system

Abstract

The system according to the embodiment aims to fairly and efficiently evaluate the thinking ability and judgment ability of a learner. [Solution] The system according to the embodiment comprises a collection unit, an analysis unit, an evaluation unit, and a storage unit. The collection unit collects data on learners' note-taking and behavior. The analysis unit analyzes the data collected by the collection unit and evaluates the learners' thinking ability and judgment ability. The evaluation unit evaluates the learners against goals based on the evaluation results obtained by the analysis unit. The storage unit stores the data collected by the collection unit over the long term.

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 techniques make it difficult to fairly and efficiently evaluate thinking and judgment skills, and there is room for improvement.

[0005] The system according to the embodiment aims to fairly and efficiently evaluate the thinking ability and judgment ability of a learner. [Means for solving the problem]

[0006] The system according to the embodiment includes a collection unit, an analysis unit, an evaluation unit, and a storage unit. The collection unit collects data on learners' note-taking and behavior. The analysis unit analyzes the data collected by the collection unit and evaluates the learners' thinking ability and judgment ability. The evaluation unit evaluates the learners against the goals based on the evaluation results obtained by the analysis unit. The storage unit stores the data collected by the collection unit over the long term. [Effects of the Invention]

[0007] The system according to the embodiment can fairly and efficiently evaluate the thinking ability and judgment ability of a learner. [Brief explanation of the drawings]

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

[0028] (Example 1) The AI ​​system for generating evaluations according to an embodiment of the present invention evaluates thinking and judgment skills that cannot be assessed by test scores. This system aims to evaluate students by comparing their writing with their goals (objectives). Specifically, it accumulates data on a student's notes and behavior over the long term and performs evaluations based on this data. For example, the AI ​​system for generating evaluations collects data on a student's notes and behavior, including handwritten notes, digital notes, audio recordings, and video recordings. It then analyzes the collected data to evaluate their thinking and judgment skills. This evaluation is performed by comparing the data with the goals (objectives). For example, when evaluating a text proposing a solution to a specific problem, the system evaluates how closely the text matches the goal. Furthermore, accumulating data over a long period of time is expected to improve evaluation accuracy. For example, the system records in detail the thought process a student went through to arrive at a solution and performs evaluation based on this data. This enables not only short-term evaluation but also long-term evaluation. This system enables fair and efficient evaluation of thinking and judgment skills, and is expected to improve evaluation methods in educational settings. This allows the Evaluate generation AI system to fairly and efficiently evaluate learners' thinking and judgment abilities.

[0029] The evaluation generation AI system according to the embodiment includes a collection unit, an analysis unit, an evaluation unit, and a storage unit. The collection unit collects data on learners' note-taking and behavior. Examples of the data on learners' note-taking and behavior include, but are not limited to, handwritten notes, digital notes, audio recordings, and video recordings. For example, the collection unit digitizes and reads handwritten notes using scanning technology. The collection unit can also directly read notes submitted in digital format. Furthermore, the collection unit can analyze audio recordings and convert them into text data. For example, the collection unit scans handwritten notes with a high-resolution scanner and converts them into text information using OCR technology. Digital notes submitted in a specific file format can be directly read. Audio recordings are converted into text data using voice recognition technology. The analysis unit analyzes the data collected by the collection unit and evaluates the learners' thinking ability and judgment. The analysis is performed based on criteria such as, but not limited to, logical thinking, problem-solving ability, and decision-making accuracy. For example, the analysis unit may use text mining technology to analyze note-taking to evaluate a learner's thinking ability and judgment. The analysis unit may also use audio analysis technology to analyze audio recordings to evaluate a learner's thinking ability and judgment. Furthermore, the analysis unit may analyze video recordings to evaluate a learner's behavioral patterns. For example, the analysis unit may use text mining technology to extract particularly important information from note-taking and perform evaluation based on that information. Audio analysis technology may extract particularly important utterances from audio recordings and perform evaluation based on that information. Video analysis technology may extract particularly important actions from video recordings and perform evaluation based on that information. The evaluation unit may compare the evaluation results obtained by the analysis unit with the goal (objective). Evaluations may be based on criteria such as, but not limited to, logical thought processes, creative solutions, and goal achievement. For example, the evaluation unit may evaluate logical consistency and understanding of causal relationships to evaluate logical thought processes. To evaluate creative solutions, the evaluation unit may evaluate originality and feasibility. To evaluate goal achievement, the evaluation unit may evaluate the goal achievement rate and progress. The storage unit stores the data collected by the collection unit for a long period of time.The data may be stored, for example, using cloud storage or a physical storage method, but is not limited to these examples. For example, the storage unit stores data for a long period of time using cloud storage. Physical storage methods include an external hard disk and a tape drive. This allows the Evaluate generation AI system according to the embodiment to fairly and efficiently evaluate a learner's thinking ability and judgment ability. Some or all of the above-described processes in the collection unit, analysis unit, evaluation unit, and storage unit may be performed using AI, for example, or without AI. For example, the collection unit may input image data acquired by scanning handwritten notes into the generation AI and cause the generation AI to generate text data from the image data. The analysis unit may input the data collected by the collection unit into the generation AI and cause the generation AI to evaluate the thinking ability and judgment ability. The evaluation unit may input the evaluation results obtained by the analysis unit into the generation AI and cause the generation AI to evaluate the data against the goal (purpose). The storage unit may input the data collected by the collection unit into the generation AI and cause the generation AI to perform long-term storage.

[0030] The evaluation unit can perform evaluation based on evaluation criteria for logical thought processes, creative solutions, and goal achievement. For example, to evaluate a learner's logical thought processes, the evaluation unit evaluates logical consistency and understanding of causal relationships. For example, to evaluate logical consistency, the evaluation unit checks for logical contradictions in the learner's writing. To evaluate understanding of causal relationships, the evaluation unit can also evaluate how the solution presented by the learner is related to the cause of the problem. Furthermore, to evaluate creative solutions, the evaluation unit evaluates originality and feasibility. For example, the evaluation unit evaluates how different the solution presented by the learner is from other common solutions. The evaluation unit can also evaluate whether the presented solution is feasible. Furthermore, to evaluate goal achievement, the evaluation unit evaluates the rate of goal achievement and progress. For example, the evaluation unit evaluates to what extent the learner has achieved the set goal. The evaluation unit can also evaluate the extent of progress the learner has shown to evaluate the learner's progress. This allows the evaluation unit to evaluate a learner's ability based on a variety of evaluation criteria.

[0031] The collection unit can analyze patterns of a learner's past note-taking and behavior and select an appropriate collection method. For example, the collection unit identifies a time period in which the learner wrote a lot of information in the past and collects data during that time period. For example, the collection unit can analyze the time stamps of the learner's note-taking and confirm that a lot of information was written during a specific time period. The collection unit can also analyze the learner's behavior regarding a specific assignment and apply the same collection method to similar assignments. For example, the collection unit can analyze the learner's note-taking and behavior regarding past assignments and apply the same collection method to similar assignments. The collection unit can also analyze the note-taking format and writing style used by the learner in the past and customize the collection method based on the results. For example, the collection unit can analyze the note-taking format (e.g., handwritten notes, digital notes) and writing style (e.g., bullet points, sentence format) used by the learner in the past and customize the collection method based on the results. This enables efficient data collection by selecting the optimal collection method based on past data. Some or all of the above-described processing in the collection unit may be performed using, for example, AI, or without AI. For example, the collection unit can input data on a learner's past note-taking and behavior into the generation AI, and have the generation AI select the optimal collection method.

[0032] When collecting data, the collection unit can filter the data based on the learner's current learning situation or areas of interest. For example, the collection unit collects only data related to the task the learner is currently working on. For example, the collection unit filters the data based on keywords related to the learner's current task. The collection unit can also prioritize collecting relevant data based on the learner's areas of interest. For example, the collection unit analyzes the learner's past learning history and social media activity to filter data related to the learner's areas of interest. The collection unit can also collect data according to the learner's learning progress by taking into account the learner's learning progress. For example, the collection unit filters data based on the learner's test results and assignment submission status. In this way, by filtering data based on the learner's current situation and interests, it is possible to collect relevant data. Some or all of the above-mentioned processing in the collection unit may be performed using, or without, AI. For example, the collection unit can input data on the learner's current learning situation and areas of interest to the generation AI and have the generation AI perform data filtering.

[0033] When collecting data, the collection unit can prioritize collecting highly relevant data by taking into account the learner's geographical location information. For example, if the learner is in a specific location, the collection unit prioritizes collecting data related to that location. For example, the collection unit obtains the learner's geographical location information using GPS data and filters data related to that location. Furthermore, if the learner is traveling, the collection unit can also collect data related to the learner's destination. For example, the collection unit analyzes the learner's travel route and filters data related to the destination. Furthermore, if the learner is interested in a specific region, the collection unit can prioritize collecting data related to that region. For example, the collection unit analyzes the learner's social media activity and filters data related to the region in which the learner expressed interest. This allows for more appropriate data to be collected by collecting highly relevant data based on the geographical location information. Some or all of the above-described processing by the collection unit may be performed using, or without, AI. For example, the collection unit may input the learner's geographical location information to a generation AI and cause the generation AI to filter highly relevant data.

[0034] When collecting data, the collection unit can analyze the learner's social media activities and collect related data. The collection unit collects related data based on, for example, information shared by the learner on social media. For example, the collection unit analyzes the learner's social media posts and filters the related data. The collection unit can also analyze the activities of accounts and groups followed by the learner to collect related data. For example, the collection unit analyzes the posts of accounts followed by the learner and filters the related data. The collection unit can also collect related data based on topics in which the learner shows interest on social media. For example, the collection unit analyzes the posts that the learner has "liked" or "shared" and filters the related data. In this way, by collecting related data based on social media activities, data according to the learner's interests can be collected. Some or all of the above-mentioned processing by the collection unit may be performed using, for example, AI, or may be performed without using AI. For example, the collection unit can input data on the learner's social media activities to the generation AI and cause the generation AI to collect related data.

[0035] During analysis, the analysis unit can adjust the level of detail of the analysis based on the importance of the data. For example, the analysis unit performs a detailed analysis on data with high importance. For example, the analysis unit evaluates the importance of the data and performs a detailed analysis on data with high importance. The analysis unit can also perform a simplified analysis on data with low importance. For example, the analysis unit evaluates the importance of the data and performs a simplified analysis on data with low importance. The analysis unit can also perform an analysis with an appropriate level of detail on data with medium importance. For example, the analysis unit evaluates the importance of the data and performs an analysis with an appropriate level of detail on data with medium importance. This enables efficient analysis by adjusting the level of detail of the analysis according to the importance of the data. Some or all of the above-described 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 importance of the data to the generation AI and cause the generation AI to adjust the level of detail of the analysis.

[0036] During analysis, the analysis unit can apply different analysis algorithms depending on the data category. For example, the analysis unit applies a natural language processing algorithm to text data. For example, the analysis unit uses natural language processing technology to analyze text data. The analysis unit can also apply a statistical analysis algorithm to numerical data. For example, the analysis unit uses statistical analysis technology to analyze numerical data. The analysis unit can also apply an image recognition algorithm to image data. For example, the analysis unit uses image recognition technology to analyze image data. This enables more accurate analysis by applying an analysis algorithm depending on the data category. 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 data of each category, namely text data, numerical data, and image data, into the generation AI and cause the generation AI to apply an appropriate analysis algorithm.

[0037] During analysis, the analysis unit can determine the analysis priority based on the time when the data was collected. The analysis unit, for example, prioritizes analysis of the most recent data. For example, the analysis unit prioritizes analysis of the most recent data based on the time when the data was collected. The analysis unit can also analyze current data while referring to past data. For example, the analysis unit compares past data with current data to derive analysis results. The analysis unit can also prioritize analysis of data collected during a specific period. For example, the analysis unit performs analysis based on data collected during a specific period. This enables efficient analysis by determining the analysis priority based on the time when the data was collected. 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 when the data was collected into the generation AI and have the generation AI determine the analysis priority.

[0038] During analysis, the analysis unit can adjust the order of analysis based on the relevance of the data. The analysis unit, for example, prioritizes analysis of highly relevant data. For example, the analysis unit evaluates the relevance of the data and prioritizes analysis of highly relevant data. The analysis unit can also postpone analysis of less relevant data. For example, the analysis unit evaluates the relevance of the data and postpones analysis of less relevant data. The analysis unit can also dynamically adjust the order of analysis based on the relevance of the data. For example, the analysis unit evaluates the relevance of the data and dynamically adjusts the order of analysis. 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, for example, AI, 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.

[0039] The evaluation unit can improve the accuracy of the evaluation based on the interrelationships between data during evaluation. The evaluation unit, for example, correlates and evaluates the learner's note-taking and speech and behavior data. For example, the evaluation unit correlates and evaluates the learner's note-taking and audio recordings. The evaluation unit can also evaluate by comparing the learner's past evaluation results with current data. For example, the evaluation unit compares the learner's past evaluation results with current note-taking and evaluates. The evaluation unit can also analyze the correlations between the learner's data and improve the accuracy of the evaluation. For example, the evaluation unit analyzes the correlations between the learner's note-taking and video recordings and evaluates them. This improves the accuracy of the evaluation by taking the interrelationships between the data into consideration. Some or all of the above-mentioned processing in the evaluation unit may be performed using, for example, AI, or may be performed without AI. For example, the evaluation unit can input the learner's note-taking, audio recording, and video recording data into a generation AI and have the generation AI evaluate the interrelationships.

[0040] The evaluation unit can perform evaluation based on the learner's attribute information during evaluation. The evaluation unit adjusts the evaluation criteria according to, for example, the learner's age or grade. For example, the evaluation unit sets the evaluation criteria based on the learner's age or grade. The evaluation unit can also perform evaluation based on the learner's learning style and interests. For example, the evaluation unit performs evaluation based on the learner's learning style (visual, auditory, experiential, etc.) and interests. The evaluation unit can also perform evaluation with reference to the learner's past learning history. For example, the evaluation unit analyzes the learner's past learning history and performs evaluation based on that. This enables more appropriate evaluation by taking the learner's attribute information into consideration. Some or all of the above-mentioned processing in the evaluation unit may be performed using, for example, AI, or may be performed without AI. For example, the evaluation unit can input the learner's attribute information into the generation AI and cause the generation AI to adjust the evaluation criteria.

[0041] The evaluation unit can perform evaluation based on the geographic distribution of data during evaluation. For example, if learners are in different regions, the evaluation unit can perform evaluation taking into account the characteristics of those regions. For example, the evaluation unit can set evaluation criteria based on the geographic distribution of learners. Furthermore, if a learner is interested in a particular region, the evaluation unit can also perform evaluation by focusing on data related to that region. For example, the evaluation unit can analyze the learner's social media activity and perform evaluation based on data related to the region of interest. Furthermore, the evaluation unit can perform evaluation by referring to the learner's geographic movement history. For example, the evaluation unit can analyze the learner's movement route and perform evaluation based on that. This enables more appropriate evaluation by taking the geographic distribution of data into consideration. Some or all of the above-mentioned processing in the evaluation unit can be performed using, for example, AI, or without AI. For example, the evaluation unit can input data on the learners' geographic distribution into the generation AI and cause the generation AI to adjust the evaluation criteria.

[0042] During evaluation, the evaluation unit can improve the accuracy of the evaluation based on literature related to the data. The evaluation unit, for example, performs the evaluation by referring to academic papers related to the learner's data. For example, the evaluation unit analyzes the learner's note-taking and behavior data and performs the evaluation by referring to related academic papers. The evaluation unit can also perform the evaluation by referring to books and articles related to the learner's data. For example, the evaluation unit analyzes the learner's note-taking and behavior data and performs the evaluation by referring to related books and articles. The evaluation unit can also perform the evaluation by referring to past research results related to the learner's data. For example, the evaluation unit analyzes the learner's note-taking and behavior data and performs the evaluation by referring to past research results related to the data. In this way, by referring to related literature, the accuracy of the evaluation is improved. Some or all of the above-mentioned processing in the evaluation unit may be performed using, for example, AI, or may be performed without using AI. For example, the evaluation unit can input the learner's data into a generation AI and have the generation AI refer to related literature and perform the evaluation.

[0043] The storage unit can optimize the storage algorithm by referring to past storage data during storage. The storage unit, for example, analyzes past storage data and selects an optimal storage method. For example, the storage unit analyzes patterns of past storage data and selects an optimal storage method. The storage unit can also analyze patterns of past storage data and adjust the storage algorithm. For example, the storage unit evaluates the quality of past storage data and improves the storage algorithm. The storage unit can also evaluate the quality of past storage data and improve the storage algorithm. For example, the storage unit evaluates the quality of past storage data and improves the storage algorithm. In this way, the storage algorithm can be optimized by referring to past storage data. Some or all of the above-mentioned processing in the storage unit may be performed using, for example, AI, or may be performed without using AI. For example, the storage unit can input past storage data to a generation AI and cause the generation AI to optimize the storage algorithm.

[0044] The storage unit can weight the stored data based on the time the data was collected during storage. For example, the storage unit assigns a higher weight to the most recent data. For example, the storage unit assigns a higher weight to the most recent data based on the time the data was collected. The storage unit can also assign a lower weight to older data. For example, the storage unit assigns a lower weight to older data based on the time the data was collected. The storage unit can also assign an appropriate weight to data collected during a specific period. For example, the storage unit assigns an appropriate weight to data collected during a specific period. In this way, by weighting the stored data based on the time the data was collected, more important data can be appropriately stored. Some or all of the above-described processing in the storage unit may be performed using, for example, AI, or may be performed without using AI. For example, the storage unit can input the time the data was collected to the generation AI and have the generation AI perform weighting of the stored data.

[0045] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0046] When collecting learner data, the collection unit can customize the collection method based on the learner's learning style. For example, for visual learners, data collection can be focused on visual content, and for auditory learners, data collection can be focused on audio recordings. Also, for experiential learners, data collection can be focused on recording actual actions and experiments. This makes it possible to collect data according to the learner's learning style, and more accurate evaluations can be expected.

[0047] When analyzing learner data, the analysis unit can evaluate the reliability of the data and prioritize analysis of highly reliable data. For example, it can evaluate reliability based on the data collection method and collection environment, and analyze highly reliable data in detail. It can also analyze data with low reliability using complementary data. This improves the accuracy of the analysis results and enables more reliable evaluation.

[0048] When evaluating a learner's data, the evaluation unit can adjust the level of detail of the evaluation based on the learner's level of goal achievement. For example, if goal achievement is high, a detailed evaluation can be conducted and specific advice for further growth can be provided. On the other hand, if goal achievement is low, a brief evaluation can be conducted that focuses on basic areas for improvement. This allows the learner to receive an appropriate evaluation according to their level of goal achievement, improving their learning effectiveness.

[0049] When collecting data on a learner, the collection unit can optimize the collection method based on the learner's learning history. For example, it can prioritize the application of collection methods that have been effective in the past and avoid methods that have been less effective. It can also adjust the collection method according to the learner's progress to collect optimal data. This enables efficient data collection based on the learner's learning history.

[0050] When analyzing learner data, the analysis unit can select an analysis algorithm taking into account the diversity of the data. For example, a natural language processing algorithm can be applied to text data, and an image recognition algorithm can be applied to image data. It is also possible to apply an audio analysis algorithm to audio data, and a video analysis algorithm to video data. This enables appropriate analysis according to the diversity of the data, improving the accuracy of the analysis results.

[0051] The processing flow of the first embodiment will be briefly explained below.

[0052] Step 1: The collection unit collects data on learners' note-taking and behavior. Specifically, this includes handwritten notes, digital notes, audio recordings, video recordings, etc. The collection unit digitizes and reads handwritten notes using scanning technology. It can also directly read notes submitted in digital format. It can also analyze audio recordings and convert them into text data. For example, handwritten notes can be scanned with a high-resolution scanner and converted into text information using OCR technology. Digital notes submitted in a specific file format can be directly read. Audio recordings can be converted into text data using voice recognition technology. Step 2: The analysis unit analyzes the data collected by the collection unit and evaluates the learner's thinking ability and judgment. The analysis is based on criteria such as logical thinking, problem-solving ability, and decision-making accuracy. For example, text mining technology can be used to analyze note-taking to evaluate thinking ability and judgment. Audio analysis technology can also be used to analyze audio recordings to evaluate thinking ability and judgment. Furthermore, video recordings can be analyzed to evaluate the learner's behavioral patterns. Step 3: The evaluation section evaluates the results of the analysis section against the goals. The evaluation is based on criteria such as logical thought process, creative solutions, and goal achievement. For example, the evaluation evaluates logical consistency and understanding of causal relationships, originality and feasibility, and the rate of goal achievement and progress. Step 4: The storage unit stores the data collected by the collection unit for the long term. The data is stored using cloud storage or a physical storage method. For example, the data is stored for the long term using cloud storage. Physical storage methods include an external hard disk or tape drive.

[0053] (Example 2) The AI ​​system for generating evaluations according to an embodiment of the present invention evaluates thinking and judgment skills that cannot be assessed by test scores. This system aims to evaluate students by comparing their writing with their goals (objectives). Specifically, it accumulates data on a student's notes and behavior over the long term and performs evaluations based on this data. For example, the AI ​​system for generating evaluations collects data on a student's notes and behavior, including handwritten notes, digital notes, audio recordings, and video recordings. It then analyzes the collected data to evaluate their thinking and judgment skills. This evaluation is performed by comparing the data with the goals (objectives). For example, when evaluating a text proposing a solution to a specific problem, the system evaluates how closely the text matches the goal. Furthermore, accumulating data over a long period of time is expected to improve evaluation accuracy. For example, the system records in detail the thought process a student went through to arrive at a solution and performs evaluation based on this data. This enables not only short-term evaluation but also long-term evaluation. This system enables fair and efficient evaluation of thinking and judgment skills, and is expected to improve evaluation methods in educational settings. This allows the Evaluate generation AI system to fairly and efficiently evaluate learners' thinking and judgment abilities.

[0054] The evaluation generation AI system according to the embodiment includes a collection unit, an analysis unit, an evaluation unit, and a storage unit. The collection unit collects data on learners' note-taking and behavior. Examples of the data on learners' note-taking and behavior include, but are not limited to, handwritten notes, digital notes, audio recordings, and video recordings. For example, the collection unit digitizes and reads handwritten notes using scanning technology. The collection unit can also directly read notes submitted in digital format. Furthermore, the collection unit can analyze audio recordings and convert them into text data. For example, the collection unit scans handwritten notes with a high-resolution scanner and converts them into text information using OCR technology. Digital notes submitted in a specific file format can be directly read. Audio recordings are converted into text data using voice recognition technology. The analysis unit analyzes the data collected by the collection unit and evaluates the learners' thinking ability and judgment. The analysis is performed based on criteria such as, but not limited to, logical thinking, problem-solving ability, and decision-making accuracy. For example, the analysis unit may use text mining technology to analyze note-taking to evaluate a learner's thinking ability and judgment. The analysis unit may also use audio analysis technology to analyze audio recordings to evaluate a learner's thinking ability and judgment. Furthermore, the analysis unit may analyze video recordings to evaluate a learner's behavioral patterns. For example, the analysis unit may use text mining technology to extract particularly important information from note-taking and perform evaluation based on that information. Audio analysis technology may extract particularly important utterances from audio recordings and perform evaluation based on that information. Video analysis technology may extract particularly important actions from video recordings and perform evaluation based on that information. The evaluation unit may compare the evaluation results obtained by the analysis unit with the goal (objective). Evaluations may be based on criteria such as, but not limited to, logical thought processes, creative solutions, and goal achievement. For example, the evaluation unit may evaluate logical consistency and understanding of causal relationships to evaluate logical thought processes. To evaluate creative solutions, the evaluation unit may evaluate originality and feasibility. To evaluate goal achievement, the evaluation unit may evaluate the goal achievement rate and progress. The storage unit stores the data collected by the collection unit for a long period of time.The data may be stored, for example, using cloud storage or a physical storage method, but is not limited to these examples. For example, the storage unit stores data for a long period of time using cloud storage. Physical storage methods include an external hard disk and a tape drive. This allows the Evaluate generation AI system according to the embodiment to fairly and efficiently evaluate a learner's thinking ability and judgment ability. Some or all of the above-described processes in the collection unit, analysis unit, evaluation unit, and storage unit may be performed using AI, for example, or without AI. For example, the collection unit may input image data acquired by scanning handwritten notes into the generation AI and cause the generation AI to generate text data from the image data. The analysis unit may input the data collected by the collection unit into the generation AI and cause the generation AI to evaluate the thinking ability and judgment ability. The evaluation unit may input the evaluation results obtained by the analysis unit into the generation AI and cause the generation AI to evaluate the data against the goal (purpose). The storage unit may input the data collected by the collection unit into the generation AI and cause the generation AI to perform long-term storage.

[0055] The evaluation unit can perform evaluation based on evaluation criteria for logical thought processes, creative solutions, and goal achievement. For example, to evaluate a learner's logical thought processes, the evaluation unit evaluates logical consistency and understanding of causal relationships. For example, to evaluate logical consistency, the evaluation unit checks for logical contradictions in the learner's writing. To evaluate understanding of causal relationships, the evaluation unit can also evaluate how the solution presented by the learner is related to the cause of the problem. Furthermore, to evaluate creative solutions, the evaluation unit evaluates originality and feasibility. For example, the evaluation unit evaluates how different the solution presented by the learner is from other common solutions. The evaluation unit can also evaluate whether the presented solution is feasible. Furthermore, to evaluate goal achievement, the evaluation unit evaluates the rate of goal achievement and progress. For example, the evaluation unit evaluates to what extent the learner has achieved the set goal. The evaluation unit can also evaluate the extent of progress the learner has shown to evaluate the learner's progress. This allows the evaluation unit to evaluate a learner's ability based on a variety of evaluation criteria.

[0056] The collection unit can estimate the learner's emotions and adjust the timing of data collection based on the estimated learner's emotions. For example, the collection unit collects data when the learner is concentrating and increases the collection frequency when the level of concentration estimated by the emotion engine is high. For example, the collection unit captures the learner's facial expressions with a camera and estimates the learner's level of concentration using the emotion engine. The collection unit can also reduce the collection frequency when the learner is tired and resume collection after a break. For example, the collection unit can record the learner's voice and estimate the learner's fatigue level using voice analysis technology. The collection unit can also pause collection when the learner is feeling stressed until the stress is reduced and resume collection when the learner is relaxed. For example, the collection unit can collect the learner's biometric data (heart rate and electrodermal activity) using a sensor and estimate the learner's stress level using the emotion engine. This allows for more appropriate data collection by adjusting the timing of data collection according to the learner's emotions. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or generative AI. The generation AI may be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the collection unit may be performed using AI, or may be performed without using AI. For example, the collection unit may input image data of a learner captured by a camera into the generation AI and cause the generation AI to estimate the learner's emotions.

[0057] The collection unit can analyze patterns of a learner's past note-taking and behavior and select an appropriate collection method. For example, the collection unit identifies a time period in which the learner wrote a lot of information in the past and collects data during that time period. For example, the collection unit can analyze the time stamps of the learner's note-taking and confirm that a lot of information was written during a specific time period. The collection unit can also analyze the learner's behavior regarding a specific assignment and apply the same collection method to similar assignments. For example, the collection unit can analyze the learner's note-taking and behavior regarding past assignments and apply the same collection method to similar assignments. The collection unit can also analyze the note-taking format and writing style used by the learner in the past and customize the collection method based on the results. For example, the collection unit can analyze the note-taking format (e.g., handwritten notes, digital notes) and writing style (e.g., bullet points, sentence format) used by the learner in the past and customize the collection method based on the results. This enables efficient data collection by selecting the optimal collection method based on past data. Some or all of the above-described processing in the collection unit may be performed using, for example, AI, or without AI. For example, the collection unit can input data on a learner's past note-taking and behavior into the generation AI, and have the generation AI select the optimal collection method.

[0058] When collecting data, the collection unit can filter the data based on the learner's current learning situation or areas of interest. For example, the collection unit collects only data related to the task the learner is currently working on. For example, the collection unit filters the data based on keywords related to the learner's current task. The collection unit can also prioritize collecting relevant data based on the learner's areas of interest. For example, the collection unit analyzes the learner's past learning history and social media activity to filter data related to the learner's areas of interest. The collection unit can also collect data according to the learner's learning progress by taking into account the learner's learning progress. For example, the collection unit filters data based on the learner's test results and assignment submission status. In this way, by filtering data based on the learner's current situation and interests, it is possible to collect relevant data. Some or all of the above-mentioned processing in the collection unit may be performed using, or without, AI. For example, the collection unit can input data on the learner's current learning situation and areas of interest to the generation AI and have the generation AI perform data filtering.

[0059] The collection unit can estimate the learner's emotions and prioritize the data to be collected based on the estimated learner's emotions. For example, if the learner is excited, the collection unit prioritizes collecting data related to creative ideas. For example, the collection unit captures the learner's facial expressions with a camera and estimates the learner's level of excitement using an emotion engine. Alternatively, if the learner is relaxed, the collection unit can prioritize collecting data requiring deep thinking. For example, the collection unit records the learner's voice and estimates the learner's level of relaxation using voice analysis technology. Alternatively, if the learner is stressed, the collection unit can prioritize collecting data related to simple tasks. For example, the collection unit collects the learner's biometric data (heart rate and electrodermal activity) using a sensor and estimates the learner's stress level using an emotion engine. This allows for prioritized data collection based on the learner's emotions, thereby enabling more important data to be collected preferentially. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or a generative AI. The generative AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the collection unit may be performed using, for example, AI, or may be performed without using AI. For example, the collection unit may input image data of the learner taken by a camera to the generation AI and cause the generation AI to estimate the learner's emotions.

[0060] When collecting data, the collection unit can prioritize collecting highly relevant data by taking into account the learner's geographical location information. For example, if the learner is in a specific location, the collection unit prioritizes collecting data related to that location. For example, the collection unit obtains the learner's geographical location information using GPS data and filters data related to that location. Furthermore, if the learner is traveling, the collection unit can also collect data related to the learner's destination. For example, the collection unit analyzes the learner's travel route and filters data related to the destination. Furthermore, if the learner is interested in a specific region, the collection unit can prioritize collecting data related to that region. For example, the collection unit analyzes the learner's social media activity and filters data related to the region in which the learner expressed interest. This allows for more appropriate data to be collected by collecting highly relevant data based on the geographical location information. Some or all of the above-described processing by the collection unit may be performed using, or without, AI. For example, the collection unit may input the learner's geographical location information to a generation AI and cause the generation AI to filter highly relevant data.

[0061] When collecting data, the collection unit can analyze the learner's social media activities and collect related data. The collection unit collects related data based on, for example, information shared by the learner on social media. For example, the collection unit analyzes the learner's social media posts and filters the related data. The collection unit can also analyze the activities of accounts and groups followed by the learner to collect related data. For example, the collection unit analyzes the posts of accounts followed by the learner and filters the related data. The collection unit can also collect related data based on topics in which the learner shows interest on social media. For example, the collection unit analyzes the posts that the learner has "liked" or "shared" and filters the related data. In this way, by collecting related data based on social media activities, data according to the learner's interests can be collected. Some or all of the above-mentioned processing by the collection unit may be performed using, for example, AI, or may be performed without using AI. For example, the collection unit can input data on the learner's social media activities to the generation AI and cause the generation AI to collect related data.

[0062] The analysis unit can estimate the learner's emotions and adjust the presentation method of the analysis based on the estimated learner's emotions. For example, if the learner is relaxed, the analysis unit provides detailed analysis results. For example, the analysis unit captures the learner's facial expressions with a camera and estimates the learner's level of relaxation using an emotion engine. If the learner is in a hurry, the analysis unit can provide concise analysis results that focus on the main points. For example, the analysis unit records the learner's voice and estimates the learner's level of hurry using voice analysis technology. If the learner is excited, the analysis unit can provide analysis results using visually stimulating graphs or charts. For example, the analysis unit collects the learner's biometric data (heart rate and electrodermal activity) using a sensor and estimates the learner's level of excitement using an emotion engine. This allows the analysis results to be more easily understood by adjusting the presentation method of the analysis according to the learner's emotions. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generative AI. The generative AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-mentioned processes in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit may input image data of a learner taken by a camera to the generation AI and cause the generation AI to estimate the learner's emotions.

[0063] During analysis, the analysis unit can adjust the level of detail of the analysis based on the importance of the data. For example, the analysis unit performs a detailed analysis on data with high importance. For example, the analysis unit evaluates the importance of the data and performs a detailed analysis on data with high importance. The analysis unit can also perform a simplified analysis on data with low importance. For example, the analysis unit evaluates the importance of the data and performs a simplified analysis on data with low importance. The analysis unit can also perform an analysis with an appropriate level of detail on data with medium importance. For example, the analysis unit evaluates the importance of the data and performs an analysis with an appropriate level of detail on data with medium importance. This enables efficient analysis by adjusting the level of detail of the analysis according to the importance of the data. Some or all of the above-described 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 importance of the data to the generation AI and cause the generation AI to adjust the level of detail of the analysis.

[0064] During analysis, the analysis unit can apply different analysis algorithms depending on the data category. For example, the analysis unit applies a natural language processing algorithm to text data. For example, the analysis unit uses natural language processing technology to analyze text data. The analysis unit can also apply a statistical analysis algorithm to numerical data. For example, the analysis unit uses statistical analysis technology to analyze numerical data. The analysis unit can also apply an image recognition algorithm to image data. For example, the analysis unit uses image recognition technology to analyze image data. This enables more accurate analysis by applying an analysis algorithm depending on the data category. 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 data of each category, namely text data, numerical data, and image data, into the generation AI and cause the generation AI to apply an appropriate analysis algorithm.

[0065] The analysis unit can estimate the learner's emotions and adjust the length of the analysis based on the estimated learner's emotions. For example, if the learner is in a hurry, the analysis unit provides a short and concise analysis result. For example, the analysis unit captures the learner's facial expressions with a camera and estimates the learner's level of hurry using an emotion engine. The analysis unit can also provide a detailed analysis result if the learner is relaxed. For example, the analysis unit records the learner's voice and estimates the learner's level of relaxation using audio analysis technology. The analysis unit can also provide a visually stimulating analysis result if the learner is excited. For example, the analysis unit collects the learner's biometric data (heart rate and electrodermal activity) using a sensor and estimates the learner's level of excitement using an emotion engine. This allows the length of the analysis to be adjusted according to the learner's emotions, thereby providing more appropriate analysis results. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generative AI. The generative AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-mentioned processes in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit may input image data of a learner taken by a camera to the generation AI and cause the generation AI to estimate the learner's emotions.

[0066] During analysis, the analysis unit can determine the analysis priority based on the time when the data was collected. The analysis unit, for example, prioritizes analysis of the most recent data. For example, the analysis unit prioritizes analysis of the most recent data based on the time when the data was collected. The analysis unit can also analyze current data while referring to past data. For example, the analysis unit compares past data with current data to derive analysis results. The analysis unit can also prioritize analysis of data collected during a specific period. For example, the analysis unit performs analysis based on data collected during a specific period. This enables efficient analysis by determining the analysis priority based on the time when the data was collected. 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 when the data was collected into the generation AI and have the generation AI determine the analysis priority.

[0067] During analysis, the analysis unit can adjust the order of analysis based on the relevance of the data. The analysis unit, for example, prioritizes analysis of highly relevant data. For example, the analysis unit evaluates the relevance of the data and prioritizes analysis of highly relevant data. The analysis unit can also postpone analysis of less relevant data. For example, the analysis unit evaluates the relevance of the data and postpones analysis of less relevant data. The analysis unit can also dynamically adjust the order of analysis based on the relevance of the data. For example, the analysis unit evaluates the relevance of the data and dynamically adjusts the order of analysis. 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, for example, AI, 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.

[0068] The evaluation unit can estimate the learner's emotions and adjust the evaluation criteria based on the estimated learner's emotions. For example, if the learner is relaxed, the evaluation unit applies detailed evaluation criteria. For example, the evaluation unit captures the learner's facial expressions with a camera and estimates the learner's level of relaxation using an emotion engine. Alternatively, if the learner is in a hurry, the evaluation unit can apply simplified evaluation criteria. For example, the evaluation unit records the learner's voice and estimates the learner's level of hurry using voice analysis technology. Alternatively, if the learner is excited, the evaluation unit can apply visually stimulating evaluation criteria. For example, the evaluation unit collects the learner's biometric data (heart rate and electrodermal activity) using a sensor and estimates the learner's level of excitement using an emotion engine. This allows for more appropriate evaluation by adjusting the evaluation criteria according to the learner's emotions. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generative AI. The generative AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the evaluation unit may be performed using, for example, AI, or may be performed without using AI. For example, the evaluation unit may input image data of the learner taken by a camera to the generation AI and cause the generation AI to estimate the learner's emotions.

[0069] The evaluation unit can improve the accuracy of the evaluation based on the interrelationships between data during evaluation. The evaluation unit, for example, correlates and evaluates the learner's note-taking and speech and behavior data. For example, the evaluation unit correlates and evaluates the learner's note-taking and audio recordings. The evaluation unit can also evaluate by comparing the learner's past evaluation results with current data. For example, the evaluation unit compares the learner's past evaluation results with current note-taking and evaluates. The evaluation unit can also analyze the correlations between the learner's data and improve the accuracy of the evaluation. For example, the evaluation unit analyzes the correlations between the learner's note-taking and video recordings and evaluates them. This improves the accuracy of the evaluation by taking the interrelationships between the data into consideration. Some or all of the above-mentioned processing in the evaluation unit may be performed using, for example, AI, or may be performed without AI. For example, the evaluation unit can input the learner's note-taking, audio recording, and video recording data into a generation AI and have the generation AI evaluate the interrelationships.

[0070] The evaluation unit can perform evaluation based on the learner's attribute information during evaluation. The evaluation unit adjusts the evaluation criteria according to, for example, the learner's age or grade. For example, the evaluation unit sets the evaluation criteria based on the learner's age or grade. The evaluation unit can also perform evaluation based on the learner's learning style and interests. For example, the evaluation unit performs evaluation based on the learner's learning style (visual, auditory, experiential, etc.) and interests. The evaluation unit can also perform evaluation with reference to the learner's past learning history. For example, the evaluation unit analyzes the learner's past learning history and performs evaluation based on that. This enables more appropriate evaluation by taking the learner's attribute information into consideration. Some or all of the above-mentioned processing in the evaluation unit may be performed using, for example, AI, or may be performed without AI. For example, the evaluation unit can input the learner's attribute information into the generation AI and cause the generation AI to adjust the evaluation criteria.

[0071] The evaluation unit can estimate the learner's emotions and adjust the display order of the evaluation results based on the estimated learner's emotions. For example, if the learner is relaxed, the evaluation unit can display detailed evaluation results first. For example, the evaluation unit can capture the learner's facial expressions with a camera and estimate the learner's level of relaxation using an emotion engine. If the learner is in a hurry, the evaluation unit can also display evaluation results that focus on the key points first. For example, the evaluation unit can record the learner's voice and estimate the learner's level of hurry using audio analysis technology. If the learner is excited, the evaluation unit can also display visually stimulating evaluation results first. For example, the evaluation unit can collect the learner's biometric data (heart rate and electrodermal activity) using a sensor and estimate the learner's level of excitement using an emotion engine. This allows the evaluation results to be more easily understood by adjusting the display order of the evaluation results according to the learner's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or a generative AI. The generative AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the evaluation unit may be performed using, for example, AI, or may be performed without using AI. For example, the evaluation unit may input image data of the learner taken by a camera to the generation AI and cause the generation AI to estimate the learner's emotions.

[0072] The evaluation unit can perform evaluation based on the geographic distribution of data during evaluation. For example, if learners are in different regions, the evaluation unit can perform evaluation taking into account the characteristics of those regions. For example, the evaluation unit can set evaluation criteria based on the geographic distribution of learners. Furthermore, if a learner is interested in a particular region, the evaluation unit can also perform evaluation by focusing on data related to that region. For example, the evaluation unit can analyze the learner's social media activity and perform evaluation based on data related to the region of interest. Furthermore, the evaluation unit can perform evaluation by referring to the learner's geographic movement history. For example, the evaluation unit can analyze the learner's movement route and perform evaluation based on that. This enables more appropriate evaluation by taking the geographic distribution of data into consideration. Some or all of the above-mentioned processing in the evaluation unit can be performed using, for example, AI, or without AI. For example, the evaluation unit can input data on the learners' geographic distribution into the generation AI and cause the generation AI to adjust the evaluation criteria.

[0073] During evaluation, the evaluation unit can improve the accuracy of the evaluation based on literature related to the data. The evaluation unit, for example, performs the evaluation by referring to academic papers related to the learner's data. For example, the evaluation unit analyzes the learner's note-taking and behavior data and performs the evaluation by referring to related academic papers. The evaluation unit can also perform the evaluation by referring to books and articles related to the learner's data. For example, the evaluation unit analyzes the learner's note-taking and behavior data and performs the evaluation by referring to related books and articles. The evaluation unit can also perform the evaluation by referring to past research results related to the learner's data. For example, the evaluation unit analyzes the learner's note-taking and behavior data and performs the evaluation by referring to past research results related to the data. In this way, by referring to related literature, the accuracy of the evaluation is improved. Some or all of the above-mentioned processing in the evaluation unit may be performed using, for example, AI, or may be performed without using AI. For example, the evaluation unit can input the learner's data into a generation AI and have the generation AI refer to related literature and perform the evaluation.

[0074] The storage unit can estimate the learner's emotions and select stored data based on the estimated learner's emotions. For example, if the learner is relaxed, the storage unit prioritizes storing detailed note descriptions. For example, the storage unit may capture the learner's facial expressions with a camera, estimate the learner's level of relaxation using an emotion engine, and store detailed note descriptions. Furthermore, if the learner is in a hurry, the storage unit can prioritize storing note descriptions that emphasize the key points. For example, the storage unit may record the learner's voice, estimate the learner's level of hurry using audio analysis technology, and store note descriptions that emphasize the key points. Furthermore, if the learner is excited, the storage unit can prioritize storing visually stimulating note descriptions. For example, the storage unit may collect the learner's biometric data (heart rate and electrodermal activity) using a sensor, estimate the learner's level of excitement using an emotion engine, and store visually stimulating note descriptions. This allows for more appropriate data to be stored by selecting stored data based on the learner's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. The generation AI may 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 storage unit may be performed using AI, for example, or may be performed without using AI. For example, the storage unit may input image data of a learner captured by a camera into the generation AI, and cause the generation AI to estimate the learner's emotions.

[0075] The storage unit can optimize the storage algorithm by referring to past storage data during storage. The storage unit, for example, analyzes past storage data and selects an optimal storage method. For example, the storage unit analyzes patterns of past storage data and selects an optimal storage method. The storage unit can also analyze patterns of past storage data and adjust the storage algorithm. For example, the storage unit evaluates the quality of past storage data and improves the storage algorithm. The storage unit can also evaluate the quality of past storage data and improve the storage algorithm. For example, the storage unit evaluates the quality of past storage data and improves the storage algorithm. In this way, the storage algorithm can be optimized by referring to past storage data. Some or all of the above-mentioned processing in the storage unit may be performed using, for example, AI, or may be performed without using AI. For example, the storage unit can input past storage data to a generation AI and cause the generation AI to optimize the storage algorithm.

[0076] The storage unit can estimate the learner's emotions and adjust the storage frequency based on the estimated learner's emotions. For example, if the learner is relaxed, the storage unit stores data frequently. For example, the storage unit may capture the learner's facial expressions with a camera, estimate the learner's level of relaxation using an emotion engine, and store data frequently. Furthermore, if the learner is in a hurry, the storage unit can reduce the storage frequency and store only important data. For example, the storage unit may record the learner's voice, estimate the learner's level of hurry using voice analysis technology, and reduce the storage frequency. Furthermore, if the learner is excited, the storage unit can adjust the storage frequency according to changes in the learner's emotions. For example, the storage unit may collect the learner's biometric data (heart rate and electrodermal activity) using a sensor, estimate the learner's level of excitement using an emotion engine, and adjust the storage frequency. This allows for more appropriate data storage by adjusting the storage frequency according to the learner's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. The generation AI may 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 storage unit may be performed using AI, for example, or may be performed without using AI. For example, the storage unit may input image data of a learner captured by a camera into the generation AI, and cause the generation AI to estimate the learner's emotions.

[0077] The storage unit can weight the stored data based on the time the data was collected during storage. For example, the storage unit assigns a higher weight to the most recent data. For example, the storage unit assigns a higher weight to the most recent data based on the time the data was collected. The storage unit can also assign a lower weight to older data. For example, the storage unit assigns a lower weight to older data based on the time the data was collected. The storage unit can also assign an appropriate weight to data collected during a specific period. For example, the storage unit assigns an appropriate weight to data collected during a specific period. In this way, by weighting the stored data based on the time the data was collected, more important data can be appropriately stored. Some or all of the above-described processing in the storage unit may be performed using, for example, AI, or may be performed without using AI. For example, the storage unit can input the time the data was collected to the generation AI and have the generation AI perform weighting of the stored data. === Hard Collateral 1-1 === Each of the multiple elements, including the collection unit, analysis unit, evaluation unit, and storage 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 on the learner's notes and behavior using the camera 42 and microphone 38B of the smart device 14, and digitizes the data using the control unit 46A. The analysis unit is realized, for example, by the specific processing unit 290 of the data processing device 12, and analyzes the collected data to evaluate the learner's thinking ability and judgment ability. The evaluation unit is realized, for example, by the specific processing unit 290 of the data processing device 12, and evaluates the learner's thinking ability and judgment ability based on the analysis results in relation to the goal (purpose). The storage unit stores the data for a long period of time using, for example, the storage 32 of the data processing device 12. === Hard Collateral 1-2 === Each of the multiple elements, including the collection unit, analysis unit, evaluation unit, and storage 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 on the learner's notes and behavior using the camera 42 and microphone 238 of the smart glasses 214, and digitizes the data using the control unit 46A. The analysis unit, realized, for example, by the specific processing unit 290 of the data processing device 12, analyzes the collected data and evaluates the learner's thinking ability and judgment ability. The evaluation unit, realized, for example, by the specific processing unit 290 of the data processing device 12, evaluates the learner's thinking ability and judgment ability based on the analysis results in accordance with the goal (purpose). The storage unit, for example, stores the data long-term using the storage 32 of the data processing device 12. === Hard Collateral 1-3 === Each of the multiple elements including the collection unit, analysis unit, evaluation unit, and storage 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 on the learner's notes and behavior using the camera 42 and microphone 238 of the headset-type terminal 314, and digitizes the data using the control unit 46A. The analysis unit is realized, for example, by the specific processing unit 290 of the data processing device 12, and analyzes the collected data to evaluate the learner's thinking ability and judgment ability. The evaluation unit is realized, for example, by the specific processing unit 290 of the data processing device 12, and evaluates the learner's thinking ability and judgment ability based on the analysis results in relation to the goal (purpose). The storage unit stores the data for a long period of time using, for example, the storage 32 of the data processing device 12. === Hard Collateral 1-4 === Each of the multiple elements including the collection unit, analysis unit, evaluation unit, and storage 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 on the learner's note-taking and behavior using the camera 42 and microphone 238 of the robot 414, and digitizes the data using the control unit 46A. The analysis unit is realized, for example, by the specific processing unit 290 of the data processing device 12, and analyzes the collected data to evaluate the learner's thinking ability and judgment ability. The evaluation unit is realized, for example, by the specific processing unit 290 of the data processing device 12, and evaluates the learner's thinking ability and judgment ability based on the analysis results in relation to the goal (purpose). The storage unit stores the data for a long period of time using, for example, the storage 32 of the data processing device 12.

[0078] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0079] When collecting learner data, the collection unit can customize the collection method based on the learner's learning style. For example, for visual learners, data collection can be focused on visual content, and for auditory learners, data collection can be focused on audio recordings. Also, for experiential learners, data collection can be focused on recording actual actions and experiments. This makes it possible to collect data according to the learner's learning style, and more accurate evaluations can be expected.

[0080] The evaluation unit can estimate the learner's emotions and adjust the evaluation feedback method based on the estimated emotions. For example, if the learner is feeling down, feedback including encouraging words can be provided to help the learner gain confidence. If the learner is excited, feedback encouraging further challenges can be provided. Furthermore, if the learner is relaxed, detailed feedback can be provided to encourage deeper understanding. This provides appropriate feedback according to the learner's emotions, improving learning effectiveness.

[0081] When analyzing learner data, the analysis unit can evaluate the reliability of the data and prioritize analysis of highly reliable data. For example, it can evaluate reliability based on the data collection method and collection environment, and analyze highly reliable data in detail. It can also analyze data with low reliability using complementary data. This improves the accuracy of the analysis results and enables more reliable evaluation.

[0082] The collection unit can estimate the learner's emotions and adjust the data collection method based on the estimated emotions. For example, if the learner is feeling stressed, data can be collected in a relaxing environment. Also, if the learner is concentrating, data can be collected in a quiet environment so as not to disturb the learner's concentration. Furthermore, if the learner is excited, data can be collected to take advantage of the learner's excitement and elicit creative ideas. This makes it possible to collect appropriate data according to the learner's emotions.

[0083] When evaluating a learner's data, the evaluation unit can adjust the level of detail of the evaluation based on the learner's level of goal achievement. For example, if goal achievement is high, a detailed evaluation can be conducted and specific advice for further growth can be provided. On the other hand, if goal achievement is low, a brief evaluation can be conducted that focuses on basic areas for improvement. This allows the learner to receive an appropriate evaluation according to their level of goal achievement, improving their learning effectiveness.

[0084] The analysis unit can estimate the learner's emotions and adjust the visualization method of the analysis based on the estimated emotions. For example, if the learner is relaxed, the analysis results can be visualized using detailed graphs and charts. If the learner is in a hurry, the analysis results can be visualized using simple icons and symbols. Furthermore, if the learner is excited, the analysis results can be visualized using colorful visuals. This provides appropriate visualizations according to the learner's emotions and facilitates understanding of the analysis results.

[0085] When collecting data on a learner, the collection unit can optimize the collection method based on the learner's learning history. For example, it can prioritize the application of collection methods that have been effective in the past and avoid methods that have been less effective. It can also adjust the collection method according to the learner's progress to collect optimal data. This enables efficient data collection based on the learner's learning history.

[0086] The evaluation unit can estimate the learner's emotions and adjust the timing of evaluation based on the estimated emotions. For example, if the learner is relaxed, it can select the timing to provide a detailed evaluation. If the learner is in a hurry, it can quickly provide a concise evaluation. Furthermore, if the learner is excited, it can take advantage of the learner's excitement to provide proactive feedback. This provides appropriate evaluation timing according to the learner's emotions, improving learning effectiveness.

[0087] When analyzing learner data, the analysis unit can select an analysis algorithm taking into account the diversity of the data. For example, a natural language processing algorithm can be applied to text data, and an image recognition algorithm can be applied to image data. It is also possible to apply an audio analysis algorithm to audio data, and a video analysis algorithm to video data. This enables appropriate analysis according to the diversity of the data, improving the accuracy of the analysis results.

[0088] The evaluation unit can estimate the learner's emotions and adjust the content of the evaluation feedback based on the estimated emotions. For example, if the learner is relaxed, detailed feedback can be provided to help the learner understand deeply. If the learner is in a hurry, concise feedback that focuses on the main points can be provided. Furthermore, if the learner is excited, proactive feedback can be provided by taking advantage of that excitement. This provides appropriate feedback according to the learner's emotions, improving learning effectiveness.

[0089] The processing flow of the second embodiment will be briefly explained below.

[0090] Step 1: The collection unit collects data on learners' note-taking and behavior. Specifically, this includes handwritten notes, digital notes, audio recordings, video recordings, etc. The collection unit digitizes and reads handwritten notes using scanning technology. It can also directly read notes submitted in digital format. It can also analyze audio recordings and convert them into text data. For example, handwritten notes can be scanned with a high-resolution scanner and converted into text information using OCR technology. Digital notes submitted in a specific file format can be directly read. Audio recordings can be converted into text data using voice recognition technology. Step 2: The analysis unit analyzes the data collected by the collection unit and evaluates the learner's thinking ability and judgment. The analysis is based on criteria such as logical thinking, problem-solving ability, and decision-making accuracy. For example, text mining technology can be used to analyze note-taking to evaluate thinking ability and judgment. Audio analysis technology can also be used to analyze audio recordings to evaluate thinking ability and judgment. Furthermore, video recordings can be analyzed to evaluate the learner's behavioral patterns. Step 3: The evaluation section evaluates the results of the analysis section against the goals. The evaluation is based on criteria such as logical thought process, creative solutions, and goal achievement. For example, the evaluation evaluates logical consistency and understanding of causal relationships, originality and feasibility, and the rate of goal achievement and progress. Step 4: The storage unit stores the data collected by the collection unit for the long term. The data is stored using cloud storage or a physical storage method. For example, the data is stored for the long term using cloud storage. Physical storage methods include an external hard disk or tape drive.

[0091] 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.

[0092] 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 the generative AI include a neural network (NN) and a neural network (NN). 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 (e.g., still image data or video data). 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 one or more data formats of voice data, text data, image data, etc. 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 may perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processing of each of the above-mentioned parts is performed by an AI, the processing may be performed in part or entirely by the AI, but is not limited to these examples. The processing performed by an AI including the generative AI may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by an AI including the generative AI.

[0093] 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.

[0094] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0095] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0096] 3, the data processing system 210 includes a data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0097] 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.

[0098] 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.

[0099] 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.

[0100] 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).

[0101] 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.

[0102] 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.

[0103] 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.

[0104] 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.

[0105] 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.

[0106] 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.

[0107] 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.

[0108] 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 including an instruction, as well as inference data such as audio data indicating speech, text data indicating text, and image data indicating an image (e.g., still image data or video data). 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 one or more data formats, such as audio data, text data, and image 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 models 58 include AIs other than the generation AI. Examples of AIs other than the generation AI include, but are not limited to, linear regression, logistic regression, decision trees, random forests, support vector machines (SVMs), k-means clustering, convolutional neural networks (CNNs), recurrent neural networks (RNNs), generative adversarial networks (GANs), and naive Bayes. These AIs can perform various types of processing, but are not limited to these examples. The AI ​​may also be an AI agent. When the processing of each of the above-described parts is performed by an AI, the processing may be performed in part or entirely by the AI, but is not limited to these examples. Processing performed by an AI, including the generation AI, may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by an AI, including the generation AI.

[0109] 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.

[0110] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

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

[0112] 5, the data processing system 310 includes the data processing device 12 and a headset type terminal 314. An example of the data processing device 12 is a server.

[0113] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0114] The headset type terminal 314 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication I / F 44, and a display 343. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, the camera 42, and the display 343 are also connected to the bus 52.

[0115] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0116] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0117] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0118] Fig. 6 shows an example of the main functions of the data processing device 12 and the headset type terminal 314. As shown in Fig. 6, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0119] The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0120] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0121] In the headset type terminal 314, the 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.

[0122] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0123] The specific processing unit 290 transmits the result of the specific processing to the headset type terminal 314. In the headset type terminal 314, the control unit 46A causes the speaker 240 and the display 343 to output the result of the specific processing. The microphone 238 acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0124] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt including an instruction, as well as inference data such as audio data indicating speech, text data indicating text, and image data indicating an image (e.g., still image data or video data). 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 one or more data formats, such as audio data, text data, and image 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 models 58 include AIs other than the generation AI. Examples of AIs other than the generation AI include, but are not limited to, linear regression, logistic regression, decision trees, random forests, support vector machines (SVMs), k-means clustering, convolutional neural networks (CNNs), recurrent neural networks (RNNs), generative adversarial networks (GANs), and naive Bayes. These AIs can perform various types of processing, but are not limited to these examples. The AI ​​may also be an AI agent. When the processing of each of the above-described parts is performed by an AI, the processing may be performed in part or entirely by the AI, but is not limited to these examples. Processing performed by an AI, including the generation AI, may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by an AI, including the generation AI.

[0125] The data processing system 310 according to the third embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 310 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the headset type terminal 314, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the headset type terminal 314. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the headset type terminal 314 or an external device, etc., and the headset type terminal 314 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0126] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0127] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0128] 7, a 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.

[0129] 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.

[0130] 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.

[0131] 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.

[0132] 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).

[0133] 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.

[0134] 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.

[0135] 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.

[0136] 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.

[0137] 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.

[0138] 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.

[0139] 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.

[0140] 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.

[0141] 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 including an instruction, as well as inference data such as audio data indicating speech, text data indicating text, and image data indicating an image (e.g., still image data or video data). 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 one or more data formats, such as audio data, text data, and image 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 models 58 include AIs other than the generation AI. Examples of AIs other than the generation AI include, but are not limited to, linear regression, logistic regression, decision trees, random forests, support vector machines (SVMs), k-means clustering, convolutional neural networks (CNNs), recurrent neural networks (RNNs), generative adversarial networks (GANs), and naive Bayes. These AIs can perform various types of processing, but are not limited to these examples. The AI ​​may also be an AI agent. When the processing of each of the above-described parts is performed by an AI, the processing may be performed in part or entirely by the AI, but is not limited to these examples. Processing performed by an AI, including the generation AI, may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by an AI, including the generation AI.

[0142] 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.

[0143] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0144] 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.

[0145] 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.

[0146] 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.

[0147] 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).

[0148] 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 indicated, and when they approach the ideal, a state of pleasure is indicated. 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 indicated, and when they approach the ideal, a state of pleasure is indicated. 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.

[0149] 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."

[0150] 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.

[0151] 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.

[0152] 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.

[0153] 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.

[0154] 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.

[0155] 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.

[0156] 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.

[0157] 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.

[0158] 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.

[0159] 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.

[0160] 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.

[0161] 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.

[0162] [Explanation of symbols]

[0163] 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 section that collects data on learners' notes and behavior; an analysis unit that analyzes the data collected by the collection unit and evaluates thinking ability and judgment ability; an evaluation unit that performs evaluation based on the evaluation result obtained by the analysis unit in comparison with a target; a storage unit that stores the data collected by the collection unit for a long period of time; A system characterized by:

2. The evaluation unit Evaluation is based on logical thought processes, creative solutions, and goal achievement criteria 2. The system of claim 1.

3. The collecting unit Estimate learner emotions and adjust data collection timing based on the estimated learner emotions.

2. The system of claim 1.

4. The collecting unit Analyze learners' past note-taking and behavioral patterns and select an appropriate collection method 2. The system of claim 1.

5. The collecting unit As data is collected, it can be filtered based on the learner's current learning status or areas of interest.

2. The system of claim 1.

6. The collecting unit Estimate learner emotions and prioritize data collection based on the estimated learner emotions 2. The system of claim 1.

7. The collecting unit When collecting data, prioritize the collection of relevant data based on the learner's geographic location 2. The system of claim 1.

8. The collecting unit During data collection, analyze learners' social media activity and collect relevant data.

2. The system of claim 1.

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