Information processing device

Abstract

To solve the problem that the range of a patent claim is difficult to be understood by ordinary people.SOLUTION: An information processing device for creating a drawing showing a configuration from the range of a patent claim by using a computer comprises: input means for inputting the range of a patent claim; acquisition means for processing the inputted range of a patent claim by a large-scale language model, and acquiring a code written by a language for creating a drawing illustrating the inputted range of a patent claim; and drawing means for drawing a block diagram or a flowchart for illustrating the inputted range of a patent claim according to the code obtained by the acquisition means. The acquisition means draws a component as a block when the component is included in the inputted range of a patent claim.SELECTED DRAWING: Figure 8

Description

[Technical Field]

[0001] The present invention relates to an information processing device. [Background technology]

[0002] In a patent application, the scope of the claims defines the subject of examination, and after the patent is granted, the scope of the rights. In both cases, the subject of examination and the scope of the rights are determined based on the textual information described in the claims.

[0003] For example, Patent Document 1 below discloses a patent document preparation device, method, computer program, computer-readable recording medium, server, and system that can reduce the time required to prepare patent documents. [Prior art documents] [Patent documents]

[0004] [Patent Document 1] Japanese Patent Application Laid-Open No. 2024-20595 Summary of the Invention [Problem to be solved by the invention]

[0005] The scope of patent claims is often difficult for the average person to understand, and determining the subject matter of examination and the scope of rights from the scope of patent claims requires skill and is a time-consuming task even for experienced practitioners. [Means for solving the problem]

[0006] The present invention has been made to solve the above-mentioned problems, and provides an information processing device that uses a computer to create a drawing showing the structure of a patent claim, the information processing device comprising: an input means for inputting the patent claim; an acquisition means for processing the inputted patent claim using a large-scale language model and acquiring code written in a language for creating a drawing illustrating the inputted patent claim; and a drawing means for drawing a block diagram or flowchart illustrating the inputted patent claim in accordance with the code acquired by the acquisition means, wherein when a component is included in the inputted patent claim, the acquisition means draws the component as a block. [Brief explanation of the drawings]

[0007] [Figure 1] 1 is a block diagram showing a system configuration in an embodiment of the program of the present invention; [Figure 2] 3 is a flowchart showing a processing flow in the present embodiment. [Figure 3] FIG. 10 is a diagram showing the results of drawing based on code written in the Dot language output from a claim by a computer program according to this embodiment. [Figure 4] FIG. 10 is a diagram showing the results of drawing based on code written in the Mermaid language output from a claim by a computer program according to this embodiment. [Figure 5] FIG. 10 is a diagram showing the results of drawing based on code written in the Mermaid language output from a claim by a computer program according to this embodiment. [Figure 6] This is a continuation of Figure 5. [Figure 7] This is a continuation of Figure 6. [Figure 8] FIG. 10 is a diagram showing the results of drawing based on code written in the Dot language output from a claim by a computer program according to this embodiment. [Figure 9] This is a continuation of Figure 8. DETAILED DESCRIPTION OF THE INVENTION

[0008] FIG. 1 is a block diagram showing a system configuration in one embodiment of the program of the present invention.

[0009] This system includes an input unit 10 for inputting claims (or even just claims), a conversion unit 20 for inputting the input claims as prompts into the LLM, analyzing them, and converting them into code, a drawing unit 30 for generating drawings from the converted code, and a display unit 40 for displaying the generated drawings. Some of these components may be executed by an external computer. In other words, the processing of this embodiment may be distributed across multiple computers.

[0010] The input unit 10 for inputting claims may input a prompt sentence for generating an image (or a code for generating an image) along with the claims. The input unit 10 may also automatically add an appropriate prompt sentence to the input claims, and send the input claims to the conversion unit 20, which analyzes the input claims using an LLM and converts them into code. The claims may also be input as a prompt and sent to the conversion unit 20.

[0011] The conversion unit 20 may generate not only the code for creating the drawing but also text to explain the drawing. In this way, embodiments that cover the scope of the claims can be automatically created. The conversion unit 20 may also directly generate image data instead of the code for creating the drawing.

[0012] The conversion unit 20 may write the names of the elements of the claims within the blocks (boxes represented by solid or dotted lines) in the drawing, or may also write descriptions of those elements. Only descriptions may be written. Descriptions other than the elements may be written outside the blocks. If elements (tangible or intangible) are exchanged between blocks, the flow may be represented by arrows. The names of the elements in that flow may be written next to the arrows.

[0013] If there are main components (for example, the hardware configuration of a device) and sub-components (such as signals exchanged between hardware), the representation of the blocks may be changed to make it clear whether they are main or sub. For example, this may be done by changing the line type. Components may also be color-coded by type. Main components may be drawn as software modules, and sub-components may be drawn as information exchanged between modules. Blocks may be nested, with blocks written within blocks. Explanations may also be written within blocks. The top-level configuration may be represented by a block, and the configurations within it may be illustrated as blocks within that block.

[0014] When the entered claims are written in a first language, the drawings may be written in a second language (machine translation may be performed).

[0015] The conversion unit 20 may process only independent claims, only claim 1, or dependent claims as well. Independent and dependent claims may be represented on a single sheet. The dependent relationships may be expressed in a way that makes them clear (for example, the dependent relationships between claims represented by each block may be expressed by connecting the blocks with arrows pointing from the dependent to the dependent or vice versa, or by lines connecting the two). Each claim may be defined as a subgraph, and the dependent relationships between them may be indicated by arrows. When drawing a process such as a flowchart based on the scope of a claim, normal processes may be drawn with rectangular blocks, and conditional branches may be represented with diamonds. The configuration and means of a device or system may be represented with rectangular blocks, and the processes performed within them may be represented as flows within those blocks.

[0016] The procedure for creating drawings according to this embodiment will be described below. First, a user inputs claims through the input unit 10. These may be input automatically from the Internet, a repository, a database, or the like. The input claims are sent (as prompts or together with other prompts) to the conversion unit 20, where they are linguistically analyzed by the LLM. The LLM analyzes the claim text and extracts the elements of the invention and their relationships. The extracted information is then converted into code for creating drawings. This code defines the type of drawing, the layout of the elements, the connections, and so on. The conversion unit 20 may be an external computer connected via the Internet or the like.

[0017] The code generated by the conversion unit 20 is passed to the drawing unit 30, which then generates the actual diagram. The drawing unit 30 uses the information contained in the code to draw a block diagram or flowchart. The layout of components and the drawing of connecting lines are automatically optimized. Arrows are also included on the diagram to represent process flow, time series, and signal flow. The generated diagram can be output in various formats, including vector graphics, bitmap, JPEG, and GIF. It can also be output as a script in Mermaid or Dot language. This makes it easy to use the generated diagram in other software or embed it in a web page.

[0018] The generated drawing is sent to the display unit 40 and presented to the user. The user can then modify the drawing as necessary to obtain the final drawing. The drawing can be modified using the editing functions provided in the display unit 40. For example, it is possible to change the position and size of blocks, add or delete connecting lines, edit text, etc. It is also possible to convert the drawing format or output it to other software.

[0019] FIG. 2 is a flowchart showing the processing flow in this embodiment. First, claims are input (S10). Next, the input claims are analyzed by LLM and converted into code for creating drawings (S20). Based on this code, a block diagram or flowchart is drawn (S30). When drawing, the drawing format (vector graphics, bitmap, JPEG, GIF, etc.) and output format (image file, Mermaid notation, Dot language, etc.) are specified (S35). Arrows are also drawn on the drawing to represent process flow, time series, and signal flow. Finally, the generated drawing is displayed (S40), and the user can make modifications as needed (S50). FIG. 2 shows arrows representing the time flow between each process.

[0020] This embodiment makes it possible to largely automate the process of creating drawings from patent claims, significantly reducing the time and effort required to prepare a patent application. Furthermore, by using linguistic analysis by LLM, it becomes possible to more accurately extract the structure of an invention from the description of claims. Furthermore, the generated drawings can be output in various formats, making it easy to integrate with other software.

[0021] The LLM algorithm and drawing generation method can be replaced with other methods. The system configuration can also take a form other than that shown in Figure 1. For example, the input unit 10 and the display unit 40 can be integrated to simplify the user interface.

[0022] Conversely to the above explanation, an image or a code describing an image may be input and converted by the LLM into character data for explaining the claims or embodiments.

[0023] A computer is used to implement the present invention. Specific examples of computers include personal computers (desktop, laptop), smartphones, tablets, servers, game consoles, smart watches, home appliances (smart TVs, smart refrigerators, etc.), and control systems. Here, for example, a personal computer, smartphone, tablet, etc. is used as a client computer (client). The client accesses the server using a web browser and sends information, which the server processes. The processed information (HTML document, JSON format data, etc.) is sent to the client, and the information is displayed on the client's web browser. Furthermore, processing may be performed only within a single computer, and there may be no need to send or receive information to or from an external device.

[0024] The processed information may be sent to the client via e-mail or messenger software and displayed there. Information may also be sent from the client to the server via e-mail or messenger software.

[0025] The server and the client are connected to the Internet. The server sends data to the client that sent the request. Both the server and the client are computers and have the following components:

[0026] Central Processing Unit (CPU): Executes program instructions and processes data. A CPU has multiple cores, each capable of processing tasks independently.

[0027] Memory (RAM): A high-speed storage device that temporarily stores programs and data while the computer is running. The CPU directly accesses it and reads and writes data. RAM is volatile memory, meaning that data is lost when the power is turned off.

[0028] Storage Device: A device that provides long-term data storage, such as a hard disk drive (HDD) or solid-state drive (SSD). These store the operating system, applications, user data, etc.

[0029] Motherboard: A board that connects all hardware components and provides power and data communication. The motherboard contains the CPU socket, RAM slots, expansion slots, I / O ports, etc.

[0030] Graphics Processing Unit (GPU): A specialized processor for graphics and image processing. GPUs are used for 3D graphics rendering, video decoding / encoding, machine learning tasks, etc.

[0031] Power Supply Unit (PSU): A device that provides power to a computer's hardware components. A PSU converts AC power into the DC voltage required by the computer.

[0032] Cooling system: A system that manages a computer's temperature and prevents damage from overheating. Cooling systems include heat sinks, fans, and liquid coolers.

[0033] Input devices: These are devices that allow a user to enter information into a computer, such as a keyboard, mouse, touchpad, or touchscreen.

[0034] Output devices: Monitors, printers, speakers, etc. are devices that communicate information from a computer to a user. A monitor displays images and text, a printer prints documents, and speakers output sound. Any output device can be used to communicate information from a computer to a user.

[0035] Network Interface: An interface that connects a computer to a network, such as an Ethernet port or Wi-Fi adapter, allowing access to the Internet or sharing resources on a local network.

[0036] Expansion Card: A card that plugs into a motherboard's expansion slot to provide additional functionality or performance, such as a graphics card, sound card, or network card.

[0037] Optical drive: A device for reading and writing optical media such as CDs, DVDs, and Blu-ray discs. In computers, the optical drive is sometimes omitted.

[0038] Case: An enclosure that protects and houses the hardware components of a computer. The case provides access to the hardware, supports the cooling system, and influences the design and shape of the computer system.

[0039] These components work together to make the whole computer system function: when you use your computer, these components work together to process data, perform tasks, and display or output information.

[0040] Below we list some of the software that is important for running a computer and explain the functions and operations of each.

[0041] Operating System (OS): The underlying software that manages a computer's hardware and software resources and allows users and applications to access them. Typical operating systems include Windows, macOS, and Linux.

[0042] Device driver: Software that is responsible for communication between the operating system and the hardware devices and peripherals in a computer. Device drivers are necessary for the correct operation of hardware such as keyboards, mice, printers, and graphics cards.

[0043] System software: Software that supports the basic functions of a computer, such as file management, system settings, disk management, and network connections. Examples include File Explorer and Disk Utility.

[0044] Security software: Software that protects your computer from security threats. This includes antivirus software, firewalls, and anti-malware tools.

[0045] Web browser: Software that displays web pages on the Internet and allows users to browse and search for online information. Popular web browsers include Google Chrome, Mozilla Firefox, and Microsoft Edge.

[0046] Utility software: A group of software for performing tasks related to improving productivity such as document creation, spreadsheet creation, and presentation creation. This also includes software that performs the processes for implementing the present invention.

[0047] Communications software: Software used for computer-based communication, such as email clients, instant messengers, and video conferencing tools.

[0048] Multimedia software: Software for playing, editing, and creating multimedia content such as audio, images, and video.

[0049] Backup software: Software that regularly backs up data on your computer, reducing the risk of data loss or system failure.

[0050] Development tools: Tools used to develop software and applications using programming languages ​​and development frameworks. These include integrated development environments (IDEs), text editors, and version control systems.

[0051] These software programs provide the basic functions required for a computer to operate and help users perform various tasks efficiently. Each software program is designed for a specific purpose and works together to improve the overall functionality of the system.

[0052] The process by which the server returns data in response to a client request is as follows. HTTP, HTTPS, etc. are used as communication protocols.

[0053] The client (usually a web browser) sends an HTTP request to the server by specifying the URL and HTTP method (GET, POST, etc.). The server analyzes the received HTTP request and processes it according to the content of the request.

[0054] If the required data or resources are available on the server side, the server retrieves the data from a database, file system, etc. The server-side program (PHP, Python, Ruby, etc.) processes the retrieved data or resources and generates a result. The server creates an HTTP response to return the generated result (HTML, JSON, XML, etc.) to the client. At this time, a status code (200 OK, 404 Not Found, etc.) and header information are also set. The server then sends the created HTTP response to the client.

[0055] The client analyzes the received HTTP response and displays or processes it in the appropriate format. For example, a web browser displays HTML, and JavaScript processes JSON data.

[0056] Through these processes, the server returns data in response to an HTTP request from the client. The protocol used for this exchange is HTTP (HyperText Transfer Protocol), which makes it possible to exchange information over the web. As mentioned above, messenger software or email can also be used for communication between the client and server.

[0057] It should be noted that instead of displaying the data on a web browser, the data may be displayed on the client terminal via email or messenger software.

[0058] In this embodiment, a computer program and an information processing device are provided that automatically create drawings showing the structure of a patent from the scope of claims using a computer. Creating drawings that visually express the structure of an invention is an important task, but doing it manually takes time and effort. Therefore, the computer program and information processing device in this embodiment analyze the scope of claims using language processing technology and automatically generate drawings, thereby significantly streamlining this task.

[0059] The program and information processing device have two main components. The first component is a conversion component that analyzes input claims using large-scale language models (LLMs) and converts them into code for creating drawings. This process extracts the elements of the invention and their relationships from the claims. LLMs can be deep learning models specialized for natural language processing, such as GPT-3, BERT, XLNet, and RoBERTa. These models are pre-trained with large amounts of text data and have excellent capabilities for understanding context and extracting meaningful information. The conversion component uses LLMs to analyze the sentence structure of the claims and identify subjects (main elements), predicates (relationships between elements), and objects (subordinate elements). This information is then used to output program code (e.g., SVG code) that generates vector data for computer graphics.

[0060] The second means is a drawing means that illustrates the claims in the form of a block diagram or flowchart in accordance with the code obtained by the conversion means. The converted code includes information such as the type of diagram (block diagram or flowchart), the layout of each component, and their connections (although at least some of this information may be omitted). Based on this information, the drawing means automatically generates a diagram that appropriately expresses the content of the claims. At this time, arrows are added to the diagram to represent the process flow, time series, and signal flow. A specific implementation of the drawing means could utilize visualization libraries such as Python's Matplotlib or JavaScript's D3.js. These libraries provide a wide range of functions for drawing shapes from vector data.

[0061] As described above, the computer program and information processing device of this embodiment combine language processing and graphic drawing technologies to automatically generate drawings from patent claims. The conversion means uses advanced natural language analysis using LLM to accurately understand the content of claims without human intervention and extract the necessary and sufficient information for drawing. Furthermore, the drawing means applies computer graphics technology to allow the program to autonomously create easy-to-read and understand drawings based on the extracted information. This significantly reduces the workload of drawing creation, which has previously been done manually, and contributes to the efficiency of patent application procedures.

[0062] It is also possible to process claims directly to create image data in various formats, such as vector graphics, bitmaps, JPEG, GIF, and SVG, without converting them into drawing code. In this case, the results of language analysis by the LLM are directly input into a drawing library or data conversion library to create the drawing. For example, by implementing a program that converts the output of the LLM into SVG or PNG image data, drawings can be generated more directly.

[0063] Furthermore, this invention can be used not only to create drawings required for patent applications, but also to illustrate patent publications and patent claims. In other words, by retrieving published patent documents from a database and analyzing the claims therein to create drawings, it is possible to visually express the contents of existing patents in an easy-to-understand manner. This is expected to improve the efficiency of patent searches and prior art searches. (Example of display) FIG. 3 is a diagram showing the results of drawing based on code written in the Dot language output from a claim by a computer program according to this embodiment.

[0064] Here, the results of drawing based on the description of claim 1 of the claims are displayed. Claim 1 states, "An information processing device that uses a computer to create drawings showing the configuration of a patent claim, an input means for inputting claims; an acquisition means for processing the input claims through a large-scale language model (LLM) to acquire code written in a language for creating drawings illustrating the input claims; a drawing means for drawing a block diagram or a flowchart illustrating the input claims in accordance with the code obtained by the obtaining means, When a component is included in the input claim, the acquiring means draws the component as a block. Assume that the following is defined. The elements of this claim are input means, acquisition means, and drawing means, which are illustrated as blocks. Each block describes the steps of the process executed in that block in the form of a flowchart. The acquisition means includes a statement indicating its configuration, such as "an acquisition means for processing the input claim through a large-scale language model (LLM) and acquiring code written in a language for creating a drawing illustrating the input claim," and a statement indicating the operation and function of the configuration, such as "when the input claim includes a component, the acquisition means draws the component as a block." In this case, only the former may be described within the block for the "acquisition means," and the latter may be described outside the block, or both may be described within a box as shown in Figure 3. The former and latter processes may be described as separate flowcharts, or as a single flowchart as shown in Figure 3.

[0065] Also, it is possible to describe only the flow chart portion of FIG. 3 and not draw the blocks.

[0066] FIG. 4 is a diagram showing the results of drawing based on code written in the Mermaid language output from a claim of a patent by a computer program according to this embodiment.

[0067] In addition to the description in Figure 3, the preamble of the claim ("An information processing device that uses a computer to create drawings showing the configuration from the claims") is also included in the illustration.

[0068] Figures 5 to 7 are diagrams showing the results of drawing based on code written in Mermaid language output from a claim by a computer program in this embodiment. Figures 5 to 7 are diagrams that have been divided from a single figure for ease of viewing, and were originally drawn on a single sheet.

[0069] This diagram shows the results of analyzing the scope of claims, including claims 1 to 5. Each claim is shown as a block representing the highest concept. The dependent relationships between claims are indicated by arrows.

[0070] Furthermore, the elements included in each claim are described as blocks within the block representing that claim. Here, only the name of the block is written within the block representing the element. An explanation of the function, operation, etc. of each block is written near the block (or near the arrow leading from the block). The relationship between blocks representing elements is expressed by arrows. For example, if a signal or information is sent from block A to block B, an arrow is drawn from block A to block B, and the name or explanation of the element (signal, information, etc.) being sent is written overlapping or near the arrow.

[0071] As shown in the figure, the relationship between blocks representing elements within one claim is indicated by an arrow, and the relationship between blocks representing elements within different claims is also indicated by an arrow. Lines may be used instead of arrows. Lines may be straight, curved, or dotted.

[0072] Furthermore, a lead line may be added from the block, and an explanation of that block may be written. The specification (such as the embodiment of the invention) may also be analyzed using LLM along with the claims, and the terms of the embodiment corresponding to the claims, the position where they appear (paragraph number, page, line), etc. may be displayed. The terms of the embodiment corresponding to the elements in the claims, the position where they appear (paragraph number, page, line), etc. may also be displayed. These may be written within the block of the claim or element, or may be written nearby. They may also be displayed in a table format separate from the figures.

[0073] The claim blocks may be drawn in order starting from claim 1, or the order may be automatically determined for layout reasons or other reasons to make the relationships easier to understand as shown in the drawing.

[0074] 8 and 9 are diagrams showing the results of drawing based on code written in the Dot language output from a claim by a computer program according to this embodiment. Figures 8 and 9 are diagrams that have been divided into parts for ease of viewing, and were originally drawn on a single sheet.

[0075] Here, the shape of the blocks is changed depending on the type of element. Also, arrows indicating the dependency of claims do not need to be drawn (for example, when the dependency can be understood by describing the relationship between elements across claims). (Variation 1) In the above embodiment, we have described a method for generating drawings from claims by analyzing the claim text using LLM and generating code for creating drawings. In Variation 1, rule-based natural language processing technology is used instead of LLM. Specifically, a syntactic analysis of the claim text is performed, and rules are defined for extracting key components and their relationships from the resulting syntax tree. These rules are created manually in advance based on knowledge about how claims are written. While rule-based methods are less versatile than LLMs, they are considered suitable for claim writing because they can perform analysis specialized for specific sentence structures. (Variation 2) In the above embodiment, when generating drawings from claims, they are expressed in the form of block diagrams or flowcharts. In Variation 2, other types of drawings, such as circuit diagrams and sequence diagrams, can also be generated. Circuit diagrams are used to represent the configuration of electrical and electronic circuits, and are suitable when the claims describe the components of a circuit and their interconnections. Sequence diagrams, on the other hand, are used to represent the chronological interactions between multiple components, and are useful when the claims describe the operation of a communication system or software. Expanding the types of drawings allows patents in a wider range of technical fields to be accommodated. (Variation 3) In the above embodiment, drawings are generated from a single claim. In Variation 3, drawings are generated by combining multiple claims. Claims may consist of independent claims and dependent claims. Dependent claims cite the independent claims and add further limitations. Therefore, by analyzing the independent claims and dependent claims together, it is possible to generate drawings that express the overall structure of the invention. In this case, one possible method is to create a basic drawing from the independent claims and then add the content of the dependent claims to further refine the drawing.

[0076] The above describes modified examples of the present invention. Modification 1 proposes a method of using rule-based natural language processing technology instead of LLM. Modification 2 shows a method of responding to patents in various technical fields by expanding the types of drawings to be generated. Modification 3 explains a method of combining multiple claims to create drawings. These modifications further develop the automatic drawing generation technology in the above embodiment, and are expected to contribute to improving the efficiency of patent application work.

[0077] The invention can also be limited by the following points. (Limitation 1) When analyzing patent claims, we use natural language processing and take into account terminology and expression patterns specific to the technical field to which the invention pertains, enabling us to perform a more accurate analysis. This allows us to more accurately extract the characteristics of the invention in that technical field and reflect them in the drawings. (Limitation 2) When creating diagrams, to express the relationships between the components of an invention, rather than just using simple arrows, arrows of different shapes and colors are used depending on the type of relationship. For example, by distinguishing between arrows that indicate data flow and arrows that indicate control flow, the operation of the invention can be expressed in more detail. (Limitation 3) To provide an interface that allows users to modify and adjust drawings automatically. Specifically, it provides a function that allows users to change the layout of components in the generated drawings or add new elements. This enables semi-automatic drawing creation that includes human judgment, rather than completely automatic generation. (Limitation 4) The effects of the invention described in the claims are added as text information in the drawings. Because the effects of the invention may not be directly apparent from the claims, they are added to the drawings while also referencing the description in the specification. This makes it possible to more clearly show the technical significance of the invention. (Limitation 5) The system will have a function to check the consistency of drawings generated based on the claims and descriptions in the specification by comparing them. Specifically, it will check whether the components and relationships contained in the drawings deviate from the descriptions in the specification, and will output a warning if any deviations are found. This will prevent inconsistencies between the claims and the description in the specification. (Limitation 6) The numerical ranges stated in the claims should be reflected in the drawings. For example, if the claim states that the temperature is in the range of 50°C to 100°C, this temperature range should be clearly indicated in the corresponding part of the drawings. This will allow the technical features of the invention to be expressed in more detail.

[0078] We have proposed six points for limiting an invention. These points clarify the technical features of an invention from various perspectives, such as how to analyze the scope of the patent claims, how to express the drawings, the user interface, and checking consistency with the specification. By combining these points, it is expected that stronger patent rights can be obtained.

[0079] The following configurations of the invention are also possible. (Example 1: Generating claims using LLM) It is also possible to generate patent claims by converting the invention structure extracted from drawings into text data and then inputting it into an LLM. Large-scale language models such as GPT-3 and T5 can be used as LLMs. These models learn the sentence structure and expression patterns of patent claims by pre-training with large amounts of patent document data. Therefore, when text data representing the invention structure is input into an LLM, it can be automatically verbalized according to the claim format. Fine-tuning the LLM in this process enables more appropriate sentence generation. (Example 2: Generating claims using ontology) Each technical field has its own unique terminology and expressions used in patent claims. Therefore, one method is to build an ontology that systematizes knowledge in each technical field in advance and use it to generate claims. The ontology describes the key concepts and terms in that technical field, as well as their relationships. By mapping the components of the invention extracted from drawings to this ontology, appropriate terminology can be selected and claims written in a manner appropriate to the technical field. Using an ontology also makes it possible to add more detailed invention limitations to claims. (Example 3: Rule-based claim generation) There is a rule-based method for defining sentence structures and expression patterns when generating claims from drawings. For example, rules such as "element A in the drawing should be described as the main element of the claim" or "if element A and element B are connected, use the expression 'has a structure in which A and B are connected'" are defined in advance. These rules are then applied based on the analysis of the drawings to generate the claim text. Rule-based methods are less flexible than LLM, but have the advantage of being able to strictly control how claims are written. (Example 4: Creating multiple claims) When the amount of information contained in the drawings is large, one method is to gradually limit the structure of the invention by generating multiple claims. Specifically, first, extract the main components of the invention from the entire drawing and describe them as independent claims. Next, focus on the detailed structure and operation in the drawing and describe them as limitations in dependent claims. At this time, to properly express the relationship between the independent claim and the dependent claim, use expressions such as "the above" to link them. By generating multiple claims, the structure of the invention can be protected from multiple angles. (Example 5: Creating claims by combining drawings and specifications) There is a method for generating more complete patent claims by utilizing the contents of the specification in addition to the drawings. First, the basic structure of the invention is extracted from the drawings and used as the outline of the claims. Next, detailed descriptions and effects of the invention not shown in the drawings are extracted from the specification and added to the claims. LLM and rule-based methods can be applied to analyzing the specification. By combining the information from the drawings and specification, it is possible to incorporate more essential features of the invention into the claims.

[0080] We have proposed five specific methods for automatically generating patent claims from drawings: a method that utilizes LLM, a method that uses ontology, a rule-based method, a method that generates multiple claims, and a method that combines drawings and specifications. By appropriately selecting and combining these methods, we believe it will be possible to automatically generate patent claims with greater accuracy.

[0081] The configurations of other embodiments will be described below.

[0082] In this embodiment, a system is provided that automatically generates embodiments based on the configuration of the invention described in the claims. Embodiments in a patent specification are descriptions that specifically explain the invention described in the claims. However, refining the content of the claims and providing clear and sufficient explanations requires advanced intellectual work. The system in this embodiment analyzes the description of the claims using natural language processing technology and automatically generates embodiment sentences based on the results, thereby significantly streamlining this work.

[0083] This system is broadly composed of three functional blocks. The first is an analysis unit that analyzes the claims and extracts the elements of the invention and their relationships. The second is a generation unit that generates a sentence for an embodiment based on the analysis results. The third is a proofreading unit that evaluates the quality of the generated sentence and makes corrections as necessary. Each functional block is explained in detail below. (Configuration of the analysis section) The analysis unit contains multiple modules that receive the claim text as input and analyze it using language processing techniques. First, the morphological analysis module divides the claim text into words and identifies the part of speech and inflected forms of each word. Next, the syntactic analysis module analyzes the dependency relationships between words to clarify the syntactic structure of the text. Finally, the semantic analysis module extracts the components of the invention and their relationships based on the syntactic structure. Using LLM in this process enables advanced semantic understanding. (Configuration of the generation unit) The generation unit has the function of converting the information about the configuration of the invention obtained by the analysis unit into a description of the embodiment. Specifically, by combining a template-based generation module and a neural network-based generation module, it generates more natural and readable text. The template-based generation module generates a basic description of the embodiment by applying the components of the invention to a pre-prepared text template. On the other hand, the neural network-based generation module uses LLM to generate text that explains the embodiment in more detailed and flexible terms. By appropriately combining the outputs of both modules, a description of the embodiment that conforms to the format of a patent specification is automatically created. (Configuration of the calibration section) The proofreading unit has the function of evaluating the generated text of an embodiment and improving its quality. Specifically, it consists of a proofreading module that points out formal errors in the text based on the description rules of patent specifications, and a proofreading module that points out semantic errors in the text based on the technical content of the invention. The former determines whether the text complies with the description requirements under the Patent Act and suggests corrections as necessary. The latter determines whether the relationships between the components of the invention are properly explained and provides additional explanations if there are any unclear points. The proofreading unit's functions can improve the quality of the text of an automatically generated embodiment.

[0084] As described above, the system in this embodiment automates a series of processes, including analyzing claims, generating embodiments, and proofreading text. The analysis unit applies various natural language processing techniques to extract detailed information about the configuration of the invention. The generation unit combines template-based and neural network-based techniques to efficiently create descriptions that conform to the format of a patent specification. The proofreading unit checks the quality of the text from both the requirements of the Patent Act and the technical content of the invention, thereby improving the completeness of the automatically generated text. By linking these functional blocks, a system for automatically generating embodiments of an invention from claims is realized.

[0085] By using this system, it is possible to significantly reduce the human burden involved in preparing patent specifications and improve the efficiency of the patent application process. In addition, by automatically generating an embodiment that accurately describes the technical content of an invention, it is expected to contribute to improving the quality of patent rights.

[0086] Next, a computer program for automatically generating claims from embodiments of the invention, and the configuration of an information processing device will be described in detail.

[0087] In this embodiment, a system is provided that automatically generates patent claims from embodiments of an invention described in a patent specification. The claims are important details for determining the scope of patent protection, and must limit the structure of the invention as necessary and sufficient. However, extracting the essential features of an invention from the description of the embodiments and creating appropriate claims requires advanced intellectual work. The system in this embodiment analyzes the description of the embodiments using natural language processing technology and automatically generates the text of the claims based on the results, thereby significantly streamlining this process.

[0088] This system is broadly composed of three functional blocks. The first is an analysis unit that analyzes the description of the embodiment and extracts the components of the invention and their relationships. The second is a generation unit that generates the text of the claims based on the analysis results. The third is a proofreading unit that evaluates the quality of the generated text and makes corrections as necessary. Each functional block is explained in detail below. (Configuration of the analysis section) The analysis unit includes multiple modules that receive the sentence of the embodiment as input and analyze it using language processing techniques. First, the morphological analysis module divides the sentence into words and identifies the part of speech and inflected forms of each word. Next, the syntactic analysis module analyzes the dependency relationships between words to clarify the syntactic structure of the sentence. Furthermore, the semantic analysis module extracts the components of the invention and their relationships based on the syntactic structure. In this process, the use of LLM enables advanced semantic understanding. Furthermore, since an embodiment may include multiple examples, the essential features of the invention are extracted by comparative analysis of these examples. (Configuration of the generation unit) The generation unit has the function of converting the information about the invention structure obtained by the analysis unit into the text of patent claims. Specifically, it consists of a module that generates independent claims and a module that generates dependent claims. In generating independent claims, the essential elements of the invention extracted from the embodiments are written in a predetermined format. In this process, LLM is used to learn the sentence structure and expression patterns of claims, allowing for the generation of more natural and appropriate text. Meanwhile, in generating dependent claims, in addition to the structure of the independent claims, desirable structures and effects of the invention described in the embodiments are added as limitations. This automatically creates a group of claims that gradually limit the structure of the invention. (Configuration of the calibration section) The proofreading unit has the function of evaluating the generated claim text and improving its quality. Specifically, it consists of a proofreading module that points out formal errors in the claims based on the description requirements of the Patent Act, and a proofreading module that points out substantive errors in the claims based on the technical content of the invention. The former determines whether the claim description format complies with the Patent Act Enforcement Regulations and suggests corrections as necessary. The latter determines whether the content of the claim deviates from the description of the embodiment and suggests corrections if there is an inconsistency. The proofreading unit's functions can improve the quality of the automatically generated claim text.

[0089] As described above, the system in this embodiment automates a series of processes, including analyzing embodiments of the invention, generating claims, and proofreading text. The analysis unit applies various natural language processing techniques to extract detailed information about the configuration of the invention. The generation unit efficiently creates a group of claims that progressively limit the essential and preferred configurations of the invention by appropriately combining independent claims and dependent claims. The proofreading unit checks the quality of the text from both the requirements of the Patent Act and the technical content of the invention, thereby improving the completeness of the automatically generated text. By linking these functional blocks, a system for automatically generating claims from embodiments of the invention is realized.

[0090] By using this system, it is possible to significantly reduce the human burden involved in preparing patent specifications and improve the efficiency of the patent application process. Furthermore, by automatically generating patent claims that adequately limit the essential and preferred configurations of an invention, it is expected to contribute to improving the quality of patent rights.

[0091] Furthermore, a modified example of processing with LLM will be described with reference to past patent publications. (Variation 1: Learning a sentence generation model using past patent publications) This modification aims to generate more natural and appropriate sentences by using sentences from past patent publications as training data when generating sentences for patent claims and embodiments of inventions. Specifically, correspondences between patent claims and embodiments of inventions are extracted from a large number of patent publications and input into the LLM as training data. The LLM then learns the sentence structure and expression patterns of patent claims and embodiments of inventions from this training data. This enables the generation of sentences that conform to the writing styles frequently found in patent publications. Furthermore, by selectively training patent publications related to a specific technical field, it becomes possible to generate sentences that appropriately use terms and expressions specific to that technical field. (Variation 2: Extraction of elements of inventions using past patent publications) In this modified version, when generating claims from embodiments of an invention, knowledge of invention elements extracted from past patent publications is utilized to generate more appropriate claims. Specifically, the system extracts the elements of inventions described in claims and their relationships between superordinate and subordinate concepts from a large number of patent publications, and builds a database of invention elements. Then, by referencing this database when analyzing embodiments, the system extracts the elements of inventions more accurately. Furthermore, when generating claims, the system combines the extracted elements based on the relationships between superordinate and subordinate concepts to create claims that appropriately limit the essential and preferred elements of the invention. This allows the system to utilize past knowledge to generate appropriate claims even from embodiments in which the elements of the invention are insufficiently described. (Variation 3: Determining claim format using past patent publications) In this variant, when generating embodiments of inventions from patent claims, knowledge of claim writing styles learned from past patent publications is utilized to generate more appropriate embodiments. Specifically, an LLM that has learned the relationship between claim writing styles and their appropriateness from a large number of patent publications is used to judge the quality of the generated claims. Then, based on the judgment results, explanatory text that conforms to the claim writing style is automatically added when generating embodiments. For example, if a claim uses the expression "comprises...", a sentence that specifically explains the configuration is added to the embodiment. This makes it possible to efficiently generate embodiments that are consistent with the content of the claims.

[0092] We have proposed three variations of LLM processing that utilize past patent publications. Variation 1 uses patent publications as training data, enabling more natural and appropriate sentence generation. Variation 2 extracts knowledge of the components of an invention from patent publications and uses this knowledge to generate claims. Variation 3 learns knowledge of the claim writing format from patent publications and uses this knowledge to generate embodiments. All of these variations aim to improve the accuracy of automatic generation of patent specifications by effectively utilizing past knowledge.

[0093] Furthermore, we explain the configuration of a computer that uses LLM to check patent specifications and claims before filing.

[0094] In this embodiment, a system is provided that automatically checks the content of patent specifications and claims before filing a patent application. This system uses LLM to analyze the text of the specification and claims and evaluate their consistency and appropriateness. The purpose of this is to improve the quality of the specification and claims before filing and increase the likelihood of patent rights being granted.

[0095] This system is broadly composed of four functional blocks. The first is the input unit that receives the text of the patent specification and claims as input. The second is the analysis unit that analyzes the input text and extracts the components of the invention and their relationships. The third is the evaluation unit that evaluates the consistency and appropriateness of the specification and claims based on the analysis results. The fourth is the output unit that presents the evaluation results to the user and suggests modifications as necessary. Each functional block is explained in detail below. (Input section configuration) The input section provides an interface for receiving the text data of the patent specification and claims created by the user. Specifically, it has a screen that allows users to upload files and copy and paste text. It also allows users to input the specification and claims separately, making it easy to distinguish between the two texts. (Configuration of the analysis section) The analysis unit has the functionality to analyze the input text of the specification and claims using LLM. Specifically, it is equipped with modules for morphological analysis, syntactic analysis, and semantic analysis, and performs detailed analysis of the structure and semantic content of the text. In particular, it places emphasis on extracting the components of the invention and their relationships, clarifying the correspondence between the specification and claims. It also checks whether the claim format complies with the Patent Law Enforcement Regulations. (Configuration of evaluation section) The evaluation unit has the function of evaluating the consistency and appropriateness of the specification and claims based on the results of the analysis unit. Specifically, the evaluation is carried out from the following perspectives: -Are the essential elements of the invention described in both the specification and the claims? Whether the elements recited in the claims are explained in the specification to the extent that they can be implemented - Whether the claims are supported by the descriptions in the specification - Whether the claim format complies with the Patent Law Enforcement Regulations These evaluation items are scored using LLM to quantitatively assess the quality of the specification and claims. (Output section configuration) The output section presents the results of the evaluation section to the user and provides an interface to assist in revising the specification and claims. Specifically, it visualizes areas with low evaluation scores and displays comments explaining the reasons. It also uses LLM to automatically generate revision suggestions for areas that require revision and presents them to the user. Users can refer to this information to improve the content of the specification and claims.

[0096] As described above, the system in this embodiment uses LLM to automatically check the contents of patent specifications and claims. The input section receives the text of the specification and claims, and the analysis section analyzes them in detail. The evaluation section then evaluates the consistency and appropriateness of the specification and claims, and the output section presents the results to the user. By linking these functional blocks, a system is realized that efficiently improves the quality of specifications and claims before patent applications are filed.

[0097] By using this system, it is possible to significantly reduce the human burden during the preparation stage of a patent application and support the creation of higher quality patent application documents. In addition, by ensuring the consistency and appropriateness of the specification and claims before filing, it is expected that reasons for rejection will be prevented during patent examination and the likelihood of patent rights being established will be increased.

[0098] Below, we explain the configuration of a computer that uses LLM to search past documents for the purpose of examining novelty and inventive step, based on input claims.

[0099] In this embodiment, a system is provided that uses LLM to efficiently search related past literature to evaluate the novelty and inventive step of a patent application. This system analyzes the content of the input patent claims and extracts keywords that describe the characteristics of the invention. It then uses these keywords to search past patent documents and other technical literature to identify literature related to the novelty and inventive step of the invention. This aims to reduce the burden of prior art searches on examiners and improve the quality of examinations.

[0100] This system is broadly composed of four functional blocks. The first is an input unit that receives the text of the patent claims as input. The second is an analysis unit that analyzes the input text and extracts keywords that represent the characteristics of the invention. The third is a search unit that uses the extracted keywords to search past literature. The fourth is an output unit that presents search results to the user and provides information for evaluating the novelty and inventive step of the invention. Each functional block is explained in detail below. (Input section configuration) The input unit provides an interface for receiving the claim text of the patent application being examined as text data. Specifically, by entering a patent application number, the corresponding claim text is automatically retrieved from the patent publication database. It is also possible to input the claim text directly. (Configuration of the analysis section) The analysis unit has the function of analyzing the input claim text using LLM. Specifically, it is equipped with morphological analysis, syntactic analysis, and semantic analysis modules, and performs detailed analysis of the structure and semantic content of the text. In particular, it focuses on the components of the invention and their relationships, and extracts keywords that represent the characteristics of the invention. When extracting keywords, it takes into account not only the text of the claims but also the content of the specification. In addition, it uses LLM to generate synonyms and related words for the extracted keywords, improving the comprehensiveness of the search. (Search section configuration) The search unit has the function of searching past patent documents and other technical literature using the keywords extracted by the analysis unit. Specifically, it searches patent publication databases and academic literature databases for documents containing the keywords. In this process, LLM is used to collect a wide range of related literature by taking into account the variety of keyword combinations and expressions. LLM is also used to generate summaries of the contents of the documents found in the search results and evaluate their relevance to the invention. This allows examiners to efficiently understand prior art. (Output section configuration) The output section presents the user with information about the documents retrieved by the search section, providing an interface to assist in the evaluation of the novelty and inventive step of the invention. Specifically, it displays a list of documents from the search results, and shows each document's summary and a score for its relevance to the invention. It also highlights parts of the documents that correspond to the elements of the invention, allowing comparison with the claims. Furthermore, it uses LLM to automatically generate findings regarding the novelty and inventive step of the invention, supporting the examiner's judgment. Examiners can use this information to compile the results of their prior art searches.

[0101] As described above, the system in this embodiment uses LLM to analyze the content of patent claims and efficiently search for related past literature. The input unit receives the claim text, and the analysis unit extracts keywords that describe the characteristics of the invention. The search unit then uses the keywords to search for related literature, and the output unit presents the results to the user. By linking these functional blocks, a system is realized that supports prior art searches during patent examination.

[0102] The system will significantly reduce the burden of prior art searches on examiners, improving the efficiency and quality of examinations. It is also expected that by providing objective information on the novelty and inventive step of inventions, it will contribute to ensuring fairness and consistency in examinations.

[0103] Below, we will explain the configuration of a computer using LLM that examines whether the invention described in the claims has novelty and inventive step over prior documents.

[0104] This embodiment provides a system that automatically evaluates the novelty and inventive step of patent application inventions based on comparison with prior art documents. This system uses LLM to analyze the structure of the invention described in the claims and the structure of the invention described in prior art documents, and identifies the differences between them to determine whether the invention is novel or inventive. This aims to support examiners' judgments and improve the efficiency and quality of examinations.

[0105] This system is broadly composed of five functional blocks. The first is an input unit that receives the patent claims and text from prior documents as input. The second is an analysis unit that analyzes the input text and extracts the elements of the invention and their relationships. The third is a comparison unit that compares the patent claims with the structures of the inventions in prior documents and identifies the differences. The fourth is an evaluation unit that evaluates the presence or absence of novelty and inventive step based on the identified differences. The fifth is an output unit that presents the evaluation results to the user and supports the examiner's decision. Each functional block is explained in detail below. (Input section configuration) The input section provides an interface for receiving the text data of the claims of the patent application under examination and the text data of the related prior documents. Specifically, by inputting the patent application number and the document number or URL of the prior document, the respective text data is automatically retrieved from the patent publication database and the literature database. (Configuration of the analysis section) The analysis unit has the functionality to analyze input claims and prior literature sentences using LLM. Specifically, it is equipped with morphological analysis, syntactic analysis, and semantic analysis modules to perform detailed analysis of sentence structure and semantic content. In particular, it focuses on the components of an invention and their relationships, expressing them in a unified format. This allows the structure of inventions in claims and prior literature to be compared using a common standard. (Configuration of comparison section) The comparison unit compares the claims extracted by the analysis unit with the structures of inventions in prior documents to identify differences. Specifically, it uses LLM to determine whether the elements described in the claims are also described in the prior documents. It also compares the relationships between elements and the specific content of the elements to evaluate whether the claimed invention is identical to the invention in the prior document or whether it could be easily arrived at from the invention in the prior document. (Configuration of evaluation section) The Evaluation Department has the function of evaluating the novelty and inventive step of the claimed invention based on the differences identified by the Comparison Department. Specifically, if the claimed invention contains new elements compared to the invention in the prior document, or if it combines known elements in a way that would not be easily conceivable, it is determined to have novelty and inventive step. On the other hand, if the claimed invention is identical to the invention in the prior document, or if it could be easily conceived from the invention in the prior document, it is determined to lack novelty and inventive step. This determination is made using the LLM, taking into account the common general knowledge of a person skilled in the art. (Output section configuration) The output section presents the results of the evaluation section to the user and provides an interface to support the examiner's decision. Specifically, it displays a comparison table between the claimed invention and inventions in prior documents, clearly indicating the differences between their respective components. It also displays the evaluation results for novelty and inventive step, along with information on the prior documents that serve as the basis. Furthermore, it uses LLM to automatically generate explanatory text about the evaluation results, helping the examiner understand them. The examiner can refer to this information when making their final decision.

[0106] As described above, the system in this embodiment uses LLM to analyze the claims and the contents of prior documents, automatically evaluating the novelty and inventive step of an invention. The input section receives the claims and the text of the prior document, and the analysis section extracts the configuration of the invention. The comparison section then identifies differences between the two, and the evaluation section determines whether the invention has novelty or inventive step based on those differences. Finally, the output section presents the evaluation results to the user. By linking these functional blocks, a system is realized that supports the determination of the patentability of inventions during patent examination.

[0107] By using this system, it is possible to significantly reduce the workload of examiners and improve the efficiency and quality of examinations. Furthermore, by clearly indicating the basis for determining the novelty and inventive step of an invention, it is expected to contribute to fulfilling accountability to applicants. However, it is important to note that this system is merely a tool to support examiners' judgments, and the final decision on patentability should be made based on the examiner's specialized knowledge and experience.

[0108] In the above-described embodiment, a computer program and an information processing device are provided that automatically generate patent claims from drawings showing the configuration of an invention using a computer. In a patent application, claims are important disclosures for identifying the technical scope of an invention. However, verbalizing the content of drawings to create appropriate claims requires advanced intellectual work. The computer program and information processing device in this embodiment analyze the information contained in the drawings using natural language processing technology and image recognition technology, and automatically generate patent claims based on the results, thereby significantly streamlining this process.

[0109] The program and information processing device have two main components. The first component is an analysis component that uses a computer vision algorithm to analyze the input drawing and extract the components contained in the drawing and their relationships. The analysis component uses image recognition technologies such as object detection, segmentation, and OCR to identify each element in the drawing and obtain their labels. Furthermore, the analysis component recognizes the lines and arrows that indicate the connections between elements to understand the relationships between elements. This allows the configuration of the invention expressed in the drawing to be extracted in a format that is understandable by a computer.

[0110] The second means is a conversion means that converts the information about the configuration of the invention obtained by the analysis means into text in accordance with the format of the patent claims. The conversion means describes the components extracted from the drawings as the main elements of the claims. It also verbalizes the relationships between the components using expressions such as "comprises..." or "connected to...". In this process, it generates appropriate sentences by taking into account grammatical rules regarding how claims are written and the use of terminology in each technical field. The output of the conversion means is text data that conforms to the format of the patent claims.

[0111] As described above, the computer program and information processing device of this embodiment combine image analysis of drawings with natural language generation technology to automatically create patent claims from drawings. The analysis means uses a computer vision algorithm to accurately recognize the structure of the invention depicted in the drawings and express it in a format that can be processed by a computer. Furthermore, the conversion means implements rules for verbalizing the extracted structure of the invention, allowing for the automatic generation of text that conforms to the format of the claims. This significantly reduces the workload of verbalizing the contents of drawings and contributes to the efficiency of patent application procedures.

[0112] In the above explanation, the types of drawings are not limited to block diagrams and flowcharts, but can also be applied to various other drawings that express the configuration of an invention, such as circuit diagrams and structural diagrams. Furthermore, it is possible to analyze drawings published in patent documents, as well as drawings at the time of patent application, and generate corresponding patent claims. This makes it useful as an auxiliary tool for analyzing the scope of rights of existing patents.

[0113] Next, a multimodal embodiment will be described in which the configurations described in the claims and the corresponding elements of the drawings are displayed in association with each other.

[0114] In this embodiment, a system is provided that visually correlates the structure of an invention described in the claims with the corresponding elements in the drawings to aid in understanding patent specifications. This system uses LLM to analyze the text of the claims and the images in the drawings, identifying the correspondence between them and visualizing the structure of the invention in an easy-to-understand manner. This allows users to intuitively grasp the contents of patent specifications, thereby improving the convenience of examiners and general users.

[0115] This system is broadly composed of four functional blocks. The first is an input unit that receives the text of the claims and images of the drawings as input. The second is an analysis unit that analyzes the input text and drawings and extracts the elements of the invention and their relationships. The third is a matching unit that matches the elements of the claims with the elements of the drawings. The fourth is an output unit that visually presents the results of the matching. Each functional block is explained in detail below. (Input section configuration) The input section extracts the claims and images of drawings from patent specifications and provides an interface for receiving them as text data and image data, respectively. Specifically, by inputting a patent publication document file or PDF file, the claims and drawings are automatically extracted. It is also possible to upload the claims and drawings separately. Images can be either vector data or raster data. (Configuration of the analysis section) The analysis unit has the functionality to analyze the input claim text and drawing images using LLM. Specifically, it applies morphological analysis, syntactic analysis, and semantic analysis modules to the claim text to extract the components of the invention and their relationships. On the other hand, it applies image recognition technologies such as object detection, segmentation, and OCR to the drawing images to identify each element in the drawing and obtain their labels. This allows it to extract information about the structure of the invention from both the claims and drawings. (Configuration of the mapping part) The matching unit has the function of matching the elements of claims extracted by the analysis unit with elements in drawings. Specifically, it uses LLM to evaluate the semantic similarity between words and phrases representing elements of claims and the labels of elements in drawings. It then identifies highly similar combinations as corresponding elements. It also improves the accuracy of the matching by comparing the relationships between elements in claims with the positional relationships of elements in drawings. This automatically identifies the correspondence between the text of claims and drawings. (Output section configuration) The output unit provides an interface for visually presenting the results of the matching unit. Specifically, the output unit displays the claim text and the drawings side by side, and clearly shows the correspondence between corresponding elements and drawing elements by drawing lines. Furthermore, the output unit highlights the elements in the claim text and the corresponding elements in the drawings in the same color, allowing the user to intuitively understand the visual correspondence. Through these displays, the user can easily grasp the relationship between the configuration of the invention described in the claims and the specific embodiments shown in the drawings.

[0116] As described above, the system in this embodiment uses LLM to analyze the claims and drawings and visually present the correspondence between them. The input unit receives the claims and drawings, and the analysis unit extracts their respective components. The matching unit then matches the components of the claims with the elements of the drawings, and the output unit visually presents the results. By linking these functional blocks, a multimodal system is realized that allows users to intuitively understand the contents of patent specifications.

[0117] Using this system will make it easier to understand the contents of patent specifications, which is expected to improve the efficiency of examiners' examinations and promote the use of patent information by general users. Furthermore, by clearly showing the correspondence between claims and drawings, it is believed to contribute to an accurate understanding of the technical scope of inventions. However, it should be noted that the correspondence determined by this system is merely the result of automatic processing, and the final judgment must be made by a human being, taking into account the entire description of the patent specification.

[0118] The following describes a computer configuration that associates each component of the claims with the description of the embodiment of the invention in which it is described.

[0119] In this embodiment, a system is provided that automatically associates and presents the elements of the invention described in the claims with the corresponding description of the embodiments of the invention to aid in understanding patent specifications. This system uses LLM to analyze the text of the claims and the text of the embodiments of the invention, and by identifying the correspondence between the two, it correlates and presents the description of the configuration of the invention. This allows the content of the claims to be understood in conjunction with the specific description of the embodiments of the invention, enabling a deeper understanding of the content of the patent specification.

[0120] This system is broadly composed of four functional blocks. The first is an input unit that receives the claims and the text of the embodiments of the invention as input. The second is an analysis unit that analyzes the input text and extracts the elements of the invention and their relationships. The third is a matching unit that matches the elements of the claims with the description of the embodiments. The fourth is an output unit that presents the results of the matching to the user. Each functional block is explained in detail below. (Input section configuration) The input unit extracts the claims and the description of the preferred embodiment from the patent specification and provides an interface for receiving them as text data. Specifically, by inputting a patent publication document file or PDF file, the claims and the description of the preferred embodiment are automatically extracted. It is also possible to upload the claims and the description of the preferred embodiment separately. (Configuration of the analysis section) The analysis unit has the function of analyzing the input claims and embodiments using LLM. Specifically, it applies morphological analysis, syntactic analysis, and semantic analysis modules to perform a detailed analysis of the structure and meaning of the sentences. In particular, for claims, it focuses on the elements of the invention and their relationships, and expresses them in a unified format. On the other hand, for embodiments, it focuses on how the elements of the invention are specifically realized, and identifies where they are described. This allows it to extract information about the structure of the invention from both the claims and embodiments. (Configuration of the mapping part) The matching unit has the function of matching the claim elements extracted by the analysis unit with the description of the embodiment. Specifically, it uses LLM to evaluate the semantic similarity between the words and phrases representing the claim elements and the description of the embodiment. Then, highly similar combinations are identified as corresponding elements and description locations. In addition, the accuracy of the matching is improved by comparing the relationship between the elements in the claim and the context of the description in the embodiment. This automatically matches each element of the claim with the description of the embodiment where it is specifically explained. (Output section configuration) The output unit provides an interface for presenting the results of the matching unit to the user. Specifically, it displays the claims and the sentences of the embodiments of the invention side by side, and sets links between each element of the claim and the corresponding description of the embodiment. When the user clicks on an element of the claim, they are able to jump to the corresponding description of the embodiment, and vice versa. In addition, the elements of the claim and the corresponding description of the embodiment are highlighted in the same color, allowing the user to intuitively understand the visual correspondence. Through these functions, the user can easily grasp the relationship between the configuration of the invention described in the claims and its specific embodiment.

[0121] As described above, the system in this embodiment uses LLM to analyze the claims and the description of the embodiment of the invention and present the correspondence between them. The input unit receives the claims and the description of the embodiment, and the analysis unit extracts their respective components and description content. The matching unit then matches the components of the claims with the description of the embodiment, and the output unit presents the results to the user. By linking these functional blocks, a system is realized that provides a deeper understanding of the contents of patent specifications.

[0122] By using this system, it is possible to understand the structure of the invention described in the claims in relation to the specific description of the embodiment of the invention, enabling accurate understanding of the contents of the patent specification. This is important for accurately interpreting the technical scope of a patent right, and is expected to contribute to the utilization of patent information and the efficiency of patent examination. However, it should be noted that the correspondence created by this system is merely the result of automatic processing, and the final judgment must be made by a human being, taking into account the entire description of the patent specification.

[0123] Below, we will explain a system for preparing an argument and amendment in response to a notice of rejection issued against a patent application.

[0124] In this embodiment, when a notice of rejection is issued for a patent application, a system is provided that analyzes the reasons for rejection and semi-automatically prepares a written argument and amendment in response. This system uses LLM to understand the content of the notice of rejection, construct a logical argument against it, and generate any necessary amendments. This aims to support the response work of applicants and attorneys and enable efficient and effective acquisition of patent rights.

[0125] This system is broadly composed of five functional blocks. The first is an input unit that receives the contents of the notice of reasons for refusal as input. The second is an analysis unit that analyzes the contents of the input notice of reasons for refusal and identifies the prior art and legal provisions that form the basis for the refusal. The third is an opinion generation unit that constructs the logic of a rebuttal to the reasons for refusal and generates the contents of an opinion. The fourth is an amendment generation unit that generates proposed amendments to resolve the reasons for refusal. The fifth is an output unit that presents the contents of the generated opinion and amendment to the user and makes any necessary corrections. Each functional block is explained in detail below. (Input section configuration) The input section provides an interface for receiving the contents of the Office Action Notice issued by the Patent Office as text data. Specifically, by uploading the Office Action Notice document file or PDF file, the contents are automatically extracted. It is also possible to input information on prior art documents cited in the Office Action Notice. (Configuration of the analysis section) The analysis unit has the functionality to analyze the content of the input rejection notice using LLM. Specifically, it applies morphological analysis, syntactic analysis, and semantic analysis modules to analyze the sentence structure of the rejection notice. It then extracts specific parts of prior art documents and provisions of laws and regulations such as the Patent Act that form the basis of the rejection. It also compares the elements of the invention pointed out in the rejection notice with the elements described in the prior art, clarifying the similarities and differences between them. This allows the content of the rejection notice to be organized in a format that can be processed by a computer. (Configuration of opinion generation unit) The opinion generation unit has the function of constructing a logical argument for the rejection based on the content of the reasons for rejection organized by the analysis unit, and generating the content of the opinion. Specifically, using LLM, it focuses on the differences between the elements of the invention and those of the prior art and constructs a logical argument for asserting the novelty and inventive step of the invention. It also analyzes the interpretation of the legal provisions cited in the notice of rejection and argues why they should not be applied to the present invention. By documenting these arguments in accordance with the format of an opinion, it presents a logical argument for the rejection. (Configuration of the amendment generation unit) The amendment generator has the function of generating proposed amendments to resolve the reasons for refusal based on the content of the reasons for refusal compiled by the analysis unit. Specifically, it uses LLM to identify deficiencies in the elements of the invention pointed out in the notice of reasons for refusal and unclear areas of difference from the prior art, and proposes amendments to clarify these. It also generates amendments that limit the elements of the invention to highlight the differences from the prior art. These amendments are reflected in the claims and description to create amendments to resolve the reasons for refusal. (Output section configuration) The output section provides an interface for presenting the contents of the opinion and amendments generated by the opinion generation section and amendment generation section to the user. Specifically, it displays the text of the generated opinion and amendment on the screen, allowing the user to check their contents. It also provides an editing function so that the user can make corrections as needed. It also has a function for checking whether the contents of the opinion and amendments comply with the requirements of laws and regulations such as the Patent Act, and notifies the user of any deficiencies. Through these functions, the user can carefully review the contents of the opinion and amendments generated by the system and complete the final submission.

[0126] As described above, the system in this embodiment uses the LLM to analyze the contents of an Office Action and semi-automatically prepare an argument and amendment in response to the Office Action. The input unit receives the contents of the Office Action, and the analysis unit organizes the reasons for refusal. The opinion generation unit and amendment generation unit then generate the logic for refuting the reasons for refusal and proposed amendments, and the output unit presents the results to the user. By linking these functional blocks, a system for efficiently dealing with reasons for refusal in patent applications is realized.

[0127] Using this system can significantly reduce the time and effort required to respond to office action notices, making it possible to obtain patent rights more quickly and reliably. Furthermore, by utilizing LLM, the process of responding to office action notices, which previously relied on human expertise, can be automated while maintaining a certain level of quality. However, it should be noted that the generation of opinions and amendments by this system is merely supplementary, and the final decision must still be made by a human being, taking into account the interpretation of laws and regulations such as the Patent Act and the technical significance of the invention. (Other configurations) The patent claim processing system using a large-scale language model (LLM) analyzes the input claim text using an LLM. The LLM uses deep learning models specialized for natural language processing, such as GPT-3, BERT, XLNet, and RoBERTa. The LLM analyzes the claim text structure and identifies subjects (main components), predicates (relationships between elements), objects (subordinate components), etc.

[0128] To generate code for the diagram, the LLM analysis results in program code (e.g., SVG code) that generates vector data for computer graphics from the identified components and relationship information. The generated code includes information such as the type of diagram (block diagram or flowchart), the layout of each component, and their connections.

[0129] Drawings based on code are automatically generated based on generated code (SVG, etc.) to appropriately express the content of the claims. Visualization libraries such as Python's Matplotlib and JavaScript's D3.js are used to draw the drawings. When drawing, arrows are automatically added to the drawing to represent processing flow, time series, and signal flow. Components are drawn as blocks, and nested structures (blocks within blocks) can also be expressed.

[0130] 1. Patent Claim Analysis Using the LLM - Morphological analysis, syntactic analysis, and semantic analysis are applied sequentially to the input patent claim text. - Morphological analysis divides a sentence into words and identifies the part of speech and inflections of each word. - Syntactic analysis analyzes the dependency relationships between words and generates a syntax tree for the sentence. - Semantic analysis identifies semantic roles such as subject, predicate, and object based on the syntax tree. - LLM uses models pre-trained on a large amount of patent literature, specializing in understanding the meaning of sentences. - The input to LLM is a tokenized version of the patent claim text. - The output of LLM is a vector representation that shows the semantic role of each word and the relationships between words. - From these vector representations, the components of the invention and their relationships are extracted.

[0131] 2. Code Generation Algorithm for Drawing - Extract the information necessary for drawing from the analysis results of LLM. - Information extracted includes the names of components, their relationships, and importance. - Generate SVG code based on this information. - Code generation is performed by applying extracted information to a pre-prepared template. - Templates are available for each type of drawing (block diagram, flowchart, etc.). - Convert components into SVG rectangle elements and relationships into line and arrow elements. - Element placement is automatically determined based on importance. - Use different types of arrows (solid lines, dotted lines, etc.) and colors to express relationships.

[0132] 3. Drawing Algorithm - The generated SVG code is passed to a drawing library to generate the drawing. - Python's Matplotlib and JavaScript's D3.js are used as drawing libraries. - These libraries have the ability to interpret SVG code and generate graphics. - When drawing, the placement of components and the calculation of line and arrow paths are automatically optimized. - Drawing blocks within blocks is achieved using a recursive algorithm. - When dealing with multiple claims, each claim is generated as an independent drawing, and then an overall diagram showing the relationships between them is generated.

[0133] By combining natural language processing using LLM and vector graphics generation using SVG, we have achieved a process for automatically generating drawings from patent claims.

[0134] As described above, drawings may be created by processing the claims, or drawings may be created by processing the embodiments and examples of the invention. Claims, embodiments and examples of the invention may also be created from the drawings.

[0135] The processes and flows in this embodiment may be executed by a plurality of software programs.

[0136] The above-described embodiments and the elements contained therein (part of the configurations, part of the processes) can be combined or replaced to create new and different embodiments.

[0137] The processes in the above-described embodiments may be performed by software or by using hardware circuits. A program for executing the processes in the above-described embodiments may be provided, or the program may be recorded on a recording medium such as a CD-ROM, a flexible disk, a hard disk, a ROM, RAM, or a memory card and provided to a user. The program is executed by a computer such as a CPU. The program may also be downloaded to a device via a communication line such as the Internet.

[0138] The above-described embodiments should be considered to be illustrative in all respects and not restrictive. The scope of the present invention is defined by the claims, not by the above description, and is intended to include all modifications within the meaning and scope of the claims.

Claims

1. An information processing device that uses a computer to create drawings showing the configuration of a patent claim, an input means for inputting claims; an acquisition means for processing the input claims through a large-scale language model to acquire code written in a language for creating drawings illustrating the input claims; a drawing means for drawing a block diagram or a flowchart illustrating the input claims in accordance with the code obtained by the obtaining means, The information processing device, wherein the acquisition means, when a component is included in the input claim, draws the component as a block.

2. The information processing apparatus according to claim 1 , wherein said drawing means draws a plurality of said blocks and writes arrows between the blocks to represent at least one of a process flow, a time sequence, and a signal flow.

3. 3. The information processing apparatus according to claim 1, wherein said drawing means draws, within said block, blocks belonging to said block.

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