system

Abstract

The system according to this embodiment aims to accurately determine whether or not a work created by a generative AI infringes on copyright. [Solution] The system according to the embodiment comprises a reception unit, an evaluation unit, a calculation unit, and a judgment unit. The reception unit uploads the materials. The evaluation unit inspects the materials uploaded by the reception unit and evaluates the copy-paste rate of the information existing on the Web. The calculation unit calculates the generation AI creation rate based on the copy-paste rate evaluated by the evaluation unit. The judgment unit uses the generation AI creation rate calculated by the calculation unit as a basis for making decisions such as resubmission or rejection.

Description

Technical Field

[0006] , ,

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

Background Art

[0002] Patent Document 1 discloses a method for controlling a persona chatbot performed by at least one processor, the method including: receiving a user utterance; adding the user utterance to a prompt including an instruction sentence related to an explanation of a character of the chatbot; encoding the prompt; and inputting the encoded prompt into a language model to generate a chatbot utterance as a response to the user utterance.

Prior Art Documents

Patent Documents

[0003]

Patent Document 1

Summary of the Invention

Problems to be Solved by the Invention

[0004] In the prior art, there is a problem that it is difficult to accurately determine the presence or absence of creations by generative AI and copyright infringement.

[0005] The system according to the embodiment aims to accurately determine the presence or absence of creations by generative AI and copyright infringement.

Means for Solving the Problems

[0006] The system according to this embodiment comprises a reception unit, an evaluation unit, a calculation unit, and a judgment unit. The reception unit uploads the materials. The evaluation unit inspects the materials uploaded by the reception unit and evaluates the copy-paste rate of information existing on the web. The calculation unit calculates the generation AI creation rate based on the copy-paste rate evaluated by the evaluation unit. The judgment unit uses the generation AI creation rate calculated by the calculation unit as a basis for making decisions such as resubmission or rejection. [Effects of the Invention]

[0007] The system according to this embodiment can accurately determine whether a work created by the AI ​​is infringing on copyright. [Brief explanation of the drawing]

[0008] [Figure 1] This is a conceptual diagram showing an example of the configuration of a data processing system according to the first embodiment. [Figure 2] This is a conceptual diagram showing an example of the essential functions of a data processing device and a smart device according to the first embodiment. [Figure 3] This is a conceptual diagram showing an example of the configuration of a data processing system according to the second embodiment. [Figure 4] This is a conceptual diagram showing an example of the main functions of a data processing device and smart glasses according to the second embodiment. [Figure 5] This is a conceptual diagram showing an example of the configuration of a data processing system according to the third embodiment. [Figure 6] This is a conceptual diagram showing an example of the main functions of a data processing device and a headset-type terminal according to the third embodiment. [Figure 7] This is a conceptual diagram showing an example of the configuration of a data processing system according to the fourth embodiment. [Figure 8] This is a conceptual diagram showing an example of the main functions of a data processing device and a robot according to the fourth embodiment. [Figure 9] This shows an emotion map where multiple emotions are mapped. [Figure 10] This shows an emotion map where multiple emotions are mapped. [Modes for carrying out the invention]

[0009] Hereinafter, an example of an embodiment of the system relating to the technology of this disclosure will be described with reference to the attached drawings.

[0010] First, let's explain the terminology used in the following explanation.

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

[0012] In the following embodiments, signed RAM (Random Access Memory) is a memory that temporarily stores information and is used as work memory by the processor.

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

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

[0015] In the following embodiments, "A and / or B" is synonymous with "at least one of A and B". That is, "A and / or B" means that it may be only A, only B, or a combination of A and B. Also, in this specification, when expressing three or more matters connected by "and / or", the same concept as "A and / or B" is applied.

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

[0017] As shown in FIG. 1, the 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, the RAM 30, and the storage 32 are connected to a bus 34. Also, the database 24 and the communication I / F 26 are 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 comprises a computer 36, a receiving device 38, an output device 40, a camera 42, and a communication interface 44. The computer 36 comprises a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The receiving device 38, output device 40, and camera 42 are also connected to the bus 52.

[0020] The reception device 38 is equipped with a touch panel 38A and a microphone 38B, and accepts user input. The touch panel 38A accepts user input via touch by detecting contact with an object (e.g., a pen or finger). The microphone 38B accepts user input via voice by detecting the user's voice. The control unit 46A transmits data indicating the user input received by the touch panel 38A and microphone 38B to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 (see Figure 2) acquires the data indicating the user input.

[0021] The output device 40 includes a display 40A and a speaker 40B, and presents data to the user by outputting the data in a form perceptible to the user (e.g., audio and / or text). The display 40A displays visible information such as text and images according to instructions from the processor 46. The speaker 40B outputs audio according to instructions from the processor 46. The camera 42 is a small digital camera equipped with an optical system such as a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor.

[0022] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various types of information between processor 46 and processor 28 via network 54.

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

[0024] As shown in Figure 2, in the data processing device 12, a specific processing 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" related to the technology of this 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 processing is realized by the processor 28 operating as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

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

[0026] In the smart device 14, specific processing is performed by the processor 46. The storage 50 stores a specific processing program 60. The specific processing program 60 is used in conjunction 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 a control unit 46A according to the specific processing program 60 executed on the RAM 48. The smart device 14 also has a data generation model 58 and an emotion identification model 59, similar to the data generation model and emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0027] Furthermore, other devices besides the data processing device 12 may also 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 processing results (such as prediction results) using the data generation model 58 by communicating with the server device having the data generation model 58. The data processing device 12 may also be a server device or a terminal device owned by a user (e.g., a mobile phone, robot, home appliance, etc.). Next, an example of processing by the data processing system 10 according to the first embodiment will be described.

[0028] (Example of form 1) The AI ​​generation checker system according to an embodiment of the present invention is a specialized service that determines whether a document created using AI generation infringes copyright. The AI ​​generation checker system allows users to upload documents, and the AI ​​generation checker evaluates the copy-paste rate and AI-generated text, calculating the AI ​​generation rate. The results can be used as a basis for decisions such as resubmission or rejection. For example, the AI ​​generation checker system allows users to upload documents. Next, the AI ​​generation checker system inspects the documents, evaluating and scoring the copy-paste rate of information existing on the web and the text that appears to have been generated by AI. It calculates the AI ​​generation rate, which can then be used as a basis for decisions such as resubmission or rejection. For example, if the AI ​​generation rate is 70% or higher, resubmission or rejection may be required. This service targets content creators, publishers, advertising agencies, corporate legal departments, and educational institutions. Challenges faced by these targets include the time and cost involved in verifying whether created content infringes copyright on other works, the need for specialized knowledge, and the inability to determine whether the creation reflects the submitter's own abilities. To address this challenge, generative AI automatically analyzes content, checking for similarities to other works and the presence of citations. This reduces the risk of copyright infringement and misrepresentation of AI-generated content. Furthermore, because the AI ​​makes judgments based on the latest copyright laws, legal risks are minimized. The market size for this service is expected to reach several hundred billion yen, driven by the rapid growth of the content creation and legal services markets. Now is the time to enter this market, as generative AI technology has advanced to the point where it can analyze content similarity and citations with high accuracy. This will create an environment where everyone can create and publish content with peace of mind, minimizing the risk of copyright infringement. This will promote creative activity and create a healthy digital content market. As a result, the generative AI checker system can determine whether documents created using generative AI infringe copyright.

[0029] The generative AI checker system according to this embodiment comprises a reception unit, an evaluation unit, a calculation unit, and a judgment unit. The reception unit receives materials from the user. The materials include, but are not limited to, text documents, images, and audio files. The reception unit receives the materials uploaded by the user and stores them in the system. The evaluation unit inspects the uploaded materials and evaluates the copy-paste rate of information existing on the Web. The evaluation unit calculates the similarity between the materials and the information on the Web using algorithms such as cosine similarity and the Jaccard coefficient. The evaluation unit can also calculate the similarity between the materials and the information on the Web using the Jaccard coefficient. Furthermore, the evaluation unit analyzes the features of the text using a model that has learned the features of the text generated by the generative AI. The evaluation unit analyzes the writing style and vocabulary usage patterns using a model that has learned the features of the text generated by the generative AI. The evaluation unit analyzes stylistic features using a model that has learned the characteristics of text generated by the generative AI. The evaluation unit can also analyze vocabulary usage patterns using a model that has learned the characteristics of text generated by the generative AI. The calculation unit calculates the generative AI creation rate based on the copy-paste rate evaluated by the evaluation unit. The calculation unit calculates the generative AI creation rate by combining the copy-paste rate and the evaluation results of the AI-generated text. The calculation unit can also combine the copy-paste rate and the evaluation results of the AI-generated text to calculate the generative AI creation rate. The judgment unit uses the generative AI creation rate calculated by the calculation unit as a basis for making decisions such as resubmission or rejection. For example, the judgment unit decides to resubmit or reject the work if the generative AI creation rate is 70% or higher. Furthermore, the judgment unit can also reject a submission if the AI ​​generation rate is 70% or higher. This allows the AI ​​generation checker system according to the embodiment to perform all steps from uploading materials to evaluation, calculating the AI ​​generation rate, and making a judgment in a consistent manner.

[0030] The reception desk receives materials uploaded by users. These materials include, but are not limited to, text documents, images, and audio files. The reception desk receives the materials uploaded by users and stores them within the system. Specifically, when a user uploads materials through the system interface, the reception desk receives the materials, converts them to the appropriate format, and stores them in the database. The reception desk automatically retrieves metadata of the uploaded materials (e.g., file name, upload date and time, file size, etc.) and records this information in the database. Furthermore, the reception desk assigns appropriate tags according to the type and content of the materials to enable efficient subsequent processing. For example, text documents are tagged "text," images are tagged "image," and audio files are tagged "audio." This allows the reception desk to centrally manage the diverse materials uploaded by users and improve the overall processing efficiency of the system. The reception desk also has a checking function to verify the integrity and completeness of uploaded materials, and can notify the user and prompt them to re-upload if a file is corrupted or uploaded in an incorrect format. This ensures that the reception department can guarantee the quality of the documents entered into the system, and that subsequent evaluation and calculation processes are carried out accurately.

[0031] The evaluation unit inspects the uploaded materials and assesses the copy-paste rate of information existing on the web. The evaluation unit calculates the similarity between the materials and the information on the web using algorithms such as cosine similarity and the Jaccard coefficient. Specifically, the evaluation unit first processes the content of the materials using a text analysis engine to generate document feature vectors. Next, it compares these with a database collected from information on the web and quantifies the similarity between the materials and the information on the web by calculating cosine similarity. Cosine similarity is a method of evaluating similarity by calculating the angle between two vectors; a value closer to 1 indicates greater similarity. The Jaccard coefficient is a method of calculating the ratio of the common part to the combined part of two sets, and this is also used to evaluate similarity. Furthermore, the evaluation unit analyzes the features of the text using a model that has learned the features of text generated by the generative AI. Specifically, it analyzes the writing style and vocabulary usage patterns using a deep learning model that has learned the features of text generated by the generative AI. This model is trained using a large amount of generated AI documents as learning data, and can detect the unique writing style and vocabulary patterns of generated AI with high accuracy. Based on these analysis results, the evaluation unit assesses the likelihood that the document was created by a generated AI and performs a comprehensive evaluation in conjunction with the plagiarism rate. This allows the evaluation unit to assess the originality of the document and the degree of involvement of generated AI with high accuracy.

[0032] The calculation unit calculates the generation AI creation rate based on the copy-paste rate evaluated by the evaluation unit. For example, the calculation unit calculates the generation AI creation rate by combining the copy-paste rate and the evaluation results of the AI-generated text. Specifically, the calculation unit integrates the copy-paste rate and the feature analysis results of the generated AI text provided by the evaluation unit and calculates the generation AI creation rate using its own algorithm. This algorithm is designed so that the generation AI creation rate is higher when the copy-paste rate is high or when strong features of the generated AI text are detected. For example, if the copy-paste rate is 50% and strong features of the generated AI text are detected, the generation AI creation rate may be calculated as 80%. Based on these calculation results, the calculation unit quantitatively evaluates to what extent the document was created by the generation AI. Furthermore, the calculation unit can improve the accuracy of the evaluation by adjusting the parameters and weights used in the calculation process of the generation AI creation rate. For example, by optimizing the parameters according to a specific field or type of document, it is possible to calculate a more accurate generation AI creation rate. In addition, the calculation unit can continuously improve the accuracy of the algorithm by utilizing past evaluation data and respond to the latest generation AI technologies. This allows the calculation unit to calculate the AI ​​generation rate for generating data with high accuracy, thereby improving the overall evaluation accuracy of the system.

[0033] The decision-making unit uses the generation AI creation rate calculated by the calculation unit as a basis for deciding whether to resubmit or reject the document. For example, if the generation AI creation rate is 70% or higher, the decision-making unit will decide to resubmit or reject the document. Specifically, if the generation AI creation rate exceeds a certain threshold, the decision-making unit will automatically send a notification to the user requesting resubmission. For example, if the generation AI creation rate is 70% or higher, it will send the user a message such as "You need to resubmit your document" and guide them through the resubmission procedure. If the generation AI creation rate is 80% or higher, it will decide to reject the document and notify the user that "Your document has been rejected." The decision-making unit can flexibly set these decision criteria and adjust the thresholds according to specific conditions and situations. For example, by setting different thresholds depending on the type and use of the document, such as academic papers or business reports, more appropriate decisions can be made. Furthermore, the decision-making unit can collect feedback from users and use it to improve the decision criteria and processes. For example, it can review the decision criteria and improve the system based on opinions and requests from users who have received notifications of resubmission or rejection. This allows the decision-making unit to make appropriate decisions based on the AI ​​generation rate, thereby improving the overall reliability of the system and user satisfaction.

[0034] The evaluation unit can evaluate the plagiarism rate using an algorithm that calculates the similarity between the document and the information on the web. For example, the evaluation unit can use cosine similarity to calculate the similarity between the document and the information on the web. The evaluation unit can also use the Jaccard coefficient to calculate the similarity between the document and the information on the web. This improves the accuracy of the plagiarism rate evaluation by calculating the similarity with the information on the web. Some or all of the above processing in the evaluation unit may be performed using AI, for example, or without AI. For example, the evaluation unit can evaluate the plagiarism rate using an AI model that takes the document and the information on the web as input and outputs a similarity score.

[0035] The evaluation unit can analyze the characteristics of text using a model that has learned the characteristics of text generated by the generative AI. For example, the evaluation unit can analyze the style and vocabulary usage patterns using a model that has learned the characteristics of text generated by the generative AI. For example, the evaluation unit can analyze the style characteristics using a model that has learned the characteristics of text generated by the generative AI. The evaluation unit can also analyze the vocabulary usage patterns using a model that has learned the characteristics of text generated by the generative AI. This improves the accuracy of the evaluation by analyzing the characteristics of text generated by the generative AI. Some or all of the above processing in the evaluation unit may be performed using AI, for example, or without AI. For example, the evaluation unit can analyze the characteristics of text using a model that has learned the characteristics of text generated by the generative AI.

[0036] The calculation unit can calculate the AI ​​generation rate by combining the copy-paste rate and the evaluation results of the AI-generated text. The calculation unit can, for example, combine the copy-paste rate and the evaluation results of the AI-generated text to calculate the AI ​​generation rate. The calculation unit can, for example, combine the copy-paste rate and the evaluation results of the AI-generated text to calculate the AI ​​generation rate. The calculation unit can also combine the copy-paste rate and the evaluation results of the AI-generated text to calculate the AI ​​generation rate. By combining the copy-paste rate and the evaluation results of the AI-generated text, the accuracy of calculating the AI ​​generation rate is improved. Some or all of the above processing in the calculation unit may be performed using AI, for example, or without using AI. For example, the calculation unit can calculate the AI ​​generation rate using an AI model that takes the copy-paste rate and the evaluation results of the AI-generated text as input and outputs the AI ​​generation rate.

[0037] The decision unit can determine whether to resubmit or reject an entry if the AI ​​generation rate is 70% or higher. For example, the decision unit may decide to resubmit an entry if the AI ​​generation rate is 70% or higher. The decision unit may also decide to reject an entry if the AI ​​generation rate is 70% or higher. This allows for appropriate evaluation by determining whether to resubmit or reject an entry when the AI ​​generation rate is high. Some or all of the above processing in the decision unit may be performed using AI, for example, or without AI. For example, the decision unit can make a decision using an AI model that takes the AI ​​generation rate as input and outputs a decision to resubmit or reject an entry.

[0038] The reception desk can analyze a user's past upload history and select the optimal upload method. For example, the reception desk may prioritize suggesting upload methods (file format, upload time, etc.) that the user has frequently used in the past. For example, the reception desk may suggest the optimal upload method for a specific time period based on the user's past upload history. The reception desk can also select the optimal upload method based on the types of materials the user has uploaded in the past. In this way, by analyzing past upload history, the reception desk can provide the optimal upload method. Some or all of the above processing in the reception desk may be performed using AI, for example, or not using AI. For example, the reception desk can input the user's past upload history data into a generating AI and have the generating AI select the optimal upload method.

[0039] The reception desk can filter uploaded materials based on the user's current projects and areas of interest. For example, the reception desk can filter to upload only materials related to the user's current project. For example, the reception desk can prioritize uploading highly relevant materials based on the user's areas of interest. The reception desk can also filter to upload appropriate materials according to the progress of the user's project. This allows for the uploading of highly relevant materials by filtering based on the user's projects and areas of interest. Some or all of the above processing in the reception desk may be performed using AI, for example, or not using AI. For example, the reception desk can input the user's project information and area of ​​interest data into a generating AI and have the generating AI perform the filtering.

[0040] The reception desk can prioritize uploading highly relevant materials by considering the user's geographical location when uploading materials. For example, if the user is in a specific region, the reception desk will prioritize uploading materials related to that region. The reception desk can also filter highly relevant materials based on the user's geographical location. Furthermore, if the user is on the move, the reception desk can upload the most suitable materials based on their current location. This allows for the priority uploading of highly relevant materials by considering geographical location. Some or all of the above processing in the reception desk may be performed using AI, for example, or without AI. For example, the reception desk can input the user's geographical location data into a generating AI and have the generating AI select highly relevant materials.

[0041] The reception desk can analyze a user's social media activity when they upload materials and upload relevant materials. For example, the reception desk can upload relevant materials based on the user's social media activity. For example, the reception desk can upload the most relevant materials based on the information the user has shared on social media. The reception desk can also upload highly relevant materials based on the user's areas of interest on social media. In this way, by analyzing social media activity, highly relevant materials can be uploaded. Some or all of the above processing in the reception desk may be performed using AI, for example, or not using AI. For example, the reception desk can input the user's social media activity data into a generating AI and have the generating AI select relevant materials.

[0042] The evaluation unit can adjust the level of detail in its evaluation based on the importance of the materials. For example, the evaluation unit will perform a detailed evaluation and check every detail for important materials. For example, it will perform a standard evaluation for general materials. The evaluation unit can also perform a simplified evaluation for materials of low importance. This allows for efficient evaluation by adjusting the level of detail based on the importance of the materials. Some or all of the above processes in the evaluation unit may be performed using AI, for example, or without AI. For example, the evaluation unit can input material importance data into a generating AI and have the generating AI perform the adjustment of the level of detail in the evaluation.

[0043] The evaluation unit can apply different evaluation algorithms depending on the category of the material during the evaluation process. For example, in the case of an academic paper, the evaluation unit can apply a specialized evaluation algorithm. For example, in the case of a business report, the evaluation unit can apply a business-specific evaluation algorithm. Furthermore, in the case of creative content, the evaluation unit can also apply an evaluation algorithm that emphasizes creativity. This allows for appropriate evaluation by applying an evaluation algorithm according to the category of the material. Some or all of the above processing in the evaluation unit may be performed using AI, for example, or without AI. For example, the evaluation unit can input the category data of the material into a generating AI and have the generating AI execute the application of the evaluation algorithm.

[0044] The evaluation unit can determine the priority of evaluations based on the submission timing of the materials during the evaluation process. For example, the evaluation unit may prioritize evaluating materials with approaching deadlines. For example, the evaluation unit may prioritize evaluating materials submitted earlier. The evaluation unit may also postpone evaluating materials submitted later. This allows for efficient evaluation by determining the priority of evaluations based on submission timing. Some or all of the above processes in the evaluation unit may be performed using AI, for example, or without AI. For example, the evaluation unit can input the submission timing data of the materials into a generating AI and have the generating AI determine the evaluation priority.

[0045] The evaluation unit can adjust the order of evaluation based on the relevance of the materials during the evaluation process. For example, the evaluation unit may prioritize evaluating materials with highly relevant content, or postpone evaluating materials with less relevant content. The evaluation unit can also dynamically adjust the order of evaluation based on the relevance of the materials. This allows for more efficient evaluation by adjusting the order of evaluation based on the relevance of the materials. Some or all of the above processing in the evaluation unit may be performed using AI, for example, or without AI. For example, the evaluation unit can input data on the relevance of the materials into a generating AI and have the generating AI perform the adjustment of the evaluation order.

[0046] The calculation unit can adjust the weighting when combining the copy-paste rate and the evaluation result of the AI-generated text during calculation. For example, if the copy-paste rate is high, the calculation unit reduces the weight of the evaluation result of the AI-generated text. For example, if the evaluation result of the AI-generated text is high, the calculation unit reduces the weight of the copy-paste rate. The calculation unit can also dynamically adjust the balance between the copy-paste rate and the evaluation result of the AI-generated text. By adjusting the weighting of the copy-paste rate and the evaluation result of the AI-generated text, it becomes possible to calculate an appropriate generation AI creation rate. Some or all of the above processing in the calculation unit may be performed using AI, for example, or without AI. For example, the calculation unit can input the copy-paste rate and the evaluation result data of the AI-generated text into the generation AI and have the generation AI perform the weighting adjustment.

[0047] The calculation unit can apply different calculation algorithms to each category of material during the calculation process. For example, in the case of academic papers, the calculation unit applies a specialized calculation algorithm. For example, in the case of business reports, the calculation unit applies a calculation algorithm specifically tailored to business. Furthermore, in the case of creative content, the calculation unit can also apply a calculation algorithm that emphasizes creativity. By applying different calculation algorithms to each category of material, it becomes possible to calculate an appropriate generation AI generation rate. Some or all of the above-described processes in the calculation unit may be performed using AI, for example, or without AI. For example, the calculation unit can input the category data of the material into the generation AI and have the generation AI execute the application of the calculation algorithm.

[0048] The calculation unit can determine the priority of the generation AI creation rate based on the submission date of the documents during calculation. For example, the calculation unit may prioritize calculating the generation AI creation rate for documents with approaching deadlines. For example, the calculation unit may prioritize calculating the generation AI creation rate for documents with earlier submission dates. The calculation unit may also postpone calculating the generation AI creation rate for documents with later submission dates. This allows for efficient calculation by determining the priority of the generation AI creation rate based on the submission date. Some or all of the above processing in the calculation unit may be performed using AI, for example, or without using AI. For example, the calculation unit may input the document submission date data into the generation AI and have the generation AI perform the determination of the priority of the generation AI creation rate.

[0049] The calculation unit can adjust the order of AI generation rates based on the relevance of the materials during calculation. For example, the calculation unit prioritizes calculating the AI ​​generation rate for materials with high relevance. For example, the calculation unit postpones calculating the AI ​​generation rate for materials with low relevance. The calculation unit can also dynamically adjust the order of AI generation rates based on the relevance of the materials. This allows for efficient calculation by adjusting the order of AI generation rates based on the relevance of the materials. Some or all of the above processing in the calculation unit may be performed using AI, for example, or without AI. For example, the calculation unit can input material relevance data into the generating AI and have the generating AI perform the adjustment of the order of AI generation rates.

[0050] The decision-making unit can make the optimal decision by referring to past data on the generation rate of the generated AI when making a decision. For example, the decision-making unit can make a decision to resubmit or reject based on past data on the generation rate of the generated AI. For example, the decision-making unit can extract a specific pattern from past data and make the optimal decision. The decision-making unit can also analyze past data and dynamically adjust the criteria for resubmission or rejection. This makes it possible to make the optimal decision by referring to past data. Some or all of the above processing in the decision-making unit may be performed using AI, for example, or without using AI. For example, the decision-making unit can input past generation rate data of the generated AI into the generating AI and cause the generating AI to make the optimal decision.

[0051] The decision-making unit can apply different criteria to each category of material when making a decision. For example, in the case of an academic paper, the decision-making unit applies specialized criteria. For example, in the case of a business report, the decision-making unit applies business-specific criteria. Furthermore, in the case of creative content, the decision-making unit can also apply criteria that emphasize creativity. This allows for appropriate judgments by applying different criteria to each category of material. Some or all of the above processing in the decision-making unit may be performed using AI, for example, or without AI. For example, the decision-making unit can input material category data into a generating AI and have the generating AI perform the application of the judgment criteria.

[0052] The decision-making unit can determine the priority of decisions based on the submission dates of the documents. For example, the decision-making unit may prioritize documents with approaching deadlines. For example, it may prioritize documents with earlier submission dates. The decision-making unit may also postpone decisions regarding documents with later submission dates. This allows for efficient decision-making by prioritizing decisions based on submission dates. Some or all of the above processing in the decision-making unit may be performed using AI, for example, or without AI. For example, the decision-making unit can input document submission date data into a generating AI and have the generating AI determine the priority of decisions.

[0053] The decision-making unit can adjust the order of decisions based on the relevance of the materials during the decision-making process. For example, the decision-making unit may prioritize decisions based on the relevance of the materials' content. For example, it may postpone decisions based on the relevance of the materials' content. The decision-making unit can also dynamically adjust the order of decisions based on the relevance of the materials. This allows for more efficient decision-making by adjusting the order of decisions based on the relevance of the materials. Some or all of the above-described processes in the decision-making unit may be performed using AI, for example, or without AI. For example, the decision-making unit can input data on the relevance of the materials into a generating AI and have the generating AI perform the adjustment of the order of decisions.

[0054] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0055] The generating AI checker system can further analyze the user's past evaluation results and dynamically adjust the evaluation criteria. For example, it can extract specific patterns from past evaluation results and optimize the evaluation criteria. It can learn the characteristics of materials that the user has previously given high ratings to and relax the evaluation criteria for materials with similar characteristics. It can also learn the characteristics of materials that have received low ratings in the past and tighten the evaluation criteria for materials with similar characteristics. This enables dynamic adjustment of evaluation criteria based on past evaluation results, improving the accuracy of the evaluation. Some or all of the above processing in the evaluation unit may be performed using AI, for example, or without AI. For example, the evaluation unit can input past evaluation result data into the generating AI and have the generating AI perform the adjustment of the evaluation criteria.

[0056] The generating AI checker system can further adjust its evaluation criteria to take into account the user's project progress. For example, in the early stages of a project, flexible evaluation criteria can be applied to allow the user to freely experiment with ideas. In the middle stages of the project, standard evaluation criteria can be applied to monitor progress. In the final stages of the project, strict evaluation criteria can be applied to ensure final quality. This allows for adjustment of evaluation criteria according to the project's progress and provides appropriate feedback. Some or all of the above processes in the evaluation unit may be performed using AI, for example, or not. For example, the evaluation unit can input project progress data into the generating AI and have the generating AI perform the adjustment of evaluation criteria.

[0057] The generating AI checker system can also dynamically adjust its evaluation algorithm based on the user's past evaluation results. For example, it can extract specific trends from past evaluation results and optimize the evaluation algorithm. It can learn the characteristics of materials that the user has given high ratings to in the past and relax the evaluation algorithm for materials with similar characteristics. It can also learn the characteristics of materials that have given low ratings in the past and tighten the evaluation algorithm for materials with similar characteristics. This enables dynamic adjustment of the evaluation algorithm based on past evaluation results, improving the accuracy of the evaluation. Some or all of the above processing in the evaluation unit may be performed using AI, for example, or without AI. For example, the evaluation unit can input past evaluation result data into the generating AI and have the generating AI perform the adjustment of the evaluation algorithm.

[0058] The generating AI checker system can further prioritize evaluations by considering the progress of the user's project. For example, if a project deadline is approaching, it will prioritize evaluating materials related to that deadline. In the middle of a project, it will prioritize evaluating materials for progress checks. In the early stages of a project, it can also prioritize evaluating materials for idea generation. This allows for the determination of evaluation priorities according to the project's progress, resulting in efficient evaluation. Some or all of the above-described processes in the evaluation unit may be performed using AI, for example, or not. For example, the evaluation unit can input project progress data into the generating AI and have the generating AI determine the evaluation priorities.

[0059] The generating AI checker system can also dynamically adjust the level of detail of evaluations based on the user's past evaluation results. For example, it can extract specific trends from past evaluation results and optimize the level of detail of the evaluations. For materials that the user has previously given high ratings to, it can perform a detailed evaluation and provide further areas for improvement. Conversely, for materials that have received low ratings in the past, it can perform a simplified evaluation and indicate basic areas for improvement. This enables dynamic adjustment of the level of detail of evaluations based on past evaluation results, thereby improving the accuracy of the evaluations. Some or all of the above processing in the evaluation unit may be performed using AI, for example, or without AI. For example, the evaluation unit can input past evaluation result data into the generating AI and have the generating AI perform the adjustment of the level of detail of the evaluations.

[0060] The following briefly describes the processing flow for example form 1.

[0061] Step 1: The reception desk receives the materials uploaded by the user. These materials may include, but are not limited to, text documents, images, and audio files. The reception desk receives the materials uploaded by the user and stores them in the system. Step 2: The evaluation unit inspects the uploaded materials and assesses the rate of copy-pasting of information found on the web. The evaluation unit uses algorithms such as cosine similarity and Jaccard coefficients to calculate the similarity between the materials and the information on the web. It also uses a model that has learned the characteristics of text generated by the generative AI to analyze the writing style and vocabulary usage patterns. Step 3: The calculation unit calculates the AI ​​generation rate based on the copy-paste rate evaluated by the evaluation unit. The calculation unit combines the copy-paste rate and the evaluation results of the AI-generated text to calculate the AI ​​generation rate. Step 4: The judgment unit uses the generation AI creation rate calculated by the calculation unit as a basis for deciding whether to resubmit or fail the application. For example, if the generation AI creation rate is 70% or higher, the unit will decide to resubmit or fail the application.

[0062] (Example of form 2) The AI ​​generation checker system according to an embodiment of the present invention is a specialized service that determines whether a document created using AI generation infringes copyright. The AI ​​generation checker system allows users to upload documents, and the AI ​​generation checker evaluates the copy-paste rate and AI-generated text, calculating the AI ​​generation rate. The results can be used as a basis for decisions such as resubmission or rejection. For example, the AI ​​generation checker system allows users to upload documents. Next, the AI ​​generation checker system inspects the documents, evaluating and scoring the copy-paste rate of information existing on the web and the text that appears to have been generated by AI. It calculates the AI ​​generation rate, which can then be used as a basis for decisions such as resubmission or rejection. For example, if the AI ​​generation rate is 70% or higher, resubmission or rejection may be required. This service targets content creators, publishers, advertising agencies, corporate legal departments, and educational institutions. Challenges faced by these targets include the time and cost involved in verifying whether created content infringes copyright on other works, the need for specialized knowledge, and the inability to determine whether the creation reflects the submitter's own abilities. To address this challenge, generative AI automatically analyzes content, checking for similarities to other works and the presence of citations. This reduces the risk of copyright infringement and misrepresentation of AI-generated content. Furthermore, because the AI ​​makes judgments based on the latest copyright laws, legal risks are minimized. The market size for this service is expected to reach several hundred billion yen, driven by the rapid growth of the content creation and legal services markets. Now is the time to enter this market, as generative AI technology has advanced to the point where it can analyze content similarity and citations with high accuracy. This will create an environment where everyone can create and publish content with peace of mind, minimizing the risk of copyright infringement. This will promote creative activity and create a healthy digital content market. As a result, the generative AI checker system can determine whether documents created using generative AI infringe copyright.

[0063] The generative AI checker system according to this embodiment comprises a reception unit, an evaluation unit, a calculation unit, and a judgment unit. The reception unit receives materials from the user. The materials include, but are not limited to, text documents, images, and audio files. The reception unit receives the materials uploaded by the user and stores them in the system. The evaluation unit inspects the uploaded materials and evaluates the copy-paste rate of information existing on the Web. The evaluation unit calculates the similarity between the materials and the information on the Web using algorithms such as cosine similarity and the Jaccard coefficient. The evaluation unit can also calculate the similarity between the materials and the information on the Web using the Jaccard coefficient. Furthermore, the evaluation unit analyzes the features of the text using a model that has learned the features of the text generated by the generative AI. The evaluation unit analyzes the writing style and vocabulary usage patterns using a model that has learned the features of the text generated by the generative AI. The evaluation unit analyzes stylistic features using a model that has learned the characteristics of text generated by the generative AI. The evaluation unit can also analyze vocabulary usage patterns using a model that has learned the characteristics of text generated by the generative AI. The calculation unit calculates the generative AI creation rate based on the copy-paste rate evaluated by the evaluation unit. The calculation unit calculates the generative AI creation rate by combining the copy-paste rate and the evaluation results of the AI-generated text. The calculation unit can also combine the copy-paste rate and the evaluation results of the AI-generated text to calculate the generative AI creation rate. The judgment unit uses the generative AI creation rate calculated by the calculation unit as a basis for making decisions such as resubmission or rejection. For example, the judgment unit decides to resubmit or reject the work if the generative AI creation rate is 70% or higher. Furthermore, the judgment unit can also reject a submission if the AI ​​generation rate is 70% or higher. This allows the AI ​​generation checker system according to the embodiment to perform all steps from uploading materials to evaluation, calculating the AI ​​generation rate, and making a judgment in a consistent manner.

[0064] The reception desk receives materials uploaded by users. These materials include, but are not limited to, text documents, images, and audio files. The reception desk receives the materials uploaded by users and stores them within the system. Specifically, when a user uploads materials through the system interface, the reception desk receives the materials, converts them to the appropriate format, and stores them in the database. The reception desk automatically retrieves metadata of the uploaded materials (e.g., file name, upload date and time, file size, etc.) and records this information in the database. Furthermore, the reception desk assigns appropriate tags according to the type and content of the materials to enable efficient subsequent processing. For example, text documents are tagged "text," images are tagged "image," and audio files are tagged "audio." This allows the reception desk to centrally manage the diverse materials uploaded by users and improve the overall processing efficiency of the system. The reception desk also has a checking function to verify the integrity and completeness of uploaded materials, and can notify the user and prompt them to re-upload if a file is corrupted or uploaded in an incorrect format. This ensures that the reception department can guarantee the quality of the documents entered into the system, and that subsequent evaluation and calculation processes are carried out accurately.

[0065] The evaluation unit inspects the uploaded materials and assesses the copy-paste rate of information existing on the web. The evaluation unit calculates the similarity between the materials and the information on the web using algorithms such as cosine similarity and the Jaccard coefficient. Specifically, the evaluation unit first processes the content of the materials using a text analysis engine to generate document feature vectors. Next, it compares these with a database collected from information on the web and quantifies the similarity between the materials and the information on the web by calculating cosine similarity. Cosine similarity is a method of evaluating similarity by calculating the angle between two vectors; a value closer to 1 indicates greater similarity. The Jaccard coefficient is a method of calculating the ratio of the common part to the combined part of two sets, and this is also used to evaluate similarity. Furthermore, the evaluation unit analyzes the features of the text using a model that has learned the features of text generated by the generative AI. Specifically, it analyzes the writing style and vocabulary usage patterns using a deep learning model that has learned the features of text generated by the generative AI. This model is trained using a large amount of generated AI documents as learning data, and can detect the unique writing style and vocabulary patterns of generated AI with high accuracy. Based on these analysis results, the evaluation unit assesses the likelihood that the document was created by a generated AI and performs a comprehensive evaluation in conjunction with the plagiarism rate. This allows the evaluation unit to assess the originality of the document and the degree of involvement of generated AI with high accuracy.

[0066] The calculation unit calculates the generation AI creation rate based on the copy-paste rate evaluated by the evaluation unit. For example, the calculation unit calculates the generation AI creation rate by combining the copy-paste rate and the evaluation results of the AI-generated text. Specifically, the calculation unit integrates the copy-paste rate and the feature analysis results of the generated AI text provided by the evaluation unit and calculates the generation AI creation rate using its own algorithm. This algorithm is designed so that the generation AI creation rate is higher when the copy-paste rate is high or when strong features of the generated AI text are detected. For example, if the copy-paste rate is 50% and strong features of the generated AI text are detected, the generation AI creation rate may be calculated as 80%. Based on these calculation results, the calculation unit quantitatively evaluates to what extent the document was created by the generation AI. Furthermore, the calculation unit can improve the accuracy of the evaluation by adjusting the parameters and weights used in the calculation process of the generation AI creation rate. For example, by optimizing the parameters according to a specific field or type of document, it is possible to calculate a more accurate generation AI creation rate. In addition, the calculation unit can continuously improve the accuracy of the algorithm by utilizing past evaluation data and respond to the latest generation AI technologies. This allows the calculation unit to calculate the AI ​​generation rate for generating data with high accuracy, thereby improving the overall evaluation accuracy of the system.

[0067] The decision-making unit uses the generation AI creation rate calculated by the calculation unit as a basis for deciding whether to resubmit or reject the document. For example, if the generation AI creation rate is 70% or higher, the decision-making unit will decide to resubmit or reject the document. Specifically, if the generation AI creation rate exceeds a certain threshold, the decision-making unit will automatically send a notification to the user requesting resubmission. For example, if the generation AI creation rate is 70% or higher, it will send the user a message such as "You need to resubmit your document" and guide them through the resubmission procedure. If the generation AI creation rate is 80% or higher, it will decide to reject the document and notify the user that "Your document has been rejected." The decision-making unit can flexibly set these decision criteria and adjust the thresholds according to specific conditions and situations. For example, by setting different thresholds depending on the type and use of the document, such as academic papers or business reports, more appropriate decisions can be made. Furthermore, the decision-making unit can collect feedback from users and use it to improve the decision criteria and processes. For example, it can review the decision criteria and improve the system based on opinions and requests from users who have received notifications of resubmission or rejection. This allows the decision-making unit to make appropriate decisions based on the AI ​​generation rate, thereby improving the overall reliability of the system and user satisfaction.

[0068] The evaluation unit can evaluate the plagiarism rate using an algorithm that calculates the similarity between the document and the information on the web. For example, the evaluation unit can use cosine similarity to calculate the similarity between the document and the information on the web. The evaluation unit can also use the Jaccard coefficient to calculate the similarity between the document and the information on the web. This improves the accuracy of the plagiarism rate evaluation by calculating the similarity with the information on the web. Some or all of the above processing in the evaluation unit may be performed using AI, for example, or without AI. For example, the evaluation unit can evaluate the plagiarism rate using an AI model that takes the document and the information on the web as input and outputs a similarity score.

[0069] The evaluation unit can analyze the characteristics of text using a model that has learned the characteristics of text generated by the generative AI. For example, the evaluation unit can analyze the style and vocabulary usage patterns using a model that has learned the characteristics of text generated by the generative AI. For example, the evaluation unit can analyze the style characteristics using a model that has learned the characteristics of text generated by the generative AI. The evaluation unit can also analyze the vocabulary usage patterns using a model that has learned the characteristics of text generated by the generative AI. This improves the accuracy of the evaluation by analyzing the characteristics of text generated by the generative AI. Some or all of the above processing in the evaluation unit may be performed using AI, for example, or without AI. For example, the evaluation unit can analyze the characteristics of text using a model that has learned the characteristics of text generated by the generative AI.

[0070] The calculation unit can calculate the AI ​​generation rate by combining the copy-paste rate and the evaluation results of the AI-generated text. The calculation unit can, for example, combine the copy-paste rate and the evaluation results of the AI-generated text to calculate the AI ​​generation rate. The calculation unit can, for example, combine the copy-paste rate and the evaluation results of the AI-generated text to calculate the AI ​​generation rate. The calculation unit can also combine the copy-paste rate and the evaluation results of the AI-generated text to calculate the AI ​​generation rate. By combining the copy-paste rate and the evaluation results of the AI-generated text, the accuracy of calculating the AI ​​generation rate is improved. Some or all of the above processing in the calculation unit may be performed using AI, for example, or without using AI. For example, the calculation unit can calculate the AI ​​generation rate using an AI model that takes the copy-paste rate and the evaluation results of the AI-generated text as input and outputs the AI ​​generation rate.

[0071] The decision unit can determine whether to resubmit or reject an entry if the AI ​​generation rate is 70% or higher. For example, the decision unit may decide to resubmit an entry if the AI ​​generation rate is 70% or higher. The decision unit may also decide to reject an entry if the AI ​​generation rate is 70% or higher. This allows for appropriate evaluation by determining whether to resubmit or reject an entry when the AI ​​generation rate is high. Some or all of the above processing in the decision unit may be performed using AI, for example, or without AI. For example, the decision unit can make a decision using an AI model that takes the AI ​​generation rate as input and outputs a decision to resubmit or reject an entry.

[0072] The reception desk can estimate the user's emotions and adjust the timing of document uploads based on the estimated emotions. For example, if the user is stressed, the reception desk can simplify the upload procedure to allow for faster uploads. If the user is relaxed, for example, the reception desk can provide detailed upload options and suggest a customizable upload method. The reception desk can also prioritize voice input to allow for faster document uploads if the user is in a hurry. This improves user convenience by adjusting the upload timing according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the reception desk may be performed using AI or not using AI. For example, the reception desk can input user facial expression data into a generative AI and have the generative AI perform emotion estimation.

[0073] The reception desk can analyze a user's past upload history and select the optimal upload method. For example, the reception desk may prioritize suggesting upload methods (file format, upload time, etc.) that the user has frequently used in the past. For example, the reception desk may suggest the optimal upload method for a specific time period based on the user's past upload history. The reception desk can also select the optimal upload method based on the types of materials the user has uploaded in the past. In this way, by analyzing past upload history, the reception desk can provide the optimal upload method. Some or all of the above processing in the reception desk may be performed using AI, for example, or not using AI. For example, the reception desk can input the user's past upload history data into a generating AI and have the generating AI select the optimal upload method.

[0074] The reception desk can filter uploaded materials based on the user's current projects and areas of interest. For example, the reception desk can filter to upload only materials related to the user's current project. For example, the reception desk can prioritize uploading highly relevant materials based on the user's areas of interest. The reception desk can also filter to upload appropriate materials according to the progress of the user's project. This allows for the uploading of highly relevant materials by filtering based on the user's projects and areas of interest. Some or all of the above processing in the reception desk may be performed using AI, for example, or not using AI. For example, the reception desk can input the user's project information and area of ​​interest data into a generating AI and have the generating AI perform the filtering.

[0075] The reception desk can estimate the user's emotions and determine the priority of documents to upload based on the estimated emotions. For example, if the user is stressed, the reception desk will prioritize uploading important documents. If the user is relaxed, the reception desk will prioritize uploading detailed documents. The reception desk can also prioritize uploading documents that can be processed quickly if the user is in a hurry. This enables efficient uploading by prioritizing documents according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the reception desk may be performed using AI or not. For example, the reception desk can input user emotion data into a generative AI and have the generative AI determine the priority of documents.

[0076] The reception desk can prioritize uploading highly relevant materials by considering the user's geographical location when uploading materials. For example, if the user is in a specific region, the reception desk will prioritize uploading materials related to that region. The reception desk can also filter highly relevant materials based on the user's geographical location. Furthermore, if the user is on the move, the reception desk can upload the most suitable materials based on their current location. This allows for the priority uploading of highly relevant materials by considering geographical location. Some or all of the above processing in the reception desk may be performed using AI, for example, or without AI. For example, the reception desk can input the user's geographical location data into a generating AI and have the generating AI select highly relevant materials.

[0077] The reception desk can analyze a user's social media activity when they upload materials and upload relevant materials. For example, the reception desk can upload relevant materials based on the user's social media activity. For example, the reception desk can upload the most relevant materials based on the information the user has shared on social media. The reception desk can also upload highly relevant materials based on the user's areas of interest on social media. In this way, by analyzing social media activity, highly relevant materials can be uploaded. Some or all of the above processing in the reception desk may be performed using AI, for example, or not using AI. For example, the reception desk can input the user's social media activity data into a generating AI and have the generating AI select relevant materials.

[0078] The evaluation unit can estimate the user's emotions and adjust the evaluation criteria for the copy-paste rate based on the estimated user emotions. For example, if the user is stressed, the evaluation unit can relax the evaluation criteria and perform a flexible evaluation. For example, if the user is relaxed, the evaluation unit can apply strict evaluation criteria. The evaluation unit can also set criteria for a quick evaluation if the user is in a hurry. This allows for flexible evaluation by adjusting the evaluation criteria according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the evaluation unit may be performed using AI, for example, or not using AI. For example, the evaluation unit can input user emotion data into the generative AI and have the generative AI perform the adjustment of the evaluation criteria.

[0079] The evaluation unit can adjust the level of detail in its evaluation based on the importance of the materials. For example, the evaluation unit will perform a detailed evaluation and check every detail for important materials. For example, it will perform a standard evaluation for general materials. The evaluation unit can also perform a simplified evaluation for materials of low importance. This allows for efficient evaluation by adjusting the level of detail based on the importance of the materials. Some or all of the above processes in the evaluation unit may be performed using AI, for example, or without AI. For example, the evaluation unit can input material importance data into a generating AI and have the generating AI perform the adjustment of the level of detail in the evaluation.

[0080] The evaluation unit can apply different evaluation algorithms depending on the category of the material during the evaluation process. For example, in the case of an academic paper, the evaluation unit can apply a specialized evaluation algorithm. For example, in the case of a business report, the evaluation unit can apply a business-specific evaluation algorithm. Furthermore, in the case of creative content, the evaluation unit can also apply an evaluation algorithm that emphasizes creativity. This allows for appropriate evaluation by applying an evaluation algorithm according to the category of the material. Some or all of the above processing in the evaluation unit may be performed using AI, for example, or without AI. For example, the evaluation unit can input the category data of the material into a generating AI and have the generating AI execute the application of the evaluation algorithm.

[0081] The evaluation unit can estimate the user's emotions and adjust the display method of the evaluation results based on the estimated user emotions. For example, if the user is nervous, the evaluation unit provides a simple and highly visible display method. For example, if the user is relaxed, the evaluation unit provides a display method that includes detailed information. The evaluation unit can also provide a concise display method if the user is in a hurry. By adjusting the display method according to the user's emotions, highly visible displays are possible. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or a generative AI. The generative AI is a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to these examples. Some or all of the above processing in the evaluation unit may be performed using AI, for example, or without AI. For example, the evaluation unit can input user emotion data into a generative AI and have the generative AI adjust the display method of the evaluation results.

[0082] The evaluation unit can determine the priority of evaluations based on the submission timing of the materials during the evaluation process. For example, the evaluation unit may prioritize evaluating materials with approaching deadlines. For example, the evaluation unit may prioritize evaluating materials submitted earlier. The evaluation unit may also postpone evaluating materials submitted later. This allows for efficient evaluation by determining the priority of evaluations based on submission timing. Some or all of the above processes in the evaluation unit may be performed using AI, for example, or without AI. For example, the evaluation unit can input the submission timing data of the materials into a generating AI and have the generating AI determine the evaluation priority.

[0083] The evaluation unit can adjust the order of evaluation based on the relevance of the materials during the evaluation process. For example, the evaluation unit may prioritize evaluating materials with highly relevant content, or postpone evaluating materials with less relevant content. The evaluation unit can also dynamically adjust the order of evaluation based on the relevance of the materials. This allows for more efficient evaluation by adjusting the order of evaluation based on the relevance of the materials. Some or all of the above processing in the evaluation unit may be performed using AI, for example, or without AI. For example, the evaluation unit can input data on the relevance of the materials into a generating AI and have the generating AI perform the adjustment of the evaluation order.

[0084] The calculation unit can estimate the user's emotions and adjust the calculation method for generating AI based on the estimated user emotions. For example, if the user is stressed, the calculation unit simplifies the calculation method and provides results quickly. For example, if the user is relaxed, the calculation unit applies a more detailed calculation method. The calculation unit can also set up a method to provide calculation results quickly if the user is in a hurry. This allows for flexible calculation by adjusting the calculation method according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generating AI. The generating AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the calculation unit may be performed using AI or not using AI. For example, the calculation unit can input user emotion data into the generating AI and have the generating AI adjust the calculation method for generating AI.

[0085] The calculation unit can adjust the weighting when combining the copy-paste rate and the evaluation result of the AI-generated text during calculation. For example, if the copy-paste rate is high, the calculation unit reduces the weight of the evaluation result of the AI-generated text. For example, if the evaluation result of the AI-generated text is high, the calculation unit reduces the weight of the copy-paste rate. The calculation unit can also dynamically adjust the balance between the copy-paste rate and the evaluation result of the AI-generated text. By adjusting the weighting of the copy-paste rate and the evaluation result of the AI-generated text, it becomes possible to calculate an appropriate generation AI creation rate. Some or all of the above processing in the calculation unit may be performed using AI, for example, or without AI. For example, the calculation unit can input the copy-paste rate and the evaluation result data of the AI-generated text into the generation AI and have the generation AI perform the weighting adjustment.

[0086] The calculation unit can apply different calculation algorithms to each category of material during the calculation process. For example, in the case of academic papers, the calculation unit applies a specialized calculation algorithm. For example, in the case of business reports, the calculation unit applies a calculation algorithm specifically tailored to business. Furthermore, in the case of creative content, the calculation unit can also apply a calculation algorithm that emphasizes creativity. By applying different calculation algorithms to each category of material, it becomes possible to calculate an appropriate generation AI generation rate. Some or all of the above-described processes in the calculation unit may be performed using AI, for example, or without AI. For example, the calculation unit can input the category data of the material into the generation AI and have the generation AI execute the application of the calculation algorithm.

[0087] The calculation unit can estimate the user's emotions and adjust the display method of the generated AI creation rate based on the estimated user emotions. For example, if the user is nervous, the calculation unit provides a simple and highly visible display method. For example, if the user is relaxed, the calculation unit provides a display method that includes detailed information. The calculation unit can also provide a concise display method if the user is in a hurry. By adjusting the display method according to the user's emotions, a highly visible display is possible. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or a generated AI. The generated AI is 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 processing in the calculation unit may be performed using AI, for example, or without AI. For example, the calculation unit can input user emotion data into the generated AI and have the generated AI adjust the display method of the generated AI creation rate.

[0088] The calculation unit can determine the priority of the generation AI creation rate based on the submission date of the documents during calculation. For example, the calculation unit may prioritize calculating the generation AI creation rate for documents with approaching deadlines. For example, the calculation unit may prioritize calculating the generation AI creation rate for documents with earlier submission dates. The calculation unit may also postpone calculating the generation AI creation rate for documents with later submission dates. This allows for efficient calculation by determining the priority of the generation AI creation rate based on the submission date. Some or all of the above processing in the calculation unit may be performed using AI, for example, or without using AI. For example, the calculation unit may input the document submission date data into the generation AI and have the generation AI perform the determination of the priority of the generation AI creation rate.

[0089] The calculation unit can adjust the order of AI generation rates based on the relevance of the materials during calculation. For example, the calculation unit prioritizes calculating the AI ​​generation rate for materials with high relevance. For example, the calculation unit postpones calculating the AI ​​generation rate for materials with low relevance. The calculation unit can also dynamically adjust the order of AI generation rates based on the relevance of the materials. This allows for efficient calculation by adjusting the order of AI generation rates based on the relevance of the materials. Some or all of the above processing in the calculation unit may be performed using AI, for example, or without AI. For example, the calculation unit can input material relevance data into the generating AI and have the generating AI perform the adjustment of the order of AI generation rates.

[0090] The decision-making unit can estimate the user's emotions and adjust the criteria for resubmission or rejection based on the estimated emotions. For example, if the user is stressed, the decision-making unit may relax the criteria and make a flexible decision. For example, if the user is relaxed, the decision-making unit may apply strict criteria. The decision-making unit can also set criteria for quick decision-making if the user is in a hurry. This allows for flexible decision-making by adjusting the criteria according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the decision-making unit may be performed using AI, for example, or not using AI. For example, the decision-making unit can input user emotion data into a generative AI and have the generative AI adjust the criteria for resubmission or rejection.

[0091] The decision-making unit can make the optimal decision by referring to past data on the generation rate of the generated AI when making a decision. For example, the decision-making unit can make a decision to resubmit or reject based on past data on the generation rate of the generated AI. For example, the decision-making unit can extract a specific pattern from past data and make the optimal decision. The decision-making unit can also analyze past data and dynamically adjust the criteria for resubmission or rejection. This makes it possible to make the optimal decision by referring to past data. Some or all of the above processing in the decision-making unit may be performed using AI, for example, or without using AI. For example, the decision-making unit can input past generation rate data of the generated AI into the generating AI and cause the generating AI to make the optimal decision.

[0092] The decision-making unit can apply different criteria to each category of material when making a decision. For example, in the case of an academic paper, the decision-making unit applies specialized criteria. For example, in the case of a business report, the decision-making unit applies business-specific criteria. Furthermore, in the case of creative content, the decision-making unit can also apply criteria that emphasize creativity. This allows for appropriate judgments by applying different criteria to each category of material. Some or all of the above processing in the decision-making unit may be performed using AI, for example, or without AI. For example, the decision-making unit can input material category data into a generating AI and have the generating AI perform the application of the judgment criteria.

[0093] The decision unit can estimate the user's emotions and adjust the display method of the decision result based on the estimated user emotions. For example, if the user is nervous, the decision unit provides a simple and highly visible display method. For example, if the user is relaxed, the decision unit provides a display method that includes detailed information. The decision unit can also provide a concise display method if the user is in a hurry. By adjusting the display method according to the user's emotions, highly visible displays are possible. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or a generative AI. The generative AI is a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to these examples. Some or all of the above processing in the decision unit may be performed using AI, for example, or without AI. For example, the decision unit can input user emotion data into the generative AI and have the generative AI adjust the display method of the decision result.

[0094] The decision-making unit can determine the priority of decisions based on the submission dates of the documents. For example, the decision-making unit may prioritize documents with approaching deadlines. For example, it may prioritize documents with earlier submission dates. The decision-making unit may also postpone decisions regarding documents with later submission dates. This allows for efficient decision-making by prioritizing decisions based on submission dates. Some or all of the above processing in the decision-making unit may be performed using AI, for example, or without AI. For example, the decision-making unit can input document submission date data into a generating AI and have the generating AI determine the priority of decisions.

[0095] The decision-making unit can adjust the order of decisions based on the relevance of the materials during the decision-making process. For example, the decision-making unit may prioritize decisions based on the relevance of the materials' content. For example, it may postpone decisions based on the relevance of the materials' content. The decision-making unit can also dynamically adjust the order of decisions based on the relevance of the materials. This allows for more efficient decision-making by adjusting the order of decisions based on the relevance of the materials. Some or all of the above-described processes in the decision-making unit may be performed using AI, for example, or without AI. For example, the decision-making unit can input data on the relevance of the materials into a generating AI and have the generating AI perform the adjustment of the order of decisions.

[0096] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0097] The generative AI checker system can further estimate the user's emotions and adjust the feedback method of the evaluation results based on the estimated emotions. For example, if the user is stressed, the evaluation results will be summarized concisely, and positive feedback will be prioritized. If the user is relaxed, detailed evaluation results will be provided, and specific areas for improvement will be indicated. If the user is in a hurry, concise feedback can be provided quickly. This can improve user satisfaction by providing feedback that is tailored to the user's emotions. Emotion estimation can be achieved using, for example, an emotion engine or generative AI. Generative AI may be, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the evaluation unit may be performed using AI, or not using AI. For example, the evaluation unit can input user emotion data into the generative AI and have the generative AI adjust the feedback method.

[0098] The generating AI checker system can further analyze the user's past evaluation results and dynamically adjust the evaluation criteria. For example, it can extract specific patterns from past evaluation results and optimize the evaluation criteria. It can learn the characteristics of materials that the user has previously given high ratings to and relax the evaluation criteria for materials with similar characteristics. It can also learn the characteristics of materials that have received low ratings in the past and tighten the evaluation criteria for materials with similar characteristics. This enables dynamic adjustment of evaluation criteria based on past evaluation results, improving the accuracy of the evaluation. Some or all of the above processing in the evaluation unit may be performed using AI, for example, or without AI. For example, the evaluation unit can input past evaluation result data into the generating AI and have the generating AI perform the adjustment of the evaluation criteria.

[0099] The generative AI checker system can further estimate the user's emotions and adjust the evaluation order of materials based on the estimated emotions. For example, if the user is stressed, important materials will be evaluated preferentially. If the user is relaxed, materials requiring detailed evaluation will be evaluated preferentially. Also, if the user is in a hurry, materials that can be evaluated quickly will be evaluated preferentially. This allows for adjustment of the evaluation order according to the user's emotions, resulting in efficient evaluation. Emotion estimation can be achieved using, for example, an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the evaluation unit may be performed using AI, or not using AI. For example, the evaluation unit can input user emotion data into the generative AI and have the generative AI perform the adjustment of the evaluation order.

[0100] The generating AI checker system can further adjust its evaluation criteria to take into account the user's project progress. For example, in the early stages of a project, flexible evaluation criteria can be applied to allow the user to freely experiment with ideas. In the middle stages of the project, standard evaluation criteria can be applied to monitor progress. In the final stages of the project, strict evaluation criteria can be applied to ensure final quality. This allows for adjustment of evaluation criteria according to the project's progress and provides appropriate feedback. Some or all of the above processes in the evaluation unit may be performed using AI, for example, or not. For example, the evaluation unit can input project progress data into the generating AI and have the generating AI perform the adjustment of evaluation criteria.

[0101] The generative AI checker system can further estimate the user's emotions and adjust the notification method of the evaluation results based on the estimated emotions. For example, if the user is stressed, the evaluation results can be notified via email with a detailed explanation. If the user is relaxed, the evaluation results can be displayed on a dashboard with interactive feedback. If the user is in a hurry, the evaluation results can be quickly notified via SMS. This allows for adjustment of the notification method according to the user's emotions, ensuring a smooth receipt of evaluation results. Emotion estimation can be achieved using, for example, an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the evaluation unit may be performed using AI or not using AI. For example, the evaluation unit can input user emotion data into the generative AI and have the generative AI adjust the notification method.

[0102] The generating AI checker system can also dynamically adjust its evaluation algorithm based on the user's past evaluation results. For example, it can extract specific trends from past evaluation results and optimize the evaluation algorithm. It can learn the characteristics of materials that the user has given high ratings to in the past and relax the evaluation algorithm for materials with similar characteristics. It can also learn the characteristics of materials that have given low ratings in the past and tighten the evaluation algorithm for materials with similar characteristics. This enables dynamic adjustment of the evaluation algorithm based on past evaluation results, improving the accuracy of the evaluation. Some or all of the above processing in the evaluation unit may be performed using AI, for example, or without AI. For example, the evaluation unit can input past evaluation result data into the generating AI and have the generating AI perform the adjustment of the evaluation algorithm.

[0103] The generative AI checker system can further estimate the user's emotions and adjust the display format of the evaluation results based on the estimated emotions. For example, if the user is stressed, the evaluation results can be summarized concisely and displayed in a visually easy-to-understand format. If the user is relaxed, detailed evaluation results can be provided using interactive graphs and charts. If the user is in a hurry, the system can also quickly display a concise evaluation result. This allows for adjustment of the display format according to the user's emotions, making it easier to understand the evaluation results. Emotion estimation can be achieved using, for example, an emotion engine or generative AI. Generative AI may be, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the evaluation unit may be performed using AI, or not using AI. For example, the evaluation unit can input user emotion data into the generative AI and have the generative AI perform the adjustment of the display format.

[0104] The generating AI checker system can further prioritize evaluations by considering the progress of the user's project. For example, if a project deadline is approaching, it will prioritize evaluating materials related to that deadline. In the middle of a project, it will prioritize evaluating materials for progress checks. In the early stages of a project, it can also prioritize evaluating materials for idea generation. This allows for the determination of evaluation priorities according to the project's progress, resulting in efficient evaluation. Some or all of the above-described processes in the evaluation unit may be performed using AI, for example, or not. For example, the evaluation unit can input project progress data into the generating AI and have the generating AI determine the evaluation priorities.

[0105] The generative AI checker system can further estimate the user's emotions and provide a function to support the interpretation of evaluation results based on the estimated emotions. For example, if the user is stressed, the system can concisely summarize the key points of the evaluation results and present them in an easy-to-understand format. If the user is relaxed, it can provide a detailed interpretation and explain the background and reasons for the evaluation results. If the user is in a hurry, it can also quickly present the important points of the evaluation results. This enables support for interpreting evaluation results in accordance with the user's emotions, deepening their understanding of the evaluation results. Emotion estimation is achieved using, for example, an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the evaluation unit may be performed using AI, or not using AI. For example, the evaluation unit can input user emotion data into the generative AI and have the generative AI perform the interpretation support function.

[0106] The generating AI checker system can also dynamically adjust the level of detail of evaluations based on the user's past evaluation results. For example, it can extract specific trends from past evaluation results and optimize the level of detail of the evaluations. For materials that the user has previously given high ratings to, it can perform a detailed evaluation and provide further areas for improvement. Conversely, for materials that have received low ratings in the past, it can perform a simplified evaluation and indicate basic areas for improvement. This enables dynamic adjustment of the level of detail of evaluations based on past evaluation results, thereby improving the accuracy of the evaluations. Some or all of the above processing in the evaluation unit may be performed using AI, for example, or without AI. For example, the evaluation unit can input past evaluation result data into the generating AI and have the generating AI perform the adjustment of the level of detail of the evaluations.

[0107] The following briefly describes the processing flow for example form 2.

[0108] Step 1: The reception desk receives the materials uploaded by the user. These materials may include, but are not limited to, text documents, images, and audio files. The reception desk receives the materials uploaded by the user and stores them in the system. Step 2: The evaluation unit inspects the uploaded materials and assesses the rate of copy-pasting of information found on the web. The evaluation unit uses algorithms such as cosine similarity and Jaccard coefficients to calculate the similarity between the materials and the information on the web. It also uses a model that has learned the characteristics of text generated by the generative AI to analyze the writing style and vocabulary usage patterns. Step 3: The calculation unit calculates the AI ​​generation rate based on the copy-paste rate evaluated by the evaluation unit. The calculation unit combines the copy-paste rate and the evaluation results of the AI-generated text to calculate the AI ​​generation rate. Step 4: The judgment unit uses the generation AI creation rate calculated by the calculation unit as a basis for deciding whether to resubmit or fail the application. For example, if the generation AI creation rate is 70% or higher, the unit will decide to resubmit or fail the application.

[0109] 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 user input for the result of the specific processing. The control unit 46A transmits the audio data indicating 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.

[0110] Data generation model 58 is a form of so-called generative AI (Artificial Intelligence). An example of data generation model 58 is ChatGPT (registered trademark) (Internet search).<URL: https: / / openai.com / blog / chatgpt> Examples of generative AI include text generation AI, image generation AI, and multimodal generation AI. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and with inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats from audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, 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), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each of the above parts is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example.Furthermore, processing performed by AI, including generative AI, may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by AI, including generative AI.

[0111] Furthermore, the processing performed by the data processing system 10 described above is carried out by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart device 14, but it may also be carried out by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart device 14. In addition, 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.

[0112] Each of the multiple elements described above, including the reception unit, evaluation unit, calculation unit, and judgment unit, is implemented in at least one of the smart device 14 and the data processing unit 12. For example, the reception unit is implemented by the control unit 46A of the smart device 14, which receives the materials uploaded by the user and stores them in the system. The evaluation unit is implemented by the specific processing unit 290 of the data processing unit 12, which inspects the uploaded materials and evaluates the copy-paste rate of the information existing on the Web. The calculation unit is implemented by the specific processing unit 290 of the data processing unit 12, which calculates the generation AI creation rate based on the copy-paste rate evaluated by the evaluation unit. The judgment unit is implemented by the specific processing unit 290 of the data processing unit 12, which uses the generation AI creation rate calculated by the calculation unit as a basis for making decisions such as resubmission or rejection. The correspondence between each unit and the device or control unit is not limited to the example described above, and various changes are possible.

[0113] [Second Embodiment] Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment.

[0114] As shown in Figure 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.

[0115] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN and / or LAN.

[0116] The smart glasses 214 include a computer 36, a microphone 238, a speaker 240, a camera 42, and a communication interface 44. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, and camera 42 are also connected to the bus 52.

[0117] The microphone 238 receives voice signals from the user and accepts instructions from the user. The microphone 238 captures the voice signals from the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0118] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, which captures images of the area around the user (for example, an imaging range defined by a field of view equivalent to the field of vision of a typical healthy person).

[0119] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0120] Figure 4 shows an example of the main functions of the data processing device 12 and the smart glasses 214. As shown in Figure 4, the data processing device 12 performs specific processing by the processor 28. The storage 32 stores the specific processing program 56.

[0121] The processor 28 reads a 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 acting as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0122] Storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0123] In the smart glasses 214, specific processing is performed by the processor 46. The storage 50 stores a specific processing program 60. 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 acting as a control unit 46A according to the specific processing program 60 executed on the RAM 48. The smart glasses 214 also have a data generation model 58 and an emotion identification model 59, similar to the data generation model and emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0124] Furthermore, other devices besides the data processing device 12 may also 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 obtains processing results (such as prediction results) using the data generation model 58 by communicating with the server device that has the data generation model 58. Also, the data processing device 12 may be a server device or a terminal device owned by the user (for example, a mobile phone, robot, home appliance, etc.).

[0125] 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 user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0126] The data generation model 58 is a so-called generative AI. An example of a 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 is input with prompts containing instructions, and inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats such as audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVM), k-means clustering, convolutional neural networks (CNN), recurrent neural networks (RNN), generative adversarial networks (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each part described above is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example. Also, processing performed by an AI including a generative AI may be replaced by rule-based processing, and rule-based processing may be replaced by processing performed by an AI including a generative AI.

[0127] 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 performed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart glasses 214, but it may also be performed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart glasses 214. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the smart glasses 214 or an external device, and the smart glasses 214 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0128] Each of the multiple elements described above, including the reception unit, evaluation unit, calculation unit, and judgment unit, is implemented, for example, in at least one of the smart glasses 214 and the data processing unit 12. For example, the reception unit is implemented by the control unit 46A of the smart glasses 214, which receives the materials uploaded by the user and stores them in the system. The evaluation unit is implemented, for example, by the specific processing unit 290 of the data processing unit 12, which inspects the uploaded materials and evaluates the copy-paste rate of the information existing on the Web. The calculation unit is implemented, for example, by the specific processing unit 290 of the data processing unit 12, which calculates the generation AI creation rate based on the copy-paste rate evaluated by the evaluation unit. The judgment unit is implemented, for example, by the specific processing unit 290 of the data processing unit 12, which uses the generation AI creation rate calculated by the calculation unit as a basis for making decisions such as resubmission or rejection. The correspondence between each unit and the device or control unit is not limited to the example described above, and various changes are possible.

[0129] [Third Embodiment] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[0130] As shown in Figure 5, the data processing system 310 includes a data processing device 12 and a headset terminal 314. An example of the data processing device 12 is a server.

[0131] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN and / or LAN.

[0132] The headset terminal 314 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication interface 44, and a display 343. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, camera 42, and display 343 are also connected to the bus 52.

[0133] The microphone 238 receives voice signals from the user and accepts instructions from the user. The microphone 238 captures the voice signals from the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0134] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, which captures images of the area around the user (for example, an imaging range defined by a field of view equivalent to the field of vision of a typical healthy person).

[0135] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0136] Figure 6 shows an example of the main functions of the data processing device 12 and the headset terminal 314. As shown in Figure 6, the data processing device 12 performs specific processing using the processor 28. The storage 32 stores the specific processing program 56.

[0137] The processor 28 reads a 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 acting as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0138] Storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0139] In the headset terminal 314, specific processing is performed by the processor 46. The storage 50 stores a specific program 60. The processor 46 reads the specific program 60 from the storage 50 and executes the read specific program 60 on the RAM 48. The specific processing is realized by the processor 46 acting as a control unit 46A according to the specific program 60 executed on the RAM 48. The headset terminal 314 also has a data generation model 58 and an emotion identification model 59, similar to the data generation model and emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0140] Furthermore, other devices besides the data processing device 12 may also 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 obtains processing results (such as prediction results) using the data generation model 58 by communicating with the server device that has the data generation model 58. Also, the data processing device 12 may be a server device or a terminal device owned by the user (for example, a mobile phone, robot, home appliance, etc.).

[0141] The specific processing unit 290 transmits the result of the specific processing to the headset terminal 314. In the headset terminal 314, the control unit 46A causes the speaker 240 and display 343 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0142] The data generation model 58 is a so-called generative AI. An example of a 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 is input with prompts containing instructions, and inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats such as audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVM), k-means clustering, convolutional neural networks (CNN), recurrent neural networks (RNN), generative adversarial networks (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each part described above is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example. Also, processing performed by an AI including a generative AI may be replaced by rule-based processing, and rule-based processing may be replaced by processing performed by an AI including a generative AI.

[0143] 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 performed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the headset terminal 314, but may also be performed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the headset terminal 314. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the headset terminal 314 or an external device, and the headset terminal 314 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0144] Each of the multiple elements described above, including the reception unit, evaluation unit, calculation unit, and judgment unit, is implemented in at least one of the headset terminal 314 and the data processing unit 12. For example, the reception unit is implemented by the control unit 46A of the headset terminal 314, which receives the materials uploaded by the user and stores them in the system. The evaluation unit is implemented by the specific processing unit 290 of the data processing unit 12, which inspects the uploaded materials and evaluates the copy-paste rate of the information existing on the Web. The calculation unit is implemented by the specific processing unit 290 of the data processing unit 12, which calculates the generation AI creation rate based on the copy-paste rate evaluated by the evaluation unit. The judgment unit is implemented by the specific processing unit 290 of the data processing unit 12, which uses the generation AI creation rate calculated by the calculation unit as a basis for making decisions such as resubmission or rejection. The correspondence between each unit and the device or control unit is not limited to the example described above, and various changes are possible.

[0145] [Fourth Embodiment] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[0146] As shown in Figure 7, the data processing system 410 includes a data processing device 12 and a robot 414. An example of the data processing device 12 is a server.

[0147] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN and / or LAN.

[0148] The robot 414 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication interface 44, and a controlled object 443. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, camera 42, and controlled object 443 are also connected to the bus 52.

[0149] The microphone 238 receives voice signals from the user and accepts instructions from the user. The microphone 238 captures the voice signals from the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0150] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS image sensor or CCD image sensor, which captures images of the area around the user (for example, an imaging range defined by a field of view equivalent to the field of vision of a typical healthy person).

[0151] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0152] The controlled 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 robot 414's emotions can be expressed by controlling these motors. The robot 414's facial expressions can also be expressed by controlling the illumination state of the LEDs in its eyes.

[0153] Figure 8 shows an example of the main functions of the data processing device 12 and the robot 414. As shown in Figure 8, the data processing device 12 performs specific processing using the processor 28. The storage 32 stores the specific processing program 56.

[0154] The processor 28 reads a 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 acting as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0155] Storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0156] In robot 414, specific processing is performed by processor 46. A specific program 60 is stored in storage 50. Processor 46 reads the specific program 60 from storage 50 and executes it on RAM 48. The specific processing is achieved by processor 46 acting as a control unit 46A according to the specific program 60 executed on RAM 48. Robot 414 also has data generation model 58 and emotion identification model 59, similar to those of the robot, and can perform processing similar to that of the specific processing unit 290 using these models.

[0157] Furthermore, other devices besides the data processing device 12 may also 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 obtains processing results (such as prediction results) using the data generation model 58 by communicating with the server device that has the data generation model 58. Also, the data processing device 12 may be a server device or a terminal device owned by the user (for example, a mobile phone, robot, home appliance, etc.).

[0158] 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 controlled object 443 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0159] The data generation model 58 is a so-called generative AI. An example of a 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 is input with prompts containing instructions, and inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats such as audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVM), k-means clustering, convolutional neural networks (CNN), recurrent neural networks (RNN), generative adversarial networks (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each part described above is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example. Also, processing performed by an AI including a generative AI may be replaced by rule-based processing, and rule-based processing may be replaced by processing performed by an AI including a generative AI.

[0160] 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 performed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the robot 414, but it may also be performed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the robot 414. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the robot 414 or an external device, and the robot 414 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0161] Each of the multiple elements described above, including the reception unit, evaluation unit, calculation unit, and judgment unit, is implemented in at least one of the robot 414 and the data processing unit 12. For example, the reception unit is implemented by the control unit 46A of the robot 414, which receives the materials uploaded by the user and stores them in the system. The evaluation unit is implemented by the specific processing unit 290 of the data processing unit 12, which inspects the uploaded materials and evaluates the copy-paste rate of the information existing on the Web. The calculation unit is implemented by the specific processing unit 290 of the data processing unit 12, which calculates the generation AI creation rate based on the copy-paste rate evaluated by the evaluation unit. The judgment unit is implemented by the specific processing unit 290 of the data processing unit 12, which uses the generation AI creation rate calculated by the calculation unit as a basis for making decisions such as resubmission or rejection. The correspondence between each unit and the device or control unit is not limited to the example described above, and various changes are possible.

[0162] Furthermore, the emotion identification model 59, acting 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 a specific mapping, which is an emotion map (see Figure 9). Similarly, the emotion identification model 59 may also determine the robot's emotion, and the identification processing unit 290 may perform identification processing using the robot's emotion.

[0163] Figure 9 shows the emotion map 400, in which multiple emotions are mapped. In the emotion map 400, emotions are arranged in concentric circles radiating from the center. The closer to the center of the concentric circles, the more primitive the emotions are located. Further out of the concentric circles, emotions representing states and actions arising from mental states are located. Emotion is a concept that includes feelings and mental states. On the left side of the concentric circles, emotions that are generally generated from reactions occurring in the brain are located. On the right side of the concentric circles, emotions that are generally induced by situational judgment are located. Above and below the concentric circles, emotions that are generally generated from reactions occurring in the brain and induced by situational judgment are located. In addition, the emotion of "pleasure" is located on the upper side of the concentric circles, and the emotion of "displeasure" is located on the lower side. Thus, in the emotion map 400, multiple emotions are mapped based on the structure in which emotions arise, and emotions that are likely to occur simultaneously are mapped close together.

[0164] These emotions are distributed at the 3 o'clock position on the Emotion Map 400, and usually fluctuate between feelings of security and anxiety. In the right half of the Emotion Map 400, situational awareness takes precedence over internal feelings, resulting in a calm impression.

[0165] The inside of the Emotion Map 400 represents inner thoughts, while the outside represents actions. Therefore, the further you go from the outside of the Emotion Map 400, the more visible (expressed in actions) your emotions become.

[0166] Here, human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, it results in discomfort, and when they approach the ideal, it results in pleasure. Similarly, in robots, cars, and motorcycles, emotions can be created based on various balances, such as posture and battery level. When these balances deviate from the ideal, it results in discomfort, and when they approach the ideal, it results in pleasure. The emotion map can be generated based, for example, on Dr. Mitsuyoshi's emotion map (Research on a system for analyzing brain physiological signals of speech emotion recognition and emotion, Tokushima University, doctoral dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map contains emotions belonging to a region called "response," where sensation is dominant. The right half of the emotion map contains emotions belonging to a region called "situation," where situational awareness is dominant.

[0167] The emotion map defines two emotions that promote learning. One is the emotion around the middle of the negative "repentance" and "reflection" on the situation side. In other words, it is 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 the emotion around the positive "desire" on the reaction side. In other words, it is when the robot has positive feelings such as "I want more" or "I want to know more."

[0168] The emotion identification model 59 inputs user input into a pre-trained neural network, obtains emotion values ​​representing each emotion shown in the emotion map 400, and determines the user's emotion. This neural network is pre-trained based on multiple training data sets, which are combinations of user input and emotion values ​​representing each emotion shown in the emotion map 400. Furthermore, this neural network is trained so that emotions located close together have similar values, as shown in the emotion map 900 in Figure 10. Figure 10 shows an example where multiple emotions such as "reassured," "calm," and "confident" have similar emotion values.

[0169] In the above embodiment, an example was given in which a specific process is performed by a single computer 22. However, the technology of this disclosure is not limited thereto, and a distributed processing method for the specific process may be used, which includes computer 22 and multiple other computers.

[0170] In the above embodiment, an example was given in which the specific processing program 56 is stored in the storage 32, but the technology of this disclosure is not limited thereto. For example, the specific processing program 56 may be stored in a portable, computer-readable, non-temporary storage medium such as a USB (Universal Serial Bus) memory. The specific processing program 56 stored in the non-temporary storage medium is installed in the computer 22 of the data processing device 12. The processor 28 executes specific processing according to the specific processing program 56.

[0171] 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.

[0172] Furthermore, it is not necessary to store the entirety 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 the entirety of the specific processing program 56 in the storage 32; it is acceptable to store only a portion of the specific processing program 56.

[0173] The following types of processors can be used as hardware resources to perform specific processing. Examples of processors include a CPU, a general-purpose processor that functions as a hardware resource to perform specific processing by executing software, i.e., a program. Other examples of processors include dedicated electrical circuits, such as FPGAs (Field-Programmable Gate Arrays), PLDs (Programmable Logic Devices), or ASICs (Application Specific Integrated Circuits), which have circuit configurations specifically designed to perform specific processing. All of these processors have built-in or connected memory, and all of them perform specific processing by using memory.

[0174] The hardware resource that performs a specific process may consist of one of these various processors, or it may consist of 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). Alternatively, the hardware resource that performs a specific process may consist of a single processor.

[0175] Examples of configurations using a single processor include, firstly, a configuration in which one or more CPUs and software are combined to form a single processor, and this processor functions as a hardware resource that performs a specific process. Secondly, there is a configuration using a processor that realizes the functions of the entire system, including multiple hardware resources that perform a specific process, on a single IC chip, as exemplified by SoCs (System-on-a-chip). In this way, a specific process is realized using one or more of the above types of processors as hardware resources.

[0176] Furthermore, the hardware structure of these various processors can more specifically utilize electrical circuits that combine circuit elements such as semiconductor devices. Also, the specific processing described above is merely an example. Therefore, it goes without saying that unnecessary steps can be deleted, new steps added, or the processing order rearranged, as long as it does not deviate from the main purpose.

[0177] Furthermore, although the above-described examples were divided into four embodiments, some or all of these embodiments may be combined. Also, the smart device 14, smart glasses 214, headset terminal 314, and robot 414 are just examples, and they may be combined, or other devices may be used. Also, although the above-described examples were divided into two embodiments, Embodiment 1 and Embodiment 2, these may be combined.

[0178] The descriptions and illustrations presented above are detailed explanations of the technical aspects of this disclosure and are merely examples of the technical aspects. For example, the above descriptions of the structure, function, operation, and effect are examples of the structure, function, operation, and effect of the technical aspects of this disclosure. Therefore, it goes without saying that you may delete unnecessary parts, add new elements, or replace elements in the descriptions and illustrations presented above, as long as you do not deviate from the essence of the technical aspects of this disclosure. Furthermore, in order to avoid confusion and facilitate understanding of the technical aspects of this disclosure, explanations of common technical knowledge and other things that do not require special explanation to enable the implementation of the technical aspects of this disclosure have been omitted from the descriptions and illustrations presented above.

[0179] All documents, patent applications, and technical standards described herein are incorporated by reference to the same extent as if each individual document, patent application, and technical standard were specifically and individually noted to be incorporated by reference.

[0180] (Note 1) The reception area for uploading documents, An evaluation unit that inspects the materials uploaded by the aforementioned reception unit and evaluates the rate of copy-pasting of information existing on the web, A calculation unit calculates the generation AI creation rate based on the copy-paste rate evaluated by the evaluation unit, The system includes a judgment unit that uses the generation AI creation rate calculated by the calculation unit as a basis for making decisions such as resubmission or rejection. A system characterized by the following features. (Note 2) The evaluation unit, We evaluate the copy-paste rate using an algorithm that calculates the similarity to information existing on the web. The system described in Appendix 1, characterized by the features described herein. (Note 3) The evaluation unit, The text features are analyzed using a model that has learned the characteristics of text generated by a generative AI. The system described in Appendix 1, characterized by the features described herein. (Note 4) The calculation unit described above, The AI ​​generation rate is calculated by combining the copy-paste rate and the evaluation results of the AI-generated text. The system described in Appendix 1, characterized by the features described herein. (Note 5) The unit that makes the determination said, If the AI ​​generation success rate is 70% or higher, the submission will be deemed unsuccessful and may require resubmission. The system described in Appendix 1, characterized by the features described herein. (Note 6) The aforementioned reception unit is It estimates the user's emotions and adjusts the timing of document uploads based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 7) The aforementioned reception unit is Analyze the user's past upload history and select the optimal upload method. The system described in Appendix 1, characterized by the features described herein. (Note 8) The aforementioned reception unit is When uploading documents, filtering is performed based on the user's current projects and areas of interest. The system described in Appendix 1, characterized by the features described herein. (Note 9) The aforementioned reception unit is It estimates the user's emotions and determines the priority of uploaded materials based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 10) The aforementioned reception unit is When uploading documents, the system prioritizes uploading documents that are highly relevant, taking into account the user's geographical location. The system described in Appendix 1, characterized by the features described herein. (Note 11) The aforementioned reception unit is When uploading documents, the system analyzes the user's social media activity and uploads relevant documents. The system described in Appendix 1, characterized by the features described herein. (Note 12) The evaluation unit, We estimate user sentiment and adjust the evaluation criteria for copy-paste rates based on the estimated user sentiment. The system described in Appendix 1, characterized by the features described herein. (Note 13) The evaluation unit, During the evaluation, adjust the level of detail based on the importance of the materials. The system described in Appendix 1, characterized by the features described herein. (Note 14) The evaluation unit, During evaluation, different evaluation algorithms are applied depending on the category of the document. The system described in Appendix 1, characterized by the features described herein. (Note 15) The evaluation unit, The system estimates the user's emotions and adjusts how the evaluation results are displayed based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 16) The evaluation unit, During the evaluation process, priority will be determined based on the submission timing of the documents. The system described in Appendix 1, characterized by the features described herein. (Note 17) The evaluation unit, During the evaluation process, the order of evaluation will be adjusted based on the relevance of the materials. The system described in Appendix 1, characterized by the features described herein. (Note 18) The calculation unit described above, We estimate the user's emotions and adjust the calculation method for generating AI based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 19) The calculation unit described above, When calculating, adjust the weighting used when combining the copy-paste rate and the evaluation results of the AI-generated text. The system described in Appendix 1, characterized by the features described herein. (Note 20) The calculation unit described above, When calculating, different calculation algorithms are applied for each category of data. The system described in Appendix 1, characterized by the features described herein. (Note 21) The calculation unit described above, The system estimates the user's emotions and adjusts how the AI ​​generation rate is displayed based on the estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 22) The calculation unit described above, During calculation, the priority of the generated AI creation rate is determined based on the submission timing of the documents. The system described in Appendix 1, characterized by the features described herein. (Note 23) The calculation unit described above, During calculation, the order of generated AI creation rates is adjusted based on the relevance of the data. The system described in Appendix 1, characterized by the features described herein. (Note 24) The unit that makes the determination said, We estimate the user's emotions and adjust the criteria for resubmission or rejection based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 25) The unit that makes the determination said, When making a decision, the system refers to past data on the success rate of generating AI to make the optimal decision. The system described in Appendix 1, characterized by the features described herein. (Note 26) The unit that makes the determination said, When making a decision, different criteria will be applied to each category of material. The system described in Appendix 1, characterized by the features described herein. (Note 27) The unit that makes the determination said, The system estimates the user's emotions and adjusts how the decision results are displayed based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 28) The unit that makes the determination said, When making a decision, the priority of the decision will be determined based on the timing of document submission. The system described in Appendix 1, characterized by the features described herein. (Note 29) The unit that makes the determination said, When making a decision, adjust the order of decisions based on the relevance of the materials. The system described in Appendix 1, characterized by the features described herein. [Explanation of symbols]

[0181] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Devices 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robots

Claims

1. The reception desk for uploading documents, An evaluation unit that inspects the materials uploaded by the aforementioned reception unit and evaluates the rate of copy-pasting of information existing on the web, A calculation unit calculates the generation AI creation rate based on the copy-paste rate evaluated by the evaluation unit, The system includes a judgment unit that uses the generation AI creation rate calculated by the calculation unit as a basis for making decisions such as resubmission or rejection. A system characterized by the following features.

2. The evaluation unit, We evaluate the copy-paste rate using an algorithm that calculates the similarity to information existing on the web. The system according to feature 1.

3. The evaluation unit, The text features are analyzed using a model that has learned the characteristics of text generated by a generative AI. The system according to feature 1.

4. The calculation unit described above, The AI ​​generation rate is calculated by combining the copy-paste rate and the evaluation results of the AI-generated text. The system according to feature 1.

5. The unit that makes the determination said, If the AI ​​generation success rate is 70% or higher, the submission will be deemed unsuccessful and may require resubmission. The system according to feature 1.

6. The aforementioned reception unit is It estimates the user's emotions and adjusts the timing of document uploads based on those estimated emotions. The system according to feature 1.

7. The aforementioned reception unit is Analyze the user's past upload history and select the optimal upload method. The system according to feature 1.

8. The aforementioned reception unit is When uploading documents, filtering is performed based on the user's current projects and areas of interest. The system according to feature 1.

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