system

Abstract

Provide a system. A means for a user to make a content generation request; A means for the server to generate content using artificial intelligence; means for the server to check the generated content against copyright and patent databases; A means for the server to determine whether or not there is a right infringement based on the collation result; A means for the server to regenerate the content if a copyright infringement is confirmed; a means for providing the user with non-infringing server-generated content; A system including:

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] In recent years, technology that uses generative AI to automatically generate content such as text and images has become widely used, but there is a possibility that the generated content may infringe existing copyrights or patents. Such infringements pose legal risks, making it essential to verify the rights of the generated content. However, these verification tasks are time-consuming, and regeneration of content when infringement is confirmed also requires time and effort. Therefore, there is a need for efficient rights verification and regeneration of generated content. [Means for solving the problem]

[0005] The present invention solves the above-mentioned problems by providing a system including: a means for a user to make a content generation request; a means for a server to generate content using generation artificial intelligence; a means for the server to verify the generated content against a copyright database and a patent database; a means for the server to determine whether or not a right infringement has occurred based on the verification result; a means for the server to regenerate the content if a right infringement is confirmed; and a means for the server to provide the generated content to the user if the content does not infringe. This allows for efficient rights verification of the generated content and regeneration as necessary, thereby providing content that users can use with peace of mind.

[0006] "Generative AI" refers to algorithms such as machine learning models that automatically generate content such as text and images based on user requests.

[0007] A "content generation request" is an instruction sent by a user to a server using a terminal to generate content such as new text or images.

[0008] A "server" is a computer system that receives a user request and executes a series of processes, from content generation to rights verification and result notification.

[0009] A "copyright database" is a data storage that aggregates information about existing copyrighted works and makes it possible to collate and search them.

[0010] A "patent database" is a data storage that aggregates existing patent information and enables it to be collated and searched.

[0011] "Infringement" is when the content generated infringes an existing copyright or patent.

[0012] "Regeneration" refers to the process of discarding existing generated content and generating new content if infringement is confirmed.

[0013] A "user" is an individual or legal entity that makes a content generation request and receives generated content from a server.

[0014] "Notification" refers to the act of the server notifying the user of information such as the result of rights verification and the progress of regeneration. [Brief explanation of the drawings]

[0015] [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. [Figure 11] FIG. 3 is a sequence diagram showing a processing flow of the data processing system according to the first embodiment. [Figure 12] FIG. 10 is a sequence diagram showing the flow of processing in the data processing system in Application Example 1. [Figure 13]FIG. 10 is a sequence diagram showing the flow of processing in the data processing system according to the second embodiment when an emotion engine is combined. [Figure 14] FIG. 10 is a sequence diagram showing the flow of processing in the data processing system in Application Example 2 when an emotion engine is combined. DETAILED DESCRIPTION OF THE INVENTION

[0016] 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.

[0017] First, the terms used in the following description will be explained.

[0018] 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, a 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), and an APU (Accelerated Processing Unit).

[0019] 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.

[0020] 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.

[0021] 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), Bluetooth (registered trademark), etc.

[0022] 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."

[0023] [First embodiment]

[0024] FIG. 1 shows an example of the configuration of a data processing system 10 according to the first embodiment.

[0025] 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.

[0026] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 is an example of a "computer" according to the technology of the present disclosure. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, the RAM 30, and the storage 32 are connected to a bus 34. 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).

[0027] 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.

[0028] The reception device 38 includes a touch panel 38A, a microphone 38B, and the like, and receives user input. The touch panel 38A detects contact with an indicator (for example, a pen or a finger) to receive user input by the touch of the indicator. 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 acquires the data indicating the user input.

[0029] The output device 40 includes a display 40A and a speaker 40B, and presents data to the user 20 by outputting the data in a form of expression that the user 20 can perceive (for example, audio and / or text). The display 40A displays visible information such as text and images in accordance with instructions from the processor 46. The speaker 40B outputs audio in accordance with instructions from the processor 46. The 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.

[0030] 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.

[0031] FIG. 2 shows an example of the main functions of the data processing device 12 and the smart device 14.

[0032] 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.

[0033] 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.

[0034] In the smart device 14, the processor 46 performs the reception output process. The storage 50 stores a reception output program 60. The reception output program 60 is used in conjunction with the specific processing program 56 by the data processing system 10. The processor 46 reads the reception output program 60 from the storage 50 and executes the read reception output program 60 on the RAM 48. The reception output process is realized by the processor 46 operating as the control unit 46A in accordance with the reception output program 60 executed on the RAM 48.

[0035] Next, a description will be given of the specific processing performed by the specific processing unit 290 of the data processing device 12. In the following description, the data processing device 12 will be referred to as a "server" and the smart device 14 will be referred to as a "terminal."

[0036] The present invention relates to a content generation system using a generation AI, and specific embodiments are described below.

[0037] First, a user requests content creation using a device. For example, the user enters "Please create a new blog post" into an input form on the device and presses the submit button. This request can include themes and keywords specified by the user.

[0038] The server then receives the user's request and generates the content using generative AI, which uses natural language generation models (NLGMs) to generate blog posts based on themes and keywords specified by the user. The generated content is temporarily stored on the server.

[0039] The generated content is then checked against copyright and patent databases by the server, which accesses these databases to determine whether the generated content violates existing copyrights or patents. During this checking process, the server evaluates whether certain phrases or themes match existing copyrighted works.

[0040] As a result of the comparison, the server determines whether or not there is a copyright infringement. For example, if part of the generated blog post matches an existing copyrighted work, the server determines that it is an infringement. On the other hand, if there is no match, it determines that there is no copyright infringement.

[0041] The server notifies the user of this decision. If there is no infringement of rights, the generated content is provided to the user as is. On the other hand, if a rights infringement is confirmed, the server notifies the user that the content will be generated again and re-executes the generation AI. In this case, the newly generated content also undergoes the same procedure and rights confirmation is performed again.

[0042] Once the final non-infringing content is generated, including if regeneration is required, the server will provide it to the user. For example, once it has been confirmed that a newly generated blog post is non-infringing, the server will notify the user, "This is the final content," and provide the content.

[0043] This system allows users to use generated content with peace of mind and avoids the risk of copyright infringement.

[0044] The processing flow will be explained below.

[0045] Step 1:

[0046] A user requests content creation using a device. The user enters "Please create a new blog post" into the device's input form and presses the submit button.

[0047] Step 2:

[0048] The server receives the user's request, which is then forwarded to the server and parsed for specific generation instructions (themes, keywords, etc.).

[0049] Step 3:

[0050] The server generates content using generative AI, which invokes a natural language generation model to generate blog posts based on user requests, taking into account themes and keywords specified in the process.

[0051] Step 4:

[0052] The server temporarily stores the generated content. The generated blog post is stored in temporary storage on the server, ready for matching in the next step.

[0053] Step 5:

[0054] The server checks the generated content against copyright and patent databases. The server accesses these databases to verify that the generated content does not infringe on existing copyrights or patents.

[0055] Step 6:

[0056] The server analyzes the results of the database comparison and determines whether there is any copyright infringement. For example, if part of the generated blog post matches an existing copyrighted work, the server determines that it is a copyright infringement.

[0057] Step 7:

[0058] The server notifies the user of the result of the rights check. If there is no infringement, the generated content is provided to the user as is. If an infringement is confirmed, the user is notified that the content must be regenerated.

[0059] Step 8:

[0060] If the server finds a copyright infringement, it will regenerate the content. The server will restart the generation AI and generate new content, adjusting parameters to avoid the infringing parts if possible.

[0061] Step 9:

[0062] The server again checks the reproduced content against copyright and patent databases to determine whether the reproduced content is infringing.

[0063] Step 10:

[0064] The server provides the final content to the user. If the reproduced content does not infringe any rights, the server notifies the user, "This is the final content," and provides the content.

[0065] Example 1

[0066] Next, a description will be given of Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the smart device 14 will be referred to as a "terminal."

[0067] In today's world, there is a growing demand for users to use generative AI to generate content quickly and safely. However, there is a risk that the generated content may infringe copyright or patents, which can make users hesitant to use the generated content. Furthermore, the regeneration process in the event of confirmed infringement is not automated, which can be time-consuming for users. These issues result in a lack of efficiency and reliability in the content generation process. Furthermore, there are insufficient means of providing appropriate input to generative AI models, and insufficient mechanisms for storing and notifying matching results.

[0068] The specific processing by the specific processing unit 290 of the data processing device 12 in the first embodiment is realized by the following means.

[0069] In this invention, the server includes: means for a user to make a content generation request; means for the server to generate content using a generative AI model; means for the server to check the generated content against a copyright database and a patent database; means for the server to determine whether or not there is infringement of rights based on the check result; means for the server to generate the content again if infringement of rights is confirmed; means for the server to provide the generated content to the user if it does not infringe rights; means for generating and inputting a prompt sentence to the generative AI model; and means for saving the check result. This allows users to use the generated content with peace of mind, and makes it possible to improve the efficiency and reliability of the content generation process.

[0070] A "user" is a user of the system who requests content generation.

[0071] A "content generation request" is a request made by a user to generate new content using the generation AI.

[0072] The "server" is a central management device that generates content using generative AI, checks copyrights and patents, and provides the content to users.

[0073] A "generative artificial intelligence model" is an artificial intelligence system for generating content in natural language based on user requests.

[0074] A "prompt" is an instruction entered into a generative AI model, which defines the conditions and theme for content generation.

[0075] "Content" refers to text data such as blog posts and documents generated by generative AI models.

[0076] The "copyright database" is a database that holds information on existing copyrighted works and compares it with generated content.

[0077] A "patent database" is a database that holds existing patent information and compares it with generated content.

[0078] The "matching result" is information regarding whether or not the generated content infringes any rights, obtained by matching with the copyright database and the patent database.

[0079] "Infringement" refers to a situation in which the generated content violates an existing copyright or patent.

[0080] "Regeneration" is the process of re-running the generation AI to generate new content if a copyright infringement is confirmed.

[0081] This invention relates to a content generation system using generative AI, and in particular to a system that automatically generates content requested by users and provides it after checking that there is no infringement of copyright or patent rights.

[0082] Specifically, a user first makes a content generation request using their own device (such as a PC or smartphone). The user enters a request into an input form, such as "Please generate a new blog post" or "Please generate a blog post about the future of AI technology," and presses the submit button. This request can include the theme and keywords of the content to be generated.

[0083] Next, the server receives the user's request and generates a prompt for the generative AI model (e.g., GPT-3 (registered trademark)). The prompt is an instruction input to the generative model, such as "Please write a blog post that provides a detailed explanation of the future prospects for AI technology."

[0084] The generated prompt sentence is input into a generative AI model, and the server generates content based on the specified theme and keywords. The generated content is temporarily stored on the server.

[0085] The server then checks the generated content against copyright and patent databases to determine if there are any infringements. Specifically, it accesses databases such as Google® Patents, PubMed, USPTO, and WIPO to determine if the generated content violates existing copyrights or patents. The server then stores the results of the checks against these databases.

[0086] Based on the matching results, the server determines whether the generated content violates existing copyrights or patents. If there is a match, it is deemed to be a copyright infringement and notifies the user that the content will be regenerated. To regenerate the content, the server uses a new artificial intelligence model to generate a new prompt sentence and generate the content. The same matching process is repeated to verify the rights.

[0087] When the final content is generated and confirmed to be free of infringement, the server provides it to the user. For example, the server may notify the user, "This is the final content," and provide the generated blog post. The server may also notify the user of the results of the verification of the generated content, informing them that the resulting content is free of infringement.

[0088] In this way, a content generation system using a generative AI model can enable users to use the generated content with peace of mind, while reducing the risk of copyright infringement.

[0089] The flow of the identification process in the first embodiment will be described with reference to FIG.

[0090] Step 1:

[0091] The user inputs a content generation request using a terminal and presses a send button.

[0092] Specifically, the user enters "Please generate a blog post about the future of AI technology" into the input form on the device and presses the send button.

[0093] Input: Request details (including themes and keywords)

[0094] Output: User request data

[0095] Step 2:

[0096] The server receives the user request data and generates a prompt sentence.

[0097] Specifically, the server receives a request to "generate a blog post about the future of AI technology" and generates a prompt that says, "Please create a blog post that provides a detailed explanation of the future prospects for AI technology."

[0098] Input: User request data

[0099] Output: Generated prompt statement

[0100] Step 3:

[0101] The server inputs a prompt sentence into the artificial intelligence model to generate content.

[0102] Specifically, the server inputs the generated prompt sentence into a generative artificial intelligence model (e.g., GPT-3) and generates a "blog post about the future of AI technology."

[0103] Input: Generate prompt statement

[0104] Output: Generated content

[0105] Step 4:

[0106] The server temporarily stores the generated content.

[0107] Specifically, the server saves a "blog article about the future of AI technology" in a temporary storage area on the server.

[0108] Input: Generated content

[0109] Output: Stored generated content

[0110] Step 5:

[0111] The server checks the generated content against copyright and patent databases.

[0112] Specifically, the server accesses databases such as Google Patents, PubMed, USPTO, and WIPO to collate "blog articles about the future of AI technology."

[0113] Input: Saved generated content

[0114] Output: Matching result data

[0115] Step 6:

[0116] The server determines whether or not there is a copyright infringement based on the matching result data.

[0117] Specifically, the server analyzes the results of the comparison and determines whether any part of the content matches an existing patent. If there is a match, it is determined to be an infringement, and if not, it is determined to be no problem.

[0118] Input: Matching result data

[0119] Output: Infringement judgment result

[0120] Step 7:

[0121] The server notifies the user of the result of the determination as to whether or not there is a copyright infringement.

[0122] Specifically, the server sends a notification to the user saying, "The generated content does not infringe any rights. Here is the final content."

[0123] Input: Infringement judgment result

[0124] Output: User notification

[0125] Step 8:

[0126] If a copyright infringement is confirmed, the server generates the content again.

[0127] Specifically, the server uses the generative artificial intelligence model again to generate a new prompt sentence and generate the content again.

[0128] Input: Infringement judgment result

[0129] Output: Regenerated content

[0130] Step 9:

[0131] The server ultimately provides non-infringing content to the user.

[0132] Specifically, the server provides the newly generated blog post to the user along with a message saying, "Here is the final content."

[0133] Input: Regenerated content

[0134] Output: Providing the final content to the user

[0135] (Application example 1)

[0136] Next, a description will be given of Application Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the smart device 14 will be referred to as a "terminal."

[0137] In recent years, automation has progressed in content generation and distribution, but there is a risk that the generated content may infringe existing copyrights. Furthermore, users are required to quickly obtain legal content in real time using smartphones or smart glasses. However, current technology does not adequately provide a system that meets these requirements. To solve this issue, a system that includes rights verification and regeneration processes for generated content is needed.

[0138] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 1 is realized by the following means.

[0139] In this invention, the server includes: a means for a user to make a content generation request; a means for generating content using a generative AI model; a means for verifying the generated content against a copyright database and a patent database; a means for determining whether or not there is a copyright infringement based on the verification result; a means for regenerating the content if a copyright infringement is confirmed; a means for providing the generated content to the user if the generated content does not infringe, an interface means for receiving prompts for generating content and verifying copyright in real time and inputting them into the generative AI model; and an application means that can be installed on a smartphone or smart glasses. This allows users to safely obtain legal content in real time.

[0140] A "content generation request" is a request made by a user to create new content using a generative AI model.

[0141] A "generative AI model" is an artificial intelligence system that uses natural language generation technology to create content based on themes and keywords specified by the user.

[0142] "Copyright database" means a database that stores information about existing copyrighted works and is used to verify whether generated content infringes existing copyrights.

[0143] "Patent Database" means a database that stores existing patent information and is used to check whether generated content infringes existing patent rights.

[0144] The "matching result" refers to the result of checking whether the generated content matches the copyright database and the patent database.

[0145] The "interface means" is a mechanism that receives prompt statements from the user and inputs them into the generative AI model.

[0146] The "application means" is software that can be installed on a smartphone or smart glasses, and allows a user to make content generation requests and receive generated content.

[0147] A "prompt" is an instruction provided by a user to a generative AI model, and specifically indicates the theme and keywords of the content to be generated.

[0148] "Regeneration means" refers to the process of regenerating content when the generated content infringes an existing copyright or patent.

[0149] The "means for determining infringement of rights" is a mechanism for determining whether or not there is infringement of rights based on the results of comparing the generated content with a copyright database and a patent database.

[0150] In this invention, a user first makes a content generation request using an application installed on a smartphone or smart glasses. The user then inputs a "prompt statement" through the application interface, specifying the theme and keywords of the content they want to generate. For example, the user might input, "Please generate a blog post about recent technology trends."

[0151] When a user submits a request, the prompt is sent to the server, which uses a generative AI model (e.g., a model using natural language generation technology) to generate content based on the specified theme.

[0152] The generated content is temporarily stored on a server, which then checks it against copyright and patent databases to see if there are any matches to existing copyrighted works or patents. During this process, it evaluates whether specific phrases or themes match existing entries in the database.

[0153] Based on the comparison results, the server determines whether there is any copyright infringement. If the generated content does not match any existing copyrighted work or patent, it is determined that there is no copyright infringement and the content is provided to the user. On the other hand, if a copyright infringement is confirmed, the server notifies the user that the content will be generated again and re-runs the generative AI model using a prompt. In this case, the newly generated content also undergoes the same procedure and is again checked for copyright.

[0154] Once the server has generated the final non-infringing content, it is sent to the user's smartphone or smart glasses. For example, the user will be notified with a message saying, "This is the final content," and can view the generated blog post within the application.

[0155] This system allows users to safely use generated content with the confidence that it will not infringe on existing copyrights or patents, and enables content creation and review in real time on portable devices such as smartphones and smart glasses.

[0156] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[0157] Step 1:

[0158] A user launches an application on their smartphone or smart glasses and inputs a prompt through the interface, for example, "Please generate a blog post about recent technology trends." This input is then sent to the server.

[0159] Input: prompt statement

[0160] Output: Request sent to the server

[0161] Step 2:

[0162] The server inputs the received prompt sentence into a generative AI model to generate content based on the specified theme. By using a generative AI model (for example, a natural language generation model), text is generated that follows the theme specified by the user.

[0163] Input: prompt statement

[0164] Output: Generated content (e.g. blog posts)

[0165] Step 3:

[0166] The server temporarily stores the generated content and then checks it against copyright and patent databases, using APIs to access these databases and check whether the generated content matches existing data.

[0167] Input: Generated content

[0168] Output: Matching result (whether or not there is a copyright infringement)

[0169] Step 4:

[0170] The server determines whether or not there is a copyright infringement based on the comparison results. If there is no copyright infringement, the content is provided to the user as is. If a copyright infringement is confirmed, new content is generated using the generative AI model again.

[0171] Input: Matching result

[0172] Output: Judgment based on whether or not there is a copyright infringement

[0173] Step 5:

[0174] If the server regenerates the content, the newly generated content will go through the same verification process, with rights verification repeated, until finally, non-infringing content is generated.

[0175] Input: Regenerated content

[0176] Output: Matching result and whether or not there is a copyright infringement

[0177] Step 6:

[0178] The server then generates non-infringing content and sends it to the user's smartphone or smart glasses, where the user can view and use the content through the application.

[0179] Input: Non-infringing generated content

[0180] Output: Providing content to the user

[0181] Step 7:

[0182] The server notifies the user that the content has been provided. For example, a message such as "This is the final content" is sent to the user, and the user can check the content within the application.

[0183] Input: Final generated content

[0184] Output: Notify the user and display the provided content

[0185] Furthermore, an emotion engine that estimates the user's emotion may be combined. That is, the identification processing unit 290 may estimate the user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

[0186] The present invention relates to a system in which a user makes a content generation request, a server generates the content using a generation artificial intelligence, and then adjusts the generated content based on the user's emotions using an emotion engine. Specific embodiments are described below.

[0187] First, a user requests content creation using a device. For example, the user enters "Please create a new blog post" into an input form on the device and presses the submit button. This request can include a theme, keywords, and even the user's current emotional state (e.g., happy, sad, stressed, etc.).

[0188] The server then receives the request and generates the content using generative AI, which uses a natural language generation model to generate blog posts based on themes and keywords specified by the user. The generated content is then stored in temporary storage on the server.

[0189] The generated content is then checked against copyright and patent databases by the server, which accesses these databases to ensure the generated content does not infringe on existing copyrighted works or patents. This checking process involves assessing whether certain phrases or themes match existing copyrighted works.

[0190] As a result of the comparison, the server determines whether or not there is a copyright infringement. For example, if part of the generated blog post matches an existing copyrighted work, the server determines that it is an infringement. On the other hand, if there is no match, it determines that there is no copyright infringement.

[0191] This is where the emotion engine comes in. The server sends the user's emotion data to the emotion engine, which evaluates how well the generated content matches the user's current emotion. For example, if the user specifies an emotional state of "fun," the generated blog post will be evaluated to see if it is enjoyable.

[0192] The server receives the emotion engine's evaluation results and adjusts the content as needed. For example, if the generated content does not match the user's emotional state, it initiates a regeneration process to generate new content. This regeneration process also checks against copyright and patent databases, just as it did the first time.

[0193] The server then provides the final content to the user. For example, once it has determined that the regenerated blog post is not infringing and matches the user's emotional state, the server notifies the user, "Here is the final content," and provides the content.

[0194] As a concrete example, if a user requests a blog post on the theme of "fun everyday events" and also specifies the emotional state as "fun," the generation AI will generate a blog post about fun events, and the emotion engine will evaluate the content. If the generated article is confirmed to be fun and does not infringe on any rights, the server will provide the article to the user. If the content is not fun or if there is any infringement of rights, the system will regenerate the article and provide the optimal content.

[0195] This system allows users to use content that matches their own emotions with peace of mind, while also avoiding the risk of copyright infringement.

[0196] The processing flow will be explained below.

[0197] Step 1:

[0198] A user uses a device to request content creation. The user enters "Please generate a new blog post" into the device's input form and presses the submit button. The request can include a topic, keywords, and even the user's current emotional state (e.g., happy, sad, stressed, etc.).

[0199] Step 2:

[0200] The server receives the user's request, which is then forwarded to the server, where it is analyzed for specific generation instructions (themes, keywords, emotional state, etc.).

[0201] Step 3:

[0202] The server generates content using generative AI. The generative AI invokes a natural language generation model to generate blog posts based on the user's request. The generation process reflects the specified themes and keywords. The generated content is stored in temporary storage on the server.

[0203] Step 4:

[0204] The server checks the generated content against copyright and patent databases. The server accesses these databases to determine whether the generated content violates existing copyrights or patents. It evaluates whether specific phrases or themes match existing copyrighted works.

[0205] Step 5:

[0206] The server analyzes the database match and determines whether or not there is a copyright infringement. If part of the generated blog post matches an existing copyrighted work, the server determines that it is an infringement. On the other hand, if there is no match, it determines that there is no copyright infringement.

[0207] Step 6:

[0208] The server sends the user's emotional data to the emotion engine, which analyzes the user's emotional state and evaluates how well the generated content matches the user's current emotion.

[0209] Step 7:

[0210] The server receives the emotion engine's evaluation results and adjusts the content as needed. For example, if the generated content does not match the user's emotional state, it initiates a regeneration process to generate new content.

[0211] Step 8:

[0212] The server runs a regeneration process, relaunching the generation AI to generate new content. This regeneration process also checks against copyright and patent databases.

[0213] Step 9:

[0214] The server again checks the reproduced content against copyright and patent databases to determine whether the reproduced content is infringing.

[0215] Step 10:

[0216] The server again sends the regenerated content to the emotion engine to evaluate whether it matches the user's emotional state.

[0217] Step 11:

[0218] The server provides the final content to the user. Once the reproduced content is verified as non-infringing and appropriate for the user's emotional state, the server notifies the user, "Here is the final content," and provides the content.

[0219] Example 2

[0220] Next, a description will be given of Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the smart device 14 will be referred to as a "terminal."

[0221] There exists a problem that it is difficult to obtain high-quality content that matches a user's emotions while avoiding the risk of copyright infringement of user-generated content. Furthermore, if generated content is provided without considering the user's emotions, the user experience may be poor. Therefore, there is a need for a system that provides copyright-free content while also adapting to the user's emotional state.

[0222] The identification process by the identification processing unit 290 of the data processing device 12 in the second embodiment is realized by the following means. In this invention, the server includes a means for making an information generation request, a means for generating information using a generative intelligence system, and a means for verifying the generated information by checking it against a copyright database and a patent database. This makes it possible to verify whether the generated information infringes any rights, and furthermore, by using an emotion engine that adjusts information based on the user's emotion data, it becomes possible to provide information that matches the user's emotional state.

[0223] An "information generation request" is an action in which a user requests that information be generated based on a specific theme or keyword.

[0224] A "generative intelligence system" is a system that uses artificial intelligence technology to automatically generate information based on conditions specified by the user.

[0225] "Information" refers to all content generated by generative intelligence systems, including blog posts, reports, and articles.

[0226] "Copyright database" means a database that stores information about existing copyrighted works and is used to verify whether generated information infringes existing rights.

[0227] A "patent database" is a database that stores existing patent information and is used to verify whether generated information infringes patent rights.

[0228] An "emotion engine" is a system that receives a user's emotional data as input and evaluates whether the generated information matches the user's emotional state.

[0229] "Regeneration" is the process of reusing the generative intelligence system to generate new information.

[0230] The "matching result" is the evaluation result when the generated information is matched with the copyright database and patent database, and indicates whether or not there is a copyright infringement.

[0231] The present invention relates to a system in which a user makes an information generation request, a server generates information using a generative intelligence system, and then adjusts the generated information based on the user's emotions using an emotion engine.

[0232] First, the user uses a device to request information generation. The user enters the request details (e.g., "Please generate a new blog post") into the device's input form, specifies a theme or keyword (e.g., "fun events in daily life") and the user's current emotional state (e.g., "fun"), and presses the send button.

[0233] Next, the server receives the request and generates and inputs a prompt sentence to a generative intelligence system (e.g., a natural language generation model).The server uses the generative intelligence system to generate information based on themes and keywords specified by the user, and saves the generated information in temporary storage (e.g., cloud storage).

[0234] The generated information is then checked against copyright and patent databases by the server, which accesses these databases to verify that the generated information does not infringe on existing copyrighted works or patents. This checking process evaluates whether certain phrases or themes match existing copyrighted works.

[0235] As a result of the comparison, the server determines whether there is a copyright infringement. If the generated information matches an existing copyrighted work, the server determines that it is an infringement and starts the regeneration process. On the other hand, if there is no match, it determines that there is no copyright infringement.

[0236] An emotion engine is introduced. The server sends the user's emotion data to the emotion engine, which evaluates whether the generated information matches the user's current emotion. For example, if the user specifies an emotional state of "fun," the generated information is evaluated to see if it is fun.

[0237] The server receives the emotion engine's evaluation results and adjusts the information as needed. If the generated information does not match the user's emotional state, it initiates a regeneration process to generate new information. This regeneration process also checks against copyright and patent databases.

[0238] The server provides the final information to the user. Once it has confirmed that the reproduced information does not infringe on any rights and is appropriate for the user's emotional state, the server notifies the user, "This is the final information," and provides the information.

[0239] As a concrete example, if a user requests a blog post on the theme of "fun everyday events" and also specifies the emotional state of "fun," the generative intelligence system will generate a blog post about fun events, and the emotion engine will evaluate its content. If the generated post is confirmed to be fun and does not infringe on any copyrights, the server will provide the post to the user. If the content is not fun or if there is any copyright infringement, the post will be regenerated to provide the most appropriate information.

[0240] This invention allows users to use information that matches their own emotions with peace of mind, and also avoids the risk of infringement of rights.

[0241] Prompt Sentence Examples

[0242] Theme: Fun everyday events

[0243] Emotional state: Happy

[0244] The flow of the identification process in the second embodiment will be described with reference to FIG.

[0245] Step 1:

[0246] The user uses a terminal to make an information generation request. Specifically, the user enters "Please generate a new blog post" into the terminal's input form, specifies a theme and keywords (e.g., "fun events in daily life") and the user's current emotional state (e.g., "fun"), and presses the send button.

[0247] Input: User request (topic, keywords, emotional state)

[0248] Output: Request data sent to the server

[0249] Step 2:

[0250] The server receives the user's request and converts the request data into a prompt sentence to be input to a generative intelligence system (e.g., a natural language generation model).

[0251] Input: User request data

[0252] Output: Prompt sentence for the generative intelligence system

[0253] Step 3:

[0254] The server generates information using a generative intelligence system. A prompt is input into the generative intelligence system, which generates information based on the specified theme or keywords. The generated information is saved in temporary storage (e.g., cloud storage).

[0255] Input: prompt statement

[0256] Output: The generated information (content)

[0257] Step 4:

[0258] The server checks the generated information against copyright and patent databases, specifically comparing words and phrases in the generated information with existing data in the databases to evaluate whether there are any matches.

[0259] Input: Generated information

[0260] Output: Matching result (whether or not there is a copyright infringement)

[0261] Step 5:

[0262] The server determines whether or not there is a copyright infringement based on the comparison result. If a copyright infringement is confirmed in the generated information, the server starts the process of generating the information again.

[0263] Input: Matching result

[0264] Output: Infringement decision result

[0265] Step 6:

[0266] The server sends the user's emotional data to the emotion engine, which evaluates whether the generated information matches the user's current emotion. Specifically, it evaluates whether the generated information matches the user's specified emotional state (e.g., "fun").

[0267] Input: Generated information and user emotion data

[0268] Output: Emotion evaluation result

[0269] Step 7:

[0270] The server receives the emotion engine's evaluation results and adjusts the information as needed. If the generated information does not match the user's emotional state, it initiates a regeneration process to generate new information. This regeneration process also checks against copyright and patent databases.

[0271] Input: Emotion evaluation result

[0272] Output: The adjusted or regenerated information

[0273] Step 8:

[0274] The server provides the final information to the user. After verifying that the reproduced information is not infringing and is appropriate for the user's emotional state, the server notifies the user, "This is the final information," and provides the information.

[0275] Input: Reconciled or regenerated information

[0276] Output: Notifying the user and providing final information

[0277] (Application example 2)

[0278] Next, a description will be given of Application Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the smart device 14 will be referred to as a "terminal."

[0279] Conventional content generation systems have difficulty generating content that matches the user's emotional state, and there is a high risk that the generated content may infringe copyright or patent rights. Furthermore, if the generated content does not match the user's emotional state, the regeneration process is complicated and time-consuming.

[0280] The specific processing by the specific processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means. In this invention, the server includes means for adjusting the generated content based on the user's emotional state, means for generating content, and means for checking the generated content against a copyright database and a patent database. This makes it possible to efficiently generate content that matches the user's emotions and avoid infringement of rights.

[0281] A "content generation request" refers to a user requesting that a generative AI create content based on a specific theme or keyword.

[0282] "Generative AI" refers to an AI system that uses natural language processing technology to automatically generate content such as text based on specified themes or keywords.

[0283] "Copyright database" refers to a database that records information about existing copyrighted works.

[0284] A "patent database" refers to a database that contains technologies and documents for which patents have been applied.

[0285] An "emotion engine" refers to a system that analyzes a user's emotional state and adjusts content to match that emotion.

[0286] "Matching result" refers to the result of evaluating whether the generated content matches existing copyrighted works or patent documents.

[0287] "Infringement" refers to a situation in which the generated content violates existing copyrights or patents.

[0288] "Regeneration" refers to the process of regenerating new content when the generated content infringes rights.

[0289] The present invention relates to a system for efficiently generating content based on a user's emotional state. The system consists of a process in which a user makes a content generation request, generates content using a generative AI model, then checks it against copyright and patent databases, and adapts it to the user's emotions using an emotion engine.

[0290] Main components of the system

[0291] 1. User Device:

[0292] The user uses the terminal to input the theme, keywords, and emotional state, and then sends a content generation request.

[0293] 2. Server:

[0294] Generative AI model: A generative AI model receives requests from users and generates content based on themes and keywords specified.

[0295] Copyright and patent database matching tool: A tool that checks the generated content against existing copyright and patent databases to ensure that the generated content does not violate any existing copyrights or patents.

[0296] Emotion engine: Uses user emotional data to evaluate generated content and adjust it as needed.

[0297] System operation procedure

[0298] First, the user inputs a theme, keywords, and emotional state from the terminal. For example, if the theme is "fun everyday events," the keywords are set as "park, small animals, family," and the emotional state is specified as "fun." This input is then sent to the server.

[0299] The server uses the generative AI model to generate content based on the specified theme or keywords, and this generated content is temporarily stored in data storage on the server.

[0300] Next, the server's copyright and patent database matching tools check the generated content against existing data to determine if there is any infringement. If there is any infringement, the regeneration process is initiated.

[0301] If the generated content is not infringing, the server uses an emotion engine to evaluate whether the content matches the user's specified emotional state, for example, whether the generated content is enjoyable based on the user's specified emotional state of "enjoyment."

[0302] If necessary, the emotion engine adjusts the content and generates the final content. This final content is provided to the user. An example of a prompt sentence is "Please create a new blog post. The theme is 'fun everyday events' and the keywords are 'parks, small animals, and family'. My current emotion is 'fun'."

[0303] This allows users to efficiently generate content that matches their emotions while avoiding the risk of copyright infringement.

[0304] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[0305] Step 1:

[0306] A user uses a terminal to make a content generation request. Specifically, the user inputs a theme, keywords, and emotional state, and then presses the send button, which sends the request to the server. As input, the user sends a prompt statement such as, "Please create a new blog post. The theme is 'fun everyday events,' and the keywords are 'parks, small animals, and family.' My current emotion is 'fun.'" As output, this request is sent to the server.

[0307] Step 2:

[0308] The server analyzes the received request and passes it to the generative AI model. The input includes the theme, keywords, and emotional state submitted by the user. This information is converted into a prompt sentence and set as input for the generative AI model. The output is the input data that the generative AI model uses to generate content.

[0309] Step 3:

[0310] The generative AI model generates content based on the specified theme and keywords. The input includes a prompt sentence passed from the server. As a data operation, the generative AI model performs natural language processing and generates new text content. As an output, the generated text content is returned to the server.

[0311] Step 4:

[0312] The server stores the generated content in temporary storage and then checks it against copyright and patent databases. The input includes the generated text content. The data processing involves comparing the generated content with existing copyright and patent databases to find matches. The output is the matching results.

[0313] Step 5:

[0314] The server determines whether or not there is a copyright infringement based on the comparison results. The input includes the comparison results between the generated text content and the database. The data calculation determines whether or not there is a copyright infringement if there is a match, and determines that there is no copyright infringement if there is not. The output is a determination result regarding whether or not there is a copyright infringement.

[0315] Step 6:

[0316] The server uses an emotion engine to adjust the generated content to fit the user's emotional state. The input includes the generated text content and the user's emotional state. As a data operation, the emotion engine analyzes the content of the content and adjusts it to fit the user's emotional state. The output is the adjusted text content.

[0317] Step 7:

[0318] If the infringement is confirmed and the content matches the emotional state, the final content is provided to the user. The input includes the adjusted text content and the result of the determination that there is no infringement. The server sends the adjusted content to the user's device and provides it as the final content. The output is that the user can view the final content.

[0319] 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.

[0320] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (registered trademark) (Internet search engine).<URL: https: / / openai.com / blog / chatgpt> ), Gemini (registered trademark) (Internet search <url: https: gemini.google.com ?hl="ja">) and other generation AIs. 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 voice, text data indicating text, and image data indicating an image is also input. 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.

[0321] In the above embodiment, an example in which the specific process is performed by the data processing device 12 has been given, but the technology of the present disclosure is not limited to this, and the specific process may be performed by the smart device 14.

[0322] [Second embodiment]

[0323] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0324] 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.

[0325] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 is an example of a "computer" according to the technology of the present disclosure. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, the RAM 30, and the storage 32 are connected to a bus 34. 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).

[0326] 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.

[0327] The microphone 238 receives instructions and the like from the user 20 by receiving voice uttered by the user 20. The microphone 238 captures the voice uttered by the user 20, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio in accordance with instructions from the processor 46.

[0328] 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 surroundings of user 20 (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0329] 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. 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.

[0330] 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.

[0331] 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 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.

[0332] 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.

[0333] In the smart glasses 214, the reception output process is performed by the processor 46. A reception output program 60 is stored in the storage 50. The processor 46 reads the reception output program 60 from the storage 50 and executes the read reception output program 60 on the RAM 48. The reception output process is realized by the processor 46 operating as the control unit 46A in accordance with the reception output program 60 executed on the RAM 48.

[0334] Next, a description will be given of the identification process performed by the identification processing unit 290 of the data processing device 12. In the following description, the data processing device 12 will be referred to as the "server" and the smart glasses 214 will be referred to as the "terminal."

[0335] The present invention relates to a content generation system using a generation AI, and specific embodiments are described below.

[0336] First, a user requests content creation using a device. For example, the user enters "Please create a new blog post" into an input form on the device and presses the submit button. This request can include themes and keywords specified by the user.

[0337] The server then receives the user's request and generates the content using generative AI, which uses natural language generation models (NLGMs) to generate blog posts based on themes and keywords specified by the user. The generated content is temporarily stored on the server.

[0338] The generated content is then checked against copyright and patent databases by the server, which accesses these databases to determine whether the generated content violates existing copyrights or patents. During this checking process, the server evaluates whether certain phrases or themes match existing copyrighted works.

[0339] As a result of the comparison, the server determines whether or not there is a copyright infringement. For example, if part of the generated blog post matches an existing copyrighted work, the server determines that it is an infringement. On the other hand, if there is no match, it determines that there is no copyright infringement.

[0340] The server notifies the user of this decision. If there is no infringement of rights, the generated content is provided to the user as is. On the other hand, if a rights infringement is confirmed, the server notifies the user that the content will be generated again and re-executes the generation AI. In this case, the newly generated content also undergoes the same procedure and rights confirmation is performed again.

[0341] Once the final non-infringing content is generated, including if regeneration is required, the server will provide it to the user. For example, once it has been confirmed that a newly generated blog post is non-infringing, the server will notify the user, "This is the final content," and provide the content.

[0342] This system allows users to use generated content with peace of mind and avoids the risk of copyright infringement.

[0343] The processing flow will be explained below.

[0344] Step 1:

[0345] A user requests content creation using a device. The user enters "Please create a new blog post" into the device's input form and presses the submit button.

[0346] Step 2:

[0347] The server receives the user's request, which is then forwarded to the server and parsed for specific generation instructions (themes, keywords, etc.).

[0348] Step 3:

[0349] The server generates content using generative AI, which invokes a natural language generation model to generate blog posts based on user requests, taking into account themes and keywords specified in the process.

[0350] Step 4:

[0351] The server temporarily stores the generated content. The generated blog post is stored in temporary storage on the server, ready for matching in the next step.

[0352] Step 5:

[0353] The server checks the generated content against copyright and patent databases. The server accesses these databases to verify that the generated content does not infringe on existing copyrights or patents.

[0354] Step 6:

[0355] The server analyzes the results of the database comparison and determines whether there is any copyright infringement. For example, if part of the generated blog post matches an existing copyrighted work, the server determines that it is a copyright infringement.

[0356] Step 7:

[0357] The server notifies the user of the result of the rights check. If there is no infringement, the generated content is provided to the user as is. If an infringement is confirmed, the user is notified that the content must be regenerated.

[0358] Step 8:

[0359] If the server finds a copyright infringement, it will regenerate the content. The server will restart the generation AI and generate new content, adjusting parameters to avoid the infringing parts if possible.

[0360] Step 9:

[0361] The server again checks the reproduced content against copyright and patent databases to determine whether the reproduced content is infringing.

[0362] Step 10:

[0363] The server provides the final content to the user. If the reproduced content does not infringe any rights, the server notifies the user, "This is the final content," and provides the content.

[0364] Example 1

[0365] Next, a description will be given of Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the smart glasses 214 will be referred to as a "terminal."

[0366] In today's world, there is a growing demand for users to use generative AI to generate content quickly and safely. However, there is a risk that the generated content may infringe copyright or patents, which can make users hesitant to use the generated content. Furthermore, the regeneration process in the event of confirmed infringement is not automated, which can be time-consuming for users. These issues result in a lack of efficiency and reliability in the content generation process. Furthermore, there are insufficient means of providing appropriate input to generative AI models, and insufficient mechanisms for storing and notifying matching results.

[0367] The specific processing by the specific processing unit 290 of the data processing device 12 in the first embodiment is realized by the following means.

[0368] In this invention, the server includes: means for a user to make a content generation request; means for the server to generate content using a generative AI model; means for the server to check the generated content against a copyright database and a patent database; means for the server to determine whether or not there is infringement of rights based on the check result; means for the server to generate the content again if infringement of rights is confirmed; means for the server to provide the generated content to the user if it does not infringe rights; means for generating and inputting a prompt sentence to the generative AI model; and means for saving the check result. This allows users to use the generated content with peace of mind, and makes it possible to improve the efficiency and reliability of the content generation process.

[0369] A "user" is a user of the system who requests content generation.

[0370] A "content generation request" is a request made by a user to generate new content using the generation AI.

[0371] The "server" is a central management device that generates content using generative AI, checks copyrights and patents, and provides the content to users.

[0372] A "generative artificial intelligence model" is an artificial intelligence system for generating content in natural language based on user requests.

[0373] A "prompt" is an instruction entered into a generative AI model, which defines the conditions and theme for content generation.

[0374] "Content" refers to text data such as blog posts and documents generated by generative AI models.

[0375] The "copyright database" is a database that holds information on existing copyrighted works and compares it with generated content.

[0376] A "patent database" is a database that holds existing patent information and compares it with generated content.

[0377] The "matching result" is information regarding whether or not the generated content infringes any rights, obtained by matching with the copyright database and the patent database.

[0378] "Infringement" refers to a situation in which the generated content violates an existing copyright or patent.

[0379] "Regeneration" is the process of re-running the generation AI to generate new content if a copyright infringement is confirmed.

[0380] This invention relates to a content generation system using generative AI, and in particular to a system that automatically generates content requested by users and provides it after checking that there is no infringement of copyright or patent rights.

[0381] Specifically, a user first makes a content generation request using their own device (such as a PC or smartphone). The user enters a request into an input form, such as "Please generate a new blog post" or "Please generate a blog post about the future of AI technology," and presses the submit button. This request can include the theme and keywords of the content to be generated.

[0382] Next, the server receives the user's request and generates a prompt for the generative AI model (e.g., GPT-3). The prompt is an instruction to be input to the generative model, such as "Please write a blog post that provides a detailed explanation of the future prospects for AI technology."

[0383] The generated prompt sentence is input into a generative AI model, and the server generates content based on the specified theme and keywords. The generated content is temporarily stored on the server.

[0384] The server then checks the generated content against copyright and patent databases to determine if there are any infringements. Specifically, it accesses databases such as Google Patents, PubMed, USPTO, and WIPO to determine whether the generated content violates existing copyrights or patents. The server then stores the results of the checks against these databases.

[0385] Based on the matching results, the server determines whether the generated content violates existing copyrights or patents. If there is a match, it is deemed to be a copyright infringement and notifies the user that the content will be regenerated. To regenerate the content, the server uses a new artificial intelligence model to generate a new prompt sentence and generate the content. The same matching process is repeated to verify the rights.

[0386] When the final content is generated and confirmed to be free of infringement, the server provides it to the user. For example, the server may notify the user, "This is the final content," and provide the generated blog post. The server may also notify the user of the results of the verification of the generated content, informing them that the resulting content is free of infringement.

[0387] In this way, a content generation system using a generative AI model can enable users to use the generated content with peace of mind, while reducing the risk of copyright infringement.

[0388] The flow of the identification process in the first embodiment will be described with reference to FIG.

[0389] Step 1:

[0390] The user inputs a content generation request using a terminal and presses a send button.

[0391] Specifically, the user enters "Please generate a blog post about the future of AI technology" into the input form on the device and presses the send button.

[0392] Input: Request details (including themes and keywords)

[0393] Output: User request data

[0394] Step 2:

[0395] The server receives the user request data and generates a prompt sentence.

[0396] Specifically, the server receives a request to "generate a blog post about the future of AI technology" and generates a prompt that says, "Please create a blog post that provides a detailed explanation of the future prospects for AI technology."

[0397] Input: User request data

[0398] Output: Generated prompt statement

[0399] Step 3:

[0400] The server inputs a prompt sentence into the artificial intelligence model to generate content.

[0401] Specifically, the server inputs the generated prompt sentence into a generative artificial intelligence model (e.g., GPT-3) and generates a "blog post about the future of AI technology."

[0402] Input: Generate prompt statement

[0403] Output: Generated content

[0404] Step 4:

[0405] The server temporarily stores the generated content.

[0406] Specifically, the server saves a "blog article about the future of AI technology" in a temporary storage area on the server.

[0407] Input: Generated content

[0408] Output: Stored generated content

[0409] Step 5:

[0410] The server checks the generated content against copyright and patent databases.

[0411] Specifically, the server accesses databases such as Google Patents, PubMed, USPTO, and WIPO to collate "blog articles about the future of AI technology."

[0412] Input: Saved generated content

[0413] Output: Matching result data

[0414] Step 6:

[0415] The server determines whether or not there is a copyright infringement based on the matching result data.

[0416] Specifically, the server analyzes the results of the comparison and determines whether any part of the content matches an existing patent. If there is a match, it is determined to be an infringement, and if not, it is determined to be no problem.

[0417] Input: Matching result data

[0418] Output: Infringement judgment result

[0419] Step 7:

[0420] The server notifies the user of the result of the determination as to whether or not there is a copyright infringement.

[0421] Specifically, the server sends a notification to the user saying, "The generated content does not infringe any rights. Here is the final content."

[0422] Input: Infringement judgment result

[0423] Output: User notification

[0424] Step 8:

[0425] If a copyright infringement is confirmed, the server generates the content again.

[0426] Specifically, the server uses the generative artificial intelligence model again to generate a new prompt sentence and generate the content again.

[0427] Input: Infringement judgment result

[0428] Output: Regenerated content

[0429] Step 9:

[0430] The server ultimately provides non-infringing content to the user.

[0431] Specifically, the server provides the newly generated blog post to the user along with a message saying, "Here is the final content."

[0432] Input: Regenerated content

[0433] Output: Providing the final content to the user

[0434] (Application example 1)

[0435] Next, a description will be given of Application Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the smart glasses 214 will be referred to as a "terminal."

[0436] In recent years, automation has progressed in content generation and distribution, but there is a risk that the generated content may infringe existing copyrights. Furthermore, users are required to quickly obtain legal content in real time using smartphones or smart glasses. However, current technology does not adequately provide a system that meets these requirements. To solve this issue, a system that includes rights verification and regeneration processes for generated content is needed.

[0437] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 1 is realized by the following means.

[0438] In this invention, the server includes: a means for a user to make a content generation request; a means for generating content using a generative AI model; a means for verifying the generated content against a copyright database and a patent database; a means for determining whether or not there is a copyright infringement based on the verification result; a means for regenerating the content if a copyright infringement is confirmed; a means for providing the generated content to the user if the generated content does not infringe, an interface means for receiving prompts for generating content and verifying copyright in real time and inputting them into the generative AI model; and an application means that can be installed on a smartphone or smart glasses. This allows users to safely obtain legal content in real time.

[0439] A "content generation request" is a request made by a user to create new content using a generative AI model.

[0440] A "generative AI model" is an artificial intelligence system that uses natural language generation technology to create content based on themes and keywords specified by the user.

[0441] "Copyright database" means a database that stores information about existing copyrighted works and is used to verify whether generated content infringes existing copyrights.

[0442] "Patent Database" means a database that stores existing patent information and is used to check whether generated content infringes existing patent rights.

[0443] The "matching result" refers to the result of checking whether the generated content matches the copyright database and the patent database.

[0444] The "interface means" is a mechanism that receives prompt statements from the user and inputs them into the generative AI model.

[0445] The "application means" is software that can be installed on a smartphone or smart glasses, and allows a user to make content generation requests and receive generated content.

[0446] A "prompt" is an instruction provided by a user to a generative AI model, and specifically indicates the theme and keywords of the content to be generated.

[0447] "Regeneration means" refers to the process of regenerating content when the generated content infringes an existing copyright or patent.

[0448] The "means for determining infringement of rights" is a mechanism for determining whether or not there is infringement of rights based on the results of comparing the generated content with a copyright database and a patent database.

[0449] In this invention, a user first makes a content generation request using an application installed on a smartphone or smart glasses. The user then inputs a "prompt statement" through the application interface, specifying the theme and keywords of the content they want to generate. For example, the user might input, "Please generate a blog post about recent technology trends."

[0450] When a user submits a request, the prompt is sent to the server, which uses a generative AI model (e.g., a model using natural language generation technology) to generate content based on the specified theme.

[0451] The generated content is temporarily stored on a server, which then checks it against copyright and patent databases to see if there are any matches to existing copyrighted works or patents. During this process, it evaluates whether specific phrases or themes match existing entries in the database.

[0452] Based on the comparison results, the server determines whether there is any copyright infringement. If the generated content does not match any existing copyrighted work or patent, it is determined that there is no copyright infringement and the content is provided to the user. On the other hand, if a copyright infringement is confirmed, the server notifies the user that the content will be generated again and re-runs the generative AI model using a prompt. In this case, the newly generated content also undergoes the same procedure and is again checked for copyright.

[0453] Once the server has generated the final non-infringing content, it is sent to the user's smartphone or smart glasses. For example, the user will be notified with a message saying, "This is the final content," and can view the generated blog post within the application.

[0454] This system allows users to safely use generated content with the confidence that it will not infringe on existing copyrights or patents, and enables content creation and review in real time on portable devices such as smartphones and smart glasses.

[0455] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[0456] Step 1:

[0457] A user launches an application on their smartphone or smart glasses and inputs a prompt through the interface, for example, "Please generate a blog post about recent technology trends." This input is then sent to the server.

[0458] Input: prompt statement

[0459] Output: Request sent to the server

[0460] Step 2:

[0461] The server inputs the received prompt sentence into a generative AI model to generate content based on the specified theme. By using a generative AI model (for example, a natural language generation model), text is generated that follows the theme specified by the user.

[0462] Input: prompt statement

[0463] Output: Generated content (e.g. blog posts)

[0464] Step 3:

[0465] The server temporarily stores the generated content and then checks it against copyright and patent databases, using APIs to access these databases and check whether the generated content matches existing data.

[0466] Input: Generated content

[0467] Output: Matching result (whether or not there is a copyright infringement)

[0468] Step 4:

[0469] The server determines whether or not there is a copyright infringement based on the comparison results. If there is no copyright infringement, the content is provided to the user as is. If a copyright infringement is confirmed, new content is generated using the generative AI model again.

[0470] Input: Matching result

[0471] Output: Judgment based on whether or not there is a copyright infringement

[0472] Step 5:

[0473] If the server regenerates the content, the newly generated content will go through the same verification process, with rights verification repeated, until finally, non-infringing content is generated.

[0474] Input: Regenerated content

[0475] Output: Matching result and whether or not there is a copyright infringement

[0476] Step 6:

[0477] The server then generates non-infringing content and sends it to the user's smartphone or smart glasses, where the user can view and use the content through the application.

[0478] Input: Non-infringing generated content

[0479] Output: Providing content to the user

[0480] Step 7:

[0481] The server notifies the user that the content has been provided. For example, a message such as "This is the final content" is sent to the user, and the user can check the content within the application.

[0482] Input: Final generated content

[0483] Output: Notify the user and display the provided content

[0484] Furthermore, an emotion engine that estimates the user's emotion may be further combined. That is, the identification processing unit 290 may estimate the user's emotion using the emotion identification model 59, and perform identification processing using the user's emotion.

[0485] The present invention relates to a system in which a user makes a content generation request, a server generates the content using a generation artificial intelligence, and then adjusts the generated content based on the user's emotions using an emotion engine. Specific embodiments are described below.

[0486] First, a user requests content creation using a device. For example, the user enters "Please create a new blog post" into an input form on the device and presses the submit button. This request can include a theme, keywords, and even the user's current emotional state (e.g., happy, sad, stressed, etc.).

[0487] The server then receives the request and generates the content using generative AI, which uses a natural language generation model to generate blog posts based on themes and keywords specified by the user. The generated content is then stored in temporary storage on the server.

[0488] The generated content is then checked against copyright and patent databases by the server, which accesses these databases to ensure the generated content does not infringe on existing copyrighted works or patents. This checking process involves assessing whether certain phrases or themes match existing copyrighted works.

[0489] As a result of the comparison, the server determines whether or not there is a copyright infringement. For example, if part of the generated blog post matches an existing copyrighted work, the server determines that it is an infringement. On the other hand, if there is no match, it determines that there is no copyright infringement.

[0490] This is where the emotion engine comes in. The server sends the user's emotion data to the emotion engine, which evaluates how well the generated content matches the user's current emotion. For example, if the user specifies an emotional state of "fun," the generated blog post will be evaluated to see if it is enjoyable.

[0491] The server receives the emotion engine's evaluation results and adjusts the content as needed. For example, if the generated content does not match the user's emotional state, it initiates a regeneration process to generate new content. This regeneration process also checks against copyright and patent databases, just as it did the first time.

[0492] The server then provides the final content to the user. For example, once it has determined that the regenerated blog post is not infringing and matches the user's emotional state, the server notifies the user, "Here is the final content," and provides the content.

[0493] As a concrete example, if a user requests a blog post on the theme of "fun everyday events" and also specifies the emotional state as "fun," the generation AI will generate a blog post about fun events, and the emotion engine will evaluate the content. If the generated article is confirmed to be fun and does not infringe on any rights, the server will provide the article to the user. If the content is not fun or if there is any infringement of rights, the system will regenerate the article and provide the optimal content.

[0494] This system allows users to use content that matches their own emotions with peace of mind, while also avoiding the risk of copyright infringement.

[0495] The processing flow will be explained below.

[0496] Step 1:

[0497] A user uses a device to request content creation. The user enters "Please generate a new blog post" into the device's input form and presses the submit button. The request can include a topic, keywords, and even the user's current emotional state (e.g., happy, sad, stressed, etc.).

[0498] Step 2:

[0499] The server receives the user's request, which is then forwarded to the server, where it is analyzed for specific generation instructions (themes, keywords, emotional state, etc.).

[0500] Step 3:

[0501] The server generates content using generative AI. The generative AI invokes a natural language generation model to generate blog posts based on the user's request. The generation process reflects the specified themes and keywords. The generated content is stored in temporary storage on the server.

[0502] Step 4:

[0503] The server checks the generated content against copyright and patent databases. The server accesses these databases to determine whether the generated content violates existing copyrights or patents. It evaluates whether specific phrases or themes match existing copyrighted works.

[0504] Step 5:

[0505] The server analyzes the database match and determines whether or not there is a copyright infringement. If part of the generated blog post matches an existing copyrighted work, the server determines that it is an infringement. On the other hand, if there is no match, it determines that there is no copyright infringement.

[0506] Step 6:

[0507] The server sends the user's emotional data to the emotion engine, which analyzes the user's emotional state and evaluates how well the generated content matches the user's current emotion.

[0508] Step 7:

[0509] The server receives the emotion engine's evaluation results and adjusts the content as needed. For example, if the generated content does not match the user's emotional state, it initiates a regeneration process to generate new content.

[0510] Step 8:

[0511] The server runs a regeneration process, relaunching the generation AI to generate new content. This regeneration process also checks against copyright and patent databases.

[0512] Step 9:

[0513] The server again checks the reproduced content against copyright and patent databases to determine whether the reproduced content is infringing.

[0514] Step 10:

[0515] The server again sends the regenerated content to the emotion engine to evaluate whether it matches the user's emotional state.

[0516] Step 11:

[0517] The server provides the final content to the user. Once the reproduced content is verified as non-infringing and appropriate for the user's emotional state, the server notifies the user, "Here is the final content," and provides the content.

[0518] Example 2

[0519] Next, a description will be given of Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the smart glasses 214 will be referred to as a "terminal."

[0520] There exists a problem that it is difficult to obtain high-quality content that matches a user's emotions while avoiding the risk of copyright infringement of user-generated content. Furthermore, if generated content is provided without considering the user's emotions, the user experience may be poor. Therefore, there is a need for a system that provides copyright-free content while also adapting to the user's emotional state.

[0521] The identification process by the identification processing unit 290 of the data processing device 12 in the second embodiment is realized by the following means. In this invention, the server includes a means for making an information generation request, a means for generating information using a generative intelligence system, and a means for verifying the generated information by checking it against a copyright database and a patent database. This makes it possible to verify whether the generated information infringes any rights, and furthermore, by using an emotion engine that adjusts information based on the user's emotion data, it becomes possible to provide information that matches the user's emotional state.

[0522] An "information generation request" is an action in which a user requests that information be generated based on a specific theme or keyword.

[0523] A "generative intelligence system" is a system that uses artificial intelligence technology to automatically generate information based on conditions specified by the user.

[0524] "Information" refers to all content generated by generative intelligence systems, including blog posts, reports, and articles.

[0525] "Copyright database" means a database that stores information about existing copyrighted works and is used to verify whether generated information infringes existing rights.

[0526] A "patent database" is a database that stores existing patent information and is used to verify whether generated information infringes patent rights.

[0527] An "emotion engine" is a system that receives a user's emotional data as input and evaluates whether the generated information matches the user's emotional state.

[0528] "Regeneration" is the process of reusing the generative intelligence system to generate new information.

[0529] The "matching result" is the evaluation result when the generated information is matched with the copyright database and patent database, and indicates whether or not there is a copyright infringement.

[0530] The present invention relates to a system in which a user makes an information generation request, a server generates information using a generative intelligence system, and then adjusts the generated information based on the user's emotions using an emotion engine.

[0531] First, the user uses a device to request information generation. The user enters the request details (e.g., "Please generate a new blog post") into the device's input form, specifies a theme or keyword (e.g., "fun events in daily life") and the user's current emotional state (e.g., "fun"), and presses the send button.

[0532] Next, the server receives the request and generates and inputs a prompt sentence to a generative intelligence system (e.g., a natural language generation model).The server uses the generative intelligence system to generate information based on themes and keywords specified by the user, and saves the generated information in temporary storage (e.g., cloud storage).

[0533] The generated information is then checked against copyright and patent databases by the server, which accesses these databases to verify that the generated information does not infringe on existing copyrighted works or patents. This checking process evaluates whether certain phrases or themes match existing copyrighted works.

[0534] As a result of the comparison, the server determines whether there is a copyright infringement. If the generated information matches an existing copyrighted work, the server determines that it is an infringement and starts the regeneration process. On the other hand, if there is no match, it determines that there is no copyright infringement.

[0535] An emotion engine is introduced. The server sends the user's emotion data to the emotion engine, which evaluates whether the generated information matches the user's current emotion. For example, if the user specifies an emotional state of "fun," the generated information is evaluated to see if it is fun.

[0536] The server receives the emotion engine's evaluation results and adjusts the information as needed. If the generated information does not match the user's emotional state, it initiates a regeneration process to generate new information. This regeneration process also checks against copyright and patent databases.

[0537] The server provides the final information to the user. Once it has confirmed that the reproduced information does not infringe on any rights and is appropriate for the user's emotional state, the server notifies the user, "This is the final information," and provides the information.

[0538] As a concrete example, if a user requests a blog post on the theme of "fun everyday events" and also specifies the emotional state of "fun," the generative intelligence system will generate a blog post about fun events, and the emotion engine will evaluate its content. If the generated post is confirmed to be fun and does not infringe on any copyrights, the server will provide the post to the user. If the content is not fun or if there is any copyright infringement, the post will be regenerated to provide the most appropriate information.

[0539] This invention allows users to use information that matches their own emotions with peace of mind, and also avoids the risk of infringement of rights.

[0540] Prompt Sentence Examples

[0541] Theme: Fun everyday events

[0542] Emotional state: Happy

[0543] The flow of the identification process in the second embodiment will be described with reference to FIG.

[0544] Step 1:

[0545] The user uses a terminal to make an information generation request. Specifically, the user enters "Please generate a new blog post" into the terminal's input form, specifies a theme and keywords (e.g., "fun events in daily life") and the user's current emotional state (e.g., "fun"), and presses the send button.

[0546] Input: User request (topic, keywords, emotional state)

[0547] Output: Request data sent to the server

[0548] Step 2:

[0549] The server receives the user's request and converts the request data into a prompt sentence to be input to a generative intelligence system (e.g., a natural language generation model).

[0550] Input: User request data

[0551] Output: Prompt sentence for the generative intelligence system

[0552] Step 3:

[0553] The server generates information using a generative intelligence system. A prompt is input into the generative intelligence system, which generates information based on the specified theme or keywords. The generated information is saved in temporary storage (e.g., cloud storage).

[0554] Input: prompt statement

[0555] Output: The generated information (content)

[0556] Step 4:

[0557] The server checks the generated information against copyright and patent databases, specifically comparing words and phrases in the generated information with existing data in the databases to evaluate whether there are any matches.

[0558] Input: Generated information

[0559] Output: Matching result (whether or not there is a copyright infringement)

[0560] Step 5:

[0561] The server determines whether or not there is a copyright infringement based on the comparison result. If a copyright infringement is confirmed in the generated information, the server starts the process of generating the information again.

[0562] Input: Matching result

[0563] Output: Infringement decision result

[0564] Step 6:

[0565] The server sends the user's emotional data to the emotion engine, which evaluates whether the generated information matches the user's current emotion. Specifically, it evaluates whether the generated information matches the user's specified emotional state (e.g., "fun").

[0566] Input: Generated information and user emotion data

[0567] Output: Emotion evaluation result

[0568] Step 7:

[0569] The server receives the emotion engine's evaluation results and adjusts the information as needed. If the generated information does not match the user's emotional state, it initiates a regeneration process to generate new information. This regeneration process also checks against copyright and patent databases.

[0570] Input: Emotion evaluation result

[0571] Output: The adjusted or regenerated information

[0572] Step 8:

[0573] The server provides the final information to the user. After verifying that the reproduced information is not infringing and is appropriate for the user's emotional state, the server notifies the user, "This is the final information," and provides the information.

[0574] Input: Reconciled or regenerated information

[0575] Output: Notifying the user and providing final information

[0576] (Application example 2)

[0577] Next, a description will be given of Application Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the smart glasses 214 will be referred to as a "terminal."

[0578] Conventional content generation systems have difficulty generating content that matches the user's emotional state, and there is a high risk that the generated content may infringe copyright or patent rights. Furthermore, if the generated content does not match the user's emotional state, the regeneration process is complicated and time-consuming.

[0579] The specific processing by the specific processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means. In this invention, the server includes means for adjusting the generated content based on the user's emotional state, means for generating content, and means for checking the generated content against a copyright database and a patent database. This makes it possible to efficiently generate content that matches the user's emotions and avoid infringement of rights.

[0580] A "content generation request" refers to a user requesting that a generative AI create content based on a specific theme or keyword.

[0581] "Generative AI" refers to an AI system that uses natural language processing technology to automatically generate content such as text based on specified themes or keywords.

[0582] "Copyright database" refers to a database that records information about existing copyrighted works.

[0583] A "patent database" refers to a database that contains technologies and documents for which patents have been applied.

[0584] An "emotion engine" refers to a system that analyzes a user's emotional state and adjusts content to match that emotion.

[0585] "Matching result" refers to the result of evaluating whether the generated content matches existing copyrighted works or patent documents.

[0586] "Infringement" refers to a situation in which the generated content violates existing copyrights or patents.

[0587] "Regeneration" refers to the process of regenerating new content when the generated content infringes rights.

[0588] The present invention relates to a system for efficiently generating content based on a user's emotional state. The system consists of a process in which a user makes a content generation request, generates content using a generative AI model, then checks it against copyright and patent databases, and adapts it to the user's emotions using an emotion engine.

[0589] Main components of the system

[0590] 1. User Device:

[0591] The user uses the terminal to input the theme, keywords, and emotional state, and then sends a content generation request.

[0592] 2. Server:

[0593] Generative AI model: A generative AI model receives requests from users and generates content based on themes and keywords specified.

[0594] Copyright and patent database matching tool: A tool that checks the generated content against existing copyright and patent databases to ensure that the generated content does not violate any existing copyrights or patents.

[0595] Emotion engine: Uses user emotional data to evaluate generated content and adjust it as needed.

[0596] System operation procedure

[0597] First, the user inputs a theme, keywords, and emotional state from the terminal. For example, if the theme is "fun everyday events," the keywords are set as "park, small animals, family," and the emotional state is specified as "fun." This input is then sent to the server.

[0598] The server uses the generative AI model to generate content based on the specified theme or keywords, and this generated content is temporarily stored in data storage on the server.

[0599] Next, the server's copyright and patent database matching tools check the generated content against existing data to determine if there is any infringement. If there is any infringement, the regeneration process is initiated.

[0600] If the generated content is not infringing, the server uses an emotion engine to evaluate whether the content matches the user's specified emotional state, for example, whether the generated content is enjoyable based on the user's specified emotional state of "enjoyment."

[0601] If necessary, the emotion engine adjusts the content and generates the final content. This final content is provided to the user. An example of a prompt sentence is "Please create a new blog post. The theme is 'fun everyday events' and the keywords are 'parks, small animals, and family'. My current emotion is 'fun'."

[0602] This allows users to efficiently generate content that matches their emotions while avoiding the risk of copyright infringement.

[0603] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[0604] Step 1:

[0605] A user uses a terminal to make a content generation request. Specifically, the user inputs a theme, keywords, and emotional state, and then presses the send button, which sends the request to the server. As input, the user sends a prompt statement such as, "Please create a new blog post. The theme is 'fun everyday events,' and the keywords are 'parks, small animals, and family.' My current emotion is 'fun.'" As output, this request is sent to the server.

[0606] Step 2:

[0607] The server analyzes the received request and passes it to the generative AI model. The input includes the theme, keywords, and emotional state submitted by the user. This information is converted into a prompt sentence and set as input for the generative AI model. The output is the input data that the generative AI model uses to generate content.

[0608] Step 3:

[0609] The generative AI model generates content based on the specified theme and keywords. The input includes a prompt sentence passed from the server. As a data operation, the generative AI model performs natural language processing and generates new text content. As an output, the generated text content is returned to the server.

[0610] Step 4:

[0611] The server stores the generated content in temporary storage and then checks it against copyright and patent databases. The input includes the generated text content. The data processing involves comparing the generated content with existing copyright and patent databases to find matches. The output is the matching results.

[0612] Step 5:

[0613] The server determines whether or not there is a copyright infringement based on the comparison results. The input includes the comparison results between the generated text content and the database. The data calculation determines whether or not there is a copyright infringement if there is a match, and determines that there is no copyright infringement if there is not. The output is a determination result regarding whether or not there is a copyright infringement.

[0614] Step 6:

[0615] The server uses an emotion engine to adjust the generated content to fit the user's emotional state. The input includes the generated text content and the user's emotional state. As a data operation, the emotion engine analyzes the content of the content and adjusts it to fit the user's emotional state. The output is the adjusted text content.

[0616] Step 7:

[0617] If the infringement is confirmed and the content matches the emotional state, the final content is provided to the user. The input includes the adjusted text content and the result of the determination that there is no infringement. The server sends the adjusted content to the user's device and provides it as the final content. The output is that the user can view the final content.

[0618] 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.

[0619] 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> ), Gemini (Internet search <url: https: gemini.google.com ?hl="ja">) and other generation AIs. 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 voice, text data indicating text, and image data indicating an image is also input. 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.

[0620] In the above embodiment, an example in which the specific processing is performed by the data processing device 12 has been given, but the technology of the present disclosure is not limited to this, and the specific processing may be performed by the smart glasses 214.

[0621] [Third embodiment]

[0622] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0623] 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.

[0624] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 is an example of a "computer" according to the technology of the present disclosure. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, the RAM 30, and the storage 32 are connected to a bus 34. 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).

[0625] 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.

[0626] The microphone 238 receives instructions and the like from the user 20 by receiving voice uttered by the user 20. The microphone 238 captures the voice uttered by the user 20, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio in accordance with instructions from the processor 46.

[0627] 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 surroundings of user 20 (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0628] 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. 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.

[0629] 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.

[0630] 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 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.

[0631] 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.

[0632] In the headset type terminal 314, a reception output process is performed by the processor 46. A reception output program 60 is stored in the storage 50. The processor 46 reads the reception output program 60 from the storage 50 and executes the read reception output program 60 on the RAM 48. The reception output process is realized by the processor 46 operating as the control unit 46A in accordance with the reception output program 60 executed on the RAM 48.

[0633] Next, a description will be given of the identification process performed by the identification processing unit 290 of the data processing device 12. In the following description, the data processing device 12 will be referred to as the "server" and the headset type terminal 314 will be referred to as the "terminal."

[0634] The present invention relates to a content generation system using a generation AI, and specific embodiments are described below.

[0635] First, a user requests content creation using a device. For example, the user enters "Please create a new blog post" into an input form on the device and presses the submit button. This request can include themes and keywords specified by the user.

[0636] The server then receives the user's request and generates the content using generative AI, which uses natural language generation models (NLGMs) to generate blog posts based on themes and keywords specified by the user. The generated content is temporarily stored on the server.

[0637] The generated content is then checked against copyright and patent databases by the server, which accesses these databases to determine whether the generated content violates existing copyrights or patents. During this checking process, the server evaluates whether certain phrases or themes match existing copyrighted works.

[0638] As a result of the comparison, the server determines whether or not there is a copyright infringement. For example, if part of the generated blog post matches an existing copyrighted work, the server determines that it is an infringement. On the other hand, if there is no match, it determines that there is no copyright infringement.

[0639] The server notifies the user of this decision. If there is no infringement of rights, the generated content is provided to the user as is. On the other hand, if a rights infringement is confirmed, the server notifies the user that the content will be generated again and re-executes the generation AI. In this case, the newly generated content also undergoes the same procedure and rights confirmation is performed again.

[0640] Once the final non-infringing content is generated, including if regeneration is required, the server will provide it to the user. For example, once it has been confirmed that a newly generated blog post is non-infringing, the server will notify the user, "This is the final content," and provide the content.

[0641] This system allows users to use generated content with peace of mind and avoids the risk of copyright infringement.

[0642] The processing flow will be explained below.

[0643] Step 1:

[0644] A user requests content creation using a device. The user enters "Please create a new blog post" into the device's input form and presses the submit button.

[0645] Step 2:

[0646] The server receives the user's request, which is then forwarded to the server and parsed for specific generation instructions (themes, keywords, etc.).

[0647] Step 3:

[0648] The server generates content using generative AI, which invokes a natural language generation model to generate blog posts based on user requests, taking into account themes and keywords specified in the process.

[0649] Step 4:

[0650] The server temporarily stores the generated content. The generated blog post is stored in temporary storage on the server, ready for matching in the next step.

[0651] Step 5:

[0652] The server checks the generated content against copyright and patent databases. The server accesses these databases to verify that the generated content does not infringe on existing copyrights or patents.

[0653] Step 6:

[0654] The server analyzes the results of the database comparison and determines whether there is any copyright infringement. For example, if part of the generated blog post matches an existing copyrighted work, the server determines that it is a copyright infringement.

[0655] Step 7:

[0656] The server notifies the user of the result of the rights check. If there is no infringement, the generated content is provided to the user as is. If an infringement is confirmed, the user is notified that the content must be regenerated.

[0657] Step 8:

[0658] If the server finds a copyright infringement, it will regenerate the content. The server will restart the generation AI and generate new content, adjusting parameters to avoid the infringing parts if possible.

[0659] Step 9:

[0660] The server again checks the reproduced content against copyright and patent databases to determine whether the reproduced content is infringing.

[0661] Step 10:

[0662] The server provides the final content to the user. If the reproduced content does not infringe any rights, the server notifies the user, "This is the final content," and provides the content.

[0663] Example 1

[0664] Next, a description will be given of Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the headset type terminal 314 will be referred to as a "terminal."

[0665] In today's world, there is a growing demand for users to use generative AI to generate content quickly and safely. However, there is a risk that the generated content may infringe copyright or patents, which can make users hesitant to use the generated content. Furthermore, the regeneration process in the event of confirmed infringement is not automated, which can be time-consuming for users. These issues result in a lack of efficiency and reliability in the content generation process. Furthermore, there are insufficient means of providing appropriate input to generative AI models, and insufficient mechanisms for storing and notifying matching results.

[0666] The specific processing by the specific processing unit 290 of the data processing device 12 in the first embodiment is realized by the following means.

[0667] In this invention, the server includes: means for a user to make a content generation request; means for the server to generate content using a generative AI model; means for the server to check the generated content against a copyright database and a patent database; means for the server to determine whether or not there is infringement of rights based on the check result; means for the server to generate the content again if infringement of rights is confirmed; means for the server to provide the generated content to the user if it does not infringe rights; means for generating and inputting a prompt sentence to the generative AI model; and means for saving the check result. This allows users to use the generated content with peace of mind, and makes it possible to improve the efficiency and reliability of the content generation process.

[0668] A "user" is a user of the system who requests content generation.

[0669] A "content generation request" is a request made by a user to generate new content using the generation AI.

[0670] The "server" is a central management device that generates content using generative AI, checks copyrights and patents, and provides the content to users.

[0671] A "generative artificial intelligence model" is an artificial intelligence system for generating content in natural language based on user requests.

[0672] A "prompt" is an instruction entered into a generative AI model, which defines the conditions and theme for content generation.

[0673] "Content" refers to text data such as blog posts and documents generated by generative AI models.

[0674] The "copyright database" is a database that holds information on existing copyrighted works and compares it with generated content.

[0675] A "patent database" is a database that holds existing patent information and compares it with generated content.

[0676] The "matching result" is information regarding whether or not the generated content infringes any rights, obtained by matching with the copyright database and the patent database.

[0677] "Infringement" refers to a situation in which the generated content violates an existing copyright or patent.

[0678] "Regeneration" is the process of re-running the generation AI to generate new content if a copyright infringement is confirmed.

[0679] This invention relates to a content generation system using generative AI, and in particular to a system that automatically generates content requested by users and provides it after checking that there is no infringement of copyright or patent rights.

[0680] Specifically, a user first makes a content generation request using their own device (such as a PC or smartphone). The user enters a request into an input form, such as "Please generate a new blog post" or "Please generate a blog post about the future of AI technology," and presses the submit button. This request can include the theme and keywords of the content to be generated.

[0681] Next, the server receives the user's request and generates a prompt for the generative AI model (e.g., GPT-3). The prompt is an instruction to be input to the generative model, such as "Please write a blog post that provides a detailed explanation of the future prospects for AI technology."

[0682] The generated prompt sentence is input into a generative AI model, and the server generates content based on the specified theme and keywords. The generated content is temporarily stored on the server.

[0683] The server then checks the generated content against copyright and patent databases to determine if there are any infringements. Specifically, it accesses databases such as Google Patents, PubMed, USPTO, and WIPO to determine whether the generated content violates existing copyrights or patents. The server then stores the results of the checks against these databases.

[0684] Based on the matching results, the server determines whether the generated content violates existing copyrights or patents. If there is a match, it is deemed to be a copyright infringement and notifies the user that the content will be regenerated. To regenerate the content, the server uses a new artificial intelligence model to generate a new prompt sentence and generate the content. The same matching process is repeated to verify the rights.

[0685] When the final content is generated and confirmed to be free of infringement, the server provides it to the user. For example, the server may notify the user, "This is the final content," and provide the generated blog post. The server may also notify the user of the results of the verification of the generated content, informing them that the resulting content is free of infringement.

[0686] In this way, a content generation system using a generative AI model can enable users to use the generated content with peace of mind, while reducing the risk of copyright infringement.

[0687] The flow of the identification process in the first embodiment will be described with reference to FIG.

[0688] Step 1:

[0689] The user inputs a content generation request using a terminal and presses a send button.

[0690] Specifically, the user enters "Please generate a blog post about the future of AI technology" into the input form on the device and presses the send button.

[0691] Input: Request details (including themes and keywords)

[0692] Output: User request data

[0693] Step 2:

[0694] The server receives the user request data and generates a prompt sentence.

[0695] Specifically, the server receives a request to "generate a blog post about the future of AI technology" and generates a prompt that says, "Please create a blog post that provides a detailed explanation of the future prospects for AI technology."

[0696] Input: User request data

[0697] Output: Generated prompt statement

[0698] Step 3:

[0699] The server inputs a prompt sentence into the artificial intelligence model to generate content.

[0700] Specifically, the server inputs the generated prompt sentence into a generative artificial intelligence model (e.g., GPT-3) and generates a "blog post about the future of AI technology."

[0701] Input: Generate prompt statement

[0702] Output: Generated content

[0703] Step 4:

[0704] The server temporarily stores the generated content.

[0705] Specifically, the server saves a "blog article about the future of AI technology" in a temporary storage area on the server.

[0706] Input: Generated content

[0707] Output: Stored generated content

[0708] Step 5:

[0709] The server checks the generated content against copyright and patent databases.

[0710] Specifically, the server accesses databases such as Google Patents, PubMed, USPTO, and WIPO to collate "blog articles about the future of AI technology."

[0711] Input: Saved generated content

[0712] Output: Matching result data

[0713] Step 6:

[0714] The server determines whether or not there is a copyright infringement based on the matching result data.

[0715] Specifically, the server analyzes the results of the comparison and determines whether any part of the content matches an existing patent. If there is a match, it is determined to be an infringement, and if not, it is determined to be no problem.

[0716] Input: Matching result data

[0717] Output: Infringement judgment result

[0718] Step 7:

[0719] The server notifies the user of the result of the determination as to whether or not there is a copyright infringement.

[0720] Specifically, the server sends a notification to the user saying, "The generated content does not infringe any rights. Here is the final content."

[0721] Input: Infringement judgment result

[0722] Output: User notification

[0723] Step 8:

[0724] If a copyright infringement is confirmed, the server generates the content again.

[0725] Specifically, the server uses the generative artificial intelligence model again to generate a new prompt sentence and generate the content again.

[0726] Input: Infringement judgment result

[0727] Output: Regenerated content

[0728] Step 9:

[0729] The server ultimately provides non-infringing content to the user.

[0730] Specifically, the server provides the newly generated blog post to the user along with a message saying, "Here is the final content."

[0731] Input: Regenerated content

[0732] Output: Providing the final content to the user

[0733] (Application example 1)

[0734] Next, a description will be given of Application Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the headset type terminal 314 will be referred to as a "terminal."

[0735] In recent years, automation has progressed in content generation and distribution, but there is a risk that the generated content may infringe existing copyrights. Furthermore, users are required to quickly obtain legal content in real time using smartphones or smart glasses. However, current technology does not adequately provide a system that meets these requirements. To solve this issue, a system that includes rights verification and regeneration processes for generated content is needed.

[0736] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 1 is realized by the following means.

[0737] In this invention, the server includes: a means for a user to make a content generation request; a means for generating content using a generative AI model; a means for verifying the generated content against a copyright database and a patent database; a means for determining whether or not there is a copyright infringement based on the verification result; a means for regenerating the content if a copyright infringement is confirmed; a means for providing the generated content to the user if the generated content does not infringe, an interface means for receiving prompts for generating content and verifying copyright in real time and inputting them into the generative AI model; and an application means that can be installed on a smartphone or smart glasses. This allows users to safely obtain legal content in real time.

[0738] A "content generation request" is a request made by a user to create new content using a generative AI model.

[0739] A "generative AI model" is an artificial intelligence system that uses natural language generation technology to create content based on themes and keywords specified by the user.

[0740] "Copyright database" means a database that stores information about existing copyrighted works and is used to verify whether generated content infringes existing copyrights.

[0741] "Patent Database" means a database that stores existing patent information and is used to check whether generated content infringes existing patent rights.

[0742] The "matching result" refers to the result of checking whether the generated content matches the copyright database and the patent database.

[0743] The "interface means" is a mechanism that receives prompt statements from the user and inputs them into the generative AI model.

[0744] The "application means" is software that can be installed on a smartphone or smart glasses, and allows a user to make content generation requests and receive generated content.

[0745] A "prompt" is an instruction provided by a user to a generative AI model, and specifically indicates the theme and keywords of the content to be generated.

[0746] "Regeneration means" refers to the process of regenerating content when the generated content infringes an existing copyright or patent.

[0747] The "means for determining infringement of rights" is a mechanism for determining whether or not there is infringement of rights based on the results of comparing the generated content with a copyright database and a patent database.

[0748] In this invention, a user first makes a content generation request using an application installed on a smartphone or smart glasses. The user then inputs a "prompt statement" through the application interface, specifying the theme and keywords of the content they want to generate. For example, the user might input, "Please generate a blog post about recent technology trends."

[0749] When a user submits a request, the prompt is sent to the server, which uses a generative AI model (e.g., a model using natural language generation technology) to generate content based on the specified theme.

[0750] The generated content is temporarily stored on a server, which then checks it against copyright and patent databases to see if there are any matches to existing copyrighted works or patents. During this process, it evaluates whether specific phrases or themes match existing entries in the database.

[0751] Based on the comparison results, the server determines whether there is any copyright infringement. If the generated content does not match any existing copyrighted work or patent, it is determined that there is no copyright infringement and the content is provided to the user. On the other hand, if a copyright infringement is confirmed, the server notifies the user that the content will be generated again and re-runs the generative AI model using a prompt. In this case, the newly generated content also undergoes the same procedure and is again checked for copyright.

[0752] Once the server has generated the final non-infringing content, it is sent to the user's smartphone or smart glasses. For example, the user will be notified with a message saying, "This is the final content," and can view the generated blog post within the application.

[0753] This system allows users to safely use generated content with the confidence that it will not infringe on existing copyrights or patents, and enables content creation and review in real time on portable devices such as smartphones and smart glasses.

[0754] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[0755] Step 1:

[0756] A user launches an application on their smartphone or smart glasses and inputs a prompt through the interface, for example, "Please generate a blog post about recent technology trends." This input is then sent to the server.

[0757] Input: prompt statement

[0758] Output: Request sent to the server

[0759] Step 2:

[0760] The server inputs the received prompt sentence into a generative AI model to generate content based on the specified theme. By using a generative AI model (for example, a natural language generation model), text is generated that follows the theme specified by the user.

[0761] Input: prompt statement

[0762] Output: Generated content (e.g. blog posts)

[0763] Step 3:

[0764] The server temporarily stores the generated content and then checks it against copyright and patent databases, using APIs to access these databases and check whether the generated content matches existing data.

[0765] Input: Generated content

[0766] Output: Matching result (whether or not there is a copyright infringement)

[0767] Step 4:

[0768] The server determines whether or not there is a copyright infringement based on the comparison results. If there is no copyright infringement, the content is provided to the user as is. If a copyright infringement is confirmed, new content is generated using the generative AI model again.

[0769] Input: Matching result

[0770] Output: Judgment based on whether or not there is a copyright infringement

[0771] Step 5:

[0772] If the server regenerates the content, the newly generated content will go through the same verification process, with rights verification repeated, until finally, non-infringing content is generated.

[0773] Input: Regenerated content

[0774] Output: Matching result and whether or not there is a copyright infringement

[0775] Step 6:

[0776] The server then generates non-infringing content and sends it to the user's smartphone or smart glasses, where the user can view and use the content through the application.

[0777] Input: Non-infringing generated content

[0778] Output: Providing content to the user

[0779] Step 7:

[0780] The server notifies the user that the content has been provided. For example, a message such as "This is the final content" is sent to the user, and the user can check the content within the application.

[0781] Input: Final generated content

[0782] Output: Notify the user and display the provided content

[0783] Furthermore, an emotion engine that estimates the user's emotion may be further combined. That is, the identification processing unit 290 may estimate the user's emotion using the emotion identification model 59, and perform identification processing using the user's emotion.

[0784] The present invention relates to a system in which a user makes a content generation request, a server generates the content using a generation artificial intelligence, and then adjusts the generated content based on the user's emotions using an emotion engine. Specific embodiments are described below.

[0785] First, a user requests content creation using a device. For example, the user enters "Please create a new blog post" into an input form on the device and presses the submit button. This request can include a theme, keywords, and even the user's current emotional state (e.g., happy, sad, stressed, etc.).

[0786] The server then receives the request and generates the content using generative AI, which uses a natural language generation model to generate blog posts based on themes and keywords specified by the user. The generated content is then stored in temporary storage on the server.

[0787] The generated content is then checked against copyright and patent databases by the server, which accesses these databases to ensure the generated content does not infringe on existing copyrighted works or patents. This checking process involves assessing whether certain phrases or themes match existing copyrighted works.

[0788] As a result of the comparison, the server determines whether or not there is a copyright infringement. For example, if part of the generated blog post matches an existing copyrighted work, the server determines that it is an infringement. On the other hand, if there is no match, it determines that there is no copyright infringement.

[0789] This is where the emotion engine comes in. The server sends the user's emotion data to the emotion engine, which evaluates how well the generated content matches the user's current emotion. For example, if the user specifies an emotional state of "fun," the generated blog post will be evaluated to see if it is enjoyable.

[0790] The server receives the emotion engine's evaluation results and adjusts the content as needed. For example, if the generated content does not match the user's emotional state, it initiates a regeneration process to generate new content. This regeneration process also checks against copyright and patent databases, just as it did the first time.

[0791] The server then provides the final content to the user. For example, once it has determined that the regenerated blog post is not infringing and matches the user's emotional state, the server notifies the user, "Here is the final content," and provides the content.

[0792] As a concrete example, if a user requests a blog post on the theme of "fun everyday events" and also specifies the emotional state as "fun," the generation AI will generate a blog post about fun events, and the emotion engine will evaluate the content. If the generated article is confirmed to be fun and does not infringe on any rights, the server will provide the article to the user. If the content is not fun or if there is any infringement of rights, the system will regenerate the article and provide the optimal content.

[0793] This system allows users to use content that matches their own emotions with peace of mind, while also avoiding the risk of copyright infringement.

[0794] The processing flow will be explained below.

[0795] Step 1:

[0796] A user uses a device to request content creation. The user enters "Please generate a new blog post" into the device's input form and presses the submit button. The request can include a topic, keywords, and even the user's current emotional state (e.g., happy, sad, stressed, etc.).

[0797] Step 2:

[0798] The server receives the user's request, which is then forwarded to the server, where it is analyzed for specific generation instructions (themes, keywords, emotional state, etc.).

[0799] Step 3:

[0800] The server generates content using generative AI. The generative AI invokes a natural language generation model to generate blog posts based on the user's request. The generation process reflects the specified themes and keywords. The generated content is stored in temporary storage on the server.

[0801] Step 4:

[0802] The server checks the generated content against copyright and patent databases. The server accesses these databases to determine whether the generated content violates existing copyrights or patents. It evaluates whether specific phrases or themes match existing copyrighted works.

[0803] Step 5:

[0804] The server analyzes the database match and determines whether or not there is a copyright infringement. If part of the generated blog post matches an existing copyrighted work, the server determines that it is an infringement. On the other hand, if there is no match, it determines that there is no copyright infringement.

[0805] Step 6:

[0806] The server sends the user's emotional data to the emotion engine, which analyzes the user's emotional state and evaluates how well the generated content matches the user's current emotion.

[0807] Step 7:

[0808] The server receives the emotion engine's evaluation results and adjusts the content as needed. For example, if the generated content does not match the user's emotional state, it initiates a regeneration process to generate new content.

[0809] Step 8:

[0810] The server runs a regeneration process, relaunching the generation AI to generate new content. This regeneration process also checks against copyright and patent databases.

[0811] Step 9:

[0812] The server again checks the reproduced content against copyright and patent databases to determine whether the reproduced content is infringing.

[0813] Step 10:

[0814] The server again sends the regenerated content to the emotion engine to evaluate whether it matches the user's emotional state.

[0815] Step 11:

[0816] The server provides the final content to the user. Once the reproduced content is verified as non-infringing and appropriate for the user's emotional state, the server notifies the user, "Here is the final content," and provides the content.

[0817] Example 2

[0818] Next, a description will be given of Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the headset type terminal 314 will be referred to as a "terminal."

[0819] There exists a problem that it is difficult to obtain high-quality content that matches a user's emotions while avoiding the risk of copyright infringement of user-generated content. Furthermore, if generated content is provided without considering the user's emotions, the user experience may be poor. Therefore, there is a need for a system that provides copyright-free content while also adapting to the user's emotional state.

[0820] The identification process by the identification processing unit 290 of the data processing device 12 in the second embodiment is realized by the following means. In this invention, the server includes a means for making an information generation request, a means for generating information using a generative intelligence system, and a means for verifying the generated information by checking it against a copyright database and a patent database. This makes it possible to verify whether the generated information infringes any rights, and furthermore, by using an emotion engine that adjusts information based on the user's emotion data, it becomes possible to provide information that matches the user's emotional state.

[0821] An "information generation request" is an action in which a user requests that information be generated based on a specific theme or keyword.

[0822] A "generative intelligence system" is a system that uses artificial intelligence technology to automatically generate information based on conditions specified by the user.

[0823] "Information" refers to all content generated by generative intelligence systems, including blog posts, reports, and articles.

[0824] "Copyright database" means a database that stores information about existing copyrighted works and is used to verify whether generated information infringes existing rights.

[0825] A "patent database" is a database that stores existing patent information and is used to verify whether generated information infringes patent rights.

[0826] An "emotion engine" is a system that receives a user's emotional data as input and evaluates whether the generated information matches the user's emotional state.

[0827] "Regeneration" is the process of reusing the generative intelligence system to generate new information.

[0828] The "matching result" is the evaluation result when the generated information is matched with the copyright database and patent database, and indicates whether or not there is a copyright infringement.

[0829] The present invention relates to a system in which a user makes an information generation request, a server generates information using a generative intelligence system, and then adjusts the generated information based on the user's emotions using an emotion engine.

[0830] First, the user uses a device to request information generation. The user enters the request details (e.g., "Please generate a new blog post") into the device's input form, specifies a theme or keyword (e.g., "fun events in daily life") and the user's current emotional state (e.g., "fun"), and presses the send button.

[0831] Next, the server receives the request and generates and inputs a prompt sentence to a generative intelligence system (e.g., a natural language generation model).The server uses the generative intelligence system to generate information based on themes and keywords specified by the user, and saves the generated information in temporary storage (e.g., cloud storage).

[0832] The generated information is then checked against copyright and patent databases by the server, which accesses these databases to verify that the generated information does not infringe on existing copyrighted works or patents. This checking process evaluates whether certain phrases or themes match existing copyrighted works.

[0833] As a result of the comparison, the server determines whether there is a copyright infringement. If the generated information matches an existing copyrighted work, the server determines that it is an infringement and starts the regeneration process. On the other hand, if there is no match, it determines that there is no copyright infringement.

[0834] An emotion engine is introduced. The server sends the user's emotion data to the emotion engine, which evaluates whether the generated information matches the user's current emotion. For example, if the user specifies an emotional state of "fun," the generated information is evaluated to see if it is fun.

[0835] The server receives the emotion engine's evaluation results and adjusts the information as needed. If the generated information does not match the user's emotional state, it initiates a regeneration process to generate new information. This regeneration process also checks against copyright and patent databases.

[0836] The server provides the final information to the user. Once it has confirmed that the reproduced information does not infringe on any rights and is appropriate for the user's emotional state, the server notifies the user, "This is the final information," and provides the information.

[0837] As a concrete example, if a user requests a blog post on the theme of "fun everyday events" and also specifies the emotional state of "fun," the generative intelligence system will generate a blog post about fun events, and the emotion engine will evaluate its content. If the generated post is confirmed to be fun and does not infringe on any copyrights, the server will provide the post to the user. If the content is not fun or if there is any copyright infringement, the post will be regenerated to provide the most appropriate information.

[0838] This invention allows users to use information that matches their own emotions with peace of mind, and also avoids the risk of infringement of rights.

[0839] Prompt Sentence Examples

[0840] Theme: Fun everyday events

[0841] Emotional state: Happy

[0842] The flow of the identification process in the second embodiment will be described with reference to FIG.

[0843] Step 1:

[0844] The user uses a terminal to make an information generation request. Specifically, the user enters "Please generate a new blog post" into the terminal's input form, specifies a theme and keywords (e.g., "fun events in daily life") and the user's current emotional state (e.g., "fun"), and presses the send button.

[0845] Input: User request (topic, keywords, emotional state)

[0846] Output: Request data sent to the server

[0847] Step 2:

[0848] The server receives the user's request and converts the request data into a prompt sentence to be input to a generative intelligence system (e.g., a natural language generation model).

[0849] Input: User request data

[0850] Output: Prompt sentence for the generative intelligence system

[0851] Step 3:

[0852] The server generates information using a generative intelligence system. A prompt is input into the generative intelligence system, which generates information based on the specified theme or keywords. The generated information is saved in temporary storage (e.g., cloud storage).

[0853] Input: prompt statement

[0854] Output: The generated information (content)

[0855] Step 4:

[0856] The server checks the generated information against copyright and patent databases, specifically comparing words and phrases in the generated information with existing data in the databases to evaluate whether there are any matches.

[0857] Input: Generated information

[0858] Output: Matching result (whether or not there is a copyright infringement)

[0859] Step 5:

[0860] The server determines whether or not there is a copyright infringement based on the comparison result. If a copyright infringement is confirmed in the generated information, the server starts the process of generating the information again.

[0861] Input: Matching result

[0862] Output: Infringement decision result

[0863] Step 6:

[0864] The server sends the user's emotional data to the emotion engine, which evaluates whether the generated information matches the user's current emotion. Specifically, it evaluates whether the generated information matches the user's specified emotional state (e.g., "fun").

[0865] Input: Generated information and user emotion data

[0866] Output: Emotion evaluation result

[0867] Step 7:

[0868] The server receives the emotion engine's evaluation results and adjusts the information as needed. If the generated information does not match the user's emotional state, it initiates a regeneration process to generate new information. This regeneration process also checks against copyright and patent databases.

[0869] Input: Emotion evaluation result

[0870] Output: The adjusted or regenerated information

[0871] Step 8:

[0872] The server provides the final information to the user. After verifying that the reproduced information is not infringing and is appropriate for the user's emotional state, the server notifies the user, "This is the final information," and provides the information.

[0873] Input: Reconciled or regenerated information

[0874] Output: Notifying the user and providing final information

[0875] (Application example 2)

[0876] Next, a description will be given of Application Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the headset type terminal 314 will be referred to as a "terminal."

[0877] Conventional content generation systems have difficulty generating content that matches the user's emotional state, and there is a high risk that the generated content may infringe copyright or patent rights. Furthermore, if the generated content does not match the user's emotional state, the regeneration process is complicated and time-consuming.

[0878] The specific processing by the specific processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means. In this invention, the server includes means for adjusting the generated content based on the user's emotional state, means for generating content, and means for checking the generated content against a copyright database and a patent database. This makes it possible to efficiently generate content that matches the user's emotions and avoid infringement of rights.

[0879] A "content generation request" refers to a user requesting that a generative AI create content based on a specific theme or keyword.

[0880] "Generative AI" refers to an AI system that uses natural language processing technology to automatically generate content such as text based on specified themes or keywords.

[0881] "Copyright database" refers to a database that records information about existing copyrighted works.

[0882] A "patent database" refers to a database that contains technologies and documents for which patents have been applied.

[0883] An "emotion engine" refers to a system that analyzes a user's emotional state and adjusts content to match that emotion.

[0884] "Matching result" refers to the result of evaluating whether the generated content matches existing copyrighted works or patent documents.

[0885] "Infringement" refers to a situation in which the generated content violates existing copyrights or patents.

[0886] "Regeneration" refers to the process of regenerating new content when the generated content infringes rights.

[0887] The present invention relates to a system for efficiently generating content based on a user's emotional state. The system consists of a process in which a user makes a content generation request, generates content using a generative AI model, then checks it against copyright and patent databases, and adapts it to the user's emotions using an emotion engine.

[0888] Main components of the system

[0889] 1. User Device:

[0890] The user uses the terminal to input the theme, keywords, and emotional state, and then sends a content generation request.

[0891] 2. Server:

[0892] Generative AI model: A generative AI model receives requests from users and generates content based on themes and keywords specified.

[0893] Copyright and patent database matching tool: A tool that checks the generated content against existing copyright and patent databases to ensure that the generated content does not violate any existing copyrights or patents.

[0894] Emotion engine: Uses user emotional data to evaluate generated content and adjust it as needed.

[0895] System operation procedure

[0896] First, the user inputs a theme, keywords, and emotional state from the terminal. For example, if the theme is "fun everyday events," the keywords are set as "park, small animals, family," and the emotional state is specified as "fun." This input is then sent to the server.

[0897] The server uses the generative AI model to generate content based on the specified theme or keywords, and this generated content is temporarily stored in data storage on the server.

[0898] Next, the server's copyright and patent database matching tools check the generated content against existing data to determine if there is any infringement. If there is any infringement, the regeneration process is initiated.

[0899] If the generated content is not infringing, the server uses an emotion engine to evaluate whether the content matches the user's specified emotional state, for example, whether the generated content is enjoyable based on the user's specified emotional state of "enjoyment."

[0900] If necessary, the emotion engine adjusts the content and generates the final content. This final content is provided to the user. An example of a prompt sentence is "Please create a new blog post. The theme is 'fun everyday events' and the keywords are 'parks, small animals, and family'. My current emotion is 'fun'."

[0901] This allows users to efficiently generate content that matches their emotions while avoiding the risk of copyright infringement.

[0902] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[0903] Step 1:

[0904] A user uses a terminal to make a content generation request. Specifically, the user inputs a theme, keywords, and emotional state, and then presses the send button, which sends the request to the server. As input, the user sends a prompt statement such as, "Please create a new blog post. The theme is 'fun everyday events,' and the keywords are 'parks, small animals, and family.' My current emotion is 'fun.'" As output, this request is sent to the server.

[0905] Step 2:

[0906] The server analyzes the received request and passes it to the generative AI model. The input includes the theme, keywords, and emotional state submitted by the user. This information is converted into a prompt sentence and set as input for the generative AI model. The output is the input data that the generative AI model uses to generate content.

[0907] Step 3:

[0908] The generative AI model generates content based on the specified theme and keywords. The input includes a prompt sentence passed from the server. As a data operation, the generative AI model performs natural language processing and generates new text content. As an output, the generated text content is returned to the server.

[0909] Step 4:

[0910] The server stores the generated content in temporary storage and then checks it against copyright and patent databases. The input includes the generated text content. The data processing involves comparing the generated content with existing copyright and patent databases to find matches. The output is the matching results.

[0911] Step 5:

[0912] The server determines whether or not there is a copyright infringement based on the comparison results. The input includes the comparison results between the generated text content and the database. The data calculation determines whether or not there is a copyright infringement if there is a match, and determines that there is no copyright infringement if there is not. The output is a determination result regarding whether or not there is a copyright infringement.

[0913] Step 6:

[0914] The server uses an emotion engine to adjust the generated content to fit the user's emotional state. The input includes the generated text content and the user's emotional state. As a data operation, the emotion engine analyzes the content of the content and adjusts it to fit the user's emotional state. The output is the adjusted text content.

[0915] Step 7:

[0916] If the infringement is confirmed and the content matches the emotional state, the final content is provided to the user. The input includes the adjusted text content and the result of the determination that there is no infringement. The server sends the adjusted content to the user's device and provides it as the final content. The output is that the user can view the final content.

[0917] 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.

[0918] 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> ), Gemini (Internet search <url: https: gemini.google.com ?hl="ja">) and other generation AIs. 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 voice, text data indicating text, and image data indicating an image is also input. 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.

[0919] In the above embodiment, an example was given in which the specific processing is performed by the data processing device 12, but the technology of the present disclosure is not limited to this, and the specific processing may be performed by the headset type terminal 314.

[0920] [Fourth embodiment]

[0921] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0922] 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.

[0923] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 is an example of a "computer" according to the technology of the present disclosure. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, the RAM 30, and the storage 32 are connected to a bus 34. 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).

[0924] 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.

[0925] The microphone 238 receives instructions and the like from the user 20 by receiving voice uttered by the user 20. The microphone 238 captures the voice uttered by the user 20, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio in accordance with instructions from the processor 46.

[0926] 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 surroundings of user 20 (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0927] 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. 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.

[0928] The control object 443 includes a display device, LEDs in the eyes, and motors for driving 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.

[0929] 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.

[0930] 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 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.

[0931] 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.

[0932] In the robot 414, the processor 46 performs the reception output process. A reception output program 60 is stored in the storage 50. The processor 46 reads the reception output program 60 from the storage 50 and executes the read reception output program 60 on the RAM 48. The reception output process is realized by the processor 46 operating as the control unit 46A in accordance with the reception output program 60 executed on the RAM 48.

[0933] Next, a description will be given of the specific processing performed by the specific processing unit 290 of the data processing device 12. In the following description, the data processing device 12 will be referred to as a "server" and the robot 414 will be referred to as a "terminal."

[0934] The present invention relates to a content generation system using a generation AI, and specific embodiments are described below.

[0935] First, a user requests content creation using a device. For example, the user enters "Please create a new blog post" into an input form on the device and presses the submit button. This request can include themes and keywords specified by the user.

[0936] The server then receives the user's request and generates the content using generative AI, which uses natural language generation models (NLGMs) to generate blog posts based on themes and keywords specified by the user. The generated content is temporarily stored on the server.

[0937] The generated content is then checked against copyright and patent databases by the server, which accesses these databases to determine whether the generated content violates existing copyrights or patents. During this checking process, the server evaluates whether certain phrases or themes match existing copyrighted works.

[0938] As a result of the comparison, the server determines whether or not there is a copyright infringement. For example, if part of the generated blog post matches an existing copyrighted work, the server determines that it is an infringement. On the other hand, if there is no match, it determines that there is no copyright infringement.

[0939] The server notifies the user of this decision. If there is no infringement of rights, the generated content is provided to the user as is. On the other hand, if a rights infringement is confirmed, the server notifies the user that the content will be generated again and re-executes the generation AI. In this case, the newly generated content also undergoes the same procedure and rights confirmation is performed again.

[0940] Once the final non-infringing content is generated, including if regeneration is required, the server will provide it to the user. For example, once it has been confirmed that a newly generated blog post is non-infringing, the server will notify the user, "This is the final content," and provide the content.

[0941] This system allows users to use generated content with peace of mind and avoids the risk of copyright infringement.

[0942] The processing flow will be explained below.

[0943] Step 1:

[0944] A user requests content creation using a device. The user enters "Please create a new blog post" into the device's input form and presses the submit button.

[0945] Step 2:

[0946] The server receives the user's request, which is then forwarded to the server and parsed for specific generation instructions (themes, keywords, etc.).

[0947] Step 3:

[0948] The server generates content using generative AI, which invokes a natural language generation model to generate blog posts based on user requests, taking into account themes and keywords specified in the process.

[0949] Step 4:

[0950] The server temporarily stores the generated content. The generated blog post is stored in temporary storage on the server, ready for matching in the next step.

[0951] Step 5:

[0952] The server checks the generated content against copyright and patent databases. The server accesses these databases to verify that the generated content does not infringe on existing copyrights or patents.

[0953] Step 6:

[0954] The server analyzes the results of the database comparison and determines whether there is any copyright infringement. For example, if part of the generated blog post matches an existing copyrighted work, the server determines that it is a copyright infringement.

[0955] Step 7:

[0956] The server notifies the user of the result of the rights check. If there is no infringement, the generated content is provided to the user as is. If an infringement is confirmed, the user is notified that the content must be regenerated.

[0957] Step 8:

[0958] If the server finds a copyright infringement, it will regenerate the content. The server will restart the generation AI and generate new content, adjusting parameters to avoid the infringing parts if possible.

[0959] Step 9:

[0960] The server again checks the reproduced content against copyright and patent databases to determine whether the reproduced content is infringing.

[0961] Step 10:

[0962] The server provides the final content to the user. If the reproduced content does not infringe any rights, the server notifies the user, "This is the final content," and provides the content.

[0963] Example 1

[0964] Next, a description will be given of Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the robot 414 will be referred to as a "terminal."

[0965] In today's world, there is a growing demand for users to use generative AI to generate content quickly and safely. However, there is a risk that the generated content may infringe copyright or patents, which can make users hesitant to use the generated content. Furthermore, the regeneration process in the event of confirmed infringement is not automated, which can be time-consuming for users. These issues result in a lack of efficiency and reliability in the content generation process. Furthermore, there are insufficient means of providing appropriate input to generative AI models, and insufficient mechanisms for storing and notifying matching results.

[0966] The specific processing by the specific processing unit 290 of the data processing device 12 in the first embodiment is realized by the following means.

[0967] In this invention, the server includes: means for a user to make a content generation request; means for the server to generate content using a generative AI model; means for the server to check the generated content against a copyright database and a patent database; means for the server to determine whether or not there is infringement of rights based on the check result; means for the server to generate the content again if infringement of rights is confirmed; means for the server to provide the generated content to the user if it does not infringe rights; means for generating and inputting a prompt sentence to the generative AI model; and means for saving the check result. This allows users to use the generated content with peace of mind, and makes it possible to improve the efficiency and reliability of the content generation process.

[0968] A "user" is a user of the system who requests content generation.

[0969] A "content generation request" is a request made by a user to generate new content using the generation AI.

[0970] The "server" is a central management device that generates content using generative AI, checks copyrights and patents, and provides the content to users.

[0971] A "generative artificial intelligence model" is an artificial intelligence system for generating content in natural language based on user requests.

[0972] A "prompt" is an instruction entered into a generative AI model, which defines the conditions and theme for content generation.

[0973] "Content" refers to text data such as blog posts and documents generated by generative AI models.

[0974] The "copyright database" is a database that holds information on existing copyrighted works and compares it with generated content.

[0975] A "patent database" is a database that holds existing patent information and compares it with generated content.

[0976] The "matching result" is information regarding whether or not the generated content infringes any rights, obtained by matching with the copyright database and the patent database.

[0977] "Infringement" refers to a situation in which the generated content violates an existing copyright or patent.

[0978] "Regeneration" is the process of re-running the generation AI to generate new content if a copyright infringement is confirmed.

[0979] This invention relates to a content generation system using generative AI, and in particular to a system that automatically generates content requested by users and provides it after checking that there is no infringement of copyright or patent rights.

[0980] Specifically, a user first makes a content generation request using their own device (such as a PC or smartphone). The user enters a request into an input form, such as "Please generate a new blog post" or "Please generate a blog post about the future of AI technology," and presses the submit button. This request can include the theme and keywords of the content to be generated.

[0981] Next, the server receives the user's request and generates a prompt for the generative AI model (e.g., GPT-3). The prompt is an instruction to be input to the generative model, such as "Please write a blog post that provides a detailed explanation of the future prospects for AI technology."

[0982] The generated prompt sentence is input into a generative AI model, and the server generates content based on the specified theme and keywords. The generated content is temporarily stored on the server.

[0983] The server then checks the generated content against copyright and patent databases to determine if there are any infringements. Specifically, it accesses databases such as Google Patents, PubMed, USPTO, and WIPO to determine whether the generated content violates existing copyrights or patents. The server then stores the results of the checks against these databases.

[0984] Based on the matching results, the server determines whether the generated content violates existing copyrights or patents. If there is a match, it is deemed to be a copyright infringement and notifies the user that the content will be regenerated. To regenerate the content, the server uses a new artificial intelligence model to generate a new prompt sentence and generate the content. The same matching process is repeated to verify the rights.

[0985] When the final content is generated and confirmed to be free of infringement, the server provides it to the user. For example, the server may notify the user, "This is the final content," and provide the generated blog post. The server may also notify the user of the results of the verification of the generated content, informing them that the resulting content is free of infringement.

[0986] In this way, a content generation system using a generative AI model can enable users to use the generated content with peace of mind, while reducing the risk of copyright infringement.

[0987] The flow of the identification process in the first embodiment will be described with reference to FIG.

[0988] Step 1:

[0989] The user inputs a content generation request using a terminal and presses a send button.

[0990] Specifically, the user enters "Please generate a blog post about the future of AI technology" into the input form on the device and presses the send button.

[0991] Input: Request details (including themes and keywords)

[0992] Output: User request data

[0993] Step 2:

[0994] The server receives the user request data and generates a prompt sentence.

[0995] Specifically, the server receives a request to "generate a blog post about the future of AI technology" and generates a prompt that says, "Please create a blog post that provides a detailed explanation of the future prospects for AI technology."

[0996] Input: User request data

[0997] Output: Generated prompt statement

[0998] Step 3:

[0999] The server inputs a prompt sentence into the artificial intelligence model to generate content.

[1000] Specifically, the server inputs the generated prompt sentence into a generative artificial intelligence model (e.g., GPT-3) and generates a "blog post about the future of AI technology."

[1001] Input: Generate prompt statement

[1002] Output: Generated content

[1003] Step 4:

[1004] The server temporarily stores the generated content.

[1005] Specifically, the server saves a "blog article about the future of AI technology" in a temporary storage area on the server.

[1006] Input: Generated content

[1007] Output: Stored generated content

[1008] Step 5:

[1009] The server checks the generated content against copyright and patent databases.

[1010] Specifically, the server accesses databases such as Google Patents, PubMed, USPTO, and WIPO to collate "blog articles about the future of AI technology."

[1011] Input: Saved generated content

[1012] Output: Matching result data

[1013] Step 6:

[1014] The server determines whether or not there is a copyright infringement based on the matching result data.

[1015] Specifically, the server analyzes the results of the comparison and determines whether any part of the content matches an existing patent. If there is a match, it is determined to be an infringement, and if not, it is determined to be no problem.

[1016] Input: Matching result data

[1017] Output: Infringement judgment result

[1018] Step 7:

[1019] The server notifies the user of the result of the determination as to whether or not there is a copyright infringement.

[1020] Specifically, the server sends a notification to the user saying, "The generated content does not infringe any rights. Here is the final content."

[1021] Input: Infringement judgment result

[1022] Output: User notification

[1023] Step 8:

[1024] If a copyright infringement is confirmed, the server generates the content again.

[1025] Specifically, the server uses the generative artificial intelligence model again to generate a new prompt sentence and generate the content again.

[1026] Input: Infringement judgment result

[1027] Output: Regenerated content

[1028] Step 9:

[1029] The server ultimately provides non-infringing content to the user.

[1030] Specifically, the server provides the newly generated blog post to the user along with a message saying, "Here is the final content."

[1031] Input: Regenerated content

[1032] Output: Providing the final content to the user

[1033] (Application example 1)

[1034] Next, a description will be given of Application Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the robot 414 will be referred to as a "terminal."

[1035] In recent years, automation has progressed in content generation and distribution, but there is a risk that the generated content may infringe existing copyrights. Furthermore, users are required to quickly obtain legal content in real time using smartphones or smart glasses. However, current technology does not adequately provide a system that meets these requirements. To solve this issue, a system that includes rights verification and regeneration processes for generated content is needed.

[1036] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 1 is realized by the following means.

[1037] In this invention, the server includes: a means for a user to make a content generation request; a means for generating content using a generative AI model; a means for verifying the generated content against a copyright database and a patent database; a means for determining whether or not there is a copyright infringement based on the verification result; a means for regenerating the content if a copyright infringement is confirmed; a means for providing the generated content to the user if the generated content does not infringe, an interface means for receiving prompts for generating content and verifying copyright in real time and inputting them into the generative AI model; and an application means that can be installed on a smartphone or smart glasses. This allows users to safely obtain legal content in real time.

[1038] A "content generation request" is a request made by a user to create new content using a generative AI model.

[1039] A "generative AI model" is an artificial intelligence system that uses natural language generation technology to create content based on themes and keywords specified by the user.

[1040] "Copyright database" means a database that stores information about existing copyrighted works and is used to verify whether generated content infringes existing copyrights.

[1041] "Patent Database" means a database that stores existing patent information and is used to check whether generated content infringes existing patent rights.

[1042] The "matching result" refers to the result of checking whether the generated content matches the copyright database and the patent database.

[1043] The "interface means" is a mechanism that receives prompt statements from the user and inputs them into the generative AI model.

[1044] The "application means" is software that can be installed on a smartphone or smart glasses, and allows a user to make content generation requests and receive generated content.

[1045] A "prompt" is an instruction provided by a user to a generative AI model, and specifically indicates the theme and keywords of the content to be generated.

[1046] "Regeneration means" refers to the process of regenerating content when the generated content infringes an existing copyright or patent.

[1047] The "means for determining infringement of rights" is a mechanism for determining whether or not there is infringement of rights based on the results of comparing the generated content with a copyright database and a patent database.

[1048] In this invention, a user first makes a content generation request using an application installed on a smartphone or smart glasses. The user then inputs a "prompt statement" through the application interface, specifying the theme and keywords of the content they want to generate. For example, the user might input, "Please generate a blog post about recent technology trends."

[1049] When a user submits a request, the prompt is sent to the server, which uses a generative AI model (e.g., a model using natural language generation technology) to generate content based on the specified theme.

[1050] The generated content is temporarily stored on a server, which then checks it against copyright and patent databases to see if there are any matches to existing copyrighted works or patents. During this process, it evaluates whether specific phrases or themes match existing entries in the database.

[1051] Based on the comparison results, the server determines whether there is any copyright infringement. If the generated content does not match any existing copyrighted work or patent, it is determined that there is no copyright infringement and the content is provided to the user. On the other hand, if a copyright infringement is confirmed, the server notifies the user that the content will be generated again and re-runs the generative AI model using a prompt. In this case, the newly generated content also undergoes the same procedure and is again checked for copyright.

[1052] Once the server has generated the final non-infringing content, it is sent to the user's smartphone or smart glasses. For example, the user will be notified with a message saying, "This is the final content," and can view the generated blog post within the application.

[1053] This system allows users to safely use generated content with the confidence that it will not infringe on existing copyrights or patents, and enables content creation and review in real time on portable devices such as smartphones and smart glasses.

[1054] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[1055] Step 1:

[1056] A user launches an application on their smartphone or smart glasses and inputs a prompt through the interface, for example, "Please generate a blog post about recent technology trends." This input is then sent to the server.

[1057] Input: prompt statement

[1058] Output: Request sent to the server

[1059] Step 2:

[1060] The server inputs the received prompt sentence into a generative AI model to generate content based on the specified theme. By using a generative AI model (for example, a natural language generation model), text is generated that follows the theme specified by the user.

[1061] Input: prompt statement

[1062] Output: Generated content (e.g. blog posts)

[1063] Step 3:

[1064] The server temporarily stores the generated content and then checks it against copyright and patent databases, using APIs to access these databases and check whether the generated content matches existing data.

[1065] Input: Generated content

[1066] Output: Matching result (whether or not there is a copyright infringement)

[1067] Step 4:

[1068] The server determines whether or not there is a copyright infringement based on the comparison results. If there is no copyright infringement, the content is provided to the user as is. If a copyright infringement is confirmed, new content is generated using the generative AI model again.

[1069] Input: Matching result

[1070] Output: Judgment based on whether or not there is a copyright infringement

[1071] Step 5:

[1072] If the server regenerates the content, the newly generated content will go through the same verification process, with rights verification repeated, until finally, non-infringing content is generated.

[1073] Input: Regenerated content

[1074] Output: Matching result and whether or not there is a copyright infringement

[1075] Step 6:

[1076] The server then generates non-infringing content and sends it to the user's smartphone or smart glasses, where the user can view and use the content through the application.

[1077] Input: Non-infringing generated content

[1078] Output: Providing content to the user

[1079] Step 7:

[1080] The server notifies the user that the content has been provided. For example, a message such as "This is the final content" is sent to the user, and the user can check the content within the application.

[1081] Input: Final generated content

[1082] Output: Notify the user and display the provided content

[1083] Furthermore, an emotion engine that estimates the user's emotion may be further combined. That is, the identification processing unit 290 may estimate the user's emotion using the emotion identification model 59, and perform identification processing using the user's emotion.

[1084] The present invention relates to a system in which a user makes a content generation request, a server generates the content using a generation artificial intelligence, and then adjusts the generated content based on the user's emotions using an emotion engine. Specific embodiments are described below.

[1085] First, a user requests content creation using a device. For example, the user enters "Please create a new blog post" into an input form on the device and presses the submit button. This request can include a theme, keywords, and even the user's current emotional state (e.g., happy, sad, stressed, etc.).

[1086] The server then receives the request and generates the content using generative AI, which uses a natural language generation model to generate blog posts based on themes and keywords specified by the user. The generated content is then stored in temporary storage on the server.

[1087] The generated content is then checked against copyright and patent databases by the server, which accesses these databases to ensure the generated content does not infringe on existing copyrighted works or patents. This checking process involves assessing whether certain phrases or themes match existing copyrighted works.

[1088] As a result of the comparison, the server determines whether or not there is a copyright infringement. For example, if part of the generated blog post matches an existing copyrighted work, the server determines that it is an infringement. On the other hand, if there is no match, it determines that there is no copyright infringement.

[1089] This is where the emotion engine comes in. The server sends the user's emotion data to the emotion engine, which evaluates how well the generated content matches the user's current emotion. For example, if the user specifies an emotional state of "fun," the generated blog post will be evaluated to see if it is enjoyable.

[1090] The server receives the emotion engine's evaluation results and adjusts the content as needed. For example, if the generated content does not match the user's emotional state, it initiates a regeneration process to generate new content. This regeneration process also checks against copyright and patent databases, just as it did the first time.

[1091] The server then provides the final content to the user. For example, once it has determined that the regenerated blog post is not infringing and matches the user's emotional state, the server notifies the user, "Here is the final content," and provides the content.

[1092] As a concrete example, if a user requests a blog post on the theme of "fun everyday events" and also specifies the emotional state as "fun," the generation AI will generate a blog post about fun events, and the emotion engine will evaluate the content. If the generated article is confirmed to be fun and does not infringe on any rights, the server will provide the article to the user. If the content is not fun or if there is any infringement of rights, the system will regenerate the article and provide the optimal content.

[1093] This system allows users to use content that matches their own emotions with peace of mind, while also avoiding the risk of copyright infringement.

[1094] The processing flow will be explained below.

[1095] Step 1:

[1096] A user uses a device to request content creation. The user enters "Please generate a new blog post" into the device's input form and presses the submit button. The request can include a topic, keywords, and even the user's current emotional state (e.g., happy, sad, stressed, etc.).

[1097] Step 2:

[1098] The server receives the user's request, which is then forwarded to the server, where it is analyzed for specific generation instructions (themes, keywords, emotional state, etc.).

[1099] Step 3:

[1100] The server generates content using generative AI. The generative AI invokes a natural language generation model to generate blog posts based on the user's request. The generation process reflects the specified themes and keywords. The generated content is stored in temporary storage on the server.

[1101] Step 4:

[1102] The server checks the generated content against copyright and patent databases. The server accesses these databases to determine whether the generated content violates existing copyrights or patents. It evaluates whether specific phrases or themes match existing copyrighted works.

[1103] Step 5:

[1104] The server analyzes the database match and determines whether or not there is a copyright infringement. If part of the generated blog post matches an existing copyrighted work, the server determines that it is an infringement. On the other hand, if there is no match, it determines that there is no copyright infringement.

[1105] Step 6:

[1106] The server sends the user's emotional data to the emotion engine, which analyzes the user's emotional state and evaluates how well the generated content matches the user's current emotion.

[1107] Step 7:

[1108] The server receives the emotion engine's evaluation results and adjusts the content as needed. For example, if the generated content does not match the user's emotional state, it initiates a regeneration process to generate new content.

[1109] Step 8:

[1110] The server runs a regeneration process, relaunching the generation AI to generate new content. This regeneration process also checks against copyright and patent databases.

[1111] Step 9:

[1112] The server again checks the reproduced content against copyright and patent databases to determine whether the reproduced content is infringing.

[1113] Step 10:

[1114] The server again sends the regenerated content to the emotion engine to evaluate whether it matches the user's emotional state.

[1115] Step 11:

[1116] The server provides the final content to the user. Once the reproduced content is verified as non-infringing and appropriate for the user's emotional state, the server notifies the user, "Here is the final content," and provides the content.

[1117] Example 2

[1118] Next, a description will be given of Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the robot 414 will be referred to as a "terminal."

[1119] There exists a problem that it is difficult to obtain high-quality content that matches a user's emotions while avoiding the risk of copyright infringement of user-generated content. Furthermore, if generated content is provided without considering the user's emotions, the user experience may be poor. Therefore, there is a need for a system that provides copyright-free content while also adapting to the user's emotional state.

[1120] The identification process by the identification processing unit 290 of the data processing device 12 in the second embodiment is realized by the following means. In this invention, the server includes a means for making an information generation request, a means for generating information using a generative intelligence system, and a means for verifying the generated information by checking it against a copyright database and a patent database. This makes it possible to verify whether the generated information infringes any rights, and furthermore, by using an emotion engine that adjusts information based on the user's emotion data, it becomes possible to provide information that matches the user's emotional state.

[1121] An "information generation request" is an action in which a user requests that information be generated based on a specific theme or keyword.

[1122] A "generative intelligence system" is a system that uses artificial intelligence technology to automatically generate information based on conditions specified by the user.

[1123] "Information" refers to all content generated by generative intelligence systems, including blog posts, reports, and articles.

[1124] "Copyright database" means a database that stores information about existing copyrighted works and is used to verify whether generated information infringes existing rights.

[1125] A "patent database" is a database that stores existing patent information and is used to verify whether generated information infringes patent rights.

[1126] An "emotion engine" is a system that receives a user's emotional data as input and evaluates whether the generated information matches the user's emotional state.

[1127] "Regeneration" is the process of reusing the generative intelligence system to generate new information.

[1128] The "matching result" is the evaluation result when the generated information is matched with the copyright database and patent database, and indicates whether or not there is a copyright infringement.

[1129] The present invention relates to a system in which a user makes an information generation request, a server generates information using a generative intelligence system, and then adjusts the generated information based on the user's emotions using an emotion engine.

[1130] First, the user uses a device to request information generation. The user enters the request details (e.g., "Please generate a new blog post") into the device's input form, specifies a theme or keyword (e.g., "fun events in daily life") and the user's current emotional state (e.g., "fun"), and presses the send button.

[1131] Next, the server receives the request and generates and inputs a prompt sentence to a generative intelligence system (e.g., a natural language generation model).The server uses the generative intelligence system to generate information based on themes and keywords specified by the user, and saves the generated information in temporary storage (e.g., cloud storage).

[1132] The generated information is then checked against copyright and patent databases by the server, which accesses these databases to verify that the generated information does not infringe on existing copyrighted works or patents. This checking process evaluates whether certain phrases or themes match existing copyrighted works.

[1133] As a result of the comparison, the server determines whether there is a copyright infringement. If the generated information matches an existing copyrighted work, the server determines that it is an infringement and starts the regeneration process. On the other hand, if there is no match, it determines that there is no copyright infringement.

[1134] An emotion engine is introduced. The server sends the user's emotion data to the emotion engine, which evaluates whether the generated information matches the user's current emotion. For example, if the user specifies an emotional state of "fun," the generated information is evaluated to see if it is fun.

[1135] The server receives the emotion engine's evaluation results and adjusts the information as needed. If the generated information does not match the user's emotional state, it initiates a regeneration process to generate new information. This regeneration process also checks against copyright and patent databases.

[1136] The server provides the final information to the user. Once it has confirmed that the reproduced information does not infringe on any rights and is appropriate for the user's emotional state, the server notifies the user, "This is the final information," and provides the information.

[1137] As a concrete example, if a user requests a blog post on the theme of "fun everyday events" and also specifies the emotional state of "fun," the generative intelligence system will generate a blog post about fun events, and the emotion engine will evaluate its content. If the generated post is confirmed to be fun and does not infringe on any copyrights, the server will provide the post to the user. If the content is not fun or if there is any copyright infringement, the post will be regenerated to provide the most appropriate information.

[1138] This invention allows users to use information that matches their own emotions with peace of mind, and also avoids the risk of infringement of rights.

[1139] Prompt Sentence Examples

[1140] Theme: Fun everyday events

[1141] Emotional state: Happy

[1142] The flow of the identification process in the second embodiment will be described with reference to FIG.

[1143] Step 1:

[1144] The user uses a terminal to make an information generation request. Specifically, the user enters "Please generate a new blog post" into the terminal's input form, specifies a theme and keywords (e.g., "fun events in daily life") and the user's current emotional state (e.g., "fun"), and presses the send button.

[1145] Input: User request (topic, keywords, emotional state)

[1146] Output: Request data sent to the server

[1147] Step 2:

[1148] The server receives the user's request and converts the request data into a prompt sentence to be input to a generative intelligence system (e.g., a natural language generation model).

[1149] Input: User request data

[1150] Output: Prompt sentence for the generative intelligence system

[1151] Step 3:

[1152] The server generates information using a generative intelligence system. A prompt is input into the generative intelligence system, which generates information based on the specified theme or keywords. The generated information is saved in temporary storage (e.g., cloud storage).

[1153] Input: prompt statement

[1154] Output: The generated information (content)

[1155] Step 4:

[1156] The server checks the generated information against copyright and patent databases, specifically comparing words and phrases in the generated information with existing data in the databases to evaluate whether there are any matches.

[1157] Input: Generated information

[1158] Output: Matching result (whether or not there is a copyright infringement)

[1159] Step 5:

[1160] The server determines whether or not there is a copyright infringement based on the comparison result. If a copyright infringement is confirmed in the generated information, the server starts the process of generating the information again.

[1161] Input: Matching result

[1162] Output: Infringement decision result

[1163] Step 6:

[1164] The server sends the user's emotional data to the emotion engine, which evaluates whether the generated information matches the user's current emotion. Specifically, it evaluates whether the generated information matches the user's specified emotional state (e.g., "fun").

[1165] Input: Generated information and user emotion data

[1166] Output: Emotion evaluation result

[1167] Step 7:

[1168] The server receives the emotion engine's evaluation results and adjusts the information as needed. If the generated information does not match the user's emotional state, it initiates a regeneration process to generate new information. This regeneration process also checks against copyright and patent databases.

[1169] Input: Emotion evaluation result

[1170] Output: The adjusted or regenerated information

[1171] Step 8:

[1172] The server provides the final information to the user. After verifying that the reproduced information is not infringing and is appropriate for the user's emotional state, the server notifies the user, "This is the final information," and provides the information.

[1173] Input: Reconciled or regenerated information

[1174] Output: Notifying the user and providing final information

[1175] (Application example 2)

[1176] Next, a description will be given of Application Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the robot 414 will be referred to as a "terminal."

[1177] Conventional content generation systems have difficulty generating content that matches the user's emotional state, and there is a high risk that the generated content may infringe copyright or patent rights. Furthermore, if the generated content does not match the user's emotional state, the regeneration process is complicated and time-consuming.

[1178] The specific processing by the specific processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means. In this invention, the server includes means for adjusting the generated content based on the user's emotional state, means for generating content, and means for checking the generated content against a copyright database and a patent database. This makes it possible to efficiently generate content that matches the user's emotions and avoid infringement of rights.

[1179] A "content generation request" refers to a user requesting that a generative AI create content based on a specific theme or keyword.

[1180] "Generative AI" refers to an AI system that uses natural language processing technology to automatically generate content such as text based on specified themes or keywords.

[1181] "Copyright database" refers to a database that records information about existing copyrighted works.

[1182] A "patent database" refers to a database that contains technologies and documents for which patents have been applied.

[1183] An "emotion engine" refers to a system that analyzes a user's emotional state and adjusts content to match that emotion.

[1184] "Matching result" refers to the result of evaluating whether the generated content matches existing copyrighted works or patent documents.

[1185] "Infringement" refers to a situation in which the generated content violates existing copyrights or patents.

[1186] "Regeneration" refers to the process of regenerating new content when the generated content infringes rights.

[1187] The present invention relates to a system for efficiently generating content based on a user's emotional state. The system consists of a process in which a user makes a content generation request, generates content using a generative AI model, then checks it against copyright and patent databases, and adapts it to the user's emotions using an emotion engine.

[1188] Main components of the system

[1189] 1. User Device:

[1190] The user uses the terminal to input the theme, keywords, and emotional state, and then sends a content generation request.

[1191] 2. Server:

[1192] Generative AI model: A generative AI model receives requests from users and generates content based on themes and keywords specified.

[1193] Copyright and patent database matching tool: A tool that checks the generated content against existing copyright and patent databases to ensure that the generated content does not violate any existing copyrights or patents.

[1194] Emotion engine: Uses user emotional data to evaluate generated content and adjust it as needed.

[1195] System operation procedure

[1196] First, the user inputs a theme, keywords, and emotional state from the terminal. For example, if the theme is "fun everyday events," the keywords are set as "park, small animals, family," and the emotional state is specified as "fun." This input is then sent to the server.

[1197] The server uses the generative AI model to generate content based on the specified theme or keywords, and this generated content is temporarily stored in data storage on the server.

[1198] Next, the server's copyright and patent database matching tools check the generated content against existing data to determine if there is any infringement. If there is any infringement, the regeneration process is initiated.

[1199] If the generated content is not infringing, the server uses an emotion engine to evaluate whether the content matches the user's specified emotional state, for example, whether the generated content is enjoyable based on the user's specified emotional state of "enjoyment."

[1200] If necessary, the emotion engine adjusts the content and generates the final content. This final content is provided to the user. An example of a prompt sentence is "Please create a new blog post. The theme is 'fun everyday events' and the keywords are 'parks, small animals, and family'. My current emotion is 'fun'."

[1201] This allows users to efficiently generate content that matches their emotions while avoiding the risk of copyright infringement.

[1202] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[1203] Step 1:

[1204] A user uses a terminal to make a content generation request. Specifically, the user inputs a theme, keywords, and emotional state, and then presses the send button, which sends the request to the server. As input, the user sends a prompt statement such as, "Please create a new blog post. The theme is 'fun everyday events,' and the keywords are 'parks, small animals, and family.' My current emotion is 'fun.'" As output, this request is sent to the server.

[1205] Step 2:

[1206] The server analyzes the received request and passes it to the generative AI model. The input includes the theme, keywords, and emotional state submitted by the user. This information is converted into a prompt sentence and set as input for the generative AI model. The output is the input data that the generative AI model uses to generate content.

[1207] Step 3:

[1208] The generative AI model generates content based on the specified theme and keywords. The input includes a prompt sentence passed from the server. As a data operation, the generative AI model performs natural language processing and generates new text content. As an output, the generated text content is returned to the server.

[1209] Step 4:

[1210] The server stores the generated content in temporary storage and then checks it against copyright and patent databases. The input includes the generated text content. The data processing involves comparing the generated content with existing copyright and patent databases to find matches. The output is the matching results.

[1211] Step 5:

[1212] The server determines whether or not there is a copyright infringement based on the comparison results. The input includes the comparison results between the generated text content and the database. The data calculation determines whether or not there is a copyright infringement if there is a match, and determines that there is no copyright infringement if there is not. The output is a determination result regarding whether or not there is a copyright infringement.

[1213] Step 6:

[1214] The server uses an emotion engine to adjust the generated content to fit the user's emotional state. The input includes the generated text content and the user's emotional state. As a data operation, the emotion engine analyzes the content of the content and adjusts it to fit the user's emotional state. The output is the adjusted text content.

[1215] Step 7:

[1216] If the infringement is confirmed and the content matches the emotional state, the final content is provided to the user. The input includes the adjusted text content and the result of the determination that there is no infringement. The server sends the adjusted content to the user's device and provides it as the final content. The output is that the user can view the final content.

[1217] 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.

[1218] 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> ), Gemini (Internet search <url: https: gemini.google.com ?hl="ja">) and other generation AIs. 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 voice, text data indicating text, and image data indicating an image is also input. 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.

[1219] In the above embodiment, an example was given in which the specific processing is performed by the data processing device 12, but the technology of the present disclosure is not limited to this, and the specific processing may be performed by the robot 414.

[1220] 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.

[1221] 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 includes both affect 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.

[1222] 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.

[1223] 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).

[1224] Human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, a state of discomfort is indicated, and when they approach the ideal, a state of pleasure is indicated. Emotions can also be created for robots, automobiles, and motorcycles, based on various balances, such as posture and remaining battery life. When these balances deviate from the ideal, a state of discomfort is indicated, and when they approach the ideal, a state of pleasure is indicated. An emotion map can be generated, for example, based on Dr. Mitsuyoshi's emotion map (Research on Voice Emotion Recognition and Emotional Brain Physiological Signal Analysis Systems, Tokushima University, Doctoral Dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map lists emotions belonging to the "reaction" domain, where sensation is dominant. The right half of the emotion map lists emotions belonging to the "situation" domain, where situational awareness is dominant.

[1225] 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."

[1226] 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.

[1227] The system according to the present disclosure has been described above mainly with respect to the functions of the data processing device 12, but the system according to the present disclosure is not necessarily implemented on a server. The system according to the present disclosure may be implemented as a general information processing system. The present disclosure may be implemented, for example, as a software program running on a personal computer or an application running on a smartphone, etc. The method according to the present disclosure may be provided to users in the form of SaaS (Software as a Service).

[1228] In the above embodiment, an example was given in which the specific processing is performed by one computer 22, but the technology of the present disclosure is not limited to this, and the specific processing may be distributed and performed by a plurality of computers including the computer 22. For example, the data generation model 58 may be provided in an external device of the data processing device 12, and data may be generated in the external device in accordance with input data.

[1229] 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.

[1230] 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.

[1231] 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.

[1232] The hardware resource for executing a specific process can be any of the following processors: An example of a processor 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. Another example of a processor is 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.

[1233] The hardware resource that executes the specific processing 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 processing may be a single processor.

[1234] 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.

[1235] 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.

[1236] 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.

[1237] 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.

[1238] The following is further disclosed regarding the above embodiment.

[1239] (Claim 1)

[1240] a means for a user to make a content generation request;

[1241] A means for the server to generate content using artificial intelligence;

[1242] means for the server to check the generated content against copyright and patent databases;

[1243] A means for the server to determine whether or not there is a right infringement based on the collation result;

[1244] A means for the server to regenerate the content if a copyright infringement is confirmed;

[1245] a means for providing the user with non-infringing server-generated content;

[1246] A system including:

[1247] (Claim 2)

[1248] 2. The system according to claim 1, further comprising means for notifying the user of the result of the collation of the content generated by the server, and informing the user that there is no infringement of rights or that the content will be reproduced.

[1249] (Claim 3)

[1250] 2. The system of claim 1, wherein the regenerating means includes reusing the generating artificial intelligence to generate new content.

[1251] "Example 1"

[1252] (Claim 1)

[1253] a means for a user to make a content generation request;

[1254] A means for the server to generate content using a generative artificial intelligence model;

[1255] means for the server to check the generated content against copyright and patent databases;

[1256] A means for the server to determine whether or not there is a right infringement based on the collation result;

[1257] A means for the server to regenerate the content if a copyright infringement is confirmed;

[1258] a means for providing the user with non-infringing server-generated content;

[1259] means for generating and inputting a prompt sentence to the generative artificial intelligence model;

[1260] a means for storing the matching results;

[1261] A system including:

[1262] (Claim 2)

[1263] 2. The system according to claim 1, further comprising means for notifying the user of the result of the collation of the content generated by the server, and informing the user that there is no infringement of rights or that the content will be reproduced.

[1264] (Claim 3)

[1265] 2. The system of claim 1, wherein the regenerating means includes reusing the generative artificial intelligence model to generate new content.

[1266] "Application Example 1"

[1267] (Claim 1)

[1268] a means for a user to make a content generation request;

[1269] A means for the server to generate content using the generative AI model;

[1270] means for the server to check the generated content against copyright and patent databases;

[1271] A means for the server to determine whether or not there is a right infringement based on the collation result;

[1272] A means for the server to regenerate the content if a copyright infringement is confirmed;

[1273] a means for providing the user with non-infringing server-generated content;

[1274] an interface means for receiving prompts for generating content and verifying copyright in real time and inputting the prompts into the generative AI model;

[1275] an application means that can be installed on a smartphone or smart glasses;

[1276] A system including:

[1277] (Claim 2)

[1278] 2. The system according to claim 1, further comprising means for notifying the user of the result of the collation of the content generated by the server, and informing the user that there is no infringement of rights or that the content will be reproduced.

[1279] (Claim 3)

[1280] 2. The system of claim 1, wherein the regenerating means includes reusing the generating artificial intelligence to generate new content.

[1281] "Example 2: Combining Emotion Engines"

[1282] (Claim 1)

[1283] a means for a user to make an information generation request;

[1284] A means for the server to generate information using a generative intelligence system;

[1285] means for the server to verify the generated information against copyright and patent databases;

[1286] A means for the server to determine whether or not there is a right infringement based on the collation result;

[1287] a means for adjusting information by an emotion engine that the server evaluates based on the user's emotion data;

[1288] A means for generating the information again if the server detects a copyright infringement;

[1289] a means for the server to provide the user with information that is not infringing;

[1290] A system including:

[1291] (Claim 2)

[1292] 2. The system according to claim 1, further comprising means for notifying the user of the result of the collation of the information generated by the server, and informing the user that there is no infringement of rights or that the information is to be reproduced.

[1293] (Claim 3)

[1294] 10. The system of claim 1, wherein the regenerating means includes re-using the generative intelligence system to generate new information.

[1295] "Application example 2 when combining emotion engines"

[1296] (Claim 1)

[1297] a means for a user to make a content generation request;

[1298] A means for the server to generate content using artificial intelligence;

[1299] means for the server to check the generated content against copyright and patent databases;

[1300] A means for the server to determine whether or not there is a right infringement based on the collation result;

[1301] means for adjusting the server-generated content based on the user's emotional state;

[1302] A means for the server to regenerate the content if a copyright infringement is confirmed;

[1303] a means for providing the user with non-infringing server-generated content;

[1304] A system including:

[1305] (Claim 2)

[1306] 2. The system according to claim 1, further comprising means for notifying the user of the result of the collation of the content generated by the server, and informing the user that there is no infringement of rights or that the content will be reproduced.

[1307] (Claim 3)

[1308] 2. The system of claim 1, wherein the regenerating means includes reusing the generating artificial intelligence to generate new content. [Explanation of symbols]

[1309] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Device 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robot< / url:> < / url:> < / url:> < / url:>

Claims

1. a means for a user to make a content generation request; A means for the server to generate content using artificial intelligence; means for the server to check the generated content against copyright and patent databases; A means for the server to determine whether or not there is a right infringement based on the collation result; A means for the server to regenerate the content if a copyright infringement is confirmed; a means for providing the user with non-infringing server-generated content; A system including:

2. 2. The system according to claim 1, further comprising means for notifying the user of the result of the collation of the content generated by the server, and informing the user that there is no infringement of rights or that the content will be reproduced.

3. 2. The system according to claim 1, wherein the regenerating means includes regenerating new content by using the generating artificial intelligence again.

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