For example, if the user is nervous, the analysis unit can generate a short and concise response. If the user is relaxed, the analysis unit can also generate a response that includes detailed explanations. If the user is in a hurry, the analysis unit can also generate a short response that gets to the point. This allows for adjusting the length of the response based on the user's emotions to provide a more appropriate response. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI.

[0081] During analysis, the analysis unit can determine the analysis priority based on the submission time of the input information. The analysis unit determines the analysis priority based on, for example, the urgency or recency of the submission time. For example, the analysis unit prioritizes analysis of the most recent input information. The analysis unit can also prioritize analysis of input information with high urgency. The analysis unit can also determine the analysis priority based on a deadline specified by the user. In this way, efficient analysis is achieved by determining the analysis priority based on the submission time of the input information.

[0082] During analysis, the analysis unit can adjust the order of analysis based on the relevance of the input information. The analysis unit adjusts the order of analysis based on, for example, the relevance or importance of the information. For example, the analysis unit prioritizes analysis of highly relevant input information. The analysis unit can also postpone analysis of less relevant input information. The analysis unit can also adjust the order of analysis based on the relevance specified by the user. In this way, efficient analysis is achieved by adjusting the order of analysis based on the relevance of the input information.

[0083] During analysis, the analysis unit can adjust the use of technical terms in the analysis according to the user's level of expertise. The analysis unit evaluates the user's level of expertise based on, for example, the user's occupation, educational background, past usage history, etc., and selects appropriate technical terms to provide analysis results. For example, if the user has technical expertise, the analysis unit can provide analysis results that use a lot of technical terms. Also, if the user does not have technical expertise, the analysis unit can provide analysis results in simple language. Also, the analysis unit can select appropriate technical terms to provide analysis results according to the user's level of expertise. In this way, appropriate analysis results can be provided by adjusting the use of technical terms according to the user's level of expertise.

[0084] The output unit can estimate the user's emotion and adjust the output method based on the estimated user's emotion. The output unit can estimate the user's emotion using, for example, facial expression recognition technology and adjust the output method. For example, if the user is nervous, the output unit can output voice in a gentle voice. If the user is relaxed, the output unit can output voice in a casual voice. If the user is in a hurry, the output unit can output voice in a quick and concise voice. This allows for more appropriate feedback by adjusting the output method based on the user's emotion. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI.

[0085] The output unit can adjust the level of detail of the output based on the importance of the response when outputting. The output unit adjusts the level of detail of the output based on, for example, the urgency or relevance of the response. For example, the output unit outputs a detailed explanation for a response with high importance. The output unit can also output a simplified explanation for a response with low importance. The output unit can also output a quick explanation for a response with high urgency. In this way, adjusting the level of detail of the output based on the importance of the response provides efficient feedback.

[0086] The output unit can apply different output formats depending on the category of the response at the time of output. The output unit applies an appropriate output format depending on the category of the response, such as text output, audio output, or image output. For example, the output unit outputs a text response in text format. The output unit can also output an audio response in audio format. The output unit can also output an image response in image format. In this way, by applying an appropriate output format depending on the category of the response, optimal feedback can be provided to the user.

[0087] The output unit can improve the accuracy of the output by referring to the user's past output results when outputting. For example, the output unit improves the accuracy of the output by referring to the user's past output results using a method of using past data. For example, the output unit provides highly accurate output for similar responses based on the user's past output results. The output unit can also extract specific patterns from the user's past output results to improve the accuracy of the output. The output unit can also analyze the user's past output results and optimize the output algorithm. In this way, the accuracy of the output is improved by referring to the past output results.

[0088] The output unit can estimate the user's emotion and adjust the output order based on the estimated user's emotion. The output unit estimates the user's emotion using, for example, facial expression recognition technology and adjusts the output order. For example, if the user is nervous, the output unit can prioritize outputting responses with lower importance. Also, if the user is relaxed, the output unit can prioritize outputting responses with higher importance. Also, if the user is in a hurry, the output unit can prioritize outputting responses with higher urgency. In this way, adjusting the output order based on the user's emotion provides more appropriate feedback. Emotion estimation is realized using an emotion estimation function, for example, using an emotion engine or a generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI.

[0089] The output unit can determine the output priority based on the submission time of the response at the time of output. The output unit determines the output priority based on, for example, the urgency or recency of the submission time. For example, the output unit prioritizes output of the most recent response. The output unit can also prioritize output of responses with high urgency. The output unit can also determine the output priority based on a deadline specified by the user. In this way, efficient feedback is provided by determining the output priority based on the submission time of the response.

[0090] The output unit can adjust the order of output based on the relevance of responses when outputting. The output unit adjusts the order of output based on, for example, the relevance or importance of responses. For example, the output unit prioritizes output of highly relevant responses. The output unit can also postpone output of less relevant responses. The output unit can also adjust the order of output based on the relevance specified by the user. In this way, efficient feedback can be provided by adjusting the order of output based on the relevance of responses.

[0091] The output unit can adjust the use of technical terms in the output according to the user's level of expertise at the time of output. The output unit evaluates the user's level of expertise based on, for example, the user's occupation, educational background, past usage history, etc., and selects and outputs appropriate technical terms. For example, if the user has technical expertise, the output unit can output using a lot of technical terms. Alternatively, if the user does not have technical expertise, the output unit can output using simple language. Alternatively, the output unit can select and output appropriate technical terms according to the user's level of expertise. In this way, appropriate feedback can be provided by adjusting the use of technical terms according to the user's level of expertise. === Hard Collateral 1-1 === Each of the multiple elements including the above-mentioned reception unit, analysis unit, and output unit is realized, for example, by at least one of the smart device 14 and the data processing device 12. For example, the reception unit is realized by the reception device 38 of the smart device 14 and receives text, voice, or image input from a user. For example, the analysis unit is realized by the specific processing unit 290 of the data processing device 12 and analyzes the received information using a generation AI and generates an appropriate response. For example, the output unit is realized by the output device 40 of the smart device 14 and outputs the response generated by the analysis unit in voice or text. === Hard Collateral 1-2 === Each of the multiple elements, including the above-described reception unit, analysis unit, and output unit, is realized, for example, by at least one of the smart glasses 214 and the data processing device 12. For example, the reception unit is realized by the microphone 238 or camera 42 of the smart glasses 214 and receives text, voice, or image input from the user. For example, the analysis unit is realized by the specific processing unit 290 of the data processing device 12 and analyzes the received information using a generation AI and generates an appropriate response. For example, the output unit is realized by the speaker 240 of the smart glasses 214 and outputs the response generated by the analysis unit as voice or text. === Hard Collateral 1-3 === Each of the multiple elements including the above-mentioned reception unit, analysis unit, and output unit is realized, for example, by at least one of the headset-type terminal 314 and the data processing device 12. For example, the reception unit is realized by the microphone 238 or camera 42 of the headset-type terminal 314 and receives text, voice, or image input from the user. For example, the analysis unit is realized by the specific processing unit 290 of the data processing device 12 and analyzes the received information using a generation AI to generate an appropriate response. For example, the output unit is realized by the speaker 240 or display 343 of the headset-type terminal 314 and outputs the response generated by the analysis unit in voice or text. === Hard Collateral 1-4 === Each of the multiple elements including the above-mentioned reception unit, analysis unit, and output unit is realized, for example, by at least one of the robot 414 and the data processing device 12. For example, the reception unit is realized by the microphone 238 or camera 42 of the robot 414 and receives text, voice, or image input from the user. For example, the analysis unit is realized by the specific processing unit 290 of the data processing device 12 and analyzes the received information using a generation AI and generates an appropriate response. For example, the output unit is realized by the speaker 240 or display device of the robot 414 and outputs the response generated by the analysis unit in voice or text.

[0092] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0093] The reception unit can also infer the user's interests and concerns based on the user's input, and provide related information based on the inferred interests and concerns. For example, if the user inputs a question about travel, the reception unit can infer that the user is interested in travel and provide travel-related information preferentially. Also, if the user inputs a topic about a specific sport, the reception unit can provide the latest news and information related to that sport. Furthermore, if the user inputs a question about cooking, the reception unit can suggest cooking-related recipes and cooking methods. This makes it possible to provide more personalized information based on the user's interests and concerns.

[0094] The analysis unit can also estimate the user's stress level based on the user's input and adjust the tone of the response based on the estimated stress level. For example, if the analysis unit estimates that the user is feeling stressed, it can generate a response in a gentle tone. If the analysis unit estimates that the user is relaxed, it can generate a response in a casual tone. Furthermore, if the analysis unit estimates that the user is in a hurry, it can generate a concise and quick response. In this way, adjusting the tone of the response according to the user's stress level enables more appropriate communication.

[0095] The analysis unit can also estimate the user's health condition based on the user's input and generate a response based on the estimated health condition. For example, if the user is estimated to be tired, the analysis unit can generate a response encouraging rest. If the user is estimated to be healthy, the analysis unit can generate a response including a suggestion to stay active. Furthermore, if the user is estimated to be ill, medical information and advice can be provided. This makes it possible to provide an appropriate response according to the user's health condition.

[0096] The output unit can also estimate the user's learning style based on the user's input and adjust the format of the response based on the estimated learning style. For example, if the user is estimated to have a visual learning style, the output unit can generate a response that includes images and diagrams. If the user is estimated to have an auditory learning style, the output unit can generate a response that includes audio explanations. Furthermore, if the user is estimated to have an experiential learning style, the output unit can generate a response that encourages actual experience. This makes it possible to provide appropriate responses according to the user's learning style.

[0097] The reception unit can also estimate the user's cultural background based on the user's input and generate a response based on the estimated cultural background. For example, if the user inputs a topic related to a specific culture, information related to that culture can be provided preferentially. Also, if the user inputs a question about a different culture, accurate information about that culture can be provided. Furthermore, if the user is estimated to have a multicultural background, information about different cultures can be provided in a balanced manner. This makes it possible to provide an appropriate response according to the user's cultural background.

[0098] The analysis unit can also estimate the user's emotions based on the user's input and adjust the content of the response based on the estimated emotions. For example, if the analysis unit estimates that the user is sad, it can generate a response including words of comfort. If the analysis unit estimates that the user is happy, it can generate a response including words of congratulation. Furthermore, if the analysis unit estimates that the user is angry, it can generate a response that encourages the user to stay calm. This makes it possible to provide an appropriate response according to the user's emotions.

[0099] The reception unit can also infer the user's interests and concerns based on the user's input, and provide related information based on the inferred interests and concerns. For example, if the user inputs a question about travel, the reception unit can infer that the user is interested in travel and provide travel-related information preferentially. Also, if the user inputs a topic about a specific sport, the reception unit can provide the latest news and information related to that sport. Furthermore, if the user inputs a question about cooking, the reception unit can suggest cooking-related recipes and cooking methods. This makes it possible to provide more personalized information based on the user's interests and concerns.

[0100] The analysis unit can also estimate the user's stress level based on the user's input and adjust the tone of the response based on the estimated stress level. For example, if the analysis unit estimates that the user is feeling stressed, it can generate a response in a gentle tone. If the analysis unit estimates that the user is relaxed, it can generate a response in a casual tone. Furthermore, if the analysis unit estimates that the user is in a hurry, it can generate a concise and quick response. In this way, adjusting the tone of the response according to the user's stress level enables more appropriate communication.

[0101] The analysis unit can also estimate the user's health condition based on the user's input and generate a response based on the estimated health condition. For example, if the user is estimated to be tired, the analysis unit can generate a response encouraging rest. If the user is estimated to be healthy, the analysis unit can generate a response including a suggestion to stay active. Furthermore, if the user is estimated to be ill, medical information and advice can be provided. This makes it possible to provide an appropriate response according to the user's health condition.

[0102] The output unit can also estimate the user's learning style based on the user's input and adjust the format of the response based on the estimated learning style. For example, if the user is estimated to have a visual learning style, the output unit can generate a response that includes images and diagrams. If the user is estimated to have an auditory learning style, the output unit can generate a response that includes audio explanations. Furthermore, if the user is estimated to have an experiential learning style, the output unit can generate a response that encourages actual experience. This makes it possible to provide appropriate responses according to the user's learning style.

[0103] The processing flow of the second embodiment will be briefly explained below.

[0104] Step 1: The reception unit receives text, audio, and image input from the user. For example, text formats include plain text, rich text, and HTML, and audio formats include WAV, MP3, and AAC. Image resolutions can range from low to high. Step 2: The analysis unit uses the generation AI to analyze the information received by the reception unit and generate an appropriate response. For example, the generation AI can analyze text using natural language processing technology and generate an appropriate response. The generation AI can also analyze speech using speech recognition technology and generate an appropriate response. Furthermore, the generation AI can analyze images using image recognition technology and generate comments related to the images. Step 3: The output unit outputs the response generated by the analysis unit as voice or text. For example, the output unit outputs the response generated by speech synthesis technology as voice. The output unit can also output the response generated by text generation technology as text.

[0105] The specific processing unit 290 transmits the result of the specific processing to the smart device 14. In the smart device 14, the control unit 46A causes the output device 40 to output the result of the specific processing. The microphone 38B acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0106] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (registered trademark) (Internet search engine).<URL: https: / / openai.com / blog / chatgpt> Examples of generative AIs include the data generation model 58, such as a neural network model (e.g., a neural network model), and a neural network model (e.g., a neural network model). The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating speech, text data indicating text, and image data indicating an image is also input to the data generation model 58. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specification processing unit 290 performs the above-mentioned specification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0107] Furthermore, the processing by the data processing system 10 described above is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart device 14, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart device 14. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the smart device 14 or an external device, and the smart device 14 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0108] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0109] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0110] 3, the data processing system 210 includes the data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0111] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0112] The smart glasses 214 include a computer 36, a microphone 238, a speaker 240, a camera 42, and a communication I / F 44. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, and the camera 42 are also connected to the bus 52.

[0113] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0114] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0115] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0116] Fig. 4 shows an example of the main functions of the data processing device 12 and the smart glasses 214. As shown in Fig. 4, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0117] The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0118] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0119] In the smart glasses 214, the specific processing is performed by the processor 46. A specific processing program 60 is stored in the storage 50. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. The smart glasses 214 also have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0120] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0121] The specific processing unit 290 transmits the result of the specific processing to the smart glasses 214. In the smart glasses 214, the control unit 46A causes the speaker 240 to output the result of the specific processing. The microphone 238 acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0122] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0123] The data processing system 210 according to the second embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 210 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart glasses 214, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart glasses 214. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the smart glasses 214 or an external device, etc., and the smart glasses 214 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0124] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0125] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0126] 5, the data processing system 310 includes the data processing device 12 and a headset type terminal 314. An example of the data processing device 12 is a server.

[0127] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0128] The headset type terminal 314 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication I / F 44, and a display 343. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, the camera 42, and the display 343 are also connected to the bus 52.

[0129] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0130] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0131] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0132] Fig. 6 shows an example of the main functions of the data processing device 12 and the headset type terminal 314. As shown in Fig. 6, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0133] The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0134] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0135] In the headset type terminal 314, the identification process is performed by the processor 46. A identification program 60 is stored in the storage 50. The processor 46 reads the identification program 60 from the storage 50 and executes the read identification program 60 on the RAM 48. The identification process is realized by the processor 46 operating as a control unit 46A in accordance with the identification program 60 executed on the RAM 48. Note that the headset type terminal 314 has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can also perform processing similar to that of the identification processing unit 290 using these models.

[0136] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0137] The specific processing unit 290 transmits the result of the specific processing to the headset type terminal 314. In the headset type terminal 314, the control unit 46A causes the speaker 240 and the display 343 to output the result of the specific processing. The microphone 238 acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0138] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0139] The data processing system 310 according to the third embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 310 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the headset type terminal 314, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the headset type terminal 314. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the headset type terminal 314 or an external device, etc., and the headset type terminal 314 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0140] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0141] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0142] 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.

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

[0144] The robot 414 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication I / F 44, and a control target 443. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, the camera 42, and the control target 443 are also connected to the bus 52.

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

[0146] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS image sensor or a CCD image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

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

[0148] The control object 443 includes a display device, LEDs in the eyes, and motors that drive the arms, hands, and feet. The posture and gestures of the robot 414 are controlled by controlling the motors of the arms, hands, and feet. Some of the emotions of the robot 414 can be expressed by controlling these motors. In addition, the facial expressions of the robot 414 can also be expressed by controlling the light emission state of the LEDs in the eyes of the robot 414.

[0149] Fig. 8 shows an example of the main functions of the data processing device 12 and the robot 414. As shown in Fig. 8, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0150] The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0151] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0152] In the robot 414, the processor 46 performs the identification process. The storage 50 stores the identification program 60. The processor 46 reads the identification program 60 from the storage 50 and executes the read identification program 60 on the RAM 48. The identification process is realized by the processor 46 operating as the control unit 46A in accordance with the identification program 60 executed on the RAM 48. The robot 414 also has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can perform the same process as the identification processing unit 290 using these models.

[0153] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0154] The specific processing unit 290 transmits the result of the specific processing to the robot 414. In the robot 414, the control unit 46A causes the speaker 240 and the control target 443 to output the result of the specific processing. The microphone 238 acquires voice indicating a user input regarding the result of the specific processing. The control unit 46A transmits voice data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the voice data.

[0155] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0156] The data processing system 410 according to the fourth embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 410 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the robot 414, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the robot 414. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the robot 414 or an external device, etc., and the robot 414 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0157] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0158] The emotion identification model 59 as an emotion engine may determine the user's emotion according to a specific mapping. Specifically, the emotion identification model 59 may determine the user's emotion according to an emotion map (see FIG. 9), which is a specific mapping. Similarly, the emotion identification model 59 may determine the robot's emotion, and the identification processing unit 290 may perform identification processing using the robot's emotion.

[0159] FIG. 9 illustrates an emotion map 400 on which multiple emotions are mapped. In the emotion map 400, emotions are arranged in concentric circles radiating from the center. Emotions closer to the center of the concentric circles are more primitive. Emotions representing states and behaviors arising from a state of mind are arranged on the outer edges of the concentric circles. The concept of emotion encompasses both emotions and mental states. Emotions generally generated from reactions occurring in the brain are arranged on the left side of the concentric circles. Emotions generally induced by situational judgment are arranged on the right side of the concentric circles. Emotions generally generated from reactions occurring in the brain and induced by situational judgment are arranged on the upper and lower sides of the concentric circles. Furthermore, the emotion of "pleasure" is arranged on the upper side of the concentric circles, and the emotion of "discomfort" is arranged on the lower side. In this way, in the emotion map 400, multiple emotions are mapped based on the structure by which emotions are generated, and emotions that tend to occur simultaneously are mapped close to each other.

[0160] These emotions are distributed in the 3 o'clock direction on emotion map 400, and typically fluctuate between relief and anxiety. In the right half of emotion map 400, situational awareness dominates over internal sensations, resulting in a sense of calm.

[0161] The inside of emotion map 400 represents what is going on in the mind, and the outside of emotion map 400 represents behavior, so the further you go outside emotion map 400, the more visible the emotions become (the more they are expressed in behavior).

[0162] Human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, a state of discomfort is expressed, and when they approach the ideal, a state of pleasure is expressed. Emotions can also be created for robots, cars, and motorcycles, based on various balances, such as posture and remaining battery life. When these balances deviate from the ideal, a state of discomfort is expressed, and when they approach the ideal, a state of pleasure is expressed. An emotion map can be generated, for example, based on Dr. Mitsuyoshi's emotion map (Research on speech emotion recognition and brain physiological signal analysis systems for emotions, Tokushima University, doctoral dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map lists emotions belonging to the area called "reaction," where sensation is dominant. The right half of the emotion map lists emotions belonging to the area called "situation," where situational awareness is dominant.

[0163] The emotion map defines two emotions that promote learning. One is a negative emotion on the situation side, around the middle of "repentance" or "reflection." In other words, this occurs when the robot experiences negative emotions such as "I never want to feel this way again" or "I don't want to be scolded again." The other is a positive emotion on the response side, around "desire." In other words, this occurs when the robot experiences positive feelings such as "I want more" or "I want to know more."

[0164] The emotion identification model 59 inputs user input into a pre-trained neural network, obtains emotion values ​​indicating each emotion shown in the emotion map 400, and determines the user's emotion. This neural network is pre-trained based on multiple pieces of training data that are combinations of user input and emotion values ​​indicating each emotion shown in the emotion map 400. Furthermore, this neural network is trained so that emotions that are located close to each other have similar values, as in the emotion map 900 shown in FIG. 10. FIG. 10 shows an example in which multiple emotions, "relieved," "calm," and "reassuring," have similar emotion values.

[0165] In the above embodiment, an example was given in which a specific process is performed by one computer 22, but the technology disclosed herein is not limited to this, and distributed processing of the specific process may be performed by multiple computers including computer 22.

[0166] In the above embodiment, an example in which the specific processing program 56 is stored in the storage 32 has been described, but the technology of the present disclosure is not limited to this. For example, the specific processing program 56 may be stored in a portable, computer-readable, non-transitory storage medium such as a USB (Universal Serial Bus) memory. The specific processing program 56 stored in the non-transitory storage medium is installed in the computer 22 of the data processing device 12. The processor 28 executes the specific processing in accordance with the specific processing program 56.

[0167] 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.

[0168] It is not necessary to store all of the specific processing program 56 in a storage device such as a server connected to the data processing device 12 via the network 54, or to store all of the specific processing program 56 in the storage 32; only a portion of the specific processing program 56 may be stored.

[0169] The hardware resource for executing a specific process can be any of the following types of processors: A processor, for example, is a CPU, which is a general-purpose processor that functions as a hardware resource for executing a specific process by executing software, i.e., a program. A processor also includes a dedicated electrical circuit, such as an FPGA (Field-Programmable Gate Array), a PLD (Programmable Logic Device), or an ASIC (Application Specific Integrated Circuit), which is a processor with a circuit configuration designed specifically for executing a specific process. Each processor has built-in or connected memory, and each processor uses the memory to execute the specific process.

[0170] The hardware resource that executes the specific process may be configured with one of these various processors, or may be configured with a combination of two or more processors of the same or different types (for example, a combination of multiple FPGAs, or a combination of a CPU and an FPGA). Also, the hardware resource that executes the specific process may be a single processor.

[0171] As an example of a system configured with a single processor, first, one processor is configured by combining one or more CPUs and software, and this processor functions as a hardware resource that executes a specific process. Second, there is a system that uses a processor that realizes the functions of an entire system including multiple hardware resources that execute a specific process on a single IC chip, as typified by SoC (System-on-a-chip). In this way, a specific process is realized using one or more of the above-mentioned various processors as hardware resources.

[0172] Furthermore, the hardware structure of these various processors can be, more specifically, an electric circuit that combines circuit elements such as semiconductor devices. The specific processing described above is merely an example. Therefore, it goes without saying that unnecessary steps may be deleted, new steps may be added, or the processing order may be rearranged, without departing from the spirit of the invention.

[0173] In the above example, the first to fourth embodiments have been described separately, but some or all of these embodiments may be combined. The smart device 14, smart glasses 214, headset terminal 314, and robot 414 are merely examples, and they may be combined, or other devices may be used. In the above example, the first and second embodiments have been described separately, but they may be combined.

[0174] The above-described description and illustrations are a detailed explanation of the parts related to the technology of the present disclosure and are merely an example of the technology of the present disclosure. For example, the above description of the configuration, functions, actions, and effects is an explanation of an example of the configuration, functions, actions, and effects of the parts related to the technology of the present disclosure. Therefore, it goes without saying that unnecessary parts may be deleted, new elements may be added, or replacements may be made to the above-described description and illustrations within the scope of the gist of the technology of the present disclosure. Furthermore, to avoid confusion and facilitate understanding of the parts related to the technology of the present disclosure, the above-described description and illustrations omit explanations of common technical knowledge that do not require particular explanation to enable the implementation of the technology of the present disclosure.

[0175] All publications, patent applications, and technical standards mentioned in this specification are herein incorporated by reference to the same extent as if each individual publication, patent application, or technical standard was specifically and individually indicated to be incorporated by reference.

[0176] [Explanation of symbols]

[0177] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Device 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robot

Claims

1. a reception unit that receives input of text, voice, or image from a user; an analysis unit that analyzes the information received by the reception unit and generates a response; an output unit that outputs the response generated by the analysis unit in voice or text form; A system characterized by:

2. The analysis unit Analyzes user-entered text or speech and generates a response 2. The system of claim 1.

3. The analysis unit Analyzes images uploaded by users and generates comments related to the images 2. The system of claim 1.

4. The output unit Output the generated response as speech or text 2. The system of claim 1.

5. The reception unit Accepts voice input from the user 2. The system of claim 1.

6. The analysis unit Analyzes voice input and generates appropriate responses 2. The system of claim 1.

7. The reception unit Estimate the user's emotions and adjust the timing of input acceptance based on the estimated user emotions.

2. The system of claim 1.

8. The reception unit Analyze the user's past input history and select the reception method 2. The system of claim 1.

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