System

Abstract

An object of a system according to an embodiment is to determine whether or not a material uploaded by a user is a product created by a generation AI.SOLUTION: A system includes an upload unit, an inspection unit, a calculation unit, and a utilization unit. The upload unit uploads a material from a user. The inspection unit inspects the material uploaded by the upload unit. The calculation unit calculates a generation AI creation ratio on the basis of a result inspected by the inspection unit. The utilization unit utilizes the generation AI creation ratio calculated by the calculation unit.SELECTED DRAWING: Figure 1

Description

[Technical Field]

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

[0002] Patent document 1 discloses a persona chatbot control method performed by at least one processor, the method including the steps of receiving a user utterance, adding the user utterance to a prompt including an instruction sentence related to a description of the chatbot character, encoding the prompt, and inputting the encoded prompt into a language model to generate a chatbot utterance in response to the user utterance. [Prior art documents] [Patent documents]

[0003] [Patent Document 1] Japanese Patent Publication No. 2022-180282 Summary of the Invention [Problem to be solved by the invention]

[0004] With conventional technology, it was difficult to determine whether materials uploaded by users were created by generative AI.

[0005] The system according to the embodiment aims to determine whether materials uploaded by users have been created by a generative AI. [Means for solving the problem]

[0006] The system according to the embodiment includes an upload unit, an inspection unit, a calculation unit, and an utilization unit. The upload unit uploads materials from a user. The inspection unit inspects the materials uploaded by the upload unit. The calculation unit calculates a generation AI creation rate based on the results of the inspection by the inspection unit. The utilization unit utilizes the generation AI creation rate calculated by the calculation unit. [Effects of the Invention]

[0007] The system according to the embodiment can determine whether the material uploaded by the user was created by a generative AI. [Brief explanation of the drawings]

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

[0025] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

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

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

[0028] (Example 1) The AI-generated product judgment system according to an embodiment of the present invention is a specialized service for determining whether a product was created by AI-generated products. In this system, a AI-generated product checker inspects materials uploaded by users, calculates the AI-generated product creation rate, and companies and schools utilize the results. As a result, the AI-generated product judgment system prevents fraudulent creations by AI-generated products and allows companies and schools to make accurate judgments.

[0029] A generative AI creation assessment system according to an embodiment includes an upload unit, an inspection unit, a calculation unit, and an utilization unit. The upload unit uploads documents from a user. For example, documents such as resumes and school reports can be uploaded in PDF or Word file format. The upload unit can also automatically extract metadata from the documents and use it as reference information for inspection. For example, the upload unit can obtain the creation date and creator information from the PDF file properties and save it as reference information for inspection. The upload unit can also simultaneously upload a log file recording the document creation process and perform inspection based on the log. For example, a log file containing the editing history of a word processing software can be uploaded. The inspection unit inspects the documents uploaded by the upload unit. For example, a generative AI checker analyzes the document's writing style and vocabulary frequency to detect patterns unique to the generative AI. The inspection unit can also compare the content of the document with other documents generated by the generative AI and evaluate the similarity to increase the likelihood that the document is a generative AI creation. For example, the inspection unit can compare the content of the document with a database of documents generated by the generative AI. Furthermore, the inspection unit can use the emotion estimation function to evaluate the emotional consistency of the document's text and determine whether it was created by a generative AI. For example, it can analyze the emotional expressions in the text and determine whether there is consistency. The calculation unit calculates the generative AI creation rate based on the inspection results by the inspection unit. For example, when calculating the generative AI creation rate, it can perform an individual evaluation for each section of the document and calculate the generative AI creation rate for each section. The calculation unit can also calculate a more accurate generative AI creation rate by taking into account the context and thematic consistency of the document. For example, it can perform context analysis and evaluate thematic consistency. Furthermore, the calculation unit can use the emotion estimation function to analyze emotional fluctuations in the document's text and correct the generative AI creation rate based on those fluctuations. For example, it can analyze fluctuations in the emotion score and reflect them in the generative AI creation rate. The utilization unit utilizes the generative AI creation rate calculated by the calculation unit. For example, it can automatically link the calculated generative AI creation rate to a company or school's evaluation system to streamline the evaluation process. The utilization unit can also provide feedback on the calculation results to users and provide them with suggestions for improvements and points to note in the generative AI's creations.For example, it points out areas where the AI ​​has a high rate of creation and suggests areas for improvement. Furthermore, the utilization unit can use the emotion estimation function to analyze the user's emotional response to the calculation results and revise the evaluation criteria based on that response. For example, it can adjust the evaluation criteria based on the user's emotional score. This allows the AI-created product assessment system according to the embodiment to prevent fraudulent creations by AI-created products and enable companies and schools to make accurate assessments. For example, when reviewing resumes, eliminating works created by AI-created products allows applicants' true abilities to be evaluated. Furthermore, when reviewing school reports, it can accurately determine whether the student created the report themselves.

[0030] The upload unit automatically extracts metadata from documents and can use it as reference information for inspections. For example, when a user uploads documents, the system automatically extracts metadata. For example, the system obtains the creation date and creator information from the properties of a PDF file and saves it as reference information for inspections. This allows the metadata from documents to be automatically extracted and used as reference information for inspections.

[0031] The upload unit simultaneously uploads a log file that records the document creation process, and can perform an inspection based on that log. For example, when a user uploads a document, the upload unit simultaneously uploads the log file that records the creation process. For example, a log file that includes the editing history of a word processing software is uploaded. This allows an inspection to be performed based on the log file that records the document creation process.

[0032] The upload unit also supports voice input or handwritten input, allowing documents in a wider variety of formats to be subject to inspection. For example, the upload unit supports voice input for uploading documents, converting the voice data into text for inspection. For example, voice recognition technology is used to convert the voice data into text. The upload unit also supports handwritten input, converting handwritten documents into digital data for inspection. For example, handwritten character recognition technology is used to convert handwritten documents into digital data. This allows for voice input or handwritten input, allowing documents in a wider variety of formats to be subject to inspection.

[0033] The upload unit enables direct uploading from different platforms, thereby improving user convenience. The upload unit enables direct uploading from cloud storage, for example, allowing users to easily upload materials. For example, direct uploading from Google Drive or Dropbox is supported. The upload unit also enables direct uploading from email, allowing users to directly upload email attachments. This enables direct uploading from different platforms, thereby improving user convenience.

[0034] The inspection unit can analyze the writing style of the material and the frequency of vocabulary use to detect patterns specific to the generative AI. For example, the inspection unit uses a generative AI checker to analyze the writing style of the material and detect patterns specific to the generative AI. For example, it determines the use of generative AI based on the structure of sentences and the characteristics of expression. The inspection unit can also analyze the frequency of vocabulary use to detect patterns specific to the generative AI. For example, it analyzes the frequency of specific vocabulary and determines the use of generative AI. In this way, it is possible to analyze the writing style of the material and the frequency of vocabulary use to detect patterns specific to the generative AI.

[0035] The inspection unit compares the contents of the document with other documents generated by the generative AI and evaluates the similarity, thereby increasing the likelihood that the document was created by the generative AI. The inspection unit, for example, compares the contents of the document with other documents generated by the generative AI and evaluates the similarity. For example, it compares the contents with a document database generated by the generative AI. The inspection unit can also use cosine similarity or the Jaccard coefficient to evaluate the similarity. For example, cosine similarity is used to calculate the angle between two vectors in a vector space model, and the smaller the angle, the higher the similarity is determined to be. In this way, the possibility that the document was created by the generative AI can be increased by comparing the contents of the document with other documents generated by the generative AI and evaluating the similarity.

[0036] The inspection unit can also analyze document images and diagrams to detect the possibility that the visual content was generated by generative AI. For example, the inspection unit uses a generative AI checker to analyze document images and diagrams to detect visual content that may have been generated by generative AI. For example, it uses image recognition technology to detect patterns that are unique to generative AI. The inspection unit can also determine the use of generative AI based on specific patterns and features when analyzing diagrams. For example, it can analyze the structure and design features of diagrams to determine the use of generative AI. This makes it possible to analyze document images and diagrams to detect the possibility that the visual content was generated by generative AI.

[0037] The inspection unit can use different generative AI models to evaluate materials from multiple perspectives and make a comprehensive judgment. The inspection unit can, for example, use different generative AI models to evaluate materials and make a comprehensive judgment. For example, it can analyze document features using GPT-3 or BERT. The inspection unit can also combine multiple generative AI models to make an evaluation. For example, it can analyze document features using both GPT-3 and BERT and make a comprehensive judgment. This allows it to evaluate materials from multiple perspectives and make a comprehensive judgment using different generative AI models.

[0038] The calculation unit can perform an individual evaluation for each section of the document and calculate the generation AI creation rate for each section. The calculation unit, for example, performs an individual evaluation for each section of the document and calculates the generation AI creation rate. For example, the calculation unit performs an evaluation for each section, such as the introduction, main body, and conclusion, and calculates the generation AI creation rate for each. The calculation unit can also perform an evaluation for each chapter or paragraph in the evaluation for each section. For example, the calculation unit can perform an evaluation for each chapter and calculate the generation AI creation rate. This makes it possible to perform an individual evaluation for each section of the document and calculate the generation AI creation rate for each section.

[0039] The calculation unit can calculate a more accurate generation AI creation rate by taking into account the context of the material and the consistency of the theme. The calculation unit calculates the generation AI creation rate by taking into account, for example, the context of the material and the consistency of the theme. For example, it performs context analysis and evaluates the consistency of the theme. In addition, the calculation unit can make a judgment in evaluating the consistency of the theme based on the consistency of the context and the continuity of the theme. For example, it analyzes the consistency of the context and evaluates the consistency of the theme. This allows a more accurate generation AI creation rate to be calculated by taking into account the context of the material and the consistency of the theme.

[0040] The calculation unit can combine the calculation result of the generative AI creation rate with other evaluation indicators to perform a comprehensive evaluation. The calculation unit, for example, combines the calculation result of the generative AI creation rate with the grammatical error rate to perform a comprehensive evaluation. For example, the calculation unit corrects the generative AI creation rate based on the frequency of grammatical errors. The calculation unit can also combine vocabulary diversity as an evaluation indicator. For example, vocabulary diversity is evaluated and reflected in the generative AI creation rate. This allows the calculation result of the generative AI creation rate to be combined with other evaluation indicators to perform a comprehensive evaluation.

[0041] The calculation unit can customize the calculation results of the generative AI creation rate for different industries or applications, and provide an evaluation that meets specific needs. For example, the calculation unit can customize the calculation results of the generative AI creation rate for different industries and applications, and provide an evaluation that meets specific needs. For example, the calculation unit can perform an evaluation that specializes in technical documents or marketing materials. The calculation unit can also customize the evaluation for different applications. For example, the calculation unit can perform an evaluation that meets educational applications or business applications. This makes it possible to customize the calculation results of the generative AI creation rate for different industries and applications, and provide an evaluation that meets specific needs.

[0042] The Utilization Department can automatically link the calculated results of the generative AI creation rate to the evaluation system of a company or school, thereby streamlining the evaluation process. For example, the Utilization Department can automatically link the calculated results of the generative AI creation rate to a company's evaluation system, thereby streamlining the evaluation process. For example, the generative AI creation rate can be automatically reflected in the hiring process. The Utilization Department can also link the generative AI creation rate to a school's grade evaluation system. For example, the generative AI creation rate can be automatically reflected in report evaluations. In this way, the calculated results of the generative AI creation rate can be automatically linked to the evaluation system of a company or school, thereby streamlining the evaluation process.

[0043] The utilization unit can provide feedback on the calculation results to the user and provide improvements and points to note for the generated AI creation. For example, the utilization unit can provide feedback on the calculation results of the generated AI creation rate to the user and provide improvements and points to note for the generated AI creation. For example, it can point out areas where the generated AI creation rate is high and suggest improvements. The utilization unit can also provide specific grammar corrections and content improvements in the feedback. For example, it can point out grammar corrections and suggest specific correction methods. In this way, the calculation results can be provided as feedback to the user and improvements and points to note for the generated AI creation can be provided.

[0044] The utilization department can integrate the calculation results with other evaluation data and perform a comprehensive evaluation. For example, the utilization department can integrate the calculation results of the generative AI creation rate with other evaluation data and perform a comprehensive evaluation. For example, the utilization department can perform an evaluation in combination with interview results and test scores. The utilization department can also use different evaluation criteria when integrating the evaluation data. For example, the utilization department can perform an evaluation based on international standards or industry standards. This allows the calculation results to be integrated with other evaluation data and perform a comprehensive evaluation.

[0045] The utilization department can apply the calculation results to different evaluation criteria and provide an evaluation from a global perspective. For example, the utilization department can apply the calculation results of the generative AI creation rate to different evaluation criteria and provide an evaluation from a global perspective. For example, the utilization department can perform an evaluation based on international standards or industry standards. In addition, the utilization department can perform an evaluation according to a specific industry or use when applying the evaluation criteria. For example, an evaluation specialized for the medical industry or the education industry. This allows the calculation results to be applied to different evaluation criteria and provide an evaluation from a global perspective.

[0046] The Utilization Department can automatically link the calculated results of the generative AI creation rate to the evaluation system of a company or school, thereby streamlining the evaluation process. For example, the Utilization Department can automatically link the calculated results of the generative AI creation rate to a company's evaluation system, thereby streamlining the evaluation process. For example, the generative AI creation rate can be automatically reflected in the hiring process. The Utilization Department can also link the generative AI creation rate to a school's grade evaluation system. For example, the generative AI creation rate can be automatically reflected in report evaluations. In this way, the calculated results of the generative AI creation rate can be automatically linked to the evaluation system of a company or school, thereby streamlining the evaluation process.

[0047] The utilization department can integrate the calculation results with other evaluation data and perform a comprehensive evaluation. For example, the utilization department can integrate the calculation results of the generative AI creation rate with other evaluation data and perform a comprehensive evaluation. For example, the utilization department can perform an evaluation in combination with interview results and test scores. The utilization department can also use different evaluation criteria when integrating the evaluation data. For example, the utilization department can perform an evaluation based on international standards or industry standards. This allows the calculation results to be integrated with other evaluation data and perform a comprehensive evaluation.

[0048] The utilization department can apply the calculation results to different evaluation criteria and provide an evaluation from a global perspective. For example, the utilization department can apply the calculation results of the generative AI creation rate to different evaluation criteria and provide an evaluation from a global perspective. For example, the utilization department can perform an evaluation based on international standards or industry standards. In addition, the utilization department can perform an evaluation according to a specific industry or use when applying the evaluation criteria. For example, an evaluation specialized for the medical industry or the education industry. This allows the calculation results to be applied to different evaluation criteria and provide an evaluation from a global perspective.

[0049] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0050] The generative AI creation assessment system can further include a behavior analysis unit that analyzes the user's behavior history. The behavior analysis unit, for example, analyzes the keyboard input speed and mouse movements when the user creates a document to detect the use of generative AI. For example, if the keyboard input speed is abnormally fast or the mouse movements have a certain pattern, the use of generative AI can be suspected. The behavior analysis unit can also analyze the application usage history when the user creates a document to detect the use of generative AI. For example, if a specific generative AI tool is frequently used, it can determine that the document is likely created by generative AI. This makes it possible to analyze the user's behavior history and detect the use of generative AI.

[0051] The generative AI creation judgment system can further include an external data reference unit for evaluating the credibility of materials. The external data reference unit, for example, compares the content of the material with public databases on the Internet to evaluate its credibility. For example, it compares it with academic papers or news articles to confirm whether the content of the material matches existing information. The external data reference unit can also verify the source of the material and evaluate its credibility. For example, it can check whether the cited literature actually exists and evaluate the credibility of the material. This makes it possible to evaluate the credibility of materials by referencing external data.

[0052] The generative AI creation assessment system can further include a visual analysis unit that analyzes the visual characteristics of the document. The visual analysis unit, for example, analyzes the characteristics of images and graphs contained in the document to detect the use of generative AI. For example, it analyzes the image resolution and color patterns to detect images that may have been generated by generative AI. The visual analysis unit can also analyze the layout and design characteristics of the document to detect the use of generative AI. For example, if a specific design template is used, it can determine that the document is likely to have been created by generative AI. This makes it possible to analyze the visual characteristics of the document and detect the use of generative AI.

[0053] The system for determining generative AI creations can further include an audio analysis unit that analyzes the audio data of the materials. The audio analysis unit, for example, analyzes the audio data included in the materials to detect the use of generative AI. For example, it analyzes the tone and rhythm of the audio to detect audio that may have been generated by generative AI. The audio analysis unit can also convert the content of the audio data into text and analyze that text. For example, if the content of the audio data matches a specific pattern, it can determine that the audio is likely to have been created by generative AI. This makes it possible to analyze the audio data of the materials and detect the use of generative AI.

[0054] The generative AI creation judgment system can further include a profile analysis unit that analyzes the profile of the creator of the material. The profile analysis unit, for example, analyzes the creator's past creations and activity history to detect the use of generative AI. For example, it compares past creations with current materials and evaluates consistency in writing style and expression. The profile analysis unit can also evaluate the creator's expertise and skills and determine whether the content of the material matches those skills. For example, if the material contains content that requires specific expertise, it evaluates whether the content matches the creator's skills. This makes it possible to analyze the profile of the creator of the material and detect the use of generative AI.

[0055] The processing flow of the first embodiment will be briefly explained below.

[0056] Step 1: The upload unit uploads documents from the user. For example, documents such as resumes or school reports can be uploaded in PDF or Word file format. The upload unit can also automatically extract metadata from the documents and use it as reference information for the inspection. For example, it can obtain the creation date and creator information from the PDF file properties and save it as reference information for the inspection. Furthermore, the upload unit can simultaneously upload a log file that records the document creation process and perform the inspection based on that log. For example, it can upload a log file that contains the editing history of a word processing software. Step 2: The inspection unit inspects the materials uploaded by the upload unit. For example, the generation AI checker analyzes the material's writing style and vocabulary frequency to detect patterns unique to the generation AI. The inspection unit can also compare the content of the material with other documents generated by the generation AI and evaluate the degree of similarity, thereby increasing the likelihood that the material was created by the generation AI. For example, it can compare the content with a database of documents generated by the generation AI. Furthermore, the inspection unit can use an emotion estimation function to evaluate the consistency of emotions from the text of the material and determine whether it was created by the generation AI. For example, it can analyze emotional expressions in the text and determine whether there is consistency. Step 3: The calculation unit calculates the generation AI creation rate based on the results of the inspection by the inspection unit. For example, when calculating the generation AI creation rate, it evaluates each section of the document individually and calculates the generation AI creation rate for each section. The calculation unit can also calculate a more accurate generation AI creation rate by taking into account the context and thematic consistency of the document. For example, it can perform context analysis and evaluate thematic consistency. Furthermore, the calculation unit can use an emotion estimation function to analyze fluctuations in emotions toward the text of the document and correct the generation AI creation rate based on those fluctuations. For example, it can analyze fluctuations in emotion scores and reflect them in the generation AI creation rate. Step 4: The utilization unit utilizes the generative AI creation rate calculated by the calculation unit. For example, the calculation results of the generative AI creation rate can be automatically linked to the evaluation system of a company or school, streamlining the evaluation process. The utilization unit can also provide feedback on the calculation results to the user and provide suggestions for improvements and points to note in the generative AI creations. For example, it can point out areas where the generative AI creation rate is high and suggest areas for improvement. Furthermore, the utilization unit can use the emotion estimation function to analyze the user's emotional reaction to the calculation results and revise the evaluation criteria based on that reaction. For example, it can adjust the evaluation criteria based on the user's emotional score.

[0057] (Example 2) The AI-generated product judgment system according to an embodiment of the present invention is a specialized service for determining whether a product was created by AI-generated products. In this system, a AI-generated product checker inspects materials uploaded by users, calculates the AI-generated product creation rate, and companies and schools utilize the results. As a result, the AI-generated product judgment system prevents fraudulent creations by AI-generated products and allows companies and schools to make accurate judgments.

[0058] A generative AI creation assessment system according to an embodiment includes an upload unit, an inspection unit, a calculation unit, and an utilization unit. The upload unit uploads documents from a user. For example, documents such as resumes and school reports can be uploaded in PDF or Word file format. The upload unit can also automatically extract metadata from the documents and use it as reference information for inspection. For example, the upload unit can obtain the creation date and creator information from the PDF file properties and save it as reference information for inspection. The upload unit can also simultaneously upload a log file recording the document creation process and perform inspection based on the log. For example, a log file containing the editing history of a word processing software can be uploaded. The inspection unit inspects the documents uploaded by the upload unit. For example, a generative AI checker analyzes the document's writing style and vocabulary frequency to detect patterns unique to the generative AI. The inspection unit can also compare the content of the document with other documents generated by the generative AI and evaluate the similarity to increase the likelihood that the document is a generative AI creation. For example, the inspection unit can compare the content of the document with a database of documents generated by the generative AI. Furthermore, the inspection unit can use the emotion estimation function to evaluate the emotional consistency of the document's text and determine whether it was created by a generative AI. For example, it can analyze the emotional expressions in the text and determine whether there is consistency. The calculation unit calculates the generative AI creation rate based on the inspection results by the inspection unit. For example, when calculating the generative AI creation rate, it can perform an individual evaluation for each section of the document and calculate the generative AI creation rate for each section. The calculation unit can also calculate a more accurate generative AI creation rate by taking into account the context and thematic consistency of the document. For example, it can perform context analysis and evaluate thematic consistency. Furthermore, the calculation unit can use the emotion estimation function to analyze emotional fluctuations in the document's text and correct the generative AI creation rate based on those fluctuations. For example, it can analyze fluctuations in the emotion score and reflect them in the generative AI creation rate. The utilization unit utilizes the generative AI creation rate calculated by the calculation unit. For example, it can automatically link the calculated generative AI creation rate to a company or school's evaluation system to streamline the evaluation process. The utilization unit can also provide feedback on the calculation results to users and provide them with suggestions for improvements and points to note in the generative AI's creations.For example, it points out areas where the AI ​​has a high rate of creation and suggests areas for improvement. Furthermore, the utilization unit can use the emotion estimation function to analyze the user's emotional response to the calculation results and revise the evaluation criteria based on that response. For example, it can adjust the evaluation criteria based on the user's emotional score. This allows the AI-created product assessment system according to the embodiment to prevent fraudulent creations by AI-created products and enable companies and schools to make accurate assessments. For example, when reviewing resumes, eliminating works created by AI-created products allows applicants' true abilities to be evaluated. Furthermore, when reviewing school reports, it can accurately determine whether the student created the report themselves.

[0059] The upload unit automatically extracts metadata from documents and can use it as reference information for inspections. For example, when a user uploads documents, the system automatically extracts metadata. For example, the system obtains the creation date and creator information from the properties of a PDF file and saves it as reference information for inspections. This allows the metadata from documents to be automatically extracted and used as reference information for inspections.

[0060] The upload unit simultaneously uploads a log file that records the document creation process, and can perform an inspection based on that log. For example, when a user uploads a document, the upload unit simultaneously uploads the log file that records the creation process. For example, a log file that includes the editing history of a word processing software is uploaded. This allows an inspection to be performed based on the log file that records the document creation process.

[0061] The uploading unit can use the emotion estimation function to analyze the user's emotion at the time of uploading and use the result as information to supplement the credibility of the materials. For example, the uploading unit analyzes the user's emotion in real time when uploading materials and uses the result to evaluate the credibility of the materials. For example, the uploading unit analyzes the user's facial expression and voice using a camera or microphone. This allows the user's emotion to be analyzed and used as information to supplement the credibility of the materials.

[0062] The upload unit also supports voice input or handwritten input, allowing documents in a wider variety of formats to be subject to inspection. For example, the upload unit supports voice input for uploading documents, converting the voice data into text for inspection. For example, voice recognition technology is used to convert the voice data into text. The upload unit also supports handwritten input, converting handwritten documents into digital data for inspection. For example, handwritten character recognition technology is used to convert handwritten documents into digital data. This allows for voice input or handwritten input, allowing documents in a wider variety of formats to be subject to inspection.

[0063] The upload unit enables direct uploading from different platforms, thereby improving user convenience. The upload unit enables direct uploading from cloud storage, for example, allowing users to easily upload materials. For example, direct uploading from Google Drive or Dropbox is supported. The upload unit also enables direct uploading from email, allowing users to directly upload email attachments. This enables direct uploading from different platforms, thereby improving user convenience.

[0064] The upload unit can use the emotion estimation function to provide real-time feedback on the user's emotions at the time of uploading, allowing the user to submit documents with peace of mind. For example, the upload unit can analyze the user's emotions in real time when uploading documents and provide feedback on the results. For example, the upload unit can use a camera or microphone to analyze the user's facial expressions and voice, providing a sense of security. This allows the user's emotions to be provided in real time, allowing the user to submit documents with peace of mind.

[0065] The inspection unit can analyze the writing style of the material and the frequency of vocabulary use to detect patterns specific to the generative AI. For example, the inspection unit uses a generative AI checker to analyze the writing style of the material and detect patterns specific to the generative AI. For example, it determines the use of generative AI based on the structure of sentences and the characteristics of expression. The inspection unit can also analyze the frequency of vocabulary use to detect patterns specific to the generative AI. For example, it analyzes the frequency of specific vocabulary and determines the use of generative AI. In this way, it is possible to analyze the writing style of the material and the frequency of vocabulary use to detect patterns specific to the generative AI.

[0066] The inspection unit compares the contents of the document with other documents generated by the generative AI and evaluates the similarity, thereby increasing the likelihood that the document was created by the generative AI. The inspection unit, for example, compares the contents of the document with other documents generated by the generative AI and evaluates the similarity. For example, it compares the contents with a document database generated by the generative AI. The inspection unit can also use cosine similarity or the Jaccard coefficient to evaluate the similarity. For example, cosine similarity is used to calculate the angle between two vectors in a vector space model, and the smaller the angle, the higher the similarity is determined to be. In this way, the possibility that the document was created by the generative AI can be increased by comparing the contents of the document with other documents generated by the generative AI and evaluating the similarity.

[0067] The inspection unit can use the emotion estimation function to evaluate the emotional consistency from the text of the document and determine whether it was created by generative AI. The inspection unit, for example, uses the emotion estimation function to evaluate the emotional consistency from the text of the document. For example, it analyzes emotional expressions in the text and determines whether there is consistency. In addition, the inspection unit can make a judgment in evaluating the emotional consistency based on emotional fluctuations in the context and the degree of agreement of emotions. For example, it analyzes emotional fluctuations in the context and determines whether there is consistency. In this way, it is possible to use the emotion estimation function to evaluate the emotional consistency from the text of the document and determine whether it was created by generative AI.

[0068] The inspection unit can also analyze document images and diagrams to detect the possibility that the visual content was generated by generative AI. For example, the inspection unit uses a generative AI checker to analyze document images and diagrams to detect visual content that may have been generated by generative AI. For example, it uses image recognition technology to detect patterns that are unique to generative AI. The inspection unit can also determine the use of generative AI based on specific patterns and features when analyzing diagrams. For example, it can analyze the structure and design features of diagrams to determine the use of generative AI. This makes it possible to analyze document images and diagrams to detect the possibility that the visual content was generated by generative AI.

[0069] The inspection unit can use different generative AI models to evaluate materials from multiple perspectives and make a comprehensive judgment. The inspection unit can, for example, use different generative AI models to evaluate materials and make a comprehensive judgment. For example, it can analyze document features using GPT-3 or BERT. The inspection unit can also combine multiple generative AI models to make an evaluation. For example, it can analyze document features using both GPT-3 and BERT and make a comprehensive judgment. This allows it to evaluate materials from multiple perspectives and make a comprehensive judgment using different generative AI models.

[0070] The calculation unit can perform an individual evaluation for each section of the document and calculate the generation AI creation rate for each section. The calculation unit, for example, performs an individual evaluation for each section of the document and calculates the generation AI creation rate. For example, the calculation unit performs an evaluation for each section, such as the introduction, main body, and conclusion, and calculates the generation AI creation rate for each. The calculation unit can also perform an evaluation for each chapter or paragraph in the evaluation for each section. For example, the calculation unit can perform an evaluation for each chapter and calculate the generation AI creation rate. This makes it possible to perform an individual evaluation for each section of the document and calculate the generation AI creation rate for each section.

[0071] The calculation unit can calculate a more accurate generation AI creation rate by taking into account the context of the material and the consistency of the theme. The calculation unit calculates the generation AI creation rate by taking into account, for example, the context of the material and the consistency of the theme. For example, it performs context analysis and evaluates the consistency of the theme. In addition, the calculation unit can make a judgment in evaluating the consistency of the theme based on the consistency of the context and the continuity of the theme. For example, it analyzes the consistency of the context and evaluates the consistency of the theme. This allows a more accurate generation AI creation rate to be calculated by taking into account the context of the material and the consistency of the theme.

[0072] The calculation unit can use the emotion estimation function to analyze emotional fluctuations in response to the text of the document and correct the generation AI creation rate based on those fluctuations. The calculation unit, for example, uses the emotion estimation function to analyze emotional fluctuations in response to the text of the document and corrects the generation AI creation rate based on that data. For example, it analyzes fluctuations in emotion scores and reflects this in the generation AI creation rate. The calculation unit can also make a judgment based on temporal changes in emotion and the intensity of emotion in evaluating emotional fluctuations. For example, it analyzes temporal changes in emotion and corrects the generation AI creation rate. This makes it possible to use the emotion estimation function to analyze emotional fluctuations in response to the text of the document and correct the generation AI creation rate based on those fluctuations.

[0073] The calculation unit can combine the calculation result of the generative AI creation rate with other evaluation indicators to perform a comprehensive evaluation. The calculation unit, for example, combines the calculation result of the generative AI creation rate with the grammatical error rate to perform a comprehensive evaluation. For example, the calculation unit corrects the generative AI creation rate based on the frequency of grammatical errors. The calculation unit can also combine vocabulary diversity as an evaluation indicator. For example, vocabulary diversity is evaluated and reflected in the generative AI creation rate. This allows the calculation result of the generative AI creation rate to be combined with other evaluation indicators to perform a comprehensive evaluation.

[0074] The calculation unit can customize the calculation results of the generative AI creation rate for different industries or applications, and provide an evaluation that meets specific needs. For example, the calculation unit can customize the calculation results of the generative AI creation rate for different industries and applications, and provide an evaluation that meets specific needs. For example, the calculation unit can perform an evaluation that specializes in technical documents or marketing materials. The calculation unit can also customize the evaluation for different applications. For example, the calculation unit can perform an evaluation that meets educational applications or business applications. This makes it possible to customize the calculation results of the generative AI creation rate for different industries and applications, and provide an evaluation that meets specific needs.

[0075] The calculation unit can use the emotion estimation function to collect the user's emotional reactions to the calculation results of the generative AI creation rate and adjust the evaluation criteria based on those reactions. The calculation unit, for example, uses the emotion estimation function to collect the user's emotional reactions to the calculation results of the generative AI creation rate and adjust the evaluation criteria based on that data. For example, the calculation unit reviews the evaluation criteria based on the user's emotional score. The calculation unit can also use real-time emotion analysis in collecting the emotional reactions. For example, the calculation unit analyzes the user's emotions in real time and reflects them in the evaluation criteria. This allows the emotion estimation function to collect the user's emotional reactions to the calculation results of the generative AI creation rate and adjust the evaluation criteria based on those reactions.

[0076] The Utilization Department can automatically link the calculated results of the generative AI creation rate to the evaluation system of a company or school, thereby streamlining the evaluation process. For example, the Utilization Department can automatically link the calculated results of the generative AI creation rate to a company's evaluation system, thereby streamlining the evaluation process. For example, the generative AI creation rate can be automatically reflected in the hiring process. The Utilization Department can also link the generative AI creation rate to a school's grade evaluation system. For example, the generative AI creation rate can be automatically reflected in report evaluations. In this way, the calculated results of the generative AI creation rate can be automatically linked to the evaluation system of a company or school, thereby streamlining the evaluation process.

[0077] The utilization unit can provide feedback on the calculation results to the user and provide improvements and points to note for the generated AI creation. For example, the utilization unit can provide feedback on the calculation results of the generated AI creation rate to the user and provide improvements and points to note for the generated AI creation. For example, it can point out areas where the generated AI creation rate is high and suggest improvements. The utilization unit can also provide specific grammar corrections and content improvements in the feedback. For example, it can point out grammar corrections and suggest specific correction methods. In this way, the calculation results can be provided as feedback to the user and improvements and points to note for the generated AI creation can be provided.

[0078] The utilization unit can use the emotion estimation function to analyze the user's emotional reaction to the calculation results and revise the evaluation criteria based on that reaction. The utilization unit, for example, uses the emotion estimation function to analyze the user's emotional reaction to the calculation results and revise the evaluation criteria based on that data. For example, the utilization unit adjusts the evaluation criteria based on the user's emotion score. The utilization unit can also use real-time emotion analysis in analyzing the emotional reaction. For example, the utilization unit analyzes the user's emotion in real time and reflects it in the evaluation criteria. This allows the emotion estimation function to analyze the user's emotional reaction to the calculation results and revise the evaluation criteria based on that reaction.

[0079] The utilization department can integrate the calculation results with other evaluation data and perform a comprehensive evaluation. For example, the utilization department can integrate the calculation results of the generative AI creation rate with other evaluation data and perform a comprehensive evaluation. For example, the utilization department can perform an evaluation in combination with interview results and test scores. The utilization department can also use different evaluation criteria when integrating the evaluation data. For example, the utilization department can perform an evaluation based on international standards or industry standards. This allows the calculation results to be integrated with other evaluation data and perform a comprehensive evaluation.

[0080] The utilization department can apply the calculation results to different evaluation criteria and provide an evaluation from a global perspective. For example, the utilization department can apply the calculation results of the generative AI creation rate to different evaluation criteria and provide an evaluation from a global perspective. For example, the utilization department can perform an evaluation based on international standards or industry standards. In addition, the utilization department can perform an evaluation according to a specific industry or use when applying the evaluation criteria. For example, an evaluation specialized for the medical industry or the education industry. This allows the calculation results to be applied to different evaluation criteria and provide an evaluation from a global perspective.

[0081] The utilization unit can use the emotion estimation function to monitor the user's emotional response to the calculation results in real time, thereby improving the transparency of the evaluation process. For example, the utilization unit can use the emotion estimation function to monitor the user's emotional response to the calculation results in real time, and improve the transparency of the evaluation process based on that data. For example, the utilization unit can review the evaluation criteria based on the user's emotion score. The utilization unit can also analyze the user's emotions in real time monitoring and provide feedback on the results. For example, the utilization unit can analyze the user's emotions in real time and provide a sense of security. This allows the utilization unit to use the emotion estimation function to monitor the user's emotional response to the calculation results in real time, thereby improving the transparency of the evaluation process.

[0082] The Utilization Department can automatically link the calculated results of the generative AI creation rate to the evaluation system of a company or school, thereby streamlining the evaluation process. For example, the Utilization Department can automatically link the calculated results of the generative AI creation rate to a company's evaluation system, thereby streamlining the evaluation process. For example, the generative AI creation rate can be automatically reflected in the hiring process. The Utilization Department can also link the generative AI creation rate to a school's grade evaluation system. For example, the generative AI creation rate can be automatically reflected in report evaluations. In this way, the calculated results of the generative AI creation rate can be automatically linked to the evaluation system of a company or school, thereby streamlining the evaluation process.

[0083] The utilization unit can use the emotion estimation function to analyze the user's emotional reaction to the calculation results and revise the evaluation criteria based on that reaction. The utilization unit, for example, uses the emotion estimation function to analyze the user's emotional reaction to the calculation results and revise the evaluation criteria based on that data. For example, the utilization unit adjusts the evaluation criteria based on the user's emotion score. The utilization unit can also use real-time emotion analysis in analyzing the emotional reaction. For example, the utilization unit analyzes the user's emotion in real time and reflects it in the evaluation criteria. This allows the emotion estimation function to analyze the user's emotional reaction to the calculation results and revise the evaluation criteria based on that reaction.

[0084] The utilization department can integrate the calculation results with other evaluation data and perform a comprehensive evaluation. For example, the utilization department can integrate the calculation results of the generative AI creation rate with other evaluation data and perform a comprehensive evaluation. For example, the utilization department can perform an evaluation in combination with interview results and test scores. The utilization department can also use different evaluation criteria when integrating the evaluation data. For example, the utilization department can perform an evaluation based on international standards or industry standards. This allows the calculation results to be integrated with other evaluation data and perform a comprehensive evaluation.

[0085] The utilization department can apply the calculation results to different evaluation criteria and provide an evaluation from a global perspective. For example, the utilization department can apply the calculation results of the generative AI creation rate to different evaluation criteria and provide an evaluation from a global perspective. For example, the utilization department can perform an evaluation based on international standards or industry standards. In addition, the utilization department can perform an evaluation according to a specific industry or use when applying the evaluation criteria. For example, an evaluation specialized for the medical industry or the education industry. This allows the calculation results to be applied to different evaluation criteria and provide an evaluation from a global perspective.

[0086] The utilization unit can use the emotion estimation function to monitor the user's emotional response to the calculation results in real time, thereby improving the transparency of the evaluation process. For example, the utilization unit can use the emotion estimation function to monitor the user's emotional response to the calculation results in real time, and improve the transparency of the evaluation process based on that data. For example, the utilization unit can review the evaluation criteria based on the user's emotion score. The utilization unit can also analyze the user's emotions in real time monitoring and provide feedback on the results. For example, the utilization unit can analyze the user's emotions in real time and provide a sense of security. This allows the utilization unit to use the emotion estimation function to monitor the user's emotional response to the calculation results in real time, thereby improving the transparency of the evaluation process.

[0087] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0088] The generative AI creation assessment system can further include a behavior analysis unit that analyzes the user's behavior history. The behavior analysis unit, for example, analyzes the keyboard input speed and mouse movements when the user creates a document to detect the use of generative AI. For example, if the keyboard input speed is abnormally fast or the mouse movements have a certain pattern, the use of generative AI can be suspected. The behavior analysis unit can also analyze the application usage history when the user creates a document to detect the use of generative AI. For example, if a specific generative AI tool is frequently used, it can determine that the document is likely created by generative AI. This makes it possible to analyze the user's behavior history and detect the use of generative AI.

[0089] The generative AI creation judgment system can further include an external data reference unit for evaluating the credibility of materials. The external data reference unit, for example, compares the content of the material with public databases on the Internet to evaluate its credibility. For example, it compares it with academic papers or news articles to confirm whether the content of the material matches existing information. The external data reference unit can also verify the source of the material and evaluate its credibility. For example, it can check whether the cited literature actually exists and evaluate the credibility of the material. This makes it possible to evaluate the credibility of materials by referencing external data.

[0090] The generative AI creation assessment system can further include a visual analysis unit that analyzes the visual characteristics of the document. The visual analysis unit, for example, analyzes the characteristics of images and graphs contained in the document to detect the use of generative AI. For example, it analyzes the image resolution and color patterns to detect images that may have been generated by generative AI. The visual analysis unit can also analyze the layout and design characteristics of the document to detect the use of generative AI. For example, if a specific design template is used, it can determine that the document is likely to have been created by generative AI. This makes it possible to analyze the visual characteristics of the document and detect the use of generative AI.

[0091] The system for determining generative AI creations can further include an audio analysis unit that analyzes the audio data of the materials. The audio analysis unit, for example, analyzes the audio data included in the materials to detect the use of generative AI. For example, it analyzes the tone and rhythm of the audio to detect audio that may have been generated by generative AI. The audio analysis unit can also convert the content of the audio data into text and analyze that text. For example, if the content of the audio data matches a specific pattern, it can determine that the audio is likely to have been created by generative AI. This makes it possible to analyze the audio data of the materials and detect the use of generative AI.

[0092] The generative AI creation judgment system can further include a profile analysis unit that analyzes the profile of the creator of the material. The profile analysis unit, for example, analyzes the creator's past creations and activity history to detect the use of generative AI. For example, it compares past creations with current materials and evaluates consistency in writing style and expression. The profile analysis unit can also evaluate the creator's expertise and skills and determine whether the content of the material matches those skills. For example, if the material contains content that requires specific expertise, it evaluates whether the content matches the creator's skills. This makes it possible to analyze the profile of the creator of the material and detect the use of generative AI.

[0093] The generative AI creation judgment system can further include an emotion evaluation unit that estimates the user's emotions and evaluates the credibility of the material based on those emotions. The emotion evaluation unit, for example, analyzes the user's emotions in real time when uploading materials and uses the results to evaluate the credibility of the material. For example, it may use a camera or microphone to analyze the user's facial expressions and voice to evaluate the level of tension or anxiety. The emotion evaluation unit can also analyze the user's emotional response to the content of the material and evaluate the credibility of the material based on that response. For example, it may evaluate whether the user's emotions toward the content of the material are consistent. This makes it possible to estimate the user's emotions and evaluate the credibility of the material based on those emotions.

[0094] The generative AI creation judgment system can further include an emotional feedback unit that estimates the user's emotions and provides feedback based on those emotions. The emotional feedback unit, for example, analyzes the user's emotions in real time when uploading materials and provides feedback based on those emotions. For example, it can analyze the user's facial expressions and voice using a camera or microphone to provide a sense of security. The emotional feedback unit can also analyze the user's emotional response to the material inspection results and adjust the feedback based on those responses. For example, if the user's emotions regarding the inspection results are negative, it can provide feedback that specifically indicates areas for improvement. This makes it possible to estimate the user's emotions and provide feedback based on those emotions.

[0095] The generative AI creation judgment system can further include an emotion adjustment unit that estimates the user's emotions and adjusts the evaluation criteria based on those emotions. The emotion adjustment unit, for example, analyzes the user's emotional response to the material inspection results in real time and adjusts the evaluation criteria based on that data. For example, it reviews the evaluation criteria based on the user's emotional score. The emotion adjustment unit can also use real-time emotion analysis to collect emotional responses. For example, it analyzes the user's emotions in real time and reflects them in the evaluation criteria. This makes it possible to estimate the user's emotions and adjust the evaluation criteria based on those emotions.

[0096] The generative AI creation judgment system can further include an emotion improvement unit that estimates the user's emotions and suggests improvements to the material based on those emotions. The emotion improvement unit, for example, analyzes the user's emotional response to the material inspection results in real time and suggests improvements based on that data. For example, it suggests specific improvements based on the user's emotion score. The emotion improvement unit can also use real-time emotion analysis to collect the emotional responses. For example, it analyzes the user's emotions in real time and suggests improvements based on the results. This makes it possible to estimate the user's emotions and suggest improvements to the material based on those emotions.

[0097] The generative AI creation judgment system can further include an emotion evaluation providing unit that estimates the user's emotions and provides evaluation results for the material based on those emotions. The emotion evaluation providing unit, for example, analyzes the user's emotional response to the material inspection results in real time and provides evaluation results based on that data. For example, it adjusts the evaluation results based on the user's emotion score. The emotion evaluation providing unit can also use real-time emotion analysis in collecting emotional responses. For example, it analyzes the user's emotions in real time and provides evaluation results based on the results. This makes it possible to estimate the user's emotions and provide evaluation results for the material based on those emotions.

[0098] The processing flow of the second embodiment will be briefly explained below.

[0099] Step 1: The upload unit uploads documents from the user. For example, documents such as resumes or school reports can be uploaded in PDF or Word file format. The upload unit can also automatically extract metadata from the documents and use it as reference information for the inspection. For example, it can obtain the creation date and creator information from the PDF file properties and save it as reference information for the inspection. Furthermore, the upload unit can simultaneously upload a log file that records the document creation process and perform the inspection based on that log. For example, it can upload a log file that contains the editing history of a word processing software. Step 2: The inspection unit inspects the materials uploaded by the upload unit. For example, the generation AI checker analyzes the material's writing style and vocabulary frequency to detect patterns unique to the generation AI. The inspection unit can also compare the content of the material with other documents generated by the generation AI and evaluate the degree of similarity, thereby increasing the likelihood that the material was created by the generation AI. For example, it can compare the content with a database of documents generated by the generation AI. Furthermore, the inspection unit can use an emotion estimation function to evaluate the consistency of emotions from the text of the material and determine whether it was created by the generation AI. For example, it can analyze emotional expressions in the text and determine whether there is consistency. Step 3: The calculation unit calculates the generation AI creation rate based on the results of the inspection by the inspection unit. For example, when calculating the generation AI creation rate, it evaluates each section of the document individually and calculates the generation AI creation rate for each section. The calculation unit can also calculate a more accurate generation AI creation rate by taking into account the context and thematic consistency of the document. For example, it can perform context analysis and evaluate thematic consistency. Furthermore, the calculation unit can use an emotion estimation function to analyze fluctuations in emotions toward the text of the document and correct the generation AI creation rate based on those fluctuations. For example, it can analyze fluctuations in emotion scores and reflect them in the generation AI creation rate. Step 4: The utilization unit utilizes the generative AI creation rate calculated by the calculation unit. For example, the calculation results of the generative AI creation rate can be automatically linked to the evaluation system of a company or school, streamlining the evaluation process. The utilization unit can also provide feedback on the calculation results to the user and provide suggestions for improvements and points to note in the generative AI creations. For example, it can point out areas where the generative AI creation rate is high and suggest areas for improvement. Furthermore, the utilization unit can use the emotion estimation function to analyze the user's emotional reaction to the calculation results and revise the evaluation criteria based on that reaction. For example, it can adjust the evaluation criteria based on the user's emotional score.

[0100] The specific processing unit 290 transmits the result of the specific processing to the smart device 14. In the smart device 14, the control unit 46A causes the output device 40 to output the result of the specific processing. The microphone 38B acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0101] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (Internet Search<URL: https: / / openai.com / blog / chatgpt> Examples of generative AIs include the data generation model 58, such as a neural network model (e.g., a neural network model), and a neural network model (e.g., a neural network model). The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating speech, text data indicating text, and image data indicating an image is also input to the data generation model 58. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specification processing unit 290 performs the above-mentioned specification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0102] Furthermore, the processing by the data processing system 10 described above is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart device 14, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart device 14. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the smart device 14 or an external device, and the smart device 14 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0103] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0104] 3, the data processing system 210 includes the data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0105] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0106] The smart glasses 214 include a computer 36, a microphone 238, a speaker 240, a camera 42, and a communication I / F 44. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, and the camera 42 are also connected to the bus 52.

[0107] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0108] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0109] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0110] Fig. 4 shows an example of the main functions of the data processing device 12 and the smart glasses 214. As shown in Fig. 4, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0111] The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0112] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

[0113] In the smart glasses 214, the specific processing is performed by the processor 46. A specific processing program 60 is stored in the storage 50. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. Note that the smart glasses 214 may have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59.

[0114] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0115] The specific processing unit 290 transmits the result of the specific processing to the smart glasses 214. In the smart glasses 214, the control unit 46A causes the speaker 240 to output the result of the specific processing. The microphone 238 acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0116] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0117] The data processing system 210 according to the second embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 210 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart glasses 214, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart glasses 214. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the smart glasses 214 or an external device, etc., and the smart glasses 214 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0118] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0119] 5, the data processing system 310 includes the data processing device 12 and a headset type terminal 314. An example of the data processing device 12 is a server.

[0120] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0121] The headset type terminal 314 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication I / F 44, and a display 343. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, the camera 42, and the display 343 are also connected to the bus 52.

[0122] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0123] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0124] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0125] Fig. 6 shows an example of the main functions of the data processing device 12 and the headset type terminal 314. As shown in Fig. 6, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0126] The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0127] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

[0128] In the headset type terminal 314, the identification process is performed by the processor 46. A identification program 60 is stored in the storage 50. The processor 46 reads the identification program 60 from the storage 50 and executes the read identification program 60 on the RAM 48. The identification process is realized by the processor 46 operating as a control unit 46A in accordance with the identification program 60 executed on the RAM 48. Note that the headset type terminal 314 may also have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59.

[0129] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0130] The specific processing unit 290 transmits the result of the specific processing to the headset type terminal 314. In the headset type terminal 314, the control unit 46A causes the speaker 240 and the display 343 to output the result of the specific processing. The microphone 238 acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0131] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0132] The data processing system 310 according to the third embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 310 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the headset type terminal 314, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the headset type terminal 314. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the headset type terminal 314 or an external device, etc., and the headset type terminal 314 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0133] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0134] 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.

[0135] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0136] The robot 414 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication I / F 44, and a control target 443. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, the camera 42, and the control target 443 are also connected to the bus 52.

[0137] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0138] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS image sensor or a CCD image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0139] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0140] The control object 443 includes a display device, LEDs in the eyes, and motors that drive the arms, hands, and feet. The posture and gestures of the robot 414 are controlled by controlling the motors of the arms, hands, and feet. Some of the emotions of the robot 414 can be expressed by controlling these motors. In addition, the facial expressions of the robot 414 can also be expressed by controlling the light emission state of the LEDs in the eyes of the robot 414.

[0141] Fig. 8 shows an example of the main functions of the data processing device 12 and the robot 414. As shown in Fig. 8, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0142] The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0143] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

[0144] In the robot 414, the processor 46 performs the identification process. A identification program 60 is stored in the storage 50. The processor 46 reads the identification program 60 from the storage 50 and executes the read identification program 60 on the RAM 48. The identification process is realized by the processor 46 operating as a control unit 46A in accordance with the identification program 60 executed on the RAM 48. The robot 414 may have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59.

[0145] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0146] The specific processing unit 290 transmits the result of the specific processing to the robot 414. In the robot 414, the control unit 46A causes the speaker 240 and the control target 443 to output the result of the specific processing. The microphone 238 acquires voice indicating a user input regarding the result of the specific processing. The control unit 46A transmits voice data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the voice data.

[0147] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0148] The data processing system 410 according to the fourth embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 410 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the robot 414, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the robot 414. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the robot 414 or an external device, etc., and the robot 414 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0149] The emotion identification model 59 as an emotion engine may determine the user's emotion according to a specific mapping. Specifically, the emotion identification model 59 may determine the user's emotion according to an emotion map (see FIG. 9), which is a specific mapping. Similarly, the emotion identification model 59 may determine the robot's emotion, and the identification processing unit 290 may perform identification processing using the robot's emotion.

[0150] FIG. 9 illustrates an emotion map 400 on which multiple emotions are mapped. In the emotion map 400, emotions are arranged in concentric circles radiating from the center. Emotions closer to the center of the concentric circles are more primitive. Emotions representing states and behaviors arising from a state of mind are arranged on the outer edges of the concentric circles. The concept of emotion encompasses both emotions and mental states. Emotions generally generated from reactions occurring in the brain are arranged on the left side of the concentric circles. Emotions generally induced by situational judgment are arranged on the right side of the concentric circles. Emotions generally generated from reactions occurring in the brain and induced by situational judgment are arranged on the upper and lower sides of the concentric circles. Furthermore, the emotion of "pleasure" is arranged on the upper side of the concentric circles, and the emotion of "discomfort" is arranged on the lower side. In this way, in the emotion map 400, multiple emotions are mapped based on the structure by which emotions are generated, and emotions that tend to occur simultaneously are mapped close to each other.

[0151] These emotions are distributed in the 3 o'clock direction on emotion map 400, and typically fluctuate between relief and anxiety. In the right half of emotion map 400, situational awareness dominates over internal sensations, resulting in a sense of calm.

[0152] The inside of emotion map 400 represents what is going on in the mind, and the outside of emotion map 400 represents behavior, so the further you go outside emotion map 400, the more visible the emotions become (the more they are expressed in behavior).

[0153] Human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, a state of discomfort is expressed, and when they approach the ideal, a state of pleasure is expressed. Emotions can also be created for robots, cars, and motorcycles, based on various balances, such as posture and remaining battery life. When these balances deviate from the ideal, a state of discomfort is expressed, and when they approach the ideal, a state of pleasure is expressed. An emotion map can be generated, for example, based on Dr. Mitsuyoshi's emotion map (Research on speech emotion recognition and brain physiological signal analysis systems for emotions, Tokushima University, doctoral dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map lists emotions belonging to the area called "reaction," where sensation is dominant. The right half of the emotion map lists emotions belonging to the area called "situation," where situational awareness is dominant.

[0154] The emotion map defines two emotions that promote learning. One is a negative emotion on the situation side, around the middle of "repentance" or "reflection." In other words, this occurs when the robot experiences negative emotions such as "I never want to feel this way again" or "I don't want to be scolded again." The other is a positive emotion on the response side, around "desire." In other words, this occurs when the robot experiences positive feelings such as "I want more" or "I want to know more."

[0155] The emotion identification model 59 inputs user input into a pre-trained neural network, obtains emotion values ​​indicating each emotion shown in the emotion map 400, and determines the user's emotion. This neural network is pre-trained based on multiple pieces of training data that are combinations of user input and emotion values ​​indicating each emotion shown in the emotion map 400. Furthermore, this neural network is trained so that emotions that are located close to each other have similar values, as in the emotion map 900 shown in FIG. 10. FIG. 10 shows an example in which multiple emotions, "relieved," "calm," and "reassuring," have similar emotion values.

[0156] In the above embodiment, an example was given in which a specific process is performed by one computer 22, but the technology disclosed herein is not limited to this, and distributed processing of the specific process may be performed by multiple computers including computer 22.

[0157] In the above embodiment, an example in which the specific processing program 56 is stored in the storage 32 has been described, but the technology of the present disclosure is not limited to this. For example, the specific processing program 56 may be stored in a portable, computer-readable, non-transitory storage medium such as a USB (Universal Serial Bus) memory. The specific processing program 56 stored in the non-transitory storage medium is installed in the computer 22 of the data processing device 12. The processor 28 executes the specific processing in accordance with the specific processing program 56.

[0158] 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.

[0159] It is not necessary to store all of the specific processing program 56 in a storage device such as a server connected to the data processing device 12 via the network 54, or to store all of the specific processing program 56 in the storage 32; only a portion of the specific processing program 56 may be stored.

[0160] The hardware resource for executing a specific process can be any of the following types of processors: A processor, for example, is a CPU, which is a general-purpose processor that functions as a hardware resource for executing a specific process by executing software, i.e., a program. A processor also includes a dedicated electrical circuit, such as an FPGA (Field-Programmable Gate Array), a PLD (Programmable Logic Device), or an ASIC (Application Specific Integrated Circuit), which is a processor with a circuit configuration designed specifically for executing a specific process. Each processor has built-in or connected memory, and each processor uses the memory to execute the specific process.

[0161] The hardware resource that executes the specific process may be configured with one of these various processors, or may be configured with a combination of two or more processors of the same or different types (for example, a combination of multiple FPGAs, or a combination of a CPU and an FPGA). Also, the hardware resource that executes the specific process may be a single processor.

[0162] As an example of a system configured with a single processor, first, one processor is configured by combining one or more CPUs and software, and this processor functions as a hardware resource that executes a specific process. Second, there is a system that uses a processor that realizes the functions of an entire system including multiple hardware resources that execute a specific process on a single IC chip, as typified by SoC (System-on-a-chip). In this way, a specific process is realized using one or more of the above-mentioned various processors as hardware resources.

[0163] Furthermore, the hardware structure of these various processors can be, more specifically, an electric circuit that combines circuit elements such as semiconductor devices. The specific processing described above is merely an example. Therefore, it goes without saying that unnecessary steps may be deleted, new steps may be added, or the processing order may be rearranged, without departing from the spirit of the invention.

[0164] In the above example, the first to fourth embodiments have been described separately, but some or all of these embodiments may be combined. The smart device 14, smart glasses 214, headset terminal 314, and robot 414 are merely examples, and they may be combined, or other devices may be used. In the above example, the first and second embodiments have been described separately, but they may be combined.

[0165] The above-described description and illustrations are a detailed explanation of the parts related to the technology of the present disclosure and are merely an example of the technology of the present disclosure. For example, the above description of the configuration, functions, actions, and effects is an explanation of an example of the configuration, functions, actions, and effects of the parts related to the technology of the present disclosure. Therefore, it goes without saying that unnecessary parts may be deleted, new elements may be added, or replacements may be made to the above-described description and illustrations within the scope of the gist of the technology of the present disclosure. Furthermore, to avoid confusion and facilitate understanding of the parts related to the technology of the present disclosure, the above-described description and illustrations omit explanations of common technical knowledge that do not require particular explanation to enable the implementation of the technology of the present disclosure.

[0166] All publications, patent applications, and technical standards mentioned in this specification are herein incorporated by reference to the same extent as if each individual publication, patent application, or technical standard was specifically and individually indicated to be incorporated by reference. [Explanation of symbols]

[0167] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Device 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robot

Claims

1. an upload unit for uploading materials from users; an inspection unit that inspects the materials uploaded by the upload unit; a calculation unit that calculates a generation AI creation rate based on the inspection result by the inspection unit; and a utilization unit that utilizes the generated AI creation rate calculated by the calculation unit. A system characterized by:

2. The upload unit Automatically extract metadata from materials and use it as reference information for inspection 2. The system of claim 1.

3. The inspection unit Analyze the writing style and frequency of vocabulary in the document to detect patterns specific to generative AI 2. The system of claim 1.

4. The calculation unit Each section of the document is evaluated individually to calculate the AI ​​generation rate for each section.

2. The system of claim 1.

5. The utilization part is The results of the AI ​​creation rate calculations will be automatically linked to the evaluation systems of companies and schools, streamlining the evaluation process.

2. The system of claim 1.

6. The upload unit Analyzing user sentiment at the time of uploading and using it as information to supplement the authenticity of the material 2. The system of claim 1.

7. The inspection unit Evaluate the emotional consistency of the text of the document to determine whether it was created by generative AI 2. The system of claim 1.

8. The calculation unit Analyzes emotional fluctuations in response to the text of documents and adjusts the AI ​​generation rate based on those fluctuations 2. The system of claim 1.

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