System

Abstract

An object of a system according to an embodiment is to improve the degree of understanding of communication in accordance with a way of transmission and prerequisite knowledge of a partner.SOLUTION: A system according to an embodiment includes a transmission method adjustment unit, a premise knowledge grasping unit, a feedback collection unit, and an understanding level improvement unit. The transmission method adjustment unit generates a method of transmitting the content desired to be transmitted by the user in an optimal form. The prerequisite knowledge grasping part grasps the prerequisite knowledge of the other party. The feedback collection unit collects feedback from the other party. The comprehension degree improvement part combines the information of the transmission method adjustment part, the premise knowledge grasping part and the feedback collection part to improve the comprehension degree of the communication.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] With conventional technology, the level of understanding in communication is heavily influenced by the way the information is conveyed and the other person's prior knowledge, which can lead to communication errors.

[0005] The system according to the embodiment aims to improve the level of understanding of communication in accordance with the way of communication and the prior knowledge of the other party. [Means for solving the problem]

[0006] The system according to the embodiment includes a communication style adjustment unit, a prior knowledge understanding unit, a feedback collection unit, and a comprehension improvement unit. The communication style adjustment unit generates a method for optimally communicating the content that the user wants to communicate. The prior knowledge understanding unit understands the other party's prior knowledge. The feedback collection unit collects feedback from the other party. The comprehension improvement unit improves the comprehension of communication by combining information from the communication style adjustment unit, prior knowledge understanding unit, and feedback collection unit. [Effects of the Invention]

[0007] The system according to the embodiment can improve the level of understanding of communication according to the way of communication and the prior knowledge of the other party. [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) The communication comprehension improvement system according to the embodiment of the present invention is a system that adjusts the user's communication style, grasps the other party's prior knowledge, collects feedback, and improves the level of comprehension, thereby improving the level of comprehension of communication and reducing the risk of mistakes.

[0029] A communication comprehension improvement system according to an embodiment includes a communication style adjustment unit, a prior knowledge understanding unit, a feedback collection unit, and a comprehension improvement unit. The communication style adjustment unit generates a method for optimally communicating the content a user wants to communicate. For example, the generation AI generates an optimal communication style based on the content input by the user, tailored to the other party's prior knowledge and level of understanding. The prior knowledge understanding unit understands the other party's prior knowledge. For example, the generation AI analyzes the other party's past communication history and social media data to infer the other party's prior knowledge. The feedback collection unit collects feedback from the other party. For example, the generation AI collects feedback such as which parts the other party found difficult to understand and which parts were helpful. The comprehension improvement unit combines information from the communication style adjustment unit, the prior knowledge understanding unit, and the feedback collection unit to improve the communication comprehension. For example, the generation AI adjusts the difficulty of the explanation based on the other party's level of understanding, thereby conveying information in a way that is easier for the other party to understand. This allows the communication comprehension improvement system to improve the communication comprehension and reduce the risk of mistakes.

[0030] The communication style adjustment unit can analyze the user's voice tone and speaking habits and generate the optimal communication style based on that. In the communication style adjustment unit, for example, the generation AI analyzes the user's voice tone and generates a way to convey information to the other person in an appropriate tone. For example, it determines when to speak in a calm tone or an excited tone. The communication style adjustment unit also analyzes the user's speaking habits and suggests a way to speak that is easy for the other person to understand. For example, it instructs a user who speaks quickly to speak more slowly. The communication style adjustment unit also analyzes the user's voice tone and speaking habits in combination and generates the optimal communication style for the other person. For example, it suggests a way to attract the other person's attention by changing the tone of voice. This makes it possible to generate the optimal communication style based on the user's voice tone and speaking habits.

[0031] The communication style adjustment unit learns the user's past successful communication patterns and can suggest the optimal communication style based on them. For example, the generation AI in the communication style adjustment unit analyzes the user's past successful communication patterns and suggests the optimal communication style in similar situations. For example, it reuses specific phrases or expressions. The communication style adjustment unit also learns the user's past successful communication patterns and generates the optimal communication style for new situations. For example, it suggests effective communication styles even for different people. The communication style adjustment unit also predicts the other person's reaction based on the user's past successful communication patterns and suggests the optimal communication style. For example, it uses phrases that will elicit a positive reaction from the other person. This makes it possible to suggest the optimal communication style based on past successful communication patterns.

[0032] The communication style adjustment unit can analyze the user's gestures and facial expressions and generate the optimal way of communicating based on that. In this communication style adjustment unit, for example, the generation AI analyzes the user's gestures and suggests appropriate gestures to the other person. For example, it uses hand movements to indicate points to emphasize. In addition, the communication style adjustment unit analyzes the user's facial expressions and suggests facial expressions that are easy for the other person to understand. For example, it uses a smile to convey friendliness. In addition, the communication style adjustment unit analyzes the user's gestures and facial expressions in combination and generates the optimal way of communicating to the other person. For example, it conveys information by linking hand movements and facial expressions. This makes it possible to generate the optimal way of communicating based on the user's gestures and facial expressions.

[0033] The communication style adjustment unit generates communication styles that correspond to different cultural spheres and languages, thereby supporting international communication. For example, the generation AI in the communication style adjustment unit learns communication styles in different cultural spheres and proposes the optimal communication style. For example, differences in the use of honorifics and gestures are taken into consideration. The communication style adjustment unit also generates communication styles that correspond to different languages ​​and conveys information in the recipient's native language. For example, it translates from English to Spanish. The communication style adjustment unit also supports international communication by generating a combination of communication styles that correspond to different cultural spheres and languages. For example, it uses phrases that take cultural background into consideration. This allows the generation AI to generate communication styles that correspond to different cultural spheres and languages, thereby supporting international communication.

[0034] The prior knowledge understanding unit can infer prior knowledge by analyzing the other person's social media and public profile. In the prior knowledge understanding unit, for example, the generation AI analyzes the content of the other person's social media posts to understand the other person's interests and concerns. For example, it identifies the topics that the other person frequently posts on. In addition, the prior knowledge understanding unit analyzes the other person's public profile to understand the other person's expertise and work history. For example, it infers prior knowledge based on the other person's work history and educational background. In addition, the generation AI analyzes the other person's social media and public profile in combination to comprehensively understand the other person's prior knowledge. For example, it infers prior knowledge based on the content of the other person's posts and work history. This makes it possible to infer prior knowledge based on the other person's social media and public profile.

[0035] The prerequisite knowledge understanding unit can analyze the other party's past learning history and work experience to determine their level of expertise. For example, the generation AI analyzes the other party's past learning history to determine their level of expertise. For example, it infers their level of expertise based on the courses they have taken and the qualifications they have obtained. The prerequisite knowledge understanding unit also analyzes the other party's work history to determine their level of expertise. For example, it infers their level of expertise based on the projects they have been in charge of and their position. The generation AI also analyzes the other party's learning history and work experience to comprehensively determine their level of expertise. For example, it infers their level of expertise based on their educational background and work experience. This makes it possible to determine their level of expertise based on their past learning history and work experience.

[0036] The prior knowledge understanding unit can infer prior knowledge by analyzing the other party's past purchasing history and behavioral patterns. In the prior knowledge understanding unit, for example, the generation AI analyzes the other party's past purchasing history to understand the other party's interests and concerns. For example, prior knowledge is inferred based on the products and services purchased by the other party. In addition, the generation AI analyzes the other party's behavioral patterns to understand the other party's interests and concerns. For example, prior knowledge is inferred based on the websites and apps the other party frequently visits. In addition, the generation AI analyzes the other party's purchasing history and behavioral patterns in combination to comprehensively understand the other party's prior knowledge. For example, prior knowledge is inferred based on the other party's purchase history and website browsing history. This makes it possible to infer prior knowledge based on the other party's past purchasing history and behavioral patterns.

[0037] The prior knowledge understanding unit can infer prior knowledge by analyzing information about the communities and groups to which the other party belongs. In the prior knowledge understanding unit, for example, the generation AI analyzes information about the community to which the other party belongs to understand the other party's interests and concerns. For example, prior knowledge is inferred based on the online forums and groups in which the other party participates. In addition, the prior knowledge understanding unit analyzes information about the groups to which the other party belongs to understand the other party's interests and concerns. For example, prior knowledge is inferred based on the expert groups or hobby circles to which the other party belongs. In addition, the generation AI combines and analyzes information about the other party's communities and groups to comprehensively understand the other party's prior knowledge. For example, prior knowledge is inferred based on the forums and circles in which the other party participates. This makes it possible to infer prior knowledge based on information about the communities and groups to which the other party belongs.

[0038] The feedback collection unit can analyze the other person's non-verbal reactions, such as facial expressions and gestures, to evaluate the level of understanding. In the feedback collection unit, for example, the generation AI analyzes the other person's facial expressions to evaluate the level of understanding. For example, if the other person shows a confused expression, it determines that the explanation for that part is insufficient. In addition, the feedback collection unit analyzes the other person's gestures to evaluate the level of understanding. For example, if the other person tilts their head, it determines that the explanation for that part is unclear. In addition, the feedback collection unit analyzes the other person's facial expressions and gestures in combination to comprehensively evaluate the level of understanding. For example, if the other person smiles, it determines that the explanation for that part has been understood. This makes it possible to evaluate the level of understanding based on the other person's non-verbal reactions.

[0039] The feedback collection unit can analyze the content and frequency of the other party's questions and evaluate the level of understanding. In the feedback collection unit, for example, the generation AI analyzes the content of the other party's questions and evaluates the level of understanding. For example, if the other party asks a specific question, it determines that the explanation for that part is understood. In addition, the feedback collection unit analyzes the frequency of the other party's questions and evaluates the level of understanding. For example, if the other party asks questions frequently, it determines that the explanation for that part is insufficient. In addition, the feedback collection unit analyzes a combination of the content and frequency of the other party's questions and evaluates the level of understanding comprehensively. For example, if the other party does not ask a question, it determines that the explanation for that part is sufficient. This makes it possible to evaluate the level of understanding based on the content and frequency of the other party's questions.

[0040] The feedback collection unit can analyze the other person's physiological responses, such as heart rate and electrodermal activity, to evaluate the level of understanding. In the feedback collection unit, for example, the generation AI analyzes the other person's heart rate to evaluate the level of understanding. For example, if the heart rate increases, it determines that that part of the explanation has not been understood. In the feedback collection unit, the generation AI analyzes the other person's electrodermal activity to evaluate the level of understanding. For example, if the electrodermal activity changes, it determines that that part of the explanation is unclear. In the feedback collection unit, the generation AI analyzes the other person's heart rate and electrodermal activity in combination to comprehensively evaluate the level of understanding. For example, if the heart rate and electrodermal activity are stable, it determines that that part of the explanation has been understood. This makes it possible to evaluate the level of understanding based on the other person's physiological responses.

[0041] The feedback collection unit can analyze the search and sharing of related information, which is the other party's post-communication behavior, and evaluate the level of understanding. For example, the generation AI analyzes the other party's post-communication search history to evaluate the level of understanding. For example, if the other party searches for related information, it determines that the explanation of that part is insufficient. The feedback collection unit also analyzes the other party's post-communication sharing history to evaluate the level of understanding. For example, if the other party shares information, it determines that the explanation of that part is understood. The feedback collection unit also analyzes the other party's search history and sharing history in combination to comprehensively evaluate the level of understanding. For example, if the other party searches for and shares information, it determines that the explanation of that part is particularly interesting. This makes it possible to evaluate the level of understanding based on the other party's post-communication behavior.

[0042] The comprehension improvement unit can adjust the difficulty of the explanation in real time according to the other party's level of understanding. In the comprehension improvement unit, for example, the generation AI analyzes the other party's level of understanding in real time and adjusts the difficulty of the explanation. For example, it converts technical terms into simple language so that the other party can easily understand. In addition, the comprehension improvement unit analyzes the other party's level of understanding in real time and adjusts the level of detail in the explanation. For example, it adds specific examples so that the other party can easily understand. In addition, the comprehension improvement unit analyzes the other party's level of understanding in real time and adjusts the speed of the explanation. For example, it instructs the other party to speak more slowly so that the other party can easily understand. This allows the difficulty of the explanation to be adjusted in real time according to the other party's level of understanding.

[0043] The comprehension improvement unit can automatically generate supplementary materials and additional information based on the other party's level of understanding. In the comprehension improvement unit, for example, the generation AI analyzes the other party's level of understanding and automatically generates supplementary materials as needed. For example, detailed explanations are added to parts that are difficult for the other party to understand. In addition, the comprehension improvement unit analyzes the other party's level of understanding and automatically generates additional information as needed. For example, information related to topics that interest the other party is provided. In addition, the comprehension improvement unit analyzes the other party's level of understanding and automatically generates visual materials as needed. For example, diagrams and graphs are added to make it easier for the other party to understand. In this way, supplementary materials and additional information can be automatically generated based on the other party's level of understanding.

[0044] The comprehension improvement unit can combine different media such as text, audio, and video to provide the optimal explanation method. In the comprehension improvement unit, for example, the generation AI combines text and audio to provide the optimal explanation method to the other party. For example, it explains using text while supplementing with audio. In addition, the comprehension improvement unit can combine audio and video to provide the optimal explanation method to the other party. For example, it explains visually using video while supplementing with audio. In addition, the comprehension improvement unit can combine text, audio, and video to provide the optimal explanation method to the other party. For example, it explains an overview using text, shows details using video, and supplements with audio. In this way, the optimal explanation method can be provided by combining different media.

[0045] The comprehension improvement unit can provide an explanation method that suits the other person's visual, auditory, and experiential learning styles. For example, the generation AI of the comprehension improvement unit provides an explanation method using diagrams and graphs in accordance with the other person's visual learning style. For example, it creates materials that are visually easy to understand. The comprehension improvement unit also provides an explanation method using audio in accordance with the other person's auditory learning style. For example, it creates an audio guide. The comprehension improvement unit also provides an explanation method through actual experience in accordance with the other person's experiential learning style. For example, it performs simulations and demonstrations. This makes it possible to provide an explanation method that suits the other person's learning style.

[0046] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0047] The communication comprehension improvement system may further include a history analysis unit that analyzes the user's past communication history and learns successful patterns. The history analysis unit, for example, identifies phrases and tones that were particularly effective in the user's past communications and suggests reusing them in similar situations. The history analysis unit can also predict the other party's reaction based on the user's past communication history and generate an optimal way of communicating. For example, it may learn that specific phrases and tones are effective for a specific party and suggest that. Furthermore, the history analysis unit can generate an optimal way of communicating for a new situation based on the user's past communication history. For example, it may suggest an effective way of communicating with a different party. This makes it possible to suggest an optimal way of communicating based on the user's past successful communication patterns.

[0048] The communication comprehension improvement system can further include a voice analysis unit that analyzes the user's voice tone and speaking habits and generates the optimal way of communicating based on the results. The voice analysis unit, for example, analyzes the user's voice tone and generates a way of communicating information to the other party in an appropriate tone. For example, it determines when to speak in a calm tone or when to speak in an excited tone. The voice analysis unit also analyzes the user's speaking habits and suggests a way of speaking that is easy for the other party to understand. For example, it instructs a user who speaks quickly to speak more slowly. The voice analysis unit also analyzes the user's voice tone and speaking habits in combination to generate the optimal way of communicating for the other party. For example, it suggests a way to attract the other party's attention by changing the tone of voice. This makes it possible to generate the optimal way of communicating based on the user's voice tone and speaking habits.

[0049] The communication comprehension improvement system can further include a non-verbal analysis unit that analyzes the user's gestures and facial expressions and generates the optimal way of communicating based on the results. The non-verbal analysis unit, for example, analyzes the user's gestures and suggests appropriate gestures to the other person. For example, it uses hand movements to indicate points to emphasize. The non-verbal analysis unit also analyzes the user's facial expressions and suggests facial expressions that are easy for the other person to understand. For example, it uses a smile to convey friendliness. The non-verbal analysis unit also analyzes the user's gestures and facial expressions in combination to generate the optimal way of communicating to the other person. For example, it conveys information by linking hand movements and facial expressions. This makes it possible to generate the optimal way of communicating based on the user's gestures and facial expressions.

[0050] The communication comprehension improvement system can further include a cultural adaptation unit that generates ways of communicating that correspond to different cultures and languages, thereby supporting international communication. The cultural adaptation unit, for example, learns communication styles in different cultures and proposes the optimal way of communicating. For example, it takes into account differences in the use of honorifics and gestures. The cultural adaptation unit also generates ways of communicating that correspond to different languages ​​and conveys information in the recipient's native language. For example, it translates information from English to Spanish. The cultural adaptation unit also generates a combination of ways of communicating that correspond to different cultures and languages, thereby supporting international communication. For example, it uses phrases that take cultural background into consideration. This makes it possible to generate ways of communicating that correspond to different cultures and languages, thereby supporting international communication.

[0051] The communication comprehension improvement system can further include a social analysis unit that analyzes the other person's social media and public profile to infer prior knowledge. The social analysis unit, for example, analyzes the content of the other person's social media posts to understand the other person's interests. For example, it identifies topics that the other person frequently posts about. The social analysis unit also analyzes the other person's public profile to understand the other person's expertise and work history. For example, it infers prior knowledge based on the other person's work history and educational background. The social analysis unit also analyzes the other person's social media and public profile in combination to comprehensively understand the other person's prior knowledge. For example, it infers prior knowledge based on the other person's post content and work history. This makes it possible to infer prior knowledge based on the other person's social media and public profile.

[0052] The processing flow of the first embodiment will be briefly explained below.

[0053] Step 1: The communication adjustment unit generates the optimal way to communicate what the user wants to say. For example, the generation AI generates the optimal way to communicate based on the content entered by the user, taking into account the other person's prior knowledge and level of understanding. Step 2: The prior knowledge understanding unit understands the other person's prior knowledge. For example, the generation AI analyzes the other person's past communication history and social media data to infer the other person's prior knowledge. Step 3: The feedback collection section collects feedback from the other party. For example, the generation AI collects feedback such as which parts the other party found difficult to understand and which parts were helpful. Step 4: The comprehension improvement unit improves the level of understanding of communication by combining information from the communication adjustment unit, the prior knowledge understanding unit, and the feedback collection unit. For example, the generation AI adjusts the difficulty of the explanation to match the other person's level of comprehension, and conveys information in a way that is easier for the other person to understand.

[0054] (Example 2) The communication comprehension improvement system according to the embodiment of the present invention is a system that adjusts the user's communication style, grasps the other party's prior knowledge, collects feedback, and improves the level of comprehension, thereby improving the level of comprehension of communication and reducing the risk of mistakes.

[0055] A communication comprehension improvement system according to an embodiment includes a communication style adjustment unit, a prior knowledge understanding unit, a feedback collection unit, and a comprehension improvement unit. The communication style adjustment unit generates a method for optimally communicating the content a user wants to communicate. For example, the generation AI generates an optimal communication style based on the content input by the user, tailored to the other party's prior knowledge and level of understanding. The prior knowledge understanding unit understands the other party's prior knowledge. For example, the generation AI analyzes the other party's past communication history and social media data to infer the other party's prior knowledge. The feedback collection unit collects feedback from the other party. For example, the generation AI collects feedback such as which parts the other party found difficult to understand and which parts were helpful. The comprehension improvement unit combines information from the communication style adjustment unit, the prior knowledge understanding unit, and the feedback collection unit to improve the communication comprehension. For example, the generation AI adjusts the difficulty of the explanation based on the other party's level of understanding, thereby conveying information in a way that is easier for the other party to understand. This allows the communication comprehension improvement system to improve the communication comprehension and reduce the risk of mistakes.

[0056] The communication style adjustment unit can analyze the user's voice tone and speaking habits and generate the optimal communication style based on that. In the communication style adjustment unit, for example, the generation AI analyzes the user's voice tone and generates a way to convey information to the other person in an appropriate tone. For example, it determines when to speak in a calm tone or an excited tone. The communication style adjustment unit also analyzes the user's speaking habits and suggests a way to speak that is easy for the other person to understand. For example, it instructs a user who speaks quickly to speak more slowly. The communication style adjustment unit also analyzes the user's voice tone and speaking habits in combination and generates the optimal communication style for the other person. For example, it suggests a way to attract the other person's attention by changing the tone of voice. This makes it possible to generate the optimal communication style based on the user's voice tone and speaking habits.

[0057] The communication style adjustment unit learns the user's past successful communication patterns and can suggest the optimal communication style based on them. For example, the generation AI in the communication style adjustment unit analyzes the user's past successful communication patterns and suggests the optimal communication style in similar situations. For example, it reuses specific phrases or expressions. The communication style adjustment unit also learns the user's past successful communication patterns and generates the optimal communication style for new situations. For example, it suggests effective communication styles even for different people. The communication style adjustment unit also predicts the other person's reaction based on the user's past successful communication patterns and suggests the optimal communication style. For example, it uses phrases that will elicit a positive reaction from the other person. This makes it possible to suggest the optimal communication style based on past successful communication patterns.

[0058] The communication style adjustment unit can use the emotion estimation function to analyze the emotional state of the user and generate a communication style that corresponds to the emotion. For example, the communication style adjustment unit uses the emotion estimation function to suggest that the user speak in a relaxed tone if the user is nervous. For example, it displays a message encouraging the user to take a deep breath. The communication style adjustment unit also uses the emotion estimation function to instruct the user to speak in a calm tone if the user is excited. For example, it suggests that the user slow down their speaking speed. The communication style adjustment unit also uses the emotion estimation function to generate an optimal communication style that corresponds to the user's emotional state. For example, if the user is sad, it uses words of encouragement. In this way, it is possible to generate an optimal communication style based on the user's emotional state.

[0059] The communication style adjustment unit can analyze the user's gestures and facial expressions and generate the optimal way of communicating based on that. In this communication style adjustment unit, for example, the generation AI analyzes the user's gestures and suggests appropriate gestures to the other person. For example, it uses hand movements to indicate points to emphasize. In addition, the communication style adjustment unit analyzes the user's facial expressions and suggests facial expressions that are easy for the other person to understand. For example, it uses a smile to convey friendliness. In addition, the communication style adjustment unit analyzes the user's gestures and facial expressions in combination and generates the optimal way of communicating to the other person. For example, it conveys information by linking hand movements and facial expressions. This makes it possible to generate the optimal way of communicating based on the user's gestures and facial expressions.

[0060] The communication style adjustment unit generates communication styles that correspond to different cultural spheres and languages, thereby supporting international communication. For example, the generation AI in the communication style adjustment unit learns communication styles in different cultural spheres and proposes the optimal communication style. For example, differences in the use of honorifics and gestures are taken into consideration. The communication style adjustment unit also generates communication styles that correspond to different languages ​​and conveys information in the recipient's native language. For example, it translates from English to Spanish. The communication style adjustment unit also supports international communication by generating a combination of communication styles that correspond to different cultural spheres and languages. For example, it uses phrases that take cultural background into consideration. This allows the generation AI to generate communication styles that correspond to different cultural spheres and languages, thereby supporting international communication.

[0061] The communication style adjustment unit can use the emotion estimation function to analyze the emotional state of the other party in real time and generate a corresponding communication style. For example, the communication style adjustment unit uses the emotion estimation function to suggest speaking in a relaxed tone if the other party is nervous. For example, using words that make the other party feel at ease. The communication style adjustment unit also uses the emotion estimation function to instruct speaking in a calm tone if the other party is excited. For example, using words that calm the other party. The communication style adjustment unit also uses the emotion estimation function to generate an optimal communication style according to the other party's emotional state. For example, if the other party is sad, using words of encouragement. In this way, an optimal communication style can be generated in real time based on the other party's emotional state.

[0062] The prior knowledge understanding unit can infer prior knowledge by analyzing the other person's social media and public profile. In the prior knowledge understanding unit, for example, the generation AI analyzes the content of the other person's social media posts to understand the other person's interests and concerns. For example, it identifies the topics that the other person frequently posts on. In addition, the prior knowledge understanding unit analyzes the other person's public profile to understand the other person's expertise and work history. For example, it infers prior knowledge based on the other person's work history and educational background. In addition, the generation AI analyzes the other person's social media and public profile in combination to comprehensively understand the other person's prior knowledge. For example, it infers prior knowledge based on the content of the other person's posts and work history. This makes it possible to infer prior knowledge based on the other person's social media and public profile.

[0063] The prerequisite knowledge understanding unit can analyze the other party's past learning history and work experience to determine their level of expertise. For example, the generation AI analyzes the other party's past learning history to determine their level of expertise. For example, it infers their level of expertise based on the courses they have taken and the qualifications they have obtained. The prerequisite knowledge understanding unit also analyzes the other party's work history to determine their level of expertise. For example, it infers their level of expertise based on the projects they have been in charge of and their position. The generation AI also analyzes the other party's learning history and work experience to comprehensively determine their level of expertise. For example, it infers their level of expertise based on their educational background and work experience. This makes it possible to determine their level of expertise based on their past learning history and work experience.

[0064] The prior knowledge understanding unit can use the emotion estimation function to analyze the interests and concerns of the other party and infer prior knowledge based on that. For example, if the other party shows a strong interest in a particular topic, the prior knowledge understanding unit can use the emotion estimation function to infer prior knowledge related to that topic. For example, it can identify a topic that the other party is excited about. Furthermore, if the other party shows positive emotions about a particular topic, the prior knowledge understanding unit can infer prior knowledge related to that topic. For example, it can identify a topic that the other party is happy about. Furthermore, the prior knowledge understanding unit can use the emotion estimation function to analyze the other party's interests and concerns and infer prior knowledge based on that. For example, it can infer prior knowledge based on topics that the other party frequently talks about. In this way, it is possible to infer prior knowledge based on the other party's interests and concerns.

[0065] The prior knowledge understanding unit can infer prior knowledge by analyzing the other party's past purchasing history and behavioral patterns. In the prior knowledge understanding unit, for example, the generation AI analyzes the other party's past purchasing history to understand the other party's interests and concerns. For example, prior knowledge is inferred based on the products and services purchased by the other party. In addition, the generation AI analyzes the other party's behavioral patterns to understand the other party's interests and concerns. For example, prior knowledge is inferred based on the websites and apps the other party frequently visits. In addition, the generation AI analyzes the other party's purchasing history and behavioral patterns in combination to comprehensively understand the other party's prior knowledge. For example, prior knowledge is inferred based on the other party's purchase history and website browsing history. This makes it possible to infer prior knowledge based on the other party's past purchasing history and behavioral patterns.

[0066] The prior knowledge understanding unit can infer prior knowledge by analyzing information about the communities and groups to which the other party belongs. In the prior knowledge understanding unit, for example, the generation AI analyzes information about the community to which the other party belongs to understand the other party's interests and concerns. For example, prior knowledge is inferred based on the online forums and groups in which the other party participates. In addition, the prior knowledge understanding unit analyzes information about the groups to which the other party belongs to understand the other party's interests and concerns. For example, prior knowledge is inferred based on the expert groups or hobby circles to which the other party belongs. In addition, the generation AI combines and analyzes information about the other party's communities and groups to comprehensively understand the other party's prior knowledge. For example, prior knowledge is inferred based on the forums and circles in which the other party participates. This makes it possible to infer prior knowledge based on information about the communities and groups to which the other party belongs.

[0067] The prior knowledge understanding unit can use the emotion estimation function to analyze the other party's past feedback and infer prior knowledge based on that. The prior knowledge understanding unit, for example, uses the emotion estimation function to infer prior knowledge related to topics on which the other party has given positive feedback in the past. For example, it identifies topics on which the other party has given high ratings. The prior knowledge understanding unit also uses the emotion estimation function to infer prior knowledge related to topics on which the other party has given negative feedback in the past. For example, it identifies topics on which the other party has given low ratings. The prior knowledge understanding unit also uses the emotion estimation function to analyze the other party's past feedback and infer prior knowledge based on that. For example, it infers prior knowledge based on topics on which the other party frequently gives feedback. This makes it possible to infer prior knowledge based on the other party's past feedback.

[0068] The feedback collection unit can analyze the other person's non-verbal reactions, such as facial expressions and gestures, to evaluate the level of understanding. In the feedback collection unit, for example, the generation AI analyzes the other person's facial expressions to evaluate the level of understanding. For example, if the other person shows a confused expression, it determines that the explanation for that part is insufficient. In addition, the feedback collection unit analyzes the other person's gestures to evaluate the level of understanding. For example, if the other person tilts their head, it determines that the explanation for that part is unclear. In addition, the feedback collection unit analyzes the other person's facial expressions and gestures in combination to comprehensively evaluate the level of understanding. For example, if the other person smiles, it determines that the explanation for that part has been understood. This makes it possible to evaluate the level of understanding based on the other person's non-verbal reactions.

[0069] The feedback collection unit can analyze the content and frequency of the other party's questions and evaluate the level of understanding. In the feedback collection unit, for example, the generation AI analyzes the content of the other party's questions and evaluates the level of understanding. For example, if the other party asks a specific question, it determines that the explanation for that part is understood. In addition, the feedback collection unit analyzes the frequency of the other party's questions and evaluates the level of understanding. For example, if the other party asks questions frequently, it determines that the explanation for that part is insufficient. In addition, the feedback collection unit analyzes a combination of the content and frequency of the other party's questions and evaluates the level of understanding comprehensively. For example, if the other party does not ask a question, it determines that the explanation for that part is sufficient. This makes it possible to evaluate the level of understanding based on the content and frequency of the other party's questions.

[0070] The feedback collection unit can use the emotion estimation function to analyze the emotional response of the other party and evaluate the level of understanding. For example, using the emotion estimation function, the feedback collection unit determines that if the other party shows positive emotion, that part of the explanation has been understood. For example, if the other party smiles. Furthermore, using the emotion estimation function, the feedback collection unit determines that if the other party shows negative emotion, that part of the explanation is insufficient. For example, if the other party shows a confused expression. Furthermore, using the emotion estimation function, the feedback collection unit comprehensively analyzes the other party's emotional response and evaluates the level of understanding. For example, if the other party is excited, it determines that that part of the explanation is particularly interesting. This makes it possible to evaluate the level of understanding based on the other party's emotional response.

[0071] The feedback collection unit can analyze the other person's physiological responses, such as heart rate and electrodermal activity, to evaluate the level of understanding. In the feedback collection unit, for example, the generation AI analyzes the other person's heart rate to evaluate the level of understanding. For example, if the heart rate increases, it determines that that part of the explanation has not been understood. In the feedback collection unit, the generation AI analyzes the other person's electrodermal activity to evaluate the level of understanding. For example, if the electrodermal activity changes, it determines that that part of the explanation is unclear. In the feedback collection unit, the generation AI analyzes the other person's heart rate and electrodermal activity in combination to comprehensively evaluate the level of understanding. For example, if the heart rate and electrodermal activity are stable, it determines that that part of the explanation has been understood. This makes it possible to evaluate the level of understanding based on the other person's physiological responses.

[0072] The feedback collection unit can analyze the search and sharing of related information, which is the other party's post-communication behavior, and evaluate the level of understanding. For example, the generation AI analyzes the other party's post-communication search history to evaluate the level of understanding. For example, if the other party searches for related information, it determines that the explanation of that part is insufficient. The feedback collection unit also analyzes the other party's post-communication sharing history to evaluate the level of understanding. For example, if the other party shares information, it determines that the explanation of that part is understood. The feedback collection unit also analyzes the other party's search history and sharing history in combination to comprehensively evaluate the level of understanding. For example, if the other party searches for and shares information, it determines that the explanation of that part is particularly interesting. This makes it possible to evaluate the level of understanding based on the other party's post-communication behavior.

[0073] The feedback collection unit can use the emotion estimation function to analyze the emotional tone of the other party's feedback and evaluate the level of understanding. For example, using the emotion estimation function, the feedback collection unit determines that a part of the explanation has been understood if the other party's feedback has a positive tone. For example, if the other party expresses gratitude. The feedback collection unit can also use the emotion estimation function to determine that a part of the explanation has been insufficient if the other party's feedback has a negative tone. For example, if the other party expresses dissatisfaction. The feedback collection unit can also use the emotion estimation function to comprehensively analyze the emotional tone of the other party's feedback and evaluate the level of understanding. For example, if the other party is excited, the feedback collection unit can determine that a part of the explanation is particularly interesting. This makes it possible to evaluate the level of understanding based on the emotional tone of the other party's feedback.

[0074] The comprehension improvement unit can adjust the difficulty of the explanation in real time according to the other party's level of understanding. In the comprehension improvement unit, for example, the generation AI analyzes the other party's level of understanding in real time and adjusts the difficulty of the explanation. For example, it converts technical terms into simple language so that the other party can easily understand. In addition, the comprehension improvement unit analyzes the other party's level of understanding in real time and adjusts the level of detail in the explanation. For example, it adds specific examples so that the other party can easily understand. In addition, the comprehension improvement unit analyzes the other party's level of understanding in real time and adjusts the speed of the explanation. For example, it instructs the other party to speak more slowly so that the other party can easily understand. This allows the difficulty of the explanation to be adjusted in real time according to the other party's level of understanding.

[0075] The comprehension improvement unit can automatically generate supplementary materials and additional information based on the other party's level of understanding. In the comprehension improvement unit, for example, the generation AI analyzes the other party's level of understanding and automatically generates supplementary materials as needed. For example, detailed explanations are added to parts that are difficult for the other party to understand. In addition, the comprehension improvement unit analyzes the other party's level of understanding and automatically generates additional information as needed. For example, information related to topics that interest the other party is provided. In addition, the comprehension improvement unit analyzes the other party's level of understanding and automatically generates visual materials as needed. For example, diagrams and graphs are added to make it easier for the other party to understand. In this way, supplementary materials and additional information can be automatically generated based on the other party's level of understanding.

[0076] The comprehension improvement unit can use the emotion estimation function to select an explanation method according to the emotional state of the other party, thereby improving the level of comprehension. For example, the comprehension improvement unit can use the emotion estimation function to suggest that the other party explain in a relaxed tone if they are nervous. For example, use words that make them feel at ease. The comprehension improvement unit can also use the emotion estimation function to instruct the other party to explain in a calm tone if they are excited. For example, use words that calm the other party. The comprehension improvement unit can also use the emotion estimation function to select an optimal explanation method according to the other party's emotional state. For example, use words of encouragement if the other party is sad. This allows the explanation method to be selected according to the other party's emotional state, thereby improving the level of comprehension.

[0077] The comprehension improvement unit can combine different media such as text, audio, and video to provide the optimal explanation method. In the comprehension improvement unit, for example, the generation AI combines text and audio to provide the optimal explanation method to the other party. For example, it explains using text while supplementing with audio. In addition, the comprehension improvement unit can combine audio and video to provide the optimal explanation method to the other party. For example, it explains visually using video while supplementing with audio. In addition, the comprehension improvement unit can combine text, audio, and video to provide the optimal explanation method to the other party. For example, it explains an overview using text, shows details using video, and supplements with audio. In this way, the optimal explanation method can be provided by combining different media.

[0078] The comprehension improvement unit can provide an explanation method that suits the other person's visual, auditory, and experiential learning styles. For example, the generation AI of the comprehension improvement unit provides an explanation method using diagrams and graphs in accordance with the other person's visual learning style. For example, it creates materials that are visually easy to understand. The comprehension improvement unit also provides an explanation method using audio in accordance with the other person's auditory learning style. For example, it creates an audio guide. The comprehension improvement unit also provides an explanation method through actual experience in accordance with the other person's experiential learning style. For example, it performs simulations and demonstrations. This makes it possible to provide an explanation method that suits the other person's learning style.

[0079] The comprehension improvement unit can use the emotion estimation function to monitor the emotional reactions of the other party in real time and continuously adjust the optimal explanation method. The comprehension improvement unit, for example, uses the emotion estimation function to monitor the emotional reactions of the other party in real time and adjust the explanation method. For example, if the other party is confused, the explanation is simplified. The comprehension improvement unit also uses the emotion estimation function to monitor the emotional reactions of the other party in real time and adjust the speed of the explanation. For example, if the other party is excited, the explanation is slowed down. The comprehension improvement unit also uses the emotion estimation function to monitor the emotional reactions of the other party in real time and adjust the level of detail of the explanation. For example, specific examples are added to make it easier for the other party to understand. In this way, the comprehension improvement unit can monitor the emotional reactions of the other party in real time and continuously adjust the optimal explanation method.

[0080] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0081] The communication comprehension improvement system may further include a history analysis unit that analyzes the user's past communication history and learns successful patterns. The history analysis unit, for example, identifies phrases and tones that were particularly effective in the user's past communications and suggests reusing them in similar situations. The history analysis unit can also predict the other party's reaction based on the user's past communication history and generate an optimal way of communicating. For example, it may learn that specific phrases and tones are effective for a specific party and suggest that. Furthermore, the history analysis unit can generate an optimal way of communicating for a new situation based on the user's past communication history. For example, it may suggest an effective way of communicating with a different party. This makes it possible to suggest an optimal way of communicating based on the user's past successful communication patterns.

[0082] The communication comprehension improvement system can further include a voice analysis unit that analyzes the user's voice tone and speaking habits and generates the optimal way of communicating based on the results. The voice analysis unit, for example, analyzes the user's voice tone and generates a way of communicating information to the other party in an appropriate tone. For example, it determines when to speak in a calm tone or when to speak in an excited tone. The voice analysis unit also analyzes the user's speaking habits and suggests a way of speaking that is easy for the other party to understand. For example, it instructs a user who speaks quickly to speak more slowly. The voice analysis unit also analyzes the user's voice tone and speaking habits in combination to generate the optimal way of communicating for the other party. For example, it suggests a way to attract the other party's attention by changing the tone of voice. This makes it possible to generate the optimal way of communicating based on the user's voice tone and speaking habits.

[0083] The communication comprehension improvement system can further include a non-verbal analysis unit that analyzes the user's gestures and facial expressions and generates the optimal way of communicating based on the results. The non-verbal analysis unit, for example, analyzes the user's gestures and suggests appropriate gestures to the other person. For example, it uses hand movements to indicate points to emphasize. The non-verbal analysis unit also analyzes the user's facial expressions and suggests facial expressions that are easy for the other person to understand. For example, it uses a smile to convey friendliness. The non-verbal analysis unit also analyzes the user's gestures and facial expressions in combination to generate the optimal way of communicating to the other person. For example, it conveys information by linking hand movements and facial expressions. This makes it possible to generate the optimal way of communicating based on the user's gestures and facial expressions.

[0084] The communication comprehension improvement system can further include a cultural adaptation unit that generates ways of communicating that correspond to different cultures and languages, thereby supporting international communication. The cultural adaptation unit, for example, learns communication styles in different cultures and proposes the optimal way of communicating. For example, it takes into account differences in the use of honorifics and gestures. The cultural adaptation unit also generates ways of communicating that correspond to different languages ​​and conveys information in the recipient's native language. For example, it translates information from English to Spanish. The cultural adaptation unit also generates a combination of ways of communicating that correspond to different cultures and languages, thereby supporting international communication. For example, it uses phrases that take cultural background into consideration. This makes it possible to generate ways of communicating that correspond to different cultures and languages, thereby supporting international communication.

[0085] The communication comprehension improvement system can further include a social analysis unit that analyzes the other person's social media and public profile to infer prior knowledge. The social analysis unit, for example, analyzes the content of the other person's social media posts to understand the other person's interests. For example, it identifies topics that the other person frequently posts about. The social analysis unit also analyzes the other person's public profile to understand the other person's expertise and work history. For example, it infers prior knowledge based on the other person's work history and educational background. The social analysis unit also analyzes the other person's social media and public profile in combination to comprehensively understand the other person's prior knowledge. For example, it infers prior knowledge based on the other person's post content and work history. This makes it possible to infer prior knowledge based on the other person's social media and public profile.

[0086] The communication comprehension improvement system can further include an emotion analysis unit that uses the emotion estimation function to analyze the user's emotional state and generate a way of communicating that corresponds to the emotion. For example, if the user is nervous, the emotion analysis unit suggests that the user speak in a relaxed tone. For example, it displays a message encouraging the user to take a deep breath. Furthermore, if the user is excited, the emotion analysis unit instructs the user to speak in a calm tone. For example, it suggests that the user slow down their speaking speed. Furthermore, the emotion analysis unit generates an optimal way of communicating that corresponds to the user's emotional state. For example, if the user is sad, it uses words of encouragement. In this way, an optimal way of communicating can be generated based on the user's emotional state.

[0087] The communication comprehension improvement system can further include an emotion real-time analysis unit that uses the emotion estimation function to analyze the emotional state of the other party in real time and generate a corresponding way of communicating. For example, if the other party is nervous, the emotion real-time analysis unit suggests speaking in a relaxed tone. For example, use words that make the other party feel at ease. Furthermore, if the other party is excited, the emotion real-time analysis unit instructs the other party to speak in a calm tone. For example, use words that calm the other party. Furthermore, the emotion real-time analysis unit generates an optimal way of communicating according to the other party's emotional state. For example, if the other party is sad, use words of encouragement. In this way, an optimal way of communicating can be generated in real time based on the other party's emotional state.

[0088] The communication comprehension improvement system can further include an interest analysis unit that uses an emotion estimation function to analyze the interests and concerns of the other party and infer prior knowledge based on the analysis. For example, if the other party shows a strong interest in a particular topic, the interest analysis unit infers prior knowledge related to that topic. For example, it identifies topics that the other party is excited about. Furthermore, if the other party shows positive emotions about a particular topic, the interest analysis unit infers prior knowledge related to that topic. For example, it identifies topics that the other party is happy about. Furthermore, the interest analysis unit analyzes the other party's interests and concerns and infers prior knowledge based on the analysis. For example, it infers prior knowledge based on topics that the other party frequently talks about. In this way, prior knowledge can be inferred based on the other party's interests and concerns.

[0089] The communication comprehension improvement system can further include a feedback analysis unit that uses the emotion estimation function to analyze the other party's past feedback and infer prior knowledge based on the analysis. The feedback analysis unit, for example, infers prior knowledge related to topics on which the other party has given positive feedback in the past. For example, it identifies topics on which the other party has given high ratings. The feedback analysis unit also infers prior knowledge related to topics on which the other party has given negative feedback in the past. For example, it identifies topics on which the other party has given low ratings. The feedback analysis unit also analyzes the other party's past feedback and infers prior knowledge based on the analysis. For example, it infers prior knowledge based on topics on which the other party frequently gives feedback. This makes it possible to infer prior knowledge based on the other party's past feedback.

[0090] The communication comprehension improvement system can further include an emotion evaluation unit that uses an emotion estimation function to analyze the emotional response of the other party and evaluate the level of comprehension. For example, if the other party shows positive emotion, the emotion evaluation unit determines that that part of the explanation has been understood. For example, if the other party shows a smile. Also, if the other party shows negative emotion, the emotion evaluation unit determines that that part of the explanation is insufficient. For example, if the other party shows a confused expression. Also, the emotion evaluation unit comprehensively analyzes the other party's emotional response and evaluates the level of comprehension. For example, if the other party is excited, it determines that that part of the explanation is particularly interesting. This makes it possible to evaluate the level of comprehension based on the other party's emotional response.

[0091] The processing flow of the second embodiment will be briefly explained below.

[0092] Step 1: The communication adjustment unit generates the optimal way to communicate what the user wants to say. For example, the generation AI generates the optimal way to communicate based on the content entered by the user, taking into account the other person's prior knowledge and level of understanding. Step 2: The prior knowledge understanding unit understands the other person's prior knowledge. For example, the generation AI analyzes the other person's past communication history and social media data to infer the other person's prior knowledge. Step 3: The feedback collection section collects feedback from the other party. For example, the generation AI collects feedback such as which parts the other party found difficult to understand and which parts were helpful. Step 4: The comprehension improvement unit improves the level of understanding of communication by combining information from the communication adjustment unit, the prior knowledge understanding unit, and the feedback collection unit. For example, the generation AI adjusts the difficulty of the explanation to match the other person's level of comprehension, and conveys information in a way that is easier for the other person to understand.

[0093] 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.

[0094] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (Internet Search<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.

[0095] 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.

[0096] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0097] 3, the data processing system 210 includes a data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0098] 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.

[0099] 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.

[0100] 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.

[0101] 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).

[0102] 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.

[0103] 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.

[0104] 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.

[0105] 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.

[0106] 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.

[0107] 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.

[0108] 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.

[0109] 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.

[0110] 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.

[0111] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0112] 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.

[0113] 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.

[0114] 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.

[0115] 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.

[0116] 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).

[0117] 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.

[0118] 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.

[0119] 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.

[0120] 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.

[0121] In the headset type terminal 314, 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. 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 specific processing unit 290 using these models.

[0122] 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.

[0123] 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.

[0124] 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.

[0125] 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.

[0126] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0127] 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.

[0128] 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.

[0129] 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.

[0130] 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.

[0131] 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).

[0132] 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.

[0133] 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.

[0134] 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.

[0135] 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.

[0136] 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.

[0137] In the robot 414, 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 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 processing similar to that of the specific processing unit 290 using these models.

[0138] 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.

[0139] 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.

[0140] 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.

[0141] 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.

[0142] 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.

[0143] 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.

[0144] 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.

[0145] 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).

[0146] 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.

[0147] 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."

[0148] 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.

[0149] 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.

[0150] 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.

[0151] 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.

[0152] 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.

[0153] 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.

[0154] 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.

[0155] 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.

[0156] 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.

[0157] 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.

[0158] 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.

[0159] 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. [Explanation of symbols]

[0160] 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 communication method adjustment unit that generates a method for optimally communicating content that the user wants to communicate; a prior knowledge grasping unit for grasping the prior knowledge of the other party; a feedback collection unit that collects feedback from the other party; and a comprehension improvement unit that improves the level of understanding of communication by combining information from the communication style adjustment unit, the prerequisite knowledge understanding unit, and the feedback collection unit. A system characterized by:

2. The transmission method adjustment unit Analyzing the user's tone of voice and speaking habits and generating the optimal way of communicating based on that.

2. The system of claim 1.

3. The transmission method adjustment unit Learns the user's past successful communication patterns and suggests the best way to communicate based on them 2. The system of claim 1.

4. The transmission method adjustment unit Analyzing the emotional state of the user and generating a way of communicating according to the emotion 2. The system of claim 1.

5. The transmission method adjustment unit Analyze the user's gestures and facial expressions and generate the optimal way to communicate based on them 2. The system of claim 1.

6. The transmission method adjustment unit Generate ways of communicating that correspond to different cultures and languages, and support international communication 2. The system of claim 1.

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