System

Abstract

An object of the system according to the embodiment is to efficiently confirm infringement of patent and infringement of copyrights.SOLUTION: A system according to an embodiment includes a patent infringement confirming part and a copy right infringement confirming part. A patent infringement confirming part retrieves a patent database and confirms the possibility of patent infringement. The copyright infringement confirmation unit searches the copyright database and confirms the possibility of copyright infringement.SELECTED DRAWING: Figure 1

Description

[Technical Field]

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

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

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

[0004] With conventional technology, checking for patent and copyright infringement when announcing a new service or product was complicated and difficult to do efficiently.

[0005] The system according to the embodiment aims to efficiently check for patent infringement and copyright infringement. [Means for solving the problem]

[0006] The system according to the embodiment includes a patent infringement confirmation unit and a copyright infringement confirmation unit. The patent infringement confirmation unit searches a patent database to confirm possible patent infringement. The copyright infringement confirmation unit searches a copyright database to confirm possible copyright infringement. [Effects of the Invention]

[0007] The system according to the embodiment can efficiently check for patent infringement and copyright infringement. [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 verification system according to an embodiment of the present invention uses a generative AI to automatically verify the risk of patent infringement and copyright infringement when companies or individuals create, publish, post on social media, or download new services, new products, inventions, images and data information used in document creation, videos, music, audio, illustrations, etc. This automatic verification system reduces concerns about patent infringement and copyright infringement, allowing content to be created, published, and shared with peace of mind.

[0029] An automatic confirmation system according to an embodiment includes a patent infringement confirmation unit and a copyright infringement confirmation unit. The patent infringement confirmation unit searches a patent database to confirm possible patent infringement. For example, the patent infringement confirmation unit searches the patent database based on information entered by a user regarding a new service, new product, or invention to confirm whether similar patents exist. The patent infringement confirmation unit evaluates the content of patent documents and the scope of patent claims to determine possible patent infringement. The copyright infringement confirmation unit searches a copyright database to confirm possible copyright infringement. For example, the copyright infringement confirmation unit searches the copyright database based on information about content created by the user, such as images, videos, music, audio, and illustrations, to confirm whether similar copyrighted works exist. The copyright infringement confirmation unit evaluates the content of the copyrighted works and the scope of copyright to determine possible copyright infringement. This allows the automatic confirmation system according to an embodiment to automatically confirm patent infringement and the risk of copyright infringement. For example, if a user invents a new gadget, the user can input details of the gadget into a generation AI, which then searches a patent database to confirm whether similar patents exist. When a user creates a new song, they input the song's details into the generating AI, which then searches a copyright database to see if similar works exist.

[0030] When searching patent databases, the patent infringement confirmation unit can analyze not only patent document abstracts but also drawings and diagrams to evaluate visual similarity. For example, when the generative AI searches a patent database, the patent infringement confirmation unit can analyze the patent document's drawings and diagrams using image recognition technology to evaluate visual similarity. For example, it can analyze machine blueprints and circuit diagrams contained in patent documents and compare them with blueprints for a new gadget entered by the user. This allows for more accurate identification of patent infringement risks by evaluating the visual similarity of patent documents.

[0031] The patent infringement confirmation unit can analyze the structure of patent claims and evaluate the scope of the patent in detail. For example, the patent infringement confirmation unit uses natural language processing technology to enable generative AI to analyze the structure of patent claims and evaluate the scope of the patent in detail. For example, it analyzes the claims of patent documents and automatically extracts the scope of the patent. This allows for a detailed evaluation of the scope of the patent, making it possible to more accurately confirm the risk of patent infringement.

[0032] The Patent Infringement Confirmation Department can simultaneously search patent databases in different countries and regions to assess the risk of international patent infringement. For example, when the generation AI searches a patent database, the Patent Infringement Confirmation Department will build a system that simultaneously searches patent databases in different countries and regions. For example, it will simultaneously search the databases of the United States Patent and Trademark Office, the Japan Patent Office, and the European Patent Office. This allows the risk of patent infringement to be assessed internationally, making it possible to confirm the risk of patent infringement from a global perspective.

[0033] When searching copyright databases, the copyright infringement verification unit can analyze not only the metadata of music and videos, but also the waveforms and frames of the actual content to evaluate similarity. For example, when the generative AI searches copyright databases, the copyright infringement verification unit builds a system that analyzes the waveforms and frames of music and videos to evaluate similarity. For example, it analyzes music waveform data to identify similar songs. This allows for more accurate identification of copyright infringement risks by analyzing the actual content of music and videos.

[0034] The Copyright Infringement Verification Unit can simultaneously search copyright databases in different countries and regions to assess the risk of international copyright infringement. For example, when the generation AI searches a copyright database, the Copyright Infringement Verification Unit will build a system that simultaneously searches copyright databases in different countries and regions. For example, it could simultaneously search the databases of the United States Copyright Office, the Japan Copyright Information Center, and the European Copyright Office. This allows the risk of copyright infringement to be assessed internationally, enabling the risk of copyright infringement to be confirmed from a global perspective.

[0035] The patent infringement confirmation unit visualizes the results of patent infringement confirmation so that the user can easily understand, and visually indicate the risk of patent infringement. The patent infringement confirmation unit develops an interface for visualizing the results of patent infringement confirmation. For example, the risk of patent infringement may be displayed in different colors. This visual indication of the risk of patent infringement allows the user to intuitively understand.

[0036] The copyright infringement confirmation unit visualizes the copyright infringement confirmation results so that the user can easily understand them, and visually indicates the risk of copyright infringement. The copyright infringement confirmation unit, for example, develops an interface for visualizing the copyright infringement confirmation results. For example, the risk of copyright infringement is displayed in different colors. This visually indicates the risk of copyright infringement, allowing the user to intuitively understand it.

[0037] When analyzing content posted to social media, the Patent Infringement Confirmation Unit comprehensively evaluates all text, images, and videos to be posted, making it possible to confirm the risk of multiple infringements. For example, the Patent Infringement Confirmation Unit constructs a system in which a generative AI comprehensively analyzes the text, images, and videos posted to social media and evaluates the risk of multiple infringements. For example, the content of the text is compared with the content of the images for evaluation. This makes it possible to comprehensively evaluate the content posted to social media and confirm the risk of multiple infringements.

[0038] The patent infringement confirmation unit can compare the results of the confirmation at the time of posting on social media with the terms and policies of the posting platform and evaluate the risk for each platform. For example, the patent infringement confirmation unit will build a system in which a generation AI compares the results of the confirmation at the time of posting on social media with the terms and policies of each posting platform and evaluates them. For example, it will evaluate the risk based on the terms and policies of Twitter and Facebook. This makes it possible to evaluate the risk for each platform by taking into account the terms and policies of the posting platform.

[0039] When analyzing content at the time of download, the patent infringement confirmation unit analyzes not only the file metadata but also the actual content, making it possible to assess the risk of infringement. For example, when the generation AI analyzes content at the time of download, the patent infringement confirmation unit will build a system that analyzes not only the file metadata but also the actual content. For example, it will analyze the waveform data of a music file and identify similar songs. This allows for a detailed analysis of content at the time of download, making it possible to accurately assess the risk of infringement.

[0040] The patent infringement confirmation unit can compare the download confirmation results with the user's past download history to evaluate past usage. For example, the patent infringement confirmation unit could build a system in which the generation AI compares the download confirmation results with the user's past download history to evaluate them. For example, it could evaluate whether the content is similar to content downloaded in the past. This makes it possible to evaluate risk based on usage by taking past download history into account.

[0041] When analyzing content at the time of download, the patent infringement confirmation unit simultaneously takes into account the terms and policies of different download platforms, making it possible to evaluate the risk for each platform. For example, the patent infringement confirmation unit will build a system that simultaneously takes into account the terms and policies of different download platforms when the generation AI analyzes content at the time of download. For example, it will simultaneously evaluate the terms and policies of Amazon, Google Play, and the Apple Store. This makes it possible to evaluate the risk for each platform by taking into account the terms and policies of different download platforms.

[0042] The patent infringement confirmation unit visualizes the download confirmation results so that they are easy for users to understand, and can visually indicate risks before downloading. The patent infringement confirmation unit develops an interface for visualizing the download confirmation results. For example, it may display risks in different colors before downloading. This allows users to intuitively understand the risks by visually indicating the risks before downloading.

[0043] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0044] The automatic verification system can also be equipped with a history management unit that saves the user's past patent infringement verification history and uses it as a reference the next time the system is verified. For example, the accuracy of risk assessment can be improved by saving the results of patent infringement risks previously verified by the user and comparing them with the past results the next time the system is verified. This allows the user to evaluate new risks while referring to past verification results.

[0045] The automatic confirmation system can also be equipped with an advisory section that provides expert advice in real time when the user checks the risk of patent infringement. For example, if the risk of patent infringement is high, the user can receive advice from an expert in real time. This allows the user to assess the risk while receiving advice from someone with expert knowledge.

[0046] The automatic confirmation system can also be equipped with a report generation unit that automatically generates and saves the results of risk assessment as a report when a user checks patent infringement risk. For example, the patent infringement risk assessment results can be automatically generated in PDF format and saved for later reference by the user. This allows users to easily share and save the results of their risk assessment.

[0047] The automatic confirmation system can also include a sharing section that allows a user to share the results of risk assessment with other users when confirming patent infringement risk. For example, the results of patent infringement risk assessment can be shared with team members to jointly assess risk. This allows multiple users to collaborate in risk assessment and make more accurate decisions.

[0048] The automatic confirmation system can also be equipped with a countermeasure suggestion unit that automatically proposes countermeasures based on the results of the risk assessment when the user checks the risk of patent infringement. For example, if the risk of patent infringement is high, the system will automatically suggest alternatives or workarounds. This allows the user to quickly take specific measures to avoid the risk.

[0049] The processing flow of the first embodiment will be briefly explained below.

[0050] Step 1: The patent infringement confirmation unit searches the patent database to confirm the possibility of patent infringement. Based on the information entered by the user about the new service, new product, or invention, the unit searches the patent database to confirm whether similar patents exist. The unit evaluates the content of the patent document and the scope of the patent claims to determine the possibility of patent infringement. Step 2: The copyright infringement verification unit searches the copyright database to check for possible copyright infringement. It searches the copyright database based on information about the content created by the user, such as images, videos, music, audio, and illustrations, to check whether similar copyrighted works exist. It evaluates the content of the copyrighted work and the scope of copyright, and determines whether there is a possibility of copyright infringement.

[0051] (Example 2) The automatic verification system according to an embodiment of the present invention uses a generative AI to automatically verify the risk of patent infringement and copyright infringement when companies or individuals create, publish, post on social media, or download new services, new products, inventions, images and data information used in document creation, videos, music, audio, illustrations, etc. This automatic verification system reduces concerns about patent infringement and copyright infringement, allowing content to be created, published, and shared with peace of mind.

[0052] An automatic confirmation system according to an embodiment includes a patent infringement confirmation unit and a copyright infringement confirmation unit. The patent infringement confirmation unit searches a patent database to confirm possible patent infringement. For example, the patent infringement confirmation unit searches the patent database based on information entered by a user regarding a new service, new product, or invention to confirm whether similar patents exist. The patent infringement confirmation unit evaluates the content of patent documents and the scope of patent claims to determine possible patent infringement. The copyright infringement confirmation unit searches a copyright database to confirm possible copyright infringement. For example, the copyright infringement confirmation unit searches the copyright database based on information about content created by the user, such as images, videos, music, audio, and illustrations, to confirm whether similar copyrighted works exist. The copyright infringement confirmation unit evaluates the content of the copyrighted works and the scope of copyright to determine possible copyright infringement. This allows the automatic confirmation system according to an embodiment to automatically confirm patent infringement and the risk of copyright infringement. For example, if a user invents a new gadget, the user can input details of the gadget into a generation AI, which then searches a patent database to confirm whether similar patents exist. When a user creates a new song, they input the song's details into the generating AI, which then searches a copyright database to see if similar works exist.

[0053] When searching patent databases, the patent infringement confirmation unit can analyze not only patent document abstracts but also drawings and diagrams to evaluate visual similarity. For example, when the generative AI searches a patent database, the patent infringement confirmation unit can analyze the patent document's drawings and diagrams using image recognition technology to evaluate visual similarity. For example, it can analyze machine blueprints and circuit diagrams contained in patent documents and compare them with blueprints for a new gadget entered by the user. This allows for more accurate identification of patent infringement risks by evaluating the visual similarity of patent documents.

[0054] The patent infringement confirmation unit can analyze the structure of patent claims and evaluate the scope of the patent in detail. For example, the patent infringement confirmation unit uses natural language processing technology to enable generative AI to analyze the structure of patent claims and evaluate the scope of the patent in detail. For example, it analyzes the claims of patent documents and automatically extracts the scope of the patent. This allows for a detailed evaluation of the scope of the patent, making it possible to more accurately confirm the risk of patent infringement.

[0055] The Patent Infringement Confirmation Department can simultaneously search patent databases in different countries and regions to assess the risk of international patent infringement. For example, when the generation AI searches a patent database, the Patent Infringement Confirmation Department will build a system that simultaneously searches patent databases in different countries and regions. For example, it will simultaneously search the databases of the United States Patent and Trademark Office, the Japan Patent Office, and the European Patent Office. This allows the risk of patent infringement to be assessed internationally, making it possible to confirm the risk of patent infringement from a global perspective.

[0056] When searching copyright databases, the copyright infringement verification unit can analyze not only the metadata of music and videos, but also the waveforms and frames of the actual content to evaluate similarity. For example, when the generative AI searches copyright databases, the copyright infringement verification unit builds a system that analyzes the waveforms and frames of music and videos to evaluate similarity. For example, it analyzes music waveform data to identify similar songs. This allows for more accurate identification of copyright infringement risks by analyzing the actual content of music and videos.

[0057] The Copyright Infringement Verification Unit can simultaneously search copyright databases in different countries and regions to assess the risk of international copyright infringement. For example, when the generation AI searches a copyright database, the Copyright Infringement Verification Unit will build a system that simultaneously searches copyright databases in different countries and regions. For example, it could simultaneously search the databases of the United States Copyright Office, the Japan Copyright Information Center, and the European Copyright Office. This allows the risk of copyright infringement to be assessed internationally, enabling the risk of copyright infringement to be confirmed from a global perspective.

[0058] The patent infringement confirmation unit can use the emotion estimation function to evaluate the anxiety and concern the user feels about the risk of patent infringement and adjust the level of detail of the risk assessment based on the emotion. The patent infringement confirmation unit, for example, uses the emotion estimation function to evaluate the anxiety and concern the user feels about the risk of patent infringement in real time. For example, it analyzes the user's facial expressions and voice and calculates an emotion score. This makes it possible to reduce the user's anxiety and concern by adjusting the level of detail of the risk assessment based on the user's emotion.

[0059] The copyright infringement confirmation unit can use the emotion estimation function to evaluate the anxiety and concern the user feels about the risk of copyright infringement and adjust the level of detail of the risk assessment based on the emotion. The copyright infringement confirmation unit, for example, uses the emotion estimation function to evaluate the anxiety and concern the user feels about the risk of copyright infringement in real time. For example, it analyzes the user's facial expressions and voice and calculates an emotion score. In this way, the level of detail of the risk assessment can be adjusted based on the user's emotion, thereby reducing the user's anxiety and concern.

[0060] The patent infringement confirmation unit visualizes the results of patent infringement confirmation so that the user can easily understand, and visually indicate the risk of patent infringement. The patent infringement confirmation unit develops an interface for visualizing the results of patent infringement confirmation. For example, the risk of patent infringement may be displayed in different colors. This visual indication of the risk of patent infringement allows the user to intuitively understand.

[0061] The copyright infringement confirmation unit visualizes the copyright infringement confirmation results so that the user can easily understand them, and visually indicates the risk of copyright infringement. The copyright infringement confirmation unit, for example, develops an interface for visualizing the copyright infringement confirmation results. For example, the risk of copyright infringement is displayed in different colors. This visually indicates the risk of copyright infringement, allowing the user to intuitively understand it.

[0062] The patent infringement confirmation unit can use the emotion estimation function to monitor in real time the emotions of a user when confirming the risk of patent infringement and provide positive feedback. The patent infringement confirmation unit, for example, uses the emotion estimation function to build a system that monitors in real time the emotions of a user when confirming the risk of patent infringement. For example, the unit analyzes the user's facial expressions and voice and calculates an emotion score. This makes it possible to monitor the user's emotions in real time and provide positive feedback, thereby reducing the user's anxiety and concerns.

[0063] The copyright infringement confirmation unit can use the emotion estimation function to monitor in real time the emotions of a user when confirming the risk of copyright infringement and provide positive feedback. The copyright infringement confirmation unit, for example, uses the emotion estimation function to build a system that monitors in real time the emotions of a user when confirming the risk of copyright infringement. For example, the copyright infringement confirmation unit analyzes the user's facial expressions and voice and calculates an emotion score. This makes it possible to monitor the user's emotions in real time and provide positive feedback to reduce the user's anxiety and concerns.

[0064] When analyzing content posted to social media, the Patent Infringement Confirmation Unit comprehensively evaluates all text, images, and videos to be posted, making it possible to confirm the risk of multiple infringements. For example, the Patent Infringement Confirmation Unit constructs a system in which a generative AI comprehensively analyzes the text, images, and videos posted to social media and evaluates the risk of multiple infringements. For example, the content of the text is compared with the content of the images for evaluation. This makes it possible to comprehensively evaluate the content posted to social media and confirm the risk of multiple infringements.

[0065] The patent infringement confirmation unit can compare the results of the confirmation at the time of posting on social media with the terms and policies of the posting platform and evaluate the risk for each platform. For example, the patent infringement confirmation unit will build a system in which a generation AI compares the results of the confirmation at the time of posting on social media with the terms and policies of each posting platform and evaluates them. For example, it will evaluate the risk based on the terms and policies of Twitter and Facebook. This makes it possible to evaluate the risk for each platform by taking into account the terms and policies of the posting platform.

[0066] The patent infringement confirmation unit can use the emotion estimation function to evaluate the anxiety and concern a user feels about the risks when posting to SNS, and adjust the level of detail of the risk assessment based on the emotion. The patent infringement confirmation unit, for example, uses the emotion estimation function to evaluate in real time the anxiety and concern a user feels about the risks when posting to SNS. For example, it analyzes the user's facial expressions and voice and calculates an emotion score. This makes it possible to reduce the user's anxiety and concern by adjusting the level of detail of the risk assessment based on the user's emotion.

[0067] When analyzing content at the time of download, the patent infringement confirmation unit analyzes not only the file metadata but also the actual content, making it possible to assess the risk of infringement. For example, when the generation AI analyzes content at the time of download, the patent infringement confirmation unit will build a system that analyzes not only the file metadata but also the actual content. For example, it will analyze the waveform data of a music file and identify similar songs. This allows for a detailed analysis of content at the time of download, making it possible to accurately assess the risk of infringement.

[0068] The patent infringement confirmation unit can compare the download confirmation results with the user's past download history to evaluate past usage. For example, the patent infringement confirmation unit could build a system in which the generation AI compares the download confirmation results with the user's past download history to evaluate them. For example, it could evaluate whether the content is similar to content downloaded in the past. This makes it possible to evaluate risk based on usage by taking past download history into account.

[0069] The patent infringement confirmation unit can use the emotion estimation function to evaluate the anxiety and concern the user feels about the risks when downloading, and adjust the level of detail of the risk assessment based on the emotion. The patent infringement confirmation unit, for example, uses the emotion estimation function to evaluate in real time the anxiety and concern the user feels about the risks when downloading. For example, it analyzes the user's facial expressions and voice and calculates an emotion score. This makes it possible to reduce the user's anxiety and concern by adjusting the level of detail of the risk assessment based on the user's emotion.

[0070] When analyzing content at the time of download, the patent infringement confirmation unit simultaneously takes into account the terms and policies of different download platforms, making it possible to evaluate the risk for each platform. For example, the patent infringement confirmation unit will build a system that simultaneously takes into account the terms and policies of different download platforms when the generation AI analyzes content at the time of download. For example, it will simultaneously evaluate the terms and policies of Amazon, Google Play, and the Apple Store. This makes it possible to evaluate the risk for each platform by taking into account the terms and policies of different download platforms.

[0071] The patent infringement confirmation unit visualizes the download confirmation results so that they are easy for users to understand, and can visually indicate risks before downloading. The patent infringement confirmation unit develops an interface for visualizing the download confirmation results. For example, it may display risks in different colors before downloading. This allows users to intuitively understand the risks by visually indicating the risks before downloading.

[0072] The patent infringement confirmation unit can use the emotion estimation function to monitor in real time the emotions of users when they confirm the risks of downloading and provide positive feedback. The patent infringement confirmation unit, for example, uses the emotion estimation function to build a system that monitors in real time the emotions of users when they confirm the risks of downloading. For example, it analyzes the user's facial expressions and voice and calculates an emotion score. This makes it possible to monitor the user's emotions in real time and provide positive feedback, thereby reducing the user's anxiety and concerns.

[0073] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0074] The automatic verification system can also be equipped with a history management unit that saves the user's past patent infringement verification history and uses it as a reference the next time the system is verified. For example, the accuracy of risk assessment can be improved by saving the results of patent infringement risks previously verified by the user and comparing them with the past results the next time the system is verified. This allows the user to evaluate new risks while referring to past verification results.

[0075] The automatic confirmation system can also be equipped with an advisory section that provides expert advice in real time when the user checks the risk of patent infringement. For example, if the risk of patent infringement is high, the user can receive advice from an expert in real time. This allows the user to assess the risk while receiving advice from someone with expert knowledge.

[0076] The automatic confirmation system can also be equipped with a report generation unit that automatically generates and saves the results of risk assessment as a report when a user checks patent infringement risk. For example, the patent infringement risk assessment results can be automatically generated in PDF format and saved for later reference by the user. This allows users to easily share and save the results of their risk assessment.

[0077] The automatic confirmation system can also include a sharing section that allows a user to share the results of risk assessment with other users when confirming patent infringement risk. For example, the results of patent infringement risk assessment can be shared with team members to jointly assess risk. This allows multiple users to collaborate in risk assessment and make more accurate decisions.

[0078] The automatic confirmation system can also be equipped with a countermeasure suggestion unit that automatically proposes countermeasures based on the results of the risk assessment when the user checks the risk of patent infringement. For example, if the risk of patent infringement is high, the system will automatically suggest alternatives or workarounds. This allows the user to quickly take specific measures to avoid the risk.

[0079] The automated verification system can also estimate the user's emotions and customize the risk assessment results based on the estimated emotions. For example, if the user is worried about the risk of patent infringement, the system can display the risk assessment results in more detail. This allows the system to provide risk assessment results that are tailored to the user's emotions and reduce the user's anxiety.

[0080] The automatic confirmation system can further estimate the user's emotions and adjust the method of notifying the risk assessment results based on the estimated emotions. For example, if the user is feeling stressed about the risk of patent infringement, the system can notify the user of the risk assessment results in a calm tone. This provides a notification method that takes the user's emotions into consideration and reduces the user's stress.

[0081] The automatic verification system can also estimate the user's emotions and provide risk assessment results in stages based on the estimated emotions. For example, if the user is surprised by the risk of patent infringement, the system can provide risk assessment results in stages to make it easier for the user to digest the information. This provides information in a way that is tailored to the user's emotions and helps the user understand.

[0082] The automatic verification system can further estimate the user's emotions and visually highlight the risk assessment results based on the estimated emotions. For example, if the user is worried about the risk of patent infringement, the system can highlight the risk assessment results in different colors. This provides a visual highlighting that corresponds to the user's emotions and attracts the user's attention.

[0083] The automatic confirmation system can further estimate the user's emotions and notify the user of the results of the risk assessment in a voice based on the estimated emotions. For example, if the user is anxious about the risk of patent infringement, the system can notify the user of the results of the risk assessment in a calm voice. This provides an audio notification that takes the user's emotions into consideration, thereby reducing the user's anxiety.

[0084] The processing flow of the second embodiment will be briefly explained below.

[0085] Step 1: The patent infringement confirmation unit searches the patent database to confirm the possibility of patent infringement. Based on the information entered by the user about the new service, new product, or invention, the unit searches the patent database to confirm whether similar patents exist. The unit evaluates the content of the patent document and the scope of the patent claims to determine the possibility of patent infringement. Step 2: The copyright infringement verification unit searches the copyright database to check for possible copyright infringement. It searches the copyright database based on information about the content created by the user, such as images, videos, music, audio, and illustrations, to check whether similar copyrighted works exist. It evaluates the content of the copyrighted work and the scope of copyright, and determines whether there is a possibility of copyright infringement.

[0086] 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.

[0087] 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.

[0088] 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.

[0089] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0090] 3, the data processing system 210 includes the data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0091] 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.

[0092] 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.

[0093] 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.

[0094] 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).

[0095] 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.

[0096] 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.

[0097] 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.

[0098] 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.

[0099] 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.

[0100] 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.

[0101] 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.

[0102] 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.

[0103] 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.

[0104] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0105] 5, the data processing system 310 includes the data processing device 12 and a headset type terminal 314. An example of the data processing device 12 is a server.

[0106] 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.

[0107] 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.

[0108] 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.

[0109] 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).

[0110] 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.

[0111] 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.

[0112] 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.

[0113] 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.

[0114] 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.

[0115] 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.

[0116] 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.

[0117] 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.

[0118] 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.

[0119] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0120] 7, a data processing system 410 includes a data processing device 12 and a robot 414. An example of the data processing device 12 is a server.

[0121] 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.

[0122] 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.

[0123] 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.

[0124] 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).

[0125] 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.

[0126] 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.

[0127] 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.

[0128] 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.

[0129] 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.

[0130] 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.

[0131] 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.

[0132] 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.

[0133] 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.

[0134] 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.

[0135] 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.

[0136] 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.

[0137] 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.

[0138] 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).

[0139] 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.

[0140] 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."

[0141] 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.

[0142] 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.

[0143] 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.

[0144] 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.

[0145] 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.

[0146] 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.

[0147] 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.

[0148] 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.

[0149] 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.

[0150] 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.

[0151] 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.

[0152] 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]

[0153] 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. the patent infringement confirmation unit that searches the patent database and confirms the possibility of patent infringement; a copyright infringement confirmation unit that searches a copyright database and confirms the possibility of copyright infringement. A system characterized by:

2. The patent infringement confirmation department: When searching the patent database, the patent documents' abstracts as well as their drawings and diagrams are analyzed to assess visual similarity.

2. The system of claim 1.

3. The copyright infringement confirmation unit: When searching the copyright database, the waveform or frame, as well as the metadata of the music or video, is analyzed to assess similarity.

2. The system of claim 1.

4. The patent infringement confirmation department: When analyzing the content posted on the aforementioned social media platforms, we comprehensively evaluate all text, images, and videos to be posted to check for multiple infringement risks.

2. The system of claim 1.

5. The patent infringement confirmation department: When analyzing content at the time of download, not only the file metadata but also its contents are analyzed to assess the risk of infringement.

2. The system of claim 1.

6. The patent infringement confirmation department: Evaluate the anxiety and concern that users feel about the risk of patent infringement and adjust the level of detail of the risk assessment based on that emotion.

2. The system of claim 1.

7. The copyright infringement confirmation unit: Evaluate users' fears and concerns about copyright infringement risk and adjust the level of detail in the risk assessment based on those feelings 2. The system of claim 1.

8. The patent infringement confirmation department: Evaluate users' fears and concerns about the risks of downloading and adjust the level of detail in the risk assessment based on those feelings.

2. The system of claim 1.

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