system

Abstract

We provide the system. [Solution] Means for obtaining a set of information including patent information, A means for performing analysis using generative artificial intelligence to identify similar patents from the aforementioned information set, Means for listing the aforementioned similar patents, A means for automatically generating a new patent application document by referring to past application documents, Means for verifying the grammar and structure of the generated patent application document, 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 method for controlling a persona chatbot, which is performed by at least one processor, including the steps of receiving a user utterance, adding the user utterance to a prompt including an instruction sentence related to an explanation of a character of the chatbot, 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 the patent application business of enterprises, there are inefficiencies due to personal work, differences in know-how between new employees and veterans, and the burden of time and labor due to a large amount of materials. In addition, the prior art search to avoid duplication with existing patents also requires a huge amount of time, which is a factor that presses the application deadline. The conventional method has a problem that there are not enough means to efficiently overcome such problems.

Means for Solving the Problems

[0005] This invention provides a means for identifying similar or overlapping patents by acquiring a set of information including patent information and analyzing this information using generative artificial intelligence. Furthermore, it includes means for automatically generating a new patent application document based on a patent summary entered by the user, referencing past application documents. This reduces the effort required in the drafting stage of documents and simplifies the process of verifying the grammar and structure of the generated documents. The system has the ability to improve accuracy by having the generative artificial intelligence continuously learn the latest patent information, thereby improving the overall efficiency of the patent application process.

[0006] "Patent information" refers to data that records details and application status of patents that have already been filed.

[0007] An "information set" is a collection of data and knowledge gathered for a specific purpose.

[0008] "Generative artificial intelligence" refers to machine learning models and algorithms that have the ability to generate new data and information.

[0009] A "similar patent" refers to an existing patent that has similar technical features or structure to a newly filed patent.

[0010] "Listing" refers to the act of selecting elements based on specific criteria and organizing them into a list.

[0011] "Application documents" refer to official documents and records submitted in order to obtain a patent.

[0012] Grammar is a system of rules concerning the form of words and the structure of sentences in a language.

[0013] "Composition" refers to the overall structure and arrangement formed by the combination of elements.

[0014] A "draft" refers to a document that shows the initial design or draft version of a document.

[0015] "User" refers to a person, company, or organization that uses a system or device.

Brief Explanation of Drawings

[0016] [Figure 1] It is a conceptual diagram showing an example of the configuration of a data processing system according to the first embodiment. [Figure 2] It is a conceptual diagram showing an example of the main functions of a data processing device and a smart device according to the first embodiment. [Figure 3] It is a conceptual diagram showing an example of the configuration of a data processing system according to the second embodiment. [Figure 4] It 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] It is a conceptual diagram showing an example of the configuration of a data processing system according to the third embodiment. [Figure 6] It 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] It is a conceptual diagram showing an example of the configuration of a data processing system according to the fourth embodiment. [Figure 8] It 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] It shows an emotion map to which multiple emotions are mapped. [Figure 10] It shows an emotion map to which multiple emotions are mapped. [Figure 11] It is a sequence diagram showing the processing flow of the data processing system in Example 1. [Figure 12] It 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 Example 2 when an 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 an emotion engine is combined.

Embodiments for Carrying out the Invention

[0017] Hereinafter, an example of an embodiment of a system according to the technology of the present disclosure will be described with reference to the accompanying drawings.

[0018] First, the terms used in the following description will be explained.

[0019] In the following embodiments, a processor with a reference number (hereinafter simply referred to as "processor") may be a single arithmetic unit or a combination of multiple arithmetic units. Also, the processor may be a single 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.

[0020] In the following embodiments, a RAM (Random Access Memory) with a reference number is a memory in which information is temporarily stored and is used as a work memory by the processor.

[0021] In the following embodiments, a storage with a reference number 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.

[0022] 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).

[0023] 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."

[0024] [First Embodiment]

[0025] Figure 1 shows an example of the configuration of the data processing system 10 according to the first embodiment.

[0026] 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.

[0027] 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).

[0028] 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.

[0029] 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.

[0030] 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.

[0031] 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.

[0032] Figure 2 shows an example of the main functions of the data processing device 12 and the smart device 14.

[0033] 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.

[0034] 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.

[0035] 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.

[0036] 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".

[0037] This patent application support system provides technology that streamlines the patent application process through the mutual cooperation of servers, terminals, and users. In particular, the server incorporates generative artificial intelligence and connects to a continuously updated patent database to perform prior art searches using the latest patent information.

[0038] First, the user inputs an overview and features of their invention into the terminal. The terminal sends this information to the server. The server retrieves relevant patent information from its database based on the input information and uses generative artificial intelligence to automatically analyze and list similar and overlapping patents. This list is provided to the user via the terminal, allowing the user to immediately evaluate the relevance. For example, when applying for a patent for a new electronic device, the server displays multiple similar technologies from the literature.

[0039] Next, the server references past patent application documents and automatically generates a draft of the new application document based on the user's input. This draft is sent to the user's terminal, where the user can add or modify as needed. Through this process, the effort involved in the initial stages of patent application can be significantly reduced.

[0040] Furthermore, the application documents revised by the user are again managed on the server side and subjected to proofreading by generative artificial intelligence. The server automatically detects document consistency and grammatical errors and provides feedback to the user. In this way, users can quickly and accurately perform a final review of their patent application documents.

[0041] This system reduces the subjective elements of patent application work and narrows the skill gap between new and experienced professionals. Furthermore, it offers the advantage of improving the success rate of applications by accurately avoiding duplicate patents.

[0042] The following describes the processing flow.

[0043] Step 1:

[0044] The user enters an overview and features of the invention into the terminal. The terminal prepares to send the input information to the server.

[0045] Step 2:

[0046] The server receives information sent by the user and accesses the patent database. The server extracts relevant keywords and obtains the necessary datasets for prior art searches.

[0047] Step 3:

[0048] The server analyzes the acquired dataset using generative artificial intelligence. It automatically identifies and lists patents that may be similar or overlapping.

[0049] Step 4:

[0050] The server sends a list of similar patents it has identified to the terminal. The terminal displays the list to the user, who then reviews the findings.

[0051] Step 5:

[0052] The server references past patent application documents and automatically generates a draft of a new application document. The generated draft reflects user input within the configuration.

[0053] Step 6:

[0054] The server sends a draft of the application document it generates to the user's terminal. The user reviews the draft on the terminal and makes additions or corrections as needed.

[0055] Step 7:

[0056] The user sends the corrected application documents back from their terminal to the server. The server then uses generative artificial intelligence to review the received documents, detecting inconsistencies and grammatical errors.

[0057] Step 8:

[0058] The server generates feedback based on the proofreading results and sends it to the user's terminal. The user receives the feedback, makes final confirmations and adjustments, and aims to complete the patent application document.

[0059] (Example 1)

[0060] 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."

[0061] The patent application process is extremely complex, requiring significant time and expertise, particularly for prior art searches and application document preparation. Furthermore, effective identification of similar patents in the early stages of the application process is crucial to avoid over-examination and duplication. Additionally, grammatical checks and consistency verification are essential to improve the accuracy of application documents, necessitating support to bridge the skill gap between new and experienced patent holders.

[0062] 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.

[0063] In this invention, the server includes means for acquiring a data set containing patent information, means for acquiring relevant patent information from the data set and analyzing similar patents using generative AI, and means for checking the grammatical errors and consistency of the generated patent application documents. This enables efficient prior art searches and improved accuracy of documents in patent application work.

[0064] "Patent information" refers to data related to patents, including information such as the content of the patent, application status, and scope of rights.

[0065] A "data set" is a collection of aggregated information consisting of multiple data items.

[0066] "Generative AI" is a type of artificial intelligence technology that has the ability to automatically generate new information or documents based on given data.

[0067] A "similar patent" is an existing patent that has technically similar characteristics to the invention for which a patent application is pending.

[0068] "Automated generation" is the process by which a system creates new documents or content without requiring human intervention.

[0069] A "grammatical error" refers to a violation of linguistic rules in written text.

[0070] "Consistency" refers to a state in which multiple data or documents have a unified, logical coherence.

[0071] "Feedback" refers to evaluations and information provided in response to specific results or reactions.

[0072] This invention provides a system in which servers, terminals, and users work together to streamline the patent application process. In this system, each component works together to automate and efficiently perform the information processing and document generation necessary for filing new patent applications.

[0073] The server is equipped with a processor and storage, and has the ability to access a database that holds patent information. In particular, the server incorporates a generative AI model (e.g., a natural language processing model), which is used to analyze input invention information and identify relevant existing patents. The server also automatically generates documents for new patent applications and performs grammatical and consistency checks.

[0074] The terminal provides an interface for users to input information. It has the function of converting user input into a digital format and sending it to the server. Furthermore, it displays patent information and draft application documents received from the server, allowing users to make corrections and verifications.

[0075] The user enters information about their invention into the terminal and proceeds with the patent application process using prompts. A specific example of a prompt is: "Regarding the invention of a new electronic device, please describe the specific features of the charging method. How does this device differ from other patented technologies?"

[0076] This system allows users to quickly and accurately compile an invention summary into patent application documents, significantly reducing the complexity of the patent application process. The server's AI generation model enables users to efficiently conduct prior art searches and automatically generate application documents, reducing manual workload. Furthermore, the document checking function improves the accuracy and success rate of applications.

[0077] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0078] Step 1:

[0079] The user uses a terminal to input an overview and features of the invention. This input is formatted as text data by the terminal. The terminal then prepares to send the input data to the server over the network. As a specific example of input, the user inputs the characteristics of a new electronic device.

[0080] Step 2:

[0081] The terminal sends the data entered by the user to the server. Here, the terminal converts the data to the appropriate protocol and transmits it without signal loss. This process allows the server to receive the input text data.

[0082] Step 3:

[0083] The server retrieves relevant patent information from the patent database based on the input data it receives. A generative AI model is used here, comparing and analyzing the input data with the extracted information for each patent through natural language processing. This process identifies related similar patent information.

[0084] Step 4:

[0085] The server uses a generative AI model to analyze and list related similar patents. The acquired patent information is organized through an AI scoring process to generate a list of similar patents. Once this list is generated, it is prepared to be sent to the terminal.

[0086] Step 5:

[0087] The terminal provides the user with a list of similar patents sent from the server. At this stage, the user can evaluate the relevance of the patents based on the provided list. The list is displayed visually on the interface and has the functionality to display detailed information when clicked.

[0088] Step 6:

[0089] The server automatically generates a draft of a new patent application document using a generative AI model based on user input and acquired patent information. In this process, the AI ​​constructs the document based on past patent application formats. This draft is then sent to the terminal.

[0090] Step 7:

[0091] The terminal displays the generated draft to the user. The user reviews the draft and makes any necessary corrections. User corrections are made in real time through a text editor and are reflected immediately.

[0092] Step 8:

[0093] The user's revised application document is sent back to the server and reviewed by a generative AI model. The server detects grammatical errors and structural problems in the document and provides feedback to the user. Based on this feedback, the user makes a final review of the document.

[0094] (Application Example 1)

[0095] 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."

[0096] The patent application process requires the efficient use of a vast and complex database of patent information to avoid overlapping patents and identify patent infringement risks early on. However, current methods often involve manual searches by humans, which are time-consuming and labor-intensive, and make it difficult to quickly reflect the latest patent information. Furthermore, the lack of real-time patent information verification and immediate warnings about relevant information makes it difficult to make quick and accurate decisions.

[0097] 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.

[0098] In this invention, the server includes means for acquiring a data set containing patent information, means for performing analysis using a generative AI model to identify similar technical documents from the data set, and equipment for acquiring visual information and using said information to analyze patent risk. This enables real-time identification and analysis of patent information. By using a generative AI model, the latest information can be quickly reflected and highly accurate risk analysis can be achieved, making it possible to quickly provide useful information to users.

[0099] "Patent information" refers to all information related to patents, including data such as technical documents and legal information concerning past and present inventions.

[0100] A "data set" is a collection of many data points gathered based on a specific purpose or condition, and is the subject of analysis and calculation.

[0101] A "generative AI model" is artificial intelligence designed to mimic human speech and behavior, and is a collection of algorithms trained to efficiently perform specific tasks.

[0102] "Visual information" refers to image and video data acquired through devices such as cameras and sensors, and is used for analysis and judgment.

[0103] "Technical documents" refer to documents that provide detailed information about research, development, and implementation in a specific technological field, and include information recorded in patent databases, etc.

[0104] "Equipment for risk analysis" refers to a set of software and hardware necessary to assess the likelihood of patent infringement or duplication, and includes a system for processing data based on specific criteria.

[0105] This invention provides a system aimed at streamlining the patent application process. The system primarily operates through the cooperation of three parties: a server, a terminal, and a user. Embodiments for each step are described below.

[0106] First, the user inputs a summary and features of their invention into a terminal. The terminal then transmits this user-entered information to a server. The server is connected to a patent database and retrieves a data set based on this information. The server uses a generative AI model to identify similar technical documents and, based on this, lists relevant patent information. This process allows the user to quickly assess whether similar inventions have existed in the past.

[0107] To acquire visual information, visual equipment such as cameras and sensors are used as needed. This visual data is used to analyze patent risk. Specifically, the data acquired from visual information is used to perform analysis to detect potential patent infringement. The results of this analysis are provided to the user in real time using a generative AI model, enabling immediate action.

[0108] For example, when a user is visually inspecting a new electronic device, the system retrieves potentially relevant patent information in the background and immediately displays a warning on the device if a patent risk exists. This allows the user to make accurate decisions in the early stages of patent application.

[0109] Examples of prompts to input into a generative AI model:

[0110] "Analyze the risks of similar technologies in the patent database. Determine if a new electronic component design infringes on existing patents and display a warning."

[0111] In this way, a form for concretely implementing the invention is provided. This allows users to efficiently proceed with the patent process and increase the likelihood of successful patent application.

[0112] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0113] Step 1:

[0114] The user enters a summary and features of the invention into the terminal. This input information is used as basic data for patent searches.

[0115] Step 2:

[0116] The terminal sends user input information to the server. This input includes an overview of the invention and keywords.

[0117] Step 3:

[0118] The server retrieves relevant data sets from the patent database based on the input information. These data sets include similar technologies and related patent documents.

[0119] Step 4:

[0120] The server uses a generative AI model to analyze the data set and identify similar technical documents. Based on the input information, it selects highly relevant patents and outputs them as analysis results.

[0121] Step 5:

[0122] The server creates a list of similar patents based on the analysis results and sends it to the terminal. The user uses this list to evaluate whether or not there are similar technologies.

[0123] Step 6:

[0124] If necessary, users can use visual equipment to capture specific devices or blueprints. This visual information is then used for risk analysis on the server.

[0125] Step 7:

[0126] The server analyzes the acquired visual information and assesses the risk of patent infringement. Image analysis includes extracting visual information and comparing it with database information.

[0127] Step 8:

[0128] The server uses a generated AI model to provide real-time feedback of risk analysis results to the user's device. Based on the displayed warnings, the user takes appropriate action.

[0129] 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.

[0130] The patent application support system of the present invention provides technology for streamlining the patent application process through the collaboration of servers, terminals, and users. This system incorporates generative artificial intelligence and an emotion engine, and includes advanced means for efficiently processing patent information.

[0131] First, the user inputs a summary and features of the patent into the terminal. The terminal receives this input and activates an emotion engine to recognize the user's emotions in real time. The emotion engine analyzes the user's emotional state during input and sends this information to the server. The server accesses the patent database to retrieve relevant patent information and performs data analysis using generative artificial intelligence.

[0132] When similar or overlapping patents are identified, the server sends this information to the terminal and displays it visually to the user. Furthermore, based on the analysis results from the emotion engine, the server adjusts the tone and style of the document to correspond to the user's emotional state. For example, if the system detects that the user is stressed, it suggests a supportive and concise document style.

[0133] Furthermore, the server automatically generates a draft of the new patent application document, referencing past patent application documents. This draft takes the user's emotional state into consideration and is designed to allow the user to intuitively make revisions and additions. The terminal displays the generated draft to the user, enabling them to make any necessary corrections.

[0134] Once the final application document is complete, the server uses generative artificial intelligence to proofread it. After verifying the grammar and structure, the server generates feedback and sends it to the user's terminal. This system allows users to efficiently and effectively manage the entire patent application process, improving the quality and speed of patent application work.

[0135] The following describes the processing flow.

[0136] Step 1:

[0137] The user enters a summary and features of the patent application into the terminal. The terminal prepares to send the entered information to the server and simultaneously activates the emotion engine to begin analyzing the user's emotions.

[0138] Step 2:

[0139] The emotion engine recognizes the user's emotional state in real time as they input data and sends the analysis results to the server. The server receives this emotion data and uses it as reference for the patent application process.

[0140] Step 3:

[0141] The server accesses the patent database and retrieves relevant patent information based on the summary information received from the user. The server uses generative artificial intelligence to analyze patents that may be similar or overlapping.

[0142] Step 4:

[0143] The server creates a list of similar patents based on the analysis results and sends it to the terminal. This list is displayed visually to the user and can be reviewed for reference.

[0144] Step 5:

[0145] The server references past patent documents and automatically generates a draft of a new patent application document based on the user's input information and emotional state. The generated draft is created with a tone and style that incorporates the analysis results of the emotion engine.

[0146] Step 6:

[0147] The terminal displays a draft of the generated patent application document to the user. The user can review the draft on the terminal and make additions or corrections as needed.

[0148] Step 7:

[0149] The user sends the revised document from their device to the server. The server then uses generative artificial intelligence to review the received document, performing checks on grammar and structure.

[0150] Step 8:

[0151] The server generates feedback based on the proofreading results and sends it to the terminal. The terminal displays the feedback to the user for final confirmation. The user then makes final adjustments based on this feedback and completes the patent application document.

[0152] (Example 2)

[0153] 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 device 14 as the "terminal".

[0154] The patent application process faces challenges such as the enormous amount of time and effort required for determining patent similarity, preparing documents, and reviewing them. Furthermore, a lack of approaches that consider the emotional state of patent applicants can lead to decreased work efficiency and lower quality of applications.

[0155] 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.

[0156] In this invention, the server includes means for acquiring a dataset containing patent information, means for identifying and analyzing similar patents from the dataset using a generative machine learning model, and means for recognizing the user's emotional state and adjusting the tone of the document. This enables increased efficiency in the patent application process and improved quality of the applications.

[0157] "Patent information" refers to a collection of data that describes the scope of patent rights and the technical content of a patent.

[0158] A "dataset" is a collection of data, including patent information and related materials.

[0159] A "generative machine learning model" is an artificial intelligence technology that has the ability to learn from data and generate new information.

[0160] A "similar patent" refers to an existing patent that has similar technical content and scope of rights to a newly applied-for patent.

[0161] "Analysis" is the process of examining data and extracting or evaluating specific information.

[0162] "Document tone" refers to the writing style and expression of a document, including the impression and emotional nuances it conveys to the reader.

[0163] "Emotional state" refers to the emotional state a user experiences at a particular moment, and it affects their comfort and efficiency during work.

[0164] This invention is an integrated system for supporting patent application procedures, in which multiple elements such as servers, terminals, and users work together, each fulfilling their respective roles, to achieve efficient patent application.

[0165] The user inputs a patent summary and features using a terminal. The terminal uses this input and simultaneously operates an emotion engine to recognize the user's emotional state in real time. The emotion engine uses a general-purpose GPU, which excels at high-speed processing, to quickly analyze the collected data. Data regarding the user's emotions is transmitted from the terminal to the server.

[0166] The server connects to a patent database and retrieves a dataset containing patent information. A general-purpose database management system is used for this process. Next, the server uses a generative AI model to analyze the retrieved dataset and identify similar patents. It is envisioned that an open-source model will be used for this generative AI model. The server sends the analysis results to the terminal and visually presents the similar patents to the user.

[0167] Furthermore, the server automatically generates a draft of a new document based on past patent application documents. During this process, the tone and style of the document are adjusted based on the user's emotional state. The terminal displays the generated draft to the user and provides an editor that allows the user to freely modify and add to it.

[0168] An example of a prompt message might be: "Please enter the features of your new smart device. The system will generate a document for your patent application." Through this prompt, the user can input the device's characteristics and the purpose of the application into the terminal.

[0169] Finally, the user-reviewed document is checked for grammar and structure by the server to improve its integrity. The server generates feedback and presents it to the user via the terminal to ensure the patent application document is proper. This comprehensive process allows users to proceed with their patent applications efficiently, resulting in fast and high-quality applications.

[0170] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0171] Step 1:

[0172] The user inputs a summary and features of the patent using a terminal. The terminal receives this input and activates an emotion engine to analyze the user's emotional state. Emotion data is generated from the input text data. The emotion engine analyzes the emotional tone of each sentence and outputs the results as emotion data.

[0173] Step 2:

[0174] The terminal sends the analyzed sentiment data to the server. The server accesses the patent database based on this data and retrieves relevant patent information. It compares the characteristics of the input patent with existing patents in the database and identifies similar patent data. As a result, it outputs a list of the similar patents found.

[0175] Step 3:

[0176] The server further analyzes the patent information obtained using a generative AI model. The generative AI model takes a list of similar patents as input and outputs more specific analysis results by examining the characteristics and related information of the relevant patents. This process accurately determines patent overlap and similarity.

[0177] Step 4:

[0178] The server automatically generates a draft of a new patent application document, taking into account similar patent information and user sentiment data. It also refers to a database of past application documents to appropriately set the style and tone. The generated document draft is output and sent to the user's terminal for further review.

[0179] Step 5:

[0180] The terminal displays a draft document sent from the server to the user. The user can make necessary corrections and additions through the interface. The corrected document is then resent from the terminal to the server, where its contents are finalized.

[0181] Step 6:

[0182] The server uses a generation AI model to proofread the received final version of the document. This process checks grammar and structure, and generates feedback with final revisions. A feedback report is then output and provided to the user.

[0183] This system streamlines the generation and verification of patent application documents, significantly reducing the workload for applicants.

[0184] (Application Example 2)

[0185] 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".

[0186] Patent applications are a highly specialized and labor-intensive process. Numerous steps are required, including the analysis of patent information, identification of related patents, and document preparation, and these tasks often become a burden for applicants. Furthermore, the efficiency of the work can vary significantly depending on the applicant's emotional state. This invention aims to solve these problems, streamline the patent application process, and reduce the burden on applicants.

[0187] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means.

[0188] In this invention, the server includes means for acquiring a data set including patent data, means for performing analysis using generative artificial intelligence to identify relevant patents, and means including an emotion recognition engine for analyzing the user's emotional state and adjusting the user interface. This makes it possible to streamline patent application processes and provide an optimal experience tailored to the user's emotions.

[0189] "Patent data" refers to a collection of data that includes all information related to a patent application.

[0190] A "dataset" refers to a series of datasets that contain a collection of various pieces of information.

[0191] "Generative artificial intelligence" refers to artificial intelligence technology that has the ability to generate useful information from large amounts of data.

[0192] "Performing an analysis" refers to the process of analyzing data to find specific results or patterns.

[0193] A "related patent" refers to another patent that is deemed to be technically similar to or related to a given patent.

[0194] "User interface" refers to the interaction design that allows users and systems to interact with each other.

[0195] An "emotion recognition engine" refers to a technology or system used to analyze a user's emotional state in real time.

[0196] "Document tone" refers to the style and emotional atmosphere that is consistently expressed throughout the entire text.

[0197] To implement this invention, three components are required: a server, a terminal, and a user.

[0198] Server Role

[0199] The server acquires a dataset containing patent data and uses generative artificial intelligence (AI) technology to identify and analyze relevant patents. The server combines the Python programming language with OpenAI's GPT-3 model to analyze the data and creates new patent application documents using the generative AI model. Furthermore, the server utilizes an emotion recognition engine to adjust the tone of the documents based on the user's emotional state. In this process, the server accesses the database using SQLAlchemy to retrieve information from past application documents.

[0200] Terminal role

[0201] The terminal receives the patent summary entered by the user and sends that information to the server. The terminal operates an emotion recognition engine, including a BERT model, which analyzes the user's input text in real time. At this time, the terminal provides the user with an appropriate interface and displays feedback from the server and the generated document.

[0202] User roles

[0203] Users input the necessary information for patent applications into a terminal and review and modify data and documents provided by the server. The system provides appropriate support based on the user's emotional state, ensuring a smooth patent application process.

[0204] For example, if a user enters an outline of a new battery technology, the device sends that information to a server, and a generating AI model searches for similar patents and displays relevant information. If the user's input is something like, "I'm busy and tired today...", the emotion recognition engine recognizes "stress" and adjusts the interface to provide support.

[0205] An example of a prompt message is, "Enter an overview of your new battery technology, search for similar patents, and draft a proposal." This allows users to proceed with patent applications efficiently.

[0206] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0207] Step 1:

[0208] The terminal provides an interface for the user to input a patent summary. Once the user completes the input, it sends the input data to the server. This data is the input and contains the patent summary information necessary for the next step.

[0209] Step 2:

[0210] Based on the received patent summary, the server begins analysis using the generative AI model GPT-3. At this stage, the server generates prompts to search for relevant similar patents in the patent database and executes database queries. The output at this stage is a list of similar patents.

[0211] Step 3:

[0212] The terminal visually displays a list of similar patents received from the server to the user. The user reviews this list and uses it as a reference to determine the most relevant patent information. The output of this step is an information display for the user to review.

[0213] Step 4:

[0214] The server generates a draft of a new patent application document, referencing past patent application documents. In this process, it utilizes a generation AI model to combine the received patent summary with similar patent data to create the new document. The input for this step is the patent summary and similar patent information, and the output is a draft of the initial application document.

[0215] Step 5:

[0216] The terminal displays a draft of the generated patent application document to the user. The user reviews the document and makes corrections or additions as needed. The output of this step is the draft after the user's revisions.

[0217] Step 6:

[0218] The server uses an emotion recognition engine to analyze the user's emotions in real time as they input. Based on that emotional state, it provides an interface adapted to the device. The input is the user's text input, and the output is an optimized user experience.

[0219] Step 7:

[0220] The server performs a final proofreading of the completed patent application document, verifying its grammar and structure. This process ensures document quality and provides a final check. The output is the final feedback and a corrected, perfect document.

[0221] 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.

[0222] 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.

[0223] 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.

[0224] [Second Embodiment]

[0225] Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment.

[0226] 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.

[0227] 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).

[0228] 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.

[0229] 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.

[0230] 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).

[0231] 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.

[0232] 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.

[0233] 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.

[0234] 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.

[0235] 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.

[0236] 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".

[0237] This patent application support system provides technology that streamlines the patent application process through the mutual cooperation of servers, terminals, and users. In particular, the server incorporates generative artificial intelligence and connects to a continuously updated patent database to perform prior art searches using the latest patent information.

[0238] First, the user inputs an overview and features of their invention into the terminal. The terminal sends this information to the server. The server retrieves relevant patent information from its database based on the input information and uses generative artificial intelligence to automatically analyze and list similar and overlapping patents. This list is provided to the user via the terminal, allowing the user to immediately evaluate the relevance. For example, when applying for a patent for a new electronic device, the server displays multiple similar technologies from the literature.

[0239] Next, the server references past patent application documents and automatically generates a draft of the new application document based on the user's input. This draft is sent to the user's terminal, where the user can add or modify as needed. Through this process, the effort involved in the initial stages of patent application can be significantly reduced.

[0240] Furthermore, the application documents revised by the user are again managed on the server side and subjected to proofreading by generative artificial intelligence. The server automatically detects document consistency and grammatical errors and provides feedback to the user. In this way, users can quickly and accurately perform a final review of their patent application documents.

[0241] This system reduces the subjective elements of patent application work and narrows the skill gap between new and experienced professionals. Furthermore, it offers the advantage of improving the success rate of applications by accurately avoiding duplicate patents.

[0242] The following describes the processing flow.

[0243] Step 1:

[0244] The user enters an overview and features of the invention into the terminal. The terminal prepares to send the input information to the server.

[0245] Step 2:

[0246] The server receives information sent by the user and accesses the patent database. The server extracts relevant keywords and obtains the necessary datasets for prior art searches.

[0247] Step 3:

[0248] The server analyzes the acquired dataset using generative artificial intelligence. It automatically identifies and lists patents that may be similar or overlapping.

[0249] Step 4:

[0250] The server sends a list of similar patents it has identified to the terminal. The terminal displays the list to the user, who then reviews the findings.

[0251] Step 5:

[0252] The server references past patent application documents and automatically generates a draft of a new application document. The generated draft reflects user input within the configuration.

[0253] Step 6:

[0254] The server sends a draft of the application document it generates to the user's terminal. The user reviews the draft on the terminal and makes additions or corrections as needed.

[0255] Step 7:

[0256] The user sends the corrected application documents back from their terminal to the server. The server then uses generative artificial intelligence to review the received documents, detecting inconsistencies and grammatical errors.

[0257] Step 8:

[0258] The server generates feedback based on the proofreading results and sends it to the user's terminal. The user receives the feedback, makes final confirmations and adjustments, and aims to complete the patent application document.

[0259] (Example 1)

[0260] 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."

[0261] The patent application process is extremely complex, requiring significant time and expertise, particularly for prior art searches and application document preparation. Furthermore, effective identification of similar patents in the early stages of the application process is crucial to avoid over-examination and duplication. Additionally, grammatical checks and consistency verification are essential to improve the accuracy of application documents, necessitating support to bridge the skill gap between new and experienced patent holders.

[0262] 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.

[0263] In this invention, the server includes means for acquiring a data set containing patent information, means for acquiring relevant patent information from the data set and analyzing similar patents using generative AI, and means for checking the grammatical errors and consistency of the generated patent application documents. This enables efficient prior art searches and improved accuracy of documents in patent application work.

[0264] "Patent information" refers to data related to patents, including information such as the content of the patent, application status, and scope of rights.

[0265] A "data set" is a collection of aggregated information consisting of multiple data items.

[0266] "Generative AI" is a type of artificial intelligence technology that has the ability to automatically generate new information or documents based on given data.

[0267] A "similar patent" is an existing patent that has technically similar characteristics to the invention for which a patent application is pending.

[0268] "Automated generation" is the process by which a system creates new documents or content without requiring human intervention.

[0269] A "grammatical error" refers to a violation of linguistic rules in written text.

[0270] "Consistency" refers to a state in which multiple data or documents have a unified, logical coherence.

[0271] "Feedback" refers to evaluations and information provided in response to specific results or reactions.

[0272] This invention provides a system in which servers, terminals, and users work together to streamline the patent application process. In this system, each component works together to automate and efficiently perform the information processing and document generation necessary for filing new patent applications.

[0273] The server is equipped with a processor and storage, and has the ability to access a database that holds patent information. In particular, the server incorporates a generative AI model (e.g., a natural language processing model), which is used to analyze input invention information and identify relevant existing patents. The server also automatically generates documents for new patent applications and performs grammatical and consistency checks.

[0274] The terminal provides an interface for users to input information. It has the function of converting user input into a digital format and sending it to the server. Furthermore, it displays patent information and draft application documents received from the server, allowing users to make corrections and verifications.

[0275] The user enters information about their invention into the terminal and proceeds with the patent application process using prompts. A specific example of a prompt is: "Regarding the invention of a new electronic device, please describe the specific features of the charging method. How does this device differ from other patented technologies?"

[0276] With this system, users can quickly and accurately compile the summary of the invention into patent application documents, significantly reducing the complexity of patent application operations. Through the server's generative AI model, users can efficiently conduct prior art searches related to patents and reduce the manual workload by automatically generating application documents. Additionally, the document checking function can improve the accuracy and success rate of applications.

[0277] The flow of the specific process in Example 1 will be described using FIG. 11.

[0278] Step 1:

[0279] The user uses the terminal to input the summary and features of the invention. This input is formatted by the terminal as text data. The terminal prepares to send the input data to the server via the network. As a specific example of the input, the user inputs the characteristics of a new electronic device.

[0280] Step 2:

[0281] The terminal sends the data input by the user to the server. Here, the terminal converts the data into an appropriate protocol and sends it to ensure no signal loss. Through this process, the server can receive the input text data.

[0282] Step 3:

[0283] Based on the received input data, the server retrieves relevant patent information from the patent database. Here, the generative AI model is utilized to compare and analyze the input data with the extracted information of each patent through natural language processing. Through this process, relevant similar patent information is identified.

[0284] Step 4:

[0285] The server analyzes and lists relevant similar patents using the generated AI model. It organizes the obtained patent information through AI-based scoring processing and generates a list of similar patents. Once this list is generated, it prepares to send it to the terminal.

[0286] Step 5:

[0287] The terminal provides the list of similar patents sent from the server to the user. At this stage, the user can evaluate the relevance of the patents based on the provided list. The list is visually displayed on the interface and has a function to display detailed information by clicking.

[0288] Step 6:

[0289] Based on the user's input information and the obtained patent information, the server automatically generates a draft of a new patent application document using the generated AI model. In this process, the AI constructs the text based on past patent application formats. Then, this draft is sent to the terminal.

[0290] Step 7:

[0291] The terminal displays the generated draft to the user. The user checks the draft and makes necessary corrections. The user's corrections are made in real-time through a text editor and are immediately reflected.

[0292] Step 8:

[0293] The application document with the user's corrections is sent back to the server and reviewed by the generated AI model. The server detects grammar mistakes and structural problems in the document and provides feedback to the user. Based on this feedback, the user makes a final confirmation of the document.

[0294] (Application Example 1)

[0295] 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."

[0296] The patent application process requires the efficient use of a vast and complex database of patent information to avoid overlapping patents and identify patent infringement risks early on. However, current methods often involve manual searches by humans, which are time-consuming and labor-intensive, and make it difficult to quickly reflect the latest patent information. Furthermore, the lack of real-time patent information verification and immediate warnings about relevant information makes it difficult to make quick and accurate decisions.

[0297] 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.

[0298] In this invention, the server includes means for acquiring a data set containing patent information, means for performing analysis using a generative AI model to identify similar technical documents from the data set, and equipment for acquiring visual information and using said information to analyze patent risk. This enables real-time identification and analysis of patent information. By using a generative AI model, the latest information can be quickly reflected and highly accurate risk analysis can be achieved, making it possible to quickly provide useful information to users.

[0299] "Patent information" refers to all information related to patents, including data such as technical documents and legal information concerning past and present inventions.

[0300] A "data set" is a collection of many data points gathered based on a specific purpose or condition, and is the subject of analysis and calculation.

[0301] A "generative AI model" is artificial intelligence designed to mimic human speech and behavior, and is a collection of algorithms trained to efficiently perform specific tasks.

[0302] "Visual information" refers to image and video data obtained through devices such as cameras and sensors, and refers to visual data used for analysis and judgment.

[0303] "Technical literature" refers to documents that provide detailed information on research, development, and implementation in a specific technical field, and refers to information recorded in patent databases and the like.

[0304] "Equipment for analyzing risks" refers to a set of software and hardware necessary for evaluating the possibility of patent infringement and duplication, and has a system for processing data based on specific criteria.

[0305] This invention provides a system aimed at improving the efficiency of the patent application process. The system mainly operates with the cooperation of a server, a terminal, and a user. The embodiments in each step will be described below.

[0306] First, the user inputs a summary and features of the invention content into the terminal. The terminal is responsible for sending the information input by this user to the server. The server is connected to the patent database and obtains a data set based on this information. The server uses a generative AI model to identify similar technical literature and lists relevant patent information based on this. Through this process, the user can quickly evaluate whether similar inventions have existed in the past.

[0307] For the acquisition of visual information, visual equipment such as cameras and sensors is used as needed. This visual data is used for analyzing patent risks. Specifically, analysis for detecting the possibility of patent infringement is performed using the data obtained from the visual information. The analysis results are provided to the user in real time using a generative AI model, enabling immediate response.

[0308] For example, when a user is visually inspecting a new electronic device, the system retrieves potentially relevant patent information in the background and immediately displays a warning on the device if a patent risk exists. This allows the user to make accurate decisions in the early stages of patent application.

[0309] Examples of prompts to input into a generative AI model:

[0310] "Analyze the risks of similar technologies in the patent database. Determine if a new electronic component design infringes on existing patents and display a warning."

[0311] In this way, a form for concretely implementing the invention is provided. This allows users to efficiently proceed with the patent process and increase the likelihood of successful patent application.

[0312] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0313] Step 1:

[0314] The user enters a summary and features of the invention into the terminal. This input information is used as basic data for patent searches.

[0315] Step 2:

[0316] The terminal sends user input information to the server. This input includes an overview of the invention and keywords.

[0317] Step 3:

[0318] The server retrieves relevant data sets from the patent database based on the input information. These data sets include similar technologies and related patent documents.

[0319] Step 4:

[0320] The server uses a generative AI model to analyze the data set and identify similar technical documents. Based on the input information, it selects highly relevant patents and outputs them as analysis results.

[0321] Step 5:

[0322] The server creates a list of similar patents based on the analysis results and sends it to the terminal. The user uses this list to evaluate whether or not there are similar technologies.

[0323] Step 6:

[0324] If necessary, users can use visual equipment to capture specific devices or blueprints. This visual information is then used for risk analysis on the server.

[0325] Step 7:

[0326] The server analyzes the acquired visual information and assesses the risk of patent infringement. Image analysis includes extracting visual information and comparing it with database information.

[0327] Step 8:

[0328] The server uses a generated AI model to provide real-time feedback of risk analysis results to the user's device. Based on the displayed warnings, the user takes appropriate action.

[0329] 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.

[0330] The patent application support system of the present invention provides technology for streamlining the patent application process through the collaboration of servers, terminals, and users. This system incorporates generative artificial intelligence and an emotion engine, and includes advanced means for efficiently processing patent information.

[0331] First, the user inputs a summary and features of the patent into the terminal. The terminal receives this input and activates an emotion engine to recognize the user's emotions in real time. The emotion engine analyzes the user's emotional state during input and sends this information to the server. The server accesses the patent database to retrieve relevant patent information and performs data analysis using generative artificial intelligence.

[0332] When similar or overlapping patents are identified, the server sends this information to the terminal and displays it visually to the user. Furthermore, based on the analysis results from the emotion engine, the server adjusts the tone and style of the document to correspond to the user's emotional state. For example, if the system detects that the user is stressed, it suggests a supportive and concise document style.

[0333] Furthermore, the server automatically generates a draft of the new patent application document, referencing past patent application documents. This draft takes the user's emotional state into consideration and is designed to allow the user to intuitively make revisions and additions. The terminal displays the generated draft to the user, enabling them to make any necessary corrections.

[0334] Once the final application document is complete, the server uses generative artificial intelligence to proofread it. After verifying the grammar and structure, the server generates feedback and sends it to the user's terminal. This system allows users to efficiently and effectively manage the entire patent application process, improving the quality and speed of patent application work.

[0335] The following describes the processing flow.

[0336] Step 1:

[0337] The user enters a summary and features of the patent application into the terminal. The terminal prepares to send the entered information to the server and simultaneously activates the emotion engine to begin analyzing the user's emotions.

[0338] Step 2:

[0339] The emotion engine recognizes the user's emotional state in real time as they input data and sends the analysis results to the server. The server receives this emotion data and uses it as reference for the patent application process.

[0340] Step 3:

[0341] The server accesses the patent database and retrieves relevant patent information based on the summary information received from the user. The server uses generative artificial intelligence to analyze patents that may be similar or overlapping.

[0342] Step 4:

[0343] The server creates a list of similar patents based on the analysis results and sends it to the terminal. This list is displayed visually to the user and can be reviewed for reference.

[0344] Step 5:

[0345] The server references past patent documents and automatically generates a draft of a new patent application document based on the user's input information and emotional state. The generated draft is created with a tone and style that incorporates the analysis results of the emotion engine.

[0346] Step 6:

[0347] The terminal displays a draft of the generated patent application document to the user. The user can review the draft on the terminal and make additions or corrections as needed.

[0348] Step 7:

[0349] The user sends the revised document from their device to the server. The server then uses generative artificial intelligence to review the received document, performing checks on grammar and structure.

[0350] Step 8:

[0351] The server generates feedback based on the proofreading results and sends it to the terminal. The terminal displays the feedback to the user for final confirmation. The user then makes final adjustments based on this feedback and completes the patent application document.

[0352] (Example 2)

[0353] 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".

[0354] The patent application process faces challenges such as the enormous amount of time and effort required for determining patent similarity, preparing documents, and reviewing them. Furthermore, a lack of approaches that consider the emotional state of patent applicants can lead to decreased work efficiency and lower quality of applications.

[0355] 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.

[0356] In this invention, the server includes means for acquiring a dataset containing patent information, means for identifying and analyzing similar patents from the dataset using a generative machine learning model, and means for recognizing the user's emotional state and adjusting the tone of the document. This enables increased efficiency in the patent application process and improved quality of the applications.

[0357] "Patent information" refers to a collection of data that describes the scope of patent rights and the technical content of a patent.

[0358] A "dataset" is a collection of data, including patent information and related materials.

[0359] A "generative machine learning model" is an artificial intelligence technology that has the ability to learn from data and generate new information.

[0360] A "similar patent" refers to an existing patent that has similar technical content and scope of rights to a newly applied-for patent.

[0361] "Analysis" is the process of examining data and extracting or evaluating specific information.

[0362] "Document tone" refers to the writing style and expression of a document, including the impression and emotional nuances it conveys to the reader.

[0363] "Emotional state" refers to the emotional state a user experiences at a particular moment, and it affects their comfort and efficiency during work.

[0364] This invention is an integrated system for supporting patent application procedures, in which multiple elements such as servers, terminals, and users work together, each fulfilling their respective roles, to achieve efficient patent application.

[0365] The user inputs a patent summary and features using a terminal. The terminal uses this input and simultaneously operates an emotion engine to recognize the user's emotional state in real time. The emotion engine uses a general-purpose GPU, which excels at high-speed processing, to quickly analyze the collected data. Data regarding the user's emotions is transmitted from the terminal to the server.

[0366] The server connects to a patent database and retrieves a dataset containing patent information. A general-purpose database management system is used for this process. Next, the server uses a generative AI model to analyze the retrieved dataset and identify similar patents. It is envisioned that an open-source model will be used for this generative AI model. The server sends the analysis results to the terminal and visually presents the similar patents to the user.

[0367] Furthermore, the server automatically generates a draft of a new document based on past patent application documents. During this process, the tone and style of the document are adjusted based on the user's emotional state. The terminal displays the generated draft to the user and provides an editor that allows the user to freely modify and add to it.

[0368] An example of a prompt message might be: "Please enter the features of your new smart device. The system will generate a document for your patent application." Through this prompt, the user can input the device's characteristics and the purpose of the application into the terminal.

[0369] Finally, the user-reviewed document is checked for grammar and structure by the server to improve its integrity. The server generates feedback and presents it to the user via the terminal to ensure the patent application document is proper. This comprehensive process allows users to proceed with their patent applications efficiently, resulting in fast and high-quality applications.

[0370] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0371] Step 1:

[0372] The user inputs a summary and features of the patent using a terminal. The terminal receives this input and activates an emotion engine to analyze the user's emotional state. Emotion data is generated from the input text data. The emotion engine analyzes the emotional tone of each sentence and outputs the results as emotion data.

[0373] Step 2:

[0374] The terminal sends the analyzed sentiment data to the server. The server accesses the patent database based on this data and retrieves relevant patent information. It compares the characteristics of the input patent with existing patents in the database and identifies similar patent data. As a result, it outputs a list of the similar patents found.

[0375] Step 3:

[0376] The server further analyzes the patent information obtained using a generative AI model. The generative AI model takes a list of similar patents as input and outputs more specific analysis results by examining the characteristics and related information of the relevant patents. This process accurately determines patent overlap and similarity.

[0377] Step 4:

[0378] The server automatically generates a draft of a new patent application document, taking into account similar patent information and user sentiment data. It also refers to a database of past application documents to appropriately set the style and tone. The generated document draft is output and sent to the user's terminal for further review.

[0379] Step 5:

[0380] The terminal displays a draft document sent from the server to the user. The user can make necessary corrections and additions through the interface. The corrected document is then resent from the terminal to the server, where its contents are finalized.

[0381] Step 6:

[0382] The server uses a generation AI model to proofread the received final version of the document. This process checks grammar and structure, and generates feedback with final revisions. A feedback report is then output and provided to the user.

[0383] This system streamlines the generation and verification of patent application documents, significantly reducing the workload for applicants.

[0384] (Application Example 2)

[0385] 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."

[0386] Patent applications are a highly specialized and labor-intensive process. Numerous steps are required, including the analysis of patent information, identification of related patents, and document preparation, and these tasks often become a burden for applicants. Furthermore, the efficiency of the work can vary significantly depending on the applicant's emotional state. This invention aims to solve these problems, streamline the patent application process, and reduce the burden on applicants.

[0387] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means.

[0388] In this invention, the server includes means for acquiring a data set including patent data, means for performing analysis using generative artificial intelligence to identify relevant patents, and means including an emotion recognition engine for analyzing the user's emotional state and adjusting the user interface. This makes it possible to streamline patent application processes and provide an optimal experience tailored to the user's emotions.

[0389] "Patent data" refers to a collection of data that includes all information related to a patent application.

[0390] A "dataset" refers to a series of datasets that contain a collection of various pieces of information.

[0391] "Generative artificial intelligence" refers to artificial intelligence technology that has the ability to generate useful information from large amounts of data.

[0392] "Performing an analysis" refers to the process of analyzing data to find specific results or patterns.

[0393] A "related patent" refers to another patent that is deemed to be technically similar to or related to a given patent.

[0394] "User interface" refers to the interaction design that allows users and systems to interact with each other.

[0395] An "emotion recognition engine" refers to a technology or system used to analyze a user's emotional state in real time.

[0396] "Document tone" refers to the style and emotional atmosphere that is consistently expressed throughout the entire text.

[0397] To implement this invention, three components are required: a server, a terminal, and a user.

[0398] Server Role

[0399] The server acquires a dataset containing patent data and uses generative artificial intelligence (AI) technology to identify and analyze relevant patents. The server combines the Python programming language with OpenAI's GPT-3 model to analyze the data and creates new patent application documents using the generative AI model. Furthermore, the server utilizes an emotion recognition engine to adjust the tone of the documents based on the user's emotional state. In this process, the server accesses the database using SQLAlchemy to retrieve information from past application documents.

[0400] Terminal role

[0401] The terminal receives the patent summary entered by the user and sends that information to the server. The terminal operates an emotion recognition engine, including a BERT model, which analyzes the user's input text in real time. At this time, the terminal provides the user with an appropriate interface and displays feedback from the server and the generated document.

[0402] User roles

[0403] Users input the necessary information for patent applications into a terminal and review and modify data and documents provided by the server. The system provides appropriate support based on the user's emotional state, ensuring a smooth patent application process.

[0404] For example, if a user enters an outline of a new battery technology, the device sends that information to a server, and a generating AI model searches for similar patents and displays relevant information. If the user's input is something like, "I'm busy and tired today...", the emotion recognition engine recognizes "stress" and adjusts the interface to provide support.

[0405] An example of a prompt message is, "Enter an overview of your new battery technology, search for similar patents, and draft a proposal." This allows users to proceed with patent applications efficiently.

[0406] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0407] Step 1:

[0408] The terminal provides an interface for the user to input a patent summary. Once the user completes the input, it sends the input data to the server. This data is the input and contains the patent summary information necessary for the next step.

[0409] Step 2:

[0410] Based on the received patent summary, the server begins analysis using the generative AI model GPT-3. At this stage, the server generates prompts to search for relevant similar patents in the patent database and executes database queries. The output at this stage is a list of similar patents.

[0411] Step 3:

[0412] The terminal visually displays a list of similar patents received from the server to the user. The user reviews this list and uses it as a reference to determine the most relevant patent information. The output of this step is an information display for the user to review.

[0413] Step 4:

[0414] The server generates a draft of a new patent application document, referencing past patent application documents. In this process, it utilizes a generation AI model to combine the received patent summary with similar patent data to create the new document. The input for this step is the patent summary and similar patent information, and the output is a draft of the initial application document.

[0415] Step 5:

[0416] The terminal displays a draft of the generated patent application document to the user. The user reviews the document and makes corrections or additions as needed. The output of this step is the draft after the user's revisions.

[0417] Step 6:

[0418] The server uses an emotion recognition engine to analyze the user's emotions in real time as they input. Based on that emotional state, it provides an interface adapted to the device. The input is the user's text input, and the output is an optimized user experience.

[0419] Step 7:

[0420] The server performs a final proofreading of the completed patent application document, verifying its grammar and structure. This process ensures document quality and provides a final check. The output is the final feedback and a corrected, perfect document.

[0421] 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.

[0422] 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.

[0423] 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.

[0424] [Third Embodiment]

[0425] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[0426] 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.

[0427] 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).

[0428] 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.

[0429] 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.

[0430] 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).

[0431] 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.

[0432] 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.

[0433] 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.

[0434] 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.

[0435] 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.

[0436] 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".

[0437] This patent application support system provides technology that streamlines the patent application process through the mutual cooperation of servers, terminals, and users. In particular, the server incorporates generative artificial intelligence and connects to a continuously updated patent database to perform prior art searches using the latest patent information.

[0438] First, the user inputs an overview and features of their invention into the terminal. The terminal sends this information to the server. The server retrieves relevant patent information from its database based on the input information and uses generative artificial intelligence to automatically analyze and list similar and overlapping patents. This list is provided to the user via the terminal, allowing the user to immediately evaluate the relevance. For example, when applying for a patent for a new electronic device, the server displays multiple similar technologies from the literature.

[0439] Next, the server references past patent application documents and automatically generates a draft of the new application document based on the user's input. This draft is sent to the user's terminal, where the user can add or modify as needed. Through this process, the effort involved in the initial stages of patent application can be significantly reduced.

[0440] Furthermore, the application documents revised by the user are again managed on the server side and subjected to proofreading by generative artificial intelligence. The server automatically detects document consistency and grammatical errors and provides feedback to the user. In this way, users can quickly and accurately perform a final review of their patent application documents.

[0441] This system reduces the subjective elements of patent application work and narrows the skill gap between new and experienced professionals. Furthermore, it offers the advantage of improving the success rate of applications by accurately avoiding duplicate patents.

[0442] The following describes the processing flow.

[0443] Step 1:

[0444] The user enters an overview and features of the invention into the terminal. The terminal prepares to send the input information to the server.

[0445] Step 2:

[0446] The server receives information sent by the user and accesses the patent database. The server extracts relevant keywords and obtains the necessary datasets for prior art searches.

[0447] Step 3:

[0448] The server analyzes the acquired dataset using generative artificial intelligence. It automatically identifies and lists patents that may be similar or overlapping.

[0449] Step 4:

[0450] The server sends a list of similar patents it has identified to the terminal. The terminal displays the list to the user, who then reviews the findings.

[0451] Step 5:

[0452] The server references past patent application documents and automatically generates a draft of a new application document. The generated draft reflects user input within the configuration.

[0453] Step 6:

[0454] The server sends a draft of the application document it generates to the user's terminal. The user reviews the draft on the terminal and makes additions or corrections as needed.

[0455] Step 7:

[0456] The user sends the corrected application documents back from their terminal to the server. The server then uses generative artificial intelligence to review the received documents, detecting inconsistencies and grammatical errors.

[0457] Step 8:

[0458] The server generates feedback based on the proofreading results and sends it to the user's terminal. The user receives the feedback, makes final confirmations and adjustments, and aims to complete the patent application document.

[0459] (Example 1)

[0460] 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."

[0461] The patent application process is extremely complex, requiring significant time and expertise, particularly for prior art searches and application document preparation. Furthermore, effective identification of similar patents in the early stages of the application process is crucial to avoid over-examination and duplication. Additionally, grammatical checks and consistency verification are essential to improve the accuracy of application documents, necessitating support to bridge the skill gap between new and experienced patent holders.

[0462] 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.

[0463] In this invention, the server includes means for acquiring a data set containing patent information, means for acquiring relevant patent information from the data set and analyzing similar patents using generative AI, and means for checking the grammatical errors and consistency of the generated patent application documents. This enables efficient prior art searches and improved accuracy of documents in patent application work.

[0464] "Patent information" refers to data related to patents, including information such as the content of the patent, application status, and scope of rights.

[0465] A "data set" is a collection of aggregated information consisting of multiple data items.

[0466] "Generative AI" is a type of artificial intelligence technology that has the ability to automatically generate new information or documents based on given data.

[0467] A "similar patent" is an existing patent that has technically similar characteristics to the invention for which a patent application is pending.

[0468] "Automated generation" is the process by which a system creates new documents or content without requiring human intervention.

[0469] A "grammatical error" refers to a violation of linguistic rules in written text.

[0470] "Consistency" refers to a state in which multiple data or documents have a unified, logical coherence.

[0471] "Feedback" refers to evaluations and information provided in response to specific results or reactions.

[0472] This invention provides a system in which servers, terminals, and users work together to streamline the patent application process. In this system, each component works together to automate and efficiently perform the information processing and document generation necessary for filing new patent applications.

[0473] The server is equipped with a processor and storage, and has the ability to access a database that holds patent information. In particular, the server incorporates a generative AI model (e.g., a natural language processing model), which is used to analyze input invention information and identify relevant existing patents. The server also automatically generates documents for new patent applications and performs grammatical and consistency checks.

[0474] The terminal provides an interface for users to input information. It has the function of converting user input into a digital format and sending it to the server. Furthermore, it displays patent information and draft application documents received from the server, allowing users to make corrections and verifications.

[0475] The user enters information about their invention into the terminal and proceeds with the patent application process using prompts. A specific example of a prompt is: "Regarding the invention of a new electronic device, please describe the specific features of the charging method. How does this device differ from other patented technologies?"

[0476] This system allows users to quickly and accurately compile an invention summary into patent application documents, significantly reducing the complexity of the patent application process. The server's AI generation model enables users to efficiently conduct prior art searches and automatically generate application documents, reducing manual workload. Furthermore, the document checking function improves the accuracy and success rate of applications.

[0477] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0478] Step 1:

[0479] The user uses a terminal to input an overview and features of the invention. This input is formatted as text data by the terminal. The terminal then prepares to send the input data to the server over the network. As a specific example of input, the user inputs the characteristics of a new electronic device.

[0480] Step 2:

[0481] The terminal sends the data entered by the user to the server. Here, the terminal converts the data to the appropriate protocol and transmits it without signal loss. This process allows the server to receive the input text data.

[0482] Step 3:

[0483] The server retrieves relevant patent information from the patent database based on the input data it receives. A generative AI model is used here, comparing and analyzing the input data with the extracted information for each patent through natural language processing. This process identifies related similar patent information.

[0484] Step 4:

[0485] The server uses a generative AI model to analyze and list related similar patents. The acquired patent information is organized through an AI scoring process to generate a list of similar patents. Once this list is generated, it is prepared to be sent to the terminal.

[0486] Step 5:

[0487] The terminal provides the user with a list of similar patents sent from the server. At this stage, the user can evaluate the relevance of the patents based on the provided list. The list is displayed visually on the interface and has the functionality to display detailed information when clicked.

[0488] Step 6:

[0489] The server automatically generates a draft of a new patent application document using a generative AI model based on user input and acquired patent information. In this process, the AI ​​constructs the document based on past patent application formats. This draft is then sent to the terminal.

[0490] Step 7:

[0491] The terminal displays the generated draft to the user. The user reviews the draft and makes any necessary corrections. User corrections are made in real time through a text editor and are reflected immediately.

[0492] Step 8:

[0493] The user's revised application document is sent back to the server and reviewed by a generative AI model. The server detects grammatical errors and structural problems in the document and provides feedback to the user. Based on this feedback, the user makes a final review of the document.

[0494] (Application Example 1)

[0495] 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."

[0496] The patent application process requires the efficient use of a vast and complex database of patent information to avoid overlapping patents and identify patent infringement risks early on. However, current methods often involve manual searches by humans, which are time-consuming and labor-intensive, and make it difficult to quickly reflect the latest patent information. Furthermore, the lack of real-time patent information verification and immediate warnings about relevant information makes it difficult to make quick and accurate decisions.

[0497] 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.

[0498] In this invention, the server includes means for acquiring a data set containing patent information, means for performing analysis using a generative AI model to identify similar technical documents from the data set, and equipment for acquiring visual information and using said information to analyze patent risk. This enables real-time identification and analysis of patent information. By using a generative AI model, the latest information can be quickly reflected and highly accurate risk analysis can be achieved, making it possible to quickly provide useful information to users.

[0499] "Patent information" refers to all information related to patents, including data such as technical documents and legal information concerning past and present inventions.

[0500] A "data set" is a collection of many data points gathered based on a specific purpose or condition, and is the subject of analysis and calculation.

[0501] A "generative AI model" is artificial intelligence designed to mimic human speech and behavior, and is a collection of algorithms trained to efficiently perform specific tasks.

[0502] "Visual information" refers to image and video data acquired through devices such as cameras and sensors, and is used for analysis and judgment.

[0503] "Technical documents" refer to documents that provide detailed information about research, development, and implementation in a specific technological field, and include information recorded in patent databases, etc.

[0504] "Equipment for risk analysis" refers to a set of software and hardware necessary to assess the likelihood of patent infringement or duplication, and includes a system for processing data based on specific criteria.

[0505] This invention provides a system aimed at streamlining the patent application process. The system primarily operates through the cooperation of three parties: a server, a terminal, and a user. Embodiments for each step are described below.

[0506] First, the user inputs a summary and features of their invention into a terminal. The terminal then transmits this user-entered information to a server. The server is connected to a patent database and retrieves a data set based on this information. The server uses a generative AI model to identify similar technical documents and, based on this, lists relevant patent information. This process allows the user to quickly assess whether similar inventions have existed in the past.

[0507] To acquire visual information, visual equipment such as cameras and sensors are used as needed. This visual data is used to analyze patent risk. Specifically, the data acquired from visual information is used to perform analysis to detect potential patent infringement. The results of this analysis are provided to the user in real time using a generative AI model, enabling immediate action.

[0508] For example, when a user is visually inspecting a new electronic device, the system retrieves potentially relevant patent information in the background and immediately displays a warning on the device if a patent risk exists. This allows the user to make accurate decisions in the early stages of patent application.

[0509] Examples of prompts to input into a generative AI model:

[0510] "Analyze the risks of similar technologies in the patent database. Determine if a new electronic component design infringes on existing patents and display a warning."

[0511] In this way, a form for concretely implementing the invention is provided. This allows users to efficiently proceed with the patent process and increase the likelihood of successful patent application.

[0512] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0513] Step 1:

[0514] The user enters a summary and features of the invention into the terminal. This input information is used as basic data for patent searches.

[0515] Step 2:

[0516] The terminal sends user input information to the server. This input includes an overview of the invention and keywords.

[0517] Step 3:

[0518] The server retrieves relevant data sets from the patent database based on the input information. These data sets include similar technologies and related patent documents.

[0519] Step 4:

[0520] The server uses a generative AI model to analyze the data set and identify similar technical documents. Based on the input information, it selects highly relevant patents and outputs them as analysis results.

[0521] Step 5:

[0522] The server creates a list of similar patents based on the analysis results and sends it to the terminal. The user uses this list to evaluate whether or not there are similar technologies.

[0523] Step 6:

[0524] If necessary, users can use visual equipment to capture specific devices or blueprints. This visual information is then used for risk analysis on the server.

[0525] Step 7:

[0526] The server analyzes the acquired visual information and assesses the risk of patent infringement. Image analysis includes extracting visual information and comparing it with database information.

[0527] Step 8:

[0528] The server uses a generated AI model to provide real-time feedback of risk analysis results to the user's device. Based on the displayed warnings, the user takes appropriate action.

[0529] 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.

[0530] The patent application support system of the present invention provides technology for streamlining the patent application process through the collaboration of servers, terminals, and users. This system incorporates generative artificial intelligence and an emotion engine, and includes advanced means for efficiently processing patent information.

[0531] First, the user inputs a summary and features of the patent into the terminal. The terminal receives this input and activates an emotion engine to recognize the user's emotions in real time. The emotion engine analyzes the user's emotional state during input and sends this information to the server. The server accesses the patent database to retrieve relevant patent information and performs data analysis using generative artificial intelligence.

[0532] When similar or overlapping patents are identified, the server sends this information to the terminal and displays it visually to the user. Furthermore, based on the analysis results from the emotion engine, the server adjusts the tone and style of the document to correspond to the user's emotional state. For example, if the system detects that the user is stressed, it suggests a supportive and concise document style.

[0533] Furthermore, the server automatically generates a draft of the new patent application document, referencing past patent application documents. This draft takes the user's emotional state into consideration and is designed to allow the user to intuitively make revisions and additions. The terminal displays the generated draft to the user, enabling them to make any necessary corrections.

[0534] Once the final application document is complete, the server uses generative artificial intelligence to proofread it. After verifying the grammar and structure, the server generates feedback and sends it to the user's terminal. This system allows users to efficiently and effectively manage the entire patent application process, improving the quality and speed of patent application work.

[0535] The following describes the processing flow.

[0536] Step 1:

[0537] The user enters a summary and features of the patent application into the terminal. The terminal prepares to send the entered information to the server and simultaneously activates the emotion engine to begin analyzing the user's emotions.

[0538] Step 2:

[0539] The emotion engine recognizes the user's emotional state in real time as they input data and sends the analysis results to the server. The server receives this emotion data and uses it as reference for the patent application process.

[0540] Step 3:

[0541] The server accesses the patent database and retrieves relevant patent information based on the summary information received from the user. The server uses generative artificial intelligence to analyze patents that may be similar or overlapping.

[0542] Step 4:

[0543] The server creates a list of similar patents based on the analysis results and sends it to the terminal. This list is displayed visually to the user and can be reviewed for reference.

[0544] Step 5:

[0545] The server references past patent documents and automatically generates a draft of a new patent application document based on the user's input information and emotional state. The generated draft is created with a tone and style that incorporates the analysis results of the emotion engine.

[0546] Step 6:

[0547] The terminal displays a draft of the generated patent application document to the user. The user can review the draft on the terminal and make additions or corrections as needed.

[0548] Step 7:

[0549] The user sends the revised document from their device to the server. The server then uses generative artificial intelligence to review the received document, performing checks on grammar and structure.

[0550] Step 8:

[0551] The server generates feedback based on the proofreading results and sends it to the terminal. The terminal displays the feedback to the user for final confirmation. The user then makes final adjustments based on this feedback and completes the patent application document.

[0552] (Example 2)

[0553] 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."

[0554] The patent application process faces challenges such as the enormous amount of time and effort required for determining patent similarity, preparing documents, and reviewing them. Furthermore, a lack of approaches that consider the emotional state of patent applicants can lead to decreased work efficiency and lower quality of applications.

[0555] 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.

[0556] In this invention, the server includes means for acquiring a dataset containing patent information, means for identifying and analyzing similar patents from the dataset using a generative machine learning model, and means for recognizing the user's emotional state and adjusting the tone of the document. This enables increased efficiency in the patent application process and improved quality of the applications.

[0557] "Patent information" refers to a collection of data that describes the scope of patent rights and the technical content of a patent.

[0558] A "dataset" is a collection of data, including patent information and related materials.

[0559] A "generative machine learning model" is an artificial intelligence technology that has the ability to learn from data and generate new information.

[0560] A "similar patent" refers to an existing patent that has similar technical content and scope of rights to a newly applied-for patent.

[0561] "Analysis" is the process of examining data and extracting or evaluating specific information.

[0562] "Document tone" refers to the writing style and expression of a document, including the impression and emotional nuances it conveys to the reader.

[0563] "Emotional state" refers to the emotional state a user experiences at a particular moment, and it affects their comfort and efficiency during work.

[0564] This invention is an integrated system for supporting patent application procedures, in which multiple elements such as servers, terminals, and users work together, each fulfilling their respective roles, to achieve efficient patent application.

[0565] The user inputs a patent summary and features using a terminal. The terminal uses this input and simultaneously operates an emotion engine to recognize the user's emotional state in real time. The emotion engine uses a general-purpose GPU, which excels at high-speed processing, to quickly analyze the collected data. Data regarding the user's emotions is transmitted from the terminal to the server.

[0566] The server connects to a patent database and retrieves a dataset containing patent information. A general-purpose database management system is used for this process. Next, the server uses a generative AI model to analyze the retrieved dataset and identify similar patents. It is envisioned that an open-source model will be used for this generative AI model. The server sends the analysis results to the terminal and visually presents the similar patents to the user.

[0567] Furthermore, the server automatically generates a draft of a new document based on past patent application documents. During this process, the tone and style of the document are adjusted based on the user's emotional state. The terminal displays the generated draft to the user and provides an editor that allows the user to freely modify and add to it.

[0568] An example of a prompt message might be: "Please enter the features of your new smart device. The system will generate a document for your patent application." Through this prompt, the user can input the device's characteristics and the purpose of the application into the terminal.

[0569] Finally, the user-reviewed document is checked for grammar and structure by the server to improve its integrity. The server generates feedback and presents it to the user via the terminal to ensure the patent application document is proper. This comprehensive process allows users to proceed with their patent applications efficiently, resulting in fast and high-quality applications.

[0570] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0571] Step 1:

[0572] The user inputs a summary and features of the patent using a terminal. The terminal receives this input and activates an emotion engine to analyze the user's emotional state. Emotion data is generated from the input text data. The emotion engine analyzes the emotional tone of each sentence and outputs the results as emotion data.

[0573] Step 2:

[0574] The terminal sends the analyzed sentiment data to the server. The server accesses the patent database based on this data and retrieves relevant patent information. It compares the characteristics of the input patent with existing patents in the database and identifies similar patent data. As a result, it outputs a list of the similar patents found.

[0575] Step 3:

[0576] The server further analyzes the patent information obtained using a generative AI model. The generative AI model takes a list of similar patents as input and outputs more specific analysis results by examining the characteristics and related information of the relevant patents. This process accurately determines patent overlap and similarity.

[0577] Step 4:

[0578] The server automatically generates a draft of a new patent application document, taking into account similar patent information and user sentiment data. It also refers to a database of past application documents to appropriately set the style and tone. The generated document draft is output and sent to the user's terminal for further review.

[0579] Step 5:

[0580] The terminal displays a draft document sent from the server to the user. The user can make necessary corrections and additions through the interface. The corrected document is then resent from the terminal to the server, where its contents are finalized.

[0581] Step 6:

[0582] The server uses a generation AI model to proofread the received final version of the document. This process checks grammar and structure, and generates feedback with final revisions. A feedback report is then output and provided to the user.

[0583] This system streamlines the generation and verification of patent application documents, significantly reducing the workload for applicants.

[0584] (Application Example 2)

[0585] 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."

[0586] Patent applications are a highly specialized and labor-intensive process. Numerous steps are required, including the analysis of patent information, identification of related patents, and document preparation, and these tasks often become a burden for applicants. Furthermore, the efficiency of the work can vary significantly depending on the applicant's emotional state. This invention aims to solve these problems, streamline the patent application process, and reduce the burden on applicants.

[0587] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means.

[0588] In this invention, the server includes means for acquiring a data set including patent data, means for performing analysis using generative artificial intelligence to identify relevant patents, and means including an emotion recognition engine for analyzing the user's emotional state and adjusting the user interface. This makes it possible to streamline patent application processes and provide an optimal experience tailored to the user's emotions.

[0589] "Patent data" refers to a collection of data that includes all information related to a patent application.

[0590] A "dataset" refers to a series of datasets that contain a collection of various pieces of information.

[0591] "Generative artificial intelligence" refers to artificial intelligence technology that has the ability to generate useful information from large amounts of data.

[0592] "Performing an analysis" refers to the process of analyzing data to find specific results or patterns.

[0593] A "related patent" refers to another patent that is deemed to be technically similar to or related to a given patent.

[0594] "User interface" refers to the interaction design that allows users and systems to interact with each other.

[0595] An "emotion recognition engine" refers to a technology or system used to analyze a user's emotional state in real time.

[0596] "Document tone" refers to the style and emotional atmosphere that is consistently expressed throughout the entire text.

[0597] To implement this invention, three components are required: a server, a terminal, and a user.

[0598] Server Role

[0599] The server acquires a dataset containing patent data and uses generative artificial intelligence (AI) technology to identify and analyze relevant patents. The server combines the Python programming language with OpenAI's GPT-3 model to analyze the data and creates new patent application documents using the generative AI model. Furthermore, the server utilizes an emotion recognition engine to adjust the tone of the documents based on the user's emotional state. In this process, the server accesses the database using SQLAlchemy to retrieve information from past application documents.

[0600] Terminal role

[0601] The terminal receives the patent summary entered by the user and sends that information to the server. The terminal operates an emotion recognition engine, including a BERT model, which analyzes the user's input text in real time. At this time, the terminal provides the user with an appropriate interface and displays feedback from the server and the generated document.

[0602] User roles

[0603] Users input the necessary information for patent applications into a terminal and review and modify data and documents provided by the server. The system provides appropriate support based on the user's emotional state, ensuring a smooth patent application process.

[0604] For example, if a user enters an outline of a new battery technology, the device sends that information to a server, and a generating AI model searches for similar patents and displays relevant information. If the user's input is something like, "I'm busy and tired today...", the emotion recognition engine recognizes "stress" and adjusts the interface to provide support.

[0605] An example of a prompt message is, "Enter an overview of your new battery technology, search for similar patents, and draft a proposal." This allows users to proceed with patent applications efficiently.

[0606] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0607] Step 1:

[0608] The terminal provides an interface for the user to input a patent summary. Once the user completes the input, it sends the input data to the server. This data is the input and contains the patent summary information necessary for the next step.

[0609] Step 2:

[0610] Based on the received patent summary, the server begins analysis using the generative AI model GPT-3. At this stage, the server generates prompts to search for relevant similar patents in the patent database and executes database queries. The output at this stage is a list of similar patents.

[0611] Step 3:

[0612] The terminal visually displays a list of similar patents received from the server to the user. The user reviews this list and uses it as a reference to determine the most relevant patent information. The output of this step is an information display for the user to review.

[0613] Step 4:

[0614] The server generates a draft of a new patent application document, referencing past patent application documents. In this process, it utilizes a generation AI model to combine the received patent summary with similar patent data to create the new document. The input for this step is the patent summary and similar patent information, and the output is a draft of the initial application document.

[0615] Step 5:

[0616] The terminal displays a draft of the generated patent application document to the user. The user reviews the document and makes corrections or additions as needed. The output of this step is the draft after the user's revisions.

[0617] Step 6:

[0618] The server uses an emotion recognition engine to analyze the user's emotions in real time as they input. Based on that emotional state, it provides an interface adapted to the device. The input is the user's text input, and the output is an optimized user experience.

[0619] Step 7:

[0620] The server performs a final proofreading of the completed patent application document, verifying its grammar and structure. This process ensures document quality and provides a final check. The output is the final feedback and a corrected, perfect document.

[0621] 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.

[0622] 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.

[0623] 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.

[0624] [Fourth Embodiment]

[0625] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[0626] 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.

[0627] 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).

[0628] 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.

[0629] 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.

[0630] 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).

[0631] 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.

[0632] 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.

[0633] 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.

[0634] 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.

[0635] 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.

[0636] 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.

[0637] 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".

[0638] This patent application support system provides technology that streamlines the patent application process through the mutual cooperation of servers, terminals, and users. In particular, the server incorporates generative artificial intelligence and connects to a continuously updated patent database to perform prior art searches using the latest patent information.

[0639] First, the user inputs an overview and features of their invention into the terminal. The terminal sends this information to the server. The server retrieves relevant patent information from its database based on the input information and uses generative artificial intelligence to automatically analyze and list similar and overlapping patents. This list is provided to the user via the terminal, allowing the user to immediately evaluate the relevance. For example, when applying for a patent for a new electronic device, the server displays multiple similar technologies from the literature.

[0640] Next, the server references past patent application documents and automatically generates a draft of the new application document based on the user's input. This draft is sent to the user's terminal, where the user can add or modify as needed. Through this process, the effort involved in the initial stages of patent application can be significantly reduced.

[0641] Furthermore, the application documents revised by the user are again managed on the server side and subjected to proofreading by generative artificial intelligence. The server automatically detects document consistency and grammatical errors and provides feedback to the user. In this way, users can quickly and accurately perform a final review of their patent application documents.

[0642] This system reduces the subjective elements of patent application work and narrows the skill gap between new and experienced professionals. Furthermore, it offers the advantage of improving the success rate of applications by accurately avoiding duplicate patents.

[0643] The following describes the processing flow.

[0644] Step 1:

[0645] The user enters an overview and features of the invention into the terminal. The terminal prepares to send the input information to the server.

[0646] Step 2:

[0647] The server receives information sent by the user and accesses the patent database. The server extracts relevant keywords and obtains the necessary datasets for prior art searches.

[0648] Step 3:

[0649] The server analyzes the acquired dataset using generative artificial intelligence. It automatically identifies and lists patents that may be similar or overlapping.

[0650] Step 4:

[0651] The server sends a list of similar patents it has identified to the terminal. The terminal displays the list to the user, who then reviews the findings.

[0652] Step 5:

[0653] The server references past patent application documents and automatically generates a draft of a new application document. The generated draft reflects user input within the configuration.

[0654] Step 6:

[0655] The server sends a draft of the application document it generates to the user's terminal. The user reviews the draft on the terminal and makes additions or corrections as needed.

[0656] Step 7:

[0657] The user sends the corrected application documents back from their terminal to the server. The server then uses generative artificial intelligence to review the received documents, detecting inconsistencies and grammatical errors.

[0658] Step 8:

[0659] The server generates feedback based on the proofreading results and sends it to the user's terminal. The user receives the feedback, makes final confirmations and adjustments, and aims to complete the patent application document.

[0660] (Example 1)

[0661] 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".

[0662] The patent application process is extremely complex, requiring significant time and expertise, particularly for prior art searches and application document preparation. Furthermore, effective identification of similar patents in the early stages of the application process is crucial to avoid over-examination and duplication. Additionally, grammatical checks and consistency verification are essential to improve the accuracy of application documents, necessitating support to bridge the skill gap between new and experienced patent holders.

[0663] 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.

[0664] In this invention, the server includes means for acquiring a data set containing patent information, means for acquiring relevant patent information from the data set and analyzing similar patents using generative AI, and means for checking the grammatical errors and consistency of the generated patent application documents. This enables efficient prior art searches and improved accuracy of documents in patent application work.

[0665] "Patent information" refers to data related to patents, including information such as the content of the patent, application status, and scope of rights.

[0666] A "data set" is a collection of aggregated information consisting of multiple data items.

[0667] "Generative AI" is a type of artificial intelligence technology that has the ability to automatically generate new information or documents based on given data.

[0668] A "similar patent" is an existing patent that has technically similar characteristics to the invention for which a patent application is pending.

[0669] "Automated generation" is the process by which a system creates new documents or content without requiring human intervention.

[0670] A "grammatical error" refers to a violation of linguistic rules in written text.

[0671] "Consistency" refers to a state in which multiple data or documents have a unified, logical coherence.

[0672] "Feedback" refers to evaluations and information provided in response to specific results or reactions.

[0673] This invention provides a system in which servers, terminals, and users work together to streamline the patent application process. In this system, each component works together to automate and efficiently perform the information processing and document generation necessary for filing new patent applications.

[0674] The server is equipped with a processor and storage, and has the ability to access a database that holds patent information. In particular, the server incorporates a generative AI model (e.g., a natural language processing model), which is used to analyze input invention information and identify relevant existing patents. The server also automatically generates documents for new patent applications and performs grammatical and consistency checks.

[0675] The terminal provides an interface for users to input information. It has the function of converting user input into a digital format and sending it to the server. Furthermore, it displays patent information and draft application documents received from the server, allowing users to make corrections and verifications.

[0676] The user enters information about their invention into the terminal and proceeds with the patent application process using prompts. A specific example of a prompt is: "Regarding the invention of a new electronic device, please describe the specific features of the charging method. How does this device differ from other patented technologies?"

[0677] This system allows users to quickly and accurately compile an invention summary into patent application documents, significantly reducing the complexity of the patent application process. The server's AI generation model enables users to efficiently conduct prior art searches and automatically generate application documents, reducing manual workload. Furthermore, the document checking function improves the accuracy and success rate of applications.

[0678] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0679] Step 1:

[0680] The user uses a terminal to input an overview and features of the invention. This input is formatted as text data by the terminal. The terminal then prepares to send the input data to the server over the network. As a specific example of input, the user inputs the characteristics of a new electronic device.

[0681] Step 2:

[0682] The terminal sends the data entered by the user to the server. Here, the terminal converts the data to the appropriate protocol and transmits it without signal loss. This process allows the server to receive the input text data.

[0683] Step 3:

[0684] The server retrieves relevant patent information from the patent database based on the input data it receives. A generative AI model is used here, comparing and analyzing the input data with the extracted information for each patent through natural language processing. This process identifies related similar patent information.

[0685] Step 4:

[0686] The server uses a generative AI model to analyze and list related similar patents. The acquired patent information is organized through an AI scoring process to generate a list of similar patents. Once this list is generated, it is prepared to be sent to the terminal.

[0687] Step 5:

[0688] The terminal provides the user with a list of similar patents sent from the server. At this stage, the user can evaluate the relevance of the patents based on the provided list. The list is displayed visually on the interface and has the functionality to display detailed information when clicked.

[0689] Step 6:

[0690] The server automatically generates a draft of a new patent application document using a generative AI model based on user input and acquired patent information. In this process, the AI ​​constructs the document based on past patent application formats. This draft is then sent to the terminal.

[0691] Step 7:

[0692] The terminal displays the generated draft to the user. The user reviews the draft and makes any necessary corrections. User corrections are made in real time through a text editor and are reflected immediately.

[0693] Step 8:

[0694] The user's revised application document is sent back to the server and reviewed by a generative AI model. The server detects grammatical errors and structural problems in the document and provides feedback to the user. Based on this feedback, the user makes a final review of the document.

[0695] (Application Example 1)

[0696] 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".

[0697] The patent application process requires the efficient use of a vast and complex database of patent information to avoid overlapping patents and identify patent infringement risks early on. However, current methods often involve manual searches by humans, which are time-consuming and labor-intensive, and make it difficult to quickly reflect the latest patent information. Furthermore, the lack of real-time patent information verification and immediate warnings about relevant information makes it difficult to make quick and accurate decisions.

[0698] 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.

[0699] In this invention, the server includes means for acquiring a data set containing patent information, means for performing analysis using a generative AI model to identify similar technical documents from the data set, and equipment for acquiring visual information and using said information to analyze patent risk. This enables real-time identification and analysis of patent information. By using a generative AI model, the latest information can be quickly reflected and highly accurate risk analysis can be achieved, making it possible to quickly provide useful information to users.

[0700] "Patent information" refers to all information related to patents, including data such as technical documents and legal information concerning past and present inventions.

[0701] A "data set" is a collection of many data points gathered based on a specific purpose or condition, and is the subject of analysis and calculation.

[0702] A "generative AI model" is artificial intelligence designed to mimic human speech and behavior, and is a collection of algorithms trained to efficiently perform specific tasks.

[0703] "Visual information" refers to image and video data acquired through devices such as cameras and sensors, and is used for analysis and judgment.

[0704] "Technical documents" refer to documents that provide detailed information about research, development, and implementation in a specific technological field, and include information recorded in patent databases, etc.

[0705] "Equipment for risk analysis" refers to a set of software and hardware necessary to assess the likelihood of patent infringement or duplication, and includes a system for processing data based on specific criteria.

[0706] This invention provides a system aimed at streamlining the patent application process. The system primarily operates through the cooperation of three parties: a server, a terminal, and a user. Embodiments for each step are described below.

[0707] First, the user inputs a summary and features of their invention into a terminal. The terminal then transmits this user-entered information to a server. The server is connected to a patent database and retrieves a data set based on this information. The server uses a generative AI model to identify similar technical documents and, based on this, lists relevant patent information. This process allows the user to quickly assess whether similar inventions have existed in the past.

[0708] To acquire visual information, visual equipment such as cameras and sensors are used as needed. This visual data is used to analyze patent risk. Specifically, the data acquired from visual information is used to perform analysis to detect potential patent infringement. The results of this analysis are provided to the user in real time using a generative AI model, enabling immediate action.

[0709] For example, when a user is visually inspecting a new electronic device, the system retrieves potentially relevant patent information in the background and immediately displays a warning on the device if a patent risk exists. This allows the user to make accurate decisions in the early stages of patent application.

[0710] Examples of prompts to input into a generative AI model:

[0711] "Analyze the risks of similar technologies in the patent database. Determine if a new electronic component design infringes on existing patents and display a warning."

[0712] In this way, a form for concretely implementing the invention is provided. This allows users to efficiently proceed with the patent process and increase the likelihood of successful patent application.

[0713] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0714] Step 1:

[0715] The user enters a summary and features of the invention into the terminal. This input information is used as basic data for patent searches.

[0716] Step 2:

[0717] The terminal sends user input information to the server. This input includes an overview of the invention and keywords.

[0718] Step 3:

[0719] The server retrieves relevant data sets from the patent database based on the input information. These data sets include similar technologies and related patent documents.

[0720] Step 4:

[0721] The server uses a generative AI model to analyze the data set and identify similar technical documents. Based on the input information, it selects highly relevant patents and outputs them as analysis results.

[0722] Step 5:

[0723] The server creates a list of similar patents based on the analysis results and sends it to the terminal. The user uses this list to evaluate whether or not there are similar technologies.

[0724] Step 6:

[0725] If necessary, users can use visual equipment to capture specific devices or blueprints. This visual information is then used for risk analysis on the server.

[0726] Step 7:

[0727] The server analyzes the acquired visual information and assesses the risk of patent infringement. Image analysis includes extracting visual information and comparing it with database information.

[0728] Step 8:

[0729] The server uses a generated AI model to provide real-time feedback of risk analysis results to the user's device. Based on the displayed warnings, the user takes appropriate action.

[0730] 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.

[0731] The patent application support system of the present invention provides technology for streamlining the patent application process through the collaboration of servers, terminals, and users. This system incorporates generative artificial intelligence and an emotion engine, and includes advanced means for efficiently processing patent information.

[0732] First, the user inputs a summary and features of the patent into the terminal. The terminal receives this input and activates an emotion engine to recognize the user's emotions in real time. The emotion engine analyzes the user's emotional state during input and sends this information to the server. The server accesses the patent database to retrieve relevant patent information and performs data analysis using generative artificial intelligence.

[0733] When similar or overlapping patents are identified, the server sends this information to the terminal and displays it visually to the user. Furthermore, based on the analysis results from the emotion engine, the server adjusts the tone and style of the document to correspond to the user's emotional state. For example, if the system detects that the user is stressed, it suggests a supportive and concise document style.

[0734] Furthermore, the server automatically generates a draft of the new patent application document, referencing past patent application documents. This draft takes the user's emotional state into consideration and is designed to allow the user to intuitively make revisions and additions. The terminal displays the generated draft to the user, enabling them to make any necessary corrections.

[0735] Once the final application document is complete, the server uses generative artificial intelligence to proofread it. After verifying the grammar and structure, the server generates feedback and sends it to the user's terminal. This system allows users to efficiently and effectively manage the entire patent application process, improving the quality and speed of patent application work.

[0736] The following describes the processing flow.

[0737] Step 1:

[0738] The user enters a summary and features of the patent application into the terminal. The terminal prepares to send the entered information to the server and simultaneously activates the emotion engine to begin analyzing the user's emotions.

[0739] Step 2:

[0740] The emotion engine recognizes the user's emotional state in real time as they input data and sends the analysis results to the server. The server receives this emotion data and uses it as reference for the patent application process.

[0741] Step 3:

[0742] The server accesses the patent database and retrieves relevant patent information based on the summary information received from the user. The server uses generative artificial intelligence to analyze patents that may be similar or overlapping.

[0743] Step 4:

[0744] The server creates a list of similar patents based on the analysis results and sends it to the terminal. This list is displayed visually to the user and can be reviewed for reference.

[0745] Step 5:

[0746] The server references past patent documents and automatically generates a draft of a new patent application document based on the user's input information and emotional state. The generated draft is created with a tone and style that incorporates the analysis results of the emotion engine.

[0747] Step 6:

[0748] The terminal displays a draft of the generated patent application document to the user. The user can review the draft on the terminal and make additions or corrections as needed.

[0749] Step 7:

[0750] The user sends the revised document from their device to the server. The server then uses generative artificial intelligence to review the received document, performing checks on grammar and structure.

[0751] Step 8:

[0752] The server generates feedback based on the proofreading results and sends it to the terminal. The terminal displays the feedback to the user for final confirmation. The user then makes final adjustments based on this feedback and completes the patent application document.

[0753] (Example 2)

[0754] 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".

[0755] The patent application process faces challenges such as the enormous amount of time and effort required for determining patent similarity, preparing documents, and reviewing them. Furthermore, a lack of approaches that consider the emotional state of patent applicants can lead to decreased work efficiency and lower quality of applications.

[0756] 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.

[0757] In this invention, the server includes means for acquiring a dataset containing patent information, means for identifying and analyzing similar patents from the dataset using a generative machine learning model, and means for recognizing the user's emotional state and adjusting the tone of the document. This enables increased efficiency in the patent application process and improved quality of the applications.

[0758] "Patent information" refers to a collection of data that describes the scope of patent rights and the technical content of a patent.

[0759] A "dataset" is a collection of data, including patent information and related materials.

[0760] A "generative machine learning model" is an artificial intelligence technology that has the ability to learn from data and generate new information.

[0761] A "similar patent" refers to an existing patent that has similar technical content and scope of rights to a newly applied-for patent.

[0762] "Analysis" is the process of examining data and extracting or evaluating specific information.

[0763] "Document tone" refers to the writing style and expression of a document, including the impression and emotional nuances it conveys to the reader.

[0764] "Emotional state" refers to the emotional state a user experiences at a particular moment, and it affects their comfort and efficiency during work.

[0765] This invention is an integrated system for supporting patent application procedures, in which multiple elements such as servers, terminals, and users work together, each fulfilling their respective roles, to achieve efficient patent application.

[0766] The user inputs a patent summary and features using a terminal. The terminal uses this input and simultaneously operates an emotion engine to recognize the user's emotional state in real time. The emotion engine uses a general-purpose GPU, which excels at high-speed processing, to quickly analyze the collected data. Data regarding the user's emotions is transmitted from the terminal to the server.

[0767] The server connects to a patent database and retrieves a dataset containing patent information. A general-purpose database management system is used for this process. Next, the server uses a generative AI model to analyze the retrieved dataset and identify similar patents. It is envisioned that an open-source model will be used for this generative AI model. The server sends the analysis results to the terminal and visually presents the similar patents to the user.

[0768] Furthermore, the server automatically generates a draft of a new document based on past patent application documents. During this process, the tone and style of the document are adjusted based on the user's emotional state. The terminal displays the generated draft to the user and provides an editor that allows the user to freely modify and add to it.

[0769] An example of a prompt message might be: "Please enter the features of your new smart device. The system will generate a document for your patent application." Through this prompt, the user can input the device's characteristics and the purpose of the application into the terminal.

[0770] Finally, the user-reviewed document is checked for grammar and structure by the server to improve its integrity. The server generates feedback and presents it to the user via the terminal to ensure the patent application document is proper. This comprehensive process allows users to proceed with their patent applications efficiently, resulting in fast and high-quality applications.

[0771] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0772] Step 1:

[0773] The user inputs a summary and features of the patent using a terminal. The terminal receives this input and activates an emotion engine to analyze the user's emotional state. Emotion data is generated from the input text data. The emotion engine analyzes the emotional tone of each sentence and outputs the results as emotion data.

[0774] Step 2:

[0775] The terminal sends the analyzed sentiment data to the server. The server accesses the patent database based on this data and retrieves relevant patent information. It compares the characteristics of the input patent with existing patents in the database and identifies similar patent data. As a result, it outputs a list of the similar patents found.

[0776] Step 3:

[0777] The server further analyzes the patent information obtained using a generative AI model. The generative AI model takes a list of similar patents as input and outputs more specific analysis results by examining the characteristics and related information of the relevant patents. This process accurately determines patent overlap and similarity.

[0778] Step 4:

[0779] The server automatically generates a draft of a new patent application document, taking into account similar patent information and user sentiment data. It also refers to a database of past application documents to appropriately set the style and tone. The generated document draft is output and sent to the user's terminal for further review.

[0780] Step 5:

[0781] The terminal displays a draft document sent from the server to the user. The user can make necessary corrections and additions through the interface. The corrected document is then resent from the terminal to the server, where its contents are finalized.

[0782] Step 6:

[0783] The server uses a generation AI model to proofread the received final version of the document. This process checks grammar and structure, and generates feedback with final revisions. A feedback report is then output and provided to the user.

[0784] This system streamlines the generation and verification of patent application documents, significantly reducing the workload for applicants.

[0785] (Application Example 2)

[0786] 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".

[0787] Patent applications are a highly specialized and labor-intensive process. Numerous steps are required, including the analysis of patent information, identification of related patents, and document preparation, and these tasks often become a burden for applicants. Furthermore, the efficiency of the work can vary significantly depending on the applicant's emotional state. This invention aims to solve these problems, streamline the patent application process, and reduce the burden on applicants.

[0788] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means.

[0789] In this invention, the server includes means for acquiring a data set including patent data, means for performing analysis using generative artificial intelligence to identify relevant patents, and means including an emotion recognition engine for analyzing the user's emotional state and adjusting the user interface. This makes it possible to streamline patent application processes and provide an optimal experience tailored to the user's emotions.

[0790] "Patent data" refers to a collection of data that includes all information related to a patent application.

[0791] A "dataset" refers to a series of datasets that contain a collection of various pieces of information.

[0792] "Generative artificial intelligence" refers to artificial intelligence technology that has the ability to generate useful information from large amounts of data.

[0793] "Performing an analysis" refers to the process of analyzing data to find specific results or patterns.

[0794] A "related patent" refers to another patent that is deemed to be technically similar to or related to a given patent.

[0795] "User interface" refers to the interaction design that allows users and systems to interact with each other.

[0796] An "emotion recognition engine" refers to a technology or system used to analyze a user's emotional state in real time.

[0797] "Document tone" refers to the style and emotional atmosphere that is consistently expressed throughout the entire text.

[0798] To implement this invention, three components are required: a server, a terminal, and a user.

[0799] Server Role

[0800] The server acquires a dataset containing patent data and uses generative artificial intelligence (AI) technology to identify and analyze relevant patents. The server combines the Python programming language with OpenAI's GPT-3 model to analyze the data and creates new patent application documents using the generative AI model. Furthermore, the server utilizes an emotion recognition engine to adjust the tone of the documents based on the user's emotional state. In this process, the server accesses the database using SQLAlchemy to retrieve information from past application documents.

[0801] Terminal role

[0802] The terminal receives the patent summary entered by the user and sends that information to the server. The terminal operates an emotion recognition engine, including a BERT model, which analyzes the user's input text in real time. At this time, the terminal provides the user with an appropriate interface and displays feedback from the server and the generated document.

[0803] User roles

[0804] Users input the necessary information for patent applications into a terminal and review and modify data and documents provided by the server. The system provides appropriate support based on the user's emotional state, ensuring a smooth patent application process.

[0805] For example, if a user enters an outline of a new battery technology, the device sends that information to a server, and a generating AI model searches for similar patents and displays relevant information. If the user's input is something like, "I'm busy and tired today...", the emotion recognition engine recognizes "stress" and adjusts the interface to provide support.

[0806] An example of a prompt message is, "Enter an overview of your new battery technology, search for similar patents, and draft a proposal." This allows users to proceed with patent applications efficiently.

[0807] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0808] Step 1:

[0809] The terminal provides an interface for the user to input a patent summary. Once the user completes the input, it sends the input data to the server. This data is the input and contains the patent summary information necessary for the next step.

[0810] Step 2:

[0811] Based on the received patent summary, the server begins analysis using the generative AI model GPT-3. At this stage, the server generates prompts to search for relevant similar patents in the patent database and executes database queries. The output at this stage is a list of similar patents.

[0812] Step 3:

[0813] The terminal visually displays a list of similar patents received from the server to the user. The user reviews this list and uses it as a reference to determine the most relevant patent information. The output of this step is an information display for the user to review.

[0814] Step 4:

[0815] The server generates a draft of a new patent application document, referencing past patent application documents. In this process, it utilizes a generation AI model to combine the received patent summary with similar patent data to create the new document. The input for this step is the patent summary and similar patent information, and the output is a draft of the initial application document.

[0816] Step 5:

[0817] The terminal displays a draft of the generated patent application document to the user. The user reviews the document and makes corrections or additions as needed. The output of this step is the draft after the user's revisions.

[0818] Step 6:

[0819] The server uses an emotion recognition engine to analyze the user's emotions in real time as they input. Based on that emotional state, it provides an interface adapted to the device. The input is the user's text input, and the output is an optimized user experience.

[0820] Step 7:

[0821] The server performs a final proofreading of the completed patent application document, verifying its grammar and structure. This process ensures document quality and provides a final check. The output is the final feedback and a corrected, perfect document.

[0822] 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.

[0823] 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.

[0824] 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.

[0825] 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.

[0826] 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.

[0827] 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.

[0828] 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.

[0829] 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.

[0830] 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."

[0831] 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.

[0832] 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.

[0833] 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.

[0834] 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.

[0835] 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.

[0836] 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.

[0837] 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.

[0838] 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.

[0839] 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.

[0840] 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.

[0841] 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.

[0842] 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 to be incorporated by reference.

[0843] The following is further disclosed regarding the embodiments described above.

[0844] (Claim 1)

[0845] Means for obtaining a set of information including patent information,

[0846] A means for performing analysis using generative artificial intelligence to identify similar patents from the aforementioned information set,

[0847] Means for listing the aforementioned similar patents,

[0848] A means for automatically generating a new patent application document by referring to past application documents,

[0849] Means for verifying the grammar and structure of the generated patent application document,

[0850] A system that includes this.

[0851] (Claim 2)

[0852] The system according to claim 1, wherein the generative artificial intelligence learns the latest patent information to improve its accuracy.

[0853] (Claim 3)

[0854] The system according to claim 1, which drafts an initial application document based on a patent summary entered by a user.

[0855] "Example 1"

[0856] (Claim 1)

[0857] A means for obtaining a data set containing patent information,

[0858] A means for obtaining relevant patent information from the aforementioned data set and analyzing similar patents using generative AI,

[0859] A means of compiling the aforementioned similar patents into a list and providing it to the user via a terminal,

[0860] A method for automatically generating new patent application documents using generative AI based on past application documents,

[0861] A means for checking the grammatical errors and consistency of the generated patent application document,

[0862] A means of re-examining the user's revised patent application document using generative AI and providing feedback as needed,

[0863] A system that includes this.

[0864] (Claim 2)

[0865] The system according to claim 1, wherein a generative AI continuously learns the latest patent data and improves the accuracy of its analysis.

[0866] (Claim 3)

[0867] The system according to claim 1, which drafts an initial patent application document based on an outline of the invention entered by the user into a terminal.

[0868] "Application Example 1"

[0869] (Claim 1)

[0870] A means for obtaining a data set containing patent information,

[0871] A means for performing analysis using a generative AI model to identify similar technical documents from the aforementioned data set,

[0872] A means for listing the aforementioned similar technical documents,

[0873] A means for automatically generating new technical application documents by referring to past technical documents,

[0874] A means for verifying the linguistic consistency and structure of the generated technical application document,

[0875] Equipment for acquiring visual information and using said information to analyze patent risk,

[0876] A means of displaying a warning to the user in accordance with information obtained from visual equipment,

[0877] A system that includes this.

[0878] (Claim 2)

[0879] The system according to claim 1, wherein the generating AI model learns the latest technological information to improve its accuracy.

[0880] (Claim 3)

[0881] The system according to claim 1, which drafts an initial application document based on a technical overview entered by the user.

[0882] "Example 2 of combining an emotion engine"

[0883] (Claim 1)

[0884] A means of obtaining a dataset containing patent information,

[0885] A means for performing analysis using a generative machine learning model to identify similar patents from the aforementioned dataset,

[0886] Means for listing the aforementioned similar patents,

[0887] A means for automatically creating a new patent application document by referring to past application documents,

[0888] Means for checking the grammar and structure of the generated patent application document,

[0889] A means of recognizing the user's emotional state and adjusting the tone of the document,

[0890] A means of displaying an interface on the device that allows users to make intuitive modifications,

[0891] A system that includes this.

[0892] (Claim 2)

[0893] The system according to claim 1, wherein the generative machine learning model learns the latest patent information and improves its accuracy.

[0894] (Claim 3)

[0895] The system according to claim 1, which drafts an initial application document based on a patent summary and sentiment entered by a user.

[0896] "Application example 2 when combining with an emotional engine"

[0897] (Claim 1)

[0898] A means for acquiring a data set including patent data,

[0899] A means for performing analysis using generative artificial intelligence to identify related patents from the aforementioned data set,

[0900] A means for listing the aforementioned related patents,

[0901] A means of automatically creating new patent application documents by referring to past application documents,

[0902] A means for verifying the grammar and structure of the aforementioned patent application document,

[0903] A means including an emotion recognition engine for analyzing the user's emotional state and adjusting the user interface,

[0904] A means of adjusting the tone of a document according to the user's emotions,

[0905] A system that includes this.

[0906] (Claim 2)

[0907] The system according to claim 1, wherein the generative artificial intelligence learns the latest patent data to improve its accuracy.

[0908] (Claim 3)

[0909] The system according to claim 1, which drafts an initial application document based on a patent summary entered by a user. [Explanation of symbols]

[0910] 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. Means for obtaining a set of information including patent information, A means for performing analysis using generative artificial intelligence to identify similar patents from the aforementioned information set, Means for listing the aforementioned similar patents, A means for automatically generating a new patent application document by referring to past application documents, Means for verifying the grammar and structure of the generated patent application document, A system that includes this.

2. The system according to claim 1, wherein the generative artificial intelligence learns the latest patent information to improve its accuracy.

3. The system according to claim 1, which drafts an initial application document based on a patent summary entered by a user.

Images