system

Abstract

We provide the system. [Solution] A means for receiving user input data from an information processing device, A generative artificial intelligence means that analyzes the user input data and identifies extracted keywords and phrases, Means for matching the aforementioned keywords or phrases with a patent information database and a trademark database, If the results of the aforementioned matching include relevant patent and trademark information, means for generating metadata indicating the relevant portion, A means for adding the metadata to the user input data and returning the edited data, A system that includes this.

Description

Technical Field

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

Background Art

[0002] Patent Document 1 discloses a persona chatbot control method performed by at least one processor, including steps of receiving a user utterance, adding the user utterance to a prompt including an instruction sentence related to an explanation of a 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

Summary of the Invention

Problems to be Solved by the Invention

[0004] In a modern business environment, the protection of various trademarks, registered trademarks, and patents is important. However, it is complicated and laborious to determine whether the content created by a user or a new idea is related to existing trademarks or patents. Furthermore, failure to display trademark or patent information appropriately may cause violations and legal problems. The present invention is to provide a system that improves the proper management and transparency of trademarks and patents in order to solve these problems.

Means for Solving the Problems

[0005] This invention provides a system that receives user input data from an information processing device and analyzes that data using generative artificial intelligence. This system includes means for comparing extracted keywords and phrases with a patent information database and a trademark database, and generating relevant metadata if a match is found. Furthermore, by providing means for attaching this metadata to the user input data and returning the edited data, the system is characterized by clearly indicating information if the user-created content is related to trademarks or patents. This improves commercial transparency and makes it possible to prevent infringement.

[0006] An "information processing device" is a device that receives input data from a user and processes and analyzes that data.

[0007] "User input data" refers to text and other forms of data provided by the user through an information processing device.

[0008] "Generative artificial intelligence" is an artificial intelligence technology that has the ability to analyze user input data and identify key keywords and phrases.

[0009] "Keywords and phrases" refer to important words and expressions extracted from user input data and used for analysis.

[0010] A "patent information database" is a database that centrally manages information on various patents and allows users to search and match patent information related to specific keywords or phrases.

[0011] A "trademark database" is a database that centrally manages information on various trademarks and allows users to search and match trademark information related to specific keywords or phrases.

[0012] "Metadata" refers to additional information added to user input data, including supplementary information such as patent information and trademark information.

[0013] "Edited data" refers to user input data to which metadata has been added based on the results of analysis by generative artificial intelligence and database matching.

[0014] "Method of returning data" refers to a method of returning edited data, which includes metadata added to the original user input data, to the user or information processing device. [Brief explanation of the drawing]

[0015] [Figure 1] This is a conceptual diagram showing an example of the configuration of a data processing system according to the first embodiment. [Figure 2] This is a conceptual diagram showing an example of the essential functions of a data processing device and a smart device according to the first embodiment. [Figure 3] This is a conceptual diagram showing an example of the configuration of a data processing system according to the second embodiment. [Figure 4] This is a conceptual diagram showing an example of the main functions of a data processing device and smart glasses according to the second embodiment. [Figure 5] This is a conceptual diagram showing an example of the configuration of a data processing system according to the third embodiment. [Figure 6] This is a conceptual diagram showing an example of the main functions of a data processing device and a headset-type terminal according to the third embodiment. [Figure 7] This is a conceptual diagram showing an example of the configuration of a data processing system according to the fourth embodiment. [Figure 8] This is a conceptual diagram showing an example of the main functions of a data processing device and a robot according to the fourth embodiment. [Figure 9] This shows an emotion map where multiple emotions are mapped. [Figure 10] This shows an emotion map where multiple emotions are mapped. [Figure 11] This is a sequence diagram showing the processing flow of the data processing system in Example 1. [Figure 12] This is a sequence diagram showing the processing flow of the data processing system in Application Example 1. [Figure 13]It is a sequence diagram showing the processing flow of the data processing system in Embodiment 2 when the emotion engine is combined. [Figure 14] It is a sequence diagram showing the processing flow of the data processing system in Application Example 2 when the emotion engine is combined.

Mode for Carrying Out the Invention

[0016] Hereinafter, an example of an embodiment of the system according to the technology of the present disclosure will be described with reference to the accompanying drawings.

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

[0018] In the following embodiments, the numbered processor (hereinafter simply referred to as "processor") may be one arithmetic unit or a combination of multiple arithmetic units. Also, the processor may be one type of arithmetic unit or a combination of multiple types of arithmetic units. Examples of arithmetic units include a CPU (Central Processing Unit), a GPU (Graphics Processing Unit), a GPGPU (General-Purpose computing on Graphics Processing Units), an APU (Accelerated Processing Unit), and the like.

[0019] In the following embodiments, the numbered RAM (Random Access Memory) is a memory in which information is temporarily stored and is used as a work memory by the processor.

[0020] In the following embodiments, the numbered storage is one or more non-volatile storage devices that store various programs and various parameters, etc. Examples of non-volatile storage devices include flash memory (SSD (Solid State Drive)), magnetic disks (e.g., hard disks), or magnetic tapes, etc.

[0021] In the following embodiments, the signed communication interface (I / F) is an interface that includes a communication processor and an antenna, etc. The communication interface manages communication between multiple computers. Examples of communication standards applicable to the communication interface include wireless communication standards such as 5G (5th Generation Mobile Communication System), Wi-Fi (registered trademark), or Bluetooth (registered trademark).

[0022] In the following embodiments, "A and / or B" is synonymous with "at least one of A and B." That is, "A and / or B" means that it may be A alone, or B alone, or a combination of A and B. Furthermore, in this specification, the same concept as "A and / or B" applies when expressing three or more things linked by "and / or."

[0023] [First Embodiment]

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

[0025] As shown in Figure 1, the data processing system 10 includes a data processing device 12 and a smart device 14. An example of the data processing device 12 is a server.

[0026] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 is an example of a "computer" related to the technology of this disclosure. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[0027] The smart device 14 comprises a computer 36, a reception device 38, an output device 40, a camera 42, and a communication interface 44. The computer 36 comprises a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The reception device 38, output device 40, and camera 42 are also connected to the bus 52.

[0028] The reception device 38 is equipped with a touch panel 38A and a microphone 38B, etc., and receives user input. The touch panel 38A receives user input by detecting contact with an object (e.g., a pen or finger). The microphone 38B receives user input by detecting the user's voice. The control unit 46A transmits data indicating the user input received by the touch panel 38A and microphone 38B to the data processing 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 perceptible to the user 20 (e.g., audio and / or text). The display 40A displays visible information such as text and images according to instructions from the processor 46. The speaker 40B outputs audio according to instructions from the processor 46. The camera 42 is a small digital camera equipped with an optical system such as a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor.

[0030] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various types of information between processor 46 and processor 28 via network 54.

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

[0032] As shown in Figure 2, in the data processing device 12, a specific processing is performed by the processor 28. A specific processing program 56 is stored in the storage 32. The specific processing program 56 is an example of a "program" related to the technology of this disclosure. The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0033] The storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[0034] In the smart device 14, the processor 46 performs the reception output processing. The storage 50 stores the reception output program 60. The reception output program 60 is used in conjunction with a 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 processing is realized by the processor 46 operating as a control unit 46A according to the reception output program 60 executed on the RAM 48.

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

[0036] This invention is a system for improving the proper management and transparency of trademarks and patents. It has the function of analyzing user input data and managing extracted keywords and phrases to add relevant trademark and patent information. The system of this invention includes an information processing device, a generative artificial intelligence system, a database matching function, a metadata generation function, and an output function for edited data.

[0037] Program overview and explanation in natural language

[0038] This system operates as follows:

[0039] 1. Obtaining and sending user input

[0040] The user launches the application and provides input through a text interface. It begins with the user entering ideas or content for a new application.

[0041] The terminal receives this input data and sends it to the server using a secure communication protocol.

[0042] 2. Text Analysis

[0043] The server utilizes generative artificial intelligence to analyze user input data. Specifically, it breaks down text and extracts key keywords and phrases.

[0044] The aforementioned analysis identifies terms related to "trademarks" and "patents."

[0045] 3. Database matching

[0046] The server compares the extracted keywords and phrases with patent information databases and trademark databases. This verifies whether the entered data is related to existing trademarks or patents.

[0047] 4. Metadata generation

[0048] The server generates metadata, including relevant trademark and patent information, based on the results of database matching. For example, it might generate information such as, "This content is related to a specific trademark."

[0049] 5. Compiling and sending the edited data

[0050] The server generates metadata which is then added to the user input data to construct the edited data. Finally, the edited data is sent back to the terminal.

[0051] 6. Displaying the results

[0052] The terminal displays the edited data received from the server in the user interface. At this stage, the user can verify which trademarks or patents the input data is related to.

[0053] Specific example

[0054] Input example

[0055] The user enters, "My idea for my new application is based on existing communication services."

[0056] System operation

[0057] 1. The user enters information.

[0058] 2. The terminal sends the input data to the server.

[0059] 3. The server analyzes the data and extracts the keyword "communication services".

[0060] 4. The server checks the patent information database and trademark database to confirm that the "communication service" is related to a specific trademark or patent.

[0061] 5. The server generates metadata stating "This content is related to an existing trademark" and adds it to the input data.

[0062] 6. The device receives the edited data and displays the message, "This idea is related to a specific trademark."

[0063] This system promotes the proper management of trademarks and patents and prevents legal problems by clarifying what user-created content is related to.

[0064] The following describes the processing flow.

[0065] Step 1:

[0066] The user launches the application and inputs ideas or content through a text interface. For example, the user might enter the text, "The idea for the new application is based on existing communication services."

[0067] Step 2:

[0068] The terminal receives user input data and sends it to the server using a secure communication protocol (e.g., HTTPS).

[0069] Step 3:

[0070] The server passes the user input data it receives to a generative artificial intelligence (AI), which then begins text analysis. Specifically, it breaks down the text and extracts key keywords and phrases. For example, it might identify the term "communication services."

[0071] Step 4:

[0072] The server sends queries to patent and trademark databases based on the extracted keywords and phrases. This verifies whether the entered data is related to existing patents or trademarks.

[0073] Step 5:

[0074] The server receives matching results from the database, and if a matching trademark or patent is found, it generates metadata containing that information. For example, information such as "This content is related to a trademark of a specific telecommunications service" might be generated.

[0075] Step 6:

[0076] The metadata generated by the server is added to the original user input data to construct the edited data. For example, the message "This idea is related to a specific trademark" is added to the original text.

[0077] Step 7:

[0078] The server returns the edited data to the terminal.

[0079] Step 8:

[0080] The terminal displays the edited data received from the server in the user interface. The user can review the edited data and recognize which trademarks or patents the input data is related to.

[0081] This process allows users to check whether their ideas or content infringe upon existing patents or trademarks, and to take appropriate action.

[0082] (Example 1)

[0083] Next, we will describe Example 1. In the following description, the data processing device 12 will be referred to as the "server," and the smart device 14 will be referred to as the "terminal."

[0084] To ensure proper management and transparency of trademarks and patents, a system is needed that allows users to quickly verify whether the data they enter is related to existing trademarks and patents. However, current systems require users to conduct their own research and investigations, which is time-consuming and laborious. Furthermore, if the information users refer to is incomplete, it becomes difficult to prevent legal risks. Against this backdrop, there is a need to develop a system that can significantly reduce the burden on users and provide accurate and timely information related to trademarks and patents.

[0085] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 1 is realized by the following means.

[0086] In this invention, the server includes means for receiving user input data from an information processing device; generative artificial intelligence means for analyzing the user input data and identifying extracted keywords and phrases; means for comparing the keywords and phrases with a patent information database and a trademark database; means for generating metadata including relevant patent and trademark information as a result of the comparison; means for adding the metadata to the user input data and returning the edited data; and means for displaying the received edited data on a user interface. This enables users to quickly and accurately obtain information related to trademarks and patents without having to conduct their own research.

[0087] An "information processing device" is a computer system that receives input data from a user and performs various data processing operations.

[0088] "User input data" refers to information that a user provides to the system through a text interface or other input means.

[0089] "Generative artificial intelligence" refers to algorithms that learn from large amounts of text data and perform text analysis and generation tailored to specific purposes.

[0090] "Keywords and phrases" are important words and phrases extracted from user input data that are highly likely to be related to trademarks or patents.

[0091] A "patent information database" is a database system that stores existing patent information and provides it in a searchable format.

[0092] A "trademark database" is a database system that stores existing trademark information and provides it in a searchable format.

[0093] "Matching" is the process of comparing extracted keywords or phrases with existing information in the database to confirm a match or relevance.

[0094] "Metadata" refers to data generated as additional information related to user input data, including details about patents and trademarks.

[0095] "Edited data" refers to a dataset in which metadata has been added to user-input data, thereby enhancing its information.

[0096] "Receiving" refers to the process by which data or information is transferred from another system or user.

[0097] "Display" refers to the process by which processed data or information is visually presented to the user.

[0098] This invention is a system for improving the proper management and transparency of trademarks and patents. It has the function of analyzing user input data and managing extracted keywords and phrases to add relevant trademark and patent information. Embodiments of this invention will be described in detail below.

[0099] This system includes an information processing device, a generative artificial intelligence system, a database matching function, a metadata generation function, and an output function for edited data.

[0100] First, the user launches the application and enters information through a text interface. At this stage, the user enters ideas or content for the new application. For example, they might enter, "My idea for my new application is based on existing communication services."

[0101] The terminal receives user input data and sends it to the server using a secure communication protocol (e.g., TLS / SSL).

[0102] Next, the server analyzes the received input data using a generating AI model (e.g., GPT-4®). This AI model breaks down the text to identify key keywords and phrases. Specifically, it extracts keywords such as "application" and "communication service." Text analysis by the AI ​​model is used to understand the context of the user input data and identify relevant terms.

[0103] The extracted keywords and phrases are cross-referenced by the server against patent information databases (e.g., the Japan Patent Office database) and trademark databases. This cross-referencing process uses SQL queries and API requests. For example, a cross-referencing is performed to check if the keyword "communication services" is related to any existing patents or trademarks. This retrieves detailed information about the relevant patents and trademarks as a result of the cross-referencing.

[0104] Next, the server generates metadata based on the database matching results, including relevant trademark and patent information. For example, information such as "This content is related to a specific trademark" is generated. The metadata includes patent numbers, patent details, and trademark owners.

[0105] The generated metadata is appended to the user input data, and a new dataset is constructed by the server. The edited data is then retransmitted to the terminal and provided to the user. This transmission uses a secure communication protocol, similar to the reception process.

[0106] Finally, the terminal displays the edited data received from the server on the user interface. Specifically, it provides information on relevant patents and trademarks in an easy-to-understand format, along with the content entered by the user. For example, information such as "This idea is related to an existing patent" is displayed, allowing the user to quickly check the legal risks.

[0107] Specific example:

[0108] Example of a prompt:

[0109] "My idea for my new application is based on existing communication services."

[0110] This system allows users to quickly and accurately obtain information related to trademarks and patents without having to conduct their own research, thereby preventing legal risks.

[0111] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0112] Step 1:

[0113] Acquiring and sending user input

[0114] The user launches the application and enters information via a text interface. For example, the user might enter, "My idea for my new application is based on existing communication services."

[0115] The terminal receives this input data and sends it to the server using a secure communication protocol (e.g., TLS / SSL).

[0116] Input: Text data entered by the user.

[0117] Output: User input data securely sent to the server.

[0118] Step 2:

[0119] Text analysis

[0120] The server analyzes the received input data using a generation AI model (e.g., GPT-4).

[0121] The server breaks down the input data and extracts key keywords and phrases. For example, "application" and "communication service" might be extracted.

[0122] Input: User text data sent to the server.

[0123] Output: A list of analyzed keywords and phrases.

[0124] Step 3:

[0125] Database matching

[0126] The server compares the extracted keywords and phrases with patent information databases and trademark databases.

[0127] The server uses SQL queries and API requests to search for items in the database that match or are related to registered information. For example, it might identify existing patents or trademarks that match "communication services."

[0128] Input: Analyzed keywords or phrases.

[0129] Output: Relevant patent and trademark information as a result of the matching process.

[0130] Step 4:

[0131] Metadata generation

[0132] The server generates metadata, including relevant patent and trademark information, based on the database matching results.

[0133] For example, information such as "This content is related to a specific trademark" is generated.

[0134] Metadata includes information such as patent numbers, patent details, and trademark owners.

[0135] Input: Database matching result.

[0136] Output: Generated metadata.

[0137] Step 5:

[0138] Compiling and sending edited data

[0139] The server adds the generated metadata to the user input data to construct a new dataset.

[0140] The server securely sends this edited data to the terminal.

[0141] Input: User input data and metadata.

[0142] Output: Edited and securely transmitted dataset.

[0143] Step 6:

[0144] Displaying Results

[0145] The terminal displays the edited data received from the server in the user interface.

[0146] For example, a user might see a message stating, "This idea is related to an existing patent."

[0147] Input: Edited data received from the server.

[0148] Output: Relevant information displayed in the user interface.

[0149] (Application Example 1)

[0150] Next, we will explain Application Example 1. In the following explanation, the data processing device 12 will be referred to as the "server," and the smart device 14 will be referred to as the "terminal."

[0151] Conventional information processing systems have made it difficult to quickly and accurately assess the trademark and patent risks associated with new ideas and projects. As a result, companies and individuals were unable to prevent legal risks when developing new products, potentially leading to problems. In particular, there was a lack of means to obtain appropriate information when real-time evaluation was required.

[0152] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 1 is realized by the following means.

[0153] In this invention, the server includes means for receiving user input data from an information processing device; generative artificial intelligence means for analyzing the user input data and identifying extracted keywords and phrases; means for comparing the keywords and phrases with a patent information database and a trademark database; means for generating metadata indicating the relevant sections if the comparison results include relevant patent and trademark information; means for attaching the metadata to the user input data and returning the edited data; means for sending and receiving data using satellite communication; and means for evaluating trademark and patent-related risks in real time. This enables users to evaluate trademark and patent-related risks related to new ideas and projects in real time.

[0154] An "information processing device" is a terminal device that receives input data from a user and transmits it to a server.

[0155] "User input data" refers to information entered by users in text format, such as new ideas or project details.

[0156] "Generative artificial intelligence" refers to an artificial intelligence system that analyzes user input data and extracts key keywords and phrases.

[0157] A "patent information database" is a database that stores existing patent information and uses it for comparison.

[0158] A "trademark database" is a database that stores existing trademark information and uses it for matching.

[0159] "Metadata" refers to data related to patents and trademarks that is added to user input data.

[0160] "Satellite communication" is a communication method that transmits and receives data via artificial satellites.

[0161] "Real-time evaluation" refers to the immediate assessment of trademark and patent-related risks associated with user input data.

[0162] "Edited data" refers to user-input data to which metadata has been added, and then returned from the server.

[0163] This invention is a system for improving the proper management and transparency of trademarks and patents. The system aims to enable companies and individuals to evaluate new ideas and projects in real time and to anticipate trademark and patent-related risks.

[0164] The system mainly includes the following components:

[0165] 1. Information processing device: A device such as a smartphone that has the function of receiving user input data.

[0166] 2. Generative Artificial Intelligence: Generative AI models such as GPT-4 are used to analyze user input data and extract key keywords and phrases.

[0167] 3. Database matching function: The extracted keywords and phrases are compared with the patent information database and the trademark database.

[0168] 4. Metadata generation function: Generates metadata including relevant patent and trademark information.

[0169] 5. Satellite communications: A means of communication for sending and receiving data.

[0170] 6. Real-time evaluation function: Evaluates trademark and patent-related risks in real time.

[0171] Specific examples of how the system works

[0172] 1. Obtaining user input

[0173] The user launches the application on their smartphone and enters details about a new idea or project. For example, they might enter, "We are developing a next-generation security camera system."

[0174] User input data is encrypted and sent to the server via a secure communication protocol (e.g., HTTPS).

[0175] 2. Data Analysis and Reconciliation

[0176] The server uses generative artificial intelligence (e.g., GPT-4) to analyze user input data and extract key keywords and phrases. For example, "security camera system" might be extracted.

[0177] The extracted keywords are compared with patent information databases and trademark databases (e.g., J-PlatPat) to obtain relevant information.

[0178] 3. Metadata generation and return

[0179] Based on the matching results, the server generates metadata that includes relevant patent and trademark information. For example, it might generate information such as, "This idea is related to an existing trademark."

[0180] The generated metadata is added to the user input data, and the edited data is sent back to the user's smartphone.

[0181] 4. Displaying the results

[0182] The terminal displays edited data received from the server in the user interface. Users can see in real time which trademarks and patents their entered ideas or projects are associated with.

[0183] Example of a prompt

[0184] Examples of prompt statements for a generative AI model are as follows:

[0185] Please identify any relevant trademarks or patents for the following idea: Idea: Developing a next-generation security camera system.

[0186] This system allows users to assess trademark and patent-related risks in real time and prevent legal troubles before they occur. Specific hardware used includes smartphones, and software includes generative AI (e.g., GPT-4), secure communication protocols (e.g., HTTPS), and patent and trademark databases (e.g., J-PlatPat).

[0187] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0188] Step 1:

[0189] Retrieving user input

[0190] The user launches the application on their smartphone and enters details of a new idea or project.

[0191] The terminal receives the input data and encrypts it.

[0192] The device sends encrypted data to the server using a secure communication protocol (e.g., HTTPS).

[0193] Input: Text data entered by the user.

[0194] Output: Encrypted data is sent to the server.

[0195] Step 2:

[0196] Data Analysis

[0197] The server analyzes the received user input data using generative artificial intelligence (e.g., GPT-4).

[0198] The server extracts key keywords and phrases from the input data.

[0199] Specific operation: Input a prompt sentence into the generative AI model and have it extract highly relevant keywords.

[0200] Input: Encrypted user input data.

[0201] Output: Extracted keywords and phrases.

[0202] Step 3:

[0203] Database matching

[0204] The server compares the extracted keywords and phrases with patent information databases and trademark databases (e.g., J-PlatPat).

[0205] The server retrieves relevant patent and trademark information as a result of the matching process.

[0206] Specific operation: Issue search queries to patent and trademark databases and retrieve results.

[0207] Input: Extracted keywords or phrases.

[0208] Output: Relevant patent and trademark information.

[0209] Step 4:

[0210] Metadata generation

[0211] Based on the matching results, the server generates metadata that includes relevant patent and trademark information.

[0212] Specific operation: The server constructs metadata based on the information it has acquired.

[0213] Input: Relevant patent and trademark information.

[0214] Output: Generated metadata.

[0215] Step 5:

[0216] Return of edited data

[0217] The server adds the generated metadata to the user input data and sends the edited data back to the user's smartphone.

[0218] Specific operation: Metadata is added after existing user input data and transferred to the user's terminal in an edited form.

[0219] Input: User input data and metadata.

[0220] Output: Edited data.

[0221] Step 6:

[0222] Displaying Results

[0223] The terminal displays the edited data received from the server in the user interface.

[0224] Specific operation: Reads received data and displays it in a format that is easy for the user to understand.

[0225] Input: Edited data.

[0226] Output: User interface displaying data.

[0227] Furthermore, an emotion engine that estimates the user's emotions may be incorporated. That is, the identification processing unit 290 may use the emotion identification model 59 to estimate the user's emotions and perform identification processing using the user's emotions.

[0228] This invention combines a system that analyzes user input data, compares extracted keywords and phrases with a patent information database and a trademark database, and adds relevant patent and trademark information, with an emotion engine that recognizes the user's emotions. This system includes an information processing device, a generative artificial intelligence system, a database matching function, a metadata generation function, an edited data output function, and an emotion engine.

[0229] Program overview and explanation in natural language

[0230] This system operates as follows:

[0231] 1. Obtaining and sending user input

[0232] The user launches the application and enters information through a text interface. For example, the user might enter the text, "The idea for the new application is based on existing communication services."

[0233] The terminal receives this input data and sends it to the server using a secure communication protocol.

[0234] 2. Text Analysis

[0235] The server receives input data and passes it to a generative artificial intelligence system to begin text analysis. Specifically, it breaks down the text and extracts key keywords and phrases. For example, it identifies the term "communication services."

[0236] 3. User emotion recognition

[0237] The server uses an emotion engine to analyze and recognize the user's emotions from user input data. For example, it extracts emotions such as "excitement" or "anxiety" from the wording and syntax within the text.

[0238] 4. Database matching

[0239] The server sends queries to patent and trademark databases based on the extracted keywords and phrases. This verifies whether the entered data is related to existing patents or trademarks.

[0240] 5. Metadata generation

[0241] The server receives the results of the database matching, and if a matching trademark or patent is found, it generates metadata containing that information. For example, information such as "This content is related to a trademark of a specific communication service" is generated.

[0242] Furthermore, metadata generation is adjusted based on the results of the emotion engine. For example, if a user indicates an "anxious" emotion, the information is adjusted to provide more detail.

[0243] 6. Compiling and sending the edited data

[0244] The metadata generated by the server is added to the original user input data to construct the edited data. For example, the message "This idea is related to a specific trademark" is added to the original text.

[0245] Based on the results from the emotion engine, the timing of presenting edited data is adjusted. For example, if the user is "excited," information is presented immediately; if they are "anxious," it is presented at an appropriate time.

[0246] 7. Displaying the results

[0247] The terminal displays the edited data received from the server in the user interface. The user can review the edited data and recognize which trademarks or patents the input data is related to.

[0248] Specific example

[0249] Input example

[0250] The user enters, "My idea for my new application is based on existing communication services."

[0251] System operation

[0252] 1. The user enters information.

[0253] 2. The terminal sends the input data to the server.

[0254] 3. The server analyzes the data and extracts the keyword "communication services".

[0255] 4. The server uses an emotion engine to detect emotions such as "anxiety" from the text.

[0256] 5. The server checks the patent information database and trademark database to confirm that the "communication service" is associated with a specific trademark or patent.

[0257] 6. The server generates metadata stating, "This content is related to the trademark of an existing communication service." This addresses the user's feeling of "anxiety" by providing more detailed information.

[0258] 7. The device receives the edited data and displays "This idea is related to a specific trademark" at the appropriate time.

[0259] This system allows users to not only check whether their ideas or content infringe on existing patents or trademarks, but also to receive appropriate information tailored to their emotional needs. This facilitates sound judgment and appropriate responses.

[0260] The following describes the processing flow.

[0261] Step 1:

[0262] The user launches the application and inputs ideas or content through a text interface. For example, the user might enter the text, "The idea for the new application is based on existing communication services."

[0263] Step 2:

[0264] The terminal receives user input data and sends it to the server using a secure communication protocol (e.g., HTTPS).

[0265] Step 3:

[0266] The server passes the user input data it receives to a generative artificial intelligence (AI), which then begins text analysis. Specifically, it breaks down the text and extracts key keywords and phrases. For example, it might identify the term "communication services."

[0267] Step 4:

[0268] The server uses an emotion engine to analyze and recognize the user's emotions from user input data. For example, it extracts emotions such as "excitement" or "anxiety" from the wording and syntax within the text.

[0269] Step 5:

[0270] The server sends queries to patent and trademark databases based on the extracted keywords and phrases. This verifies whether the entered data is related to existing patents or trademarks.

[0271] Step 6:

[0272] The server receives matching results from the database, and if a matching trademark or patent is found, it generates metadata containing that information. For example, information such as "This content is related to a trademark of a specific telecommunications service" might be generated.

[0273] Step 7:

[0274] The server adjusts metadata generation based on the results of the emotion engine. For example, if a user indicates an "anxious" emotion, the metadata is adjusted to provide more detailed information.

[0275] Step 8:

[0276] The metadata generated by the server is added to the original user input data to construct the edited data. For example, the message "This idea is related to a specific trademark" is added to the original text.

[0277] Step 9:

[0278] The server sends the edited data back to the terminal. Based on the results from the emotion engine, the timing of presentation is adjusted. For example, if the user is "excited," information is presented immediately; if they are "anxious," it is presented at an appropriate time.

[0279] Step 10:

[0280] The terminal displays the edited data received from the server on the user interface. The user can check the edited data and recognize which trademarks and patents the input data is related to, as well as additional information according to the user's emotions.

[0281] (Example 2)

[0282] Next, Example 2 will be described. In the following description, the data processing device 12 is referred to as the "server", and the smart device 14 is referred to as the "terminal".

[0283] In a conventional information processing system, although the analysis of user input data and the collation of related patent and trademark information are performed, the results cannot be adjusted based on the user's emotions. Therefore, there is a problem that the information required by the user is not provided at an appropriate timing and the display content cannot be optimized. Furthermore, since appropriate information provision according to the user's psychological state cannot be performed, it has been difficult to improve the user's satisfaction and make efficient decisions.

[0284] The specific processing by the specific processing unit 290 of the data processing device 12 in Example 2 is realized by the following means.

[0285] In this invention, the server includes means for receiving user input data from a computer device, generative artificial intelligence means for analyzing the user input data and identifying the extracted keywords and phrases, emotion recognition means for analyzing and recognizing the user's emotions from the user data, means for collating the keywords and phrases with a patent information database and a trademark database, means for generating metadata indicating the relevant location if the collation result includes relevant patent and trademark information, means for adjusting the generation and editing of metadata based on the result of the emotion recognition means, means for adding the metadata to the user input data and returning the edited data, and means for adjusting the timing of presenting the edited data. Thereby, it becomes possible to provide information at an appropriate timing and content according to the user's emotional state.

[0286] A "computer device" is an electronic device for inputting, outputting, storing, and processing data.

[0287] "User input data" refers to information such as text and voice that a user inputs to a computer device.

[0288] "Generative artificial intelligence means" refers to means that uses algorithms and programs for analyzing user input data and extracting important keywords and phrases.

[0289] "Emotion recognition means" refers to means that uses algorithms and programs for analyzing and recognizing a user's emotions from user input data.

[0290] "Patent information database" refers to a database in which information related to patents is stored and can be searched and compared.

[0291] "Trademark database" refers to a database in which information related to trademarks is stored and can be searched and compared.

[0292] "Comparison means" refers to a function for comparing and matching the extracted keywords and phrases with a patent information database and a trademark database.

[0293] "Metadata generation means" refers to a function for generating data for adding related information based on the comparison results of keywords and phrases.

[0294] "Editing means" refers to a function for adding the generated metadata to user input data and creating edited data.

[0295] [[ID=�6]] "Timing adjustment means" refers to a function for determining the optimal timing for presenting the edited data according to a user's emotions and the system's status.

[0296] This invention combines a system that analyzes user input data, compares extracted keywords and phrases with a patent information database and a trademark database, and adds relevant patent and trademark information, with an emotion engine that recognizes the user's emotions. The embodiments are described in detail below.

[0297] The entire system consists of an information processing device (computer device), a generative artificial intelligence means, a database matching means, an emotion recognition means, a metadata generation means, an editing means, and a timing adjustment means.

[0298] 1. Obtaining and sending user input

[0299] The user uses a dedicated application to enter information into a text input field. For example, they might enter, "The idea for the new application is based on existing communication services."

[0300] The terminal receives this user input data and sends it to the server using a secure communication protocol (e.g., HTTPS).

[0301] 2. Text Analysis

[0302] The server analyzes the user input data it receives using a natural language processing library (e.g., spaCy). It breaks down the text into words and phrases, extracting important keywords and phrases. In this case, "communication service" is extracted as a keyword.

[0303] 3. User emotion recognition

[0304] The server uses an EmotionML-compatible emotion engine to analyze emotions from user input data. For example, it analyzes positive and negative expressions in text to extract emotions such as "excitement" or "anxiety."

[0305] 4. Database matching

[0306] The server sends a query to match the extracted keywords and phrases with a patent information database (e.g., Google (registered trademark) Patents) and a trademark database (e.g., Trademark Electronic Search System). It checks whether the input data is related to existing patents and trademarks.

[0307] 5. Metadata Generation

[0308] When the server receives the matching results and finds matching trademarks or patents, it generates metadata containing that information. For example, it generates metadata such as "This content is related to the trademark of a specific communication service". Furthermore, based on the results of the sentiment recognition means, it adjusts the accuracy and detail level of the metadata. If the user shows "unease", it adds detailed information.

[0309] 6. Construction and Transmission of Edited Data

[0310] The server adds the generated metadata to the original user input data to create edited data. For example, it adds a message such as "This idea is related to a specific trademark". Based on the analysis results of the sentiment recognition means, it adjusts the presentation timing of the edited data. If the user is "excited", it displays immediately; if the user is "uneasy", it displays at the timing when the explanation ends.

[0311] 7. Result Display

[0312] The terminal displays the edited data received from the server on the user interface. The user can check this data and recognize which trademarks and patents the input data is related to.

[0313] Specific Example

[0314] Input Example

[0315] The user enters, "My idea for my new application is based on existing communication services."

[0316] System operation

[0317] 1. The user launches the application and enters text.

[0318] 2. The terminal sends the input data to the server.

[0319] 3. The server analyzes the received data and extracts the keyword "communication service".

[0320] 4. The server uses an emotion engine to detect the emotion of "user anxiety."

[0321] 5. The server sends queries to the patent information database and trademark database to confirm that the “communication service” is related to a specific trademark or patent.

[0322] 6. The server generates metadata stating, "This content is related to the trademark of an existing communication service." This addresses user concerns by providing more detailed information.

[0323] 7. The device displays a message in the user interface at an appropriate time stating, "This idea is related to a specific trademark."

[0324] This system allows users to quickly check whether their ideas or content are related to existing patents or trademarks. Furthermore, it provides appropriate information tailored to the user's emotions, enabling optimal decision-making and responses.

[0325] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0326] Step 1: Obtain and submit user input

[0327] The user launches a dedicated application and enters information into a text input field. For example, they might enter, "The idea for the new application is based on existing communication services."

[0328] The terminal receives this user input data and sends it to the server using a secure communication protocol (HTTPS).

[0329] Input: Text data entered by the user

[0330] Output: Text data sent to the server

[0331] Step 2: Text Analysis

[0332] The server analyzes the user input data it receives using a natural language processing library (e.g., spaCy). It breaks down the text into words and phrases, extracting important keywords and phrases. In this case, "communication service" is extracted as a keyword.

[0333] Input: Text data submitted in Step 1

[0334] Output: List of extracted keywords and phrases

[0335] Specific operation: The text is parsed on the server using spaCy's nlp function, and keywords are extracted using properties such as token.lemma_.

[0336] Step 3: User emotion recognition

[0337] The server uses an EmotionML-compatible emotion engine to analyze emotions from user input data. For example, it analyzes positive and negative expressions in text to extract emotions such as "excitement" or "anxiety."

[0338] Input: Text data submitted in Step 1

[0339] Output: Analyzed emotion data (e.g., "anxiety")

[0340] Specific operation: Analyze text via the emotion analysis API and extract the emotion status. Example: "API.emotion_analysis(text)".

[0341] Step 4: Database matching

[0342] The server sends queries to patent information databases (e.g., Google Patents) and trademark databases (e.g., Trademark Electronic Search System) using the extracted keywords and phrases. This verifies whether the entered data is related to existing patents or trademarks.

[0343] Input: Keywords and phrases extracted in Step 2

[0344] Output: List of relevant patent and trademark information

[0345] Specific operation: Execute a query using the database search API and retrieve the search results. Example: "db_search('communication service')".

[0346] Step 5: Generate metadata

[0347] The server receives the results of the database matching, and if a matching trademark or patent is found, it generates metadata containing that information. For example, information such as "This content is related to a trademark of a specific communication service" is generated.

[0348] Furthermore, the accuracy and detail of the metadata are adjusted based on the results of the emotion recognition system. If the user indicates "anxiety," more detailed information is added.

[0349] Input: Matching results from Step 4 and sentiment data from Step 3.

[0350] Output: Generated metadata

[0351] Specific operation: Use a metadata generation template to insert and output the necessary information. Example: "metadata_template.format(data)".

[0352] Step 6: Build and send the edited data

[0353] The server generates metadata and adds it to the original user input data to create edited data. For example, it might add a message such as, "This idea is related to a specific trademark."

[0354] Based on the analysis results of emotion recognition methods, the timing of presenting edited data is adjusted. If the user is "excited," it is displayed immediately; if the user is "anxious," it is displayed after the explanation is finished.

[0355] Input: User input data from Step 1, metadata from Step 5, and sentiment data from Step 3.

[0356] Output: Edited text data

[0357] Specific operation: Integrates edited text and metadata, combining them in the format, e.g., "edited_data = original_text + metadata".

[0358] Step 7: Displaying the results

[0359] The terminal displays the edited data received from the server in the user interface. The user can review this data and recognize which trademarks or patents the input data is related to.

[0360] Input: Edited text data generated in Step 6

[0361] Output: Text displayed in the user interface

[0362] Specific operation: Binds data to a UI component and displays it in the appropriate format. Example: "ui_display(edited_data)".

[0363] (Application Example 2)

[0364] Next, we will explain application example 2. In the following explanation, the data processing device 12 will be referred to as a "server" and the smart device 14 as a "terminal".

[0365] This invention aims to provide a system that analyzes user input data and provides relevant patent and trademark information, not merely providing relevant information, but providing optimal information in accordance with the user's emotions. Conventional systems often provide information without considering the user's emotions, resulting in a poor user experience. In particular, in the advertising field, the problem was that providing information that was not based on the user's emotions reduced the effectiveness of advertising.

[0366] In Application Example 2, the specific processing performed by the specific processing unit 290 of the data processing device 12 is realized by the following means. In this invention, the server includes means for receiving user input data from an information processing device, means for generating artificial intelligence that analyzes the user input data and identifies extracted keywords and phrases, means for comparing the keywords and phrases with a patent information database and a trademark database, means for generating metadata indicating the relevant sections if the comparison results include relevant patent and trademark information, means for an emotion engine that analyzes and recognizes the user's emotions, means for generating and transmitting data edited according to the user's emotions based on the metadata, and means for displaying the edited data on a user interface. This makes it possible to provide optimal information based on the user's emotions.

[0367] An "information processing device" refers to any computer system that receives input data from a user and transmits it to a server.

[0368] "Generative artificial intelligence means" refers to an algorithm or program that analyzes input data and extracts key keywords or phrases.

[0369] A "patent information database" refers to a database system that stores information about existing patents.

[0370] A "trademark database" refers to a database system that stores information about existing trademarks.

[0371] "Means for generating metadata" refers to an algorithm or program that generates metadata, including relevant patent and trademark information, based on the matching results.

[0372] "Emotional engine means" refers to an algorithm or program for analyzing and recognizing emotions from user input data.

[0373] "Means for transmitting edited data" refers to an algorithm or program for transmitting edited data to a user based on the generated metadata.

[0374] A "user interface" refers to the screens or applications that users use to input information or to view edited data.

[0375] "Data edited to reflect user emotions" refers to data that includes information and advertisements optimized to take user emotions into consideration.

[0376] The specific system for implementing this invention consists of several main components. This system aims to receive and analyze user input data, recognize the user's emotions, provide relevant patent and trademark information, and generate and present edited data based on the user's emotions at the optimal time.

[0377] Hardware and software to use

[0378] 1. Information processing device:

[0379] These are primarily devices used by users. For example, smartphones and personal computers fulfill this role. They receive user input data and send it to the server.

[0380] 2. Generative Artificial Intelligence:

[0381] Software for data analysis and keyword extraction. Specifically, it uses generative AI models such as GPT-4. This model analyzes the input text data and extracts key keywords and phrases.

[0382] 3. Database matching function:

[0383] Software for performing query searches against patent and trademark databases. This allows the system to verify whether entered keywords or phrases are related to existing patents or trademarks.

[0384] 4. Emotional Engine:

[0385] Software that analyzes and recognizes user emotions from user input data. Specifically, it uses tools such as the Google Cloud Natural Language API.

[0386] 5. Metadata generation function:

[0387] Software that generates metadata, including relevant patent and trademark information, based on matching results. It also adjusts the content and display timing of the metadata based on user sentiment.

[0388] 6. Function to send and display edited data:

[0389] Software that sends generated metadata to the user and displays it in the user interface (UI). This allows the user to see optimized information in real time.

[0390] Specific examples of the system

[0391] Let's say a user enters text into a smartphone application such as, "I'm interested in the latest smartphones. What features do they have?" The following is a specific example of how the system would function in that situation.

[0392] 1. Obtaining user input:

[0393] The user enters text and sends it to the server via an information processing device (smartphone).

[0394] 2. Text analysis:

[0395] Generative artificial intelligence (GPT-4) analyzes the input data and extracts key keywords such as "latest smartphones."

[0396] 3. Emotion recognition:

[0397] The emotion engine (Google Cloud Natural Language API) recognizes emotions such as "excitement."

[0398] 4. Database matching:

[0399] The database matching function sends queries to the patent information database and trademark database to retrieve relevant information.

[0400] 5. Metadata generation:

[0401] Metadata including relevant patent and trademark information is generated, and information corresponding to the user's "excitement" is added.

[0402] 6. Submitting and displaying edited data:

[0403] The edited data is sent to the user at the optimal time and displayed on their smartphone screen along with the message, "This idea is related to a specific trademark."

[0404] Example of a prompt

[0405] Input example:

[0406] "I'm interested in the latest smartphones. What features do they have?"

[0407] In this way, the system can provide relevant patent and trademark information through the analysis of user input data and emotion recognition, as well as provide optimal information tailored to the user's emotions. This improves advertising effectiveness and maximizes the user experience.

[0408] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0409] Step 1:

[0410] A user enters text into a smartphone application. For example, they might type, "I'm interested in the latest smartphones. What features do they have?" Once the user has finished typing, the device sends this user input data to a server using a secure communication protocol (such as HTTPS). The input data is in the format of regular text data.

[0411] Step 2:

[0412] The server analyzes the received text data. This analysis is performed using generative artificial intelligence (e.g., GPT-4). The input text data is first broken down, and key keywords and phrases are extracted. Specifically, keywords such as "latest smartphone" and "features" are extracted. The output of this step is a list of the extracted keywords and phrases.

[0413] Step 3:

[0414] The server uses an emotion engine (e.g., Google Cloud Natural Language API) to analyze the user's emotions from extracted keywords and phrases. Specifically, it analyzes the wording and syntax of the input text data to identify emotions such as "excitement" and "anxiety." The output of this step is a label indicating the user's emotion.

[0415] Step 4:

[0416] The server sends queries to patent and trademark databases based on the extracted keywords and phrases. This verifies whether the entered data is related to existing patents or trademarks. Specifically, the database engine (e.g., SQL Server) executes the queries and retrieves matching records. The output of this step is a list of relevant patent and trademark information.

[0417] Step 5:

[0418] The server generates metadata based on the matching results. Specifically, it generates metadata explaining the relevance of patent and trademark information. Furthermore, this metadata is adjusted based on the user's emotions. For example, if the user is "excited," concise and engaging information is provided; if they are "anxious," more detailed explanations are added. The output of this step is optimized metadata.

[0419] Step 6:

[0420] The server constructs edited data based on the generated metadata. Specifically, it adds information such as "This idea is related to a specific trademark" to the original input text. The timing of delivery is also adjusted according to the user's mood. For example, if the user is "excited," the information is provided immediately. The output of this step is the edited data.

[0421] Step 7:

[0422] The server sends the edited data back to the terminal, which then displays the data in its user interface. The user can then review the information displayed on their smartphone screen. For example, a message such as "This idea is related to a specific trademark" might be displayed in a timely manner. The output of this step is the final display data.

[0423] Through the steps outlined above, the system achieves everything from analyzing user input data and recognizing emotions to matching patent and trademark information, generating metadata, and presenting edited data. This system allows users to receive relevant information in real time, significantly improving the effectiveness of advertising.

[0424] The specific processing unit 290 transmits the result of the specific processing to the smart device 14. In the smart device 14, the control unit 46A causes the output device 40 to output the result of the specific processing. The microphone 38B acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0425] Data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of data generation model 58 is ChatGPT (registered trademark) (Internet search).<URL: https: / / openai.com / blog / chatgpt> ), Gemini (registered trademark) (Internet search) <url: https: gemini.google.com ?hl="ja">Examples of generative AI include the following. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and with inference data such as audio data representing speech, text data representing text, and image data representing images. The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference results in data formats such as audio data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[0426] In the above embodiment, an example was given in which specific processing is performed by the data processing device 12, but the technology of this disclosure is not limited thereto, and the specific processing may also be performed by the smart device 14.

[0427] [Second Embodiment]

[0428] Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment.

[0429] As shown in Figure 3, the data processing system 210 includes a data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0430] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 is an example of a "computer" related to the technology of this disclosure. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[0431] The smart glasses 214 include a computer 36, a microphone 238, a speaker 240, a camera 42, and a communication interface 44. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, and camera 42 are also connected to the bus 52.

[0432] The microphone 238 receives voice signals from the user 20 and receives instructions from the user 20. The microphone 238 captures the voice signals from the user 20, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0433] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the area around the user 20 (for example, an imaging range defined by a field of view equivalent to the width of a typical healthy person's field of vision).

[0434] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0435] Figure 4 shows an example of the main functions of the data processing device 12 and the smart glasses 214. As shown in Figure 4, the data processing device 12 performs specific processing using the processor 28. The storage 32 stores the specific processing program 56.

[0436] The specific processing program 56 is an example of a "program" relating to the technology of this disclosure. The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0437] The storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[0438] In the smart glasses 214, the processor 46 performs the reception output processing. The storage 50 stores the reception output program 60. 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 processing is realized by the processor 46 operating as a control unit 46A according to the reception output program 60 executed on the RAM 48.

[0439] Next, the identification processing performed by the identification processing unit 290 of the data processing device 12 will be described. 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".

[0440] This invention is a system for improving the proper management and transparency of trademarks and patents. It has the function of analyzing user input data and managing extracted keywords and phrases to add relevant trademark and patent information. The system of this invention includes an information processing device, a generative artificial intelligence system, a database matching function, a metadata generation function, and an output function for edited data.

[0441] Program overview and explanation in natural language

[0442] This system operates as follows:

[0443] 1. Obtaining and sending user input

[0444] The user launches the application and provides input through a text interface. It begins with the user entering ideas or content for a new application.

[0445] The terminal receives this input data and sends it to the server using a secure communication protocol.

[0446] 2. Text Analysis

[0447] The server utilizes generative artificial intelligence to analyze user input data. Specifically, it breaks down text and extracts key keywords and phrases.

[0448] The aforementioned analysis identifies terms related to "trademarks" and "patents."

[0449] 3. Database matching

[0450] The server compares the extracted keywords and phrases with patent information databases and trademark databases. This verifies whether the entered data is related to existing trademarks or patents.

[0451] 4. Metadata generation

[0452] The server generates metadata, including relevant trademark and patent information, based on the results of database matching. For example, it might generate information such as, "This content is related to a specific trademark."

[0453] 5. Compiling and sending the edited data

[0454] The server generates metadata which is then added to the user input data to construct the edited data. Finally, the edited data is sent back to the terminal.

[0455] 6. Displaying the results

[0456] The terminal displays the edited data received from the server in the user interface. At this stage, the user can verify which trademarks or patents the input data is related to.

[0457] Specific example

[0458] Input example

[0459] The user enters, "My idea for my new application is based on existing communication services."

[0460] System operation

[0461] 1. The user enters information.

[0462] 2. The terminal sends the input data to the server.

[0463] 3. The server analyzes the data and extracts the keyword "communication services".

[0464] 4. The server checks the patent information database and trademark database to confirm that the "communication service" is related to a specific trademark or patent.

[0465] 5. The server generates metadata stating "This content is related to an existing trademark" and adds it to the input data.

[0466] 6. The device receives the edited data and displays the message, "This idea is related to a specific trademark."

[0467] This system promotes the proper management of trademarks and patents and prevents legal problems by clarifying what user-created content is related to.

[0468] The following describes the processing flow.

[0469] Step 1:

[0470] The user launches the application and inputs ideas or content through a text interface. For example, the user might enter the text, "The idea for the new application is based on existing communication services."

[0471] Step 2:

[0472] The terminal receives user input data and sends it to the server using a secure communication protocol (e.g., HTTPS).

[0473] Step 3:

[0474] The server passes the user input data it receives to a generative artificial intelligence (AI), which then begins text analysis. Specifically, it breaks down the text and extracts key keywords and phrases. For example, it might identify the term "communication services."

[0475] Step 4:

[0476] The server sends queries to patent and trademark databases based on the extracted keywords and phrases. This verifies whether the entered data is related to existing patents or trademarks.

[0477] Step 5:

[0478] The server receives matching results from the database, and if a matching trademark or patent is found, it generates metadata containing that information. For example, information such as "This content is related to a trademark of a specific telecommunications service" might be generated.

[0479] Step 6:

[0480] The metadata generated by the server is added to the original user input data to construct the edited data. For example, the message "This idea is related to a specific trademark" is added to the original text.

[0481] Step 7:

[0482] The server returns the edited data to the terminal.

[0483] Step 8:

[0484] The terminal displays the edited data received from the server in the user interface. The user can review the edited data and recognize which trademarks or patents the input data is related to.

[0485] This process allows users to check whether their ideas or content infringe upon existing patents or trademarks, and to take appropriate action.

[0486] (Example 1)

[0487] Next, we will describe Example 1. 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."

[0488] To ensure proper management and transparency of trademarks and patents, a system is needed that allows users to quickly verify whether the data they enter is related to existing trademarks and patents. However, current systems require users to conduct their own research and investigations, which is time-consuming and laborious. Furthermore, if the information users refer to is incomplete, it becomes difficult to prevent legal risks. Against this backdrop, there is a need to develop a system that can significantly reduce the burden on users and provide accurate and timely information related to trademarks and patents.

[0489] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 1 is realized by the following means.

[0490] In this invention, the server includes means for receiving user input data from an information processing device; generative artificial intelligence means for analyzing the user input data and identifying extracted keywords and phrases; means for comparing the keywords and phrases with a patent information database and a trademark database; means for generating metadata including relevant patent and trademark information as a result of the comparison; means for adding the metadata to the user input data and returning the edited data; and means for displaying the received edited data on a user interface. This enables users to quickly and accurately obtain information related to trademarks and patents without having to conduct their own research.

[0491] An "information processing device" is a computer system that receives input data from a user and performs various data processing operations.

[0492] "User input data" refers to information that a user provides to the system through a text interface or other input means.

[0493] "Generative artificial intelligence" refers to algorithms that learn from large amounts of text data and perform text analysis and generation tailored to specific purposes.

[0494] "Keywords and phrases" are important words and phrases extracted from user input data that are highly likely to be related to trademarks or patents.

[0495] A "patent information database" is a database system that stores existing patent information and provides it in a searchable format.

[0496] A "trademark database" is a database system that stores existing trademark information and provides it in a searchable format.

[0497] "Matching" is the process of comparing extracted keywords or phrases with existing information in the database to confirm a match or relevance.

[0498] "Metadata" refers to data generated as additional information related to user input data, including details about patents and trademarks.

[0499] "Edited data" refers to a dataset in which metadata has been added to user-input data, thereby enhancing its information.

[0500] "Receiving" refers to the process by which data or information is transferred from another system or user.

[0501] "Display" refers to the process by which processed data or information is visually presented to the user.

[0502] This invention is a system for improving the proper management and transparency of trademarks and patents. It has the function of analyzing user input data and managing extracted keywords and phrases to add relevant trademark and patent information. Embodiments of this invention will be described in detail below.

[0503] This system includes an information processing device, a generative artificial intelligence system, a database matching function, a metadata generation function, and an output function for edited data.

[0504] First, the user launches the application and enters information through a text interface. At this stage, the user enters ideas or content for the new application. For example, they might enter, "My idea for my new application is based on existing communication services."

[0505] The terminal receives user input data and sends it to the server using a secure communication protocol (e.g., TLS / SSL).

[0506] Next, the server analyzes the received input data using a generative AI model (e.g., GPT-4). This AI model breaks down the text to identify key keywords and phrases. Specifically, it extracts keywords such as "application" and "communication service." Text analysis by the AI ​​model is used to understand the context of the user input data and identify relevant terms.

[0507] The extracted keywords and phrases are cross-referenced by the server against patent information databases (e.g., the Japan Patent Office database) and trademark databases. This cross-referencing process uses SQL queries and API requests. For example, a cross-referencing is performed to check if the keyword "communication services" is related to any existing patents or trademarks. This retrieves detailed information about the relevant patents and trademarks as a result of the cross-referencing.

[0508] Next, the server generates metadata based on the database matching results, including relevant trademark and patent information. For example, information such as "This content is related to a specific trademark" is generated. The metadata includes patent numbers, patent details, and trademark owners.

[0509] The generated metadata is appended to the user input data, and a new dataset is constructed by the server. The edited data is then retransmitted to the terminal and provided to the user. This transmission uses a secure communication protocol, similar to the reception process.

[0510] Finally, the terminal displays the edited data received from the server on the user interface. Specifically, it provides information on relevant patents and trademarks in an easy-to-understand format, along with the content entered by the user. For example, information such as "This idea is related to an existing patent" is displayed, allowing the user to quickly check the legal risks.

[0511] Specific example:

[0512] Example of a prompt:

[0513] "My idea for my new application is based on existing communication services."

[0514] This system allows users to quickly and accurately obtain information related to trademarks and patents without having to conduct their own research, thereby preventing legal risks.

[0515] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0516] Step 1:

[0517] Acquiring and sending user input

[0518] The user launches the application and enters information via a text interface. For example, the user might enter, "My idea for my new application is based on existing communication services."

[0519] The terminal receives this input data and sends it to the server using a secure communication protocol (e.g., TLS / SSL).

[0520] Input: Text data entered by the user.

[0521] Output: User input data securely sent to the server.

[0522] Step 2:

[0523] Text analysis

[0524] The server analyzes the received input data using a generation AI model (e.g., GPT-4).

[0525] The server breaks down the input data and extracts key keywords and phrases. For example, "application" and "communication service" might be extracted.

[0526] Input: User text data sent to the server.

[0527] Output: A list of analyzed keywords and phrases.

[0528] Step 3:

[0529] Database matching

[0530] The server compares the extracted keywords and phrases with patent information databases and trademark databases.

[0531] The server uses SQL queries and API requests to search for items in the database that match or are related to registered information. For example, it might identify existing patents or trademarks that match "communication services."

[0532] Input: Analyzed keywords or phrases.

[0533] Output: Relevant patent and trademark information as a result of the matching process.

[0534] Step 4:

[0535] Metadata generation

[0536] The server generates metadata, including relevant patent and trademark information, based on the database matching results.

[0537] For example, information such as "This content is related to a specific trademark" is generated.

[0538] Metadata includes information such as patent numbers, patent details, and trademark owners.

[0539] Input: Database matching result.

[0540] Output: Generated metadata.

[0541] Step 5:

[0542] Compiling and sending edited data

[0543] The server adds the generated metadata to the user input data to construct a new dataset.

[0544] The server securely sends this edited data to the terminal.

[0545] Input: User input data and metadata.

[0546] Output: Edited and securely transmitted dataset.

[0547] Step 6:

[0548] Displaying Results

[0549] The terminal displays the edited data received from the server in the user interface.

[0550] For example, a user might see a message stating, "This idea is related to an existing patent."

[0551] Input: Edited data received from the server.

[0552] Output: Relevant information displayed in the user interface.

[0553] (Application Example 1)

[0554] Next, we will explain Application Example 1. In the following explanation, the data processing device 12 will be referred to as the "server," and the smart glasses 214 will be referred to as the "terminal."

[0555] Conventional information processing systems have made it difficult to quickly and accurately assess the trademark and patent risks associated with new ideas and projects. As a result, companies and individuals were unable to prevent legal risks when developing new products, potentially leading to problems. In particular, there was a lack of means to obtain appropriate information when real-time evaluation was required.

[0556] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 1 is realized by the following means.

[0557] In this invention, the server includes means for receiving user input data from an information processing device; generative artificial intelligence means for analyzing the user input data and identifying extracted keywords and phrases; means for comparing the keywords and phrases with a patent information database and a trademark database; means for generating metadata indicating the relevant sections if the comparison results include relevant patent and trademark information; means for attaching the metadata to the user input data and returning the edited data; means for sending and receiving data using satellite communication; and means for evaluating trademark and patent-related risks in real time. This enables users to evaluate trademark and patent-related risks related to new ideas and projects in real time.

[0558] An "information processing device" is a terminal device that receives input data from a user and transmits it to a server.

[0559] "User input data" refers to information entered by users in text format, such as new ideas or project details.

[0560] "Generative artificial intelligence" refers to an artificial intelligence system that analyzes user input data and extracts key keywords and phrases.

[0561] A "patent information database" is a database that stores existing patent information and uses it for comparison.

[0562] A "trademark database" is a database that stores existing trademark information and uses it for matching.

[0563] "Metadata" refers to data related to patents and trademarks that is added to user input data.

[0564] "Satellite communication" is a communication method that transmits and receives data via artificial satellites.

[0565] "Real-time evaluation" refers to the immediate assessment of trademark and patent-related risks associated with user input data.

[0566] "Edited data" refers to user-input data to which metadata has been added, and then returned from the server.

[0567] This invention is a system for improving the proper management and transparency of trademarks and patents. The system aims to enable companies and individuals to evaluate new ideas and projects in real time and to anticipate trademark and patent-related risks.

[0568] The system mainly includes the following components:

[0569] 1. Information processing device: A device such as a smartphone that has the function of receiving user input data.

[0570] 2. Generative Artificial Intelligence: Generative AI models such as GPT-4 are used to analyze user input data and extract key keywords and phrases.

[0571] 3. Database matching function: The extracted keywords and phrases are compared with the patent information database and the trademark database.

[0572] 4. Metadata generation function: Generates metadata including relevant patent and trademark information.

[0573] 5. Satellite communications: A means of communication for sending and receiving data.

[0574] 6. Real-time evaluation function: Evaluates trademark and patent-related risks in real time.

[0575] Specific examples of how the system works

[0576] 1. Obtaining user input

[0577] The user launches the application on their smartphone and enters details about a new idea or project. For example, they might enter, "We are developing a next-generation security camera system."

[0578] User input data is encrypted and sent to the server via a secure communication protocol (e.g., HTTPS).

[0579] 2. Data Analysis and Reconciliation

[0580] The server uses generative artificial intelligence (e.g., GPT-4) to analyze user input data and extract key keywords and phrases. For example, "security camera system" might be extracted.

[0581] The extracted keywords are compared with patent information databases and trademark databases (e.g., J-PlatPat) to obtain relevant information.

[0582] 3. Metadata generation and return

[0583] Based on the matching results, the server generates metadata that includes relevant patent and trademark information. For example, it might generate information such as, "This idea is related to an existing trademark."

[0584] The generated metadata is added to the user input data, and the edited data is sent back to the user's smartphone.

[0585] 4. Displaying the results

[0586] The terminal displays edited data received from the server in the user interface. Users can see in real time which trademarks and patents their entered ideas or projects are associated with.

[0587] Example of a prompt

[0588] Examples of prompt statements for a generative AI model are as follows:

[0589] Please identify any relevant trademarks or patents for the following idea: Idea: Developing a next-generation security camera system.

[0590] This system allows users to assess trademark and patent-related risks in real time and prevent legal troubles before they occur. Specific hardware used includes smartphones, and software includes generative AI (e.g., GPT-4), secure communication protocols (e.g., HTTPS), and patent and trademark databases (e.g., J-PlatPat).

[0591] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0592] Step 1:

[0593] Retrieving user input

[0594] The user launches the application on their smartphone and enters details of a new idea or project.

[0595] The terminal receives the input data and encrypts it.

[0596] The device sends encrypted data to the server using a secure communication protocol (e.g., HTTPS).

[0597] Input: Text data entered by the user.

[0598] Output: Encrypted data is sent to the server.

[0599] Step 2:

[0600] Data Analysis

[0601] The server analyzes the received user input data using generative artificial intelligence (e.g., GPT-4).

[0602] The server extracts key keywords and phrases from the input data.

[0603] Specific operation: Input a prompt sentence into the generative AI model and have it extract highly relevant keywords.

[0604] Input: Encrypted user input data.

[0605] Output: Extracted keywords and phrases.

[0606] Step 3:

[0607] Database matching

[0608] The server compares the extracted keywords and phrases with patent information databases and trademark databases (e.g., J-PlatPat).

[0609] The server retrieves relevant patent and trademark information as a result of the matching process.

[0610] Specific operation: Issue search queries to patent and trademark databases and retrieve results.

[0611] Input: Extracted keywords or phrases.

[0612] Output: Relevant patent and trademark information.

[0613] Step 4:

[0614] Metadata generation

[0615] Based on the matching results, the server generates metadata that includes relevant patent and trademark information.

[0616] Specific operation: The server constructs metadata based on the information it has acquired.

[0617] Input: Relevant patent and trademark information.

[0618] Output: Generated metadata.

[0619] Step 5:

[0620] Return of edited data

[0621] The server adds the generated metadata to the user input data and sends the edited data back to the user's smartphone.

[0622] Specific operation: Metadata is added after existing user input data and transferred to the user's terminal in an edited form.

[0623] Input: User input data and metadata.

[0624] Output: Edited data.

[0625] Step 6:

[0626] Displaying Results

[0627] The terminal displays the edited data received from the server in the user interface.

[0628] Specific operation: Reads received data and displays it in a format that is easy for the user to understand.

[0629] Input: Edited data.

[0630] Output: User interface displaying data.

[0631] Furthermore, an emotion engine that estimates the user's emotions may be incorporated. That is, the identification processing unit 290 may use the emotion identification model 59 to estimate the user's emotions and perform identification processing using the user's emotions.

[0632] This invention combines a system that analyzes user input data, compares extracted keywords and phrases with a patent information database and a trademark database, and adds relevant patent and trademark information, with an emotion engine that recognizes the user's emotions. This system includes an information processing device, a generative artificial intelligence system, a database matching function, a metadata generation function, an edited data output function, and an emotion engine.

[0633] Program overview and explanation in natural language

[0634] This system operates as follows:

[0635] 1. Obtaining and sending user input

[0636] The user launches the application and enters information through a text interface. For example, the user might enter the text, "The idea for the new application is based on existing communication services."

[0637] The terminal receives this input data and sends it to the server using a secure communication protocol.

[0638] 2. Text Analysis

[0639] The server receives input data and passes it to a generative artificial intelligence system to begin text analysis. Specifically, it breaks down the text and extracts key keywords and phrases. For example, it identifies the term "communication services."

[0640] 3. User emotion recognition

[0641] The server uses an emotion engine to analyze and recognize the user's emotions from user input data. For example, it extracts emotions such as "excitement" or "anxiety" from the wording and syntax within the text.

[0642] 4. Database matching

[0643] The server sends queries to patent and trademark databases based on the extracted keywords and phrases. This verifies whether the entered data is related to existing patents or trademarks.

[0644] 5. Metadata generation

[0645] The server receives the results of the database matching, and if a matching trademark or patent is found, it generates metadata containing that information. For example, information such as "This content is related to a trademark of a specific communication service" is generated.

[0646] Furthermore, metadata generation is adjusted based on the results of the emotion engine. For example, if a user indicates an "anxious" emotion, the information is adjusted to provide more detail.

[0647] 6. Compiling and sending the edited data

[0648] The metadata generated by the server is added to the original user input data to construct the edited data. For example, the message "This idea is related to a specific trademark" is added to the original text.

[0649] Based on the results from the emotion engine, the timing of presenting edited data is adjusted. For example, if the user is "excited," information is presented immediately; if they are "anxious," it is presented at an appropriate time.

[0650] 7. Displaying the results

[0651] The terminal displays the edited data received from the server in the user interface. The user can review the edited data and recognize which trademarks or patents the input data is related to.

[0652] Specific example

[0653] Input example

[0654] The user enters, "My idea for my new application is based on existing communication services."

[0655] System operation

[0656] 1. The user enters information.

[0657] 2. The terminal sends the input data to the server.

[0658] 3. The server analyzes the data and extracts the keyword "communication services".

[0659] 4. The server uses an emotion engine to detect emotions such as "anxiety" from the text.

[0660] 5. The server checks the patent information database and trademark database to confirm that the "communication service" is associated with a specific trademark or patent.

[0661] 6. The server generates metadata stating, "This content is related to the trademark of an existing communication service." This addresses the user's feeling of "anxiety" by providing more detailed information.

[0662] 7. The device receives the edited data and displays "This idea is related to a specific trademark" at the appropriate time.

[0663] This system allows users to not only check whether their ideas or content infringe on existing patents or trademarks, but also to receive appropriate information tailored to their emotional needs. This facilitates sound judgment and appropriate responses.

[0664] The following describes the processing flow.

[0665] Step 1:

[0666] The user launches the application and inputs ideas or content through a text interface. For example, the user might enter the text, "The idea for the new application is based on existing communication services."

[0667] Step 2:

[0668] The terminal receives user input data and sends it to the server using a secure communication protocol (e.g., HTTPS).

[0669] Step 3:

[0670] The server passes the user input data it receives to a generative artificial intelligence (AI), which then begins text analysis. Specifically, it breaks down the text and extracts key keywords and phrases. For example, it might identify the term "communication services."

[0671] Step 4:

[0672] The server uses an emotion engine to analyze and recognize the user's emotions from user input data. For example, it extracts emotions such as "excitement" or "anxiety" from the wording and syntax within the text.

[0673] Step 5:

[0674] The server sends queries to patent and trademark databases based on the extracted keywords and phrases. This verifies whether the entered data is related to existing patents or trademarks.

[0675] Step 6:

[0676] The server receives matching results from the database, and if a matching trademark or patent is found, it generates metadata containing that information. For example, information such as "This content is related to a trademark of a specific telecommunications service" might be generated.

[0677] Step 7:

[0678] The server adjusts metadata generation based on the results of the emotion engine. For example, if a user indicates an "anxious" emotion, the metadata is adjusted to provide more detailed information.

[0679] Step 8:

[0680] The metadata generated by the server is added to the original user input data to construct the edited data. For example, the message "This idea is related to a specific trademark" is added to the original text.

[0681] Step 9:

[0682] The server sends the edited data back to the terminal. Based on the results from the emotion engine, the timing of presentation is adjusted. For example, if the user is "excited," information is presented immediately; if they are "anxious," it is presented at an appropriate time.

[0683] Step 10:

[0684] The terminal displays the edited data received from the server in the user interface. The user can review the edited data and recognize which trademarks or patents the input data is related to, as well as additional information tailored to the user's emotions.

[0685] (Example 2)

[0686] Next, we will describe Example 2. 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".

[0687] Conventional information processing systems analyze user input data and cross-reference it with relevant patent and trademark information, but they cannot adjust the results based on the user's emotions. Therefore, users often fail to receive the information they need at the appropriate time, and the display content cannot be optimized. Furthermore, the inability to provide appropriate information tailored to the user's psychological state makes it difficult to improve user satisfaction and facilitate efficient decision-making.

[0688] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 2 is realized by the following means.

[0689] In this invention, the server includes means for receiving user input data from a computer device; generative artificial intelligence means for analyzing the user input data and identifying extracted keywords and phrases; emotion recognition means for analyzing and recognizing the user's emotions from the user data; means for comparing the keywords and phrases with a patent information database and a trademark database; means for generating metadata indicating the relevant sections if the comparison results include relevant patent and trademark information; means for adjusting the generation and editing of metadata based on the results of the emotion recognition means; means for attaching the metadata to the user input data and returning the edited data; and means for adjusting the timing of presenting the edited data. This makes it possible to provide information at an appropriate timing and with appropriate content according to the user's emotional state.

[0690] A "computer device" is an electronic device used for inputting, outputting, storing, and processing data.

[0691] "User input data" refers to information such as text and audio that a user inputs into a computer device.

[0692] "Generative artificial intelligence means" refers to methods that use algorithms and programs to analyze user input data and extract important keywords and phrases.

[0693] "Emotion recognition means" refers to methods that use algorithms or programs to analyze and recognize a user's emotions from user input data.

[0694] A "patent information database" is a database where information related to patents is stored and can be searched and cross-referenced.

[0695] A "trademark database" is a database where information about trademarks is stored and can be searched and cross-referenced.

[0696] The "matching means" is a function for comparing and verifying extracted keywords and phrases with patent information databases and trademark databases.

[0697] A "metadata generation means" is a function that generates data to add relevant information based on the matching results of keywords and phrases.

[0698] "Editing means" refers to a function that adds generated metadata to user input data to create edited data.

[0699] A "timing adjustment mechanism" is a function that determines the optimal timing for presenting edited data, depending on the user's emotions and the system's status.

[0700] This invention combines a system that analyzes user input data, compares extracted keywords and phrases with a patent information database and a trademark database, and adds relevant patent and trademark information, with an emotion engine that recognizes the user's emotions. The embodiments are described in detail below.

[0701] The entire system consists of an information processing device (computer device), a generative artificial intelligence means, a database matching means, an emotion recognition means, a metadata generation means, an editing means, and a timing adjustment means.

[0702] 1. Obtaining and sending user input

[0703] The user uses a dedicated application to enter information into a text input field. For example, they might enter, "The idea for the new application is based on existing communication services."

[0704] The terminal receives this user input data and sends it to the server using a secure communication protocol (e.g., HTTPS).

[0705] 2. Text Analysis

[0706] The server analyzes the user input data it receives using a natural language processing library (e.g., spaCy). It breaks down the text into words and phrases, extracting important keywords and phrases. In this case, "communication service" is extracted as a keyword.

[0707] 3. User emotion recognition

[0708] The server uses an EmotionML-compatible emotion engine to analyze emotions from user input data. For example, it analyzes positive and negative expressions in text to extract emotions such as "excitement" or "anxiety."

[0709] 4. Database matching

[0710] The server sends queries to match the extracted keywords and phrases against patent information databases (e.g., Google Patents) and trademark databases (e.g., Trademark Electronic Search System). It verifies whether the input data is related to existing patents and trademarks.

[0711] 5. Metadata generation

[0712] The server receives the matching results, and if a matching trademark or patent is found, it generates metadata containing that information. For example, it might generate metadata stating, "This content is related to a trademark of a specific communication service." Furthermore, it adjusts the accuracy and detail of the metadata based on the results of sentiment recognition. If the user indicates "anxiety," more detailed information is added.

[0713] 6. Compiling and sending the edited data

[0714] The system adds metadata generated by the server to the original user input data to create edited data. For example, it might add a message such as, "This idea is related to a specific trademark." Based on the analysis results of emotion recognition, the timing of the presentation of the edited data is adjusted. If the user is "excited," it is displayed immediately; if the user is "anxious," it is displayed after the explanation is finished.

[0715] 7. Displaying the results

[0716] The terminal displays the edited data received from the server in the user interface. The user can review this data and recognize which trademarks or patents the input data is related to.

[0717] Specific example

[0718] Input example

[0719] The user enters, "My idea for my new application is based on existing communication services."

[0720] System operation

[0721] 1. The user launches the application and enters text.

[0722] 2. The terminal sends the input data to the server.

[0723] 3. The server analyzes the received data and extracts the keyword "communication service".

[0724] 4. The server uses an emotion engine to detect the emotion of "user anxiety."

[0725] 5. The server sends queries to the patent information database and trademark database to confirm that the “communication service” is related to a specific trademark or patent.

[0726] 6. The server generates metadata stating, "This content is related to the trademark of an existing communication service." This addresses user concerns by providing more detailed information.

[0727] 7. The device displays a message in the user interface at an appropriate time stating, "This idea is related to a specific trademark."

[0728] This system allows users to quickly check whether their ideas or content are related to existing patents or trademarks. Furthermore, it provides appropriate information tailored to the user's emotions, enabling optimal decision-making and responses.

[0729] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0730] Step 1: Obtain and submit user input

[0731] The user launches a dedicated application and enters information into a text input field. For example, they might enter, "The idea for the new application is based on existing communication services."

[0732] The terminal receives this user input data and sends it to the server using a secure communication protocol (HTTPS).

[0733] Input: Text data entered by the user

[0734] Output: Text data sent to the server

[0735] Step 2: Text Analysis

[0736] The server analyzes the user input data it receives using a natural language processing library (e.g., spaCy). It breaks down the text into words and phrases, extracting important keywords and phrases. In this case, "communication service" is extracted as a keyword.

[0737] Input: Text data submitted in Step 1

[0738] Output: List of extracted keywords and phrases

[0739] Specific operation: The text is parsed on the server using spaCy's nlp function, and keywords are extracted using properties such as token.lemma_.

[0740] Step 3: User emotion recognition

[0741] The server uses an EmotionML-compatible emotion engine to analyze emotions from user input data. For example, it analyzes positive and negative expressions in text to extract emotions such as "excitement" or "anxiety."

[0742] Input: Text data submitted in Step 1

[0743] Output: Analyzed emotion data (e.g., "anxiety")

[0744] Specific operation: Analyze text via the emotion analysis API and extract the emotion status. Example: "API.emotion_analysis(text)".

[0745] Step 4: Database matching

[0746] The server sends queries to patent information databases (e.g., Google Patents) and trademark databases (e.g., Trademark Electronic Search System) using the extracted keywords and phrases. This verifies whether the entered data is related to existing patents or trademarks.

[0747] Input: Keywords and phrases extracted in Step 2

[0748] Output: List of relevant patent and trademark information

[0749] Specific operation: Execute a query using the database search API and retrieve the search results. Example: "db_search('communication service')".

[0750] Step 5: Generate metadata

[0751] The server receives the results of the database matching, and if a matching trademark or patent is found, it generates metadata containing that information. For example, information such as "This content is related to a trademark of a specific communication service" is generated.

[0752] Furthermore, the accuracy and detail of the metadata are adjusted based on the results of the emotion recognition system. If the user indicates "anxiety," more detailed information is added.

[0753] Input: Matching results from Step 4 and sentiment data from Step 3.

[0754] Output: Generated metadata

[0755] Specific operation: Use a metadata generation template to insert and output the necessary information. Example: "metadata_template.format(data)".

[0756] Step 6: Build and send the edited data

[0757] The server generates metadata and adds it to the original user input data to create edited data. For example, it might add a message such as, "This idea is related to a specific trademark."

[0758] Based on the analysis results of emotion recognition methods, the timing of presenting edited data is adjusted. If the user is "excited," it is displayed immediately; if the user is "anxious," it is displayed after the explanation is finished.

[0759] Input: User input data from Step 1, metadata from Step 5, and sentiment data from Step 3.

[0760] Output: Edited text data

[0761] Specific operation: Integrates edited text and metadata, combining them in the format, e.g., "edited_data = original_text + metadata".

[0762] Step 7: Displaying the results

[0763] The terminal displays the edited data received from the server in the user interface. The user can review this data and recognize which trademarks or patents the input data is related to.

[0764] Input: Edited text data generated in Step 6

[0765] Output: Text displayed in the user interface

[0766] Specific operation: Binds data to a UI component and displays it in the appropriate format. Example: "ui_display(edited_data)".

[0767] (Application Example 2)

[0768] Next, we will explain application example 2. In the following explanation, the data processing device 12 will be referred to as the "server," and the smart glasses 214 will be referred to as the "terminal."

[0769] This invention aims to provide a system that analyzes user input data and provides relevant patent and trademark information, not merely providing relevant information, but providing optimal information in accordance with the user's emotions. Conventional systems often provide information without considering the user's emotions, resulting in a poor user experience. In particular, in the advertising field, the problem was that providing information that was not based on the user's emotions reduced the effectiveness of advertising.

[0770] In Application Example 2, the specific processing performed by the specific processing unit 290 of the data processing device 12 is realized by the following means. In this invention, the server includes means for receiving user input data from an information processing device, means for generating artificial intelligence that analyzes the user input data and identifies extracted keywords and phrases, means for comparing the keywords and phrases with a patent information database and a trademark database, means for generating metadata indicating the relevant sections if the comparison results include relevant patent and trademark information, means for an emotion engine that analyzes and recognizes the user's emotions, means for generating and transmitting data edited according to the user's emotions based on the metadata, and means for displaying the edited data on a user interface. This makes it possible to provide optimal information based on the user's emotions.

[0771] An "information processing device" refers to any computer system that receives input data from a user and transmits it to a server.

[0772] "Generative artificial intelligence means" refers to an algorithm or program that analyzes input data and extracts key keywords or phrases.

[0773] A "patent information database" refers to a database system that stores information about existing patents.

[0774] A "trademark database" refers to a database system that stores information about existing trademarks.

[0775] "Means for generating metadata" refers to an algorithm or program that generates metadata, including relevant patent and trademark information, based on the matching results.

[0776] "Emotional engine means" refers to an algorithm or program for analyzing and recognizing emotions from user input data.

[0777] "Means for transmitting edited data" refers to an algorithm or program for transmitting edited data to a user based on the generated metadata.

[0778] A "user interface" refers to the screens or applications that users use to input information or to view edited data.

[0779] "Data edited to reflect user emotions" refers to data that includes information and advertisements optimized to take user emotions into consideration.

[0780] The specific system for implementing this invention consists of several main components. This system aims to receive and analyze user input data, recognize the user's emotions, provide relevant patent and trademark information, and generate and present edited data based on the user's emotions at the optimal time.

[0781] Hardware and software to use

[0782] 1. Information processing device:

[0783] These are primarily devices used by users. For example, smartphones and personal computers fulfill this role. They receive user input data and send it to the server.

[0784] 2. Generative Artificial Intelligence:

[0785] Software for data analysis and keyword extraction. Specifically, it uses generative AI models such as GPT-4. This model analyzes the input text data and extracts key keywords and phrases.

[0786] 3. Database matching function:

[0787] Software for performing query searches against patent and trademark databases. This allows the system to verify whether entered keywords or phrases are related to existing patents or trademarks.

[0788] 4. Emotional Engine:

[0789] Software that analyzes and recognizes user emotions from user input data. Specifically, it uses tools such as the Google Cloud Natural Language API.

[0790] 5. Metadata generation function:

[0791] Software that generates metadata, including relevant patent and trademark information, based on matching results. It also adjusts the content and display timing of the metadata based on user sentiment.

[0792] 6. Function to send and display edited data:

[0793] Software that sends generated metadata to the user and displays it in the user interface (UI). This allows the user to see optimized information in real time.

[0794] Specific examples of the system

[0795] Let's say a user enters text into a smartphone application such as, "I'm interested in the latest smartphones. What features do they have?" The following is a specific example of how the system would function in that situation.

[0796] 1. Obtaining user input:

[0797] The user enters text and sends it to the server via an information processing device (smartphone).

[0798] 2. Text analysis:

[0799] Generative artificial intelligence (GPT-4) analyzes the input data and extracts key keywords such as "latest smartphones."

[0800] 3. Emotion recognition:

[0801] The emotion engine (Google Cloud Natural Language API) recognizes emotions such as "excitement."

[0802] 4. Database matching:

[0803] The database matching function sends queries to the patent information database and trademark database to retrieve relevant information.

[0804] 5. Metadata generation:

[0805] Metadata including relevant patent and trademark information is generated, and information corresponding to the user's "excitement" is added.

[0806] 6. Submitting and displaying edited data:

[0807] The edited data is sent to the user at the optimal time and displayed on their smartphone screen along with the message, "This idea is related to a specific trademark."

[0808] Example of a prompt

[0809] Input example:

[0810] "I'm interested in the latest smartphones. What features do they have?"

[0811] In this way, the system can provide relevant patent and trademark information through the analysis of user input data and emotion recognition, as well as provide optimal information tailored to the user's emotions. This improves advertising effectiveness and maximizes the user experience.

[0812] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0813] Step 1:

[0814] A user enters text into a smartphone application. For example, they might type, "I'm interested in the latest smartphones. What features do they have?" Once the user has finished typing, the device sends this user input data to a server using a secure communication protocol (such as HTTPS). The input data is in the format of regular text data.

[0815] Step 2:

[0816] The server analyzes the received text data. This analysis is performed using generative artificial intelligence (e.g., GPT-4). The input text data is first broken down, and key keywords and phrases are extracted. Specifically, keywords such as "latest smartphone" and "features" are extracted. The output of this step is a list of the extracted keywords and phrases.

[0817] Step 3:

[0818] The server uses an emotion engine (e.g., Google Cloud Natural Language API) to analyze the user's emotions from extracted keywords and phrases. Specifically, it analyzes the wording and syntax of the input text data to identify emotions such as "excitement" and "anxiety." The output of this step is a label indicating the user's emotion.

[0819] Step 4:

[0820] The server sends queries to patent and trademark databases based on the extracted keywords and phrases. This verifies whether the entered data is related to existing patents or trademarks. Specifically, the database engine (e.g., SQL Server) executes the queries and retrieves matching records. The output of this step is a list of relevant patent and trademark information.

[0821] Step 5:

[0822] The server generates metadata based on the matching results. Specifically, it generates metadata explaining the relevance of patent and trademark information. Furthermore, this metadata is adjusted based on the user's emotions. For example, if the user is "excited," concise and engaging information is provided; if they are "anxious," more detailed explanations are added. The output of this step is optimized metadata.

[0823] Step 6:

[0824] The server constructs edited data based on the generated metadata. Specifically, it adds information such as "This idea is related to a specific trademark" to the original input text. The timing of delivery is also adjusted according to the user's mood. For example, if the user is "excited," the information is provided immediately. The output of this step is the edited data.

[0825] Step 7:

[0826] The server sends the edited data back to the terminal, which then displays the data in its user interface. The user can then review the information displayed on their smartphone screen. For example, a message such as "This idea is related to a specific trademark" might be displayed in a timely manner. The output of this step is the final display data.

[0827] Through the steps outlined above, the system achieves everything from analyzing user input data and recognizing emotions to matching patent and trademark information, generating metadata, and presenting edited data. This system allows users to receive relevant information in real time, significantly improving the effectiveness of advertising.

[0828] The specific processing unit 290 transmits the result of the specific processing to the smart glasses 214. In the smart glasses 214, the control unit 46A causes the speaker 240 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0829] Data generation model 58 is a type of so-called generative AI (Artificial Intelligence). One example of data generation model 58 is ChatGPT (Internet search<URL: https: / / openai.com / blog / chatgpt> ), Gemini (Internet search) <url: https: gemini.google.com ?hl="ja">Examples of generative AI include the following. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and with inference data such as audio data representing speech, text data representing text, and image data representing images. The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference results in data formats such as audio data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[0830] In the above embodiment, an example was given in which specific processing is performed by the data processing device 12, but the technology of this disclosure is not limited thereto, and the specific processing may also be performed by the smart glasses 214.

[0831] [Third Embodiment]

[0832] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[0833] As shown in Figure 5, the data processing system 310 includes a data processing device 12 and a headset terminal 314. An example of the data processing device 12 is a server.

[0834] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 is an example of a "computer" related to the technology of this disclosure. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[0835] The headset terminal 314 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication interface 44, and a display 343. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, camera 42, and display 343 are also connected to the bus 52.

[0836] The microphone 238 receives voice signals from the user 20 and receives instructions from the user 20. The microphone 238 captures the voice signals from the user 20, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0837] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the area around the user 20 (for example, an imaging range defined by a field of view equivalent to the width of a typical healthy person's field of vision).

[0838] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0839] Figure 6 shows an example of the main functions of the data processing device 12 and the headset terminal 314. As shown in Figure 6, the data processing device 12 performs specific processing using the processor 28. The storage 32 stores the specific processing program 56.

[0840] The specific processing program 56 is an example of a "program" relating to the technology of this disclosure. The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0841] The storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[0842] In the headset terminal 314, the processor 46 performs the reception output processing. The storage 50 stores the reception output program 60. 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 processing is realized by the processor 46 operating as a control unit 46A according to the reception output program 60 executed on the RAM 48.

[0843] Next, the specific processing performed by the specific processing unit 290 of the data processing device 12 will be described. In the following description, the data processing device 12 will be referred to as the "server" and the headset terminal 314 will be referred to as the "terminal".

[0844] This invention is a system for improving the proper management and transparency of trademarks and patents. It has the function of analyzing user input data and managing extracted keywords and phrases to add relevant trademark and patent information. The system of this invention includes an information processing device, a generative artificial intelligence system, a database matching function, a metadata generation function, and an output function for edited data.

[0845] Program overview and explanation in natural language

[0846] This system operates as follows:

[0847] 1. Obtaining and sending user input

[0848] The user launches the application and provides input through a text interface. It begins with the user entering ideas or content for a new application.

[0849] The terminal receives this input data and sends it to the server using a secure communication protocol.

[0850] 2. Text Analysis

[0851] The server utilizes generative artificial intelligence to analyze user input data. Specifically, it breaks down text and extracts key keywords and phrases.

[0852] The aforementioned analysis identifies terms related to "trademarks" and "patents."

[0853] 3. Database matching

[0854] The server compares the extracted keywords and phrases with patent information databases and trademark databases. This verifies whether the entered data is related to existing trademarks or patents.

[0855] 4. Metadata generation

[0856] The server generates metadata, including relevant trademark and patent information, based on the results of database matching. For example, it might generate information such as, "This content is related to a specific trademark."

[0857] 5. Compiling and sending the edited data

[0858] The server generates metadata which is then added to the user input data to construct the edited data. Finally, the edited data is sent back to the terminal.

[0859] 6. Displaying the results

[0860] The terminal displays the edited data received from the server in the user interface. At this stage, the user can verify which trademarks or patents the input data is related to.

[0861] Specific example

[0862] Input example

[0863] The user enters, "My idea for my new application is based on existing communication services."

[0864] System operation

[0865] 1. The user enters information.

[0866] 2. The terminal sends the input data to the server.

[0867] 3. The server analyzes the data and extracts the keyword "communication services".

[0868] 4. The server checks the patent information database and trademark database to confirm that the "communication service" is related to a specific trademark or patent.

[0869] 5. The server generates metadata stating "This content is related to an existing trademark" and adds it to the input data.

[0870] 6. The device receives the edited data and displays the message, "This idea is related to a specific trademark."

[0871] This system promotes the proper management of trademarks and patents and prevents legal problems by clarifying what user-created content is related to.

[0872] The following describes the processing flow.

[0873] Step 1:

[0874] The user launches the application and inputs ideas or content through a text interface. For example, the user might enter the text, "The idea for the new application is based on existing communication services."

[0875] Step 2:

[0876] The terminal receives user input data and sends it to the server using a secure communication protocol (e.g., HTTPS).

[0877] Step 3:

[0878] The server passes the user input data it receives to a generative artificial intelligence (AI), which then begins text analysis. Specifically, it breaks down the text and extracts key keywords and phrases. For example, it might identify the term "communication services."

[0879] Step 4:

[0880] The server sends queries to patent and trademark databases based on the extracted keywords and phrases. This verifies whether the entered data is related to existing patents or trademarks.

[0881] Step 5:

[0882] The server receives matching results from the database, and if a matching trademark or patent is found, it generates metadata containing that information. For example, information such as "This content is related to a trademark of a specific telecommunications service" might be generated.

[0883] Step 6:

[0884] The metadata generated by the server is added to the original user input data to construct the edited data. For example, the message "This idea is related to a specific trademark" is added to the original text.

[0885] Step 7:

[0886] The server returns the edited data to the terminal.

[0887] Step 8:

[0888] The terminal displays the edited data received from the server in the user interface. The user can review the edited data and recognize which trademarks or patents the input data is related to.

[0889] This process allows users to check whether their ideas or content infringe upon existing patents or trademarks, and to take appropriate action.

[0890] (Example 1)

[0891] Next, we will describe Example 1. 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."

[0892] To ensure proper management and transparency of trademarks and patents, a system is needed that allows users to quickly verify whether the data they enter is related to existing trademarks and patents. However, current systems require users to conduct their own research and investigations, which is time-consuming and laborious. Furthermore, if the information users refer to is incomplete, it becomes difficult to prevent legal risks. Against this backdrop, there is a need to develop a system that can significantly reduce the burden on users and provide accurate and timely information related to trademarks and patents.

[0893] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 1 is realized by the following means.

[0894] In this invention, the server includes means for receiving user input data from an information processing device; generative artificial intelligence means for analyzing the user input data and identifying extracted keywords and phrases; means for comparing the keywords and phrases with a patent information database and a trademark database; means for generating metadata including relevant patent and trademark information as a result of the comparison; means for adding the metadata to the user input data and returning the edited data; and means for displaying the received edited data on a user interface. This enables users to quickly and accurately obtain information related to trademarks and patents without having to conduct their own research.

[0895] An "information processing device" is a computer system that receives input data from a user and performs various data processing operations.

[0896] "User input data" refers to information that a user provides to the system through a text interface or other input means.

[0897] "Generative artificial intelligence" refers to algorithms that learn from large amounts of text data and perform text analysis and generation tailored to specific purposes.

[0898] "Keywords and phrases" are important words and phrases extracted from user input data that are highly likely to be related to trademarks or patents.

[0899] A "patent information database" is a database system that stores existing patent information and provides it in a searchable format.

[0900] A "trademark database" is a database system that stores existing trademark information and provides it in a searchable format.

[0901] "Matching" is the process of comparing extracted keywords or phrases with existing information in the database to confirm a match or relevance.

[0902] "Metadata" refers to data generated as additional information related to user input data, including details about patents and trademarks.

[0903] "Edited data" refers to a dataset in which metadata has been added to user-input data, thereby enhancing its information.

[0904] "Receiving" refers to the process by which data or information is transferred from another system or user.

[0905] "Display" refers to the process by which processed data or information is visually presented to the user.

[0906] This invention is a system for improving the proper management and transparency of trademarks and patents. It has the function of analyzing user input data and managing extracted keywords and phrases to add relevant trademark and patent information. Embodiments of this invention will be described in detail below.

[0907] This system includes an information processing device, a generative artificial intelligence system, a database matching function, a metadata generation function, and an output function for edited data.

[0908] First, the user launches the application and enters information through a text interface. At this stage, the user enters ideas or content for the new application. For example, they might enter, "My idea for my new application is based on existing communication services."

[0909] The terminal receives user input data and sends it to the server using a secure communication protocol (e.g., TLS / SSL).

[0910] Next, the server analyzes the received input data using a generative AI model (e.g., GPT-4). This AI model breaks down the text to identify key keywords and phrases. Specifically, it extracts keywords such as "application" and "communication service." Text analysis by the AI ​​model is used to understand the context of the user input data and identify relevant terms.

[0911] The extracted keywords and phrases are cross-referenced by the server against patent information databases (e.g., the Japan Patent Office database) and trademark databases. This cross-referencing process uses SQL queries and API requests. For example, a cross-referencing is performed to check if the keyword "communication services" is related to any existing patents or trademarks. This retrieves detailed information about the relevant patents and trademarks as a result of the cross-referencing.

[0912] Next, the server generates metadata based on the database matching results, including relevant trademark and patent information. For example, information such as "This content is related to a specific trademark" is generated. The metadata includes patent numbers, patent details, and trademark owners.

[0913] The generated metadata is appended to the user input data, and a new dataset is constructed by the server. The edited data is then retransmitted to the terminal and provided to the user. This transmission uses a secure communication protocol, similar to the reception process.

[0914] Finally, the terminal displays the edited data received from the server on the user interface. Specifically, it provides information on relevant patents and trademarks in an easy-to-understand format, along with the content entered by the user. For example, information such as "This idea is related to an existing patent" is displayed, allowing the user to quickly check the legal risks.

[0915] Specific example:

[0916] Example of a prompt:

[0917] "My idea for my new application is based on existing communication services."

[0918] This system allows users to quickly and accurately obtain information related to trademarks and patents without having to conduct their own research, thereby preventing legal risks.

[0919] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0920] Step 1:

[0921] Acquiring and sending user input

[0922] The user launches the application and enters information via a text interface. For example, the user might enter, "My idea for my new application is based on existing communication services."

[0923] The terminal receives this input data and sends it to the server using a secure communication protocol (e.g., TLS / SSL).

[0924] Input: Text data entered by the user.

[0925] Output: User input data securely sent to the server.

[0926] Step 2:

[0927] Text analysis

[0928] The server analyzes the received input data using a generation AI model (e.g., GPT-4).

[0929] The server breaks down the input data and extracts key keywords and phrases. For example, "application" and "communication service" might be extracted.

[0930] Input: User text data sent to the server.

[0931] Output: A list of analyzed keywords and phrases.

[0932] Step 3:

[0933] Database matching

[0934] The server compares the extracted keywords and phrases with patent information databases and trademark databases.

[0935] The server uses SQL queries and API requests to search for items in the database that match or are related to registered information. For example, it might identify existing patents or trademarks that match "communication services."

[0936] Input: Analyzed keywords or phrases.

[0937] Output: Relevant patent and trademark information as a result of the matching process.

[0938] Step 4:

[0939] Metadata generation

[0940] The server generates metadata, including relevant patent and trademark information, based on the database matching results.

[0941] For example, information such as "This content is related to a specific trademark" is generated.

[0942] Metadata includes information such as patent numbers, patent details, and trademark owners.

[0943] Input: Database matching result.

[0944] Output: Generated metadata.

[0945] Step 5:

[0946] Compiling and sending edited data

[0947] The server adds the generated metadata to the user input data to construct a new dataset.

[0948] The server securely sends this edited data to the terminal.

[0949] Input: User input data and metadata.

[0950] Output: Edited and securely transmitted dataset.

[0951] Step 6:

[0952] Displaying Results

[0953] The terminal displays the edited data received from the server in the user interface.

[0954] For example, a user might see a message stating, "This idea is related to an existing patent."

[0955] Input: Edited data received from the server.

[0956] Output: Relevant information displayed in the user interface.

[0957] (Application Example 1)

[0958] Next, we will explain Application Example 1. In the following explanation, 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."

[0959] Conventional information processing systems have made it difficult to quickly and accurately assess the trademark and patent risks associated with new ideas and projects. As a result, companies and individuals were unable to prevent legal risks when developing new products, potentially leading to problems. In particular, there was a lack of means to obtain appropriate information when real-time evaluation was required.

[0960] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 1 is realized by the following means.

[0961] In this invention, the server includes means for receiving user input data from an information processing device; generative artificial intelligence means for analyzing the user input data and identifying extracted keywords and phrases; means for comparing the keywords and phrases with a patent information database and a trademark database; means for generating metadata indicating the relevant sections if the comparison results include relevant patent and trademark information; means for attaching the metadata to the user input data and returning the edited data; means for sending and receiving data using satellite communication; and means for evaluating trademark and patent-related risks in real time. This enables users to evaluate trademark and patent-related risks related to new ideas and projects in real time.

[0962] An "information processing device" is a terminal device that receives input data from a user and transmits it to a server.

[0963] "User input data" refers to information entered by users in text format, such as new ideas or project details.

[0964] "Generative artificial intelligence" refers to an artificial intelligence system that analyzes user input data and extracts key keywords and phrases.

[0965] A "patent information database" is a database that stores existing patent information and uses it for comparison.

[0966] A "trademark database" is a database that stores existing trademark information and uses it for matching.

[0967] "Metadata" refers to data related to patents and trademarks that is added to user input data.

[0968] "Satellite communication" is a communication method that transmits and receives data via artificial satellites.

[0969] "Real-time evaluation" refers to the immediate assessment of trademark and patent-related risks associated with user input data.

[0970] "Edited data" refers to user-input data to which metadata has been added, and then returned from the server.

[0971] This invention is a system for improving the proper management and transparency of trademarks and patents. The system aims to enable companies and individuals to evaluate new ideas and projects in real time and to anticipate trademark and patent-related risks.

[0972] The system mainly includes the following components:

[0973] 1. Information processing device: A device such as a smartphone that has the function of receiving user input data.

[0974] 2. Generative Artificial Intelligence: Generative AI models such as GPT-4 are used to analyze user input data and extract key keywords and phrases.

[0975] 3. Database matching function: The extracted keywords and phrases are compared with the patent information database and the trademark database.

[0976] 4. Metadata generation function: Generates metadata including relevant patent and trademark information.

[0977] 5. Satellite communications: A means of communication for sending and receiving data.

[0978] 6. Real-time evaluation function: Evaluates trademark and patent-related risks in real time.

[0979] Specific examples of how the system works

[0980] 1. Obtaining user input

[0981] The user launches the application on their smartphone and enters details about a new idea or project. For example, they might enter, "We are developing a next-generation security camera system."

[0982] User input data is encrypted and sent to the server via a secure communication protocol (e.g., HTTPS).

[0983] 2. Data Analysis and Reconciliation

[0984] The server uses generative artificial intelligence (e.g., GPT-4) to analyze user input data and extract key keywords and phrases. For example, "security camera system" might be extracted.

[0985] The extracted keywords are compared with patent information databases and trademark databases (e.g., J-PlatPat) to obtain relevant information.

[0986] 3. Metadata generation and return

[0987] Based on the matching results, the server generates metadata that includes relevant patent and trademark information. For example, it might generate information such as, "This idea is related to an existing trademark."

[0988] The generated metadata is added to the user input data, and the edited data is sent back to the user's smartphone.

[0989] 4. Displaying the results

[0990] The terminal displays edited data received from the server in the user interface. Users can see in real time which trademarks and patents their entered ideas or projects are associated with.

[0991] Example of a prompt

[0992] Examples of prompt statements for a generative AI model are as follows:

[0993] Please identify any relevant trademarks or patents for the following idea: Idea: Developing a next-generation security camera system.

[0994] This system allows users to assess trademark and patent-related risks in real time and prevent legal troubles before they occur. Specific hardware used includes smartphones, and software includes generative AI (e.g., GPT-4), secure communication protocols (e.g., HTTPS), and patent and trademark databases (e.g., J-PlatPat).

[0995] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0996] Step 1:

[0997] Retrieving user input

[0998] The user launches the application on their smartphone and enters details of a new idea or project.

[0999] The terminal receives the input data and encrypts it.

[1000] The device sends encrypted data to the server using a secure communication protocol (e.g., HTTPS).

[1001] Input: Text data entered by the user.

[1002] Output: Encrypted data is sent to the server.

[1003] Step 2:

[1004] Data Analysis

[1005] The server analyzes the received user input data using generative artificial intelligence (e.g., GPT-4).

[1006] The server extracts key keywords and phrases from the input data.

[1007] Specific operation: Input a prompt sentence into the generative AI model and have it extract highly relevant keywords.

[1008] Input: Encrypted user input data.

[1009] Output: Extracted keywords and phrases.

[1010] Step 3:

[1011] Database matching

[1012] The server compares the extracted keywords and phrases with patent information databases and trademark databases (e.g., J-PlatPat).

[1013] The server retrieves relevant patent and trademark information as a result of the matching process.

[1014] Specific operation: Issue search queries to patent and trademark databases and retrieve results.

[1015] Input: Extracted keywords or phrases.

[1016] Output: Relevant patent and trademark information.

[1017] Step 4:

[1018] Metadata generation

[1019] Based on the matching results, the server generates metadata that includes relevant patent and trademark information.

[1020] Specific operation: The server constructs metadata based on the information it has acquired.

[1021] Input: Relevant patent and trademark information.

[1022] Output: Generated metadata.

[1023] Step 5:

[1024] Return of edited data

[1025] The server adds the generated metadata to the user input data and sends the edited data back to the user's smartphone.

[1026] Specific operation: Metadata is added after existing user input data and transferred to the user's terminal in an edited form.

[1027] Input: User input data and metadata.

[1028] Output: Edited data.

[1029] Step 6:

[1030] Displaying Results

[1031] The terminal displays the edited data received from the server in the user interface.

[1032] Specific operation: Reads received data and displays it in a format that is easy for the user to understand.

[1033] Input: Edited data.

[1034] Output: User interface displaying data.

[1035] Furthermore, an emotion engine that estimates the user's emotions may be incorporated. That is, the identification processing unit 290 may use the emotion identification model 59 to estimate the user's emotions and perform identification processing using the user's emotions.

[1036] This invention combines a system that analyzes user input data, compares extracted keywords and phrases with a patent information database and a trademark database, and adds relevant patent and trademark information, with an emotion engine that recognizes the user's emotions. This system includes an information processing device, a generative artificial intelligence system, a database matching function, a metadata generation function, an edited data output function, and an emotion engine.

[1037] Program overview and explanation in natural language

[1038] This system operates as follows:

[1039] 1. Obtaining and sending user input

[1040] The user launches the application and enters information through a text interface. For example, the user might enter the text, "The idea for the new application is based on existing communication services."

[1041] The terminal receives this input data and sends it to the server using a secure communication protocol.

[1042] 2. Text Analysis

[1043] The server receives input data and passes it to a generative artificial intelligence system to begin text analysis. Specifically, it breaks down the text and extracts key keywords and phrases. For example, it identifies the term "communication services."

[1044] 3. User emotion recognition

[1045] The server uses an emotion engine to analyze and recognize the user's emotions from user input data. For example, it extracts emotions such as "excitement" or "anxiety" from the wording and syntax within the text.

[1046] 4. Database matching

[1047] The server sends queries to patent and trademark databases based on the extracted keywords and phrases. This verifies whether the entered data is related to existing patents or trademarks.

[1048] 5. Metadata generation

[1049] The server receives the results of the database matching, and if a matching trademark or patent is found, it generates metadata containing that information. For example, information such as "This content is related to a trademark of a specific communication service" is generated.

[1050] Furthermore, metadata generation is adjusted based on the results of the emotion engine. For example, if a user indicates an "anxious" emotion, the information is adjusted to provide more detail.

[1051] 6. Compiling and sending the edited data

[1052] The metadata generated by the server is added to the original user input data to construct the edited data. For example, the message "This idea is related to a specific trademark" is added to the original text.

[1053] Based on the results from the emotion engine, the timing of presenting edited data is adjusted. For example, if the user is "excited," information is presented immediately; if they are "anxious," it is presented at an appropriate time.

[1054] 7. Displaying the results

[1055] The terminal displays the edited data received from the server in the user interface. The user can review the edited data and recognize which trademarks or patents the input data is related to.

[1056] Specific example

[1057] Input example

[1058] The user enters, "My idea for my new application is based on existing communication services."

[1059] System operation

[1060] 1. The user enters information.

[1061] 2. The terminal sends the input data to the server.

[1062] 3. The server analyzes the data and extracts the keyword "communication services".

[1063] 4. The server uses an emotion engine to detect emotions such as "anxiety" from the text.

[1064] 5. The server checks the patent information database and trademark database to confirm that the "communication service" is associated with a specific trademark or patent.

[1065] 6. The server generates metadata stating, "This content is related to the trademark of an existing communication service." This addresses the user's feeling of "anxiety" by providing more detailed information.

[1066] 7. The device receives the edited data and displays "This idea is related to a specific trademark" at the appropriate time.

[1067] This system allows users to not only check whether their ideas or content infringe on existing patents or trademarks, but also to receive appropriate information tailored to their emotional needs. This facilitates sound judgment and appropriate responses.

[1068] The following describes the processing flow.

[1069] Step 1:

[1070] The user launches the application and inputs ideas or content through a text interface. For example, the user might enter the text, "The idea for the new application is based on existing communication services."

[1071] Step 2:

[1072] The terminal receives user input data and sends it to the server using a secure communication protocol (e.g., HTTPS).

[1073] Step 3:

[1074] The server passes the user input data it receives to a generative artificial intelligence (AI), which then begins text analysis. Specifically, it breaks down the text and extracts key keywords and phrases. For example, it might identify the term "communication services."

[1075] Step 4:

[1076] The server uses an emotion engine to analyze and recognize the user's emotions from user input data. For example, it extracts emotions such as "excitement" or "anxiety" from the wording and syntax within the text.

[1077] Step 5:

[1078] The server sends queries to patent and trademark databases based on the extracted keywords and phrases. This verifies whether the entered data is related to existing patents or trademarks.

[1079] Step 6:

[1080] The server receives matching results from the database, and if a matching trademark or patent is found, it generates metadata containing that information. For example, information such as "This content is related to a trademark of a specific telecommunications service" might be generated.

[1081] Step 7:

[1082] The server adjusts metadata generation based on the results of the emotion engine. For example, if a user indicates an "anxious" emotion, the metadata is adjusted to provide more detailed information.

[1083] Step 8:

[1084] The metadata generated by the server is added to the original user input data to construct the edited data. For example, the message "This idea is related to a specific trademark" is added to the original text.

[1085] Step 9:

[1086] The server sends the edited data back to the terminal. Based on the results from the emotion engine, the timing of presentation is adjusted. For example, if the user is "excited," information is presented immediately; if they are "anxious," it is presented at an appropriate time.

[1087] Step 10:

[1088] The terminal displays the edited data received from the server in the user interface. The user can review the edited data and recognize which trademarks or patents the input data is related to, as well as additional information tailored to the user's emotions.

[1089] (Example 2)

[1090] Next, we will describe Example 2. 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."

[1091] Conventional information processing systems analyze user input data and cross-reference it with relevant patent and trademark information, but they cannot adjust the results based on the user's emotions. Therefore, users often fail to receive the information they need at the appropriate time, and the display content cannot be optimized. Furthermore, the inability to provide appropriate information tailored to the user's psychological state makes it difficult to improve user satisfaction and facilitate efficient decision-making.

[1092] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 2 is realized by the following means.

[1093] In this invention, the server includes means for receiving user input data from a computer device; generative artificial intelligence means for analyzing the user input data and identifying extracted keywords and phrases; emotion recognition means for analyzing and recognizing the user's emotions from the user data; means for comparing the keywords and phrases with a patent information database and a trademark database; means for generating metadata indicating the relevant sections if the comparison results include relevant patent and trademark information; means for adjusting the generation and editing of metadata based on the results of the emotion recognition means; means for attaching the metadata to the user input data and returning the edited data; and means for adjusting the timing of presenting the edited data. This makes it possible to provide information at an appropriate timing and with appropriate content according to the user's emotional state.

[1094] A "computer device" is an electronic device used for inputting, outputting, storing, and processing data.

[1095] "User input data" refers to information such as text and audio that a user inputs into a computer device.

[1096] "Generative artificial intelligence means" refers to methods that use algorithms and programs to analyze user input data and extract important keywords and phrases.

[1097] "Emotion recognition means" refers to methods that use algorithms or programs to analyze and recognize a user's emotions from user input data.

[1098] A "patent information database" is a database where information related to patents is stored and can be searched and cross-referenced.

[1099] A "trademark database" is a database where information about trademarks is stored and can be searched and cross-referenced.

[1100] The "matching means" is a function for comparing and verifying extracted keywords and phrases with patent information databases and trademark databases.

[1101] A "metadata generation means" is a function that generates data to add relevant information based on the matching results of keywords and phrases.

[1102] "Editing means" refers to a function that adds generated metadata to user input data to create edited data.

[1103] A "timing adjustment mechanism" is a function that determines the optimal timing for presenting edited data, depending on the user's emotions and the system's status.

[1104] This invention combines a system that analyzes user input data, compares extracted keywords and phrases with a patent information database and a trademark database, and adds relevant patent and trademark information, with an emotion engine that recognizes the user's emotions. The embodiments are described in detail below.

[1105] The entire system consists of an information processing device (computer device), a generative artificial intelligence means, a database matching means, an emotion recognition means, a metadata generation means, an editing means, and a timing adjustment means.

[1106] 1. Obtaining and sending user input

[1107] The user uses a dedicated application to enter information into a text input field. For example, they might enter, "The idea for the new application is based on existing communication services."

[1108] The terminal receives this user input data and sends it to the server using a secure communication protocol (e.g., HTTPS).

[1109] 2. Text Analysis

[1110] The server analyzes the user input data it receives using a natural language processing library (e.g., spaCy). It breaks down the text into words and phrases, extracting important keywords and phrases. In this case, "communication service" is extracted as a keyword.

[1111] 3. User emotion recognition

[1112] The server uses an EmotionML-compatible emotion engine to analyze emotions from user input data. For example, it analyzes positive and negative expressions in text to extract emotions such as "excitement" or "anxiety."

[1113] 4. Database matching

[1114] The server sends queries to match the extracted keywords and phrases against patent information databases (e.g., Google Patents) and trademark databases (e.g., Trademark Electronic Search System). It verifies whether the input data is related to existing patents and trademarks.

[1115] 5. Metadata generation

[1116] The server receives the matching results, and if a matching trademark or patent is found, it generates metadata containing that information. For example, it might generate metadata stating, "This content is related to a trademark of a specific communication service." Furthermore, it adjusts the accuracy and detail of the metadata based on the results of sentiment recognition. If the user indicates "anxiety," more detailed information is added.

[1117] 6. Compiling and sending the edited data

[1118] The system adds metadata generated by the server to the original user input data to create edited data. For example, it might add a message such as, "This idea is related to a specific trademark." Based on the analysis results of emotion recognition, the timing of the presentation of the edited data is adjusted. If the user is "excited," it is displayed immediately; if the user is "anxious," it is displayed after the explanation is finished.

[1119] 7. Displaying the results

[1120] The terminal displays the edited data received from the server in the user interface. The user can review this data and recognize which trademarks or patents the input data is related to.

[1121] Specific example

[1122] Input example

[1123] The user enters, "My idea for my new application is based on existing communication services."

[1124] System operation

[1125] 1. The user launches the application and enters text.

[1126] 2. The terminal sends the input data to the server.

[1127] 3. The server analyzes the received data and extracts the keyword "communication service".

[1128] 4. The server uses an emotion engine to detect the emotion of "user anxiety."

[1129] 5. The server sends queries to the patent information database and trademark database to confirm that the “communication service” is related to a specific trademark or patent.

[1130] 6. The server generates metadata stating, "This content is related to the trademark of an existing communication service." This addresses user concerns by providing more detailed information.

[1131] 7. The device displays a message in the user interface at an appropriate time stating, "This idea is related to a specific trademark."

[1132] This system allows users to quickly check whether their ideas or content are related to existing patents or trademarks. Furthermore, it provides appropriate information tailored to the user's emotions, enabling optimal decision-making and responses.

[1133] The flow of the specific processing in Example 2 will be explained using Figure 13.

[1134] Step 1: Obtain and submit user input

[1135] The user launches a dedicated application and enters information into a text input field. For example, they might enter, "The idea for the new application is based on existing communication services."

[1136] The terminal receives this user input data and sends it to the server using a secure communication protocol (HTTPS).

[1137] Input: Text data entered by the user

[1138] Output: Text data sent to the server

[1139] Step 2: Text Analysis

[1140] The server analyzes the user input data it receives using a natural language processing library (e.g., spaCy). It breaks down the text into words and phrases, extracting important keywords and phrases. In this case, "communication service" is extracted as a keyword.

[1141] Input: Text data submitted in Step 1

[1142] Output: List of extracted keywords and phrases

[1143] Specific operation: The text is parsed on the server using spaCy's nlp function, and keywords are extracted using properties such as token.lemma_.

[1144] Step 3: User emotion recognition

[1145] The server uses an EmotionML-compatible emotion engine to analyze emotions from user input data. For example, it analyzes positive and negative expressions in text to extract emotions such as "excitement" or "anxiety."

[1146] Input: Text data submitted in Step 1

[1147] Output: Analyzed emotion data (e.g., "anxiety")

[1148] Specific operation: Analyze text via the emotion analysis API and extract the emotion status. Example: "API.emotion_analysis(text)".

[1149] Step 4: Database matching

[1150] The server sends queries to patent information databases (e.g., Google Patents) and trademark databases (e.g., Trademark Electronic Search System) using the extracted keywords and phrases. This verifies whether the entered data is related to existing patents or trademarks.

[1151] Input: Keywords and phrases extracted in Step 2

[1152] Output: List of relevant patent and trademark information

[1153] Specific operation: Execute a query using the database search API and retrieve the search results. Example: "db_search('communication service')".

[1154] Step 5: Generate metadata

[1155] The server receives the results of the database matching, and if a matching trademark or patent is found, it generates metadata containing that information. For example, information such as "This content is related to a trademark of a specific communication service" is generated.

[1156] Furthermore, the accuracy and detail of the metadata are adjusted based on the results of the emotion recognition system. If the user indicates "anxiety," more detailed information is added.

[1157] Input: Matching results from Step 4 and sentiment data from Step 3.

[1158] Output: Generated metadata

[1159] Specific operation: Use a metadata generation template to insert and output the necessary information. Example: "metadata_template.format(data)".

[1160] Step 6: Build and send the edited data

[1161] The server generates metadata and adds it to the original user input data to create edited data. For example, it might add a message such as, "This idea is related to a specific trademark."

[1162] Based on the analysis results of emotion recognition methods, the timing of presenting edited data is adjusted. If the user is "excited," it is displayed immediately; if the user is "anxious," it is displayed after the explanation is finished.

[1163] Input: User input data from Step 1, metadata from Step 5, and sentiment data from Step 3.

[1164] Output: Edited text data

[1165] Specific operation: Integrates edited text and metadata, combining them in the format, e.g., "edited_data = original_text + metadata".

[1166] Step 7: Displaying the results

[1167] The terminal displays the edited data received from the server in the user interface. The user can review this data and recognize which trademarks or patents the input data is related to.

[1168] Input: Edited text data generated in Step 6

[1169] Output: Text displayed in the user interface

[1170] Specific operation: Binds data to a UI component and displays it in the appropriate format. Example: "ui_display(edited_data)".

[1171] (Application Example 2)

[1172] Next, we will explain application example 2. In the following explanation, 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."

[1173] This invention aims to provide a system that analyzes user input data and provides relevant patent and trademark information, not merely providing relevant information, but providing optimal information in accordance with the user's emotions. Conventional systems often provide information without considering the user's emotions, resulting in a poor user experience. In particular, in the advertising field, the problem was that providing information that was not based on the user's emotions reduced the effectiveness of advertising.

[1174] In Application Example 2, the specific processing performed by the specific processing unit 290 of the data processing device 12 is realized by the following means. In this invention, the server includes means for receiving user input data from an information processing device, means for generating artificial intelligence that analyzes the user input data and identifies extracted keywords and phrases, means for comparing the keywords and phrases with a patent information database and a trademark database, means for generating metadata indicating the relevant sections if the comparison results include relevant patent and trademark information, means for an emotion engine that analyzes and recognizes the user's emotions, means for generating and transmitting data edited according to the user's emotions based on the metadata, and means for displaying the edited data on a user interface. This makes it possible to provide optimal information based on the user's emotions.

[1175] An "information processing device" refers to any computer system that receives input data from a user and transmits it to a server.

[1176] "Generative artificial intelligence means" refers to an algorithm or program that analyzes input data and extracts key keywords or phrases.

[1177] A "patent information database" refers to a database system that stores information about existing patents.

[1178] A "trademark database" refers to a database system that stores information about existing trademarks.

[1179] "Means for generating metadata" refers to an algorithm or program that generates metadata, including relevant patent and trademark information, based on the matching results.

[1180] "Emotional engine means" refers to an algorithm or program for analyzing and recognizing emotions from user input data.

[1181] "Means for transmitting edited data" refers to an algorithm or program for transmitting edited data to a user based on the generated metadata.

[1182] A "user interface" refers to the screens or applications that users use to input information or to view edited data.

[1183] "Data edited to reflect user emotions" refers to data that includes information and advertisements optimized to take user emotions into consideration.

[1184] The specific system for implementing this invention consists of several main components. This system aims to receive and analyze user input data, recognize the user's emotions, provide relevant patent and trademark information, and generate and present edited data based on the user's emotions at the optimal time.

[1185] Hardware and software to use

[1186] 1. Information processing device:

[1187] These are primarily devices used by users. For example, smartphones and personal computers fulfill this role. They receive user input data and send it to the server.

[1188] 2. Generative Artificial Intelligence:

[1189] Software for data analysis and keyword extraction. Specifically, it uses generative AI models such as GPT-4. This model analyzes the input text data and extracts key keywords and phrases.

[1190] 3. Database matching function:

[1191] Software for performing query searches against patent and trademark databases. This allows the system to verify whether entered keywords or phrases are related to existing patents or trademarks.

[1192] 4. Emotional Engine:

[1193] Software that analyzes and recognizes user emotions from user input data. Specifically, it uses tools such as the Google Cloud Natural Language API.

[1194] 5. Metadata generation function:

[1195] Software that generates metadata, including relevant patent and trademark information, based on matching results. It also adjusts the content and display timing of the metadata based on user sentiment.

[1196] 6. Function to send and display edited data:

[1197] Software that sends generated metadata to the user and displays it in the user interface (UI). This allows the user to see optimized information in real time.

[1198] Specific examples of the system

[1199] Let's say a user enters text into a smartphone application such as, "I'm interested in the latest smartphones. What features do they have?" The following is a specific example of how the system would function in that situation.

[1200] 1. Obtaining user input:

[1201] The user enters text and sends it to the server via an information processing device (smartphone).

[1202] 2. Text analysis:

[1203] Generative artificial intelligence (GPT-4) analyzes the input data and extracts key keywords such as "latest smartphones."

[1204] 3. Emotion recognition:

[1205] The emotion engine (Google Cloud Natural Language API) recognizes emotions such as "excitement."

[1206] 4. Database matching:

[1207] The database matching function sends queries to the patent information database and trademark database to retrieve relevant information.

[1208] 5. Metadata generation:

[1209] Metadata including relevant patent and trademark information is generated, and information corresponding to the user's "excitement" is added.

[1210] 6. Submitting and displaying edited data:

[1211] The edited data is sent to the user at the optimal time and displayed on their smartphone screen along with the message, "This idea is related to a specific trademark."

[1212] Example of a prompt

[1213] Input example:

[1214] "I'm interested in the latest smartphones. What features do they have?"

[1215] In this way, the system can provide relevant patent and trademark information through the analysis of user input data and emotion recognition, as well as provide optimal information tailored to the user's emotions. This improves advertising effectiveness and maximizes the user experience.

[1216] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[1217] Step 1:

[1218] A user enters text into a smartphone application. For example, they might type, "I'm interested in the latest smartphones. What features do they have?" Once the user has finished typing, the device sends this user input data to a server using a secure communication protocol (such as HTTPS). The input data is in the format of regular text data.

[1219] Step 2:

[1220] The server analyzes the received text data. This analysis is performed using generative artificial intelligence (e.g., GPT-4). The input text data is first broken down, and key keywords and phrases are extracted. Specifically, keywords such as "latest smartphone" and "features" are extracted. The output of this step is a list of the extracted keywords and phrases.

[1221] Step 3:

[1222] The server uses an emotion engine (e.g., Google Cloud Natural Language API) to analyze the user's emotions from extracted keywords and phrases. Specifically, it analyzes the wording and syntax of the input text data to identify emotions such as "excitement" and "anxiety." The output of this step is a label indicating the user's emotion.

[1223] Step 4:

[1224] The server sends queries to patent and trademark databases based on the extracted keywords and phrases. This verifies whether the entered data is related to existing patents or trademarks. Specifically, the database engine (e.g., SQL Server) executes the queries and retrieves matching records. The output of this step is a list of relevant patent and trademark information.

[1225] Step 5:

[1226] The server generates metadata based on the matching results. Specifically, it generates metadata explaining the relevance of patent and trademark information. Furthermore, this metadata is adjusted based on the user's emotions. For example, if the user is "excited," concise and engaging information is provided; if they are "anxious," more detailed explanations are added. The output of this step is optimized metadata.

[1227] Step 6:

[1228] The server constructs edited data based on the generated metadata. Specifically, it adds information such as "This idea is related to a specific trademark" to the original input text. The timing of delivery is also adjusted according to the user's mood. For example, if the user is "excited," the information is provided immediately. The output of this step is the edited data.

[1229] Step 7:

[1230] The server sends the edited data back to the terminal, which then displays the data in its user interface. The user can then review the information displayed on their smartphone screen. For example, a message such as "This idea is related to a specific trademark" might be displayed in a timely manner. The output of this step is the final display data.

[1231] Through the steps outlined above, the system achieves everything from analyzing user input data and recognizing emotions to matching patent and trademark information, generating metadata, and presenting edited data. This system allows users to receive relevant information in real time, significantly improving the effectiveness of advertising.

[1232] The specific processing unit 290 transmits the result of the specific processing to the headset terminal 314. In the headset terminal 314, the control unit 46A causes the speaker 240 and display 343 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[1233] Data generation model 58 is a type of so-called generative AI (Artificial Intelligence). One example of data generation model 58 is ChatGPT (Internet search<URL: https: / / openai.com / blog / chatgpt> ), Gemini (Internet search) <url: https: gemini.google.com ?hl="ja">Examples of generative AI include the following. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and with inference data such as audio data representing speech, text data representing text, and image data representing images. The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference results in data formats such as audio data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[1234] In the above embodiment, an example was given in which specific processing is performed by the data processing device 12, but the technology of this disclosure is not limited thereto, and specific processing may also be performed by the headset terminal 314.

[1235] [Fourth Embodiment]

[1236] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[1237] As shown in Figure 7, the data processing system 410 includes a data processing device 12 and a robot 414. An example of the data processing device 12 is a server.

[1238] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 is an example of a "computer" related to the technology of this disclosure. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[1239] The robot 414 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication interface 44, and a controlled object 443. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, camera 42, and controlled object 443 are also connected to the bus 52.

[1240] The microphone 238 receives voice signals from the user 20 and receives instructions from the user 20. The microphone 238 captures the voice signals from the user 20, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[1241] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the area around the user 20 (for example, an imaging range defined by a field of view equivalent to the width of a typical healthy person's field of vision).

[1242] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[1243] The controlled object 443 includes a display device, LEDs in the eyes, and motors that drive the arms, hands, and feet. The posture and gestures of the robot 414 are controlled by controlling the motors of the arms, hands, and feet. Some of the robot 414's emotions can be expressed by controlling these motors. Furthermore, the robot 414's facial expressions can also be expressed by controlling the illumination state of the LEDs in its eyes.

[1244] Figure 8 shows an example of the main functions of the data processing device 12 and the robot 414. As shown in Figure 8, the data processing device 12 performs specific processing using the processor 28. The storage 32 stores the specific processing program 56.

[1245] The specific processing program 56 is an example of a "program" relating to the technology of this disclosure. The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[1246] The storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[1247] In robot 414, the processor 46 performs the reception output processing. The storage 50 stores the reception output program 60. 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 processing is realized by the processor 46 operating as a control unit 46A according to the reception output program 60 executed on the RAM 48.

[1248] Next, the specific processing performed by the specific processing unit 290 of the data processing device 12 will be described. In the following description, the data processing device 12 will be referred to as the "server" and the robot 414 as the "terminal".

[1249] This invention is a system for improving the proper management and transparency of trademarks and patents. It has the function of analyzing user input data and managing extracted keywords and phrases to add relevant trademark and patent information. The system of this invention includes an information processing device, a generative artificial intelligence system, a database matching function, a metadata generation function, and an output function for edited data.

[1250] Program overview and explanation in natural language

[1251] This system operates as follows:

[1252] 1. Obtaining and sending user input

[1253] The user launches the application and provides input through a text interface. It begins with the user entering ideas or content for a new application.

[1254] The terminal receives this input data and sends it to the server using a secure communication protocol.

[1255] 2. Text Analysis

[1256] The server utilizes generative artificial intelligence to analyze user input data. Specifically, it breaks down text and extracts key keywords and phrases.

[1257] The aforementioned analysis identifies terms related to "trademarks" and "patents."

[1258] 3. Database matching

[1259] The server compares the extracted keywords and phrases with patent information databases and trademark databases. This verifies whether the entered data is related to existing trademarks or patents.

[1260] 4. Metadata generation

[1261] The server generates metadata, including relevant trademark and patent information, based on the results of database matching. For example, it might generate information such as, "This content is related to a specific trademark."

[1262] 5. Compiling and sending the edited data

[1263] The server generates metadata which is then added to the user input data to construct the edited data. Finally, the edited data is sent back to the terminal.

[1264] 6. Displaying the results

[1265] The terminal displays the edited data received from the server in the user interface. At this stage, the user can verify which trademarks or patents the input data is related to.

[1266] Specific example

[1267] Input example

[1268] The user enters, "My idea for my new application is based on existing communication services."

[1269] System operation

[1270] 1. The user enters information.

[1271] 2. The terminal sends the input data to the server.

[1272] 3. The server analyzes the data and extracts the keyword "communication services".

[1273] 4. The server checks the patent information database and trademark database to confirm that the "communication service" is related to a specific trademark or patent.

[1274] 5. The server generates metadata stating "This content is related to an existing trademark" and adds it to the input data.

[1275] 6. The device receives the edited data and displays the message, "This idea is related to a specific trademark."

[1276] This system promotes the proper management of trademarks and patents and prevents legal problems by clarifying what user-created content is related to.

[1277] The following describes the processing flow.

[1278] Step 1:

[1279] The user launches the application and inputs ideas or content through a text interface. For example, the user might enter the text, "The idea for the new application is based on existing communication services."

[1280] Step 2:

[1281] The terminal receives user input data and sends it to the server using a secure communication protocol (e.g., HTTPS).

[1282] Step 3:

[1283] The server passes the user input data it receives to a generative artificial intelligence (AI), which then begins text analysis. Specifically, it breaks down the text and extracts key keywords and phrases. For example, it might identify the term "communication services."

[1284] Step 4:

[1285] The server sends queries to patent and trademark databases based on the extracted keywords and phrases. This verifies whether the entered data is related to existing patents or trademarks.

[1286] Step 5:

[1287] The server receives matching results from the database, and if a matching trademark or patent is found, it generates metadata containing that information. For example, information such as "This content is related to a trademark of a specific telecommunications service" might be generated.

[1288] Step 6:

[1289] The metadata generated by the server is added to the original user input data to construct the edited data. For example, the message "This idea is related to a specific trademark" is added to the original text.

[1290] Step 7:

[1291] The server returns the edited data to the terminal.

[1292] Step 8:

[1293] The terminal displays the edited data received from the server in the user interface. The user can review the edited data and recognize which trademarks or patents the input data is related to.

[1294] This process allows users to check whether their ideas or content infringe upon existing patents or trademarks, and to take appropriate action.

[1295] (Example 1)

[1296] Next, we will describe Example 1. In the following description, the data processing device 12 will be referred to as the "server" and the robot 414 as the "terminal".

[1297] To ensure proper management and transparency of trademarks and patents, a system is needed that allows users to quickly verify whether the data they enter is related to existing trademarks and patents. However, current systems require users to conduct their own research and investigations, which is time-consuming and laborious. Furthermore, if the information users refer to is incomplete, it becomes difficult to prevent legal risks. Against this backdrop, there is a need to develop a system that can significantly reduce the burden on users and provide accurate and timely information related to trademarks and patents.

[1298] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 1 is realized by the following means.

[1299] In this invention, the server includes means for receiving user input data from an information processing device; generative artificial intelligence means for analyzing the user input data and identifying extracted keywords and phrases; means for comparing the keywords and phrases with a patent information database and a trademark database; means for generating metadata including relevant patent and trademark information as a result of the comparison; means for adding the metadata to the user input data and returning the edited data; and means for displaying the received edited data on a user interface. This enables users to quickly and accurately obtain information related to trademarks and patents without having to conduct their own research.

[1300] An "information processing device" is a computer system that receives input data from a user and performs various data processing operations.

[1301] "User input data" refers to information that a user provides to the system through a text interface or other input means.

[1302] "Generative artificial intelligence" refers to algorithms that learn from large amounts of text data and perform text analysis and generation tailored to specific purposes.

[1303] "Keywords and phrases" are important words and phrases extracted from user input data that are highly likely to be related to trademarks or patents.

[1304] A "patent information database" is a database system that stores existing patent information and provides it in a searchable format.

[1305] A "trademark database" is a database system that stores existing trademark information and provides it in a searchable format.

[1306] "Matching" is the process of comparing extracted keywords or phrases with existing information in the database to confirm a match or relevance.

[1307] "Metadata" refers to data generated as additional information related to user input data, including details about patents and trademarks.

[1308] "Edited data" refers to a dataset in which metadata has been added to user-input data, thereby enhancing its information.

[1309] "Receiving" refers to the process by which data or information is transferred from another system or user.

[1310] "Display" refers to the process by which processed data or information is visually presented to the user.

[1311] This invention is a system for improving the proper management and transparency of trademarks and patents. It has the function of analyzing user input data and managing extracted keywords and phrases to add relevant trademark and patent information. Embodiments of this invention will be described in detail below.

[1312] This system includes an information processing device, a generative artificial intelligence system, a database matching function, a metadata generation function, and an output function for edited data.

[1313] First, the user launches the application and enters information through a text interface. At this stage, the user enters ideas or content for the new application. For example, they might enter, "My idea for my new application is based on existing communication services."

[1314] The terminal receives user input data and sends it to the server using a secure communication protocol (e.g., TLS / SSL).

[1315] Next, the server analyzes the received input data using a generative AI model (e.g., GPT-4). This AI model breaks down the text to identify key keywords and phrases. Specifically, it extracts keywords such as "application" and "communication service." Text analysis by the AI ​​model is used to understand the context of the user input data and identify relevant terms.

[1316] The extracted keywords and phrases are cross-referenced by the server against patent information databases (e.g., the Japan Patent Office database) and trademark databases. This cross-referencing process uses SQL queries and API requests. For example, a cross-referencing is performed to check if the keyword "communication services" is related to any existing patents or trademarks. This retrieves detailed information about the relevant patents and trademarks as a result of the cross-referencing.

[1317] Next, the server generates metadata based on the database matching results, including relevant trademark and patent information. For example, information such as "This content is related to a specific trademark" is generated. The metadata includes patent numbers, patent details, and trademark owners.

[1318] The generated metadata is appended to the user input data, and a new dataset is constructed by the server. The edited data is then retransmitted to the terminal and provided to the user. This transmission uses a secure communication protocol, similar to the reception process.

[1319] Finally, the terminal displays the edited data received from the server on the user interface. Specifically, it provides information on relevant patents and trademarks in an easy-to-understand format, along with the content entered by the user. For example, information such as "This idea is related to an existing patent" is displayed, allowing the user to quickly check the legal risks.

[1320] Specific example:

[1321] Example of a prompt:

[1322] "My idea for my new application is based on existing communication services."

[1323] This system allows users to quickly and accurately obtain information related to trademarks and patents without having to conduct their own research, thereby preventing legal risks.

[1324] The flow of the specific processing in Example 1 will be explained using Figure 11.

[1325] Step 1:

[1326] Acquiring and sending user input

[1327] The user launches the application and enters information via a text interface. For example, the user might enter, "My idea for my new application is based on existing communication services."

[1328] The terminal receives this input data and sends it to the server using a secure communication protocol (e.g., TLS / SSL).

[1329] Input: Text data entered by the user.

[1330] Output: User input data securely sent to the server.

[1331] Step 2:

[1332] Text analysis

[1333] The server analyzes the received input data using a generation AI model (e.g., GPT-4).

[1334] The server breaks down the input data and extracts key keywords and phrases. For example, "application" and "communication service" might be extracted.

[1335] Input: User text data sent to the server.

[1336] Output: A list of analyzed keywords and phrases.

[1337] Step 3:

[1338] Database matching

[1339] The server compares the extracted keywords and phrases with patent information databases and trademark databases.

[1340] The server uses SQL queries and API requests to search for items in the database that match or are related to registered information. For example, it might identify existing patents or trademarks that match "communication services."

[1341] Input: Analyzed keywords or phrases.

[1342] Output: Relevant patent and trademark information as a result of the matching process.

[1343] Step 4:

[1344] Metadata generation

[1345] The server generates metadata, including relevant patent and trademark information, based on the database matching results.

[1346] For example, information such as "This content is related to a specific trademark" is generated.

[1347] Metadata includes information such as patent numbers, patent details, and trademark owners.

[1348] Input: Database matching result.

[1349] Output: Generated metadata.

[1350] Step 5:

[1351] Compiling and sending edited data

[1352] The server adds the generated metadata to the user input data to construct a new dataset.

[1353] The server securely sends this edited data to the terminal.

[1354] Input: User input data and metadata.

[1355] Output: Edited and securely transmitted dataset.

[1356] Step 6:

[1357] Displaying Results

[1358] The terminal displays the edited data received from the server in the user interface.

[1359] For example, a user might see a message stating, "This idea is related to an existing patent."

[1360] Input: Edited data received from the server.

[1361] Output: Relevant information displayed in the user interface.

[1362] (Application Example 1)

[1363] Next, we will explain Application Example 1. In the following explanation, the data processing device 12 will be referred to as the "server" and the robot 414 as the "terminal".

[1364] Conventional information processing systems have made it difficult to quickly and accurately assess the trademark and patent risks associated with new ideas and projects. As a result, companies and individuals were unable to prevent legal risks when developing new products, potentially leading to problems. In particular, there was a lack of means to obtain appropriate information when real-time evaluation was required.

[1365] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 1 is realized by the following means.

[1366] In this invention, the server includes means for receiving user input data from an information processing device; generative artificial intelligence means for analyzing the user input data and identifying extracted keywords and phrases; means for comparing the keywords and phrases with a patent information database and a trademark database; means for generating metadata indicating the relevant sections if the comparison results include relevant patent and trademark information; means for attaching the metadata to the user input data and returning the edited data; means for sending and receiving data using satellite communication; and means for evaluating trademark and patent-related risks in real time. This enables users to evaluate trademark and patent-related risks related to new ideas and projects in real time.

[1367] An "information processing device" is a terminal device that receives input data from a user and transmits it to a server.

[1368] "User input data" refers to information entered by users in text format, such as new ideas or project details.

[1369] "Generative artificial intelligence" refers to an artificial intelligence system that analyzes user input data and extracts key keywords and phrases.

[1370] A "patent information database" is a database that stores existing patent information and uses it for comparison.

[1371] A "trademark database" is a database that stores existing trademark information and uses it for matching.

[1372] "Metadata" refers to data related to patents and trademarks that is added to user input data.

[1373] "Satellite communication" is a communication method that transmits and receives data via artificial satellites.

[1374] "Real-time evaluation" refers to the immediate assessment of trademark and patent-related risks associated with user input data.

[1375] "Edited data" refers to user-input data to which metadata has been added, and then returned from the server.

[1376] This invention is a system for improving the proper management and transparency of trademarks and patents. The system aims to enable companies and individuals to evaluate new ideas and projects in real time and to anticipate trademark and patent-related risks.

[1377] The system mainly includes the following components:

[1378] 1. Information processing device: A device such as a smartphone that has the function of receiving user input data.

[1379] 2. Generative Artificial Intelligence: Generative AI models such as GPT-4 are used to analyze user input data and extract key keywords and phrases.

[1380] 3. Database matching function: The extracted keywords and phrases are compared with the patent information database and the trademark database.

[1381] 4. Metadata generation function: Generates metadata including relevant patent and trademark information.

[1382] 5. Satellite communications: A means of communication for sending and receiving data.

[1383] 6. Real-time evaluation function: Evaluates trademark and patent-related risks in real time.

[1384] Specific examples of how the system works

[1385] 1. Obtaining user input

[1386] The user launches the application on their smartphone and enters details about a new idea or project. For example, they might enter, "We are developing a next-generation security camera system."

[1387] User input data is encrypted and sent to the server via a secure communication protocol (e.g., HTTPS).

[1388] 2. Data Analysis and Reconciliation

[1389] The server uses generative artificial intelligence (e.g., GPT-4) to analyze user input data and extract key keywords and phrases. For example, "security camera system" might be extracted.

[1390] The extracted keywords are compared with patent information databases and trademark databases (e.g., J-PlatPat) to obtain relevant information.

[1391] 3. Metadata generation and return

[1392] Based on the matching results, the server generates metadata that includes relevant patent and trademark information. For example, it might generate information such as, "This idea is related to an existing trademark."

[1393] The generated metadata is added to the user input data, and the edited data is sent back to the user's smartphone.

[1394] 4. Displaying the results

[1395] The terminal displays edited data received from the server in the user interface. Users can see in real time which trademarks and patents their entered ideas or projects are associated with.

[1396] Example of a prompt

[1397] Examples of prompt statements for a generative AI model are as follows:

[1398] Please identify any relevant trademarks or patents for the following idea: Idea: Developing a next-generation security camera system.

[1399] This system allows users to assess trademark and patent-related risks in real time and prevent legal troubles before they occur. Specific hardware used includes smartphones, and software includes generative AI (e.g., GPT-4), secure communication protocols (e.g., HTTPS), and patent and trademark databases (e.g., J-PlatPat).

[1400] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[1401] Step 1:

[1402] Retrieving user input

[1403] The user launches the application on their smartphone and enters details of a new idea or project.

[1404] The terminal receives the input data and encrypts it.

[1405] The device sends encrypted data to the server using a secure communication protocol (e.g., HTTPS).

[1406] Input: Text data entered by the user.

[1407] Output: Encrypted data is sent to the server.

[1408] Step 2:

[1409] Data Analysis

[1410] The server analyzes the received user input data using generative artificial intelligence (e.g., GPT-4).

[1411] The server extracts key keywords and phrases from the input data.

[1412] Specific operation: Input a prompt sentence into the generative AI model and have it extract highly relevant keywords.

[1413] Input: Encrypted user input data.

[1414] Output: Extracted keywords and phrases.

[1415] Step 3:

[1416] Database matching

[1417] The server compares the extracted keywords and phrases with patent information databases and trademark databases (e.g., J-PlatPat).

[1418] The server retrieves relevant patent and trademark information as a result of the matching process.

[1419] Specific operation: Issue search queries to patent and trademark databases and retrieve results.

[1420] Input: Extracted keywords or phrases.

[1421] Output: Relevant patent and trademark information.

[1422] Step 4:

[1423] Metadata generation

[1424] Based on the matching results, the server generates metadata that includes relevant patent and trademark information.

[1425] Specific operation: The server constructs metadata based on the information it has acquired.

[1426] Input: Relevant patent and trademark information.

[1427] Output: Generated metadata.

[1428] Step 5:

[1429] Return of edited data

[1430] The server adds the generated metadata to the user input data and sends the edited data back to the user's smartphone.

[1431] Specific operation: Metadata is added after existing user input data and transferred to the user's terminal in an edited form.

[1432] Input: User input data and metadata.

[1433] Output: Edited data.

[1434] Step 6:

[1435] Displaying Results

[1436] The terminal displays the edited data received from the server in the user interface.

[1437] Specific operation: Reads received data and displays it in a format that is easy for the user to understand.

[1438] Input: Edited data.

[1439] Output: User interface displaying data.

[1440] Furthermore, an emotion engine that estimates the user's emotions may be incorporated. That is, the identification processing unit 290 may use the emotion identification model 59 to estimate the user's emotions and perform identification processing using the user's emotions.

[1441] This invention combines a system that analyzes user input data, compares extracted keywords and phrases with a patent information database and a trademark database, and adds relevant patent and trademark information, with an emotion engine that recognizes the user's emotions. This system includes an information processing device, a generative artificial intelligence system, a database matching function, a metadata generation function, an edited data output function, and an emotion engine.

[1442] Program overview and explanation in natural language

[1443] This system operates as follows:

[1444] 1. Obtaining and sending user input

[1445] The user launches the application and enters information through a text interface. For example, the user might enter the text, "The idea for the new application is based on existing communication services."

[1446] The terminal receives this input data and sends it to the server using a secure communication protocol.

[1447] 2. Text Analysis

[1448] The server receives input data and passes it to a generative artificial intelligence system to begin text analysis. Specifically, it breaks down the text and extracts key keywords and phrases. For example, it identifies the term "communication services."

[1449] 3. User emotion recognition

[1450] The server uses an emotion engine to analyze and recognize the user's emotions from user input data. For example, it extracts emotions such as "excitement" or "anxiety" from the wording and syntax within the text.

[1451] 4. Database matching

[1452] The server sends queries to patent and trademark databases based on the extracted keywords and phrases. This verifies whether the entered data is related to existing patents or trademarks.

[1453] 5. Metadata generation

[1454] The server receives the results of the database matching, and if a matching trademark or patent is found, it generates metadata containing that information. For example, information such as "This content is related to a trademark of a specific communication service" is generated.

[1455] Furthermore, metadata generation is adjusted based on the results of the emotion engine. For example, if a user indicates an "anxious" emotion, the information is adjusted to provide more detail.

[1456] 6. Compiling and sending the edited data

[1457] The metadata generated by the server is added to the original user input data to construct the edited data. For example, the message "This idea is related to a specific trademark" is added to the original text.

[1458] Based on the results from the emotion engine, the timing of presenting edited data is adjusted. For example, if the user is "excited," information is presented immediately; if they are "anxious," it is presented at an appropriate time.

[1459] 7. Displaying the results

[1460] The terminal displays the edited data received from the server in the user interface. The user can review the edited data and recognize which trademarks or patents the input data is related to.

[1461] Specific example

[1462] Input example

[1463] The user enters, "My idea for my new application is based on existing communication services."

[1464] System operation

[1465] 1. The user enters information.

[1466] 2. The terminal sends the input data to the server.

[1467] 3. The server analyzes the data and extracts the keyword "communication services".

[1468] 4. The server uses an emotion engine to detect emotions such as "anxiety" from the text.

[1469] 5. The server checks the patent information database and trademark database to confirm that the "communication service" is associated with a specific trademark or patent.

[1470] 6. The server generates metadata stating, "This content is related to the trademark of an existing communication service." This addresses the user's feeling of "anxiety" by providing more detailed information.

[1471] 7. The device receives the edited data and displays "This idea is related to a specific trademark" at the appropriate time.

[1472] This system allows users to not only check whether their ideas or content infringe on existing patents or trademarks, but also to receive appropriate information tailored to their emotional needs. This facilitates sound judgment and appropriate responses.

[1473] The following describes the processing flow.

[1474] Step 1:

[1475] The user launches the application and inputs ideas or content through a text interface. For example, the user might enter the text, "The idea for the new application is based on existing communication services."

[1476] Step 2:

[1477] The terminal receives user input data and sends it to the server using a secure communication protocol (e.g., HTTPS).

[1478] Step 3:

[1479] The server passes the user input data it receives to a generative artificial intelligence (AI), which then begins text analysis. Specifically, it breaks down the text and extracts key keywords and phrases. For example, it might identify the term "communication services."

[1480] Step 4:

[1481] The server uses an emotion engine to analyze and recognize the user's emotions from user input data. For example, it extracts emotions such as "excitement" or "anxiety" from the wording and syntax within the text.

[1482] Step 5:

[1483] The server sends queries to patent and trademark databases based on the extracted keywords and phrases. This verifies whether the entered data is related to existing patents or trademarks.

[1484] Step 6:

[1485] The server receives matching results from the database, and if a matching trademark or patent is found, it generates metadata containing that information. For example, information such as "This content is related to a trademark of a specific telecommunications service" might be generated.

[1486] Step 7:

[1487] The server adjusts metadata generation based on the results of the emotion engine. For example, if a user indicates an "anxious" emotion, the metadata is adjusted to provide more detailed information.

[1488] Step 8:

[1489] The metadata generated by the server is added to the original user input data to construct the edited data. For example, the message "This idea is related to a specific trademark" is added to the original text.

[1490] Step 9:

[1491] The server sends the edited data back to the terminal. Based on the results from the emotion engine, the timing of presentation is adjusted. For example, if the user is "excited," information is presented immediately; if they are "anxious," it is presented at an appropriate time.

[1492] Step 10:

[1493] The terminal displays the edited data received from the server in the user interface. The user can review the edited data and recognize which trademarks or patents the input data is related to, as well as additional information tailored to the user's emotions.

[1494] (Example 2)

[1495] Next, we will describe Example 2. In the following description, the data processing device 12 will be referred to as the "server" and the robot 414 as the "terminal".

[1496] Conventional information processing systems analyze user input data and cross-reference it with relevant patent and trademark information, but they cannot adjust the results based on the user's emotions. Therefore, users often fail to receive the information they need at the appropriate time, and the display content cannot be optimized. Furthermore, the inability to provide appropriate information tailored to the user's psychological state makes it difficult to improve user satisfaction and facilitate efficient decision-making.

[1497] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 2 is realized by the following means.

[1498] In this invention, the server includes means for receiving user input data from a computer device; generative artificial intelligence means for analyzing the user input data and identifying extracted keywords and phrases; emotion recognition means for analyzing and recognizing the user's emotions from the user data; means for comparing the keywords and phrases with a patent information database and a trademark database; means for generating metadata indicating the relevant sections if the comparison results include relevant patent and trademark information; means for adjusting the generation and editing of metadata based on the results of the emotion recognition means; means for attaching the metadata to the user input data and returning the edited data; and means for adjusting the timing of presenting the edited data. This makes it possible to provide information at an appropriate timing and with appropriate content according to the user's emotional state.

[1499] A "computer device" is an electronic device used for inputting, outputting, storing, and processing data.

[1500] "User input data" refers to information such as text and audio that a user inputs into a computer device.

[1501] "Generative artificial intelligence means" refers to methods that use algorithms and programs to analyze user input data and extract important keywords and phrases.

[1502] "Emotion recognition means" refers to methods that use algorithms or programs to analyze and recognize a user's emotions from user input data.

[1503] A "patent information database" is a database where information related to patents is stored and can be searched and cross-referenced.

[1504] A "trademark database" is a database where information about trademarks is stored and can be searched and cross-referenced.

[1505] The "matching means" is a function for comparing and verifying extracted keywords and phrases with patent information databases and trademark databases.

[1506] A "metadata generation means" is a function that generates data to add relevant information based on the matching results of keywords and phrases.

[1507] "Editing means" refers to a function that adds generated metadata to user input data to create edited data.

[1508] A "timing adjustment mechanism" is a function that determines the optimal timing for presenting edited data, depending on the user's emotions and the system's status.

[1509] This invention combines a system that analyzes user input data, compares extracted keywords and phrases with a patent information database and a trademark database, and adds relevant patent and trademark information, with an emotion engine that recognizes the user's emotions. The embodiments are described in detail below.

[1510] The entire system consists of an information processing device (computer device), a generative artificial intelligence means, a database matching means, an emotion recognition means, a metadata generation means, an editing means, and a timing adjustment means.

[1511] 1. Obtaining and sending user input

[1512] The user uses a dedicated application to enter information into a text input field. For example, they might enter, "The idea for the new application is based on existing communication services."

[1513] The terminal receives this user input data and sends it to the server using a secure communication protocol (e.g., HTTPS).

[1514] 2. Text Analysis

[1515] The server analyzes the user input data it receives using a natural language processing library (e.g., spaCy). It breaks down the text into words and phrases, extracting important keywords and phrases. In this case, "communication service" is extracted as a keyword.

[1516] 3. User emotion recognition

[1517] The server uses an EmotionML-compatible emotion engine to analyze emotions from user input data. For example, it analyzes positive and negative expressions in text to extract emotions such as "excitement" or "anxiety."

[1518] 4. Database matching

[1519] The server sends queries to match the extracted keywords and phrases against patent information databases (e.g., Google Patents) and trademark databases (e.g., Trademark Electronic Search System). It verifies whether the input data is related to existing patents and trademarks.

[1520] 5. Metadata generation

[1521] The server receives the matching results, and if a matching trademark or patent is found, it generates metadata containing that information. For example, it might generate metadata stating, "This content is related to a trademark of a specific communication service." Furthermore, it adjusts the accuracy and detail of the metadata based on the results of sentiment recognition. If the user indicates "anxiety," more detailed information is added.

[1522] 6. Compiling and sending the edited data

[1523] The system adds metadata generated by the server to the original user input data to create edited data. For example, it might add a message such as, "This idea is related to a specific trademark." Based on the analysis results of emotion recognition, the timing of the presentation of the edited data is adjusted. If the user is "excited," it is displayed immediately; if the user is "anxious," it is displayed after the explanation is finished.

[1524] 7. Displaying the results

[1525] The terminal displays the edited data received from the server in the user interface. The user can review this data and recognize which trademarks or patents the input data is related to.

[1526] Specific example

[1527] Input example

[1528] The user enters, "My idea for my new application is based on existing communication services."

[1529] System operation

[1530] 1. The user launches the application and enters text.

[1531] 2. The terminal sends the input data to the server.

[1532] 3. The server analyzes the received data and extracts the keyword "communication service".

[1533] 4. The server uses an emotion engine to detect the emotion of "user anxiety."

[1534] 5. The server sends queries to the patent information database and trademark database to confirm that the “communication service” is related to a specific trademark or patent.

[1535] 6. The server generates metadata stating, "This content is related to the trademark of an existing communication service." This addresses user concerns by providing more detailed information.

[1536] 7. The device displays a message in the user interface at an appropriate time stating, "This idea is related to a specific trademark."

[1537] This system allows users to quickly check whether their ideas or content are related to existing patents or trademarks. Furthermore, it provides appropriate information tailored to the user's emotions, enabling optimal decision-making and responses.

[1538] The flow of the specific processing in Example 2 will be explained using Figure 13.

[1539] Step 1: Obtain and submit user input

[1540] The user launches a dedicated application and enters information into a text input field. For example, they might enter, "The idea for the new application is based on existing communication services."

[1541] The terminal receives this user input data and sends it to the server using a secure communication protocol (HTTPS).

[1542] Input: Text data entered by the user

[1543] Output: Text data sent to the server

[1544] Step 2: Text Analysis

[1545] The server analyzes the user input data it receives using a natural language processing library (e.g., spaCy). It breaks down the text into words and phrases, extracting important keywords and phrases. In this case, "communication service" is extracted as a keyword.

[1546] Input: Text data submitted in Step 1

[1547] Output: List of extracted keywords and phrases

[1548] Specific operation: The text is parsed on the server using spaCy's nlp function, and keywords are extracted using properties such as token.lemma_.

[1549] Step 3: User emotion recognition

[1550] The server uses an EmotionML-compatible emotion engine to analyze emotions from user input data. For example, it analyzes positive and negative expressions in text to extract emotions such as "excitement" or "anxiety."

[1551] Input: Text data submitted in Step 1

[1552] Output: Analyzed emotion data (e.g., "anxiety")

[1553] Specific operation: Analyze text via the emotion analysis API and extract the emotion status. Example: "API.emotion_analysis(text)".

[1554] Step 4: Database matching

[1555] The server sends queries to patent information databases (e.g., Google Patents) and trademark databases (e.g., Trademark Electronic Search System) using the extracted keywords and phrases. This verifies whether the entered data is related to existing patents or trademarks.

[1556] Input: Keywords and phrases extracted in Step 2

[1557] Output: List of relevant patent and trademark information

[1558] Specific operation: Execute a query using the database search API and retrieve the search results. Example: "db_search('communication service')".

[1559] Step 5: Generate metadata

[1560] The server receives the results of the database matching, and if a matching trademark or patent is found, it generates metadata containing that information. For example, information such as "This content is related to a trademark of a specific communication service" is generated.

[1561] Furthermore, the accuracy and detail of the metadata are adjusted based on the results of the emotion recognition system. If the user indicates "anxiety," more detailed information is added.

[1562] Input: Matching results from Step 4 and sentiment data from Step 3.

[1563] Output: Generated metadata

[1564] Specific operation: Use a metadata generation template to insert and output the necessary information. Example: "metadata_template.format(data)".

[1565] Step 6: Build and send the edited data

[1566] The server generates metadata and adds it to the original user input data to create edited data. For example, it might add a message such as, "This idea is related to a specific trademark."

[1567] Based on the analysis results of emotion recognition methods, the timing of presenting edited data is adjusted. If the user is "excited," it is displayed immediately; if the user is "anxious," it is displayed after the explanation is finished.

[1568] Input: User input data from Step 1, metadata from Step 5, and sentiment data from Step 3.

[1569] Output: Edited text data

[1570] Specific operation: Integrates edited text and metadata, combining them in the format, e.g., "edited_data = original_text + metadata".

[1571] Step 7: Displaying the results

[1572] The terminal displays the edited data received from the server in the user interface. The user can review this data and recognize which trademarks or patents the input data is related to.

[1573] Input: Edited text data generated in Step 6

[1574] Output: Text displayed in the user interface

[1575] Specific operation: Binds data to a UI component and displays it in the appropriate format. Example: "ui_display(edited_data)".

[1576] (Application Example 2)

[1577] Next, we will explain application example 2. In the following explanation, the data processing device 12 will be referred to as the "server" and the robot 414 as the "terminal".

[1578] This invention aims to provide a system that analyzes user input data and provides relevant patent and trademark information, not merely providing relevant information, but providing optimal information in accordance with the user's emotions. Conventional systems often provide information without considering the user's emotions, resulting in a poor user experience. In particular, in the advertising field, the problem was that providing information that was not based on the user's emotions reduced the effectiveness of advertising.

[1579] In Application Example 2, the specific processing performed by the specific processing unit 290 of the data processing device 12 is realized by the following means. In this invention, the server includes means for receiving user input data from an information processing device, means for generating artificial intelligence that analyzes the user input data and identifies extracted keywords and phrases, means for comparing the keywords and phrases with a patent information database and a trademark database, means for generating metadata indicating the relevant sections if the comparison results include relevant patent and trademark information, means for an emotion engine that analyzes and recognizes the user's emotions, means for generating and transmitting data edited according to the user's emotions based on the metadata, and means for displaying the edited data on a user interface. This makes it possible to provide optimal information based on the user's emotions.

[1580] An "information processing device" refers to any computer system that receives input data from a user and transmits it to a server.

[1581] "Generative artificial intelligence means" refers to an algorithm or program that analyzes input data and extracts key keywords or phrases.

[1582] A "patent information database" refers to a database system that stores information about existing patents.

[1583] A "trademark database" refers to a database system that stores information about existing trademarks.

[1584] "Means for generating metadata" refers to an algorithm or program that generates metadata, including relevant patent and trademark information, based on the matching results.

[1585] "Emotional engine means" refers to an algorithm or program for analyzing and recognizing emotions from user input data.

[1586] "Means for transmitting edited data" refers to an algorithm or program for transmitting edited data to a user based on the generated metadata.

[1587] A "user interface" refers to the screens or applications that users use to input information or to view edited data.

[1588] "Data edited to reflect user emotions" refers to data that includes information and advertisements optimized to take user emotions into consideration.

[1589] The specific system for implementing this invention consists of several main components. This system aims to receive and analyze user input data, recognize the user's emotions, provide relevant patent and trademark information, and generate and present edited data based on the user's emotions at the optimal time.

[1590] Hardware and software to use

[1591] 1. Information processing device:

[1592] These are primarily devices used by users. For example, smartphones and personal computers fulfill this role. They receive user input data and send it to the server.

[1593] 2. Generative Artificial Intelligence:

[1594] Software for data analysis and keyword extraction. Specifically, it uses generative AI models such as GPT-4. This model analyzes the input text data and extracts key keywords and phrases.

[1595] 3. Database matching function:

[1596] Software for performing query searches against patent and trademark databases. This allows the system to verify whether entered keywords or phrases are related to existing patents or trademarks.

[1597] 4. Emotional Engine:

[1598] Software that analyzes and recognizes user emotions from user input data. Specifically, it uses tools such as the Google Cloud Natural Language API.

[1599] 5. Metadata generation function:

[1600] Software that generates metadata, including relevant patent and trademark information, based on matching results. It also adjusts the content and display timing of the metadata based on user sentiment.

[1601] 6. Function to send and display edited data:

[1602] Software that sends generated metadata to the user and displays it in the user interface (UI). This allows the user to see optimized information in real time.

[1603] Specific examples of the system

[1604] Let's say a user enters text into a smartphone application such as, "I'm interested in the latest smartphones. What features do they have?" The following is a specific example of how the system would function in that situation.

[1605] 1. Obtaining user input:

[1606] The user enters text and sends it to the server via an information processing device (smartphone).

[1607] 2. Text analysis:

[1608] Generative artificial intelligence (GPT-4) analyzes the input data and extracts key keywords such as "latest smartphones."

[1609] 3. Emotion recognition:

[1610] The emotion engine (Google Cloud Natural Language API) recognizes emotions such as "excitement."

[1611] 4. Database matching:

[1612] The database matching function sends queries to the patent information database and trademark database to retrieve relevant information.

[1613] 5. Metadata generation:

[1614] Metadata including relevant patent and trademark information is generated, and information corresponding to the user's "excitement" is added.

[1615] 6. Submitting and displaying edited data:

[1616] The edited data is sent to the user at the optimal time and displayed on their smartphone screen along with the message, "This idea is related to a specific trademark."

[1617] Example of a prompt

[1618] Input example:

[1619] "I'm interested in the latest smartphones. What features do they have?"

[1620] In this way, the system can provide relevant patent and trademark information through the analysis of user input data and emotion recognition, as well as provide optimal information tailored to the user's emotions. This improves advertising effectiveness and maximizes the user experience.

[1621] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[1622] Step 1:

[1623] A user enters text into a smartphone application. For example, they might type, "I'm interested in the latest smartphones. What features do they have?" Once the user has finished typing, the device sends this user input data to a server using a secure communication protocol (such as HTTPS). The input data is in the format of regular text data.

[1624] Step 2:

[1625] The server analyzes the received text data. This analysis is performed using generative artificial intelligence (e.g., GPT-4). The input text data is first broken down, and key keywords and phrases are extracted. Specifically, keywords such as "latest smartphone" and "features" are extracted. The output of this step is a list of the extracted keywords and phrases.

[1626] Step 3:

[1627] The server uses an emotion engine (e.g., Google Cloud Natural Language API) to analyze the user's emotions from extracted keywords and phrases. Specifically, it analyzes the wording and syntax of the input text data to identify emotions such as "excitement" and "anxiety." The output of this step is a label indicating the user's emotion.

[1628] Step 4:

[1629] The server sends queries to patent and trademark databases based on the extracted keywords and phrases. This verifies whether the entered data is related to existing patents or trademarks. Specifically, the database engine (e.g., SQL Server) executes the queries and retrieves matching records. The output of this step is a list of relevant patent and trademark information.

[1630] Step 5:

[1631] The server generates metadata based on the matching results. Specifically, it generates metadata explaining the relevance of patent and trademark information. Furthermore, this metadata is adjusted based on the user's emotions. For example, if the user is "excited," concise and engaging information is provided; if they are "anxious," more detailed explanations are added. The output of this step is optimized metadata.

[1632] Step 6:

[1633] The server constructs edited data based on the generated metadata. Specifically, it adds information such as "This idea is related to a specific trademark" to the original input text. The timing of delivery is also adjusted according to the user's mood. For example, if the user is "excited," the information is provided immediately. The output of this step is the edited data.

[1634] Step 7:

[1635] The server sends the edited data back to the terminal, which then displays the data in its user interface. The user can then review the information displayed on their smartphone screen. For example, a message such as "This idea is related to a specific trademark" might be displayed in a timely manner. The output of this step is the final display data.

[1636] Through the steps outlined above, the system achieves everything from analyzing user input data and recognizing emotions to matching patent and trademark information, generating metadata, and presenting edited data. This system allows users to receive relevant information in real time, significantly improving the effectiveness of advertising.

[1637] The specific processing unit 290 transmits the result of the specific processing to the robot 414. In the robot 414, the control unit 46A causes the speaker 240 and the controlled object 443 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[1638] Data generation model 58 is a type of so-called generative AI (Artificial Intelligence). One example of data generation model 58 is ChatGPT (Internet search<URL: https: / / openai.com / blog / chatgpt> ), Gemini (Internet search) <url: https: gemini.google.com ?hl="ja">Examples of generative AI include the following. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and with inference data such as audio data representing speech, text data representing text, and image data representing images. The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference results in data formats such as audio data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[1639] 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 this disclosure is not limited thereto, and the specific processing may also be performed by the robot 414.

[1640] Furthermore, the emotion identification model 59, acting as an emotion engine, may determine the user's emotion according to a specific mapping. Specifically, the emotion identification model 59 may determine the user's emotion according to a specific mapping, which is an emotion map (see Figure 9). Similarly, the emotion identification model 59 may also determine the robot's emotion, and the identification processing unit 290 may perform identification processing using the robot's emotion.

[1641] Figure 9 shows an emotion map 400 in which multiple emotions are mapped. In the emotion map 400, emotions are arranged in concentric circles radiating from the center. The closer to the center of the concentric circles, the more primitive the emotions are located. Further out of the concentric circles, emotions representing states and actions arising from mental states are located. Emotion is a concept that includes feelings and mental states. On the left side of the concentric circles, emotions that are generally generated from reactions occurring in the brain are located. On the right side of the concentric circles, emotions that are generally induced by situational judgment are located. Above and below the concentric circles, emotions that are generally generated from reactions occurring in the brain and induced by situational judgment are located. In addition, the emotion of "pleasure" is located on the upper side of the concentric circles, and the emotion of "displeasure" is located on the lower side. Thus, in the emotion map 400, multiple emotions are mapped based on the structure in which emotions arise, and emotions that are likely to occur simultaneously are mapped close together.

[1642] These emotions are distributed at the 3 o'clock position on the Emotion Map 400, and usually fluctuate between feelings of security and anxiety. In the right half of the Emotion Map 400, situational awareness takes precedence over internal feelings, resulting in a calm impression.

[1643] The inside of the Emotion Map 400 represents inner thoughts, while the outside represents actions. Therefore, the further you go from the outside of the Emotion Map 400, the more visible (expressed in actions) your emotions become.

[1644] Here, human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, it results in discomfort, and when they approach the ideal, it results in pleasure. Similarly, in robots, cars, motorcycles, etc., emotions can be created based on various balances, such as posture and battery level. When these balances deviate from the ideal, it results in discomfort, and when they approach the ideal, it results in pleasure. The emotion map can be generated, for example, based on Dr. Mitsuyoshi's emotion map (Research on a system for analyzing brain physiological signals of speech emotion recognition and emotion, Tokushima University, doctoral dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map contains emotions belonging to a region called "response," where sensation is dominant. The right half of the emotion map contains emotions belonging to a region called "situation," where situational awareness is dominant.

[1645] The emotion map defines two emotions that promote learning. One is the emotion around the middle of the negative "repentance" and "reflection" on the situation side. In other words, it is when the robot experiences negative emotions such as "I never want to feel this way again" or "I don't want to be scolded again." The other is the emotion around the positive "desire" on the reaction side. In other words, it is when the robot has positive feelings such as "I want more" or "I want to know more."

[1646] The emotion identification model 59 inputs user input into a pre-trained neural network, obtains emotion values ​​representing each emotion shown in the emotion map 400, and determines the user's emotion. This neural network is pre-trained based on multiple training data sets, which are combinations of user input and emotion values ​​representing each emotion shown in the emotion map 400. Furthermore, this neural network is trained so that emotions located close together have similar values, as shown in the emotion map 900 in Figure 10. Figure 10 shows an example where multiple emotions such as "reassured," "calm," and "confident" have similar emotion values.

[1647] The above description primarily focuses on the functions of the data processing device 12 in relation to this disclosure. However, the system related to this disclosure is not necessarily implemented on a server. The system related to this disclosure may be implemented as a general information processing system. This disclosure may be implemented, for example, as a software program that runs on a personal computer or as an application that runs on a smartphone. The method related to this disclosure may be provided to users in SaaS (Software as a Service) format.

[1648] In the above embodiment, an example was given in which a specific process is performed by a single computer 22. However, the technology of this disclosure is not limited thereto, and a distributed processing of the specific process may be performed by multiple computers, including computer 22. For example, a data generation model 58 may be provided in an external device of the data processing device 12, and the external device may generate data according to the input data.

[1649] In the above embodiment, an example was given in which the specific processing program 56 is stored in the storage 32, but the technology of this disclosure is not limited thereto. For example, the specific processing program 56 may be stored in a portable, computer-readable, non-temporary storage medium such as a USB (Universal Serial Bus) memory. The specific processing program 56 stored in the non-temporary storage medium is installed in the computer 22 of the data processing device 12. The processor 28 executes specific processing according to the specific processing program 56.

[1650] 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.

[1651] Furthermore, it is not necessary to store the entirety of the specific processing program 56 in a storage device such as a server connected to the data processing device 12 via the network 54, or to store the entirety of the specific processing program 56 in the storage 32; it is acceptable to store only a portion of the specific processing program 56.

[1652] The following types of processors can be used as hardware resources to perform specific processing. Examples of processors include a CPU, a general-purpose processor that functions as a hardware resource to perform specific processing by executing software, i.e., a program. Other examples of processors include dedicated electrical circuits, such as FPGAs (Field-Programmable Gate Arrays), PLDs (Programmable Logic Devices), or ASICs (Application Specific Integrated Circuits), which have circuit configurations specifically designed to perform specific processing. All of these processors have built-in or connected memory, and all of them perform specific processing by using memory.

[1653] The hardware resource that performs a specific process may consist of one of these various processors, or it may consist of a combination of two or more processors of the same or different types (for example, a combination of multiple FPGAs, or a combination of a CPU and an FPGA). Alternatively, the hardware resource that performs a specific process may consist of a single processor.

[1654] Examples of configurations using a single processor include, firstly, a configuration in which one or more CPUs and software are combined to form a single processor, and this processor functions as a hardware resource that performs a specific process. Secondly, there is a configuration using a processor that realizes the functions of the entire system, including multiple hardware resources that perform a specific process, on a single IC chip, as exemplified by SoCs (System-on-a-chip). In this way, a specific process is realized using one or more of the above types of processors as hardware resources.

[1655] Furthermore, the hardware structure of these various processors can more specifically utilize electrical circuits that combine circuit elements such as semiconductor devices. Also, the specific processing described above is merely an example. Therefore, it goes without saying that unnecessary steps can be deleted, new steps added, or the processing order rearranged, as long as it does not deviate from the main purpose.

[1656] The descriptions and illustrations presented above are detailed explanations of the technical aspects of this disclosure and are merely examples of the technical aspects. For example, the above descriptions of the structure, function, operation, and effect are examples of the structure, function, operation, and effect of the technical aspects of this disclosure. Therefore, it goes without saying that you may delete unnecessary parts, add new elements, or replace elements in the descriptions and illustrations presented above, as long as you do not deviate from the essence of the technical aspects of this disclosure. Furthermore, in order to avoid confusion and facilitate understanding of the technical aspects of this disclosure, explanations of common technical knowledge and the like that do not require special explanation to enable the implementation of the technical aspects of this disclosure have been omitted from the descriptions and illustrations presented above.

[1657] All documents, patent applications, and technical standards described herein are incorporated by reference to the same extent as if each individual document, patent application, and technical standard were specifically and individually noted as being incorporated by reference.

[1658] The following is further disclosed regarding the embodiments described above.

[1659] (Claim 1)

[1660] A means for receiving user input data from an information processing device,

[1661] A generative artificial intelligence means that analyzes the user input data and identifies extracted keywords and phrases,

[1662] Means for matching the aforementioned keywords or phrases with a patent information database and a trademark database,

[1663] If the results of the aforementioned matching include relevant patent and trademark information, means for generating metadata indicating the relevant portion,

[1664] A means for adding the metadata to the user input data and returning the edited data,

[1665] A system that includes this.

[1666] (Claim 2)

[1667] A means for receiving user input data from an information processing device,

[1668] A generative artificial intelligence means that analyzes the user input data and identifies extracted keywords and phrases,

[1669] A means for matching the aforementioned keywords or phrases with a patent information database and a trademark database, and generating related metadata if a match is found,

[1670] A means for combining the aforementioned metadata and the original user input data and outputting the result to a display device,

[1671] The system according to claim 1, including the following:

[1672] (Claim 3)

[1673] A means for receiving user input data from an information processing device,

[1674] A generative artificial intelligence means that analyzes the user input data and identifies extracted keywords and phrases,

[1675] Means for matching the aforementioned keywords or phrases with a patent information database and a trademark database,

[1676] A means for re-evaluating the matching results when updating the aforementioned database,

[1677] A means for updating the data edited based on the aforementioned re-evaluation results in real time,

[1678] The system according to claim 1, including the following:

[1679] "Example 1"

[1680] (Claim 1)

[1681] A means for receiving user input data from an information processing device,

[1682] A generative artificial intelligence means that analyzes the user input data and identifies extracted keywords and phrases,

[1683] Means for matching the aforementioned keywords or phrases with a patent information database and a trademark database,

[1684] As a result of the aforementioned matching, means for generating metadata including relevant patent and trademark information,

[1685] A means for adding the metadata to the user input data and returning the edited data,

[1686] A means for displaying the received edited data on the user interface,

[1687] A system that includes this.

[1688] (Claim 2)

[1689] A means for receiving user input data from an information processing device,

[1690] A generative artificial intelligence means that analyzes the user input data and identifies extracted keywords and phrases,

[1691] A means for matching the aforementioned keywords or phrases with a patent information database and a trademark database, and generating related metadata if a match is found,

[1692] A means for combining the aforementioned metadata and the original user input data and outputting the result to a display device,

[1693] The system according to claim 1, including the following:

[1694] (Claim 3)

[1695] A means for receiving user input data from an information processing device,

[1696] A generative artificial intelligence means that analyzes the user input data and identifies extracted keywords and phrases,

[1697] Means for matching the aforementioned keywords or phrases with a patent information database and a trademark database,

[1698] A means for re-evaluating the matching results when updating the aforementioned database,

[1699] A means for updating the data edited based on the aforementioned re-evaluation results in real time,

[1700] The system according to claim 1, including the following:

[1701] "Application Example 1"

[1702] (Claim 1)

[1703] A means for receiving user input data from an information processing device,

[1704] A generative artificial intelligence means that analyzes the user input data and identifies extracted keywords and phrases,

[1705] Means for matching the aforementioned keywords or phrases with a patent information database and a trademark database,

[1706] If the results of the aforementioned matching include relevant patent and trademark information, means for generating metadata indicating the relevant portion,

[1707] A means for adding the metadata to the user input data and returning the edited data,

[1708] A means of sending and receiving data using satellite communication,

[1709] A means of assessing trademark and patent-related risks in real time,

[1710] A system that includes this.

[1711] (Claim 2)

[1712] A means for combining the aforementioned metadata and the original user input data and outputting the result to a display device,

[1713] A means of sending and receiving data using satellite communication,

[1714] The system according to claim 1, including the following:

[1715] (Claim 3)

[1716] A means for re-evaluating the matching results when updating the aforementioned database,

[1717] A means for updating the data edited based on the aforementioned re-evaluation results in real time,

[1718] A means of sending and receiving data using satellite communication,

[1719] The system according to claim 1, including the following:

[1720] "Example 2 of combining an emotion engine"

[1721] (Claim 1)

[1722] A means for receiving user input data from a computer device,

[1723] A generative artificial intelligence means that analyzes the user input data and identifies extracted keywords and phrases,

[1724] An emotion recognition means for analyzing and recognizing the user's emotions from the aforementioned user input data,

[1725] Means for matching the aforementioned keywords or phrases with a patent information database and a trademark database,

[1726] If the results of the aforementioned matching include relevant patent and trademark information, means for generating metadata indicating the relevant portion,

[1727] Means for adjusting metadata generation and editing based on the results of the emotion recognition means,

[1728] A means for adding the metadata to the user input data and returning the edited data,

[1729] Means for adjusting the timing of presenting the edited data,

[1730] A system that includes this.

[1731] (Claim 2)

[1732] A means for receiving user input data from a computer device,

[1733] A generative artificial intelligence means that analyzes the user input data and identifies extracted keywords and phrases,

[1734] An emotion recognition means for analyzing and recognizing the user's emotions from the aforementioned user data,

[1735] A means for matching the aforementioned keywords or phrases with a patent information database and a trademark database, and generating related metadata if a match is found,

[1736] Means for adjusting metadata generation and editing based on the results of the emotion recognition means,

[1737] A means for combining the aforementioned metadata and the original user input data and outputting the result to a display device,

[1738] The system according to claim 1, including the following:

[1739] (Claim 3)

[1740] A means for receiving user input data from a computer device,

[1741] A generative artificial intelligence means that analyzes the user input data and identifies extracted keywords and phrases,

[1742] An emotion recognition means for analyzing and recognizing the user's emotions from the aforementioned user data,

[1743] Means for matching the aforementioned keywords or phrases with a patent information database and a trademark database,

[1744] A means for re-evaluating the matching results when updating the aforementioned database,

[1745] A means for adjusting the re-evaluation results based on the results of the emotion recognition means and updating the edited data in real time,

[1746] The system according to claim 1, including the following:

[1747] "Application example 2 when combining with an emotional engine"

[1748] (Claim 1)

[1749] A means for receiving user input data from an information processing device,

[1750] A generative artificial intelligence means that analyzes the user input data and identifies extracted keywords and phrases,

[1751] Means for matching the aforementioned keywords or phrases with a patent information database and a trademark database,

[1752] If the results of the aforementioned matching include relevant patent and trademark information, means for generating metadata indicating the relevant portion,

[1753] An emotion engine that analyzes and recognizes user emotions,

[1754] A means for generating and transmitting data edited according to the user's emotions based on the aforementioned metadata,

[1755] Means for displaying the edited data on a user interface,

[1756] A system that includes this.

[1757] (Claim 2)

[1758] The system according to claim 1, which extracts keywords and phrases from user input data and recognizes the user's emotions using an emotion engine.

[1759] (Claim 3)

[1760] The system according to claim 1, which displays keywords and phrases generated based on user input data and information corresponding to the user's emotions. [Explanation of symbols]

[1761] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Devices 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robots< / url:> < / url:> < / url:> < / url:>

Claims

1. A means for receiving user input data from an information processing device, A generative artificial intelligence means that analyzes the user input data and identifies extracted keywords and phrases, Means for matching the aforementioned keywords or phrases with a patent information database and a trademark database, If the results of the aforementioned matching include relevant patent and trademark information, means for generating metadata indicating the relevant portion, A means for adding the metadata to the user input data and returning the edited data, A system that includes this.

2. A means for receiving user input data from an information processing device, A generative artificial intelligence means that analyzes the user input data and identifies extracted keywords and phrases, A means for matching the aforementioned keywords or phrases with a patent information database and a trademark database, and generating related metadata if a match is found, A means for combining the aforementioned metadata and the original user input data and outputting the result to a display device, The system according to claim 1, including the following:

3. A means for receiving user input data from an information processing device, A generative artificial intelligence means that analyzes the user input data and identifies extracted keywords and phrases, Means for matching the aforementioned keywords or phrases with a patent information database and a trademark database, A means for re-evaluating the matching results when updating the aforementioned database, A means for updating the data edited based on the aforementioned re-evaluation results in real time, The system according to claim 1, including the following:

Images