System

Abstract

An object of a system according to an embodiment is to propose a method of appropriately conveying what a user is hard to say and to provide a specific sentence example.SOLUTION: A system according to an embodiment includes a user input information collection unit, an optimum transmission method suggestion unit, and a sentence example generation unit. The user input information collection unit collects information from a user. The optimum transmission method proposal unit proposes an optimum transmission method based on the information collected by the user input information collection unit. The sentence example generation unit generates a specific sentence example based on the transmission method proposed by the optimum transmission method proposal unit.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, it is difficult to find an appropriate way to communicate difficult things, which can reduce the effectiveness of communication.

[0005] The system according to the embodiment aims to propose a method for users to appropriately communicate things that are difficult for them to say, and to provide specific example sentences. [Means for solving the problem]

[0006] The system according to the embodiment includes a user-input information collection unit, an optimal communication method proposal unit, and a sentence example generation unit. The user-input information collection unit collects information from a user. The optimal communication method proposal unit proposes an optimal communication method based on the information collected by the user-input information collection unit. The sentence example generation unit generates specific sentence examples based on the communication method proposed by the optimal communication method proposal unit. [Effects of the Invention]

[0007] The system according to the embodiment can suggest ways for users to appropriately communicate things that are difficult for them to say, and can provide specific example sentences. [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 nonvolatile storage devices that store various programs, various parameters, etc. Examples of nonvolatile 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 the user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

[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 may have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59.

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

[0028] (Example 1) A service according to an embodiment of the present invention utilizes AI to suggest the optimal way to communicate difficult things. This service aims to ensure that what a user wants to communicate, whether it be positive feedback or suggestions for improvement, is properly conveyed to the other party and facilitates the desired outcome. In this way, the service supports the user in properly communicating with the other party, improving the quality of communication and making it easier to achieve the desired outcome.

[0029] A service according to an embodiment includes a user-input information collection unit, an optimal communication method proposal unit, and a sample sentence generation unit. The user-input information collection unit collects information from a user. For example, the user inputs information about the content they want to communicate and information about the other party (e.g., their relationship, past responses, and personality). The user-input information collection unit can also collect information entered by the user in text format. For example, the user may collect information by entering text into an input form. The user-input information collection unit can also support voice input and image input. For example, when a user provides information by voice, the information is converted into text using voice recognition technology. The optimal communication method proposal unit proposes the optimal way of communicating based on the collected information. For example, the generation AI analyzes the information entered by the user and proposes the most appropriate communication method, timing, and wording for the other party. The generation AI can also propose a calm timing and gentle language, taking into account the other party's personality and past responses. The generation AI can also learn from the user's past feedback to improve the accuracy of its proposals. The sample sentence generation unit generates specific sample sentences based on the proposed communication method. For example, in the case of feedback via email, the generation AI generates a sample sentence such as, "Thank you for your hard work. I would like to let you know some improvements you can make to the project you worked on the other day." The generation AI can also generate sample sentences that can be used in social media messages and actual conversations. Furthermore, the generation AI can learn the user's past usage history and generate more personalized sample sentences. As a result, the service according to the embodiment can support effective communication by suggesting the optimal way to communicate based on the user's information and generating specific sample sentences.

[0030] The user input information collection unit analyzes the user's past communication history and extracts specific patterns and trends, thereby enabling more accurate information collection. The user input information collection unit, for example, analyzes the user's past email and chat history to extract specific wording and expression patterns. For example, it finds similar patterns based on examples of feedback that have been successful in the past. The user input information collection unit can also analyze the user's past call records to extract specific patterns and trends. For example, it analyzes changes in wording and tone during calls to improve the accuracy of information collection. Furthermore, the user input information collection unit can analyze the user's past SNS posts to extract specific patterns and trends. For example, it analyzes the content of posts and trends in reactions to improve the accuracy of information collection. In this way, analyzing past communication history enables more accurate information collection.

[0031] The user-input information collection unit can introduce an interactive interface that provides real-time feedback on the information entered by the user and requests additional information as needed. For example, the user-input information collection unit uses AI to provide real-time feedback on the content entered by the user and complete the necessary information. For example, additional questions may be asked if the input content is insufficient. The user-input information collection unit can also use an interactive UI to check the information entered by the user in real time and provide feedback. For example, a chatbot can be used to provide immediate responses to the content entered by the user. Furthermore, the user-input information collection unit can also use an interactive form to collect the information entered by the user in real time and provide feedback. For example, appropriate advice or supplementary information can be provided for the content entered by the user. This allows for real-time feedback to be provided and necessary information to be completed, thereby improving the accuracy of information collection.

[0032] The user input information collection unit supports voice input and image input, allowing the user to provide information in ways other than words. The user input information collection unit, for example, supports voice input and converts what the user says into text. For example, when the user inputs what he or she wants to communicate verbally, voice recognition technology is used. The user input information collection unit can also support image input and analyze images provided by the user. For example, when the user provides handwritten notes or drawings as images, image recognition technology is used to convert them into text. The user input information collection unit can also support video input and analyze videos provided by the user. For example, when the user provides what he or she wants to communicate through video, video analysis technology is used to extract information. In this way, by supporting voice input and image input, the user can provide information in ways other than words.

[0033] The user input information collection unit can utilize an international database to collect information taking cultural backgrounds into consideration in order to accommodate different cultures and languages. The user input information collection unit, for example, utilizes an international database to collect information taking different cultures and languages ​​into consideration. For example, it proposes a feedback method that takes cultural differences into consideration. The user input information collection unit can also utilize a multilingual database to collect information in different languages. For example, it automatically translates and analyzes information entered by users in different languages. Furthermore, the user input information collection unit can utilize a cultural information database to collect information taking different cultural backgrounds into consideration. For example, it proposes a feedback method that takes different cultural customs and values ​​into consideration. This makes it possible to support international communication by accommodating different cultures and languages.

[0034] The optimal communication method suggestion unit can learn the user's past feedback for the communication methods suggested by the generation AI and improve the accuracy of the suggestions. The optimal communication method suggestion unit, for example, learns the user's past feedback for the communication methods suggested by the generation AI and improves the accuracy of the suggestions. For example, it learns patterns of feedback that have been successful in the past. The optimal communication method suggestion unit can also analyze the user's past feedback and improve the accuracy of the suggestions. For example, it analyzes the content and reactions of feedback that the user has received in the past and improves the accuracy of the suggestions. Furthermore, the optimal communication method suggestion unit can develop an algorithm for improving the accuracy of the suggestions based on the user's past feedback. For example, it uses a machine learning model to learn the user's feedback and improve the accuracy of the suggestions. In this way, the accuracy of the suggestions can be improved by learning the user's past feedback.

[0035] The optimal communication method suggestion unit can also provide the proposed communication method as a communication method in a different medium. For example, the optimal communication method suggestion unit can provide the proposed communication method as a video message, allowing the user to communicate visually. For example, the content of the feedback can be explained using a video. The optimal communication method suggestion unit can also provide the proposed communication method as an infographic, allowing the user to communicate in a format that is visually easy to understand. For example, the main points of the feedback can be shown using diagrams or icons. Furthermore, the optimal communication method suggestion unit can provide the proposed communication method as an audio message, allowing the user to communicate audibly. For example, the content of the feedback can be explained using audio. In this way, communication methods in different media are provided, allowing the user to select the optimal method.

[0036] The optimal communication method suggestion unit can provide communication method templates specialized for different industries and occupations, allowing the user to select from them. The optimal communication method suggestion unit can provide communication method templates specialized for different industries and occupations, allowing the user to select from them. For example, technical, design, and marketing templates are prepared. The optimal communication method suggestion unit can also suggest an optimal communication method template depending on the industry and occupation selected by the user. For example, if the user is an engineer in the IT industry, a template for technical feedback is suggested. Furthermore, the optimal communication method suggestion unit can customize a template specialized for the industry and occupation selected by the user. For example, if the user is giving feedback on a specific project, a template specialized for that project is provided. In this way, by providing templates specialized for different industries and occupations, the user can select the optimal method.

[0037] The example sentence generation unit can learn the user's past usage history from the generated example sentences and generate more personalized example sentences. The example sentence generation unit, for example, learns the user's past usage history from the generated example sentences and generates more personalized example sentences. For example, it learns patterns of example sentences used in the past. The example sentence generation unit can also analyze the user's past usage history and generate personalized example sentences. For example, it analyzes the content and reactions of example sentences used by the user in the past and generates personalized example sentences. Furthermore, the example sentence generation unit can develop an algorithm for generating personalized example sentences based on the user's past usage history. For example, it uses a machine learning model to learn the user's usage history and generate personalized example sentences. In this way, more personalized example sentences can be generated by learning the user's past usage history.

[0038] The example sentence generation unit can refer to related industry news and trend information to understand the context when generating example sentences. For example, the example sentence generation unit automatically collects related industry news and trend information when generating example sentences and reflects it in the example sentences. For example, the latest technological trends and market needs are reflected. The example sentence generation unit can also develop an algorithm for referencing related industry news and trend information when generating example sentences. For example, related news articles and academic papers are collected and reflected in the example sentences. Furthermore, the example sentence generation unit can build a system for referring to related industry news and trend information when generating example sentences and understanding the context. For example, related information is automatically collected and reflected in the example sentences. In this way, by referring to industry news and trend information, the context can be understood and more appropriate example sentences can be generated.

[0039] The example sentence generation unit can automatically translate the generated example sentences into different languages ​​to support international communication. The example sentence generation unit, for example, automatically translates the generated example sentences into different languages ​​to support international communication. For example, translation into multiple languages ​​such as English, French, and Chinese is performed. The example sentence generation unit can also develop algorithms for automatic translation into different languages. For example, machine translation technology is used to translate the generated example sentences into different languages. Furthermore, the example sentence generation unit can also build a system for automatically translating the generated example sentences into different languages ​​to support international communication. For example, neural network translation technology is used to translate the generated example sentences with high accuracy. This makes it possible to support international communication by automatically translating into different languages.

[0040] The example sentence generation unit can convert example sentences into visual notes or mind maps to make them easier to understand visually. For example, the example sentence generation unit can convert the generated example sentences into visual notes to visually display the main points of an idea. For example, important points can be indicated using diagrams or icons. The example sentence generation unit can also convert the generated example sentences into mind maps to display them in a format that is easier to understand visually. For example, the structure of the example sentences can be displayed as a mind map to visually organize related information. Furthermore, the example sentence generation unit can develop algorithms for converting the generated example sentences into visual notes or mind maps. For example, the generated example sentences can be visually displayed using handwritten notes or digital notes. In this way, converting the example sentences into visual notes or mind maps can make them easier to understand visually.

[0041] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0042] The user-input information collection unit can automatically retrieve information from related external databases based on the user's input, improving the accuracy of information collection. For example, if a user provides feedback about a specific industry, the latest trends and news in that industry can be automatically retrieved and reflected in the feedback. The user-input information collection unit can also automatically collect academic papers and research data related to the content entered by the user to strengthen the corroboration of the information. Furthermore, the user-input information collection unit can collect social media posts and comments related to the content entered by the user, reflecting real-time opinions and reactions. In this way, the accuracy of information collection can be improved by utilizing external databases.

[0043] The user-input information collection unit uses AI to automatically tag information entered by users, making it easier to organize and search for information. For example, it automatically tags the content entered by users with related keywords and categories. The user-input information collection unit can also tag information previously entered by users to promote the reuse of information. Furthermore, the user-input information collection unit can suggest related tags for the information entered by users, and users can also manually tag information. This makes it easier to organize and search for information, enabling efficient information collection.

[0044] The user input information collection unit can support voice input and image input, allowing the user to provide information in ways other than words. For example, it can support voice input and convert what the user says into text. For example, when the user inputs what they want to communicate verbally, it uses voice recognition technology. The user input information collection unit can also support image input and analyze images provided by the user. For example, when a user provides handwritten notes or drawings as images, it converts them into text using image recognition technology. The user input information collection unit can also support video input and analyze videos provided by the user. For example, when a user provides what they want to communicate through video, it uses video analysis technology to extract information. In this way, by supporting voice input and image input, the user can provide information in ways other than words.

[0045] The user input information collection unit can utilize an international database to collect information taking cultural backgrounds into consideration in order to accommodate different cultures and languages. For example, an international database can be utilized to collect information taking into consideration different cultures and languages. For example, a feedback method that takes cultural differences into consideration can be proposed. The user input information collection unit can also utilize a multilingual database to collect information in different languages. For example, information entered by a user in a different language can be automatically translated and analyzed. The user input information collection unit can also utilize a cultural information database to collect information taking into consideration different cultural backgrounds. For example, a feedback method that takes into consideration the customs and values ​​of different cultures can be proposed. This makes it possible to support international communication by accommodating different cultures and languages.

[0046] The optimal communication method suggestion unit can learn the user's past feedback for the communication methods suggested by the generation AI and improve the accuracy of the suggestions. For example, the optimal communication method suggestion unit can learn the user's past feedback for the communication methods suggested by the generation AI and improve the accuracy of the suggestions. For example, it can learn patterns of feedback that have been successful in the past. The optimal communication method suggestion unit can also analyze the user's past feedback and improve the accuracy of the suggestions. For example, it can analyze the content and reactions of feedback that the user has received in the past and improve the accuracy of the suggestions. Furthermore, the optimal communication method suggestion unit can develop an algorithm for improving the accuracy of the suggestions based on the user's past feedback. For example, it can use a machine learning model to learn the user's feedback and improve the accuracy of the suggestions. In this way, the accuracy of the suggestions can be improved by learning the user's past feedback.

[0047] The optimal communication method suggestion unit can also provide the proposed communication method as a communication method in a different medium. For example, the proposed communication method can be provided as a video message, allowing the user to communicate visually. For example, the content of the feedback can be explained using a video. The optimal communication method suggestion unit can also provide the proposed communication method as an infographic, allowing the user to communicate in a format that is visually easy to understand. For example, the main points of the feedback can be shown using diagrams or icons. Furthermore, the optimal communication method suggestion unit can provide the proposed communication method as an audio message, allowing the user to communicate audibly. For example, the content of the feedback can be explained using audio. In this way, communication methods in different media can be provided, allowing the user to select the optimal method.

[0048] The processing flow of the first embodiment will be briefly explained below.

[0049] Step 1: The user input information collection unit collects information from the user. For example, the user inputs what they want to communicate and information about the other person (such as their relationship, past reactions, and personality). The user input information collection unit can also collect information entered by the user in text format. For example, the information is collected by the user entering text into an input form. The user input information collection unit can also support voice input and image input. For example, when a user provides information by voice, it is converted into text using voice recognition technology. Step 2: The optimal communication method suggestion unit suggests the optimal way to communicate based on the collected information. For example, the generation AI analyzes the information entered by the user and suggests the most appropriate way to communicate, timing, and wording for the other person. The generation AI can also suggest calm timing and gentle language, taking into account the other person's personality and past reactions. Furthermore, the generation AI can learn from the user's past feedback and improve the accuracy of its suggestions. Step 3: The example generator generates specific example sentences based on the proposed communication method. For example, in the case of feedback via email, the generation AI generates an example sentence such as, "Thank you for your hard work. I would like to let you know some improvements to be made to the project you worked on the other day." The generation AI can also generate example sentences that can be used in social media messages and real conversations. Furthermore, the generation AI can learn from the user's past usage history and generate more personalized example sentences.

[0050] (Example 2) A service according to an embodiment of the present invention utilizes AI to suggest the optimal way to communicate difficult things. This service aims to ensure that what a user wants to communicate, whether it be positive feedback or suggestions for improvement, is properly conveyed to the other party and facilitates the desired outcome. In this way, the service supports the user in properly communicating with the other party, improving the quality of communication and making it easier to achieve the desired outcome.

[0051] A service according to an embodiment includes a user-input information collection unit, an optimal communication method proposal unit, and a sample sentence generation unit. The user-input information collection unit collects information from a user. For example, the user inputs information about the content they want to communicate and information about the other party (e.g., their relationship, past responses, and personality). The user-input information collection unit can also collect information entered by the user in text format. For example, the user may collect information by entering text into an input form. The user-input information collection unit can also support voice input and image input. For example, when a user provides information by voice, the information is converted into text using voice recognition technology. The optimal communication method proposal unit proposes the optimal way of communicating based on the collected information. For example, the generation AI analyzes the information entered by the user and proposes the most appropriate communication method, timing, and wording for the other party. The generation AI can also propose a calm timing and gentle language, taking into account the other party's personality and past responses. The generation AI can also learn from the user's past feedback to improve the accuracy of its proposals. The sample sentence generation unit generates specific sample sentences based on the proposed communication method. For example, in the case of feedback via email, the generation AI generates a sample sentence such as, "Thank you for your hard work. I would like to let you know some improvements you can make to the project you worked on the other day." The generation AI can also generate sample sentences that can be used in social media messages and actual conversations. Furthermore, the generation AI can learn the user's past usage history and generate more personalized sample sentences. As a result, the service according to the embodiment can support effective communication by suggesting the optimal way to communicate based on the user's information and generating specific sample sentences.

[0052] The user input information collection unit analyzes the user's past communication history and extracts specific patterns and trends, thereby enabling more accurate information collection. The user input information collection unit, for example, analyzes the user's past email and chat history to extract specific wording and expression patterns. For example, it finds similar patterns based on examples of feedback that have been successful in the past. The user input information collection unit can also analyze the user's past call records to extract specific patterns and trends. For example, it analyzes changes in wording and tone during calls to improve the accuracy of information collection. Furthermore, the user input information collection unit can analyze the user's past SNS posts to extract specific patterns and trends. For example, it analyzes the content of posts and trends in reactions to improve the accuracy of information collection. In this way, analyzing past communication history enables more accurate information collection.

[0053] The user-input information collection unit can introduce an interactive interface that provides real-time feedback on the information entered by the user and requests additional information as needed. For example, the user-input information collection unit uses AI to provide real-time feedback on the content entered by the user and complete the necessary information. For example, additional questions may be asked if the input content is insufficient. The user-input information collection unit can also use an interactive UI to check the information entered by the user in real time and provide feedback. For example, a chatbot can be used to provide immediate responses to the content entered by the user. Furthermore, the user-input information collection unit can also use an interactive form to collect the information entered by the user in real time and provide feedback. For example, appropriate advice or supplementary information can be provided for the content entered by the user. This allows for real-time feedback to be provided and necessary information to be completed, thereby improving the accuracy of information collection.

[0054] The user input information collecting unit can use the emotion estimation function to analyze the emotional state of the user when entering input and collect information based on the emotion. The user input information collecting unit, for example, analyzes the facial expression and tone of voice when the user enters input to estimate the emotional state. For example, if the user is nervous, it provides advice to relax. The user input information collecting unit can also use text analysis technology to estimate the emotional state from the content entered by the user. For example, it analyzes the emotional nuances of the sentences entered by the user to estimate the emotional state. Furthermore, the user input information collecting unit can collect the user's biometric data (heart rate and electrodermal activity) using a sensor and analyze the emotional state using an emotion estimation algorithm. For example, it estimates the emotional state based on fluctuations in heart rate. This makes it possible to collect information based on the emotion by analyzing the user's emotional state.

[0055] The user input information collection unit supports voice input and image input, allowing the user to provide information in ways other than words. The user input information collection unit, for example, supports voice input and converts what the user says into text. For example, when the user inputs what he or she wants to communicate verbally, voice recognition technology is used. The user input information collection unit can also support image input and analyze images provided by the user. For example, when the user provides handwritten notes or drawings as images, image recognition technology is used to convert them into text. The user input information collection unit can also support video input and analyze videos provided by the user. For example, when the user provides what he or she wants to communicate through video, video analysis technology is used to extract information. In this way, by supporting voice input and image input, the user can provide information in ways other than words.

[0056] The user input information collection unit can utilize an international database to collect information taking cultural backgrounds into consideration in order to accommodate different cultures and languages. The user input information collection unit, for example, utilizes an international database to collect information taking different cultures and languages ​​into consideration. For example, it proposes a feedback method that takes cultural differences into consideration. The user input information collection unit can also utilize a multilingual database to collect information in different languages. For example, it automatically translates and analyzes information entered by users in different languages. Furthermore, the user input information collection unit can utilize a cultural information database to collect information taking different cultural backgrounds into consideration. For example, it proposes a feedback method that takes different cultural customs and values ​​into consideration. This makes it possible to support international communication by accommodating different cultures and languages.

[0057] The user input information collecting unit can use the emotion estimation function to monitor the user's emotions in real time when they input data and provide an interface for eliciting positive emotions. The user input information collecting unit can, for example, use the emotion estimation function to monitor the user's emotions in real time when they input data and provide an interface for eliciting positive emotions. For example, the unit can display an encouraging message while the user is inputting data. The user input information collecting unit can also analyze the user's emotions in real time when they input data and provide advice for eliciting positive emotions. For example, if the user is feeling anxious, the unit can provide advice to relax. The user input information collecting unit can also monitor the user's emotions in real time when they input data and provide an interactive UI for eliciting positive emotions. For example, the user can receive positive feedback while inputting data, thereby increasing their motivation to input data. This can improve the quality of information collection by monitoring the user's emotions in real time and eliciting positive emotions.

[0058] The optimal communication method suggestion unit can learn the user's past feedback for the communication methods suggested by the generation AI and improve the accuracy of the suggestions. The optimal communication method suggestion unit, for example, learns the user's past feedback for the communication methods suggested by the generation AI and improves the accuracy of the suggestions. For example, it learns patterns of feedback that have been successful in the past. The optimal communication method suggestion unit can also analyze the user's past feedback and improve the accuracy of the suggestions. For example, it analyzes the content and reactions of feedback that the user has received in the past and improves the accuracy of the suggestions. Furthermore, the optimal communication method suggestion unit can develop an algorithm for improving the accuracy of the suggestions based on the user's past feedback. For example, it uses a machine learning model to learn the user's feedback and improve the accuracy of the suggestions. In this way, the accuracy of the suggestions can be improved by learning the user's past feedback.

[0059] The optimal communication method suggestion unit can use the emotion estimation function to predict the emotional state of the other party and suggest the most appropriate way to communicate with that emotion. For example, the optimal communication method suggestion unit can use the emotion estimation function to predict the other party's emotional state and suggest the most appropriate way to communicate with that emotion. For example, if the other party is nervous, it can suggest words to relax them. The optimal communication method suggestion unit can also develop an algorithm to predict the other party's emotional state and suggest the most appropriate way to communicate with that emotion. For example, it can predict the other party's emotional state using an emotion estimation algorithm based on the other party's past reaction data. Furthermore, the optimal communication method suggestion unit can use the emotion estimation function to monitor the other party's emotional state in real time and suggest the most appropriate way to communicate with that emotion. For example, if the other party becomes emotional during a conversation, it can choose a calm moment and suggest gentle language. In this way, it is possible to predict the other party's emotional state and suggest the most appropriate way to communicate.

[0060] The optimal communication method suggestion unit can also provide the proposed communication method as a communication method in a different medium. For example, the optimal communication method suggestion unit can provide the proposed communication method as a video message, allowing the user to communicate visually. For example, the content of the feedback can be explained using a video. The optimal communication method suggestion unit can also provide the proposed communication method as an infographic, allowing the user to communicate in a format that is visually easy to understand. For example, the main points of the feedback can be shown using diagrams or icons. Furthermore, the optimal communication method suggestion unit can provide the proposed communication method as an audio message, allowing the user to communicate audibly. For example, the content of the feedback can be explained using audio. In this way, communication methods in different media are provided, allowing the user to select the optimal method.

[0061] The optimal communication method suggestion unit can provide communication method templates specialized for different industries and occupations, allowing the user to select from them. The optimal communication method suggestion unit can provide communication method templates specialized for different industries and occupations, allowing the user to select from them. For example, technical, design, and marketing templates are prepared. The optimal communication method suggestion unit can also suggest an optimal communication method template depending on the industry and occupation selected by the user. For example, if the user is an engineer in the IT industry, a template for technical feedback is suggested. Furthermore, the optimal communication method suggestion unit can customize a template specialized for the industry and occupation selected by the user. For example, if the user is giving feedback on a specific project, a template specialized for that project is provided. In this way, by providing templates specialized for different industries and occupations, the user can select the optimal method.

[0062] The optimal communication method suggestion unit can use the emotion estimation function to collect the user's emotional reactions to the proposed communication methods in real time and dynamically adjust the proposed content. The optimal communication method suggestion unit, for example, uses the emotion estimation function to collect the user's emotional reactions to the proposed communication methods in real time and dynamically adjust the proposed content. For example, if the user is feeling anxious, the optimal communication method suggestion unit can make suggestions that give the user a sense of security. The optimal communication method suggestion unit can also analyze the user's emotional reactions in real time and dynamically adjust the proposed content. For example, the optimal communication method suggestion unit can prioritize suggestions that the user feels positive about. Furthermore, the optimal communication method suggestion unit can use the emotion estimation function to collect the user's emotional reactions in real time and develop an algorithm for dynamically adjusting the proposed content. For example, an algorithm can be developed that optimizes the proposed content based on the user's emotional data. In this way, the optimal communication method suggestion unit can provide a more effective communication method by collecting the user's emotional reactions in real time and dynamically adjusting the proposed content.

[0063] The example sentence generation unit can learn the user's past usage history from the generated example sentences and generate more personalized example sentences. The example sentence generation unit, for example, learns the user's past usage history from the generated example sentences and generates more personalized example sentences. For example, it learns patterns of example sentences used in the past. The example sentence generation unit can also analyze the user's past usage history and generate personalized example sentences. For example, it analyzes the content and reactions of example sentences used by the user in the past and generates personalized example sentences. Furthermore, the example sentence generation unit can develop an algorithm for generating personalized example sentences based on the user's past usage history. For example, it uses a machine learning model to learn the user's usage history and generate personalized example sentences. In this way, more personalized example sentences can be generated by learning the user's past usage history.

[0064] The example sentence generation unit can refer to related industry news and trend information to understand the context when generating example sentences. For example, the example sentence generation unit automatically collects related industry news and trend information when generating example sentences and reflects it in the example sentences. For example, the latest technological trends and market needs are reflected. The example sentence generation unit can also develop an algorithm for referencing related industry news and trend information when generating example sentences. For example, related news articles and academic papers are collected and reflected in the example sentences. Furthermore, the example sentence generation unit can build a system for referring to related industry news and trend information when generating example sentences and understanding the context. For example, related information is automatically collected and reflected in the example sentences. In this way, by referring to industry news and trend information, the context can be understood and more appropriate example sentences can be generated.

[0065] The example sentence generation unit can use the emotion estimation function to predict the emotional impact that the generated example sentences will have on the other person and select the optimal example sentences. For example, the example sentence generation unit can use the emotion estimation function to predict the emotional impact that the generated example sentences will have on the other person and select the optimal example sentences. For example, the example sentence generation unit can preferentially select example sentences that the other person has positive emotions. The example sentence generation unit can also develop an algorithm for predicting the emotional impact that the generated example sentences will have on the other person. For example, the example sentence generation unit can predict the emotional impact using an emotion estimation algorithm based on the other person's past reaction data. Furthermore, the example sentence generation unit can use the emotion estimation function to monitor the emotional impact that the generated example sentences will have on the other person in real time and select the optimal example sentences. For example, if the other person becomes emotional during a conversation, the example sentence generation unit can choose a calm moment and suggest using gentle language. In this way, the optimal example sentences can be selected by predicting the emotional impact that the generated example sentences will have on the other person.

[0066] The example sentence generation unit can automatically translate the generated example sentences into different languages ​​to support international communication. The example sentence generation unit, for example, automatically translates the generated example sentences into different languages ​​to support international communication. For example, translation into multiple languages ​​such as English, French, and Chinese is performed. The example sentence generation unit can also develop algorithms for automatic translation into different languages. For example, machine translation technology is used to translate the generated example sentences into different languages. Furthermore, the example sentence generation unit can also build a system for automatically translating the generated example sentences into different languages ​​to support international communication. For example, neural network translation technology is used to translate the generated example sentences with high accuracy. This makes it possible to support international communication by automatically translating into different languages.

[0067] The example sentence generation unit can convert example sentences into visual notes or mind maps to make them easier to understand visually. For example, the example sentence generation unit can convert the generated example sentences into visual notes to visually display the main points of an idea. For example, important points can be indicated using diagrams or icons. The example sentence generation unit can also convert the generated example sentences into mind maps to display them in a format that is easier to understand visually. For example, the structure of the example sentences can be displayed as a mind map to visually organize related information. Furthermore, the example sentence generation unit can develop algorithms for converting the generated example sentences into visual notes or mind maps. For example, the generated example sentences can be visually displayed using handwritten notes or digital notes. In this way, converting the example sentences into visual notes or mind maps can make them easier to understand visually.

[0068] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0069] The user-input information collection unit can automatically retrieve information from related external databases based on the user's input, improving the accuracy of information collection. For example, if a user provides feedback about a specific industry, the latest trends and news in that industry can be automatically retrieved and reflected in the feedback. The user-input information collection unit can also automatically collect academic papers and research data related to the content entered by the user to strengthen the corroboration of the information. Furthermore, the user-input information collection unit can collect social media posts and comments related to the content entered by the user, reflecting real-time opinions and reactions. In this way, the accuracy of information collection can be improved by utilizing external databases.

[0070] The user-input information collection unit uses AI to automatically tag information entered by users, making it easier to organize and search for information. For example, it automatically tags the content entered by users with related keywords and categories. The user-input information collection unit can also tag information previously entered by users to promote the reuse of information. Furthermore, the user-input information collection unit can suggest related tags for the information entered by users, and users can also manually tag information. This makes it easier to organize and search for information, enabling efficient information collection.

[0071] The user input information collection unit can use the emotion estimation function to analyze the emotional state of the user when entering input and collect information based on the emotion. For example, the emotional state can be estimated by analyzing the facial expressions and tone of voice when the user enters input. For example, if the user is nervous, advice on how to relax can be provided. The user input information collection unit can also use text analysis technology to estimate the emotional state from the content entered by the user. For example, the emotional nuances of the sentences entered by the user can be analyzed to estimate the emotional state. Furthermore, the user input information collection unit can collect the user's biometric data (heart rate and electrodermal activity) using a sensor and analyze the emotional state using an emotion estimation algorithm. For example, the emotional state can be estimated based on fluctuations in heart rate. This makes it possible to collect information based on the emotion by analyzing the user's emotional state.

[0072] The user input information collection unit can support voice input and image input, allowing the user to provide information in ways other than words. For example, it can support voice input and convert what the user says into text. For example, when the user inputs what they want to communicate verbally, it uses voice recognition technology. The user input information collection unit can also support image input and analyze images provided by the user. For example, when a user provides handwritten notes or drawings as images, it converts them into text using image recognition technology. The user input information collection unit can also support video input and analyze videos provided by the user. For example, when a user provides what they want to communicate through video, it uses video analysis technology to extract information. In this way, by supporting voice input and image input, the user can provide information in ways other than words.

[0073] The user input information collection unit can utilize an international database to collect information taking cultural backgrounds into consideration in order to accommodate different cultures and languages. For example, an international database can be utilized to collect information taking into consideration different cultures and languages. For example, a feedback method that takes cultural differences into consideration can be proposed. The user input information collection unit can also utilize a multilingual database to collect information in different languages. For example, information entered by a user in a different language can be automatically translated and analyzed. The user input information collection unit can also utilize a cultural information database to collect information taking into consideration different cultural backgrounds. For example, a feedback method that takes into consideration the customs and values ​​of different cultures can be proposed. This makes it possible to support international communication by accommodating different cultures and languages.

[0074] The user input information collecting unit can use the emotion estimation function to monitor the user's emotions in real time when they input data and provide an interface for eliciting positive emotions. For example, the emotion estimation function can be used to monitor the user's emotions in real time when they input data and provide an interface for eliciting positive emotions. For example, an encouraging message can be displayed while the user is inputting data. The user input information collecting unit can also analyze the user's emotions in real time when they input data and provide advice for eliciting positive emotions. For example, if the user is feeling anxious, advice to relax can be provided. Furthermore, the user input information collecting unit can monitor the user's emotions in real time when they input data and provide an interactive UI for eliciting positive emotions. For example, receiving positive feedback while the user is inputting data can increase the user's motivation to input data. This makes it possible to improve the quality of information collection by monitoring the user's emotions in real time and eliciting positive emotions.

[0075] The optimal communication method suggestion unit can learn the user's past feedback for the communication methods suggested by the generation AI and improve the accuracy of the suggestions. For example, the optimal communication method suggestion unit can learn the user's past feedback for the communication methods suggested by the generation AI and improve the accuracy of the suggestions. For example, it can learn patterns of feedback that have been successful in the past. The optimal communication method suggestion unit can also analyze the user's past feedback and improve the accuracy of the suggestions. For example, it can analyze the content and reactions of feedback that the user has received in the past and improve the accuracy of the suggestions. Furthermore, the optimal communication method suggestion unit can develop an algorithm for improving the accuracy of the suggestions based on the user's past feedback. For example, it can use a machine learning model to learn the user's feedback and improve the accuracy of the suggestions. In this way, the accuracy of the suggestions can be improved by learning the user's past feedback.

[0076] The optimal communication method suggestion unit can use the emotion estimation function to predict the emotional state of the other party and suggest the most appropriate way to communicate that emotion. For example, the emotion estimation function can be used to predict the other party's emotional state and suggest the most appropriate way to communicate that emotion. For example, if the other party is nervous, the optimal communication method suggestion unit can suggest words to relax the other party. The optimal communication method suggestion unit can also develop an algorithm to predict the other party's emotional state and suggest the most appropriate way to communicate that emotion. For example, the emotion estimation algorithm can predict the other party's emotional state based on the other party's past reaction data. Furthermore, the optimal communication method suggestion unit can use the emotion estimation function to monitor the other party's emotional state in real time and suggest the most appropriate way to communicate that emotion. For example, if the other party becomes emotional during a conversation, the optimal communication method suggestion unit can choose a calm moment and use gentle language. In this way, the most appropriate way to communicate can be suggested by predicting the other party's emotional state.

[0077] The optimal communication method suggestion unit can also provide the proposed communication method as a communication method in a different medium. For example, the proposed communication method can be provided as a video message, allowing the user to communicate visually. For example, the content of the feedback can be explained using a video. The optimal communication method suggestion unit can also provide the proposed communication method as an infographic, allowing the user to communicate in a format that is visually easy to understand. For example, the main points of the feedback can be shown using diagrams or icons. Furthermore, the optimal communication method suggestion unit can provide the proposed communication method as an audio message, allowing the user to communicate audibly. For example, the content of the feedback can be explained using audio. In this way, communication methods in different media can be provided, allowing the user to select the optimal method.

[0078] The optimal communication method suggestion unit can use the emotion estimation function to collect the user's emotional reactions to the proposed communication methods in real time and dynamically adjust the proposed content. For example, the emotion estimation function can be used to collect the user's emotional reactions to the proposed communication methods in real time and dynamically adjust the proposed content. For example, if the user is feeling anxious, the optimal communication method suggestion unit can make suggestions that give the user a sense of security. The optimal communication method suggestion unit can also analyze the user's emotional reactions in real time and dynamically adjust the proposed content. For example, suggestions that the user feels positive about are given priority. Furthermore, the optimal communication method suggestion unit can use the emotion estimation function to collect the user's emotional reactions in real time and develop an algorithm for dynamically adjusting the proposed content. For example, an algorithm can be developed that optimizes the proposed content based on the user's emotional data. In this way, the user's emotional reactions can be collected in real time and the proposed content can be dynamically adjusted, thereby providing a more effective communication method.

[0079] The processing flow of the second embodiment will be briefly explained below.

[0080] Step 1: The user input information collection unit collects information from the user. For example, the user inputs what they want to communicate and information about the other person (such as their relationship, past reactions, and personality). The user input information collection unit can also collect information entered by the user in text format. For example, the information is collected by the user entering text into an input form. The user input information collection unit can also support voice input and image input. For example, when a user provides information by voice, it is converted into text using voice recognition technology. Step 2: The optimal communication method suggestion unit suggests the optimal way to communicate based on the collected information. For example, the generation AI analyzes the information entered by the user and suggests the most appropriate way to communicate, timing, and wording for the other person. The generation AI can also suggest calm timing and gentle language, taking into account the other person's personality and past reactions. Furthermore, the generation AI can learn from the user's past feedback and improve the accuracy of its suggestions. Step 3: The example generator generates specific example sentences based on the proposed communication method. For example, in the case of feedback via email, the generation AI generates an example sentence such as, "Thank you for your hard work. I would like to let you know some improvements to be made to the project you worked on the other day." The generation AI can also generate example sentences that can be used in social media messages and real conversations. Furthermore, the generation AI can learn from the user's past usage history and generate more personalized example sentences.

[0081] 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.

[0082] 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.

[0083] 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.

[0084] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0085] 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.

[0086] 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.

[0087] 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.

[0088] 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.

[0089] 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).

[0090] 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.

[0091] 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.

[0092] 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.

[0093] 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 the user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

[0094] 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. Note that the smart glasses 214 may have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59.

[0095] 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.

[0096] 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.

[0097] 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.

[0098] 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.

[0099] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0100] 5, the data processing system 310 includes the data processing device 12 and a headset terminal 314. An example of the data processing device 12 is a server.

[0101] 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.

[0102] 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.

[0103] 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.

[0104] 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).

[0105] 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.

[0106] Fig. 6 shows an example of the main functions of the data processing device 12 and the headset 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.

[0107] 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.

[0108] 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 the user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

[0109] In the headset type terminal 314, the identification process is performed by the processor 46. A identification program 60 is stored in the storage 50. The processor 46 reads the identification program 60 from the storage 50 and executes the read identification program 60 on the RAM 48. The identification process is realized by the processor 46 operating as a control unit 46A in accordance with the identification program 60 executed on the RAM 48. Note that the headset type terminal 314 may also have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59.

[0110] 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.

[0111] 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.

[0112] 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.

[0113] 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.

[0114] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0115] 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.

[0116] 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.

[0117] 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.

[0118] 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.

[0119] 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).

[0120] 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.

[0121] 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.

[0122] 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.

[0123] 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.

[0124] 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 the user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

[0125] In the robot 414, the processor 46 performs the identification process. A identification program 60 is stored in the storage 50. The processor 46 reads the identification program 60 from the storage 50 and executes the read identification program 60 on the RAM 48. The identification process is realized by the processor 46 operating as a control unit 46A in accordance with the identification program 60 executed on the RAM 48. The robot 414 may have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59.

[0126] 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.

[0127] 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.

[0128] 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.

[0129] 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.

[0130] 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.

[0131] 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.

[0132] 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.

[0133] 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).

[0134] 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.

[0135] 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."

[0136] 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.

[0137] 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.

[0138] 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.

[0139] 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.

[0140] 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.

[0141] The hardware resource for executing a specific process can be any of the following processors: A CPU is a general-purpose processor that functions as a hardware resource for executing a specific process by executing software, i.e., a program. A dedicated electrical circuit, such as a field-programmable gate array (FPGA), a programmable logic device (PLD), or an application-specific integrated circuit (ASIC), is a processor with a circuit configuration specifically designed to execute a specific process. Each processor has built-in or connected memory, and uses the memory to execute the specific process.

[0142] 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.

[0143] 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.

[0144] 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.

[0145] 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.

[0146] 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.

[0147] 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]

[0148] 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 user input information collection unit that collects information from a user; an optimal communication method suggestion unit that suggests an optimal communication method based on the information collected by the user input information collection unit; a sentence example generation unit that generates specific sentence examples based on the communication methods proposed by the optimal communication method proposal unit. A system characterized by:

2. The user input information collection unit An interactive interface is introduced that provides real-time feedback to the information entered by the user and prompts for additional information as needed.

2. The system of claim 1.

3. The user input information collection unit Supports voice and image input, allowing the user to provide information in ways other than verbal 2. The system of claim 1.

4. The optimal transmission method proposing unit The generation AI learns from the user's past feedback and improves the accuracy of the suggestions it makes to convey the message.

2. The system of claim 1.

5. The optimal transmission method proposing unit The proposed method of communication is also offered as a means of communication in different media.

2. The system of claim 1.

6. The example sentence generation unit The generated sentence examples are learned from the user's past usage history to generate more personalized sentence examples.

2. The system of claim 1.

7. The example sentence generation unit The generated sentence examples are automatically translated into different languages ​​to support international communication.

2. The system of claim 1.

8. The user input information collection unit Using emotion estimation function, the emotional state of the user when inputting is analyzed and emotion-based information is collected.

2. The system of claim 1.

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