system

Abstract

Provide a system. 【Solution means】 Means for analyzing business information, Means for extracting technical ideas from the analyzed information, Means for evaluating the patentability of the extracted ideas, Means for generating a document for patent application based on the evaluation, Means for managing the review and approval of the generated document, Means for automatically filing an application with the patent office, Means for evaluating technical ideas through an application installed on a portable device and transmitting the ideas to the data processing system within the company, Means for analyzing the information input using natural language processing technology and comparing it with an existing database to evaluate novelty, A system including

Description

Technical Field

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

Background Art

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

Prior Art Documents

Patent Documents

[0003]

Patent Document 1

Summary of the Invention

Problems to be Solved by the Invention

[0004] There is a need to solve the problem that technical ideas generated daily within an enterprise are buried without being patented. If employees with little patent experience do not notice the value of their own ideas, those ideas may not be protected as patents and, as a result, business opportunities may be missed. It is required to improve such a situation and actively utilize the intellectual property of the enterprise to enhance competitiveness.

Means for Solving the Problems

[0005] This invention provides a means for analyzing business information and extracting technical ideas from it. The extracted ideas can be evaluated for patentability, and based on this evaluation, documents for patent application can be automatically generated. Furthermore, after review and approval by the intellectual property management department, the generated documents are automatically filed with the Japan Patent Office. This ensures that ideas with patent potential are quickly filed for patent applications, maximizing the utilization of a company's intangible assets.

[0006] "Business information" refers to information such as documents, databases, emails, and reports that are generated and managed on a daily basis within a company.

[0007] "Analysis" is the process of scrutinizing given information and extracting meaningful data and patterns.

[0008] "Technical ideas" refer to new technologies, methods, or innovative solutions discovered within business information.

[0009] "Extraction" is the process of taking out specific elements or information from an object.

[0010] "Patentability" refers to the criteria used to determine whether an idea or invention possesses the novelty and usefulness necessary to be protected as a patent.

[0011] "Evaluation" is the act of determining the value or performance of an object based on specific criteria.

[0012] A "patent application" is the procedure of applying to the patent office for protection of an idea or invention as a patent.

[0013] "Document generation" is the process of creating official documents based on specific information.

[0014] "Review" is the process of re-examining generated documents and information and making corrections or improvements as needed.

[0015] "Approval" refers to the act of confirming that the reviewed content meets the predetermined criteria and permitting progress to the next step.

[0016] "The Patent Office" is a government agency that officially registers inventions as patents and manages them.

[0017] "Filing an application" refers to the act of making an official application seeking specific rights or benefits.

Brief Description of Drawings

[0018] [Figure 1] It is a conceptual diagram showing an example of the configuration of a data processing system according to the first embodiment. [Figure 2] It is a conceptual diagram showing an example of the main functions of a data processing device and a smart device according to the first embodiment. [Figure 3] It is a conceptual diagram showing an example of the configuration of a data processing system according to the second embodiment. [Figure 4] It is a conceptual diagram showing an example of the main functions of a data processing device and smart glasses according to the second embodiment. [Figure 5] It is a conceptual diagram showing an example of the configuration of a data processing system according to the third embodiment. [Figure 6] It is a conceptual diagram showing an example of the main functions of a data processing device and a headset-type terminal according to the third embodiment. [Figure 7] It is a conceptual diagram showing an example of the configuration of a data processing system according to the fourth embodiment. [Figure 8] It is a conceptual diagram showing an example of the main functions of a data processing device and a robot according to the fourth embodiment. [Figure 9] It shows an emotion map to which multiple emotions are mapped. [Figure 10] It shows an emotion map to which multiple emotions are mapped. [Figure 11] It is a sequence diagram showing the processing flow of the data processing system in Example 1. [Figure 12]It is a sequence diagram showing the processing flow of the data processing system in Application Example 1. [Figure 13] It is a sequence diagram showing the processing flow of the data processing system in Example 2 when combined with an emotion engine. [Figure 14] It is a sequence diagram showing the processing flow of the data processing system in Application Example 2 when combined with an emotion engine.

Mode for Carrying Out the Invention

[0019] Hereinafter, an example of an embodiment of the system according to the technology of the present disclosure will be described with reference to the accompanying drawings.

[0020] First, the terms used in the following description will be explained.

[0021] In the following embodiments, the numbered processor (hereinafter simply referred to as "processor") may be a single arithmetic unit or a combination of multiple arithmetic units. Also, the processor may be a single type of arithmetic unit or a combination of multiple types of arithmetic units. Examples of arithmetic units include CPU (Central Processing Unit), GPU (Graphics Processing Unit), GPGPU (General-Purpose computing on Graphics Processing Units), APU (Accelerated Processing Unit), etc.

[0022] In the following embodiments, the numbered RAM (Random Access Memory) is a memory in which information is temporarily stored and is used as a work memory by the processor.

[0023] In the following embodiments, the signed storage is one or more non-volatile storage devices that store various programs and various parameters. Examples of non-volatile storage devices include flash memory (SSD (Solid State Drive)), magnetic disks (e.g., hard disks), or magnetic tapes.

[0024] In the following embodiments, the signed communication interface (I / F) is an interface that includes a communication processor and an antenna, etc. The communication interface manages communication between multiple computers. Examples of communication standards applicable to the communication interface include wireless communication standards such as 5G (5th Generation Mobile Communication System), Wi-Fi (registered trademark), or Bluetooth (registered trademark).

[0025] In the following embodiments, "A and / or B" is synonymous with "at least one of A and B." That is, "A and / or B" means that it may be A alone, or B alone, or a combination of A and B. Furthermore, in this specification, the same concept as "A and / or B" applies when expressing three or more things linked by "and / or."

[0026] [First Embodiment]

[0027] Figure 1 shows an example of the configuration of the data processing system 10 according to the first embodiment.

[0028] As shown in Figure 1, the data processing system 10 includes a data processing device 12 and a smart device 14. An example of the data processing device 12 is a server.

[0029] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 is an example of a "computer" related to the technology of this disclosure. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[0030] The smart device 14 comprises a computer 36, a reception device 38, an output device 40, a camera 42, and a communication interface 44. The computer 36 comprises a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The reception device 38, output device 40, and camera 42 are also connected to the bus 52.

[0031] The reception device 38 is equipped with a touch panel 38A and a microphone 38B, etc., and receives user input. The touch panel 38A receives user input by detecting contact with an object (e.g., a pen or finger). The microphone 38B receives user input by detecting the user's voice. The control unit 46A transmits data indicating the user input received by the touch panel 38A and microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the data indicating the user input.

[0032] The output device 40 includes a display 40A and a speaker 40B, and presents data to the user 20 by outputting the data in a form perceptible to the user 20 (e.g., audio and / or text). The display 40A displays visible information such as text and images according to instructions from the processor 46. The speaker 40B outputs audio according to instructions from the processor 46. The camera 42 is a small digital camera equipped with an optical system such as a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor.

[0033] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various types of information between processor 46 and processor 28 via network 54.

[0034] Figure 2 shows an example of the main functions of the data processing device 12 and the smart device 14.

[0035] As shown in Figure 2, in the data processing device 12, a specific processing is performed by the processor 28. A specific processing program 56 is stored in the storage 32. The specific processing program 56 is an example of a "program" related to the technology of this disclosure. The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0036] The storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[0037] In the smart device 14, the processor 46 performs the reception output processing. The storage 50 stores the reception output program 60. The reception output program 60 is used in conjunction with a specific processing program 56 by the data processing system 10. The processor 46 reads the reception output program 60 from the storage 50 and executes the read reception output program 60 on the RAM 48. The reception output processing is realized by the processor 46 operating as a control unit 46A according to the reception output program 60 executed on the RAM 48.

[0038] Next, the specific processing performed by the specific processing unit 290 of the data processing device 12 will be described. In the following description, the data processing device 12 will be referred to as the "server" and the smart device 14 as the "terminal".

[0039] This invention describes an embodiment of a system that analyzes business information within a company and automatically extracts, evaluates, and files patent applications for technical ideas. The main components of the system consist of a server, terminals, and users.

[0040] The server forms the core of the information processing in this invention. First, it collects business information such as emails, reports, and meeting materials generated by employees via the company's internal network. Next, it analyzes the collected information using natural language processing technology to extract technical problems and new technical ideas as solutions. This reveals potential patent ideas that employees may not be aware of.

[0041] The extracted ideas are cross-referenced with external patent and academic paper databases to evaluate their patentability based on novelty and inventiveness. This evaluation is performed using machine learning algorithms, and ideas deemed highly patentable are given priority.

[0042] Next, the server generates the documents necessary for filing a patent application for the idea. These documents are formatted to meet legal requirements and are output in a manner that conforms to the patent application process.

[0043] The terminal acts as the interface with the user. It receives idea summaries and patent suitability assessment reports sent from the server and displays them in a format that the user can review. The user can use the provided information to verify the value of the technical idea and provide feedback as needed.

[0044] The user (usually the intellectual property management department) reviews the patent application documents generated by the server. At this stage, a person with legal expertise reviews the content of the documents and makes final adjustments to make them suitable for submission to the patent office. Once the documents are approved by the user, the server automatically files them with the patent office in the next step.

[0045] As a concrete example, a system could identify a new method for delivering digital advertisements written in internal meeting notes. These notes are then analyzed by a server to verify the usefulness of the new algorithm. Subsequently, patentability is assessed, and the necessary documents for filing a patent application are automatically generated. Through this process, technical ideas can be quickly patented, strengthening a company's intellectual property portfolio.

[0046] The following describes the processing flow.

[0047] Step 1:

[0048] The server collects various types of business information through the company network. This includes emails, reports, meeting materials, and digital documents. Information stored in databases is also included in the extraction process.

[0049] Step 2:

[0050] The server uses natural language processing technology to analyze the collected business information. The purpose of the analysis is to identify technical problems and information that could lead to new solutions. The technical ideas extracted here are automatically listed by the system.

[0051] Step 3:

[0052] The server uses machine learning algorithms to evaluate the novelty and patentability of a technical idea. It cross-references information with external patent and academic paper databases to verify that the idea is differentiated from existing inventions. This evaluation is recorded as a score.

[0053] Step 4:

[0054] The server automatically generates the necessary documents for a patent application based on the patent evaluation results. These documents include detailed descriptions of the idea, technical scope, and effects. The documents are formatted according to the standards of the Japan Patent Office.

[0055] Step 5:

[0056] The terminal receives idea summaries and patent suitability assessment reports sent from the server. In addition, it retrieves the generated patent application documents and provides an interface for presenting them to the user.

[0057] Step 6:

[0058] The user (intellectual property management department) reviews the documents presented on the terminal, verifying their accuracy and legal compliance. They make revisions as needed and then provide final approval. Feedback is sent back to the server via the terminal.

[0059] Step 7:

[0060] The server automatically files a patent application with the patent office after receiving approval from the user. The progress of the application process is periodically notified to the terminal, allowing the user to check the status.

[0061] (Example 1)

[0062] Next, we will describe Example 1. In the following description, the data processing device 12 will be referred to as the "server," and the smart device 14 will be referred to as the "terminal."

[0063] Traditionally, the process of efficiently extracting technical ideas from the vast amount of business information generated within a company and converting them into patent applications has been time-consuming and labor-intensive. A major challenge is that technical ideas can get lost in this process, leading to missed patent opportunities. Furthermore, evaluating patentability and generating application documents requires specialized knowledge, making it difficult to achieve a rapid application process.

[0064] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 1 is realized by the following means.

[0065] In this invention, the server includes means for collecting business information, means for analyzing the collected information using natural language processing technology to extract technical problems and technical ideas, and means for comparing the extracted ideas with an external database to evaluate their patentability. This makes it possible to quickly and efficiently identify technical ideas from business information and automate the procedures necessary for patent application.

[0066] "Business information" refers to the collective term for data generated within a company, such as emails, reports, and meeting materials, and encompasses information related to the organization's activities.

[0067] "Natural language processing technology" refers to computer technologies used to analyze human language, enabling the understanding, interpretation, and generation of language data.

[0068] "Technical ideas" refer to concepts such as new methods, devices, and algorithms extracted from business information, and represent ingenious ideas that have the potential to be patented.

[0069] "Patentability" refers to the criteria used to evaluate whether an extracted technical idea can be granted a patent, and is judged based on novelty and inventiveness.

[0070] "Document generation" refers to the process of automatically creating documents necessary for a specific purpose using computer programs.

[0071] A "user terminal" refers to a device that provides an interface for operating a system, and includes computers and smartphones.

[0072] An "external database" is a database containing information that exists outside of a company, and includes information such as patents and academic papers.

[0073] The embodiments for carrying out this invention are shown below.

[0074] This system efficiently analyzes internal business information, automatically extracts and evaluates technical ideas, and links them to patent applications. The system's configuration consists primarily of servers, terminals, and users.

[0075] The server first collects business information through the company's internal network. This information includes emails, reports, and meeting materials. After collecting the information, the server uses natural language processing technologies such as Python's NLTK and spaCy to analyze the collected data. This analysis makes it possible to extract potential technical ideas and technical problems. The extracted ideas are then cross-referenced with external patent databases and academic paper databases, and their patentability is evaluated using machine learning algorithms with Scikit-learn and TENSORFLOW®. Based on the evaluation results, ideas with high patentability are given priority. Finally, the documents necessary for patent application are automatically generated using Microsoft® Word API and LaTeX.

[0076] The terminal will receive idea summaries and patent suitability assessment reports sent from the server and provide them to the user. The terminal's interface is expected to be implemented as a web-based application using Django or Flask. This will allow users to intuitively access information and provide feedback.

[0077] The user is typically a member of the intellectual property management department and performs the final review of the patent application documents generated on the server. They approve them from a legal standpoint and make final adjustments. After approval, the server automatically files the electronic application with the Japan Patent Office (JPO). This is done using the JPO's web service API.

[0078] A concrete example is a new digital advertising delivery method discussed in a company's internal meeting notes. This information is identified by the server and recognized as a new algorithm. An example of a generated prompt might be, "Please detail the procedure for extracting technical ideas and evaluating patentability regarding the new digital advertising delivery method." Based on this prompt, the technical ideas are automatically extracted, and patent applications are filed quickly and efficiently as part of the intellectual property strategy.

[0079] This format allows companies to quickly identify patentable ideas from their daily business data and automate the patent application process.

[0080] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0081] Step 1:

[0082] The server collects business information such as emails, reports, and meeting materials through the corporate network. Inputs include business data obtained from various corporate databases and mail servers. This data is organized internally by the server and output as an integrated dataset. This output is formatted to a standardized format in preparation for subsequent text analysis processing.

[0083] Step 2:

[0084] The server applies natural language processing techniques to the collected business information to perform text analysis. The input is an integrated business dataset. Using NLTK and spaCy, the server extracts noun and verb phrases from the data and identifies important key phrases. This results in analysis results that include technical issues and potential ideas. This output is used in the next step.

[0085] Step 3:

[0086] The server uses the technical ideas obtained through text analysis to cross-reference them with external patent and academic paper databases. The analysis results serve as input. The server executes machine learning algorithms using Scikit-learn and TensorFlow to evaluate patentability. As a result of this data processing, a list of patent ideas evaluated for novelty and inventiveness is output.

[0087] Step 4:

[0088] The server automatically generates patent application documents using the results of patentability assessments. The input is a list of ideas deemed highly patentable. The server uses Microsoft Word APIs and LaTeX to format the patent application documents. The output generated by this process is a concrete patent application document.

[0089] Step 5:

[0090] The terminal displays an idea summary and patent suitability assessment report sent from the server to the user. Generated application documents and assessment reports are used as input. The terminal uses a web application based on Django or Flask to output information in a format that is easy for the user to review. This output allows the user to evaluate the value of the technical idea and provide feedback as needed.

[0091] Step 6:

[0092] The user views and reviews the patent application documents provided through the terminal. The documents provided by the user are used as input. The user checks the documents from a legal perspective and sends comments to the server as needed. After the user's review, the approved application documents are output once the patent application is ready.

[0093] Step 7:

[0094] The server automatically submits the patent application documents approved by the user to the Japan Patent Office (JPO). The input is the approved patent application documents. The server utilizes the JPO's web service API to complete the application process. The output is confirmation information for the officially submitted patent application.

[0095] (Application Example 1)

[0096] Next, we will explain Application Example 1. In the following explanation, the data processing device 12 will be referred to as the "server," and the smart device 14 will be referred to as the "terminal."

[0097] There is a need to automate the process of efficiently extracting new technological ideas from business information generated within a company and quickly guiding those ideas to patent applications. Furthermore, it is necessary to provide an environment where users can evaluate ideas on the spot via portable devices and immediately send them to the company's intellectual asset management system. This aims to accelerate the patenting process within companies and maximize the value of their intellectual assets.

[0098] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 1 is realized by the following means.

[0099] In this invention, the server includes means for analyzing business information, means for extracting technical ideas, means for evaluating patentability, means for evaluating technical ideas and transmitting them to an in-house data processing system via an application installed on a portable device, and means for analyzing input information using natural language processing technology and comparing it with an existing database to evaluate novelty. This allows users to evaluate ideas directly from a portable device and quickly enter the in-house patenting process.

[0100] "Business information" refers to information related to business activities generated within a company, such as emails, reports, and meeting materials.

[0101] "Technical ideas" refer to new technical innovations and solutions extracted from business information.

[0102] "Patentability" refers to the criteria used to determine whether a particular idea possesses the novelty and inventiveness necessary to warrant a patent application.

[0103] "Portable devices" refer to computer equipment that can be carried around, such as smartphones and tablets.

[0104] "Natural language processing technology" is a technology that enables computers to understand, analyze, and generate human language.

[0105] A "data processing system" is a computer-based system that collects, analyzes, stores, and manages information.

[0106] "Novelty" refers to a property that possesses unique characteristics or elements not found in existing knowledge.

[0107] A "database" is a collection of information organized in a way that allows for efficient searching and retrieval of information.

[0108] In embodiments of the present invention, the server, terminal, and user are the main elements.

[0109] The server first continuously collects business information via the company's internal network. This business information consists of emails, meeting materials, reports, and other documents. The server analyzes this information using natural language processing technology to extract technical ideas. This analysis utilizes natural language processing libraries such as SpaCy. The server also compares the information with existing patent databases and evaluates its novelty. A proprietary patent search system is used for this purpose.

[0110] The terminal functions as an interface connecting the user and the server. The terminal has the ability to display an overview of the idea and the results of the patent suitability assessment sent from the server. Users can use this to verify the value of their idea and provide feedback.

[0111] Users can input new technical ideas using portable devices and receive immediate evaluations. This allows the ideas to be sent to the company's data processing system over time and managed as intellectual assets. An example of this is a process where a user inputs an idea for a new AI virtual assistant interface on their smartphone and has it instantly evaluated for patentability.

[0112] This entire process may also be presented as a prompt to the generative AI model, such as: "Evaluate the patent novelty of this idea: 'Food packaging made from new, environmentally friendly, reusable materials'."

[0113] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0114] Step 1:

[0115] The server collects business information through the corporate network. Emails, meeting materials, and reports are among the inputs. Using natural language processing techniques, the server analyzes the text data from these documents and extracts information that may contain technical ideas. The output obtained from this analysis consists of potential technical ideas.

[0116] Step 2:

[0117] The server evaluates the candidate technical ideas extracted in Step 1. Data processing is performed regarding novelty and inventiveness by cross-referencing with a patent database. This process includes specific keyword searches and similarity matching. The evaluation results are output, and ideas with high patentability are selected from these results.

[0118] Step 3:

[0119] The server generates patent application documents for ideas with high patentability. Input is a summary of the technical idea or patent abstract, prepared to meet legal requirements. Output is a patent application document. The document generation process uses an automated process based on standard templates.

[0120] Step 4:

[0121] The terminal displays the patent evaluation results and application document contents sent from the server. The server's output is received as input and formatted into a visually verifiable format on the screen. The user reviews this and provides feedback as needed. The final approval document, incorporating the user's approvals and revision requests, is then output.

[0122] Step 5:

[0123] The user inputs a new technical idea using a portable device. This input is sent to the server as a prompt to a generating AI model. Based on the input idea, the server immediately performs an evaluation. Through this process, the idea is sent to the company's intellectual asset management system and constitutes part of the patenting process. As output, the user is presented with the patentability evaluation result for the idea.

[0124] Furthermore, an emotion engine that estimates the user's emotions may be incorporated. That is, the identification processing unit 290 may use the emotion identification model 59 to estimate the user's emotions and perform identification processing using the user's emotions.

[0125] This invention is a system that automates the process of filing patent applications for technical ideas generated within a company, and specifically incorporates an emotion engine that recognizes user emotions. The system consists of a server, terminals, and users.

[0126] The server plays a central role in the system. First, the server collects business information such as emails, reports, and meeting materials through the company network. Next, it uses natural language processing technology to analyze this information and extract technical ideas. In this extraction process, the emotion engine evaluates the user's emotions and prioritizes extracting natural and positive expressions.

[0127] The emotion engine is used to emotionally analyze user feedback and evaluate how emotionally an idea resonates with others. This information is recorded as a result of the emotion analysis and particularly influences the patentability assessment of technical ideas and document generation.

[0128] The server uses machine learning algorithms to evaluate the novelty of the extracted ideas and score their patentability. Here again, the emotion engine takes user reactions into account, prioritizing ideas that are particularly emotionally positive and novel.

[0129] Once an idea has been evaluated, the system automatically generates a document for patent application. The generated document is required to reflect the sentiment analysis results from the sentiment engine and be the most user-friendly.

[0130] The terminal receives results sent from the server and presents them to the user. Through the terminal's interface, the user can review the idea content and application documents, and make adjustments based on feedback from the sentiment engine.

[0131] The user forms a crucial feedback loop within the system. Based on the information displayed on the terminal, they review the patent application document and verify its compliance with the law. During this review, the emotion engine assesses the user's emotional state and adjusts the interface to reduce stress.

[0132] For example, if an employee comes up with an idea for designing a new consumer application, the server can analyze the idea and use an emotion engine to evaluate user interest and enthusiasm. As a result, ideas deemed to have particularly high emotional value can be quickly compiled into a patent application document, presented to the user on a terminal, and easily approved. This process enables companies to promote technological innovation that is also positively received emotionally.

[0133] The following describes the processing flow.

[0134] Step 1:

[0135] The server collects business information such as emails, reports, and meeting materials via the company network. This ensures that the data necessary for extracting technical ideas is available.

[0136] Step 2:

[0137] The server analyzes the collected business information using natural language processing technology. The purpose of the analysis is to discover and extract descriptions of technical problems and new solutions.

[0138] Step 3:

[0139] The server uses an emotion engine to evaluate the user's emotions based on the analysis results. It measures how much the technical idea resonates emotionally with the user and records the results as metadata.

[0140] Step 4:

[0141] The server uses machine learning algorithms to evaluate the novelty of the extracted ideas. At this time, it also considers the results of the emotion engine, giving higher priority to ideas that elicit positive emotional responses.

[0142] Step 5:

[0143] The server automatically generates patent application documents based on evaluated ideas. These documents reflect both a technical overview and evaluation information from the sentiment engine.

[0144] Step 6:

[0145] The terminal receives patent application documents and sentiment evaluation results transmitted from the server and provides an interface to present them to the user. The user can use this interface to review the document content and evaluation results.

[0146] Step 7:

[0147] The user reviews the patent application document displayed on the device and makes revisions as needed. The emotion engine monitors the user's emotional state and dynamically adjusts the interface to reduce stress.

[0148] Step 8:

[0149] The server automatically files a patent application with the Japan Patent Office upon user approval. The progress of the application process is periodically notified to the user's terminal, allowing them to monitor the status.

[0150] (Example 2)

[0151] Next, we will describe Example 2. In the following description, the data processing device 12 will be referred to as the "server" and the smart device 14 as the "terminal".

[0152] In modern business activities, innovative technological ideas are frequently generated, but there is a need for a process to quickly and effectively translate these into patents. However, the general process of collecting, evaluating, and filing patent applications for ideas is cumbersome, and important ideas are sometimes overlooked, especially because user sentiment is not adequately considered. Therefore, a system is needed to facilitate patent applications that reflect user sentiment.

[0153] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 2 is realized by the following means.

[0154] In this invention, the server includes means for analyzing business data, means for extracting innovative ideas from the analyzed data, and means for evaluating the extracted innovative ideas using emotion analysis means for evaluating the user's emotions. This makes it possible to accurately evaluate innovative ideas that take user emotions into account and improve the efficiency of patent applications.

[0155] "Business data" refers to information generated within a company, such as emails, reports, and meeting materials.

[0156] "Analysis" refers to the process of analyzing data using natural language processing techniques to extract useful information and patterns.

[0157] An "innovative idea" refers to a technological idea or invention that arises in the course of business activities.

[0158] "Emotional analysis tools" refer to engines and algorithms that emotionally evaluate user opinions and feedback.

[0159] "Novelty" refers to something that is new or unique compared to existing technologies or ideas.

[0160] "Patentability" refers to the criteria used to evaluate whether a proposed innovation can be registered as a patent.

[0161] An "operating device" refers to a terminal or interface that a user uses to view information or results.

[0162] The "Japan Patent Office" refers to the public institution where patent applications are submitted and examined.

[0163] The embodiments for carrying out this invention are shown below.

[0164] The server acts as the central hub of the entire system. First, the server collects business data through the company network. This data includes emails, reports, meeting materials, and more. Next, the server analyzes the collected data using natural language processing (NLP) techniques to extract useful technical ideas. This analysis utilizes NLP libraries such as spaCy and NLTK. Furthermore, an emotion engine is used as a sentiment analysis tool to evaluate users' emotional responses. This prioritizes the extraction of technical ideas that elicit positive emotional responses.

[0165] The server uses machine learning to evaluate the novelty of extracted technical ideas and determine their patentability. Specifically, a trained model compares them with existing patent data to identify whether they are innovative. Generative AI models are used in this process. Once the evaluation is complete, the technical ideas are automatically generated as patent application documents. Natural language generation (NLG) technology is employed in this document generation process to ensure that the documents are structured in a way that complies with legal requirements.

[0166] The terminal is responsible for presenting the generated document sent from the server to the user. The user reviews and approves the content through the terminal. Furthermore, the interface is adjusted based on feedback from an emotion engine, allowing the user to review the information in a stress-free environment. This step supports the user's final review and adjustment of the patent application document.

[0167] As a concrete example, after a design meeting for a new consumer application, the server automatically analyzes the meeting minutes and extracts technical ideas. Using sentiment analysis, ideas that evoke high user interest and enthusiasm are identified, and those that elicit positive emotions are quickly processed for patent application and presented on the terminal. This allows companies to efficiently patent innovative technologies.

[0168] Examples of prompts for a generative AI model:

[0169] "Extract technical ideas from newly collected meeting materials and create a patent application document that reflects the results of sentiment analysis."

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

[0171] Step 1:

[0172] The server collects business data through the company network. It receives raw data such as emails, reports, and meeting materials as input. This data is organized into specific folders or databases and prepared for subsequent analysis. The collected data is stored within the system.

[0173] Step 2:

[0174] The server analyzes the collected business data using natural language processing (NLP) techniques. The unanalyzed business data collected in Step 1 is used as input. In this step, an NLP library (e.g., spaCy or NLTK) is used to analyze the text data and extract technical ideas and related keywords. The output consists of the extracted technical ideas and related information.

[0175] Step 3:

[0176] The server evaluates user feedback using sentiment analysis tools. It receives technical ideas extracted in step 2 and user comments as input. In this step, the sentiment engine emotionally analyzes the user comments and prioritizes ideas that evoke positive emotions. The output is a list of technical ideas with added sentiment evaluations.

[0177] Step 4:

[0178] The server uses machine learning to evaluate the novelty of technical ideas. It uses the sentiment-rated technical ideas obtained in step 3 as input. This process involves comparing the ideas to existing patent databases to assess their originality. The output is a list of evaluated ideas, including novelty and patentability scores.

[0179] Step 5:

[0180] The server automatically generates patent application documents. It takes the list of ideas evaluated in step 4 as input. Natural language generation (NLG) technology is applied to construct the patent application documents in a way that meets legal requirements. The output is the completed patent application document.

[0181] Step 6:

[0182] The terminal presents the generated document to the user. It receives the patent application document generated in step 5 as input. The user reviews the document via the terminal and makes revisions as needed. The output is the patent application document as finalized by the user.

[0183] Step 7:

[0184] The user reviews and approves the generated document. The input is the patent application document presented in step 6. The user verifies legal compliance and provides feedback. The output is the document with approval or a request for revision.

[0185] (Application Example 2)

[0186] Next, we will explain application example 2. In the following explanation, the data processing device 12 will be referred to as a "server" and the smart device 14 as a "terminal".

[0187] In today's business environment, there is a need to efficiently file patent applications for new technological ideas that emerge within a company. However, the traditional patent application process is time-consuming and labor-intensive, and in particular, it does not adequately identify and evaluate ideas based on customer emotional value. Furthermore, there is a lack of means to effectively utilize customer reactions as feedback in the early stages of product development and to quickly patent new technological concepts.

[0188] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means.

[0189] In this invention, the server includes means for analyzing business data, means for analyzing customer behavior and emotions and collecting product-related concepts that elicit positive responses, and means for evaluating the intellectual property suitability of the extracted concepts. This enables companies to quickly patent technological concepts that take into account customer emotional value and enhance their competitiveness.

[0190] "Business data" refers to all information related to business activities generated within a company, including emails, reports, meeting materials, and so on.

[0191] "Analysis" refers to a series of procedures and methods used to transform information and data into a form that is easy to understand.

[0192] A "technical concept" refers to the basic idea or plan for a new technological idea, and serves as the basis for determining whether that idea is feasible and patentable.

[0193] "Intellectual property suitability" refers to the criteria used to evaluate whether something has intellectual property value in a business context, and it particularly includes novelty, usefulness, and inventiveness.

[0194] "Document generation" refers to the process of automatically creating official documents and papers based on concepts and information.

[0195] "Customer behavior" refers to activities related to customer movements and actions in a commercial environment, including product exploration, trial use, and purchase decisions.

[0196] "Analyzing emotions" refers to procedures and techniques aimed at recognizing and understanding the emotional states that an individual exhibits in a specific situation or context.

[0197] "Positive reactions" refer to positive emotions or affirmative feedback that customers express towards a product or service.

[0198] An "intellectual property institution" refers to a public institution that manages and registers patents, trademarks, copyrights, and other intellectual property rights.

[0199] "Affective value" refers to the sensory or emotional value that an individual or group has towards a particular product or service, and is a factor that influences judgment and decision-making.

[0200] The system of this invention consists of a server, a terminal, and a user. The server is responsible for analyzing business data, analyzing customer behavior and emotions, and extracting technical concepts related to products that elicit positive responses. Specific processing includes collecting business data and analyzing it using natural language processing technology. This involves collecting customer behavior data using smartphone sensors and utilizing emotion analysis models such as IBM Watson® Tone Analyzer.

[0201] The terminal displays the results sent from the server and provides an interface for the user to review this information. Through this interface, the user can review the generated intellectual property application document and approve or modify its contents.

[0202] A concrete example of using emotional analysis based on a user's facial expressions and behavior while trying out a specific product to quickly extract a technological concept that elicits a positive response is when a server analyzes a customer's behavioral data while they try out a new electronic device in a store, capturing their smiles and enthusiastic remarks. The information extracted in this way is then evaluated as having particularly high emotional value in the patent application process.

[0203] An example of a prompt to be input to the generative AI model would be text such as, "Customer sentiment towards this product is very positive. Please generate positive and novel ideas for patent application."

[0204] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0205] Step 1:

[0206] The server collects business data. Inputs include internal company emails, reports, and meeting materials, which are stored in a database on the server. Output is data in a unified, analyzable format. In this step, customer behavior and speech are also collected via smartphone cameras and microphones, and each piece of data is tagged and identified.

[0207] Step 2:

[0208] The server analyzes the collected business data using natural language processing techniques. The input is the unified format data obtained in the previous step, and the output is a list of technical concepts. In this analysis, tools such as NLTK and the Sentiment Analysis API are used to extract important keywords and phrases from the text.

[0209] Step 3:

[0210] The server analyzes customer behavior and emotional data. Input is customer reaction data collected via cameras and microphones, and output is information related to the customer's emotional state and products that elicit positive responses. This step involves using an emotional analysis model, such as IBM Watson Tone Analyzer, to quantify the degree of positivity in the customer's emotions.

[0211] Step 4:

[0212] The server evaluates the intellectual property suitability of the analyzed technical concepts. The input is a list of technical concepts and customer sentiment data, and the output is a list of candidate concepts deemed suitable as intellectual property. Specifically, it uses machine learning algorithms such as Scikit-learn to evaluate novelty and inventiveness by comparing them with historical patent databases.

[0213] Step 5:

[0214] The server generates documents for patent applications. The input is a candidate technical concept that has passed suitability evaluation, and the output is a formal patent application document. In this step, a generative AI model is used to create prompts based on the given concepts and automatically generate the document accordingly.

[0215] Step 6:

[0216] The terminal displays the generated document to the user. The input is the patent application document, and the output is the document content on the user interface. The user uses this interface to review the content and perform specific actions such as approving or instructing revisions.

[0217] Step 7:

[0218] The user files an approved document with the patent office via the terminal. The input is the patent application document approved and corrected by the user, and the output is the digital document officially sent to the patent office. In this final step, the terminal's communication function is used to automatically perform the electronic filing process.

[0219] The specific processing unit 290 transmits the result of the specific processing to the smart device 14. In the smart device 14, the control unit 46A causes the output device 40 to output the result of the specific processing. The microphone 38B acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0220] Data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of data generation model 58 is ChatGPT (registered trademark) (Internet search).<URL: https: / / openai.com / blog / chatgpt> ), Gemini (registered trademark) (Internet search) <url: https: gemini.google.com ?hl="ja">Examples of generative AI include the following. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and with inference data such as audio data representing speech, text data representing text, and image data representing images. The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference results in data formats such as audio data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[0221] In the above embodiment, an example was given in which specific processing is performed by the data processing device 12, but the technology of this disclosure is not limited thereto, and the specific processing may also be performed by the smart device 14.

[0222] [Second Embodiment]

[0223] Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment.

[0224] As shown in Figure 3, the data processing system 210 includes a data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0225] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 is an example of a "computer" related to the technology of this disclosure. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[0226] The smart glasses 214 include a computer 36, a microphone 238, a speaker 240, a camera 42, and a communication interface 44. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, and camera 42 are also connected to the bus 52.

[0227] The microphone 238 receives voice signals from the user 20 and receives instructions from the user 20. The microphone 238 captures the voice signals from the user 20, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0228] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the area around the user 20 (for example, an imaging range defined by a field of view equivalent to the width of a typical healthy person's field of vision).

[0229] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0230] Figure 4 shows an example of the main functions of the data processing device 12 and the smart glasses 214. As shown in Figure 4, the data processing device 12 performs specific processing using the processor 28. The storage 32 stores the specific processing program 56.

[0231] The specific processing program 56 is an example of a "program" relating to the technology of this disclosure. The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0232] The storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[0233] In the smart glasses 214, the processor 46 performs the reception output processing. The storage 50 stores the reception output program 60. The processor 46 reads the reception output program 60 from the storage 50 and executes the read reception output program 60 on the RAM 48. The reception output processing is realized by the processor 46 operating as a control unit 46A according to the reception output program 60 executed on the RAM 48.

[0234] Next, the identification processing performed by the identification processing unit 290 of the data processing device 12 will be described. In the following description, the data processing device 12 will be referred to as the "server" and the smart glasses 214 will be referred to as the "terminal".

[0235] This invention describes an embodiment of a system that analyzes business information within a company and automatically extracts, evaluates, and files patent applications for technical ideas. The main components of the system consist of a server, terminals, and users.

[0236] The server forms the core of the information processing in this invention. First, it collects business information such as emails, reports, and meeting materials generated by employees via the company's internal network. Next, it analyzes the collected information using natural language processing technology to extract technical problems and new technical ideas as solutions. This reveals potential patent ideas that employees may not have been aware of.

[0237] The extracted ideas are cross-referenced with external patent and academic paper databases to evaluate their patentability based on novelty and inventiveness. This evaluation is performed using machine learning algorithms, and ideas deemed highly patentable are given priority.

[0238] Next, the server generates the documents necessary for filing a patent application for the idea. These documents are formatted to meet legal requirements and are output in a manner that conforms to the patent application process.

[0239] The terminal acts as the interface with the user. It receives idea summaries and patent suitability assessment reports sent from the server and displays them in a format that the user can review. The user can use the provided information to verify the value of the technical idea and provide feedback as needed.

[0240] The user (usually the intellectual property management department) reviews the patent application documents generated by the server. At this stage, a person with legal expertise reviews the content of the documents and makes final adjustments to make them suitable for submission to the patent office. Once the documents are approved by the user, the server automatically files them with the patent office in the next step.

[0241] As a concrete example, a system could identify a new method for delivering digital advertisements written in internal meeting notes. These notes are then analyzed by a server to verify the usefulness of the new algorithm. Subsequently, patentability is assessed, and the necessary documents for filing a patent application are automatically generated. Through this process, technical ideas can be quickly patented, strengthening a company's intellectual property portfolio.

[0242] The following describes the processing flow.

[0243] Step 1:

[0244] The server collects various types of business information through the company network. This includes emails, reports, meeting materials, and digital documents. Information stored in databases is also included in the extraction process.

[0245] Step 2:

[0246] The server uses natural language processing technology to analyze the collected business information. The purpose of the analysis is to identify technical problems and information that could lead to new solutions. The technical ideas extracted here are automatically listed by the system.

[0247] Step 3:

[0248] The server uses machine learning algorithms to evaluate the novelty and patentability of a technical idea. It cross-references information with external patent and academic paper databases to verify that the idea is differentiated from existing inventions. This evaluation is recorded as a score.

[0249] Step 4:

[0250] The server automatically generates the necessary documents for a patent application based on the patent evaluation results. These documents include detailed descriptions of the idea, technical scope, and effects. The documents are formatted according to the standards of the Japan Patent Office.

[0251] Step 5:

[0252] The terminal receives idea summaries and patent suitability assessment reports sent from the server. In addition, it retrieves the generated patent application documents and provides an interface for presenting them to the user.

[0253] Step 6:

[0254] The user (intellectual property management department) reviews the documents presented on the terminal, verifying their accuracy and legal compliance. They make revisions as needed and then provide final approval. Feedback is sent back to the server via the terminal.

[0255] Step 7:

[0256] The server automatically files a patent application with the patent office after receiving approval from the user. The progress of the application process is periodically notified to the terminal, allowing the user to check the status.

[0257] (Example 1)

[0258] Next, we will describe Example 1. In the following description, the data processing device 12 will be referred to as the "server" and the smart glasses 214 will be referred to as the "terminal".

[0259] Traditionally, the process of efficiently extracting technical ideas from the vast amount of business information generated within a company and converting them into patent applications has been time-consuming and labor-intensive. A major challenge is that technical ideas can get lost in this process, leading to missed patent opportunities. Furthermore, evaluating patentability and generating application documents requires specialized knowledge, making it difficult to achieve a rapid application process.

[0260] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 1 is realized by the following means.

[0261] In this invention, the server includes means for collecting business information, means for analyzing the collected information using natural language processing technology to extract technical problems and technical ideas, and means for comparing the extracted ideas with an external database to evaluate their patentability. This makes it possible to quickly and efficiently identify technical ideas from business information and automate the procedures necessary for patent application.

[0262] "Business information" refers to the collective term for data generated within a company, such as emails, reports, and meeting materials, and encompasses information related to the organization's activities.

[0263] "Natural language processing technology" refers to computer technologies used to analyze human language, enabling the understanding, interpretation, and generation of language data.

[0264] "Technical ideas" refer to concepts such as new methods, devices, and algorithms extracted from business information, and represent ingenious ideas that have the potential to be patented.

[0265] "Patentability" refers to the criteria used to evaluate whether an extracted technical idea can be granted a patent, and is judged based on novelty and inventiveness.

[0266] "Document generation" refers to the process of automatically creating documents necessary for a specific purpose using computer programs.

[0267] A "user terminal" refers to a device that provides an interface for operating a system, and includes computers and smartphones.

[0268] An "external database" is a database containing information that exists outside of a company, and includes information such as patents and academic papers.

[0269] The embodiments for carrying out this invention are shown below.

[0270] This system efficiently analyzes internal business information, automatically extracts and evaluates technical ideas, and links them to patent applications. The system's configuration consists primarily of servers, terminals, and users.

[0271] The server first collects business information through the company's internal network. This information includes emails, reports, and meeting materials. After collecting the information, the server uses natural language processing technologies such as Python's NLTK and spaCy to analyze the collected data. This analysis makes it possible to extract potential technical ideas and technical problems. The extracted ideas are then cross-referenced with external patent databases and academic paper databases, and their patentability is evaluated using machine learning algorithms with Scikit-learn and TensorFlow. Based on the evaluation results, ideas with high patentability are given priority. Finally, the documents necessary for patent application are automatically generated using Microsoft Word API and LaTeX.

[0272] The terminal will receive idea summaries and patent suitability assessment reports sent from the server and provide them to the user. The terminal's interface is expected to be implemented as a web-based application using Django or Flask. This will allow users to intuitively access information and provide feedback.

[0273] The user is typically a member of the intellectual property management department and performs the final review of the patent application documents generated on the server. They approve them from a legal standpoint and make final adjustments. After approval, the server automatically files the electronic application with the Japan Patent Office (JPO). This is done using the JPO's web service API.

[0274] A concrete example is a new digital advertising delivery method discussed in a company's internal meeting notes. This information is identified by the server and recognized as a new algorithm. An example of a generated prompt might be, "Please detail the procedure for extracting technical ideas and evaluating patentability regarding the new digital advertising delivery method." Based on this prompt, the technical ideas are automatically extracted, and patent applications are filed quickly and efficiently as part of the intellectual property strategy.

[0275] This format allows companies to quickly identify patentable ideas from their daily business data and automate the patent application process.

[0276] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0277] Step 1:

[0278] The server collects business information such as emails, reports, and meeting materials through the corporate network. Inputs include business data obtained from various corporate databases and mail servers. This data is organized internally by the server and output as an integrated dataset. This output is formatted to a standardized format in preparation for subsequent text analysis processing.

[0279] Step 2:

[0280] The server applies natural language processing techniques to the collected business information to perform text analysis. The input is an integrated business dataset. Using NLTK and spaCy, the server extracts noun and verb phrases from the data and identifies important key phrases. This results in analysis results that include technical issues and potential ideas. This output is used in the next step.

[0281] Step 3:

[0282] The server uses the technical ideas obtained through text analysis to cross-reference them with external patent and academic paper databases. The analysis results serve as input. The server executes machine learning algorithms using Scikit-learn and TensorFlow to evaluate patentability. As a result of this data processing, a list of patent ideas evaluated for novelty and inventiveness is output.

[0283] Step 4:

[0284] The server automatically generates a patent application document using the patentability evaluation results. The input is a list of ideas determined to have high patentability. The server uses the API of Microsoft Word or LaTeX to format the patent application documents. The output generated by this process is a specific patent application document.

[0285] Step 5:

[0286] The terminal displays the idea summary and patentability evaluation report sent from the server to the user. Here, the generated application documents and evaluation reports are used as input. The terminal uses a web application based on Django or Flask and outputs in a form that allows the user to easily check the information. With this output, the user can evaluate the value of the technical idea and provide feedback if necessary.

[0287] Step 6:

[0288] The user browses and checks the content of the patent application document provided through the terminal. Here, the documents provided by the user are used as input. The user checks the document from a legal perspective and sends comments to the server if necessary. After the user's confirmation, the approved application documents are output when the preparation for the patent application is completed.

[0289] Step 7:

[0290] The server automatically submits the patent application documents approved by the user to the Patent Office. The input is the approved patent application documents. The server utilizes the web service API of the Patent Office to complete the application process. The output is the confirmation information of the officially submitted patent application.

[0291] (Application Example 1)

[0292] Next, Application Example 1 will be described. In the following description, the data processing device 12 is referred to as the "server", and the smart glasses 214 are referred to as the "terminal".

[0293] There is a need to automate the process of efficiently extracting new technological ideas from business information generated within a company and quickly guiding those ideas to patent applications. Furthermore, it is necessary to provide an environment where users can evaluate ideas on the spot via portable devices and immediately send them to the company's intellectual asset management system. This aims to accelerate the patenting process within companies and maximize the value of their intellectual assets.

[0294] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 1 is realized by the following means.

[0295] In this invention, the server includes means for analyzing business information, means for extracting technical ideas, means for evaluating patentability, means for evaluating technical ideas and transmitting them to an in-house data processing system via an application installed on a portable device, and means for analyzing input information using natural language processing technology and comparing it with an existing database to evaluate novelty. This allows users to evaluate ideas directly from a portable device and quickly enter the in-house patenting process.

[0296] "Business information" refers to information related to business activities generated within a company, such as emails, reports, and meeting materials.

[0297] "Technical ideas" refer to new technical innovations and solutions extracted from business information.

[0298] "Patentability" refers to the criteria used to determine whether a particular idea possesses the novelty and inventiveness necessary to warrant a patent application.

[0299] "Portable devices" refer to computer equipment that can be carried around, such as smartphones and tablets.

[0300] "Natural language processing technology" is a technology that enables computers to understand, analyze, and generate human language.

[0301] A "data processing system" is a computer-based system that collects, analyzes, stores, and manages information.

[0302] "Novelty" refers to a property that possesses unique characteristics or elements not found in existing knowledge.

[0303] A "database" is a collection of information organized in a way that allows for efficient searching and retrieval of information.

[0304] In embodiments of the present invention, the server, terminal, and user are the main elements.

[0305] The server first continuously collects business information via the company's internal network. This business information consists of emails, meeting materials, reports, and other documents. The server analyzes this information using natural language processing technology to extract technical ideas. This analysis utilizes natural language processing libraries such as SpaCy. The server also compares the information with existing patent databases and evaluates its novelty. A proprietary patent search system is used for this purpose.

[0306] The terminal functions as an interface connecting the user and the server. The terminal has the ability to display an overview of the idea and the results of the patent suitability assessment sent from the server. Users can use this to verify the value of their idea and provide feedback.

[0307] Users can input new technical ideas using portable devices and receive immediate evaluations. This allows the ideas to be sent to the company's data processing system over time and managed as intellectual assets. An example of this is a process where a user inputs an idea for a new AI virtual assistant interface on their smartphone and has it instantly evaluated for patentability.

[0308] This entire process may also be provided as follows as a prompt to a generative AI model. "Please evaluate the patent novelty of this idea: 'Food packaging made of environmentally friendly and reusable new materials'".

[0309] The flow of the specific process in Application Example 1 will be described using FIG. 12.

[0310] Step 1:

[0311] The server collects business information through the corporate network. Emails, meeting materials, reports, etc. are taken in as inputs. Using natural language processing technology, text data is analyzed from these documents, and information that may contain technical ideas is extracted. The output obtained by the analysis is a candidate for a technical idea.

[0312] Step 2:

[0313] The server evaluates the candidate technical ideas extracted in Step 1. By comparing with the patent database, data processing is performed regarding novelty and inventive step. This process includes specific keyword searches and similarity matching. The evaluation result is obtained as an output, and ideas with high patent suitability are selected from the results.

[0314] Step 3:

[0315] The server generates documents for patent applications for ideas with high patent suitability. As inputs, an overview of the technical idea and the patent abstract arranged in a form conforming to legal requirements are given. As an output, patent application documents are generated. For document generation, an automatic generation process using a standard template is applied.

[0316] Step 4:

[0317] The terminal displays the patent evaluation results and application document contents sent from the server. The server's output is received as input and formatted into a visually verifiable format on the screen. The user reviews this and provides feedback as needed. The final approval document, incorporating the user's approvals and revision requests, is then output.

[0318] Step 5:

[0319] The user inputs a new technical idea using a portable device. This input is sent to the server as a prompt to a generating AI model. Based on the input idea, the server immediately performs an evaluation. Through this process, the idea is sent to the company's intellectual asset management system and constitutes part of the patenting process. As output, the user is presented with the patentability evaluation result for the idea.

[0320] Furthermore, an emotion engine that estimates the user's emotions may be incorporated. That is, the identification processing unit 290 may use the emotion identification model 59 to estimate the user's emotions and perform identification processing using the user's emotions.

[0321] This invention is a system that automates the process of filing patent applications for technical ideas generated within a company, and specifically incorporates an emotion engine that recognizes user emotions. The system consists of a server, terminals, and users.

[0322] The server plays a central role in the system. First, the server collects business information such as emails, reports, and meeting materials through the company network. Next, it uses natural language processing technology to analyze this information and extract technical ideas. In this extraction process, the emotion engine evaluates the user's emotions and prioritizes extracting natural and positive expressions.

[0323] The emotion engine is used to emotionally analyze user feedback and evaluate how emotionally an idea resonates with others. This information is recorded as a result of the emotion analysis and particularly influences the patentability assessment of technical ideas and document generation.

[0324] The server uses machine learning algorithms to evaluate the novelty of the extracted ideas and score their patentability. Here again, the emotion engine takes user reactions into account, prioritizing ideas that are particularly emotionally positive and novel.

[0325] Once an idea has been evaluated, the system automatically generates a document for patent application. The generated document is required to reflect the sentiment analysis results from the sentiment engine and be the most user-friendly.

[0326] The terminal receives results sent from the server and presents them to the user. Through the terminal's interface, the user can review the idea content and application documents, and make adjustments based on feedback from the sentiment engine.

[0327] The user forms a crucial feedback loop within the system. Based on the information displayed on the terminal, they review the patent application document and verify its compliance with the law. During this review, the emotion engine assesses the user's emotional state and adjusts the interface to reduce stress.

[0328] For example, if an employee comes up with an idea for designing a new consumer application, the server can analyze the idea and use an emotion engine to evaluate user interest and enthusiasm. As a result, ideas deemed to have particularly high emotional value can be quickly compiled into a patent application document, presented to the user on a terminal, and easily approved. This process enables companies to promote technological innovation that is also positively received emotionally.

[0329] The following describes the processing flow.

[0330] Step 1:

[0331] The server collects business information such as emails, reports, and meeting materials via the company network. This ensures that the data necessary for extracting technical ideas is available.

[0332] Step 2:

[0333] The server analyzes the collected business information using natural language processing technology. The purpose of the analysis is to discover and extract descriptions of technical problems and new solutions.

[0334] Step 3:

[0335] The server uses an emotion engine to evaluate the user's emotions based on the analysis results. It measures how much the technical idea resonates emotionally with the user and records the results as metadata.

[0336] Step 4:

[0337] The server uses machine learning algorithms to evaluate the novelty of the extracted ideas. At this time, it also considers the results of the emotion engine, giving higher priority to ideas that show positive emotional responses.

[0338] Step 5:

[0339] The server automatically generates patent application documents based on evaluated ideas. These documents reflect both a technical overview and evaluation information from the sentiment engine.

[0340] Step 6:

[0341] The terminal receives patent application documents and sentiment evaluation results sent from the server and provides an interface to present them to the user. The user can use this interface to review the document content and evaluation results.

[0342] Step 7:

[0343] The user reviews the patent application document displayed on the device and makes revisions as needed. The emotion engine monitors the user's emotional state and dynamically adjusts the interface to reduce stress.

[0344] Step 8:

[0345] The server automatically files a patent application with the Japan Patent Office upon user approval. The progress of the application process is periodically notified to the user's terminal, allowing them to monitor the status.

[0346] (Example 2)

[0347] Next, we will describe Example 2. In the following description, the data processing device 12 will be referred to as the "server" and the smart glasses 214 will be referred to as the "terminal".

[0348] In modern business activities, innovative technological ideas are frequently generated, but there is a need for a process to quickly and effectively translate these into patents. However, the general process of collecting, evaluating, and filing patent applications for ideas is cumbersome, and important ideas are sometimes overlooked, especially because user sentiment is not adequately considered. Therefore, a system is needed to facilitate patent applications that reflect user sentiment.

[0349] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 2 is realized by the following means.

[0350] In this invention, the server includes means for analyzing business data, means for extracting innovative ideas from the analyzed data, and means for evaluating the extracted innovative ideas using emotion analysis means for evaluating the user's emotions. This makes it possible to accurately evaluate innovative ideas that take user emotions into account and improve the efficiency of patent applications.

[0351] "Business data" refers to information generated within a company, such as emails, reports, and meeting materials.

[0352] "Analysis" refers to the process of analyzing data using natural language processing techniques to extract useful information and patterns.

[0353] An "innovative idea" refers to a technological idea or invention that arises in the course of business activities.

[0354] "Emotional analysis tools" refer to engines and algorithms that emotionally evaluate user opinions and feedback.

[0355] "Novelty" refers to something that is new or unique compared to existing technologies or ideas.

[0356] "Patentability" refers to the criteria used to evaluate whether a proposed innovation can be registered as a patent.

[0357] An "operating device" refers to a terminal or interface that a user uses to view information or results.

[0358] The "Japan Patent Office" refers to the public institution where patent applications are submitted and examined.

[0359] The embodiments for carrying out this invention are shown below.

[0360] The server acts as the central hub of the entire system. First, the server collects business data through the company network. This data includes emails, reports, meeting materials, and more. Next, the server analyzes the collected data using natural language processing (NLP) techniques to extract useful technical ideas. This analysis utilizes NLP libraries such as spaCy and NLTK. Furthermore, an emotion engine is used as a sentiment analysis tool to evaluate users' emotional responses. This prioritizes the extraction of technical ideas that elicit positive emotional responses.

[0361] The server uses machine learning to evaluate the novelty of extracted technical ideas and determine their patentability. Specifically, a trained model compares them with existing patent data to identify whether they are innovative. Generative AI models are used in this process. Once the evaluation is complete, the technical ideas are automatically generated as patent application documents. Natural language generation (NLG) technology is employed in this document generation process to ensure that the documents are structured in a way that complies with legal requirements.

[0362] The terminal is responsible for presenting the generated document sent from the server to the user. The user reviews and approves the content through the terminal. Furthermore, the interface is adjusted based on feedback from an emotion engine, allowing the user to review the information in a stress-free environment. This step supports the user's final review and adjustment of the patent application document.

[0363] As a concrete example, after a design meeting for a new consumer application, the server automatically analyzes the meeting minutes and extracts technical ideas. Using sentiment analysis, ideas that evoke high user interest and enthusiasm are identified, and those that elicit positive emotions are quickly processed for patent application and presented on the terminal. This allows companies to efficiently patent innovative technologies.

[0364] Examples of prompts for a generative AI model:

[0365] "Extract technical ideas from newly collected meeting materials and create a patent application document that reflects the results of sentiment analysis."

[0366] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0367] Step 1:

[0368] The server collects business data through the company network. It receives raw data such as emails, reports, and meeting materials as input. This data is organized into specific folders or databases and prepared for subsequent analysis. The collected data is stored within the system.

[0369] Step 2:

[0370] The server analyzes the collected business data using natural language processing (NLP) techniques. The unanalyzed business data collected in Step 1 is used as input. In this step, an NLP library (e.g., spaCy or NLTK) is used to analyze the text data and extract technical ideas and related keywords. The output consists of the extracted technical ideas and related information.

[0371] Step 3:

[0372] The server evaluates user feedback using sentiment analysis tools. It receives technical ideas extracted in step 2 and user comments as input. In this step, the sentiment engine emotionally analyzes the user comments and prioritizes ideas that evoke positive emotions. The output is a list of technical ideas with added sentiment evaluations.

[0373] Step 4:

[0374] The server uses machine learning to evaluate the novelty of technical ideas. It uses the sentiment-rated technical ideas obtained in step 3 as input. This process involves comparing the ideas to existing patent databases to assess their originality. The output is a list of evaluated ideas, including novelty and patentability scores.

[0375] Step 5:

[0376] The server automatically generates patent application documents. It takes the list of ideas evaluated in step 4 as input. Natural language generation (NLG) technology is applied to construct the patent application documents in a way that meets legal requirements. The output is the completed patent application document.

[0377] Step 6:

[0378] The terminal presents the generated document to the user. It receives the patent application document generated in step 5 as input. The user reviews the document via the terminal and makes revisions as needed. The output is the patent application document as finalized by the user.

[0379] Step 7:

[0380] The user reviews and approves the generated document. The input is the patent application document presented in step 6. The user verifies legal compliance and provides feedback. The output is the document with approval or a request for revision.

[0381] (Application Example 2)

[0382] Next, we will explain application example 2. In the following explanation, the data processing device 12 will be referred to as the "server," and the smart glasses 214 will be referred to as the "terminal."

[0383] In today's business environment, there is a need to efficiently file patent applications for new technological ideas that emerge within a company. However, the traditional patent application process is time-consuming and labor-intensive, and in particular, it does not adequately identify and evaluate ideas based on customer emotional value. Furthermore, there is a lack of means to effectively utilize customer reactions as feedback in the early stages of product development and to quickly patent new technological concepts.

[0384] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means.

[0385] In this invention, the server includes means for analyzing business data, means for analyzing customer behavior and emotions and collecting product-related concepts that elicit positive responses, and means for evaluating the intellectual property suitability of the extracted concepts. This enables companies to quickly patent technological concepts that take into account customer emotional value and enhance their competitiveness.

[0386] "Business data" refers to all information related to business activities generated within a company, including emails, reports, meeting materials, and so on.

[0387] "Analysis" refers to a series of procedures and methods used to transform information and data into a form that is easy to understand.

[0388] A "technical concept" refers to the basic idea or plan for a new technological idea, and serves as the basis for determining whether that idea is feasible and patentable.

[0389] "Intellectual property suitability" refers to the criteria used to evaluate whether something has intellectual property value in a business context, and it particularly includes novelty, usefulness, and inventiveness.

[0390] "Document generation" refers to the process of automatically creating official documents and papers based on concepts and information.

[0391] "Customer behavior" refers to activities related to customer movements and actions in a commercial environment, including product exploration, trial use, and purchase decisions.

[0392] "Analyzing emotions" refers to procedures and techniques aimed at recognizing and understanding the emotional states that an individual exhibits in a specific situation or context.

[0393] "Positive reactions" refer to positive emotions or affirmative feedback that customers express towards a product or service.

[0394] An "intellectual property institution" refers to a public institution that manages and registers patents, trademarks, copyrights, and other intellectual property rights.

[0395] "Affective value" refers to the sensory or emotional value that an individual or group has towards a particular product or service, and is a factor that influences judgment and decision-making.

[0396] The system of this invention consists of a server, a terminal, and a user. The server is responsible for analyzing business data, analyzing customer behavior and emotions, and extracting technical concepts related to products that elicit positive responses. Specific processing includes collecting business data and analyzing it using natural language processing technology. This involves collecting customer behavior data using smartphone sensors and utilizing emotion analysis models such as IBM Watson Tone Analyzer.

[0397] The terminal displays the results sent from the server and provides an interface for the user to review this information. Through this interface, the user can review the generated intellectual property application document and approve or modify its contents.

[0398] A concrete example of using emotional analysis based on a user's facial expressions and behavior while trying out a specific product to quickly extract a technological concept that elicits a positive response is when a server analyzes a customer's behavioral data while they try out a new electronic device in a store, capturing their smiles and enthusiastic remarks. The information extracted in this way is then evaluated as having particularly high emotional value in the patent application process.

[0399] An example of a prompt to be input to the generative AI model would be text such as, "Customer sentiment towards this product is very positive. Please generate positive and novel ideas for patent application."

[0400] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0401] Step 1:

[0402] The server collects business data. Inputs include internal company emails, reports, and meeting materials, which are stored in a database on the server. Output is data in a unified, analyzable format. In this step, customer behavior and speech are also collected via smartphone cameras and microphones, and each piece of data is tagged and identified.

[0403] Step 2:

[0404] The server analyzes the collected business data using natural language processing techniques. The input is the unified format data obtained in the previous step, and the output is a list of technical concepts. In this analysis, tools such as NLTK and the Sentiment Analysis API are used to extract important keywords and phrases from the text.

[0405] Step 3:

[0406] The server analyzes customer behavior and emotional data. Input is customer reaction data collected via cameras and microphones, and output is information related to the customer's emotional state and products that elicit positive responses. This step involves using an emotional analysis model, such as IBM Watson Tone Analyzer, to quantify the degree of positivity in the customer's emotions.

[0407] Step 4:

[0408] The server evaluates the intellectual property suitability of the analyzed technical concepts. The input is a list of technical concepts and customer sentiment data, and the output is a list of candidate concepts deemed suitable as intellectual property. Specifically, it uses machine learning algorithms such as Scikit-learn to evaluate novelty and inventiveness by comparing them with historical patent databases.

[0409] Step 5:

[0410] The server generates documents for patent applications. The input is a candidate technical concept that has passed suitability evaluation, and the output is a formal patent application document. In this step, a generative AI model is used to create prompts based on the given concepts and automatically generate the document accordingly.

[0411] Step 6:

[0412] The terminal displays the generated document to the user. The input is the patent application document, and the output is the document content on the user interface. The user uses this interface to review the content and perform specific actions such as approving or instructing revisions.

[0413] Step 7:

[0414] The user files an approved document with the patent office via the terminal. The input is the patent application document approved and corrected by the user, and the output is the digital document officially sent to the patent office. In this final step, the terminal's communication function is used to automatically perform the electronic filing process.

[0415] The specific processing unit 290 transmits the result of the specific processing to the smart glasses 214. In the smart glasses 214, the control unit 46A causes the speaker 240 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0416] Data generation model 58 is a type of so-called generative AI (Artificial Intelligence). One example of data generation model 58 is ChatGPT (Internet search<URL: https: / / openai.com / blog / chatgpt> ), Gemini (Internet search) <url: https: gemini.google.com ?hl="ja">Examples of generative AI include the following. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and with inference data such as audio data representing speech, text data representing text, and image data representing images. The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference results in data formats such as audio data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[0417] In the above embodiment, an example was given in which specific processing is performed by the data processing device 12, but the technology of this disclosure is not limited thereto, and the specific processing may also be performed by the smart glasses 214.

[0418] [Third Embodiment]

[0419] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[0420] As shown in Figure 5, the data processing system 310 includes a data processing device 12 and a headset terminal 314. An example of the data processing device 12 is a server.

[0421] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 is an example of a "computer" related to the technology of this disclosure. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[0422] The headset terminal 314 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication interface 44, and a display 343. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, camera 42, and display 343 are also connected to the bus 52.

[0423] The microphone 238 receives voice signals from the user 20 and receives instructions from the user 20. The microphone 238 captures the voice signals from the user 20, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0424] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the area around the user 20 (for example, an imaging range defined by a field of view equivalent to the width of a typical healthy person's field of vision).

[0425] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0426] Figure 6 shows an example of the main functions of the data processing device 12 and the headset terminal 314. As shown in Figure 6, the data processing device 12 performs specific processing using the processor 28. The storage 32 stores the specific processing program 56.

[0427] The specific processing program 56 is an example of a "program" relating to the technology of this disclosure. The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0428] The storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[0429] In the headset terminal 314, the processor 46 performs the reception output processing. The storage 50 stores the reception output program 60. The processor 46 reads the reception output program 60 from the storage 50 and executes the read reception output program 60 on the RAM 48. The reception output processing is realized by the processor 46 operating as a control unit 46A according to the reception output program 60 executed on the RAM 48.

[0430] Next, the specific processing performed by the specific processing unit 290 of the data processing device 12 will be described. In the following description, the data processing device 12 will be referred to as the "server" and the headset terminal 314 will be referred to as the "terminal".

[0431] This invention describes an embodiment of a system that analyzes business information within a company and automatically extracts, evaluates, and files patent applications for technical ideas. The main components of the system consist of a server, terminals, and users.

[0432] The server forms the core of the information processing in this invention. First, it collects business information such as emails, reports, and meeting materials generated by employees via the company's internal network. Next, it analyzes the collected information using natural language processing technology to extract technical problems and new technical ideas as solutions. This reveals potential patent ideas that employees may not have been aware of.

[0433] The extracted ideas are cross-referenced with external patent and academic paper databases to evaluate their patentability based on novelty and inventiveness. This evaluation is performed using machine learning algorithms, and ideas deemed highly patentable are given priority.

[0434] Next, the server generates the documents necessary for filing a patent application for the idea. These documents are formatted to meet legal requirements and are output in a manner that conforms to the patent application process.

[0435] The terminal acts as the interface with the user. It receives idea summaries and patent suitability assessment reports sent from the server and displays them in a format that the user can review. The user can use the provided information to verify the value of the technical idea and provide feedback as needed.

[0436] The user (usually the intellectual property management department) reviews the patent application documents generated by the server. At this stage, a person with legal expertise reviews the content of the documents and makes final adjustments to make them suitable for submission to the patent office. Once the documents are approved by the user, the server automatically files them with the patent office in the next step.

[0437] As a concrete example, a system could identify a new method for delivering digital advertisements written in internal meeting notes. These notes are then analyzed by a server to verify the usefulness of the new algorithm. Subsequently, patentability is assessed, and the necessary documents for filing a patent application are automatically generated. Through this process, technical ideas can be quickly patented, strengthening a company's intellectual property portfolio.

[0438] The following describes the processing flow.

[0439] Step 1:

[0440] The server collects various types of business information through the company network. This includes emails, reports, meeting materials, and digital documents. Information stored in databases is also included in the extraction process.

[0441] Step 2:

[0442] The server uses natural language processing technology to analyze the collected business information. The purpose of the analysis is to identify technical problems and information that could lead to new solutions. The technical ideas extracted here are automatically listed by the system.

[0443] Step 3:

[0444] The server uses machine learning algorithms to evaluate the novelty and patentability of a technical idea. It cross-references information with external patent and academic paper databases to verify that the idea is differentiated from existing inventions. This evaluation is recorded as a score.

[0445] Step 4:

[0446] The server automatically generates the necessary documents for a patent application based on the patent evaluation results. These documents include detailed descriptions of the idea, technical scope, and effects. The documents are formatted according to the standards of the Japan Patent Office.

[0447] Step 5:

[0448] The terminal receives idea summaries and patent suitability assessment reports sent from the server. In addition, it retrieves the generated patent application documents and provides an interface for presenting them to the user.

[0449] Step 6:

[0450] The user (intellectual property management department) reviews the documents presented on the terminal, verifying their accuracy and legal compliance. They make revisions as needed and then provide final approval. Feedback is sent back to the server via the terminal.

[0451] Step 7:

[0452] The server automatically files a patent application with the patent office after receiving approval from the user. The progress of the application process is periodically notified to the terminal, allowing the user to check the status.

[0453] (Example 1)

[0454] Next, we will describe Example 1. In the following description, the data processing device 12 will be referred to as the "server," and the headset-type terminal 314 will be referred to as the "terminal."

[0455] Traditionally, the process of efficiently extracting technical ideas from the vast amount of business information generated within a company and converting them into patent applications has been time-consuming and labor-intensive. A major challenge is that technical ideas can get lost in this process, leading to missed patent opportunities. Furthermore, evaluating patentability and generating application documents requires specialized knowledge, making it difficult to achieve a rapid application process.

[0456] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 1 is realized by the following means.

[0457] In this invention, the server includes means for collecting business information, means for analyzing the collected information using natural language processing technology to extract technical problems and technical ideas, and means for comparing the extracted ideas with an external database to evaluate their patentability. This makes it possible to quickly and efficiently identify technical ideas from business information and automate the procedures necessary for patent application.

[0458] "Business information" refers to the collective term for data generated within a company, such as emails, reports, and meeting materials, and encompasses information related to the organization's activities.

[0459] "Natural language processing technology" refers to computer technologies used to analyze human language, enabling the understanding, interpretation, and generation of language data.

[0460] "Technical ideas" refer to concepts such as new methods, devices, and algorithms extracted from business information, and represent ingenious ideas that have the potential to be patented.

[0461] "Patentability" refers to the criteria used to evaluate whether an extracted technical idea can be granted a patent, and is judged based on novelty and inventiveness.

[0462] "Document generation" refers to the process of automatically creating documents necessary for a specific purpose using computer programs.

[0463] A "user terminal" refers to a device that provides an interface for operating a system, and includes computers and smartphones.

[0464] An "external database" is a database containing information that exists outside of a company, and includes information such as patents and academic papers.

[0465] The embodiments for carrying out this invention are shown below.

[0466] This system efficiently analyzes internal business information, automatically extracts and evaluates technical ideas, and links them to patent applications. The system's configuration consists primarily of servers, terminals, and users.

[0467] The server first collects business information through the company's internal network. This information includes emails, reports, and meeting materials. After collecting the information, the server uses natural language processing technologies such as Python's NLTK and spaCy to analyze the collected data. This analysis makes it possible to extract potential technical ideas and technical problems. The extracted ideas are then cross-referenced with external patent databases and academic paper databases, and their patentability is evaluated using machine learning algorithms with Scikit-learn and TensorFlow. Based on the evaluation results, ideas with high patentability are given priority. Finally, the documents necessary for patent application are automatically generated using Microsoft Word API and LaTeX.

[0468] The terminal will receive idea summaries and patent suitability assessment reports sent from the server and provide them to the user. The terminal's interface is expected to be implemented as a web-based application using Django or Flask. This will allow users to intuitively access information and provide feedback.

[0469] The user is typically a member of the intellectual property management department and performs the final review of the patent application documents generated on the server. They approve them from a legal standpoint and make final adjustments. After approval, the server automatically files the electronic application with the Japan Patent Office (JPO). This is done using the JPO's web service API.

[0470] A concrete example is a new digital advertising delivery method discussed in a company's internal meeting notes. This information is identified by the server and recognized as a new algorithm. An example of a generated prompt might be, "Please detail the procedure for extracting technical ideas and evaluating patentability regarding the new digital advertising delivery method." Based on this prompt, the technical ideas are automatically extracted, and patent applications are filed quickly and efficiently as part of the intellectual property strategy.

[0471] This format allows companies to quickly identify patentable ideas from their daily business data and automate the patent application process.

[0472] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0473] Step 1:

[0474] The server collects business information such as emails, reports, and meeting materials through the corporate network. Inputs include business data obtained from various corporate databases and mail servers. This data is organized internally by the server and output as an integrated dataset. This output is formatted to a standardized format in preparation for subsequent text analysis processing.

[0475] Step 2:

[0476] The server applies natural language processing techniques to the collected business information to perform text analysis. The input is an integrated business dataset. Using NLTK and spaCy, the server extracts noun and verb phrases from the data and identifies important key phrases. This results in analysis results that include technical issues and potential ideas. This output is used in the next step.

[0477] Step 3:

[0478] The server uses the technical ideas obtained through text analysis to cross-reference them with external patent and academic paper databases. The analysis results serve as input. The server executes machine learning algorithms using Scikit-learn and TensorFlow to evaluate patentability. As a result of this data processing, a list of patent ideas evaluated for novelty and inventiveness is output.

[0479] Step 4:

[0480] The server automatically generates patent application documents using the results of patentability assessments. The input is a list of ideas deemed highly patentable. The server uses Microsoft Word APIs and LaTeX to format the patent application documents. The output generated by this process is a concrete patent application document.

[0481] Step 5:

[0482] The terminal displays an idea summary and patent suitability assessment report sent from the server to the user. Generated application documents and assessment reports are used as input. The terminal uses a web application based on Django or Flask to output information in a format that is easy for the user to review. This output allows the user to evaluate the value of the technical idea and provide feedback as needed.

[0483] Step 6:

[0484] The user views and reviews the patent application documents provided through the terminal. The documents provided by the user are used as input. The user checks the documents from a legal perspective and sends comments to the server as needed. After the user's review, the approved application documents are output once the patent application is ready.

[0485] Step 7:

[0486] The server automatically submits the patent application documents approved by the user to the Japan Patent Office (JPO). The input is the approved patent application documents. The server utilizes the JPO's web service API to complete the application process. The output is confirmation information for the officially submitted patent application.

[0487] (Application Example 1)

[0488] Next, we will explain Application Example 1. In the following explanation, the data processing device 12 will be referred to as the "server," and the headset-type terminal 314 will be referred to as the "terminal."

[0489] There is a need to automate the process of efficiently extracting new technological ideas from business information generated within a company and quickly guiding those ideas to patent applications. Furthermore, it is necessary to provide an environment where users can evaluate ideas on the spot via portable devices and immediately send them to the company's intellectual asset management system. This aims to accelerate the patenting process within companies and maximize the value of their intellectual assets.

[0490] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 1 is realized by the following means.

[0491] In this invention, the server includes means for analyzing business information, means for extracting technical ideas, means for evaluating patentability, means for evaluating technical ideas and transmitting them to an in-house data processing system via an application installed on a portable device, and means for analyzing input information using natural language processing technology and comparing it with an existing database to evaluate novelty. This allows users to evaluate ideas directly from a portable device and quickly enter the in-house patenting process.

[0492] "Business information" refers to information related to business activities generated within a company, such as emails, reports, and meeting materials.

[0493] "Technical ideas" refer to new technical innovations and solutions extracted from business information.

[0494] "Patentability" refers to the criteria used to determine whether a particular idea possesses the novelty and inventiveness necessary to warrant a patent application.

[0495] "Portable devices" refer to computer equipment that can be carried around, such as smartphones and tablets.

[0496] "Natural language processing technology" is a technology that enables computers to understand, analyze, and generate human language.

[0497] A "data processing system" is a computer-based system that collects, analyzes, stores, and manages information.

[0498] "Novelty" refers to a property that possesses unique characteristics or elements not found in existing knowledge.

[0499] A "database" is a collection of information organized in a way that allows for efficient searching and retrieval of information.

[0500] In embodiments of the present invention, the server, terminal, and user are the main elements.

[0501] The server first continuously collects business information via the company's internal network. This business information consists of emails, meeting materials, reports, and other documents. The server analyzes this information using natural language processing technology to extract technical ideas. This analysis utilizes natural language processing libraries such as SpaCy. The server also compares the information with existing patent databases and evaluates its novelty. A proprietary patent search system is used for this purpose.

[0502] The terminal functions as an interface connecting the user and the server. The terminal has the ability to display an overview of the idea and the results of the patent suitability assessment sent from the server. Users can use this to verify the value of their idea and provide feedback.

[0503] Users can input new technical ideas using portable devices and receive immediate evaluations. This allows the ideas to be sent to the company's data processing system over time and managed as intellectual assets. An example of this is a process where a user inputs an idea for a new AI virtual assistant interface on their smartphone and has it instantly evaluated for patentability.

[0504] This entire process may also be presented as a prompt to the generative AI model, such as: "Evaluate the patent novelty of this idea: 'Food packaging made from new, environmentally friendly, reusable materials'."

[0505] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0506] Step 1:

[0507] The server collects business information through the corporate network. Emails, meeting materials, and reports are among the inputs. Using natural language processing techniques, the server analyzes the text data from these documents and extracts information that may contain technical ideas. The output obtained from this analysis consists of potential technical ideas.

[0508] Step 2:

[0509] The server evaluates the candidate technical ideas extracted in Step 1. Data processing is performed regarding novelty and inventiveness by cross-referencing with a patent database. This process includes specific keyword searches and similarity matching. The evaluation results are output, and ideas with high patentability are selected from these results.

[0510] Step 3:

[0511] The server generates patent application documents for ideas with high patentability. Input is a summary of the technical idea or patent abstract, prepared to meet legal requirements. Output is a patent application document. The document generation process uses an automated process based on standard templates.

[0512] Step 4:

[0513] The terminal displays the patent evaluation results and application document contents sent from the server. The server's output is received as input and formatted into a visually verifiable format on the screen. The user reviews this and provides feedback as needed. The final approval document, incorporating the user's approvals and revision requests, is then output.

[0514] Step 5:

[0515] The user inputs a new technical idea using a portable device. This input is sent to the server as a prompt to a generating AI model. Based on the input idea, the server immediately performs an evaluation. Through this process, the idea is sent to the company's intellectual asset management system and constitutes part of the patenting process. As output, the user is presented with the patentability evaluation result for the idea.

[0516] Furthermore, an emotion engine that estimates the user's emotions may be incorporated. That is, the identification processing unit 290 may use the emotion identification model 59 to estimate the user's emotions and perform identification processing using the user's emotions.

[0517] This invention is a system that automates the process of filing patent applications for technical ideas generated within a company, and specifically incorporates an emotion engine that recognizes user emotions. The system consists of a server, terminals, and users.

[0518] The server plays a central role in the system. First, the server collects business information such as emails, reports, and meeting materials through the company network. Next, it uses natural language processing technology to analyze this information and extract technical ideas. In this extraction process, the emotion engine evaluates the user's emotions and prioritizes extracting natural and positive expressions.

[0519] The emotion engine is used to emotionally analyze user feedback and evaluate how emotionally an idea resonates with others. This information is recorded as a result of the emotion analysis and particularly influences the patentability assessment of technical ideas and document generation.

[0520] The server uses machine learning algorithms to evaluate the novelty of the extracted ideas and score their patentability. Here again, the emotion engine takes user reactions into account, prioritizing ideas that are particularly emotionally positive and novel.

[0521] Once an idea has been evaluated, the system automatically generates a document for patent application. The generated document is required to reflect the sentiment analysis results from the sentiment engine and be the most user-friendly.

[0522] The terminal receives results sent from the server and presents them to the user. Through the terminal's interface, the user can review the idea content and application documents, and make adjustments based on feedback from the sentiment engine.

[0523] The user forms a crucial feedback loop within the system. Based on the information displayed on the terminal, they review the patent application document and verify its compliance with the law. During this review, the emotion engine assesses the user's emotional state and adjusts the interface to reduce stress.

[0524] For example, if an employee comes up with an idea for designing a new consumer application, the server can analyze the idea and use an emotion engine to evaluate user interest and enthusiasm. As a result, ideas deemed to have particularly high emotional value can be quickly compiled into a patent application document, presented to the user on a terminal, and easily approved. This process enables companies to promote technological innovation that is also positively received emotionally.

[0525] The following describes the processing flow.

[0526] Step 1:

[0527] The server collects business information such as emails, reports, and meeting materials via the company network. This ensures that the data necessary for extracting technical ideas is available.

[0528] Step 2:

[0529] The server analyzes the collected business information using natural language processing technology. The purpose of the analysis is to discover and extract descriptions of technical problems and new solutions.

[0530] Step 3:

[0531] The server uses an emotion engine to evaluate the user's emotions based on the analysis results. It measures how much the technical idea resonates emotionally with the user and records the results as metadata.

[0532] Step 4:

[0533] The server uses machine learning algorithms to evaluate the novelty of the extracted ideas. At this time, it also considers the results of the emotion engine, giving higher priority to ideas that show positive emotional responses.

[0534] Step 5:

[0535] The server automatically generates patent application documents based on evaluated ideas. These documents reflect both a technical overview and evaluation information from the sentiment engine.

[0536] Step 6:

[0537] The terminal receives patent application documents and sentiment evaluation results sent from the server and provides an interface to present them to the user. The user can use this interface to review the document content and evaluation results.

[0538] Step 7:

[0539] The user reviews the patent application document displayed on the device and makes revisions as needed. The emotion engine monitors the user's emotional state and dynamically adjusts the interface to reduce stress.

[0540] Step 8:

[0541] The server automatically files a patent application with the Japan Patent Office upon user approval. The progress of the application process is periodically notified to the user's terminal, allowing them to monitor the status.

[0542] (Example 2)

[0543] Next, we will describe Example 2. In the following description, the data processing device 12 will be referred to as the "server," and the headset-type terminal 314 will be referred to as the "terminal."

[0544] In modern business activities, innovative technological ideas are frequently generated, but there is a need for a process to quickly and effectively translate these into patents. However, the general process of collecting, evaluating, and filing patent applications for ideas is cumbersome, and important ideas are sometimes overlooked, especially because user sentiment is not adequately considered. Therefore, a system is needed to facilitate patent applications that reflect user sentiment.

[0545] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 2 is realized by the following means.

[0546] In this invention, the server includes means for analyzing business data, means for extracting innovative ideas from the analyzed data, and means for evaluating the extracted innovative ideas using emotion analysis means for evaluating the user's emotions. This makes it possible to accurately evaluate innovative ideas that take user emotions into account and improve the efficiency of patent applications.

[0547] "Business data" refers to information generated within a company, such as emails, reports, and meeting materials.

[0548] "Analysis" refers to the process of analyzing data using natural language processing techniques to extract useful information and patterns.

[0549] An "innovative idea" refers to a technological idea or invention that arises in the course of business activities.

[0550] "Emotional analysis tools" refer to engines and algorithms that emotionally evaluate user opinions and feedback.

[0551] "Novelty" refers to something that is new or unique compared to existing technologies or ideas.

[0552] "Patentability" refers to the criteria used to evaluate whether a proposed innovation can be registered as a patent.

[0553] An "operating device" refers to a terminal or interface that a user uses to view information or results.

[0554] The "Japan Patent Office" refers to the public institution where patent applications are submitted and examined.

[0555] The embodiments for carrying out this invention are shown below.

[0556] The server acts as the central hub of the entire system. First, the server collects business data through the company network. This data includes emails, reports, meeting materials, and more. Next, the server analyzes the collected data using natural language processing (NLP) techniques to extract useful technical ideas. This analysis utilizes NLP libraries such as spaCy and NLTK. Furthermore, an emotion engine is used as a sentiment analysis tool to evaluate users' emotional responses. This prioritizes the extraction of technical ideas that elicit positive emotional responses.

[0557] The server uses machine learning to evaluate the novelty of extracted technical ideas and determine their patentability. Specifically, a trained model compares them with existing patent data to identify whether they are innovative. Generative AI models are used in this process. Once the evaluation is complete, the technical ideas are automatically generated as patent application documents. Natural language generation (NLG) technology is employed in this document generation process to ensure that the documents are structured in a way that complies with legal requirements.

[0558] The terminal is responsible for presenting the generated document sent from the server to the user. The user reviews and approves the content through the terminal. Furthermore, the interface is adjusted based on feedback from an emotion engine, allowing the user to review the information in a stress-free environment. This step supports the user's final review and adjustment of the patent application document.

[0559] As a concrete example, after a design meeting for a new consumer application, the server automatically analyzes the meeting minutes and extracts technical ideas. Using sentiment analysis, ideas that evoke high user interest and enthusiasm are identified, and those that elicit positive emotions are quickly processed for patent application and presented on the terminal. This allows companies to efficiently patent innovative technologies.

[0560] Examples of prompts for a generative AI model:

[0561] "Extract technical ideas from newly collected meeting materials and create a patent application document that reflects the results of sentiment analysis."

[0562] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0563] Step 1:

[0564] The server collects business data through the company network. It receives raw data such as emails, reports, and meeting materials as input. This data is organized into specific folders or databases and prepared for subsequent analysis. The collected data is stored within the system.

[0565] Step 2:

[0566] The server analyzes the collected business data using natural language processing (NLP) techniques. The unanalyzed business data collected in Step 1 is used as input. In this step, an NLP library (e.g., spaCy or NLTK) is used to analyze the text data and extract technical ideas and related keywords. The output consists of the extracted technical ideas and related information.

[0567] Step 3:

[0568] The server evaluates user feedback using sentiment analysis tools. It receives technical ideas extracted in step 2 and user comments as input. In this step, the sentiment engine emotionally analyzes the user comments and prioritizes ideas that evoke positive emotions. The output is a list of technical ideas with added sentiment evaluations.

[0569] Step 4:

[0570] The server uses machine learning to evaluate the novelty of technical ideas. It uses the sentiment-rated technical ideas obtained in step 3 as input. This process involves comparing the ideas to existing patent databases to assess their originality. The output is a list of evaluated ideas, including novelty and patentability scores.

[0571] Step 5:

[0572] The server automatically generates patent application documents. It takes the list of ideas evaluated in step 4 as input. Natural language generation (NLG) technology is applied to construct the patent application documents in a way that meets legal requirements. The output is the completed patent application document.

[0573] Step 6:

[0574] The terminal presents the generated document to the user. It receives the patent application document generated in step 5 as input. The user reviews the document via the terminal and makes revisions as needed. The output is the patent application document as finalized by the user.

[0575] Step 7:

[0576] The user reviews and approves the generated document. The input is the patent application document presented in step 6. The user verifies legal compliance and provides feedback. The output is the document with approval or a request for revision.

[0577] (Application Example 2)

[0578] Next, we will explain application example 2. In the following explanation, the data processing device 12 will be referred to as the "server," and the headset-type terminal 314 will be referred to as the "terminal."

[0579] In today's business environment, there is a need to efficiently file patent applications for new technological ideas that emerge within a company. However, the traditional patent application process is time-consuming and labor-intensive, and in particular, it does not adequately identify and evaluate ideas based on customer emotional value. Furthermore, there is a lack of means to effectively utilize customer reactions as feedback in the early stages of product development and to quickly patent new technological concepts.

[0580] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means.

[0581] In this invention, the server includes means for analyzing business data, means for analyzing customer behavior and emotions and collecting product-related concepts that elicit positive responses, and means for evaluating the intellectual property suitability of the extracted concepts. This enables companies to quickly patent technological concepts that take into account customer emotional value and enhance their competitiveness.

[0582] "Business data" refers to all information related to business activities generated within a company, including emails, reports, meeting materials, and so on.

[0583] "Analysis" refers to a series of procedures and methods used to transform information and data into a form that is easy to understand.

[0584] A "technical concept" refers to the basic idea or plan for a new technological idea, and serves as the basis for determining whether that idea is feasible and patentable.

[0585] "Intellectual property suitability" refers to the criteria used to evaluate whether something has intellectual property value in a business context, and it particularly includes novelty, usefulness, and inventiveness.

[0586] "Document generation" refers to the process of automatically creating official documents and papers based on concepts and information.

[0587] "Customer behavior" refers to activities related to customer movements and actions in a commercial environment, including product exploration, trial use, and purchase decisions.

[0588] "Analyzing emotions" refers to procedures and techniques aimed at recognizing and understanding the emotional states that an individual exhibits in a specific situation or context.

[0589] "Positive reactions" refer to positive emotions or affirmative feedback that customers express towards a product or service.

[0590] An "intellectual property institution" refers to a public institution that manages and registers patents, trademarks, copyrights, and other intellectual property rights.

[0591] "Affective value" refers to the sensory or emotional value that an individual or group has towards a particular product or service, and is a factor that influences judgment and decision-making.

[0592] The system of this invention consists of a server, a terminal, and a user. The server is responsible for analyzing business data, analyzing customer behavior and emotions, and extracting technical concepts related to products that elicit positive responses. Specific processing includes collecting business data and analyzing it using natural language processing technology. This involves collecting customer behavior data using smartphone sensors and utilizing emotion analysis models such as IBM Watson Tone Analyzer.

[0593] The terminal displays the results sent from the server and provides an interface for the user to review this information. Through this interface, the user can review the generated intellectual property application document and approve or modify its contents.

[0594] A concrete example of using emotional analysis based on a user's facial expressions and behavior while trying out a specific product to quickly extract a technological concept that elicits a positive response is when a server analyzes a customer's behavioral data while they try out a new electronic device in a store, capturing their smiles and enthusiastic remarks. The information extracted in this way is then evaluated as having particularly high emotional value in the patent application process.

[0595] An example of a prompt to be input to the generative AI model would be text such as, "Customer sentiment towards this product is very positive. Please generate positive and novel ideas for patent application."

[0596] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0597] Step 1:

[0598] The server collects business data. Inputs include internal company emails, reports, and meeting materials, which are stored in a database on the server. Output is data in a unified, analyzable format. In this step, customer behavior and speech are also collected via smartphone cameras and microphones, and each piece of data is tagged and identified.

[0599] Step 2:

[0600] The server analyzes the collected business data using natural language processing techniques. The input is the unified format data obtained in the previous step, and the output is a list of technical concepts. In this analysis, tools such as NLTK and the Sentiment Analysis API are used to extract important keywords and phrases from the text.

[0601] Step 3:

[0602] The server analyzes customer behavior and emotional data. Input is customer reaction data collected via cameras and microphones, and output is information related to the customer's emotional state and products that elicit positive responses. This step involves using an emotional analysis model, such as IBM Watson Tone Analyzer, to quantify the degree of positivity in the customer's emotions.

[0603] Step 4:

[0604] The server evaluates the intellectual property suitability of the analyzed technical concepts. The input is a list of technical concepts and customer sentiment data, and the output is a list of candidate concepts deemed suitable as intellectual property. Specifically, it uses machine learning algorithms such as Scikit-learn to evaluate novelty and inventiveness by comparing them with historical patent databases.

[0605] Step 5:

[0606] The server generates documents for patent applications. The input is a candidate technical concept that has passed suitability evaluation, and the output is a formal patent application document. In this step, a generative AI model is used to create prompts based on the given concepts and automatically generate the document accordingly.

[0607] Step 6:

[0608] The terminal displays the generated document to the user. The input is the patent application document, and the output is the document content on the user interface. The user uses this interface to review the content and perform specific actions such as approving or instructing revisions.

[0609] Step 7:

[0610] The user files an approved document with the patent office via the terminal. The input is the patent application document approved and corrected by the user, and the output is the digital document officially sent to the patent office. In this final step, the terminal's communication function is used to automatically perform the electronic filing process.

[0611] The specific processing unit 290 transmits the result of the specific processing to the headset terminal 314. In the headset terminal 314, the control unit 46A causes the speaker 240 and display 343 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0612] Data generation model 58 is a type of so-called generative AI (Artificial Intelligence). One example of data generation model 58 is ChatGPT (Internet search<URL: https: / / openai.com / blog / chatgpt> ), Gemini (Internet search) <url: https: gemini.google.com ?hl="ja">Examples of generative AI include the following. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and with inference data such as audio data representing speech, text data representing text, and image data representing images. The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference results in data formats such as audio data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[0613] In the above embodiment, an example was given in which specific processing is performed by the data processing device 12, but the technology of this disclosure is not limited thereto, and specific processing may also be performed by the headset terminal 314.

[0614] [Fourth Embodiment]

[0615] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[0616] As shown in Figure 7, the data processing system 410 includes a data processing device 12 and a robot 414. An example of the data processing device 12 is a server.

[0617] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 is an example of a "computer" related to the technology of this disclosure. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[0618] The robot 414 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication interface 44, and a controlled object 443. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, camera 42, and controlled object 443 are also connected to the bus 52.

[0619] The microphone 238 receives voice signals from the user 20 and receives instructions from the user 20. The microphone 238 captures the voice signals from the user 20, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0620] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the area around the user 20 (for example, an imaging range defined by a field of view equivalent to the width of a typical healthy person's field of vision).

[0621] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0622] The controlled object 443 includes a display device, LEDs in the eyes, and motors that drive the arms, hands, and feet. The posture and gestures of the robot 414 are controlled by controlling the motors of the arms, hands, and feet. Some of the robot 414's emotions can be expressed by controlling these motors. Furthermore, the robot 414's facial expressions can also be expressed by controlling the illumination state of the LEDs in its eyes.

[0623] Figure 8 shows an example of the main functions of the data processing device 12 and the robot 414. As shown in Figure 8, the data processing device 12 performs specific processing using the processor 28. The storage 32 stores the specific processing program 56.

[0624] The specific processing program 56 is an example of a "program" relating to the technology of this disclosure. The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0625] The storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[0626] In robot 414, the processor 46 performs the reception output processing. The storage 50 stores the reception output program 60. The processor 46 reads the reception output program 60 from the storage 50 and executes the read reception output program 60 on the RAM 48. The reception output processing is realized by the processor 46 operating as a control unit 46A according to the reception output program 60 executed on the RAM 48.

[0627] Next, the specific processing performed by the specific processing unit 290 of the data processing device 12 will be described. In the following description, the data processing device 12 will be referred to as the "server" and the robot 414 as the "terminal".

[0628] This invention describes an embodiment of a system that analyzes business information within a company and automatically extracts, evaluates, and files patent applications for technical ideas. The main components of the system consist of a server, terminals, and users.

[0629] The server forms the core of the information processing in this invention. First, it collects business information such as emails, reports, and meeting materials generated by employees via the company's internal network. Next, it analyzes the collected information using natural language processing technology to extract technical problems and new technical ideas as solutions. This reveals potential patent ideas that employees may not have been aware of.

[0630] The extracted ideas are cross-referenced with external patent and academic paper databases to evaluate their patentability based on novelty and inventiveness. This evaluation is performed using machine learning algorithms, and ideas deemed highly patentable are given priority.

[0631] Next, the server generates the documents necessary for filing a patent application for the idea. These documents are formatted to meet legal requirements and are output in a manner that conforms to the patent application process.

[0632] The terminal acts as the interface with the user. It receives idea summaries and patent suitability assessment reports sent from the server and displays them in a format that the user can review. The user can use the provided information to verify the value of the technical idea and provide feedback as needed.

[0633] The user (usually the intellectual property management department) reviews the patent application documents generated by the server. At this stage, a person with legal expertise reviews the content of the documents and makes final adjustments to make them suitable for submission to the patent office. Once the documents are approved by the user, the server automatically files them with the patent office in the next step.

[0634] As a concrete example, a system could identify a new method for delivering digital advertisements written in internal meeting notes. These notes are then analyzed by a server to verify the usefulness of the new algorithm. Subsequently, patentability is assessed, and the necessary documents for filing a patent application are automatically generated. Through this process, technical ideas can be quickly patented, strengthening a company's intellectual property portfolio.

[0635] The following describes the processing flow.

[0636] Step 1:

[0637] The server collects various types of business information through the company network. This includes emails, reports, meeting materials, and digital documents. Information stored in databases is also included in the extraction process.

[0638] Step 2:

[0639] The server uses natural language processing technology to analyze the collected business information. The purpose of the analysis is to identify technical problems and information that could lead to new solutions. The technical ideas extracted here are automatically listed by the system.

[0640] Step 3:

[0641] The server uses machine learning algorithms to evaluate the novelty and patentability of a technical idea. It cross-references information with external patent and academic paper databases to verify that the idea is differentiated from existing inventions. This evaluation is recorded as a score.

[0642] Step 4:

[0643] The server automatically generates the necessary documents for a patent application based on the patent evaluation results. These documents include detailed descriptions of the idea, technical scope, and effects. The documents are formatted according to the standards of the Japan Patent Office.

[0644] Step 5:

[0645] The terminal receives idea summaries and patent suitability assessment reports sent from the server. In addition, it retrieves the generated patent application documents and provides an interface for presenting them to the user.

[0646] Step 6:

[0647] The user (intellectual property management department) reviews the documents presented on the terminal, verifying their accuracy and legal compliance. They make revisions as needed and then provide final approval. Feedback is sent back to the server via the terminal.

[0648] Step 7:

[0649] The server automatically files a patent application with the patent office after receiving approval from the user. The progress of the application process is periodically notified to the terminal, allowing the user to check the status.

[0650] (Example 1)

[0651] Next, we will describe Example 1. In the following description, the data processing device 12 will be referred to as the "server" and the robot 414 as the "terminal".

[0652] Traditionally, the process of efficiently extracting technical ideas from the vast amount of business information generated within a company and converting them into patent applications has been time-consuming and labor-intensive. A major challenge is that technical ideas can get lost in this process, leading to missed patent opportunities. Furthermore, evaluating patentability and generating application documents requires specialized knowledge, making it difficult to achieve a rapid application process.

[0653] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 1 is realized by the following means.

[0654] In this invention, the server includes means for collecting business information, means for analyzing the collected information using natural language processing technology to extract technical problems and technical ideas, and means for comparing the extracted ideas with an external database to evaluate their patentability. This makes it possible to quickly and efficiently identify technical ideas from business information and automate the procedures necessary for patent application.

[0655] "Business information" refers to the collective term for data generated within a company, such as emails, reports, and meeting materials, and encompasses information related to the organization's activities.

[0656] "Natural language processing technology" refers to computer technologies used to analyze human language, enabling the understanding, interpretation, and generation of language data.

[0657] "Technical ideas" refer to concepts such as new methods, devices, and algorithms extracted from business information, and represent ingenious ideas that have the potential to be patented.

[0658] "Patentability" refers to the criteria used to evaluate whether an extracted technical idea can be granted a patent, and is judged based on novelty and inventiveness.

[0659] "Document generation" refers to the process of automatically creating documents necessary for a specific purpose using computer programs.

[0660] A "user terminal" refers to a device that provides an interface for operating a system, and includes computers and smartphones.

[0661] An "external database" is a database containing information that exists outside of a company, and includes information such as patents and academic papers.

[0662] The embodiments for carrying out this invention are shown below.

[0663] This system efficiently analyzes internal business information, automatically extracts and evaluates technical ideas, and links them to patent applications. The system's configuration consists primarily of servers, terminals, and users.

[0664] The server first collects business information through the company's internal network. This information includes emails, reports, and meeting materials. After collecting the information, the server uses natural language processing technologies such as Python's NLTK and spaCy to analyze the collected data. This analysis makes it possible to extract potential technical ideas and technical problems. The extracted ideas are then cross-referenced with external patent databases and academic paper databases, and their patentability is evaluated using machine learning algorithms with Scikit-learn and TensorFlow. Based on the evaluation results, ideas with high patentability are given priority. Finally, the documents necessary for patent application are automatically generated using Microsoft Word API and LaTeX.

[0665] The terminal will receive idea summaries and patent suitability assessment reports sent from the server and provide them to the user. The terminal's interface is expected to be implemented as a web-based application using Django or Flask. This will allow users to intuitively access information and provide feedback.

[0666] The user is typically a member of the intellectual property management department and performs the final review of the patent application documents generated on the server. They approve them from a legal standpoint and make final adjustments. After approval, the server automatically files the electronic application with the Japan Patent Office (JPO). This is done using the JPO's web service API.

[0667] A concrete example is a new digital advertising delivery method discussed in a company's internal meeting notes. This information is identified by the server and recognized as a new algorithm. An example of a generated prompt might be, "Please detail the procedure for extracting technical ideas and evaluating patentability regarding the new digital advertising delivery method." Based on this prompt, the technical ideas are automatically extracted, and patent applications are filed quickly and efficiently as part of the intellectual property strategy.

[0668] This format allows companies to quickly identify patentable ideas from their daily business data and automate the patent application process.

[0669] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0670] Step 1:

[0671] The server collects business information such as emails, reports, and meeting materials through the corporate network. Inputs include business data obtained from various corporate databases and mail servers. This data is organized internally by the server and output as an integrated dataset. This output is formatted to a standardized format in preparation for subsequent text analysis processing.

[0672] Step 2:

[0673] The server applies natural language processing techniques to the collected business information to perform text analysis. The input is an integrated business dataset. Using NLTK and spaCy, the server extracts noun and verb phrases from the data and identifies important key phrases. This results in analysis results that include technical issues and potential ideas. This output is used in the next step.

[0674] Step 3:

[0675] The server uses the technical ideas obtained through text analysis to cross-reference them with external patent and academic paper databases. The analysis results serve as input. The server executes machine learning algorithms using Scikit-learn and TensorFlow to evaluate patentability. As a result of this data processing, a list of patent ideas evaluated for novelty and inventiveness is output.

[0676] Step 4:

[0677] The server automatically generates patent application documents using the results of patentability assessments. The input is a list of ideas deemed highly patentable. The server uses Microsoft Word APIs and LaTeX to format the patent application documents. The output generated by this process is a concrete patent application document.

[0678] Step 5:

[0679] The terminal displays an idea summary and patent suitability assessment report sent from the server to the user. Generated application documents and assessment reports are used as input. The terminal uses a web application based on Django or Flask to output information in a format that is easy for the user to review. This output allows the user to evaluate the value of the technical idea and provide feedback as needed.

[0680] Step 6:

[0681] The user views and reviews the patent application documents provided through the terminal. The documents provided by the user are used as input. The user checks the documents from a legal perspective and sends comments to the server as needed. After the user's review, the approved application documents are output once the patent application is ready.

[0682] Step 7:

[0683] The server automatically submits the patent application documents approved by the user to the Japan Patent Office (JPO). The input is the approved patent application documents. The server utilizes the JPO's web service API to complete the application process. The output is confirmation information for the officially submitted patent application.

[0684] (Application Example 1)

[0685] Next, we will explain Application Example 1. In the following explanation, the data processing device 12 will be referred to as the "server" and the robot 414 as the "terminal".

[0686] There is a need to automate the process of efficiently extracting new technological ideas from business information generated within a company and quickly guiding those ideas to patent applications. Furthermore, it is necessary to provide an environment where users can evaluate ideas on the spot via portable devices and immediately send them to the company's intellectual asset management system. This aims to accelerate the patenting process within companies and maximize the value of their intellectual assets.

[0687] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 1 is realized by the following means.

[0688] In this invention, the server includes means for analyzing business information, means for extracting technical ideas, means for evaluating patentability, means for evaluating technical ideas and transmitting them to an in-house data processing system via an application installed on a portable device, and means for analyzing input information using natural language processing technology and comparing it with an existing database to evaluate novelty. This allows users to evaluate ideas directly from a portable device and quickly enter the in-house patenting process.

[0689] "Business information" refers to information related to business activities generated within a company, such as emails, reports, and meeting materials.

[0690] "Technical ideas" refer to new technical innovations and solutions extracted from business information.

[0691] "Patentability" refers to the criteria used to determine whether a particular idea possesses the novelty and inventiveness necessary to warrant a patent application.

[0692] "Portable devices" refer to computer equipment that can be carried around, such as smartphones and tablets.

[0693] "Natural language processing technology" is a technology that enables computers to understand, analyze, and generate human language.

[0694] A "data processing system" is a computer-based system that collects, analyzes, stores, and manages information.

[0695] "Novelty" refers to a property that possesses unique characteristics or elements not found in existing knowledge.

[0696] A "database" is a collection of information organized in a way that allows for efficient searching and retrieval of information.

[0697] In embodiments of the present invention, the server, terminal, and user are the main elements.

[0698] The server first continuously collects business information via the company's internal network. This business information consists of emails, meeting materials, reports, and other documents. The server analyzes this information using natural language processing technology to extract technical ideas. This analysis utilizes natural language processing libraries such as SpaCy. The server also compares the information with existing patent databases and evaluates its novelty. A proprietary patent search system is used for this purpose.

[0699] The terminal functions as an interface connecting the user and the server. The terminal has the ability to display an overview of the idea and the results of the patent suitability assessment sent from the server. Users can use this to verify the value of their idea and provide feedback.

[0700] Users can input new technical ideas using portable devices and receive immediate evaluations. This allows the ideas to be sent to the company's data processing system over time and managed as intellectual assets. An example of this is a process where a user inputs an idea for a new AI virtual assistant interface on their smartphone and has it instantly evaluated for patentability.

[0701] This entire process may also be presented as a prompt to the generative AI model, such as: "Evaluate the patent novelty of this idea: 'Food packaging made from new, environmentally friendly, reusable materials'."

[0702] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0703] Step 1:

[0704] The server collects business information through the corporate network. Emails, meeting materials, and reports are among the inputs. Using natural language processing techniques, the server analyzes the text data from these documents and extracts information that may contain technical ideas. The output obtained from this analysis consists of potential technical ideas.

[0705] Step 2:

[0706] The server evaluates the candidate technical ideas extracted in Step 1. Data processing is performed regarding novelty and inventiveness by cross-referencing with a patent database. This process includes specific keyword searches and similarity matching. The evaluation results are output, and ideas with high patentability are selected from these results.

[0707] Step 3:

[0708] The server generates patent application documents for ideas with high patentability. Input is a summary of the technical idea or patent abstract, prepared to meet legal requirements. Output is a patent application document. The document generation process uses an automated process based on standard templates.

[0709] Step 4:

[0710] The terminal displays the patent evaluation results and application document contents sent from the server. The server's output is received as input and formatted into a visually verifiable format on the screen. The user reviews this and provides feedback as needed. The final approval document, incorporating the user's approvals and revision requests, is then output.

[0711] Step 5:

[0712] The user inputs a new technical idea using a portable device. This input is sent to the server as a prompt to a generating AI model. Based on the input idea, the server immediately performs an evaluation. Through this process, the idea is sent to the company's intellectual asset management system and constitutes part of the patenting process. As output, the user is presented with the patentability evaluation result for the idea.

[0713] Furthermore, an emotion engine that estimates the user's emotions may be incorporated. That is, the identification processing unit 290 may use the emotion identification model 59 to estimate the user's emotions and perform identification processing using the user's emotions.

[0714] This invention is a system that automates the process of filing patent applications for technical ideas generated within a company, and specifically incorporates an emotion engine that recognizes user emotions. The system consists of a server, terminals, and users.

[0715] The server plays a central role in the system. First, the server collects business information such as emails, reports, and meeting materials through the company network. Next, it uses natural language processing technology to analyze this information and extract technical ideas. In this extraction process, the emotion engine evaluates the user's emotions and prioritizes extracting natural and positive expressions.

[0716] The emotion engine is used to emotionally analyze user feedback and evaluate how emotionally an idea resonates with others. This information is recorded as a result of the emotion analysis and particularly influences the patentability assessment of technical ideas and document generation.

[0717] The server uses machine learning algorithms to evaluate the novelty of the extracted ideas and score their patentability. Here again, the emotion engine takes user reactions into account, prioritizing ideas that are particularly emotionally positive and novel.

[0718] Once an idea has been evaluated, the system automatically generates a document for patent application. The generated document is required to reflect the sentiment analysis results from the sentiment engine and be the most user-friendly.

[0719] The terminal receives results sent from the server and presents them to the user. Through the terminal's interface, the user can review the idea content and application documents, and make adjustments based on feedback from the sentiment engine.

[0720] The user forms a crucial feedback loop within the system. Based on the information displayed on the terminal, they review the patent application document and verify its compliance with the law. During this review, the emotion engine assesses the user's emotional state and adjusts the interface to reduce stress.

[0721] For example, if an employee comes up with an idea for designing a new consumer application, the server can analyze the idea and use an emotion engine to evaluate user interest and enthusiasm. As a result, ideas deemed to have particularly high emotional value can be quickly compiled into a patent application document, presented to the user on a terminal, and easily approved. This process enables companies to promote technological innovation that is also positively received emotionally.

[0722] The following describes the processing flow.

[0723] Step 1:

[0724] The server collects business information such as emails, reports, and meeting materials via the company network. This ensures that the data necessary for extracting technical ideas is available.

[0725] Step 2:

[0726] The server analyzes the collected business information using natural language processing technology. The purpose of the analysis is to discover and extract descriptions of technical problems and new solutions.

[0727] Step 3:

[0728] The server uses an emotion engine to evaluate the user's emotions based on the analysis results. It measures how much the technical idea resonates emotionally with the user and records the results as metadata.

[0729] Step 4:

[0730] The server uses machine learning algorithms to evaluate the novelty of the extracted ideas. At this time, it also considers the results of the emotion engine, giving higher priority to ideas that show positive emotional responses.

[0731] Step 5:

[0732] The server automatically generates patent application documents based on evaluated ideas. These documents reflect both a technical overview and evaluation information from the sentiment engine.

[0733] Step 6:

[0734] The terminal receives patent application documents and sentiment evaluation results sent from the server and provides an interface to present them to the user. The user can use this interface to review the document content and evaluation results.

[0735] Step 7:

[0736] The user reviews the patent application document displayed on the device and makes revisions as needed. The emotion engine monitors the user's emotional state and dynamically adjusts the interface to reduce stress.

[0737] Step 8:

[0738] The server automatically files a patent application with the Japan Patent Office upon user approval. The progress of the application process is periodically notified to the user's terminal, allowing them to monitor the status.

[0739] (Example 2)

[0740] Next, we will describe Example 2. In the following description, the data processing device 12 will be referred to as the "server" and the robot 414 as the "terminal".

[0741] In modern business activities, innovative technological ideas are frequently generated, but there is a need for a process to quickly and effectively translate these into patents. However, the general process of collecting, evaluating, and filing patent applications for ideas is cumbersome, and important ideas are sometimes overlooked, especially because user sentiment is not adequately considered. Therefore, a system is needed to facilitate patent applications that reflect user sentiment.

[0742] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 2 is realized by the following means.

[0743] In this invention, the server includes means for analyzing business data, means for extracting innovative ideas from the analyzed data, and means for evaluating the extracted innovative ideas using emotion analysis means for evaluating the user's emotions. This makes it possible to accurately evaluate innovative ideas that take user emotions into account and improve the efficiency of patent applications.

[0744] "Business data" refers to information generated within a company, such as emails, reports, and meeting materials.

[0745] "Analysis" refers to the process of analyzing data using natural language processing techniques to extract useful information and patterns.

[0746] An "innovative idea" refers to a technological idea or invention that arises in the course of business activities.

[0747] "Emotional analysis tools" refer to engines and algorithms that emotionally evaluate user opinions and feedback.

[0748] "Novelty" refers to something that is new or unique compared to existing technologies or ideas.

[0749] "Patentability" refers to the criteria used to evaluate whether a proposed innovation can be registered as a patent.

[0750] An "operating device" refers to a terminal or interface that a user uses to view information or results.

[0751] The "Japan Patent Office" refers to the public institution where patent applications are submitted and examined.

[0752] The embodiments for carrying out this invention are shown below.

[0753] The server acts as the central hub of the entire system. First, the server collects business data through the company network. This data includes emails, reports, meeting materials, and more. Next, the server analyzes the collected data using natural language processing (NLP) techniques to extract useful technical ideas. This analysis utilizes NLP libraries such as spaCy and NLTK. Furthermore, an emotion engine is used as a sentiment analysis tool to evaluate users' emotional responses. This prioritizes the extraction of technical ideas that elicit positive emotional responses.

[0754] The server uses machine learning to evaluate the novelty of extracted technical ideas and determine their patentability. Specifically, a trained model compares them with existing patent data to identify whether they are innovative. Generative AI models are used in this process. Once the evaluation is complete, the technical ideas are automatically generated as patent application documents. Natural language generation (NLG) technology is employed in this document generation process to ensure that the documents are structured in a way that complies with legal requirements.

[0755] The terminal is responsible for presenting the generated document sent from the server to the user. The user reviews and approves the content through the terminal. Furthermore, the interface is adjusted based on feedback from an emotion engine, allowing the user to review the information in a stress-free environment. This step supports the user's final review and adjustment of the patent application document.

[0756] As a concrete example, after a design meeting for a new consumer application, the server automatically analyzes the meeting minutes and extracts technical ideas. Using sentiment analysis, ideas that evoke high user interest and enthusiasm are identified, and those that elicit positive emotions are quickly processed for patent application and presented on the terminal. This allows companies to efficiently patent innovative technologies.

[0757] Examples of prompts for a generative AI model:

[0758] "Extract technical ideas from newly collected meeting materials and create a patent application document that reflects the results of sentiment analysis."

[0759] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0760] Step 1:

[0761] The server collects business data through the company network. It receives raw data such as emails, reports, and meeting materials as input. This data is organized into specific folders or databases and prepared for subsequent analysis. The collected data is stored within the system.

[0762] Step 2:

[0763] The server analyzes the collected business data using natural language processing (NLP) techniques. The unanalyzed business data collected in Step 1 is used as input. In this step, an NLP library (e.g., spaCy or NLTK) is used to analyze the text data and extract technical ideas and related keywords. The output consists of the extracted technical ideas and related information.

[0764] Step 3:

[0765] The server evaluates user feedback using sentiment analysis tools. It receives technical ideas extracted in step 2 and user comments as input. In this step, the sentiment engine emotionally analyzes the user comments and prioritizes ideas that evoke positive emotions. The output is a list of technical ideas with added sentiment evaluations.

[0766] Step 4:

[0767] The server uses machine learning to evaluate the novelty of technical ideas. It uses the sentiment-rated technical ideas obtained in step 3 as input. This process involves comparing the ideas to existing patent databases to assess their originality. The output is a list of evaluated ideas, including novelty and patentability scores.

[0768] Step 5:

[0769] The server automatically generates patent application documents. It takes the list of ideas evaluated in step 4 as input. Natural language generation (NLG) technology is applied to construct the patent application documents in a way that meets legal requirements. The output is the completed patent application document.

[0770] Step 6:

[0771] The terminal presents the generated document to the user. It receives the patent application document generated in step 5 as input. The user reviews the document via the terminal and makes revisions as needed. The output is the patent application document as finalized by the user.

[0772] Step 7:

[0773] The user reviews and approves the generated document. The input is the patent application document presented in step 6. The user verifies legal compliance and provides feedback. The output is the document with approval or a request for revision.

[0774] (Application Example 2)

[0775] Next, we will explain application example 2. In the following explanation, the data processing device 12 will be referred to as the "server" and the robot 414 as the "terminal".

[0776] In today's business environment, there is a need to efficiently file patent applications for new technological ideas that emerge within a company. However, the traditional patent application process is time-consuming and labor-intensive, and in particular, it does not adequately identify and evaluate ideas based on customer emotional value. Furthermore, there is a lack of means to effectively utilize customer reactions as feedback in the early stages of product development and to quickly patent new technological concepts.

[0777] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means.

[0778] In this invention, the server includes means for analyzing business data, means for analyzing customer behavior and emotions and collecting product-related concepts that elicit positive responses, and means for evaluating the intellectual property suitability of the extracted concepts. This enables companies to quickly patent technological concepts that take into account customer emotional value and enhance their competitiveness.

[0779] "Business data" refers to all information related to business activities generated within a company, including emails, reports, meeting materials, and so on.

[0780] "Analysis" refers to a series of procedures and methods used to transform information and data into a form that is easy to understand.

[0781] A "technical concept" refers to the basic idea or plan for a new technological idea, and serves as the basis for determining whether that idea is feasible and patentable.

[0782] "Intellectual property suitability" refers to the criteria used to evaluate whether something has intellectual property value in a business context, and it particularly includes novelty, usefulness, and inventiveness.

[0783] "Document generation" refers to the process of automatically creating official documents and papers based on concepts and information.

[0784] "Customer behavior" refers to activities related to customer movements and actions in a commercial environment, including product exploration, trial use, and purchase decisions.

[0785] "Analyzing emotions" refers to procedures and techniques aimed at recognizing and understanding the emotional states that an individual exhibits in a specific situation or context.

[0786] "Positive reactions" refer to positive emotions or affirmative feedback that customers express towards a product or service.

[0787] An "intellectual property institution" refers to a public institution that manages and registers patents, trademarks, copyrights, and other intellectual property rights.

[0788] "Affective value" refers to the sensory or emotional value that an individual or group has towards a particular product or service, and is a factor that influences judgment and decision-making.

[0789] The system of this invention consists of a server, a terminal, and a user. The server is responsible for analyzing business data, analyzing customer behavior and emotions, and extracting technical concepts related to products that elicit positive responses. Specific processing includes collecting business data and analyzing it using natural language processing technology. This involves collecting customer behavior data using smartphone sensors and utilizing emotion analysis models such as IBM Watson Tone Analyzer.

[0790] The terminal displays the results sent from the server and provides an interface for the user to review this information. Through this interface, the user can review the generated intellectual property application document and approve or modify its contents.

[0791] A concrete example of using emotional analysis based on a user's facial expressions and behavior while trying out a specific product to quickly extract a technological concept that elicits a positive response is when a server analyzes a customer's behavioral data while they try out a new electronic device in a store, capturing their smiles and enthusiastic remarks. The information extracted in this way is then evaluated as having particularly high emotional value in the patent application process.

[0792] An example of a prompt to be input to the generative AI model would be text such as, "Customer sentiment towards this product is very positive. Please generate positive and novel ideas for patent application."

[0793] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0794] Step 1:

[0795] The server collects business data. Inputs include internal company emails, reports, and meeting materials, which are stored in a database on the server. Output is data in a unified, analyzable format. In this step, customer behavior and speech are also collected via smartphone cameras and microphones, and each piece of data is tagged and identified.

[0796] Step 2:

[0797] The server analyzes the collected business data using natural language processing techniques. The input is the unified format data obtained in the previous step, and the output is a list of technical concepts. In this analysis, tools such as NLTK and the Sentiment Analysis API are used to extract important keywords and phrases from the text.

[0798] Step 3:

[0799] The server analyzes customer behavior and emotional data. Input is customer reaction data collected via cameras and microphones, and output is information related to the customer's emotional state and products that elicit positive responses. This step involves using an emotional analysis model, such as IBM Watson Tone Analyzer, to quantify the degree of positivity in the customer's emotions.

[0800] Step 4:

[0801] The server evaluates the intellectual property suitability of the analyzed technical concepts. The input is a list of technical concepts and customer sentiment data, and the output is a list of candidate concepts deemed suitable as intellectual property. Specifically, it uses machine learning algorithms such as Scikit-learn to evaluate novelty and inventiveness by comparing them with historical patent databases.

[0802] Step 5:

[0803] The server generates documents for patent applications. The input is a candidate technical concept that has passed suitability evaluation, and the output is a formal patent application document. In this step, a generative AI model is used to create prompts based on the given concepts and automatically generate the document accordingly.

[0804] Step 6:

[0805] The terminal displays the generated document to the user. The input is the patent application document, and the output is the document content on the user interface. The user uses this interface to review the content and perform specific actions such as approving or instructing revisions.

[0806] Step 7:

[0807] The user files an approved document with the patent office via the terminal. The input is the patent application document approved and corrected by the user, and the output is the digital document officially sent to the patent office. In this final step, the terminal's communication function is used to automatically perform the electronic filing process.

[0808] The specific processing unit 290 transmits the result of the specific processing to the robot 414. In the robot 414, the control unit 46A causes the speaker 240 and the controlled object 443 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0809] Data generation model 58 is a type of so-called generative AI (Artificial Intelligence). One example of data generation model 58 is ChatGPT (Internet search<URL: https: / / openai.com / blog / chatgpt> ), Gemini (Internet search) <url: https: gemini.google.com ?hl="ja">Examples of generative AI include the following. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and with inference data such as audio data representing speech, text data representing text, and image data representing images. The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference results in data formats such as audio data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[0810] In the above embodiment, an example was given in which the specific processing is performed by the data processing device 12, but the technology of this disclosure is not limited thereto, and the specific processing may also be performed by the robot 414.

[0811] Furthermore, the emotion identification model 59, acting as an emotion engine, may determine the user's emotion according to a specific mapping. Specifically, the emotion identification model 59 may determine the user's emotion according to a specific mapping, which is an emotion map (see Figure 9). Similarly, the emotion identification model 59 may also determine the robot's emotion, and the identification processing unit 290 may perform identification processing using the robot's emotion.

[0812] Figure 9 shows an emotion map 400 in which multiple emotions are mapped. In the emotion map 400, emotions are arranged in concentric circles radiating from the center. The closer to the center of the concentric circles, the more primitive the emotions are located. Further out of the concentric circles, emotions representing states and actions arising from mental states are located. Emotion is a concept that includes feelings and mental states. On the left side of the concentric circles, emotions that are generally generated from reactions occurring in the brain are located. On the right side of the concentric circles, emotions that are generally induced by situational judgment are located. Above and below the concentric circles, emotions that are generally generated from reactions occurring in the brain and induced by situational judgment are located. In addition, the emotion of "pleasure" is located on the upper side of the concentric circles, and the emotion of "displeasure" is located on the lower side. Thus, in the emotion map 400, multiple emotions are mapped based on the structure in which emotions arise, and emotions that are likely to occur simultaneously are mapped close together.

[0813] These emotions are distributed at the 3 o'clock position on the Emotion Map 400, and usually fluctuate between feelings of security and anxiety. In the right half of the Emotion Map 400, situational awareness takes precedence over internal feelings, resulting in a calm impression.

[0814] The inside of the Emotion Map 400 represents inner thoughts, while the outside represents actions. Therefore, the further you go from the outside of the Emotion Map 400, the more visible (expressed in actions) your emotions become.

[0815] Here, human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, it results in discomfort, and when they approach the ideal, it results in pleasure. Similarly, in robots, cars, motorcycles, etc., emotions can be created based on various balances, such as posture and battery level. When these balances deviate from the ideal, it results in discomfort, and when they approach the ideal, it results in pleasure. The emotion map can be generated, for example, based on Dr. Mitsuyoshi's emotion map (Research on a system for analyzing brain physiological signals of speech emotion recognition and emotion, Tokushima University, doctoral dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map contains emotions belonging to a region called "response," where sensation is dominant. The right half of the emotion map contains emotions belonging to a region called "situation," where situational awareness is dominant.

[0816] The emotion map defines two emotions that promote learning. One is the emotion around the middle of the negative "repentance" and "reflection" on the situation side. In other words, it is when the robot experiences negative emotions such as "I never want to feel this way again" or "I don't want to be scolded again." The other is the emotion around the positive "desire" on the reaction side. In other words, it is when the robot has positive feelings such as "I want more" or "I want to know more."

[0817] The emotion identification model 59 inputs user input into a pre-trained neural network, obtains emotion values ​​representing each emotion shown in the emotion map 400, and determines the user's emotion. This neural network is pre-trained based on multiple training data sets, which are combinations of user input and emotion values ​​representing each emotion shown in the emotion map 400. Furthermore, this neural network is trained so that emotions located close together have similar values, as shown in the emotion map 900 in Figure 10. Figure 10 shows an example where multiple emotions such as "reassured," "calm," and "confident" have similar emotion values.

[0818] The above description primarily focuses on the functions of the data processing device 12 in relation to this disclosure. However, the system related to this disclosure is not necessarily implemented on a server. The system related to this disclosure may be implemented as a general information processing system. This disclosure may be implemented, for example, as a software program that runs on a personal computer or as an application that runs on a smartphone. The method related to this disclosure may be provided to users in SaaS (Software as a Service) format.

[0819] In the above embodiment, an example was given in which a specific process is performed by a single computer 22. However, the technology of this disclosure is not limited thereto, and a distributed processing of the specific process may be performed by multiple computers, including computer 22. For example, a data generation model 58 may be provided in an external device of the data processing device 12, and the external device may generate data according to the input data.

[0820] In the above embodiment, an example was given in which the specific processing program 56 is stored in the storage 32, but the technology of this disclosure is not limited thereto. For example, the specific processing program 56 may be stored in a portable, computer-readable, non-temporary storage medium such as a USB (Universal Serial Bus) memory. The specific processing program 56 stored in the non-temporary storage medium is installed in the computer 22 of the data processing device 12. The processor 28 executes specific processing according to the specific processing program 56.

[0821] Alternatively, the specific processing program 56 may be stored in a storage device such as a server connected to the data processing device 12 via the network 54, and the specific processing program 56 may be downloaded and installed on the computer 22 in response to a request from the data processing device 12.

[0822] Furthermore, it is not necessary to store the entirety of the specific processing program 56 in a storage device such as a server connected to the data processing device 12 via the network 54, or to store the entirety of the specific processing program 56 in the storage 32; it is acceptable to store only a portion of the specific processing program 56.

[0823] The following types of processors can be used as hardware resources to perform specific processing. Examples of processors include a CPU, a general-purpose processor that functions as a hardware resource to perform specific processing by executing software, i.e., a program. Other examples of processors include dedicated electrical circuits, such as FPGAs (Field-Programmable Gate Arrays), PLDs (Programmable Logic Devices), or ASICs (Application Specific Integrated Circuits), which have circuit configurations specifically designed to perform specific processing. All of these processors have built-in or connected memory, and all of them perform specific processing by using memory.

[0824] The hardware resource that performs a specific process may consist of one of these various processors, or it may consist of a combination of two or more processors of the same or different types (for example, a combination of multiple FPGAs, or a combination of a CPU and an FPGA). Alternatively, the hardware resource that performs a specific process may consist of a single processor.

[0825] Examples of configurations using a single processor include, firstly, a configuration in which one or more CPUs and software are combined to form a single processor, and this processor functions as a hardware resource that performs a specific process. Secondly, there is a configuration using a processor that realizes the functions of the entire system, including multiple hardware resources that perform a specific process, on a single IC chip, as exemplified by SoCs (System-on-a-chip). In this way, a specific process is realized using one or more of the above types of processors as hardware resources.

[0826] Furthermore, the hardware structure of these various processors can more specifically utilize electrical circuits that combine circuit elements such as semiconductor devices. Also, the specific processing described above is merely an example. Therefore, it goes without saying that unnecessary steps can be deleted, new steps added, or the processing order rearranged, as long as it does not deviate from the main purpose.

[0827] The descriptions and illustrations presented above are detailed explanations of the technical aspects of this disclosure and are merely examples of the technical aspects. For example, the above descriptions of the structure, function, operation, and effect are examples of the structure, function, operation, and effect of the technical aspects of this disclosure. Therefore, it goes without saying that you may delete unnecessary parts, add new elements, or replace elements in the descriptions and illustrations presented above, as long as you do not deviate from the essence of the technical aspects of this disclosure. Furthermore, in order to avoid confusion and facilitate understanding of the technical aspects of this disclosure, explanations of common technical knowledge and the like that do not require special explanation to enable the implementation of the technical aspects of this disclosure have been omitted from the descriptions and illustrations presented above.

[0828] All documents, patent applications, and technical standards described herein are incorporated by reference to the same extent as if each individual document, patent application, and technical standard were specifically and individually noted to be incorporated by reference.

[0829] The following is further disclosed regarding the embodiments described above.

[0830] (Claim 1)

[0831] Means for analyzing business information,

[0832] A means of extracting technical ideas from analyzed information,

[0833] A means for evaluating the patentability of extracted ideas,

[0834] A means for generating documents for a patent application based on an evaluation,

[0835] Means for managing the review and approval of generated documents,

[0836] A system that includes a means for automatically filing applications with the Japan Patent Office.

[0837] (Claim 2)

[0838] The system according to claim 1, which analyzes business information using natural language processing technology.

[0839] (Claim 3)

[0840] The system according to claim 1, which uses a machine learning algorithm to identify and prioritize patentable ideas.

[0841] "Example 1"

[0842] (Claim 1)

[0843] Means of collecting business information,

[0844] A means of analyzing collected information using natural language processing technology to extract technical problems and technical ideas,

[0845] A means of evaluating patentability by comparing extracted ideas with an external database,

[0846] A means for automatically generating documents for patent applications based on evaluation,

[0847] A means of presenting the generated document on the user terminal and managing the review and approval process,

[0848] A system that includes means for automatically filing approved documents with the patent office.

[0849] (Claim 2)

[0850] The system according to claim 1, which applies a machine learning algorithm to analyze business information and identify potential technical ideas.

[0851] (Claim 3)

[0852] The system according to claim 1, which generates prompt messages on the user terminal and presents information so that the user can evaluate the technical value of the extracted ideas.

[0853] "Application Example 1"

[0854] (Claim 1)

[0855] Means for analyzing business information,

[0856] A means of extracting technical ideas from analyzed information,

[0857] A means for evaluating the patentability of extracted ideas,

[0858] A means for generating documents for a patent application based on an evaluation,

[0859] Means for managing the review and approval of generated documents,

[0860] A means of automatically filing an application with the Japan Patent Office,

[0861] A means of evaluating technical ideas and transmitting those ideas to a company's data processing system through an application installed on a portable device,

[0862] A means for analyzing input information using natural language processing technology and evaluating its novelty by comparing it with existing databases,

[0863] A system that includes this.

[0864] (Claim 2)

[0865] The system according to claim 1, which analyzes business information using natural language processing technology and provides evaluation results immediately via a portable device.

[0866] (Claim 3)

[0867] The system according to claim 1, which uses a machine learning algorithm to identify potentially patentable ideas and transfers them to an internal intellectual asset management system for prioritization.

[0868] "Example 2 of combining an emotion engine"

[0869] (Claim 1)

[0870] Methods for analyzing business data,

[0871] A means of extracting innovative ideas from the analyzed data,

[0872] A means for evaluating innovative proposals extracted using an emotion analysis method that evaluates the user's emotions,

[0873] A means of using machine learning to analyze the novelty of an innovative idea and determine its patentability,

[0874] A means for automatically generating documents for patent applications,

[0875] A means of presenting and managing approval of generated documents through an operating device,

[0876] A system that includes means for automatically submitting approved documents to the patent office.

[0877] (Claim 2)

[0878] The system according to claim 1, which analyzes business data using natural language processing technology.

[0879] (Claim 3)

[0880] The system according to claim 1, which uses machine learning technology to identify patentable innovations and prioritizes them based on the results of sentiment analysis.

[0881] "Application example 2 when combining with an emotional engine"

[0882] (Claim 1)

[0883] Methods for analyzing business data,

[0884] A means of extracting technical concepts from analyzed data,

[0885] A means of evaluating the intellectual property suitability of the extracted concepts,

[0886] A means for generating documents for intellectual property applications based on evaluation,

[0887] Means for managing the review and approval of generated documents,

[0888] A means of automatically filing applications with intellectual property institutions,

[0889] A system that includes means for analyzing customer behavior and emotions and collecting concepts related to products that elicit positive responses.

[0890] (Claim 2)

[0891] The system according to claim 1, which analyzes business data using natural language processing technology to analyze customer emotional responses.

[0892] (Claim 3)

[0893] The system according to claim 1, which uses a machine learning algorithm to identify and prioritize concepts with intellectual property potential and highlight positive concepts based on customer emotional responses. [Explanation of symbols]

[0894] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Devices 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robots< / url:> < / url:> < / url:> < / url:>

Claims

1. Means for analyzing business information, A means of extracting technical ideas from analyzed information, A means for evaluating the patentability of extracted ideas, A means for generating documents for a patent application based on an evaluation, Means for managing the review and approval of generated documents, A means of automatically filing an application with the Japan Patent Office, A means of evaluating technical ideas and transmitting those ideas to a company's data processing system through an application installed on a portable device, A means for analyzing input information using natural language processing technology and evaluating its novelty by comparing it with existing databases, A system that includes this.

2. The system according to claim 1, which analyzes business information using natural language processing technology and provides evaluation results immediately via a portable device.

3. The system according to claim 1, which uses a machine learning algorithm to identify potentially patentable ideas and transfers them to an internal intellectual asset management system for prioritization.

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