System

Abstract

An object of the system according to the embodiment is to learn knowledge and skills of a specific person and reproduce the knowledge and skills.SOLUTION: A system includes a collection unit, an analysis unit, a learning unit, and a generation unit. The collection unit collects data of a target person. The analysis unit analyzes the data collected by the collection unit. The learning unit performs learning based on the data analyzed by the analysis unit. The generation unit generates a deliverable based on the content learned by the learning 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] Conventional technologies have had the problem of making it difficult to efficiently learn and reproduce the knowledge and skills of a specific person.

[0005] The system according to the embodiment aims to learn the knowledge and skills of a specific person and reproduce them. [Means for solving the problem]

[0006] The system according to the embodiment includes a collection unit, an analysis unit, a learning unit, and a generation unit. The collection unit collects data on a target person. The analysis unit analyzes the data collected by the collection unit. The learning unit performs learning based on the data analyzed by the analysis unit. The generation unit generates a deliverable based on the content learned by the learning unit. [Effects of the Invention]

[0007] The system according to the embodiment can learn the knowledge and skills of a specific person and reproduce them. [Brief explanation of the drawings]

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

[0028] (Example 1) A system according to an embodiment of the present invention uses AI to learn the words and work of a specific outstanding individual over the course of a year and generate an AI with the same level of competence one year later. This system mimics the individual's knowledge and skills and can perform similar work and creative activities. For example, the system collects data on the target individual's words and work over the course of a year, including conversations, lectures, writings, and research results. The system then trains the AI ​​on the collected data. The AI ​​uses natural language processing and machine learning techniques to analyze and learn the target individual's language, thought patterns, and expertise. Once the learning process is complete, the AI ​​possesses the same level of knowledge and skills as the individual. For example, the system can engage in literary creation and scientific research. Furthermore, the system can imitate the target individual's style and methodology, thereby generating artifacts that reflect the individual's characteristics. This allows the system to widely disseminate the knowledge and skills of outstanding individuals, thereby raising the level of knowledge throughout society. This allows the system to widely disseminate the knowledge and skills of outstanding individuals, thereby raising the level of knowledge throughout society.

[0029] The knowledge imitation system according to the embodiment includes a collection unit, an analysis unit, a learning unit, and a generation unit. The collection unit collects data on a target person. The data on the target person includes, but is not limited to, audio data, text data, and image data. The collection unit collects, for example, data on the target person's conversations, lectures, writings, and research results. For example, the collection unit can collect data in the form of audio recordings, video recordings, text files, and the like. The analysis unit analyzes the data collected by the collection unit. The analysis unit analyzes the data using, for example, natural language processing technology. Natural language processing technology includes, but is not limited to, morphological analysis, grammatical analysis, and semantic analysis. For example, the analysis unit can perform morphological analysis, grammatical analysis, and semantic analysis on the collected data. The learning unit performs learning based on the data analyzed by the analysis unit. The learning unit learns the target person's language and thought patterns using, for example, a machine learning algorithm. The machine learning algorithm includes, but is not limited to, a neural network and a support vector machine. The learning unit can learn the target person's language usage and thought patterns using, for example, a neural network. The generation unit generates a deliverable based on the content learned by the learning unit. The generation unit can generate a deliverable that imitates, for example, the target person's style or methodology. The generation unit can generate a deliverable in, for example, literary creative activities or scientific research. As a result, the knowledge imitation system according to the embodiment can generate artificial intelligence at the same level as the target person by collecting, analyzing, and learning data on the target person and generating a deliverable.

[0030] The collection unit can collect data on the target person's conversations, lectures, writings, and research results. The collection unit collects, for example, data on the target person's conversations, lectures, writings, and research results. For example, the collection unit can collect data in the form of audio recordings, video recordings, text files, etc. This allows for more accurate learning by collecting a variety of data on the target person. Some or all of the above-mentioned processing in the collection unit may be performed, for example, using AI or without AI. For example, the collection unit can input the target person's conversation data into the generation AI and have the generation AI analyze the conversation data.

[0031] The analysis unit can analyze the collected data using natural language processing technology. The analysis unit, for example, analyzes the collected data using natural language processing technology. Natural language processing technology includes, for example, morphological analysis, grammatical analysis, semantic analysis, etc., but is not limited to these examples. The analysis unit can, for example, perform morphological analysis, grammatical analysis, and semantic analysis of the collected data. As a result, the use of natural language processing technology improves the accuracy of data analysis. Some or all of the above-mentioned processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input the collected data to a generation AI and have the generation AI analyze the data.

[0032] The learning unit can learn the target person's language and thought patterns using a machine learning algorithm. The learning unit, for example, uses a machine learning algorithm to learn the target person's language and thought patterns. Machine learning algorithms include, but are not limited to, neural networks and support vector machines. The learning unit can, for example, learn the target person's language and thought patterns using a neural network. As a result, the target person's language and thought patterns can be accurately learned by using a machine learning algorithm. Some or all of the above-mentioned processing in the learning unit may be performed using, for example, AI, or may be performed without using AI. For example, the learning unit can input the analyzed data into a generation AI and cause the generation AI to learn the target person's language and thought patterns.

[0033] The generation unit can generate a workpiece that imitates the style and methodology of the target person based on the learned content. The generation unit can, for example, generate a workpiece that imitates the style and methodology of the target person based on the learned content. The generation unit can, for example, generate a workpiece in literary creative activities or scientific research. By generating a workpiece that imitates the style and methodology of the target person, it is possible to provide a workpiece of the same level as the target person. Some or all of the above-mentioned processing in the generation unit can be performed, for example, using AI or without AI. For example, the generation unit can input the learned content into a generation AI and cause the generation AI to generate a workpiece that imitates the style and methodology of the target person.

[0034] The generation unit can generate a work product in literary creative activities or scientific research. The generation unit can generate, for example, a work product in literary creative activities or scientific research. The generation unit can generate, for example, a novel, a paper, or experimental results. This enables application in a wide range of fields by generating a work product in literary creative activities or scientific research. Some or all of the above-described processing in the generation unit can be performed, for example, using AI or without AI. For example, the generation unit can input learned content into a generation AI and cause the generation AI to generate a work product in literary creative activities or scientific research.

[0035] The collection unit can analyze the target person's past activity history and select an appropriate data collection method. The collection unit, for example, analyzes the target person's past activity history and selects an appropriate data collection method. The collection unit can determine data collection priorities based on, for example, activities frequently performed by the target person in the past. The collection unit can also select the most effective data collection means (audio, text, image, etc.) from the target person's past activity history. The collection unit can also analyze the target person's past activity history and optimize the timing of data collection. In this way, the analysis of the past activity history can select the optimal data collection method. Some or all of the above-mentioned processing in the collection unit can be performed using, for example, AI, or can be performed without using AI. For example, the collection unit can input the target person's past activity history data into a generation AI and cause the generation AI to select the optimal data collection method.

[0036] The collection unit can filter data based on the target person's current project or area of ​​interest when collecting data. For example, the collection unit can filter data based on the target person's current project or area of ​​interest when collecting data. For example, the collection unit can prioritize collecting data related to a project the target person is currently working on. The collection unit can also filter and collect highly relevant data based on the target person's area of ​​interest. The collection unit can also selectively collect necessary data based on the target person's current research topic. In this way, highly relevant data can be collected by filtering data based on the target person's current project or area of ​​interest. Some or all of the above-mentioned processing in the collection unit can be performed using AI, for example, or without AI. For example, the collection unit can input data on the target person's current project or area of ​​interest to the generation AI and have the generation AI perform the filtering.

[0037] The collection unit can select an appropriate collection means depending on the input method of the target person when collecting data. For example, the collection unit selects an appropriate collection means depending on the input method (voice, text, image, etc.) of the target person when collecting data. For example, if the target person provides information by voice, the collection unit can preferentially collect voice data. Also, if the target person provides information by text, the collection unit can preferentially collect text data. Also, if the target person provides image or visual data, the collection unit can preferentially collect image data. This allows efficient data collection by selecting the optimal collection means depending on the input method. Some or all of the above-mentioned processing in the collection unit may be performed using, for example, AI, or may be performed without using AI. For example, the collection unit can input the target person's input data to a generation AI and cause the generation AI to select the optimal collection means.

[0038] The collection unit can prioritize collecting highly relevant data by taking into account the geographical location information of the target person when collecting data. For example, the collection unit prioritizes collecting highly relevant data by taking into account the geographical location information of the target person when collecting data. For example, the collection unit prioritizes collecting data related to activities performed by the target person in a specific location. The collection unit can also filter and collect highly relevant data based on the geographical location information of the target person. The collection unit can also prioritize collecting data related to activities performed by the target person while traveling. This makes it possible to efficiently collect highly relevant data by taking the geographical location information into account. Some or all of the above-described processing by the collection unit may be performed using AI, for example, or may be performed without using AI. For example, the collection unit can input the geographical location information of the target person to the generation AI and cause the generation AI to collect highly relevant data.

[0039] The collection unit can analyze the social media activities of the target person and collect relevant data when collecting data. For example, the collection unit analyzes the social media activities of the target person and collects relevant data when collecting data. For example, the collection unit collects relevant data based on information shared by the target person on social media. The collection unit can also analyze the target person's social media activities and collect highly relevant data. The collection unit can also select and collect necessary data based on the content of the target person's social media posts. In this way, highly relevant data can be collected by analyzing social media activities. Some or all of the above-mentioned processing in the collection unit can be performed using, for example, AI, or can be performed without using AI. For example, the collection unit can input the target person's social media activity data into the generation AI and cause the generation AI to collect related data.

[0040] The collection unit can customize the collection method by reflecting the target person's past feedback when collecting data. For example, the collection unit customizes the collection method by reflecting the target person's past feedback when collecting data. For example, the collection unit optimizes the data collection method based on feedback provided by the target person in the past. The collection unit can also select the type of data to collect by reflecting the target person's past feedback. The collection unit can also adjust the timing of data collection based on the target person's past feedback. In this way, the collection method can be optimized by reflecting the past feedback. Some or all of the above-mentioned processing in the collection unit may be performed using AI, for example, or may be performed without using AI. For example, the collection unit can input the target person's past feedback data into the generation AI and cause the generation AI to customize the collection method.

[0041] The analysis unit can adjust the level of detail of the analysis based on the importance of the data during analysis. The analysis unit, for example, adjusts the level of detail of the analysis based on the importance of the data during analysis. For example, the analysis unit performs a detailed analysis on important data. The analysis unit can also perform a simplified analysis on less important data. The analysis unit can also determine the priority of the analysis according to the importance of the data. This enables efficient analysis by adjusting the level of detail of the analysis based on the importance of the data. Some or all of the above-mentioned processing in the analysis unit may be performed using AI, for example, or may be performed without using AI. For example, the analysis unit can input the importance of the data to the generation AI and cause the generation AI to adjust the level of detail of the analysis.

[0042] The analysis unit can apply different analysis algorithms depending on the category of data during analysis. For example, the analysis unit applies different analysis algorithms depending on the category of data during analysis. For example, the analysis unit applies a natural language processing algorithm to conversation data. The analysis unit can also apply a scientific analysis algorithm to research result data. The analysis unit can also apply a stylistic analysis algorithm to written data. This improves analysis accuracy by applying an appropriate analysis algorithm depending on the category of data. Some or all of the above-mentioned processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input the category of data into the generation AI and cause the generation AI to apply an appropriate analysis algorithm.

[0043] The analysis unit can improve the accuracy of the analysis by referring to past analysis results of the target person during analysis. For example, the analysis unit can improve the accuracy of the analysis by referring to past analysis results of the target person during analysis. For example, the analysis unit corrects the current analysis result based on the past analysis results of the target person. The analysis unit can also optimize the analysis algorithm by referring to the past analysis results of the target person. The analysis unit can also improve the accuracy of the analysis by using the past analysis results of the target person. In this way, the accuracy of the analysis can be improved by referring to the past analysis results. Some or all of the above-mentioned processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input the past analysis results of the target person into the generation AI and cause the generation AI to improve the accuracy of the analysis.

[0044] The analysis unit can determine the analysis priority based on the time of data submission during analysis. The analysis unit, for example, determines the analysis priority based on the time of data submission during analysis. The analysis unit, for example, prioritizes analysis of the most recent data. The analysis unit can also postpone data that was submitted earlier. The analysis unit can also adjust the analysis schedule based on the time of submission. In this way, by determining the analysis priority based on the time of data submission, the most recent data can be analyzed preferentially. Some or all of the above-mentioned processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input the time of data submission to the generation AI and have the generation AI determine the analysis priority.

[0045] The analysis unit can adjust the order of analysis based on the relevance of the data during analysis. The analysis unit, for example, adjusts the order of analysis based on the relevance of the data during analysis. For example, the analysis unit prioritizes analysis of highly relevant data. The analysis unit can also postpone analysis of less relevant data. The analysis unit can also determine the order of analysis based on the relevance of the data. This enables efficient analysis by adjusting the order of analysis based on the relevance of the data. Some or all of the above-mentioned processing in the analysis unit may be performed using AI, for example, or may be performed without using AI. For example, the analysis unit can input the relevance of the data to the generation AI and cause the generation AI to adjust the order of analysis.

[0046] The analysis unit can adjust the use of technical terms in the analysis according to the expertise level of the target person during analysis. For example, the analysis unit can adjust the use of technical terms in the analysis according to the expertise level of the target person during analysis. For example, if the target person is an expert, the analysis unit can provide analysis results that use a lot of technical terms. Furthermore, if the target person is a layperson, the analysis unit can also provide analysis results that avoid technical terms. The analysis unit can also adjust the way the analysis results are expressed according to the expertise level of the target person. In this way, by adjusting the use of technical terms in the analysis according to the expertise level, it is possible to provide analysis results that are easier to understand. Some or all of the above-mentioned processing in the analysis unit can be performed using, for example, AI, or can be performed without using AI. For example, the analysis unit can input the expertise level of the target person into the generation AI and cause the generation AI to adjust the use of technical terms.

[0047] The learning unit can optimize the learning algorithm by referring to past learning data during learning. For example, the learning unit optimizes the learning algorithm by referring to past learning data during learning. The learning unit adjusts the current learning algorithm based on past learning data, for example. The learning unit can also improve learning efficiency by referring to past learning data. The learning unit can also optimize the learning algorithm by using past learning data. In this way, by referring to past learning data, the learning algorithm can be optimized and the accuracy of learning can be improved. Some or all of the above-mentioned processing in the learning unit may be performed using, for example, AI, or may be performed without using AI. For example, the learning unit can input past learning data to a generation AI and cause the generation AI to optimize the learning algorithm.

[0048] The learning unit can update the learning data by reflecting the feedback of the target person during learning. For example, the learning unit updates the learning data by reflecting the feedback of the target person during learning. For example, the learning unit modifies the learning data based on the feedback of the target person. The learning unit can also add learning data by reflecting the feedback of the target person. The learning unit can also update the learning data by using the feedback of the target person. In this way, the learning data can be updated by reflecting the feedback, and the accuracy of learning can be improved. Some or all of the above-mentioned processing in the learning unit may be performed using, for example, AI, or may be performed without using AI. For example, the learning unit can input the feedback data of the target person to the generation AI and cause the generation AI to update the learning data.

[0049] The learning unit can improve the accuracy of learning by analyzing in detail the target person's language and thought patterns during learning. The learning unit can improve the accuracy of learning by analyzing in detail the target person's language and thought patterns, for example, during learning. The learning unit can, for example, analyze in detail the target person's language and reflect the results in the learning data. The learning unit can also analyze in detail the target person's thought patterns and reflect the results in the learning algorithm. The learning unit can also improve the accuracy of learning based on the target person's language and thought patterns. In this way, the accuracy of learning can be improved by analyzing the language and thought patterns in detail. Some or all of the above-mentioned processing in the learning unit can be performed using, for example, AI, or can be performed without using AI. For example, the learning unit can input data on the target person's language and thought patterns into a generation AI and cause the generation AI to perform a detailed analysis.

[0050] The learning unit can weight the learning data based on the time of data submission during learning. For example, the learning unit weights the learning data based on the time of data submission during learning. For example, the learning unit increases the weighting of the most recent data. The learning unit can also decrease the weighting of data that was submitted earlier. The learning unit can also adjust the weighting of the learning data based on the time of submission. In this way, weighting the learning data based on the time of submission enables learning that emphasizes the most recent data. Some or all of the above-described processing in the learning unit may be performed using, for example, AI, or may be performed without using AI. For example, the learning unit can input the time of data submission to the generation AI and cause the generation AI to weight the learning data.

[0051] The learning unit can integrate information from different data sources to enrich the learning data during learning. For example, the learning unit integrates information from different data sources to enrich the learning data during learning. For example, the learning unit integrates information from different data sources to enrich the learning data. The learning unit can also optimize the learning algorithm based on information from different data sources. The learning unit can also improve the accuracy of learning by using information from different data sources. In this way, by integrating information from different data sources, the learning data can be enriched and the accuracy of learning can be improved. Some or all of the above-mentioned processing in the learning unit may be performed using, for example, AI, or may be performed without using AI. For example, the learning unit can input information from different data sources to a generation AI and cause the generation AI to integrate the information.

[0052] The learning unit can customize the learning algorithm according to the target person's field of expertise during learning. For example, the learning unit customizes the learning algorithm according to the target person's field of expertise during learning. For example, if the target person is knowledgeable in the literary field, the learning unit can apply a literary learning algorithm. Furthermore, if the target person is knowledgeable in the scientific field, the learning unit can also apply a scientific learning algorithm. Furthermore, the learning unit can customize the learning algorithm according to the target person's field of expertise. In this way, by customizing the learning algorithm according to the field of expertise, more specialized learning is possible. Some or all of the above-mentioned processing in the learning unit may be performed using AI, for example, or may be performed without using AI. For example, the learning unit can input the target person's field of expertise into the generation AI and cause the generation AI to customize the learning algorithm.

[0053] The generation unit can adjust the level of detail of the deliverable based on the importance of the learned content during generation. For example, the generation unit adjusts the level of detail of the deliverable based on the importance of the learned content during generation. For example, the generation unit generates a detailed deliverable for important content. The generation unit can also generate a simplified deliverable for less important content. The generation unit can also adjust the level of detail of the deliverable according to the importance of the learned content. This enables efficient delivery by adjusting the level of detail of the deliverable based on the importance of the learned content. Some or all of the above-described processing in the generation unit may be performed using, for example, AI, or may be performed without using AI. For example, the generation unit can input the importance of the learned content to the generation AI and cause the generation AI to adjust the level of detail of the deliverable.

[0054] The generation unit can apply an algorithm for closely imitating the style and methodology of the target person during generation. For example, the generation unit can apply an algorithm for closely imitating the style and methodology of the target person during generation. For example, the generation unit can apply an algorithm for closely imitating the writing style of the target person. The generation unit can also apply an algorithm for closely imitating the research methodology of the target person. The generation unit can also apply an algorithm for closely imitating the creative style of the target person. This allows for a more accurate deliverable to be generated by closely imitating the style and methodology. Some or all of the above-described processing in the generation unit can be performed using, for example, AI, or can be performed without using AI. For example, the generation unit can input data on the style and methodology of the target person into the generation AI and cause the generation AI to apply the imitation algorithm.

[0055] The generation unit can improve the accuracy of generation by referring to the target person's past works during generation. The generation unit can improve the accuracy of generation by referring to the target person's past works during generation, for example. The generation unit can improve the accuracy of generation by referring to the target person's past written works, for example. The generation unit can also improve the accuracy of generation by referring to the target person's past research results. The generation unit can also improve the accuracy of generation by referring to the target person's past creative activities. In this way, by referring to past works, the accuracy of generation can be improved. Some or all of the above-mentioned processing in the generation unit may be performed using, for example, AI, or may be performed without using AI. For example, the generation unit can input data of the target person's past works into the generation AI and cause the generation AI to improve the accuracy of generation.

[0056] The generation unit can generate a highly relevant deliverable by taking into account the geographical location information of the target person at the time of generation. For example, the generation unit generates a highly relevant deliverable by taking into account the geographical location information of the target person at the time of generation. For example, the generation unit generates a deliverable related to an activity performed by the target person in a specific location. The generation unit can also generate a highly relevant deliverable based on the geographical location information of the target person. The generation unit can also generate a deliverable related to an activity performed by the target person while traveling. In this way, a highly relevant deliverable can be generated by taking into account the geographical location information. Some or all of the above-described processing in the generation unit may be performed using, for example, AI, or may be performed without using AI. For example, the generation unit can input the geographical location information of the target person to the generation AI and cause the generation AI to generate a highly relevant deliverable.

[0057] The generation unit can analyze the social media activity of the target person at the time of generation and generate a related deliverable. For example, the generation unit can analyze the social media activity of the target person at the time of generation and generate a related deliverable. For example, the generation unit can generate a related deliverable based on information shared by the target person on social media. The generation unit can also analyze the target person's social media activity and generate a highly relevant deliverable. The generation unit can also generate a required deliverable based on the content of the target person's social media posts. In this way, a highly relevant deliverable can be generated by analyzing social media activity. Some or all of the above-described processing in the generation unit can be performed using, for example, AI, or can be performed without using AI. For example, the generation unit can input the target person's social media activity data into the generation AI and cause the generation AI to generate a related deliverable.

[0058] The generation unit can customize the generation method by reflecting the target person's past feedback at the time of generation. The generation unit, for example, customizes the generation method by reflecting the target person's past feedback at the time of generation. The generation unit can optimize the generation method, for example, based on feedback provided by the target person in the past. The generation unit can also select the type of deliverable to be generated by reflecting the target person's past feedback. The generation unit can also adjust the timing of generation based on the target person's past feedback. In this way, by reflecting past feedback, the generation method can be optimized and a more appropriate deliverable can be generated. Some or all of the above-mentioned processing in the generation unit may be performed, for example, using AI or without AI. For example, the generation unit can input the target person's past feedback data into the generation AI and cause the generation AI to customize the generation method.

[0059] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0060] The knowledge emulation system may further include a feedback unit. The feedback unit collects user feedback on the generated deliverables and provides it to the analysis unit. For example, the user may comment on or rate the generated deliverables. The feedback unit may also analyze the user feedback and reflect the feedback in the generation unit. This allows the quality of the generated deliverables to be improved by reflecting the user feedback.

[0061] The analysis unit can analyze the target person's past behavioral patterns and predict future behavior. For example, it can analyze what activities the target person performed at what timing in the past and predict future behavior. The analysis unit can also optimize the timing of data collection and learning based on the predicted behavior. This makes it possible to predict future behavior by analyzing past behavioral patterns, enabling efficient data collection and learning.

[0062] The generation unit can collect user feedback on the generated deliverables and provide it to the analysis unit. For example, users can comment on or rate the generated deliverables. The generation unit can also analyze the user feedback and reflect the feedback in the generation unit. In this way, by reflecting the user feedback, the quality of the generated deliverables can be improved.

[0063] The analysis unit can analyze the target person's past data and evaluate the reliability of the data. For example, the reliability of current data can be evaluated based on the past data, and the more reliable data can be analyzed preferentially. The analysis unit can also determine the priority of analysis based on the reliability of the data. This allows for more accurate analysis by evaluating the reliability of the data.

[0064] The generation unit can collect user feedback on the generated deliverables and provide it to the analysis unit. For example, users can comment on or rate the generated deliverables. The generation unit can also analyze the user feedback and reflect the feedback in the generation unit. In this way, by reflecting the user feedback, the quality of the generated deliverables can be improved.

[0065] The processing flow of the first embodiment will be briefly explained below.

[0066] Step 1: The collection unit collects data on the target person. The target person's data includes audio data, text data, image data, etc. The collection unit collects data on the target person's conversations, lectures, writings, and research results, and can collect data in the form of audio recordings, video recordings, text files, etc. Step 2: The analysis unit analyzes the data collected by the collection unit. The analysis unit analyzes the data using natural language processing technology and can perform morphological analysis, grammatical analysis, and semantic analysis. Step 3: The learning unit performs learning based on the data analyzed by the analysis unit. The learning unit uses machine learning algorithms to learn the target person's language and thought patterns, and can use neural networks, support vector machines, etc. Step 4: The generator generates a work based on the content learned by the learner. The generator generates a work that imitates the style and methodology of the target person, and can generate a work in literary creation or scientific research.

[0067] (Example 2) A system according to an embodiment of the present invention uses AI to learn the words and work of a specific outstanding individual over the course of a year and generate an AI with the same level of competence one year later. This system mimics the individual's knowledge and skills and can perform similar work and creative activities. For example, the system collects data on the target individual's words and work over the course of a year, including conversations, lectures, writings, and research results. The system then trains the AI ​​on the collected data. The AI ​​uses natural language processing and machine learning techniques to analyze and learn the target individual's language, thought patterns, and expertise. Once the learning process is complete, the AI ​​possesses the same level of knowledge and skills as the individual. For example, the system can engage in literary creation and scientific research. Furthermore, the system can imitate the target individual's style and methodology, thereby generating artifacts that reflect the individual's characteristics. This allows the system to widely disseminate the knowledge and skills of outstanding individuals, thereby raising the level of knowledge throughout society. This allows the system to widely disseminate the knowledge and skills of outstanding individuals, thereby raising the level of knowledge throughout society.

[0068] The knowledge imitation system according to the embodiment includes a collection unit, an analysis unit, a learning unit, and a generation unit. The collection unit collects data on a target person. The data on the target person includes, but is not limited to, audio data, text data, and image data. The collection unit collects, for example, data on the target person's conversations, lectures, writings, and research results. For example, the collection unit can collect data in the form of audio recordings, video recordings, text files, and the like. The analysis unit analyzes the data collected by the collection unit. The analysis unit analyzes the data using, for example, natural language processing technology. Natural language processing technology includes, but is not limited to, morphological analysis, grammatical analysis, and semantic analysis. For example, the analysis unit can perform morphological analysis, grammatical analysis, and semantic analysis on the collected data. The learning unit performs learning based on the data analyzed by the analysis unit. The learning unit learns the target person's language and thought patterns using, for example, a machine learning algorithm. The machine learning algorithm includes, but is not limited to, a neural network and a support vector machine. The learning unit can learn the target person's language usage and thought patterns using, for example, a neural network. The generation unit generates a deliverable based on the content learned by the learning unit. The generation unit can generate a deliverable that imitates, for example, the target person's style or methodology. The generation unit can generate a deliverable in, for example, literary creative activities or scientific research. As a result, the knowledge imitation system according to the embodiment can generate artificial intelligence at the same level as the target person by collecting, analyzing, and learning data on the target person and generating a deliverable.

[0069] The collection unit can collect data on the target person's conversations, lectures, writings, and research results. The collection unit collects, for example, data on the target person's conversations, lectures, writings, and research results. For example, the collection unit can collect data in the form of audio recordings, video recordings, text files, etc. This allows for more accurate learning by collecting a variety of data on the target person. Some or all of the above-mentioned processing in the collection unit may be performed, for example, using AI or without AI. For example, the collection unit can input the target person's conversation data into the generation AI and have the generation AI analyze the conversation data.

[0070] The analysis unit can analyze the collected data using natural language processing technology. The analysis unit, for example, analyzes the collected data using natural language processing technology. Natural language processing technology includes, for example, morphological analysis, grammatical analysis, semantic analysis, etc., but is not limited to these examples. The analysis unit can, for example, perform morphological analysis, grammatical analysis, and semantic analysis of the collected data. As a result, the use of natural language processing technology improves the accuracy of data analysis. Some or all of the above-mentioned processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input the collected data to a generation AI and have the generation AI analyze the data.

[0071] The learning unit can learn the target person's language and thought patterns using a machine learning algorithm. The learning unit, for example, uses a machine learning algorithm to learn the target person's language and thought patterns. Machine learning algorithms include, but are not limited to, neural networks and support vector machines. The learning unit can, for example, learn the target person's language and thought patterns using a neural network. As a result, the target person's language and thought patterns can be accurately learned by using a machine learning algorithm. Some or all of the above-mentioned processing in the learning unit may be performed using, for example, AI, or may be performed without using AI. For example, the learning unit can input the analyzed data into a generation AI and cause the generation AI to learn the target person's language and thought patterns.

[0072] The generation unit can generate a workpiece that imitates the style and methodology of the target person based on the learned content. The generation unit can, for example, generate a workpiece that imitates the style and methodology of the target person based on the learned content. The generation unit can, for example, generate a workpiece in literary creative activities or scientific research. By generating a workpiece that imitates the style and methodology of the target person, it is possible to provide a workpiece of the same level as the target person. Some or all of the above-mentioned processing in the generation unit can be performed, for example, using AI or without AI. For example, the generation unit can input the learned content into a generation AI and cause the generation AI to generate a workpiece that imitates the style and methodology of the target person.

[0073] The generation unit can generate a work product in literary creative activities or scientific research. The generation unit can generate, for example, a work product in literary creative activities or scientific research. The generation unit can generate, for example, a novel, a paper, or experimental results. This enables application in a wide range of fields by generating a work product in literary creative activities or scientific research. Some or all of the above-described processing in the generation unit can be performed, for example, using AI or without AI. For example, the generation unit can input learned content into a generation AI and cause the generation AI to generate a work product in literary creative activities or scientific research.

[0074] The collection unit can estimate the target person's emotions and adjust the timing of data collection based on the estimated emotions. For example, the collection unit estimates the target person's emotions and adjusts the timing of data collection based on the estimated emotions. For example, the collection unit collects data on conversations or lectures when the target person is relaxed. The collection unit can also collect data on writing or research results when the target person is concentrating. The collection unit can also temporarily suspend data collection when the target person is feeling stressed. This allows for more appropriate data collection by adjusting the timing of data collection according to the target person's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or a generation AI. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above-mentioned processing in the collection unit may be performed using an AI, for example, or without an AI. For example, the collection unit can input the target person's emotion data into a generation AI and have the generation AI perform emotion estimation.

[0075] The collection unit can analyze the target person's past activity history and select an appropriate data collection method. The collection unit, for example, analyzes the target person's past activity history and selects an appropriate data collection method. The collection unit can determine data collection priorities based on, for example, activities frequently performed by the target person in the past. The collection unit can also select the most effective data collection means (audio, text, image, etc.) from the target person's past activity history. The collection unit can also analyze the target person's past activity history and optimize the timing of data collection. In this way, the analysis of the past activity history can select the optimal data collection method. Some or all of the above-mentioned processing in the collection unit can be performed using, for example, AI, or can be performed without using AI. For example, the collection unit can input the target person's past activity history data into a generation AI and cause the generation AI to select the optimal data collection method.

[0076] The collection unit can filter data based on the target person's current project or area of ​​interest when collecting data. For example, the collection unit can filter data based on the target person's current project or area of ​​interest when collecting data. For example, the collection unit can prioritize collecting data related to a project the target person is currently working on. The collection unit can also filter and collect highly relevant data based on the target person's area of ​​interest. The collection unit can also selectively collect necessary data based on the target person's current research topic. In this way, highly relevant data can be collected by filtering data based on the target person's current project or area of ​​interest. Some or all of the above-mentioned processing in the collection unit can be performed using AI, for example, or without AI. For example, the collection unit can input data on the target person's current project or area of ​​interest to the generation AI and have the generation AI perform the filtering.

[0077] The collection unit can select an appropriate collection means depending on the input method of the target person when collecting data. For example, the collection unit selects an appropriate collection means depending on the input method (voice, text, image, etc.) of the target person when collecting data. For example, if the target person provides information by voice, the collection unit can preferentially collect voice data. Also, if the target person provides information by text, the collection unit can preferentially collect text data. Also, if the target person provides image or visual data, the collection unit can preferentially collect image data. This allows efficient data collection by selecting the optimal collection means depending on the input method. Some or all of the above-mentioned processing in the collection unit may be performed using, for example, AI, or may be performed without using AI. For example, the collection unit can input the target person's input data to a generation AI and cause the generation AI to select the optimal collection means.

[0078] The collection unit can estimate the emotions of the target person and determine the priority of data to be collected based on the estimated emotions. For example, the collection unit can estimate the emotions of the target person and determine the priority of data to be collected based on the estimated emotions. For example, the collection unit can prioritize collecting data on important conversations or lectures when the target person is relaxed. The collection unit can also prioritize collecting data on writings or research results when the target person is concentrating. The collection unit can also lower the priority of data collection when the target person is feeling stressed. In this way, by prioritizing data based on emotions, important data can be preferentially collected. Emotion estimation is realized using an emotion estimation function, for example, using an emotion engine or a generation AI. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above-mentioned processing in the collection unit can be performed using AI, for example, or without AI. For example, the collection unit can input the emotion data of the target person into a generation AI and have the generation AI perform emotion estimation.

[0079] The collection unit can prioritize collecting highly relevant data by taking into account the geographical location information of the target person when collecting data. For example, the collection unit prioritizes collecting highly relevant data by taking into account the geographical location information of the target person when collecting data. For example, the collection unit prioritizes collecting data related to activities performed by the target person in a specific location. The collection unit can also filter and collect highly relevant data based on the geographical location information of the target person. The collection unit can also prioritize collecting data related to activities performed by the target person while traveling. This makes it possible to efficiently collect highly relevant data by taking the geographical location information into account. Some or all of the above-described processing by the collection unit may be performed using AI, for example, or may be performed without using AI. For example, the collection unit can input the geographical location information of the target person to the generation AI and cause the generation AI to collect highly relevant data.

[0080] The collection unit can analyze the social media activities of the target person and collect relevant data when collecting data. For example, the collection unit analyzes the social media activities of the target person and collects relevant data when collecting data. For example, the collection unit collects relevant data based on information shared by the target person on social media. The collection unit can also analyze the target person's social media activities and collect highly relevant data. The collection unit can also select and collect necessary data based on the content of the target person's social media posts. In this way, highly relevant data can be collected by analyzing social media activities. Some or all of the above-mentioned processing in the collection unit can be performed using, for example, AI, or can be performed without using AI. For example, the collection unit can input the target person's social media activity data into the generation AI and cause the generation AI to collect related data.

[0081] The collection unit can customize the collection method by reflecting the target person's past feedback when collecting data. For example, the collection unit customizes the collection method by reflecting the target person's past feedback when collecting data. For example, the collection unit optimizes the data collection method based on feedback provided by the target person in the past. The collection unit can also select the type of data to collect by reflecting the target person's past feedback. The collection unit can also adjust the timing of data collection based on the target person's past feedback. In this way, the collection method can be optimized by reflecting the past feedback. Some or all of the above-mentioned processing in the collection unit may be performed using AI, for example, or may be performed without using AI. For example, the collection unit can input the target person's past feedback data into the generation AI and cause the generation AI to customize the collection method.

[0082] The analysis unit can estimate the emotion of the target person and adjust the analysis presentation method based on the estimated emotion. For example, the analysis unit can estimate the emotion of the target person and adjust the analysis presentation method based on the estimated emotion. For example, the analysis unit can provide detailed analysis results when the target person is relaxed. Furthermore, the analysis unit can provide concise analysis results that focus on the main points when the target person is in a hurry. Furthermore, the analysis unit can provide visually easy-to-understand analysis results when the target person is stressed. This allows for adjusting the analysis presentation method based on emotion to provide more appropriate analysis results. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or a generation AI. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above-mentioned processing in the analysis unit can be performed using, for example, an AI, or without an AI. For example, the analysis unit can input the emotion data of the target person into the generation AI and have the generation AI perform emotion estimation.

[0083] The analysis unit can adjust the level of detail of the analysis based on the importance of the data during analysis. The analysis unit, for example, adjusts the level of detail of the analysis based on the importance of the data during analysis. For example, the analysis unit performs a detailed analysis on important data. The analysis unit can also perform a simplified analysis on less important data. The analysis unit can also determine the priority of the analysis according to the importance of the data. This enables efficient analysis by adjusting the level of detail of the analysis based on the importance of the data. Some or all of the above-mentioned processing in the analysis unit may be performed using AI, for example, or may be performed without using AI. For example, the analysis unit can input the importance of the data to the generation AI and cause the generation AI to adjust the level of detail of the analysis.

[0084] The analysis unit can apply different analysis algorithms depending on the category of data during analysis. For example, the analysis unit applies different analysis algorithms depending on the category of data during analysis. For example, the analysis unit applies a natural language processing algorithm to conversation data. The analysis unit can also apply a scientific analysis algorithm to research result data. The analysis unit can also apply a stylistic analysis algorithm to written data. This improves analysis accuracy by applying an appropriate analysis algorithm depending on the category of data. Some or all of the above-mentioned processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input the category of data into the generation AI and cause the generation AI to apply an appropriate analysis algorithm.

[0085] The analysis unit can improve the accuracy of the analysis by referring to past analysis results of the target person during analysis. For example, the analysis unit can improve the accuracy of the analysis by referring to past analysis results of the target person during analysis. For example, the analysis unit corrects the current analysis result based on the past analysis results of the target person. The analysis unit can also optimize the analysis algorithm by referring to the past analysis results of the target person. The analysis unit can also improve the accuracy of the analysis by using the past analysis results of the target person. In this way, the accuracy of the analysis can be improved by referring to the past analysis results. Some or all of the above-mentioned processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input the past analysis results of the target person into the generation AI and cause the generation AI to improve the accuracy of the analysis.

[0086] The analysis unit can estimate the emotion of the target person and adjust the length of the analysis based on the estimated emotion. For example, the analysis unit can estimate the emotion of the target person and adjust the length of the analysis based on the estimated emotion. For example, if the target person is in a hurry, the analysis unit can provide a short and concise analysis result. If the target person is relaxed, the analysis unit can also provide a detailed analysis result. If the target person is stressed, the analysis unit can also provide a visually easy-to-understand analysis result. This allows for adjusting the length of the analysis based on the emotion to provide a more appropriate analysis result. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above-described processing in the analysis unit can be performed using, for example, an AI. For example, the analysis unit can input the emotion data of the target person into the generation AI and have the generation AI perform emotion estimation.

[0087] The analysis unit can determine the analysis priority based on the time of data submission during analysis. The analysis unit, for example, determines the analysis priority based on the time of data submission during analysis. The analysis unit, for example, prioritizes analysis of the most recent data. The analysis unit can also postpone data that was submitted earlier. The analysis unit can also adjust the analysis schedule based on the time of submission. In this way, by determining the analysis priority based on the time of data submission, the most recent data can be analyzed preferentially. Some or all of the above-mentioned processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input the time of data submission to the generation AI and have the generation AI determine the analysis priority.

[0088] The analysis unit can adjust the order of analysis based on the relevance of the data during analysis. The analysis unit, for example, adjusts the order of analysis based on the relevance of the data during analysis. For example, the analysis unit prioritizes analysis of highly relevant data. The analysis unit can also postpone analysis of less relevant data. The analysis unit can also determine the order of analysis based on the relevance of the data. This enables efficient analysis by adjusting the order of analysis based on the relevance of the data. Some or all of the above-mentioned processing in the analysis unit may be performed using AI, for example, or may be performed without using AI. For example, the analysis unit can input the relevance of the data to the generation AI and cause the generation AI to adjust the order of analysis.

[0089] The analysis unit can adjust the use of technical terms in the analysis according to the expertise level of the target person during analysis. For example, the analysis unit can adjust the use of technical terms in the analysis according to the expertise level of the target person during analysis. For example, if the target person is an expert, the analysis unit can provide analysis results that use a lot of technical terms. Furthermore, if the target person is a layperson, the analysis unit can also provide analysis results that avoid technical terms. The analysis unit can also adjust the way the analysis results are expressed according to the expertise level of the target person. In this way, by adjusting the use of technical terms in the analysis according to the expertise level, it is possible to provide analysis results that are easier to understand. Some or all of the above-mentioned processing in the analysis unit can be performed using, for example, AI, or can be performed without using AI. For example, the analysis unit can input the expertise level of the target person into the generation AI and cause the generation AI to adjust the use of technical terms.

[0090] The learning unit can estimate the emotion of the target person and select training data based on the estimated emotion. For example, the learning unit estimates the emotion of the target person and selects training data based on the estimated emotion. For example, the learning unit prioritizes learning data provided when the target person is relaxed. The learning unit can also prioritize learning data provided when the target person is concentrating. The learning unit can also lower the learning priority of data provided when the target person is feeling stressed. In this way, by selecting training data based on emotion, more appropriate data can be learned. Emotion estimation is realized using an emotion estimation function, for example, using an emotion engine or a generation AI. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above-mentioned processing in the learning unit may be performed using an AI, for example, or without an AI. For example, the learning unit can input emotion data of the target person into the generation AI and cause the generation AI to estimate the emotion.

[0091] The learning unit can optimize the learning algorithm by referring to past learning data during learning. For example, the learning unit optimizes the learning algorithm by referring to past learning data during learning. The learning unit adjusts the current learning algorithm based on past learning data, for example. The learning unit can also improve learning efficiency by referring to past learning data. The learning unit can also optimize the learning algorithm by using past learning data. In this way, by referring to past learning data, the learning algorithm can be optimized and the accuracy of learning can be improved. Some or all of the above-mentioned processing in the learning unit may be performed using, for example, AI, or may be performed without using AI. For example, the learning unit can input past learning data to a generation AI and cause the generation AI to optimize the learning algorithm.

[0092] The learning unit can update the learning data by reflecting the feedback of the target person during learning. For example, the learning unit updates the learning data by reflecting the feedback of the target person during learning. For example, the learning unit modifies the learning data based on the feedback of the target person. The learning unit can also add learning data by reflecting the feedback of the target person. The learning unit can also update the learning data by using the feedback of the target person. In this way, the learning data can be updated by reflecting the feedback, and the accuracy of learning can be improved. Some or all of the above-mentioned processing in the learning unit may be performed using, for example, AI, or may be performed without using AI. For example, the learning unit can input the feedback data of the target person to the generation AI and cause the generation AI to update the learning data.

[0093] The learning unit can improve the accuracy of learning by analyzing in detail the target person's language and thought patterns during learning. The learning unit can improve the accuracy of learning by analyzing in detail the target person's language and thought patterns, for example, during learning. The learning unit can, for example, analyze in detail the target person's language and reflect the results in the learning data. The learning unit can also analyze in detail the target person's thought patterns and reflect the results in the learning algorithm. The learning unit can also improve the accuracy of learning based on the target person's language and thought patterns. In this way, the accuracy of learning can be improved by analyzing the language and thought patterns in detail. Some or all of the above-mentioned processing in the learning unit can be performed using, for example, AI, or can be performed without using AI. For example, the learning unit can input data on the target person's language and thought patterns into a generation AI and cause the generation AI to perform a detailed analysis.

[0094] The learning unit can estimate the emotion of the target person and adjust the frequency of learning based on the estimated emotion. For example, the learning unit estimates the emotion of the target person and adjusts the frequency of learning based on the estimated emotion. For example, the learning unit increases the frequency of learning when the target person is relaxed. The learning unit can also increase the frequency of learning when the target person is concentrating. The learning unit can also decrease the frequency of learning when the target person is feeling stressed. This enables more effective learning by adjusting the frequency of learning based on emotion. Emotion estimation is realized using an emotion estimation function, for example, using an emotion engine or a generation AI. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to these examples. Some or all of the above-mentioned processing in the learning unit may be performed using an AI, for example, or without an AI. For example, the learning unit can input emotion data of the target person into the generation AI and cause the generation AI to estimate the emotion.

[0095] The learning unit can weight the learning data based on the time of data submission during learning. For example, the learning unit weights the learning data based on the time of data submission during learning. For example, the learning unit increases the weighting of the most recent data. The learning unit can also decrease the weighting of data that was submitted earlier. The learning unit can also adjust the weighting of the learning data based on the time of submission. In this way, weighting the learning data based on the time of submission enables learning that emphasizes the most recent data. Some or all of the above-described processing in the learning unit may be performed using, for example, AI, or may be performed without using AI. For example, the learning unit can input the time of data submission to the generation AI and cause the generation AI to weight the learning data.

[0096] The learning unit can integrate information from different data sources to enrich the learning data during learning. For example, the learning unit integrates information from different data sources to enrich the learning data during learning. For example, the learning unit integrates information from different data sources to enrich the learning data. The learning unit can also optimize the learning algorithm based on information from different data sources. The learning unit can also improve the accuracy of learning by using information from different data sources. In this way, by integrating information from different data sources, the learning data can be enriched and the accuracy of learning can be improved. Some or all of the above-mentioned processing in the learning unit may be performed using, for example, AI, or may be performed without using AI. For example, the learning unit can input information from different data sources to a generation AI and cause the generation AI to integrate the information.

[0097] The learning unit can customize the learning algorithm according to the target person's field of expertise during learning. For example, the learning unit customizes the learning algorithm according to the target person's field of expertise during learning. For example, if the target person is knowledgeable in the literary field, the learning unit can apply a literary learning algorithm. Furthermore, if the target person is knowledgeable in the scientific field, the learning unit can also apply a scientific learning algorithm. Furthermore, the learning unit can customize the learning algorithm according to the target person's field of expertise. In this way, by customizing the learning algorithm according to the field of expertise, more specialized learning is possible. Some or all of the above-mentioned processing in the learning unit may be performed using AI, for example, or may be performed without using AI. For example, the learning unit can input the target person's field of expertise into the generation AI and cause the generation AI to customize the learning algorithm.

[0098] The generation unit can estimate the emotion of the target person and adjust the expression method of the generated deliverable based on the estimated emotion. For example, the generation unit can estimate the emotion of the target person and adjust the expression method of the generated deliverable based on the estimated emotion. For example, if the target person is relaxed, the generation unit can use a relaxed expression method. If the target person is in a hurry, the generation unit can use a concise and to-the-point expression method. If the target person is excited, the generation unit can use a visually stimulating expression method. In this way, by adjusting the expression method of the deliverable based on the emotion, a more appropriate deliverable can be generated. Emotion estimation is realized using an emotion estimation function, for example, using an emotion engine or generation AI. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above-mentioned processing in the generation unit can be performed using AI, for example, or without AI. For example, the generation unit can input emotion data of the target person into the generation AI and cause the generation AI to estimate the emotion.

[0099] The generation unit can adjust the level of detail of the deliverable based on the importance of the learned content during generation. For example, the generation unit adjusts the level of detail of the deliverable based on the importance of the learned content during generation. For example, the generation unit generates a detailed deliverable for important content. The generation unit can also generate a simplified deliverable for less important content. The generation unit can also adjust the level of detail of the deliverable according to the importance of the learned content. This enables efficient delivery by adjusting the level of detail of the deliverable based on the importance of the learned content. Some or all of the above-described processing in the generation unit may be performed using, for example, AI, or may be performed without using AI. For example, the generation unit can input the importance of the learned content to the generation AI and cause the generation AI to adjust the level of detail of the deliverable.

[0100] The generation unit can apply an algorithm for closely imitating the style and methodology of the target person during generation. For example, the generation unit can apply an algorithm for closely imitating the style and methodology of the target person during generation. For example, the generation unit can apply an algorithm for closely imitating the writing style of the target person. The generation unit can also apply an algorithm for closely imitating the research methodology of the target person. The generation unit can also apply an algorithm for closely imitating the creative style of the target person. This allows for a more accurate deliverable to be generated by closely imitating the style and methodology. Some or all of the above-described processing in the generation unit can be performed using, for example, AI, or can be performed without using AI. For example, the generation unit can input data on the style and methodology of the target person into the generation AI and cause the generation AI to apply the imitation algorithm.

[0101] The generation unit can improve the accuracy of generation by referring to the target person's past works during generation. The generation unit can improve the accuracy of generation by referring to the target person's past works during generation, for example. The generation unit can improve the accuracy of generation by referring to the target person's past written works, for example. The generation unit can also improve the accuracy of generation by referring to the target person's past research results. The generation unit can also improve the accuracy of generation by referring to the target person's past creative activities. In this way, by referring to past works, the accuracy of generation can be improved. Some or all of the above-mentioned processing in the generation unit may be performed using, for example, AI, or may be performed without using AI. For example, the generation unit can input data of the target person's past works into the generation AI and cause the generation AI to improve the accuracy of generation.

[0102] The generation unit can estimate the emotion of the target person and determine the priority of the artifacts to be generated based on the estimated emotion. For example, the generation unit can estimate the emotion of the target person and determine the priority of the artifacts to be generated based on the estimated emotion. For example, if the target person is relaxed, the generation unit can prioritize generating important artifacts. Also, if the target person is in a hurry, the generation unit can prioritize generating concise artifacts. Also, if the target person is excited, the generation unit can prioritize generating visually stimulating artifacts. In this way, by prioritizing artifacts based on emotion, important artifacts can be generated preferentially. Emotion estimation is realized using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above-mentioned processing in the generation unit can be performed using, for example, an AI. For example, the generation unit can input emotion data of the target person into the generation AI and cause the generation AI to estimate the emotion.

[0103] The generation unit can generate a highly relevant deliverable by taking into account the geographical location information of the target person at the time of generation. For example, the generation unit generates a highly relevant deliverable by taking into account the geographical location information of the target person at the time of generation. For example, the generation unit generates a deliverable related to an activity performed by the target person in a specific location. The generation unit can also generate a highly relevant deliverable based on the geographical location information of the target person. The generation unit can also generate a deliverable related to an activity performed by the target person while traveling. In this way, a highly relevant deliverable can be generated by taking into account the geographical location information. Some or all of the above-described processing in the generation unit may be performed using, for example, AI, or may be performed without using AI. For example, the generation unit can input the geographical location information of the target person to the generation AI and cause the generation AI to generate a highly relevant deliverable.

[0104] The generation unit can analyze the social media activity of the target person at the time of generation and generate a related deliverable. For example, the generation unit can analyze the social media activity of the target person at the time of generation and generate a related deliverable. For example, the generation unit can generate a related deliverable based on information shared by the target person on social media. The generation unit can also analyze the target person's social media activity and generate a highly relevant deliverable. The generation unit can also generate a required deliverable based on the content of the target person's social media posts. In this way, a highly relevant deliverable can be generated by analyzing social media activity. Some or all of the above-described processing in the generation unit can be performed using, for example, AI, or can be performed without using AI. For example, the generation unit can input the target person's social media activity data into the generation AI and cause the generation AI to generate a related deliverable.

[0105] The generation unit can customize the generation method by reflecting the target person's past feedback at the time of generation. The generation unit, for example, customizes the generation method by reflecting the target person's past feedback at the time of generation. The generation unit can optimize the generation method, for example, based on feedback provided by the target person in the past. The generation unit can also select the type of deliverable to be generated by reflecting the target person's past feedback. The generation unit can also adjust the timing of generation based on the target person's past feedback. In this way, by reflecting past feedback, the generation method can be optimized and a more appropriate deliverable can be generated. Some or all of the above-mentioned processing in the generation unit may be performed, for example, using AI or without AI. For example, the generation unit can input the target person's past feedback data into the generation AI and cause the generation AI to customize the generation method. === Hard Collateral 1-1 === Each of the multiple elements including the collection unit, analysis unit, learning unit, and generation unit described above is realized, for example, by at least one of the smart device 14 and the data processing device 12. For example, the collection unit collects data of a target person using the camera 42 and microphone 38B of the smart device 14. The analysis unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and analyzes the collected data using natural language processing technology. The learning unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and performs learning using a machine learning algorithm based on the analyzed data. The generation unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and generates a deliverable based on the learned content. === Hard Collateral 1-2 === Each of the multiple elements including the collection unit, analysis unit, learning unit, and generation unit described above is realized, for example, by at least one of the smart glasses 214 and the data processing device 12. For example, the collection unit collects data of the target person using the camera 42 and microphone 238 of the smart glasses 214. The analysis unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and analyzes the collected data using natural language processing technology. The learning unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and performs learning using a machine learning algorithm based on the analyzed data. The generation unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and generates a deliverable based on the learned content. === Hard Collateral 1-3 === Each of the multiple elements including the collection unit, analysis unit, learning unit, and generation unit described above is realized, for example, by at least one of the headset-type terminal 314 and the data processing device 12. For example, the collection unit collects data of the target person using the camera 42 and microphone 238 of the headset-type terminal 314. The analysis unit is realized, for example, by the specific processing unit 290 of the data processing device 12, and analyzes the collected data using natural language processing technology. The learning unit is realized, for example, by the specific processing unit 290 of the data processing device 12, and performs learning using a machine learning algorithm based on the analyzed data. The generation unit is realized, for example, by the specific processing unit 290 of the data processing device 12, and generates a deliverable based on the learned content. === Hard Collateral 1-4 === Each of the multiple elements including the collection unit, analysis unit, learning unit, and generation unit described above is realized, for example, by at least one of the robot 414 and the data processing device 12. For example, the collection unit collects data of the target person using the camera 42 and microphone 238 of the robot 414. The analysis unit is realized, for example, by the specific processing unit 290 of the data processing device 12, and analyzes the collected data using natural language processing technology. The learning unit is realized, for example, by the specific processing unit 290 of the data processing device 12, and performs learning using a machine learning algorithm based on the analyzed data. The generation unit is realized, for example, by the specific processing unit 290 of the data processing device 12, and generates a deliverable based on the learned content.

[0106] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0107] The knowledge emulation system may further include a feedback unit. The feedback unit collects user feedback on the generated deliverables and provides it to the analysis unit. For example, the user may comment on or rate the generated deliverables. The feedback unit may also analyze the user feedback and reflect the feedback in the generation unit. This allows the quality of the generated deliverables to be improved by reflecting the user feedback.

[0108] The collection unit can collect biometric information of the target person and provide it to the analysis unit. For example, biometric information such as heart rate, electrodermal activity, and brain waves can be collected. The collected biometric information is used to estimate the target person's emotions and concentration level. As a result, collecting the target person's biometric information enables more accurate emotion estimation and concentration level evaluation.

[0109] The analysis unit can analyze the target person's past behavioral patterns and predict future behavior. For example, it can analyze what activities the target person performed at what timing in the past and predict future behavior. The analysis unit can also optimize the timing of data collection and learning based on the predicted behavior. This makes it possible to predict future behavior by analyzing past behavioral patterns, enabling efficient data collection and learning.

[0110] The learning unit can estimate the emotions of the target person and adjust the learning progress speed based on the estimated emotions. For example, the learning progress speed can be increased when the target person is relaxed, and decreased when the target person is feeling stressed. The learning unit can also adjust the learning content according to the target person's emotions. This allows for more effective learning by adjusting the learning progress speed and content based on emotions.

[0111] The generation unit can estimate the user's feelings toward the generated deliverable and improve the deliverable based on the estimated feelings. For example, if the user is satisfied with the generated deliverable, the style can be maintained, and if the user is dissatisfied, the style can be changed. The generation unit can also adjust the content of the deliverable based on the user's feelings. In this way, by improving the deliverable based on the user's feelings, it is possible to provide a deliverable that is more satisfying.

[0112] The generation unit can collect user feedback on the generated deliverables and provide it to the analysis unit. For example, users can comment on or rate the generated deliverables. The generation unit can also analyze the user feedback and reflect the feedback in the generation unit. In this way, by reflecting the user feedback, the quality of the generated deliverables can be improved.

[0113] The collection unit can estimate the target person's emotions and adjust the data collection method based on the estimated emotions. For example, when the target person is relaxed, detailed data can be collected, and when the target person is stressed, simplified data can be collected. The collection unit can also adjust the frequency of data collection according to the target person's emotions. In this way, by adjusting the data collection method and frequency based on emotions, more appropriate data can be collected.

[0114] The analysis unit can analyze the target person's past data and evaluate the reliability of the data. For example, the reliability of current data can be evaluated based on the past data, and the more reliable data can be analyzed preferentially. The analysis unit can also determine the priority of analysis based on the reliability of the data. This allows for more accurate analysis by evaluating the reliability of the data.

[0115] The learning unit can estimate the emotions of the target person and customize the learning content based on the estimated emotions. For example, when the target person is relaxed, the learning content can be more difficult, and when the target person is stressed, the learning content can be less difficult. The learning unit can also adjust the learning order according to the target person's emotions. This allows for more effective learning by customizing the learning content and order based on emotions.

[0116] The generation unit can collect user feedback on the generated deliverables and provide it to the analysis unit. For example, users can comment on or rate the generated deliverables. The generation unit can also analyze the user feedback and reflect the feedback in the generation unit. In this way, by reflecting the user feedback, the quality of the generated deliverables can be improved.

[0117] The processing flow of the second embodiment will be briefly explained below.

[0118] Step 1: The collection unit collects data on the target person. The target person's data includes audio data, text data, image data, etc. The collection unit collects data on the target person's conversations, lectures, writings, and research results, and can collect data in the form of audio recordings, video recordings, text files, etc. Step 2: The analysis unit analyzes the data collected by the collection unit. The analysis unit analyzes the data using natural language processing technology and can perform morphological analysis, grammatical analysis, and semantic analysis. Step 3: The learning unit performs learning based on the data analyzed by the analysis unit. The learning unit uses machine learning algorithms to learn the target person's language and thought patterns, and can use neural networks, support vector machines, etc. Step 4: The generator generates a work based on the content learned by the learner. The generator generates a work that imitates the style and methodology of the target person, and can generate a work in literary creation or scientific research.

[0119] 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.

[0120] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (registered trademark) (Internet search engine).<URL: https: / / openai.com / blog / chatgpt> Examples of generative AIs include the data generation model 58, such as a neural network model (e.g., a neural network model), and a neural network model (e.g., a neural network model). The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating speech, text data indicating text, and image data indicating an image is also input to the data generation model 58. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specification processing unit 290 performs the above-mentioned specification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0121] 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.

[0122] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0123] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0124] 3, the data processing system 210 includes the data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0125] 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.

[0126] 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.

[0127] 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.

[0128] 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).

[0129] 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.

[0130] 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.

[0131] 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.

[0132] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0133] In the smart glasses 214, the specific processing is performed by the processor 46. A specific processing program 60 is stored in the storage 50. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. The smart glasses 214 also have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0134] 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.

[0135] 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.

[0136] 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.

[0137] 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.

[0138] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0139] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0140] 5, the data processing system 310 includes the data processing device 12 and a headset type terminal 314. An example of the data processing device 12 is a server.

[0141] 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.

[0142] 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.

[0143] 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.

[0144] 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).

[0145] 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.

[0146] Fig. 6 shows an example of the main functions of the data processing device 12 and the headset type terminal 314. As shown in Fig. 6, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0147] 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.

[0148] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0149] In the headset type terminal 314, the identification process is performed by the processor 46. A identification program 60 is stored in the storage 50. The processor 46 reads the identification program 60 from the storage 50 and executes the read identification program 60 on the RAM 48. The identification process is realized by the processor 46 operating as a control unit 46A in accordance with the identification program 60 executed on the RAM 48. Note that the headset type terminal 314 has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can also perform processing similar to that of the identification processing unit 290 using these models.

[0150] 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.

[0151] 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.

[0152] 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.

[0153] 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.

[0154] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0155] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0156] 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.

[0157] 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.

[0158] 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.

[0159] 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.

[0160] 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).

[0161] 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.

[0162] 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.

[0163] 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.

[0164] 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.

[0165] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0166] In the robot 414, the processor 46 performs the identification process. The storage 50 stores the identification program 60. The processor 46 reads the identification program 60 from the storage 50 and executes the read identification program 60 on the RAM 48. The identification process is realized by the processor 46 operating as the control unit 46A in accordance with the identification program 60 executed on the RAM 48. The robot 414 also has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can perform the same process as the identification processing unit 290 using these models.

[0167] 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.

[0168] 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.

[0169] 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.

[0170] 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.

[0171] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0172] 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.

[0173] 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.

[0174] 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.

[0175] 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).

[0176] 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.

[0177] 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."

[0178] 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.

[0179] 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.

[0180] 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.

[0181] 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.

[0182] 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.

[0183] The hardware resource for executing a specific process can be any of the following types of processors: A processor, for example, is a CPU, which is a general-purpose processor that functions as a hardware resource for executing a specific process by executing software, i.e., a program. A processor also includes a dedicated electrical circuit, such as an FPGA (Field-Programmable Gate Array), a PLD (Programmable Logic Device), or an ASIC (Application Specific Integrated Circuit), which is a processor with a circuit configuration designed specifically for executing a specific process. Each processor has built-in or connected memory, and each processor uses the memory to execute the specific process.

[0184] 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.

[0185] 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.

[0186] 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.

[0187] 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.

[0188] 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.

[0189] 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.

[0190] [Explanation of symbols]

[0191] 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 collection unit that collects data of a target person; an analysis unit that analyzes the data collected by the collection unit; a learning unit that performs learning based on the data analyzed by the analysis unit; a generation unit that generates a product based on the content learned by the learning unit. A system characterized by:

2. The collecting unit Collect data on the subject's conversations, speeches, writings, and research results 2. The system of claim 1.

3. The analysis unit Analyze the collected data using natural language processing technology 2. The system of claim 1.

4. The learning unit Uses machine learning algorithms to learn a person's language and thought patterns 2. The system of claim 1.

5. The generation unit Generate artifacts based on what has been learned that mimic the style and methodology of the target person 2. The system of claim 1.

6. The generation unit To produce a work of literary creation or scientific research 2. The system of claim 1.

7. The collecting unit Estimate the target person's emotions and adjust the timing of data collection based on the estimated emotions.

2. The system of claim 1.

8. The collecting unit Analyze the target person's past activity history and select the appropriate data collection method 2. The system of claim 1.

9. The collecting unit As you collect data, filter it based on the person's current projects and interests.

2. The system of claim 1.

10. The collecting unit When collecting data, select the appropriate collection method depending on the input method of the target person.

2. The system of claim 1.

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