System

Abstract

An object of the system according to the embodiment is to automatically determine the validity and accuracy of basic information and prevent infringement of rights and ethics.SOLUTION: A system according to an embodiment includes a validity determination unit, a right infringement determination unit, and a ethics infringement determination unit. The validity determination unit determines validity and accuracy of the basic information. The right infringement determination part determines right infringement on the basis of the basic information determined by the validity determination part. The ethics infringement determination unit determines infringement of human rights or ethics on the basis of the basic information determined by the right infringement determination unit.SELECTED DRAWING: Figure 1

Description

[Technical Field]

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

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

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

[0004] Conventional technology requires manual determination of the validity and accuracy of basic information, making it difficult to use AI efficiently.

[0005] The system according to the embodiment aims to automatically determine the validity and accuracy of basic information and prevent infringement of rights and ethical violations. [Means for solving the problem]

[0006] The system according to the embodiment includes a validity determination unit, a right infringement determination unit, and an ethical violation determination unit. The validity determination unit determines the validity and accuracy of basic information. The right infringement determination unit determines right infringement based on the basic information determined by the validity determination unit. The ethical violation determination unit determines human rights or ethical violations based on the basic information determined by the right infringement determination unit. [Effects of the Invention]

[0007] The system according to the embodiment can automatically determine the validity and accuracy of basic information and prevent infringement of rights and ethical violations. [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 automatic discrimination system according to an embodiment of the present invention is a system that automatically determines the validity and accuracy of basic information when using generative AI. This system can prevent infringement of the rights of others, human rights, and ethics. As a result, the automatic discrimination system realizes efficient and effective use of AI, aiming for smooth business development.

[0029] The automatic discrimination system according to the embodiment includes a validity determination unit, a rights infringement determination unit, and an ethical violation determination unit. The validity determination unit determines the validity and accuracy of basic information. For example, the validity determination unit evaluates the reliability of input information from the generation AI and determines whether the information is reliable. The validity determination unit can also cross-reference multiple databases to confirm the consistency of the information. The validity determination unit can also use an emotion estimation function to collect users' emotional reactions to the reliability of the information and prioritize information that is emotionally more likely to be trusted. The rights infringement determination unit determines rights infringement based on the basic information determined by the validity determination unit. For example, the rights infringement determination unit analyzes information related to intellectual property rights such as copyrights and trademarks using the generation AI and issues a warning if there is a possibility of infringement. The rights infringement determination unit can also incorporate an algorithm that learns from past rights infringement cases and detects similar patterns. The rights infringement determination unit can also connect to patent databases and trademark databases in real time to reference the latest rights information. The ethical violation determination unit determines human rights and ethical infringement based on the basic information determined by the rights infringement determination unit. For example, the ethical violation determination unit analyzes information on discriminatory language or privacy violations using the generation AI and issues a warning if a problem is detected. The ethical violation determination unit can also incorporate an algorithm that learns from past ethical problem cases and detects similar patterns. The ethical violation determination unit can also refer to social trends and news in real time to reflect the latest ethical issues. This allows the automatic detection system according to the embodiment to prevent infringement of the rights of others, human rights, and ethics, and realize efficient and effective use of AI. For example, decision-making and strategy planning based on reliable information provided by the generation AI can be made, improving business success rates.

[0030] When evaluating the reliability of input information, the validity determination unit can refer to the information source's past reliability history and dynamically adjust the reliability. For example, when the generation AI evaluates the reliability of input information, the validity determination unit refers to the information source's past reliability history. For example, data from an information source that has provided highly reliable information in the past is rated highly, and conversely, data from an information source with low reliability is rated low. The validity determination unit can also dynamically adjust the reliability based on the reliability history. This allows for more accurate reliability evaluation by dynamically adjusting the reliability of the information source.

[0031] When determining the validity of information, the validity determination unit can cross-reference multiple different databases and check the consistency of the information. For example, when the generation AI determines the validity of information, the validity determination unit cross-references multiple different databases. For example, it references academic databases, news databases, industry databases, etc. to check the consistency of the information. The validity determination unit can also check the consistency and presence of contradictions in the information by cross-referencing. This makes it possible to check the consistency of the information by cross-referencing multiple databases.

[0032] When determining the validity of information, the validity determination unit can also analyze the visual elements of the information and evaluate the reliability of the visual information. For example, when the generation AI determines the validity of information, the validity determination unit also analyzes visual elements. For example, it analyzes the content of images and videos and evaluates whether the information is reliable. The validity determination unit can also use image analysis algorithms and video analysis technology to evaluate the reliability of visual information. This enables more accurate validity determination by evaluating the reliability of visual information.

[0033] When determining the validity of information, the validity determination unit can automatically translate information provided in different languages ​​and check for consistency between languages. For example, when the generation AI determines the validity of information, the validity determination unit automatically translates information provided in different languages. For example, it translates information into English, Japanese, Chinese, etc. and checks for consistency. The validity determination unit can also use a translation algorithm to check for consistency between languages. This makes it possible to evaluate the validity of international information by checking the consistency between different languages.

[0034] The infringement determination unit can incorporate an algorithm that learns from past infringement cases and detects similar patterns when determining infringement. For example, when the generative AI determines infringement, the infringement determination unit learns from past infringement cases. For example, past cases of copyright infringement or trademark infringement are registered in a database to detect similar patterns. The infringement determination unit can also use a pattern matching algorithm or machine learning model to detect similar patterns. This allows it to learn from past infringement cases and detect similar patterns, making it possible to determine the possibility of infringement with high accuracy.

[0035] When determining whether an infringement has occurred, the infringement determination unit can link with a patent database or trademark database in real time and refer to the latest rights information. For example, when the generation AI determines whether an infringement has occurred, the infringement determination unit can link with a patent database or trademark database in real time. For example, it can automatically obtain the latest patent information or trademark information and evaluate the possibility of infringement. The infringement determination unit can also use database linking technology to refer to the latest rights information. This improves the accuracy of determining whether an infringement has occurred by referencing the latest rights information in real time.

[0036] The infringement determination unit can also analyze audio data and music data when determining infringement, and detect infringement of audio and music rights. For example, when the generative AI determines infringement of rights, the infringement determination unit can also analyze audio data and music data. For example, it can analyze the content of audio and music and evaluate the possibility of copyright infringement. The infringement determination unit can also use audio analysis algorithms and music analysis technology to detect infringement of audio and music rights. This makes it possible to prevent a wider range of infringements by detecting infringement of audio and music rights.

[0037] The infringement determination unit can automatically translate rights information from different jurisdictions when determining infringement, making it possible to detect international infringement. For example, when the generation AI determines infringement, the infringement determination unit automatically translates rights information from different jurisdictions. For example, it translates patent information and trademark information from each country and checks for consistency. The infringement determination unit can also use a translation algorithm to detect international infringement. This makes it possible to detect international infringement by automatically translating rights information from different jurisdictions.

[0038] The ethical violation determination unit can incorporate an algorithm that learns from past ethical problem cases and detects similar patterns when determining human rights or ethical violations. For example, when the generative AI determines human rights or ethical violations, the ethical violation determination unit learns from past ethical problem cases. For example, past cases of discrimination or privacy violations are registered in a database to detect similar patterns. The ethical violation determination unit can also use a pattern matching algorithm or machine learning model to detect similar patterns. This allows it to learn from past ethical problem cases and detect similar patterns, making it possible to accurately determine the possibility of human rights or ethical violations.

[0039] The ethical violation determination unit can refer to social trends and news in real time to reflect the latest ethical issues when determining human rights or ethical violations. For example, when the generative AI determines human rights or ethical violations, the ethical violation determination unit can refer to social trends and news in real time. For example, it can analyze the latest news articles and social media posts to detect signs of ethical issues. The ethical violation determination unit can also use real-time data streaming technology to reflect the latest ethical issues. This allows the latest ethical issues to be reflected by referring to social trends and news in real time.

[0040] When determining whether a violation of human rights or ethics has occurred, the ethical violation determination unit analyzes not only text data but also image and video data, making it possible to detect visual ethical issues. For example, when the generative AI determines whether a violation of human rights or ethics has occurred, the ethical violation determination unit analyzes not only text data but also image and video data. For example, it analyzes the content of images and videos to evaluate the possibility of discriminatory language or privacy violations. The ethical violation determination unit can also use image analysis algorithms and video analysis technology to detect visual ethical issues. This makes it possible to detect visual ethical issues by analyzing not only text data but also image and video data.

[0041] The ethical violation detection unit can automatically translate ethical standards from different cultural spheres when determining human rights or ethical violations, and detect international ethical issues. For example, when the generative AI determines human rights or ethical violations, the ethical violation detection unit automatically translates ethical standards from different cultural spheres. For example, it translates the ethical guidelines and cultural values ​​of each country and checks for consistency. The ethical violation detection unit can also use a translation algorithm to detect international ethical issues. This makes it possible to detect international ethical issues by automatically translating ethical standards from different cultural spheres.

[0042] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0043] The automatic discrimination system may further include a behavior analysis unit that analyzes the user's behavior history. The behavior analysis unit analyzes, for example, what information the user has searched for in the past and what links the user has clicked. This allows the system to understand the user's interests and provide more appropriate information. The behavior analysis unit can also learn the user's behavior patterns and predict future behavior. For example, if the user tends to search for specific information during a specific time period, the system can prioritize providing information related to that time period. This makes it possible to provide more personalized information based on the user's behavior history.

[0044] The automatic discrimination system may further include a health monitoring unit that monitors the user's health condition. The health monitoring unit monitors, for example, the user's vital signs, such as heart rate, blood pressure, and body temperature. This allows the user's health condition to be understood in real time and a warning to be issued if an abnormality is detected. The health monitoring unit can also analyze the user's health data and predict health risks. For example, it can detect signs of an increase in a specific health risk based on past data. This allows the user's health condition to be monitored and measures to be taken early.

[0045] The automatic discrimination system may further include an environmental data collection unit that collects environmental data about the user. The environmental data collection unit collects environmental data, such as the temperature, humidity, and noise level around the user. This allows the system to understand the user's environmental conditions and provide appropriate advice. The environmental data collection unit may also analyze the environmental data and make suggestions to improve the user's comfort. For example, if the room temperature is too high, the system may suggest using air conditioning. This allows the system to support a more comfortable life based on the user's environmental conditions.

[0046] The automatic discrimination system may further include a purchase history analysis unit that analyzes the user's purchase history. The purchase history analysis unit analyzes, for example, data on products and services purchased by the user in the past. This allows the user's purchasing trends to be understood and related products and services to be suggested. The purchase history analysis unit can also learn the user's purchasing patterns and predict future purchasing behavior. For example, if a user tends to purchase specific products during a particular season, products related to that season can be suggested preferentially. This enables more personalized suggestions to be made based on the user's purchase history.

[0047] The automatic identification system may further include a social media analysis unit that analyzes a user's social media activity. The social media analysis unit, for example, analyzes what kind of posts a user makes on social media and what kind of responses they receive. This allows the system to understand the user's interests and provide related information. The social media analysis unit can also predict future trends based on the user's social media activity. For example, if a particular topic is rapidly gaining attention, the system can provide information related to that topic preferentially. This makes it possible to provide more appropriate information based on the user's social media activity.

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

[0049] Step 1: The validity determination unit determines the validity and accuracy of the basic information. For example, the validity determination unit evaluates the reliability of the information input by the generation AI and determines whether the information is reliable. The validity determination unit can also cross-reference multiple databases to check the consistency of the information. Furthermore, it can use an emotion estimation function to collect the user's emotional response to the reliability of the information and prioritize information that is emotionally more likely to be trusted. Step 2: The infringement determination unit determines whether a right has been infringed based on the basic information determined by the validity determination unit. For example, the infringement determination unit uses a generative AI to analyze information on intellectual property rights such as copyrights and trademarks, and issues a warning if there is a possibility of infringement. It can also incorporate algorithms that learn from past cases of infringement and detect similar patterns. It can also link with patent and trademark databases in real time to reference the latest rights information. Step 3: The ethical violation determination unit determines whether there are human rights or ethical violations based on the basic information determined by the rights violation determination unit. For example, the ethical violation determination unit uses generative AI to analyze information on discriminatory language or privacy violations and issue a warning if there are any problems. It can also implement algorithms that learn from past ethical issues and detect similar patterns. It can also refer to social trends and news in real time to reflect the latest ethical issues.

[0050] (Example 2) The automatic discrimination system according to an embodiment of the present invention is a system that automatically determines the validity and accuracy of basic information when using generative AI. This system can prevent infringement of the rights of others, human rights, and ethics. As a result, the automatic discrimination system realizes efficient and effective use of AI, aiming for smooth business development.

[0051] The automatic discrimination system according to the embodiment includes a validity determination unit, a rights infringement determination unit, and an ethical violation determination unit. The validity determination unit determines the validity and accuracy of basic information. For example, the validity determination unit evaluates the reliability of input information from the generation AI and determines whether the information is reliable. The validity determination unit can also cross-reference multiple databases to confirm the consistency of the information. The validity determination unit can also use an emotion estimation function to collect users' emotional reactions to the reliability of the information and prioritize information that is emotionally more likely to be trusted. The rights infringement determination unit determines rights infringement based on the basic information determined by the validity determination unit. For example, the rights infringement determination unit analyzes information related to intellectual property rights such as copyrights and trademarks using the generation AI and issues a warning if there is a possibility of infringement. The rights infringement determination unit can also incorporate an algorithm that learns from past rights infringement cases and detects similar patterns. The rights infringement determination unit can also connect to patent databases and trademark databases in real time to reference the latest rights information. The ethical violation determination unit determines human rights and ethical infringement based on the basic information determined by the rights infringement determination unit. For example, the ethical violation determination unit analyzes information on discriminatory language or privacy violations using the generation AI and issues a warning if a problem is detected. The ethical violation determination unit can also incorporate an algorithm that learns from past ethical problem cases and detects similar patterns. The ethical violation determination unit can also refer to social trends and news in real time to reflect the latest ethical issues. This allows the automatic detection system according to the embodiment to prevent infringement of the rights of others, human rights, and ethics, and realize efficient and effective use of AI. For example, decision-making and strategy planning based on reliable information provided by the generation AI can be made, improving business success rates.

[0052] When evaluating the reliability of input information, the validity determination unit can refer to the information source's past reliability history and dynamically adjust the reliability. For example, when the generation AI evaluates the reliability of input information, the validity determination unit refers to the information source's past reliability history. For example, data from an information source that has provided highly reliable information in the past is rated highly, and conversely, data from an information source with low reliability is rated low. The validity determination unit can also dynamically adjust the reliability based on the reliability history. This allows for more accurate reliability evaluation by dynamically adjusting the reliability of the information source.

[0053] When determining the validity of information, the validity determination unit can cross-reference multiple different databases and check the consistency of the information. For example, when the generation AI determines the validity of information, the validity determination unit cross-references multiple different databases. For example, it references academic databases, news databases, industry databases, etc. to check the consistency of the information. The validity determination unit can also check the consistency and presence of contradictions in the information by cross-referencing. This makes it possible to check the consistency of the information by cross-referencing multiple databases.

[0054] When determining the validity of information, the validity determination unit uses the emotion estimation function to collect the user's emotional response to the reliability of the information, and can prioritize evaluation of information that is emotionally more likely to be trusted. The validity determination unit, for example, uses the emotion estimation function to collect the user's emotional response to the reliability of the information. For example, the validity determination unit analyzes the user's facial expression and voice when viewing the information and calculates an emotion score for the reliability. The validity determination unit can also prioritize evaluation of information that is emotionally more likely to be trusted. In this way, by evaluating the reliability of information based on the user's emotional response, it is possible to prioritize evaluation of information that is emotionally more likely to be trusted.

[0055] When determining the validity of information, the validity determination unit can also analyze the visual elements of the information and evaluate the reliability of the visual information. For example, when the generation AI determines the validity of information, the validity determination unit also analyzes visual elements. For example, it analyzes the content of images and videos and evaluates whether the information is reliable. The validity determination unit can also use image analysis algorithms and video analysis technology to evaluate the reliability of visual information. This enables more accurate validity determination by evaluating the reliability of visual information.

[0056] When determining the validity of information, the validity determination unit can automatically translate information provided in different languages ​​and check for consistency between languages. For example, when the generation AI determines the validity of information, the validity determination unit automatically translates information provided in different languages. For example, it translates information into English, Japanese, Chinese, etc. and checks for consistency. The validity determination unit can also use a translation algorithm to check for consistency between languages. This makes it possible to evaluate the validity of international information by checking the consistency between different languages.

[0057] When determining the validity of information, the validity determination unit can use the emotion estimation function to monitor the user's emotional response to the validity of the information in real time and prioritize evaluation of information that elicits a positive emotional response. The validity determination unit, for example, uses the emotion estimation function to monitor the user's emotional response to the validity of the information in real time. For example, it analyzes the user's facial expression and voice and calculates an emotion score. The validity determination unit can also prioritize evaluation of information that elicits a positive emotional response. In this way, by monitoring the user's emotional response in real time, it is possible to prioritize evaluation of information that elicits a positive emotional response.

[0058] The infringement determination unit can incorporate an algorithm that learns from past infringement cases and detects similar patterns when determining infringement. For example, when the generative AI determines infringement, the infringement determination unit learns from past infringement cases. For example, past cases of copyright infringement or trademark infringement are registered in a database to detect similar patterns. The infringement determination unit can also use a pattern matching algorithm or machine learning model to detect similar patterns. This allows it to learn from past infringement cases and detect similar patterns, making it possible to determine the possibility of infringement with high accuracy.

[0059] When determining whether an infringement has occurred, the infringement determination unit can link with a patent database or trademark database in real time and refer to the latest rights information. For example, when the generation AI determines whether an infringement has occurred, the infringement determination unit can link with a patent database or trademark database in real time. For example, it can automatically obtain the latest patent information or trademark information and evaluate the possibility of infringement. The infringement determination unit can also use database linking technology to refer to the latest rights information. This improves the accuracy of determining whether an infringement has occurred by referencing the latest rights information in real time.

[0060] When determining whether a right infringement has occurred, the right infringement determination unit can use an emotion estimation function to collect the user's emotional response to the right infringement and prioritize warning of emotionally sensitive right infringements. The right infringement determination unit, for example, uses the emotion estimation function to collect the user's emotional response to the right infringement. For example, the emotion score is calculated by analyzing the user's facial expression or voice when the user points out the possibility of a right infringement. The right infringement determination unit can also prioritize warning of emotionally sensitive right infringements. This allows for a prompt response to emotionally sensitive right infringements by prioritized warning of right infringements based on the user's emotional response.

[0061] The infringement determination unit can also analyze audio data and music data when determining infringement, and detect infringement of audio and music rights. For example, when the generative AI determines infringement of rights, the infringement determination unit can also analyze audio data and music data. For example, it can analyze the content of audio and music and evaluate the possibility of copyright infringement. The infringement determination unit can also use audio analysis algorithms and music analysis technology to detect infringement of audio and music rights. This makes it possible to prevent a wider range of infringements by detecting infringement of audio and music rights.

[0062] The infringement determination unit can automatically translate rights information from different jurisdictions when determining infringement, making it possible to detect international infringement. For example, when the generation AI determines infringement, the infringement determination unit automatically translates rights information from different jurisdictions. For example, it translates patent information and trademark information from each country and checks for consistency. The infringement determination unit can also use a translation algorithm to detect international infringement. This makes it possible to detect international infringement by automatically translating rights information from different jurisdictions.

[0063] When determining whether a right infringement has occurred, the right infringement determination unit can use the emotion estimation function to monitor the user's emotional response to the right infringement in real time and prioritize evaluation of information that elicits a positive emotional response. The right infringement determination unit, for example, uses the emotion estimation function to monitor the user's emotional response to the right infringement in real time. For example, the emotion score is calculated by analyzing the user's facial expression and voice. The right infringement determination unit can also prioritize evaluation of information that elicits a positive emotional response. In this way, by monitoring the user's emotional response in real time, it is possible to prioritize evaluation of information that elicits a positive emotional response.

[0064] The ethical violation determination unit can incorporate an algorithm that learns from past ethical problem cases and detects similar patterns when determining human rights or ethical violations. For example, when the generative AI determines human rights or ethical violations, the ethical violation determination unit learns from past ethical problem cases. For example, past cases of discrimination or privacy violations are registered in a database to detect similar patterns. The ethical violation determination unit can also use a pattern matching algorithm or machine learning model to detect similar patterns. This allows it to learn from past ethical problem cases and detect similar patterns, making it possible to accurately determine the possibility of human rights or ethical violations.

[0065] The ethical violation determination unit can refer to social trends and news in real time to reflect the latest ethical issues when determining human rights or ethical violations. For example, when the generative AI determines human rights or ethical violations, the ethical violation determination unit can refer to social trends and news in real time. For example, it can analyze the latest news articles and social media posts to detect signs of ethical issues. The ethical violation determination unit can also use real-time data streaming technology to reflect the latest ethical issues. This allows the latest ethical issues to be reflected by referring to social trends and news in real time.

[0066] When determining whether a human rights or ethical violation has occurred, the ethical violation determination unit can use the emotion estimation function to collect the user's emotional reactions to the human rights or ethical violation and prioritize warning of emotionally sensitive ethical issues. The ethical violation determination unit, for example, uses the emotion estimation function to collect the user's emotional reactions to the human rights or ethical violation. For example, the ethical violation determination unit can analyze the user's facial expressions and voice when pointing out an ethical issue and calculate an emotion score. The ethical violation determination unit can also prioritize warning of emotionally sensitive ethical issues. In this way, by prioritizing warning of ethical issues based on the user's emotional reactions, emotionally sensitive ethical issues can be dealt with quickly.

[0067] When determining whether a violation of human rights or ethics has occurred, the ethical violation determination unit analyzes not only text data but also image and video data, making it possible to detect visual ethical issues. For example, when the generative AI determines whether a violation of human rights or ethics has occurred, the ethical violation determination unit analyzes not only text data but also image and video data. For example, it analyzes the content of images and videos to evaluate the possibility of discriminatory language or privacy violations. The ethical violation determination unit can also use image analysis algorithms and video analysis technology to detect visual ethical issues. This makes it possible to detect visual ethical issues by analyzing not only text data but also image and video data.

[0068] The ethical violation detection unit can automatically translate ethical standards from different cultural spheres when determining human rights or ethical violations, and detect international ethical issues. For example, when the generative AI determines human rights or ethical violations, the ethical violation detection unit automatically translates ethical standards from different cultural spheres. For example, it translates the ethical guidelines and cultural values ​​of each country and checks for consistency. The ethical violation detection unit can also use a translation algorithm to detect international ethical issues. This makes it possible to detect international ethical issues by automatically translating ethical standards from different cultural spheres.

[0069] When determining whether a violation of human rights or ethics has occurred, the ethical violation determination unit can use the emotion estimation function to monitor the user's emotional response to the violation of human rights or ethics in real time and prioritize evaluation of information that elicits a positive emotional response. The ethical violation determination unit, for example, uses the emotion estimation function to monitor the user's emotional response to the violation of human rights or ethics in real time. For example, it analyzes the user's facial expressions and voice and calculates an emotion score. The ethical violation determination unit can also prioritize evaluation of information that elicits a positive emotional response. In this way, by monitoring the user's emotional response in real time, it is possible to prioritize evaluation of information that elicits a positive emotional response.

[0070] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0071] The automatic discrimination system may further include a behavior analysis unit that analyzes the user's behavior history. The behavior analysis unit analyzes, for example, what information the user has searched for in the past and what links the user has clicked. This allows the system to understand the user's interests and provide more appropriate information. The behavior analysis unit can also learn the user's behavior patterns and predict future behavior. For example, if the user tends to search for specific information during a specific time period, the system can prioritize providing information related to that time period. This makes it possible to provide more personalized information based on the user's behavior history.

[0072] The automatic discrimination system may further include a health monitoring unit that monitors the user's health condition. The health monitoring unit monitors, for example, the user's vital signs, such as heart rate, blood pressure, and body temperature. This allows the user's health condition to be understood in real time and a warning to be issued if an abnormality is detected. The health monitoring unit can also analyze the user's health data and predict health risks. For example, it can detect signs of an increase in a specific health risk based on past data. This allows the user's health condition to be monitored and measures to be taken early.

[0073] The automatic discrimination system may further include an environmental data collection unit that collects environmental data about the user. The environmental data collection unit collects environmental data, such as the temperature, humidity, and noise level around the user. This allows the system to understand the user's environmental conditions and provide appropriate advice. The environmental data collection unit may also analyze the environmental data and make suggestions to improve the user's comfort. For example, if the room temperature is too high, the system may suggest using air conditioning. This allows the system to support a more comfortable life based on the user's environmental conditions.

[0074] The automatic discrimination system may further include a purchase history analysis unit that analyzes the user's purchase history. The purchase history analysis unit analyzes, for example, data on products and services purchased by the user in the past. This allows the user's purchasing trends to be understood and related products and services to be suggested. The purchase history analysis unit can also learn the user's purchasing patterns and predict future purchasing behavior. For example, if a user tends to purchase specific products during a particular season, products related to that season can be suggested preferentially. This enables more personalized suggestions to be made based on the user's purchase history.

[0075] The automatic identification system may further include a social media analysis unit that analyzes a user's social media activity. The social media analysis unit, for example, analyzes what kind of posts a user makes on social media and what kind of responses they receive. This allows the system to understand the user's interests and provide related information. The social media analysis unit can also predict future trends based on the user's social media activity. For example, if a particular topic is rapidly gaining attention, the system can provide information related to that topic preferentially. This makes it possible to provide more appropriate information based on the user's social media activity.

[0076] The automatic discrimination system may further include an emotion evaluation unit that estimates the user's emotion and evaluates the reliability of information based on the estimated emotion. The emotion evaluation unit, for example, analyzes the user's facial expression and voice when viewing information and calculates an emotion score. This makes it possible to evaluate the reliability of information based on the user's emotional response. The emotion evaluation unit may also prioritize evaluation of information that is likely to be emotionally trustworthy. For example, it may give a high rating to information in which the user shows positive emotion, and a low rating to information in which the user shows negative emotion. This makes it possible to provide more reliable information based on the user's emotional response.

[0077] The automatic discrimination system may further include an emotion validity evaluation unit that estimates the user's emotion and evaluates the validity of information based on the estimated emotion. The emotion validity evaluation unit, for example, analyzes the user's facial expression and voice when viewing information and calculates an emotion score. This makes it possible to evaluate the validity of information based on the user's emotional response. The emotion validity evaluation unit may also prioritize evaluation of information that is considered emotionally valid. For example, it may give a high rating to information in which the user shows positive emotion, and a low rating to information in which the user shows negative emotion. This makes it possible to provide more valid information based on the user's emotional response.

[0078] The automatic discrimination system may further include an emotion / rights evaluation unit that estimates the user's emotions and evaluates the possibility of a right infringement based on the estimated emotions. The emotion / rights evaluation unit, for example, analyzes the user's facial expression and voice when the user points out the possibility of a right infringement and calculates an emotion score. This makes it possible to evaluate the possibility of a right infringement based on the user's emotional response. The emotion / rights evaluation unit may also prioritize warnings of right infringements that are emotionally sensitive. For example, right infringements for which the user shows strong negative emotions may be given a high rating, and right infringements for which the user shows weak negative emotions may be given a low rating. This makes it possible to more quickly warn of right infringements based on the user's emotional response.

[0079] The automatic discrimination system may further include an emotion ethics evaluation unit that estimates the user's emotions and evaluates the possibility of an ethical violation based on the estimated emotions. The emotion ethics evaluation unit, for example, analyzes the user's facial expression and voice when the user points out a possible ethical violation and calculates an emotion score. This makes it possible to evaluate the possibility of an ethical violation based on the user's emotional response. The emotion ethics evaluation unit may also prioritize warnings of ethical violations that are emotionally sensitive. For example, an ethical violation for which the user expressed strong negative emotions may be given a high rating, and an ethical violation for which the user expressed weak negative emotions may be given a low rating. This makes it possible to more quickly warn of an ethical violation based on the user's emotional response.

[0080] The automatic discrimination system may further include an emotion priority adjustment unit that estimates the user's emotion and adjusts the priority of information based on the estimated emotion. The emotion priority adjustment unit, for example, analyzes the user's facial expression and voice when viewing information and calculates an emotion score. This makes it possible to adjust the priority of information based on the user's emotional response. The emotion priority adjustment unit may also prioritize providing information that is considered emotionally important. For example, information in which the user shows strong positive emotion may be given high priority, and information in which the user shows weak positive emotion may be given low priority. This makes it possible to provide more appropriate information based on the user's emotional response.

[0081] The processing flow of the second embodiment will be briefly explained below.

[0082] Step 1: The validity determination unit determines the validity and accuracy of the basic information. For example, the validity determination unit evaluates the reliability of the information input by the generation AI and determines whether the information is reliable. The validity determination unit can also cross-reference multiple databases to check the consistency of the information. Furthermore, it can use an emotion estimation function to collect the user's emotional response to the reliability of the information and prioritize information that is emotionally more likely to be trusted. Step 2: The infringement determination unit determines whether a right has been infringed based on the basic information determined by the validity determination unit. For example, the infringement determination unit uses a generative AI to analyze information on intellectual property rights such as copyrights and trademarks, and issues a warning if there is a possibility of infringement. It can also incorporate algorithms that learn from past cases of infringement and detect similar patterns. It can also link with patent and trademark databases in real time to reference the latest rights information. Step 3: The ethical violation determination unit determines whether there are human rights or ethical violations based on the basic information determined by the rights violation determination unit. For example, the ethical violation determination unit uses generative AI to analyze information on discriminatory language or privacy violations and issue a warning if there are any problems. It can also implement algorithms that learn from past ethical issues and detect similar patterns. It can also refer to social trends and news in real time to reflect the latest ethical issues.

[0083] 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.

[0084] 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.

[0085] 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.

[0086] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0087] 3, the data processing system 210 includes a data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0088] 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.

[0089] 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.

[0090] 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.

[0091] 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).

[0092] 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.

[0093] 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.

[0094] 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.

[0095] 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.

[0096] 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.

[0097] 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.

[0098] 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.

[0099] 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.

[0100] 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.

[0101] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0102] 5, the data processing system 310 includes the data processing device 12 and a headset terminal 314. An example of the data processing device 12 is a server.

[0103] 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.

[0104] 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.

[0105] 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.

[0106] 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).

[0107] 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.

[0108] 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.

[0109] 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.

[0110] 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.

[0111] 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.

[0112] 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.

[0113] 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.

[0114] 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.

[0115] 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.

[0116] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0117] 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.

[0118] 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.

[0119] 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.

[0120] 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.

[0121] 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).

[0122] 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.

[0123] 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.

[0124] 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.

[0125] 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.

[0126] 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.

[0127] 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.

[0128] 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.

[0129] 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.

[0130] 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.

[0131] 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.

[0132] 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.

[0133] 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.

[0134] 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.

[0135] 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).

[0136] 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.

[0137] 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."

[0138] 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.

[0139] 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.

[0140] 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.

[0141] 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.

[0142] 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.

[0143] 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.

[0144] 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.

[0145] 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.

[0146] 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.

[0147] 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.

[0148] 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.

[0149] 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]

[0150] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Device 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robot

Claims

1. a validity determination unit that determines the validity and accuracy of the basic information; an infringement determination unit that determines an infringement of a right based on the basic information determined by the validity determination unit; and an ethical infringement determination unit that determines whether a violation of human rights or ethics has occurred based on the basic information determined by the right infringement determination unit. A system characterized by:

2. The validity determination unit When evaluating the reliability of input information, the reliability is dynamically adjusted by referring to the past reliability history of the information source.

2. The system of claim 1.

3. The validity determination unit When determining the validity of information, the visual elements of the information are also analyzed to assess the reliability of the visual information.

2. The system of claim 1.

4. The infringement determination unit When determining whether a copyright has been infringed, an algorithm is introduced that learns from past copyright infringement cases and detects similar patterns.

2. The system of claim 1.

5. The ethical violation determination unit When determining whether or not there are violations of human rights or ethics, an algorithm will be introduced that learns from past ethical issues and detects similar patterns.

2. The system of claim 1.

6. The validity determination unit When determining the validity of information, an emotion estimation function is used to collect the user's emotional response to the reliability of the information, and information that is likely to be emotionally trustworthy is preferentially evaluated.

2. The system of claim 1.

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