system

Abstract

We provide the system. [Solution] A means for generating technical concepts based on input ideas using a natural language processing model, A means for obtaining existing patent information related to a technical concept entered from a patent database, Means for generating a new technical concept that avoids the aforementioned existing patent information, A system including means for proposing the aforementioned new technological concept to a user.

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: receiving a user utterance; adding the user utterance to a prompt including an instruction sentence related to an explanation of a character of the chatbot; encoding the prompt; and inputting the encoded prompt into a language model to generate a chatbot utterance in response to the user utterance.

Prior Art Documents

Patent Documents

[0003]

Patent Document 1

Summary of the Invention

Problems to be Solved by the Invention

[0004] The process of devising a patent idea is complex and time-consuming, and a great deal of effort is required to create a unique technical concept that does not conflict with existing patents. Therefore, it is an issue to improve the efficiency in idea generation and accelerate technological innovation within a company.

Means for Solving the Problems

[0005] This invention provides a system that generates non-competitive new technical concepts by using a natural language processing model to generate technical concepts based on input ideas and by comparing these technical concepts with existing patent information through a patent database. Furthermore, by incorporating a function to analyze user-provided materials and automatically generate technical concepts based on the results, the system significantly improves the efficiency involved in patent idea generation.

[0006] A "natural language processing model" is a machine learning algorithm or framework that enables computers to understand and generate natural language.

[0007] A "technical concept" refers to the basic ideas and components of a newly conceived idea or invention in a specific technological field.

[0008] A "patent database" is a digital information repository where existing patent information is stored, and it is used to search for and retrieve publicly available patent documents.

[0009] "Existing patent information" refers to detailed information about patents that have already been published and are registered in the patent database.

[0010] "Means for generating technical concepts" refer to methods and devices used to create new technical ideas based on input ideas and information.

[0011] "Means of proposing a technical concept" refers to methods or tools for displaying or notifying users of the generated technical idea.

[0012] "Analysis" refers to the process of breaking down a specific object into smaller parts and analyzing its properties and structure in detail.

[0013] "Means for evaluating inventiveness" refer to methods and criteria for evaluating how much a newly created technological concept has evolved compared to existing technologies. [Brief explanation of the drawing]

[0014] [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 a plurality of emotions are mapped. [Figure 10] It shows an emotion map to which a plurality of emotions are mapped. [Figure 11] It is a sequence diagram showing the processing flow of the data processing system in Example 1. [Figure 12] It is a sequence diagram showing the processing flow of the data processing system in Application Example 1. [Figure 13] It is a sequence diagram showing the processing flow of the data processing system in Example 2 when an emotion engine is combined. [Figure 14] It is a sequence diagram showing the processing flow of the data processing system in Application Example 2 when an emotion engine is combined.

Mode for Carrying Out the Invention

[0015] Hereinafter, an example of an embodiment of a system according to the technology of the present disclosure will be described with reference to the accompanying drawings.

[0016] First, the terms used in the following description will be explained.

[0017] 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 a CPU (Central Processing Unit), a GPU (Graphics Processing Unit), a GPGPU (General-Purpose computing on Graphics Processing Units), an APU (Accelerated Processing Unit), and the like.

[0018] 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.

[0019] In the following embodiments, the numbered storage is one or more non-volatile storage devices that store various programs and various parameters, etc. Examples of non-volatile storage devices include flash memory (SSD (Solid State Drive)), magnetic disks (e.g., hard disks), or magnetic tapes, and the like.

[0020] 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).

[0021] 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."

[0022] [First Embodiment]

[0023] Figure 1 shows an example of the configuration of the data processing system 10 according to the first embodiment.

[0024] 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.

[0025] 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).

[0026] 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.

[0027] 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.

[0028] 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.

[0029] 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.

[0030] Figure 2 shows an example of the main functions of the data processing device 12 and the smart device 14.

[0031] 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.

[0032] 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.

[0033] 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.

[0034] 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".

[0035] This invention is a system for efficiently generating patent ideas, combining a natural language processing model with patent database search. Specific embodiments are described below.

[0036] This system consists primarily of a user, a terminal, and a server. The user uses the terminal to input ideas and technical concepts. The terminal sends this information to the server, which then searches a patent database to retrieve existing patent information.

[0037] A natural language processing model on the server generates new technological concepts while avoiding existing patent information. This generation process evaluates novelty based on the input ideas, considering context and relevance. The generated technological concepts are then sent from the server to the terminal and presented to the user.

[0038] Furthermore, by uploading business documents provided by the user from their terminal to the server, a natural language processing model analyzes the content of the documents. This automatically generates technical concepts based on the documents and proposes them to the user. As a result, the user can obtain many technical concepts without any effort.

[0039] For example, if a user is thinking of a new digital pen technology concept, they input an outline of it into the device. The server receives the input, searches the patent database, and retrieves relevant technical information. A natural language processing model avoids existing digital pen patent information and devises a new proposal, such as a technology that enhances AI-powered handwriting recognition, and presents it to the user. Through this process, users can efficiently obtain new patent ideas.

[0040] The following describes the processing flow.

[0041] Step 1:

[0042] The user uses a device to input ideas or keywords related to patents. The device receives this input and prepares to send the data to the server.

[0043] Step 2:

[0044] The terminal converts the user's input into a predetermined format and sends it to the server. The server receives the data.

[0045] Step 3:

[0046] The server connects to the patent database and searches for existing patent information based on the entered keywords. The server finds relevant patents and collects the information.

[0047] Step 4:

[0048] The server passes existing patent information it has acquired to a natural language processing model. The model analyzes the data and generates new, non-competitive technology concepts.

[0049] Step 5:

[0050] The server converts the newly generated technological concept into a form that is easy for the user to understand and sends it to the terminal.

[0051] Step 6:

[0052] The terminal displays new technology concepts received from the server to the user. The user reviews the proposals and considers the possibilities of new ideas.

[0053] Step 7:

[0054] Users can optionally upload work documents to their devices and send them to the server. The server receives the documents and uses a natural language processing model to analyze their content.

[0055] Step 8:

[0056] Based on the analysis of the data, the server automatically generates a new patent proposal and presents it to the user again via the terminal.

[0057] (Example 1)

[0058] 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."

[0059] The problem this invention aims to solve is to efficiently and effectively create novel technical concepts and propose them to users based on diverse technical information. Furthermore, it aims to increase the likelihood of obtaining patents by generating technical concepts based on materials provided by users while avoiding existing patent information.

[0060] 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.

[0061] In this invention, the server includes means for generating a technical concept based on input information using a natural language processing model, means for generating a highly novel technical concept based on relevant information obtained from a database, and means for analyzing relevant data based on the obtained information and presenting the generated technical concept to the user. This makes it possible for the user to efficiently acquire new patent ideas and consider their patentability.

[0062] A "natural language processing model" is an artificial intelligence technology that analyzes digital text data, understands its meaning from its context, and generates appropriate technical concepts.

[0063] A "technical concept" is a conceptual explanation or proposal regarding a specific idea or technology, and includes innovative ideas that possess novelty and usefulness.

[0064] A "database" is an information management system in which patent information and technical information are systematically stored, and data can be quickly searched and retrieved as needed.

[0065] "Novelty" refers to the ability to propose value that is differentiated from others by possessing unique characteristics and elements not found in existing technologies or information.

[0066] "Analysis" is the process of thoroughly investigating and breaking down information and data to derive useful knowledge and conclusions from it.

[0067] "Related data" refers to information that is related to user input or information obtained from databases, and is useful for generating and evaluating technical concepts.

[0068] This invention is a system that enables the efficient generation of new technological concepts and mainly consists of three components: a server, a terminal, and a user. The user inputs technological ideas and concepts into the terminal and sends them to the server. The server is equipped with a natural language processing model and generates new technological concepts based on information obtained from the database and the user's input information. Furthermore, the server analyzes the materials provided by the user and uses the analysis results to generate technological concepts.

[0069] The server uses a natural language processing model to analyze input technical ideas and evaluate the novelty and inventiveness of the technical concepts. The generated technical concepts are proposed to the user, based on the principle of avoiding existing patent information obtained from patent databases. This allows the user to quickly and easily obtain new patent ideas.

[0070] The terminal has the functionality to receive user input information in digital format and upload it to the server. For example, if a user inputs "I want to think of a new digital pen technology concept" as a prompt, the server will search a patent database, analyze relevant patent information, and generate a new technology proposal while considering existing information. A concrete example of a prompt might be a question like, "How can this technology be further developed?"

[0071] In short, this system allows users to efficiently increase their chances of obtaining patents by providing ideas through a terminal, and the server generating and proposing new technologies based on those ideas. Furthermore, the accuracy and effectiveness of the entire system can be enhanced by utilizing natural language processing models and database search technologies.

[0072] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0073] Step 1:

[0074] Users input new technical ideas and concepts through their terminals. The input is in text format and can include specific technical requirements and an outline of the idea. This input is converted into digital data when sent to the server.

[0075] Step 2:

[0076] The terminal encrypts user input data and sends it to the server while maintaining security. This process ensures that user ideas are safely delivered to the server. The input is the user's ideas, and the output is the secure transfer of data to the server.

[0077] Step 3:

[0078] The server analyzes the received digital data and searches the patent database. This operation retrieves existing patent information related to the input data. The retrieved information is stored in the server's data cache. The input is the user's idea, and the output is the relevant patent information.

[0079] Step 4:

[0080] The server uses a natural language processing model to compare existing patent information in the data cache with the user's input ideas and generate new technological concepts. This model evaluates novelty based on the input information and generates new technological proposals as output.

[0081] Step 5:

[0082] The server re-analyzes the generated new technology concept to verify its practicality and patentability. This includes evaluating its inventiveness by comparing it with existing information in the database. The input is the generated technology concept, and the output is the evaluated technology proposal.

[0083] Step 6:

[0084] The server sends evaluated technical proposals to the terminal and presents them to the user. The user reviews the proposals through the terminal and decides whether to further develop their own technical ideas. The input is the evaluated technical proposals, and the output is the information presented to the user.

[0085] Step 7:

[0086] If necessary, users upload business documents from their terminals to the server. The server analyzes the content of the documents and generates new, relevant technical concepts. Through this process, the input documents become the input for the technical concepts, and new proposals are generated as output.

[0087] This entire process allows users to quickly and safely generate new patent ideas and proceed with development based on them.

[0088] (Application Example 1)

[0089] 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."

[0090] In the modern era, efficiently generating and realizing ideas for mechanical devices tailored to individualized lifestyles and home environments is a major challenge. Behind this challenge lies the need to discover original technological concepts while avoiding existing patent information. In particular, developing innovative mechanical devices that address specific needs in daily life at home is crucial for enriching users' lives.

[0091] 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.

[0092] In this invention, the server includes means for generating technical concepts based on input ideas using a natural language processing model, means for retrieving existing patent information related to the input technical concepts from a patent database, means for generating new technical concepts that circumvent existing patent information, and means for generating concepts of unique mechanical devices based on specific needs within the home. This enables users to efficiently obtain ideas for unique mechanical devices suitable for their homes.

[0093] A "natural language processing model" is an algorithm that analyzes text data, understands the meaning and context of language, and generates new information and ideas.

[0094] A "technological concept" refers to a new idea, method, or structure that can be implemented in a specific technological field.

[0095] A "patent database" is an information system that collects and provides searchable information on past and present patents.

[0096] "Means of obtaining existing patent information" refers to the process of searching for relevant technical information and documents from patent databases and utilizing their contents.

[0097] "Means for generating new technological concepts that circumvent existing patent information" refers to methods for creating novel technological ideas that do not infringe on known technologies.

[0098] "A means of generating the concept of unique mechanical devices based on specific needs within the home" refers to the process of developing original mechanical device ideas tailored to individual home environments and daily life.

[0099] "Means of proposal" refers to methods for effectively presenting generated technical concepts and ideas to users.

[0100] The system that realizes this invention is structured around three elements: a server, a terminal, and a user.

[0101] First, the user inputs ideas and technical concepts on a device such as a smartphone. The device then transmits this information to a server. The server first retrieves existing patent information related to the input technical concept using a patent database.

[0102] Next, a natural language processing model installed on the server (specifically, a model using the Transformers library) is used to generate new technological concepts based on the acquired patent information. In this process, various data analysis methods are used to avoid existing patents and create original technological ideas.

[0103] Furthermore, this system develops unique mechanical device concepts tailored to specific needs within the home. In other words, users can materialize ideas for mechanical devices suited to their lifestyle and environment with the assistance of natural language processing models.

[0104] The generated technical concepts are sent from the server to the terminal and presented to the user. For example, if a user has an idea for a "breakfast preparation robot," the server receives this idea and generates a new technical proposal—for instance, a concept for a robot with new functions that correspond to existing coffee makers and toasters. In this way, users can efficiently obtain innovative technical ideas that meet their household needs.

[0105] Examples of prompt statements are as follows:

[0106] "User: I have a new idea for a breakfast preparation robot. I want a robot that pours coffee and toasts bread. Are there any new technological concepts related to this functionality?"

[0107] Based on this user input, the system will propose appropriate new technologies.

[0108] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0109] Step 1:

[0110] Users input ideas and technical concepts into a device such as a smartphone. The input data is saved on the device as text data in natural language format. This data includes the functions and features of specific mechanical devices conceived by the user.

[0111] Step 2:

[0112] The terminal sends the entered text data to the server. Internet communication is used for data transmission, ensuring rapid data transfer from the terminal to the server.

[0113] Step 3:

[0114] The server forms a query to search the patent database based on the received data. The search query includes keywords related to the user's idea, which are used to retrieve existing patent information from the database.

[0115] Step 4:

[0116] A server retrieves relevant existing patent information from a patent database and analyzes the data using a natural language processing model. The analysis uses the Transformer library to evaluate context and relevance for generating new technological concepts that circumvent existing patents.

[0117] Step 5:

[0118] The server generates unique technological concepts based on specific needs within the home. Here, a natural language processing model uses user input data and information obtained from a patent database to form specific and novel proposals.

[0119] Step 6:

[0120] The generated new technology concept is sent from the server to the terminal. The user can receive and review this proposal on the terminal. The data presented here plays a crucial role in confirming whether the newly generated technology concept reliably meets the user's needs.

[0121] Step 7:

[0122] Users can work on specific projects and development based on the technical concepts generated through their devices. At this stage, they examine the alignment between the proposed technology and real-world needs, and work to form the final idea.

[0123] 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.

[0124] This invention combines a patent idea generation system with an emotion engine, aiming to optimize the proposed technical concepts according to the user's emotional state. Specific embodiments are described below.

[0125] This system consists primarily of a user, a terminal, a server, and an emotion engine. When a user uses the terminal to consult about a patent idea, the terminal sends the information to the server. The server searches the patent database and retrieves existing relevant patent information.

[0126] The natural language processing model embedded in the server generates new technological concepts that avoid the acquired patent information. Here, the server utilizes an emotion engine to recognize the user's emotional state. The emotion engine determines the emotional state through analysis of the user's input methods and responses, and adjusts the suggested content based on that information.

[0127] For example, if a user is experiencing stress during the idea generation process, the emotion engine will detect this, and the server will adjust the suggestions to be simpler and easier to understand. Conversely, if the user is relaxed, the system will optimize based on their emotional state, such as by increasing the number of innovative and complex suggestions.

[0128] As a concrete example, suppose a user is thinking about a new communication device technology. Based on the idea entered from the terminal, the server searches a patent database and analyzes relevant patents. While a natural language processing model generates a new technology to circumvent these, an emotion engine analyzes the user's reaction. For example, if the user smiles and says, "This idea might be great," the emotion engine receives this positive feedback and makes a proposal that includes detailed technical specifications. This entire process enables personalized technology proposals that respond to the user's emotions.

[0129] This system is an innovative solution that reduces the burden on users and generates patent ideas efficiently and effectively.

[0130] The following describes the processing flow.

[0131] Step 1:

[0132] The user enters their patent idea inquiry into a terminal. The terminal receives the input data and prepares to send it to the server.

[0133] Step 2:

[0134] The terminal formats the input data and sends it to the server. The server receives the data.

[0135] Step 3:

[0136] The server connects to a patent database and searches for existing patent information related to the entered idea. It retrieves the relevant patent information and saves it as data.

[0137] Step 4:

[0138] The server uses a natural language processing model to generate new technology concepts that circumvent existing patent information obtained. The generated technology concepts are then saved.

[0139] Step 5:

[0140] The server uses an emotion engine to analyze the user's input methods and responses to recognize their emotional state. For example, the emotion engine determines the user's emotions based on the speed at which the user operates the device and the linguistic characteristics of their input.

[0141] Step 6:

[0142] The server adjusts the technical concepts it generates based on information from the emotion engine, according to the user's emotional state. For example, if the user is stressed, the server simplifies the technical concepts to make them easier to understand. Conversely, if positive emotions are recognized, it adds more detailed suggestions.

[0143] Step 7:

[0144] The server sends the revised technical concept to the terminal. The terminal receives it and displays it to the user. The user reviews the proposal and provides feedback.

[0145] Step 8:

[0146] Based on user feedback, the server works with an emotion engine to generate further optimized technical concepts and repeats a cycle of making new suggestions.

[0147] (Example 2)

[0148] 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".

[0149] Conventional technology concept generation systems make technical suggestions without considering the user's emotional state, resulting in uniform suggestions that are unaffected by changes in the user's emotions, such as stress or relaxation. This makes it difficult to generate optimal suggestions tailored to the user's situation and to create creative technology concepts.

[0150] 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.

[0151] In this invention, the server includes means for generating technical concepts based on input ideas using a natural language processing algorithm, means for acquiring existing technical information related to the input technical concepts from a database, and means for recognizing the user's emotional state using an emotion analysis algorithm and adjusting the new technical concepts. This enables optimal suggestions and efficient generation of technical concepts that are tailored to the user's emotional state.

[0152] A "natural language processing algorithm" is a technology that enables computers to understand, analyze, and generate human language.

[0153] A "technical concept" refers to an idea or plan aimed at innovation or improvement in a specific technological field.

[0154] A "database" is a collection of information designed to allow for the efficient searching and retrieval of large amounts of data.

[0155] An "emotion analysis algorithm" is a technology for detecting and analyzing a user's emotional state, inferring emotions based on input data and the user's responses.

[0156] A "user" is someone who operates this system and receives technical suggestions.

[0157] This invention is a system composed of three main elements: a user, a terminal, and a server, designed to support the creation of patentable ideas. The user inputs technical ideas using the terminal, and this information is sent to the server as prompt messages. These prompt messages are analyzed by a natural language processing algorithm and used to generate new technical concepts.

[0158] The server accesses a database that stores existing technical information and retrieves existing technical information related to the ideas provided by the user. This retrieved information serves as important foundational data in the subsequent process of generating technical concepts. The sentiment analysis algorithm built into the server evaluates the user's emotional state and adjusts the technical suggestions to the user so that they are optimized according to the user's emotions.

[0159] As a concrete example, consider a scenario where a user is brainstorming ideas for new communication technologies. The user inputs a specific technology idea on their device, and this information is sent to a server. The server retrieves relevant technology information from its database and uses a natural language processing algorithm to construct a new technology concept based on this information. Simultaneously, a sentiment analysis algorithm analyzes the user's response and adjusts the proposed solution. For example, if the user gives a positive response such as "This idea might be great," the server will provide a proposal that includes detailed technology specifications.

[0160] An example of a prompt message would be, "I'm thinking of ideas for new communication technologies. Please tell me how to reduce stress and get innovative suggestions."

[0161] This system is an innovative means of providing users with higher-quality, personalized technology suggestions by combining generative AI models with sentiment analysis. Users can receive optimized suggestions tailored to their emotions, supporting the efficient generation of patent ideas.

[0162] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0163] Step 1:

[0164] The user uses a terminal to input prompts related to their patent idea. The terminal formats the input prompts as text data and sends them to the server. The input includes the technical field and keywords the user is considering, and the output is sent to the server as text data.

[0165] Step 2:

[0166] The server receives a prompt message sent from the terminal. Based on this data, the server uses a natural language processing algorithm to begin an initial analysis of the technical concept. The input is text data that serves as the prompt message, and the output generates summarized technical keywords and concepts.

[0167] Step 3:

[0168] The server uses the generated technical keywords to query its internal database and retrieve relevant existing technical information. The input is the parsed keywords, and the output is a list of relevant technical information or a technical summary. By executing database queries, information is collected quickly and accurately.

[0169] Step 4:

[0170] The server uses natural language processing algorithms to generate new technical concepts based on the acquired existing technical information. In this step, the existing input information and keywords from the prompt are integrated to output novel technical concepts while avoiding existing technologies.

[0171] Step 5:

[0172] The sentiment analysis algorithm built into the server analyzes the writing style of prompts and user interactions to evaluate the user's emotional state. Using prompts and response data obtained from the user interface as input, it generates the current emotional state as output. This allows for the optimization of suggested content.

[0173] Step 6:

[0174] The server optimizes new technical concepts based on the results of sentiment analysis and generates adjusted proposals. Because adjustments are made taking emotional states into account, for example, if the user is experiencing stress, a clear and simple technical proposal will be provided. An optimized technical proposal based on the input emotional data is generated as output.

[0175] Step 7:

[0176] The server sends the finalized technical proposal to the terminal and provides it to the user. The terminal receives this proposal and displays it to the user. The input is an optimized technical concept, and the output is a technical proposal presented to the user in an easy-to-understand format. This allows the user to use the proposal as a reference for further creative activities.

[0177] (Application Example 2)

[0178] 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".

[0179] When generating patent ideas, users often face complex technical information and large amounts of patent data, leading to stress. Furthermore, effectively developing ideas requires optimized suggestions tailored to the user's emotional state. However, current systems struggle to provide flexible technical suggestions that respond to user emotions and circumstances, failing to fully address individual user needs.

[0180] 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.

[0181] In this invention, the server includes means for generating technical concepts based on input ideas using a natural language processing model, means for acquiring existing information related to the input technical concepts from an information source database, means for generating new technical concepts that avoid the existing information, means for recognizing the user's emotional state and adjusting the technical concepts accordingly, and means for proposing the adjusted new technical concepts to the user. This enables more personalized technical suggestions that are tailored to the user's emotional state.

[0182] A "natural language processing model" is an artificial intelligence technology that enables computers to analyze and generate human language by understanding and generating text data.

[0183] A "source information database" is a digital repository that stores and makes searchable a variety of existing technical data, including patent information.

[0184] A "technical concept" is a new technical idea or component devised to solve a specific technical problem.

[0185] "Means for recognizing emotional states and adjusting technical concepts accordingly" refers to a function that judges emotions from the user's facial expressions and voice, and provides optimized technical suggestions according to that emotional state.

[0186] "Proposed means" refers to methods and processes for presenting generated technical concepts to users and supporting their understanding and decision-making.

[0187] This system consists primarily of a user, a terminal, a server, and an emotion engine. When a user inputs a new patent idea via the terminal, that data is sent to the server. The server incorporates a natural language processing model that generates technical concepts based on the input idea.

[0188] The server first retrieves relevant existing technical information from a database of information sources. Based on this data, it generates new technical concepts, adjusting the process to avoid using existing information. A generative AI model is used at this stage.

[0189] Furthermore, the server uses an emotion engine to analyze the user's current emotional state. This analysis is performed by sensing voice and facial expression data through the interface provided by the terminal and recognizing the emotional state. Based on the recognition results, the server adjusts the technical concept to the optimal form according to the user's emotions and then proposes it to the user.

[0190] As a specific example of its use, if a user is considering new ideas regarding energy efficiency, the server, upon receiving the query, will retrieve relevant existing technical information and, if the user is in a positive emotional state, generate and present more complex and technically advanced proposals.

[0191] Examples of prompt messages are as follows:

[0192] "A user is brainstorming ideas for new features in home appliances. Based on the sentiment analysis results, please suggest some ideas. The user's expression is smiling."

[0193] This system allows users to receive more personalized suggestions that take their emotional state into account, enabling them to efficiently generate new patent ideas.

[0194] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0195] Step 1:

[0196] The user inputs a new idea into the device. The device then sends the input data to the server. Voice input and text input are possible, and this becomes the basic data for the user's idea.

[0197] Step 2:

[0198] The server searches its information source database for relevant existing information based on the received data. Input: Idea data received from the user. Output: Relevant existing information. The server uses this information to prepare to generate new technical concepts.

[0199] Step 3:

[0200] The server's natural language processing model generates new technological concepts while avoiding existing information. Input: Existing data and user ideas. Output: New technological concepts. The server uses its generative AI model to make novel proposals that meet the user's requirements.

[0201] Step 4:

[0202] The device records the user's voice and facial expressions and sends them to the server. This generates user emotion data, which can then be analyzed by an emotion engine. Input: User's voice and facial expression data. Output: Emotional state.

[0203] Step 5:

[0204] The server uses an emotion engine to analyze the user's emotional state. Input: Voice and facial expression data. Output: User's emotional state. The server adjusts its suggestions based on this emotional state.

[0205] Step 6:

[0206] The server proposes a refined technical concept to the user. Input: New technical concept and the user's emotional state. Output: The refined technical concept presented to the user. This final proposal is presented in the form that best suits the user.

[0207] 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.

[0208] 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.

[0209] 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.

[0210] [Second Embodiment]

[0211] Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment.

[0212] 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.

[0213] 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).

[0214] 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.

[0215] 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.

[0216] 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).

[0217] 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.

[0218] 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.

[0219] 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.

[0220] 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.

[0221] 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.

[0222] 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".

[0223] This invention is a system for efficiently generating patent ideas, combining a natural language processing model with patent database search. Specific embodiments are described below.

[0224] This system consists primarily of a user, a terminal, and a server. The user uses the terminal to input ideas and technical concepts. The terminal sends this information to the server, which then searches a patent database to retrieve existing patent information.

[0225] A natural language processing model on the server generates new technological concepts while avoiding existing patent information. This generation process evaluates novelty based on the input ideas, considering context and relevance. The generated technological concepts are then sent from the server to the terminal and presented to the user.

[0226] Furthermore, by uploading business documents provided by the user from their terminal to the server, a natural language processing model analyzes the content of the documents. This automatically generates technical concepts based on the documents and proposes them to the user. As a result, the user can obtain many technical concepts without any effort.

[0227] For example, if a user is thinking of a new digital pen technology concept, they input an outline of it into the device. The server receives the input, searches the patent database, and retrieves relevant technical information. A natural language processing model avoids existing digital pen patent information and devises a new proposal, such as a technology that enhances AI-powered handwriting recognition, and presents it to the user. Through this process, users can efficiently obtain new patent ideas.

[0228] The following describes the processing flow.

[0229] Step 1:

[0230] The user uses a device to input ideas or keywords related to patents. The device receives this input and prepares to send the data to the server.

[0231] Step 2:

[0232] The terminal converts the user's input into a predetermined format and sends it to the server. The server receives the data.

[0233] Step 3:

[0234] The server connects to the patent database and searches for existing patent information based on the entered keywords. The server finds relevant patents and collects the information.

[0235] Step 4:

[0236] The server passes existing patent information it has acquired to a natural language processing model. The model analyzes the data and generates new, non-competitive technology concepts.

[0237] Step 5:

[0238] The server converts the newly generated technological concept into a form that is easy for the user to understand and sends it to the terminal.

[0239] Step 6:

[0240] The terminal displays new technology concepts received from the server to the user. The user reviews the proposals and considers the possibilities of new ideas.

[0241] Step 7:

[0242] Users can optionally upload work documents to their devices and send them to the server. The server receives the documents and uses a natural language processing model to analyze their content.

[0243] Step 8:

[0244] Based on the analysis of the data, the server automatically generates a new patent proposal and presents it to the user again via the terminal.

[0245] (Example 1)

[0246] 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."

[0247] The problem this invention aims to solve is to efficiently and effectively create novel technical concepts and propose them to users based on diverse technical information. Furthermore, it aims to increase the likelihood of obtaining patents by generating technical concepts based on materials provided by users while avoiding existing patent information.

[0248] 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.

[0249] In this invention, the server includes means for generating a technical concept based on input information using a natural language processing model, means for generating a highly novel technical concept based on relevant information obtained from a database, and means for analyzing relevant data based on the obtained information and presenting the generated technical concept to the user. This makes it possible for the user to efficiently acquire new patent ideas and consider their patentability.

[0250] A "natural language processing model" is an artificial intelligence technology that analyzes digital text data, understands its meaning from its context, and generates appropriate technical concepts.

[0251] A "technical concept" is a conceptual explanation or proposal regarding a specific idea or technology, and includes innovative ideas that possess novelty and usefulness.

[0252] A "database" is an information management system in which patent information and technical information are systematically stored, and data can be quickly searched and retrieved as needed.

[0253] "Novelty" refers to the ability to propose value that is differentiated from others by possessing unique characteristics and elements not found in existing technologies or information.

[0254] "Analysis" is the process of thoroughly investigating and breaking down information and data to derive useful knowledge and conclusions from it.

[0255] "Related data" refers to information that is related to user input or information obtained from databases, and is useful for generating and evaluating technical concepts.

[0256] This invention is a system that enables the efficient generation of new technological concepts and mainly consists of three components: a server, a terminal, and a user. The user inputs technological ideas and concepts into the terminal and sends them to the server. The server is equipped with a natural language processing model and generates new technological concepts based on information obtained from the database and the user's input information. Furthermore, the server analyzes the materials provided by the user and uses the analysis results to generate technological concepts.

[0257] The server uses a natural language processing model to analyze input technical ideas and evaluate the novelty and inventiveness of the technical concepts. The generated technical concepts are proposed to the user, based on the principle of avoiding existing patent information obtained from patent databases. This allows the user to quickly and easily obtain new patent ideas.

[0258] The terminal has the functionality to receive user input information in digital format and upload it to the server. For example, if a user inputs "I want to think of a new digital pen technology concept" as a prompt, the server will search a patent database, analyze relevant patent information, and generate a new technology proposal while considering existing information. A concrete example of a prompt might be a question like, "How can this technology be further developed?"

[0259] In short, this system allows users to efficiently increase their chances of obtaining patents by providing ideas through a terminal, and the server generating and proposing new technologies based on those ideas. Furthermore, the accuracy and effectiveness of the entire system can be enhanced by utilizing natural language processing models and database search technologies.

[0260] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0261] Step 1:

[0262] Users input new technical ideas and concepts through their terminals. The input is in text format and can include specific technical requirements and an outline of the idea. This input is converted into digital data when sent to the server.

[0263] Step 2:

[0264] The terminal encrypts user input data and sends it to the server while maintaining security. This process ensures that user ideas are safely delivered to the server. The input is the user's ideas, and the output is the secure transfer of data to the server.

[0265] Step 3:

[0266] The server analyzes the received digital data and searches the patent database. This operation retrieves existing patent information related to the input data. The retrieved information is stored in the server's data cache. The input is the user's idea, and the output is the relevant patent information.

[0267] Step 4:

[0268] The server uses a natural language processing model to compare existing patent information in the data cache with the user's input ideas and generate new technological concepts. This model evaluates novelty based on the input information and generates new technological proposals as output.

[0269] Step 5:

[0270] The server re-analyzes the generated new technology concept to verify its practicality and patentability. This includes evaluating its inventiveness by comparing it with existing information in the database. The input is the generated technology concept, and the output is the evaluated technology proposal.

[0271] Step 6:

[0272] The server sends evaluated technical proposals to the terminal and presents them to the user. The user reviews the proposals through the terminal and decides whether to further develop their own technical ideas. The input is the evaluated technical proposals, and the output is the information presented to the user.

[0273] Step 7:

[0274] If necessary, users upload business documents from their terminals to the server. The server analyzes the content of the documents and generates new, relevant technical concepts. Through this process, the input documents become the input for the technical concepts, and new proposals are generated as output.

[0275] This entire process allows users to quickly and safely generate new patent ideas and proceed with development based on them.

[0276] (Application Example 1)

[0277] Next, we will explain Application Example 1. In the following explanation, the data processing device 12 will be referred to as the "server," and the smart glasses 214 will be referred to as the "terminal."

[0278] In the modern era, efficiently generating and realizing ideas for mechanical devices tailored to individualized lifestyles and home environments is a major challenge. Behind this challenge lies the need to discover original technological concepts while avoiding existing patent information. In particular, developing innovative mechanical devices that address specific needs in daily life at home is crucial for enriching users' lives.

[0279] 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.

[0280] In this invention, the server includes means for generating a technical concept based on an idea input using a natural language processing model, means for obtaining existing patent information related to the input technical concept from a patent database, means for generating a new technical concept that avoids existing patent information, and means for generating a concept of a unique mechanical device based on specific needs within a household. As a result, users can efficiently obtain ideas for unique mechanical devices suitable for their homes.

[0281] A "natural language processing model" is an algorithm for analyzing text data, understanding the meaning and context of language, and generating new information or ideas.

[0282] A "technical concept" refers to new ideas, methods, or structures that can be implemented in a specific technical field.

[0283] A "patent database" is an information system that accumulates information on past and current patents and provides it in a searchable form.

[0284] "Means for obtaining existing patent information" is a process for searching for relevant technical information and documents from a patent database and utilizing their content.

[0285] "Means for generating a new technical concept that avoids existing patent information" is a method for creating a novel technical idea that does not conflict with known technologies.

[0286] "Means for generating a concept of a unique mechanical device based on specific needs within a household" is a process for developing ideas for innovative mechanical devices specialized for individual household environments and daily life.

[0287] "Means for proposing" is a method for effectively presenting the generated technical concepts or ideas to users.

[0288] The system for realizing this invention is configured around three elements: a server, a terminal, and a user.

[0289] First, the user inputs ideas and technical concepts on a device such as a smartphone. The device then transmits this information to a server. The server first retrieves existing patent information related to the input technical concept using a patent database.

[0290] Next, a natural language processing model installed on the server (specifically, a model using the Transformers library) is used to generate new technological concepts based on the acquired patent information. In this process, various data analysis methods are used to avoid existing patents and create original technological ideas.

[0291] Furthermore, this system develops unique mechanical device concepts tailored to specific needs within the home. In other words, users can materialize ideas for mechanical devices suited to their lifestyle and environment with the assistance of natural language processing models.

[0292] The generated technical concepts are sent from the server to the terminal and presented to the user. For example, if a user has an idea for a "breakfast preparation robot," the server receives this idea and generates a new technical proposal—for instance, a concept for a robot with new functions that correspond to existing coffee makers and toasters. In this way, users can efficiently obtain innovative technical ideas that meet their household needs.

[0293] Examples of prompt statements are as follows:

[0294] "User: I have a new idea for a breakfast preparation robot. I want a robot that pours coffee and toasts bread. Are there any new technological concepts related to this functionality?"

[0295] Based on this user input, the system will propose appropriate new technologies.

[0296] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0297] Step 1:

[0298] Users input ideas and technical concepts into a device such as a smartphone. The input data is saved on the device as text data in natural language format. This data includes the functions and features of specific mechanical devices conceived by the user.

[0299] Step 2:

[0300] The terminal sends the entered text data to the server. Internet communication is used for data transmission, ensuring rapid data transfer from the terminal to the server.

[0301] Step 3:

[0302] The server forms a query to search the patent database based on the received data. The search query includes keywords related to the user's idea, which are used to retrieve existing patent information from the database.

[0303] Step 4:

[0304] A server retrieves relevant existing patent information from a patent database and analyzes the data using a natural language processing model. The analysis uses the Transformer library to evaluate context and relevance for generating new technological concepts that circumvent existing patents.

[0305] Step 5:

[0306] The server generates unique technological concepts based on specific needs within the home. Here, a natural language processing model uses user input data and information obtained from a patent database to form specific and novel proposals.

[0307] Step 6:

[0308] The newly generated technical concept is transmitted from the server to the terminal. The user can receive and confirm this proposal on the terminal. The data presented here plays an important role in confirming whether the newly generated technical concept actually meets the user's needs.

[0309] Step 7:

[0310] Based on the technical concept generated through the terminal, the user can engage in specific projects or development. At this stage, the compatibility between the proposed technology and practical needs is considered, and work is carried out to form the final idea.

[0311] Furthermore, an emotion engine for estimating the user's emotions may be combined. That is, the specific processing unit 290 may estimate the user's emotions using the emotion recognition model 59 and perform specific processing using the user's emotions.

[0312] The present invention combines an emotion engine with a system for generating patent ideas, and aims to optimize the proposal of technical concepts according to the user's emotional state. The following describes its specific embodiments.

[0313] This system mainly consists of a user, a terminal, a server, and an emotion engine. When the user consults about a patent idea using the terminal, the terminal transmits the content to the server. The server searches the patent database and obtains existing relevant patent information.

[0314] The natural language processing model incorporated in the server generates a new technical concept so as to avoid the obtained patent information. Here, the server utilizes the emotion engine to recognize the user's emotional state. The emotion engine determines the emotional state through the analysis of the user's input method and reaction, and adjusts the proposed content based on that information.

[0315] For example, if a user is experiencing stress during the idea generation process, the emotion engine will detect this, and the server will adjust the suggestions to be simpler and easier to understand. Conversely, if the user is relaxed, the system will optimize based on their emotional state, such as by increasing the number of innovative and complex suggestions.

[0316] As a concrete example, suppose a user is thinking about a new communication device technology. Based on the idea entered from the terminal, the server searches a patent database and analyzes relevant patents. While a natural language processing model generates a new technology to circumvent these, an emotion engine analyzes the user's reaction. For example, if the user smiles and says, "This idea might be great," the emotion engine receives this positive feedback and makes a proposal that includes detailed technical specifications. This entire process enables personalized technology proposals that respond to the user's emotions.

[0317] This system is an innovative solution that reduces the burden on users and generates patent ideas efficiently and effectively.

[0318] The following describes the processing flow.

[0319] Step 1:

[0320] The user enters their patent idea inquiry into a terminal. The terminal receives the input data and prepares to send it to the server.

[0321] Step 2:

[0322] The terminal formats the input data and sends it to the server. The server receives the data.

[0323] Step 3:

[0324] The server connects to a patent database and searches for existing patent information related to the entered idea. It retrieves the relevant patent information and saves it as data.

[0325] Step 4:

[0326] The server uses a natural language processing model to generate new technology concepts that circumvent existing patent information obtained. The generated technology concepts are then saved.

[0327] Step 5:

[0328] The server uses an emotion engine to analyze the user's input methods and responses to recognize their emotional state. For example, the emotion engine determines the user's emotions based on the speed at which the user operates the device and the linguistic characteristics of their input.

[0329] Step 6:

[0330] The server adjusts the technical concepts it generates based on information from the emotion engine, according to the user's emotional state. For example, if the user is stressed, the server simplifies the technical concepts to make them easier to understand. Conversely, if positive emotions are recognized, it adds more detailed suggestions.

[0331] Step 7:

[0332] The server sends the revised technical concept to the terminal. The terminal receives it and displays it to the user. The user reviews the proposal and provides feedback.

[0333] Step 8:

[0334] Based on user feedback, the server works with an emotion engine to generate further optimized technical concepts and repeats a cycle of making new suggestions.

[0335] (Example 2)

[0336] 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".

[0337] Conventional technology concept generation systems make technical suggestions without considering the user's emotional state, resulting in uniform suggestions that are unaffected by changes in the user's emotions, such as stress or relaxation. This makes it difficult to generate optimal suggestions tailored to the user's situation and to create creative technology concepts.

[0338] 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.

[0339] In this invention, the server includes means for generating technical concepts based on input ideas using a natural language processing algorithm, means for acquiring existing technical information related to the input technical concepts from a database, and means for recognizing the user's emotional state using an emotion analysis algorithm and adjusting the new technical concepts. This enables optimal suggestions and efficient generation of technical concepts that are tailored to the user's emotional state.

[0340] A "natural language processing algorithm" is a technology that enables computers to understand, analyze, and generate human language.

[0341] A "technical concept" refers to an idea or plan aimed at innovation or improvement in a specific technological field.

[0342] A "database" is a collection of information designed to allow for the efficient searching and retrieval of large amounts of data.

[0343] An "emotion analysis algorithm" is a technology for detecting and analyzing a user's emotional state, inferring emotions based on input data and the user's responses.

[0344] A "user" is someone who operates this system and receives technical suggestions.

[0345] This invention is a system composed of three main elements: a user, a terminal, and a server, designed to support the creation of patentable ideas. The user inputs technical ideas using the terminal, and this information is sent to the server as prompt messages. These prompt messages are analyzed by a natural language processing algorithm and used to generate new technical concepts.

[0346] The server accesses a database that stores existing technical information and retrieves existing technical information related to the ideas provided by the user. This retrieved information serves as important foundational data in the subsequent process of generating technical concepts. The sentiment analysis algorithm built into the server evaluates the user's emotional state and adjusts the technical suggestions to the user so that they are optimized according to the user's emotions.

[0347] As a concrete example, consider a scenario where a user is brainstorming ideas for new communication technologies. The user inputs a specific technology idea on their device, and this information is sent to a server. The server retrieves relevant technology information from its database and uses a natural language processing algorithm to construct a new technology concept based on this information. Simultaneously, a sentiment analysis algorithm analyzes the user's response and adjusts the proposed solution. For example, if the user gives a positive response such as "This idea might be great," the server will provide a proposal that includes detailed technology specifications.

[0348] An example of a prompt message would be, "I'm thinking of ideas for new communication technologies. Please tell me how to reduce stress and get innovative suggestions."

[0349] This system is an innovative means of providing users with higher-quality, personalized technology suggestions by combining generative AI models with sentiment analysis. Users can receive optimized suggestions tailored to their emotions, supporting the efficient generation of patent ideas.

[0350] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0351] Step 1:

[0352] The user uses a terminal to input prompts related to their patent idea. The terminal formats the input prompts as text data and sends them to the server. The input includes the technical field and keywords the user is considering, and the output is sent to the server as text data.

[0353] Step 2:

[0354] The server receives a prompt message sent from the terminal. Based on this data, the server uses a natural language processing algorithm to begin an initial analysis of the technical concept. The input is text data that serves as the prompt message, and the output generates summarized technical keywords and concepts.

[0355] Step 3:

[0356] The server uses the generated technical keywords to query its internal database and retrieve relevant existing technical information. The input is the parsed keywords, and the output is a list of relevant technical information or a technical summary. By executing database queries, information is collected quickly and accurately.

[0357] Step 4:

[0358] The server uses natural language processing algorithms to generate new technical concepts based on the acquired existing technical information. In this step, the existing input information and keywords from the prompt are integrated to output novel technical concepts while avoiding existing technologies.

[0359] Step 5:

[0360] The sentiment analysis algorithm built into the server analyzes the writing style of prompts and user interactions to evaluate the user's emotional state. Using prompts and response data obtained from the user interface as input, it generates the current emotional state as output. This allows for the optimization of suggested content.

[0361] Step 6:

[0362] The server optimizes new technical concepts based on the results of sentiment analysis and generates adjusted proposals. Because adjustments are made taking emotional states into account, for example, if the user is experiencing stress, a clear and simple technical proposal will be provided. An optimized technical proposal based on the input emotional data is generated as output.

[0363] Step 7:

[0364] The server sends the finalized technical proposal to the terminal and provides it to the user. The terminal receives this proposal and displays it to the user. The input is an optimized technical concept, and the output is a technical proposal presented to the user in an easy-to-understand format. This allows the user to use the proposal as a reference for further creative activities.

[0365] (Application Example 2)

[0366] 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."

[0367] When generating patent ideas, users often face complex technical information and large amounts of patent data, leading to stress. Furthermore, effectively developing ideas requires optimized suggestions tailored to the user's emotional state. However, current systems struggle to provide flexible technical suggestions that respond to user emotions and circumstances, failing to fully address individual user needs.

[0368] 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.

[0369] In this invention, the server includes means for generating technical concepts based on input ideas using a natural language processing model, means for acquiring existing information related to the input technical concepts from an information source database, means for generating new technical concepts that avoid the existing information, means for recognizing the user's emotional state and adjusting the technical concepts accordingly, and means for proposing the adjusted new technical concepts to the user. This enables more personalized technical suggestions that are tailored to the user's emotional state.

[0370] A "natural language processing model" is an artificial intelligence technology that enables computers to analyze and generate human language by understanding and generating text data.

[0371] A "source information database" is a digital repository that stores and makes searchable a variety of existing technical data, including patent information.

[0372] A "technical concept" is a new technical idea or component devised to solve a specific technical problem.

[0373] "Means for recognizing emotional states and adjusting technical concepts accordingly" refers to a function that judges emotions from the user's facial expressions and voice, and provides optimized technical suggestions according to that emotional state.

[0374] "Proposed means" refers to methods and processes for presenting generated technical concepts to users and supporting their understanding and decision-making.

[0375] This system consists primarily of a user, a terminal, a server, and an emotion engine. When a user inputs a new patent idea via the terminal, that data is sent to the server. The server incorporates a natural language processing model that generates technical concepts based on the input idea.

[0376] The server first retrieves relevant existing technical information from a database of information sources. Based on this data, it generates new technical concepts, adjusting the process to avoid using existing information. A generative AI model is used at this stage.

[0377] Furthermore, the server uses an emotion engine to analyze the user's current emotional state. This analysis is performed by sensing voice and facial expression data through the interface provided by the terminal and recognizing the emotional state. Based on the recognition results, the server adjusts the technical concept to the optimal form according to the user's emotions and then proposes it to the user.

[0378] As a specific example of its use, if a user is considering new ideas regarding energy efficiency, the server, upon receiving the query, will retrieve relevant existing technical information and, if the user is in a positive emotional state, generate and present more complex and technically advanced proposals.

[0379] Examples of prompt messages are as follows:

[0380] "A user is brainstorming ideas for new features in home appliances. Based on the sentiment analysis results, please suggest some ideas. The user's expression is smiling."

[0381] This system allows users to receive more personalized suggestions that take their emotional state into account, enabling them to efficiently generate new patent ideas.

[0382] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0383] Step 1:

[0384] The user inputs a new idea into the device. The device then sends the input data to the server. Voice input and text input are possible, and this becomes the basic data for the user's idea.

[0385] Step 2:

[0386] The server searches its information source database for relevant existing information based on the received data. Input: Idea data received from the user. Output: Relevant existing information. The server uses this information to prepare to generate new technical concepts.

[0387] Step 3:

[0388] The server's natural language processing model generates new technological concepts while avoiding existing information. Input: Existing data and user ideas. Output: New technological concepts. The server uses its generative AI model to make novel proposals that meet the user's requirements.

[0389] Step 4:

[0390] The device records the user's voice and facial expressions and sends them to the server. This generates user emotion data, which can then be analyzed by an emotion engine. Input: User's voice and facial expression data. Output: Emotional state.

[0391] Step 5:

[0392] The server uses an emotion engine to analyze the user's emotional state. Input: Voice and facial expression data. Output: User's emotional state. The server adjusts its suggestions based on this emotional state.

[0393] Step 6:

[0394] The server proposes a refined technical concept to the user. Input: New technical concept and the user's emotional state. Output: The refined technical concept presented to the user. This final proposal is presented in the form that best suits the user.

[0395] 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.

[0396] Data generation model 58 is a type of so-called generative AI (Artificial Intelligence). An 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.

[0397] 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.

[0398] [Third Embodiment]

[0399] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[0400] 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.

[0401] 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).

[0402] 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.

[0403] 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.

[0404] 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).

[0405] 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.

[0406] 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.

[0407] 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.

[0408] 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.

[0409] 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.

[0410] 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".

[0411] This invention is a system for efficiently generating patent ideas, combining a natural language processing model with patent database search. Specific embodiments are described below.

[0412] This system consists primarily of a user, a terminal, and a server. The user uses the terminal to input ideas and technical concepts. The terminal sends this information to the server, which then searches a patent database to retrieve existing patent information.

[0413] A natural language processing model on the server generates new technological concepts while avoiding existing patent information. This generation process evaluates novelty based on the input ideas, considering context and relevance. The generated technological concepts are then sent from the server to the terminal and presented to the user.

[0414] Furthermore, by uploading business documents provided by the user from their terminal to the server, a natural language processing model analyzes the content of the documents. This automatically generates technical concepts based on the documents and proposes them to the user. As a result, the user can obtain many technical concepts without any effort.

[0415] For example, if a user is thinking of a new digital pen technology concept, they input an outline of it into the device. The server receives the input, searches the patent database, and retrieves relevant technical information. A natural language processing model avoids existing digital pen patent information and devises a new proposal, such as a technology that enhances AI-powered handwriting recognition, and presents it to the user. Through this process, users can efficiently obtain new patent ideas.

[0416] The following describes the processing flow.

[0417] Step 1:

[0418] The user uses a device to input ideas or keywords related to patents. The device receives this input and prepares to send the data to the server.

[0419] Step 2:

[0420] The terminal converts the user's input into a predetermined format and sends it to the server. The server receives the data.

[0421] Step 3:

[0422] The server connects to the patent database and searches for existing patent information based on the entered keywords. The server finds relevant patents and collects the information.

[0423] Step 4:

[0424] The server passes existing patent information it has acquired to a natural language processing model. The model analyzes the data and generates new, non-competitive technology concepts.

[0425] Step 5:

[0426] The server converts the newly generated technological concept into a form that is easy for the user to understand and sends it to the terminal.

[0427] Step 6:

[0428] The terminal displays new technology concepts received from the server to the user. The user reviews the proposals and considers the possibilities of new ideas.

[0429] Step 7:

[0430] Users can optionally upload work documents to their devices and send them to the server. The server receives the documents and uses a natural language processing model to analyze their content.

[0431] Step 8:

[0432] Based on the analysis of the data, the server automatically generates a new patent proposal and presents it to the user again via the terminal.

[0433] (Example 1)

[0434] 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."

[0435] The problem this invention aims to solve is to efficiently and effectively create novel technical concepts and propose them to users based on diverse technical information. Furthermore, it aims to increase the likelihood of obtaining patents by generating technical concepts based on materials provided by users while avoiding existing patent information.

[0436] 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.

[0437] In this invention, the server includes means for generating a technical concept based on input information using a natural language processing model, means for generating a highly novel technical concept based on relevant information obtained from a database, and means for analyzing relevant data based on the obtained information and presenting the generated technical concept to the user. This makes it possible for the user to efficiently acquire new patent ideas and consider their patentability.

[0438] A "natural language processing model" is an artificial intelligence technology that analyzes digital text data, understands its meaning from its context, and generates appropriate technical concepts.

[0439] A "technical concept" is a conceptual explanation or proposal regarding a specific idea or technology, and includes innovative ideas that possess novelty and usefulness.

[0440] A "database" is an information management system in which patent information and technical information are systematically stored, and data can be quickly searched and retrieved as needed.

[0441] "Novelty" refers to the ability to propose value that is differentiated from others by possessing unique characteristics and elements not found in existing technologies or information.

[0442] "Analysis" is the process of thoroughly investigating and breaking down information and data to derive useful knowledge and conclusions from it.

[0443] "Related data" refers to information that is related to user input or information obtained from databases, and is useful for generating and evaluating technical concepts.

[0444] This invention is a system that enables the efficient generation of new technological concepts and mainly consists of three components: a server, a terminal, and a user. The user inputs technological ideas and concepts into the terminal and sends them to the server. The server is equipped with a natural language processing model and generates new technological concepts based on information obtained from the database and the user's input information. Furthermore, the server analyzes the materials provided by the user and uses the analysis results to generate technological concepts.

[0445] The server uses a natural language processing model to analyze input technical ideas and evaluate the novelty and inventiveness of the technical concepts. The generated technical concepts are proposed to the user, based on the principle of avoiding existing patent information obtained from patent databases. This allows the user to quickly and easily obtain new patent ideas.

[0446] The terminal has the functionality to receive user input information in digital format and upload it to the server. For example, if a user inputs "I want to think of a new digital pen technology concept" as a prompt, the server will search a patent database, analyze relevant patent information, and generate a new technology proposal while considering existing information. A concrete example of a prompt might be a question like, "How can this technology be further developed?"

[0447] In short, this system allows users to efficiently increase their chances of obtaining patents by providing ideas through a terminal, and the server generating and proposing new technologies based on those ideas. Furthermore, the accuracy and effectiveness of the entire system can be enhanced by utilizing natural language processing models and database search technologies.

[0448] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0449] Step 1:

[0450] Users input new technical ideas and concepts through their terminals. The input is in text format and can include specific technical requirements and an outline of the idea. This input is converted into digital data when sent to the server.

[0451] Step 2:

[0452] The terminal encrypts user input data and sends it to the server while maintaining security. This process ensures that user ideas are safely delivered to the server. The input is the user's ideas, and the output is the secure transfer of data to the server.

[0453] Step 3:

[0454] The server analyzes the received digital data and searches the patent database. This operation retrieves existing patent information related to the input data. The retrieved information is stored in the server's data cache. The input is the user's idea, and the output is the relevant patent information.

[0455] Step 4:

[0456] The server uses a natural language processing model to compare existing patent information in the data cache with the user's input ideas and generate new technological concepts. This model evaluates novelty based on the input information and generates new technological proposals as output.

[0457] Step 5:

[0458] The server re-analyzes the generated new technology concept to verify its practicality and patentability. This includes evaluating its inventiveness by comparing it with existing information in the database. The input is the generated technology concept, and the output is the evaluated technology proposal.

[0459] Step 6:

[0460] The server sends evaluated technical proposals to the terminal and presents them to the user. The user reviews the proposals through the terminal and decides whether to further develop their own technical ideas. The input is the evaluated technical proposals, and the output is the information presented to the user.

[0461] Step 7:

[0462] If necessary, users upload business documents from their terminals to the server. The server analyzes the content of the documents and generates new, relevant technical concepts. Through this process, the input documents become the input for the technical concepts, and new proposals are generated as output.

[0463] This entire process allows users to quickly and safely generate new patent ideas and proceed with development based on them.

[0464] (Application Example 1)

[0465] 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."

[0466] In the modern era, efficiently generating and realizing ideas for mechanical devices tailored to individualized lifestyles and home environments is a major challenge. Behind this challenge lies the need to discover original technological concepts while avoiding existing patent information. In particular, developing innovative mechanical devices that address specific needs in daily life at home is crucial for enriching users' lives.

[0467] 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.

[0468] In this invention, the server includes means for generating technical concepts based on input ideas using a natural language processing model, means for retrieving existing patent information related to the input technical concepts from a patent database, means for generating new technical concepts that circumvent existing patent information, and means for generating concepts of unique mechanical devices based on specific needs within the home. This enables users to efficiently obtain ideas for unique mechanical devices suitable for their homes.

[0469] A "natural language processing model" is an algorithm that analyzes text data, understands the meaning and context of language, and generates new information and ideas.

[0470] A "technological concept" refers to a new idea, method, or structure that can be implemented in a specific technological field.

[0471] A "patent database" is an information system that collects and provides searchable information on past and present patents.

[0472] "Means of obtaining existing patent information" refers to the process of searching for relevant technical information and documents from patent databases and utilizing their contents.

[0473] "Means for generating new technological concepts that circumvent existing patent information" refers to methods for creating novel technological ideas that do not infringe on known technologies.

[0474] "A means of generating the concept of unique mechanical devices based on specific needs within the home" refers to the process of developing original mechanical device ideas tailored to individual home environments and daily life.

[0475] "Means of proposal" refers to methods for effectively presenting generated technical concepts and ideas to users.

[0476] The system that realizes this invention is structured around three elements: a server, a terminal, and a user.

[0477] First, the user inputs ideas and technical concepts on a device such as a smartphone. The device then transmits this information to a server. The server first retrieves existing patent information related to the input technical concept using a patent database.

[0478] Next, a natural language processing model installed on the server (specifically, a model using the Transformers library) is used to generate new technological concepts based on the acquired patent information. In this process, various data analysis methods are used to avoid existing patents and create original technological ideas.

[0479] Furthermore, this system develops unique mechanical device concepts tailored to specific needs within the home. In other words, users can materialize ideas for mechanical devices suited to their lifestyle and environment with the assistance of natural language processing models.

[0480] The generated technical concepts are sent from the server to the terminal and presented to the user. For example, if a user has an idea for a "breakfast preparation robot," the server receives this idea and generates a new technical proposal—for instance, a concept for a robot with new functions that correspond to existing coffee makers and toasters. In this way, users can efficiently obtain innovative technical ideas that meet their household needs.

[0481] Examples of prompt statements are as follows:

[0482] "User: I have a new idea for a breakfast preparation robot. I want a robot that pours coffee and toasts bread. Are there any new technological concepts related to this functionality?"

[0483] Based on this user input, the system will propose appropriate new technologies.

[0484] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0485] Step 1:

[0486] Users input ideas and technical concepts into a device such as a smartphone. The input data is saved on the device as text data in natural language format. This data includes the functions and features of specific mechanical devices conceived by the user.

[0487] Step 2:

[0488] The terminal sends the entered text data to the server. Internet communication is used for data transmission, ensuring rapid data transfer from the terminal to the server.

[0489] Step 3:

[0490] The server forms a query to search the patent database based on the received data. The search query includes keywords related to the user's idea, which are used to retrieve existing patent information from the database.

[0491] Step 4:

[0492] A server retrieves relevant existing patent information from a patent database and analyzes the data using a natural language processing model. The analysis uses the Transformer library to evaluate context and relevance for generating new technological concepts that circumvent existing patents.

[0493] Step 5:

[0494] The server generates unique technological concepts based on specific needs within the home. Here, a natural language processing model uses user input data and information obtained from a patent database to form specific and novel proposals.

[0495] Step 6:

[0496] The generated new technology concept is sent from the server to the terminal. The user can receive and review this proposal on the terminal. The data presented here plays a crucial role in confirming whether the newly generated technology concept reliably meets the user's needs.

[0497] Step 7:

[0498] Users can work on specific projects and development based on the technical concepts generated through their devices. At this stage, they examine the alignment between the proposed technology and real-world needs, and work to form the final idea.

[0499] 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.

[0500] This invention combines a patent idea generation system with an emotion engine, aiming to optimize the proposed technical concepts according to the user's emotional state. Specific embodiments are described below.

[0501] This system consists primarily of a user, a terminal, a server, and an emotion engine. When a user uses the terminal to consult about a patent idea, the terminal sends the information to the server. The server searches the patent database and retrieves existing relevant patent information.

[0502] The natural language processing model embedded in the server generates new technological concepts that avoid the acquired patent information. Here, the server utilizes an emotion engine to recognize the user's emotional state. The emotion engine determines the emotional state through analysis of the user's input methods and responses, and adjusts the suggested content based on that information.

[0503] For example, if a user is experiencing stress during the idea generation process, the emotion engine will detect this, and the server will adjust the suggestions to be simpler and easier to understand. Conversely, if the user is relaxed, the system will optimize based on their emotional state, such as by increasing the number of innovative and complex suggestions.

[0504] As a concrete example, suppose a user is thinking about a new communication device technology. Based on the idea entered from the terminal, the server searches a patent database and analyzes relevant patents. While a natural language processing model generates a new technology to circumvent these, an emotion engine analyzes the user's reaction. For example, if the user smiles and says, "This idea might be great," the emotion engine receives this positive feedback and makes a proposal that includes detailed technical specifications. This entire process enables personalized technology proposals that respond to the user's emotions.

[0505] This system is an innovative solution that reduces the burden on users and generates patent ideas efficiently and effectively.

[0506] The following describes the processing flow.

[0507] Step 1:

[0508] The user enters their patent idea inquiry into a terminal. The terminal receives the input data and prepares to send it to the server.

[0509] Step 2:

[0510] The terminal formats the input data and sends it to the server. The server receives the data.

[0511] Step 3:

[0512] The server connects to a patent database and searches for existing patent information related to the entered idea. It retrieves the relevant patent information and saves it as data.

[0513] Step 4:

[0514] The server uses a natural language processing model to generate new technology concepts that circumvent existing patent information obtained. The generated technology concepts are then saved.

[0515] Step 5:

[0516] The server uses an emotion engine to analyze the user's input methods and responses to recognize their emotional state. For example, the emotion engine determines the user's emotions based on the speed at which the user operates the device and the linguistic characteristics of their input.

[0517] Step 6:

[0518] The server adjusts the technical concepts it generates based on information from the emotion engine, according to the user's emotional state. For example, if the user is stressed, the server simplifies the technical concepts to make them easier to understand. Conversely, if positive emotions are recognized, it adds more detailed suggestions.

[0519] Step 7:

[0520] The server sends the revised technical concept to the terminal. The terminal receives it and displays it to the user. The user reviews the proposal and provides feedback.

[0521] Step 8:

[0522] Based on user feedback, the server works with an emotion engine to generate further optimized technical concepts and repeats a cycle of making new suggestions.

[0523] (Example 2)

[0524] 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."

[0525] Conventional technology concept generation systems make technical suggestions without considering the user's emotional state, resulting in uniform suggestions that are unaffected by changes in the user's emotions, such as stress or relaxation. This makes it difficult to generate optimal suggestions tailored to the user's situation and to create creative technology concepts.

[0526] 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.

[0527] In this invention, the server includes means for generating technical concepts based on input ideas using a natural language processing algorithm, means for acquiring existing technical information related to the input technical concepts from a database, and means for recognizing the user's emotional state using an emotion analysis algorithm and adjusting the new technical concepts. This enables optimal suggestions and efficient generation of technical concepts that are tailored to the user's emotional state.

[0528] A "natural language processing algorithm" is a technology that enables computers to understand, analyze, and generate human language.

[0529] A "technical concept" refers to an idea or plan aimed at innovation or improvement in a specific technological field.

[0530] A "database" is a collection of information designed to allow for the efficient searching and retrieval of large amounts of data.

[0531] An "emotion analysis algorithm" is a technology for detecting and analyzing a user's emotional state, inferring emotions based on input data and the user's responses.

[0532] A "user" is someone who operates this system and receives technical suggestions.

[0533] This invention is a system composed of three main elements: a user, a terminal, and a server, designed to support the creation of patentable ideas. The user inputs technical ideas using the terminal, and this information is sent to the server as prompt messages. These prompt messages are analyzed by a natural language processing algorithm and used to generate new technical concepts.

[0534] The server accesses a database that stores existing technical information and retrieves existing technical information related to the ideas provided by the user. This retrieved information serves as important foundational data in the subsequent process of generating technical concepts. The sentiment analysis algorithm built into the server evaluates the user's emotional state and adjusts the technical suggestions to the user so that they are optimized according to the user's emotions.

[0535] As a concrete example, consider a scenario where a user is brainstorming ideas for new communication technologies. The user inputs a specific technology idea on their device, and this information is sent to a server. The server retrieves relevant technology information from its database and uses a natural language processing algorithm to construct a new technology concept based on this information. Simultaneously, a sentiment analysis algorithm analyzes the user's response and adjusts the proposed solution. For example, if the user gives a positive response such as "This idea might be great," the server will provide a proposal that includes detailed technology specifications.

[0536] An example of a prompt message would be, "I'm thinking of ideas for new communication technologies. Please tell me how to reduce stress and get innovative suggestions."

[0537] This system is an innovative means of providing users with higher-quality, personalized technology suggestions by combining generative AI models with sentiment analysis. Users can receive optimized suggestions tailored to their emotions, supporting the efficient generation of patent ideas.

[0538] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0539] Step 1:

[0540] The user uses a terminal to input prompts related to their patent idea. The terminal formats the input prompts as text data and sends them to the server. The input includes the technical field and keywords the user is considering, and the output is sent to the server as text data.

[0541] Step 2:

[0542] The server receives a prompt message sent from the terminal. Based on this data, the server uses a natural language processing algorithm to begin an initial analysis of the technical concept. The input is text data that serves as the prompt message, and the output generates summarized technical keywords and concepts.

[0543] Step 3:

[0544] The server uses the generated technical keywords to query its internal database and retrieve relevant existing technical information. The input is the parsed keywords, and the output is a list of relevant technical information or a technical summary. By executing database queries, information is collected quickly and accurately.

[0545] Step 4:

[0546] The server uses natural language processing algorithms to generate new technical concepts based on the acquired existing technical information. In this step, the existing input information and keywords from the prompt are integrated to output novel technical concepts while avoiding existing technologies.

[0547] Step 5:

[0548] The sentiment analysis algorithm built into the server analyzes the writing style of prompts and user interactions to evaluate the user's emotional state. Using prompts and response data obtained from the user interface as input, it generates the current emotional state as output. This allows for the optimization of suggested content.

[0549] Step 6:

[0550] The server optimizes new technical concepts based on the results of sentiment analysis and generates adjusted proposals. Because adjustments are made taking emotional states into account, for example, if the user is experiencing stress, a clear and simple technical proposal will be provided. An optimized technical proposal based on the input emotional data is generated as output.

[0551] Step 7:

[0552] The server sends the finalized technical proposal to the terminal and provides it to the user. The terminal receives this proposal and displays it to the user. The input is an optimized technical concept, and the output is a technical proposal presented to the user in an easy-to-understand format. This allows the user to use the proposal as a reference for further creative activities.

[0553] (Application Example 2)

[0554] 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."

[0555] When generating patent ideas, users often face complex technical information and large amounts of patent data, leading to stress. Furthermore, effectively developing ideas requires optimized suggestions tailored to the user's emotional state. However, current systems struggle to provide flexible technical suggestions that respond to user emotions and circumstances, failing to fully address individual user needs.

[0556] 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.

[0557] In this invention, the server includes means for generating technical concepts based on input ideas using a natural language processing model, means for acquiring existing information related to the input technical concepts from an information source database, means for generating new technical concepts that avoid the existing information, means for recognizing the user's emotional state and adjusting the technical concepts accordingly, and means for proposing the adjusted new technical concepts to the user. This enables more personalized technical suggestions that are tailored to the user's emotional state.

[0558] A "natural language processing model" is an artificial intelligence technology that enables computers to analyze and generate human language by understanding and generating text data.

[0559] A "source information database" is a digital repository that stores and makes searchable a variety of existing technical data, including patent information.

[0560] A "technical concept" is a new technical idea or component devised to solve a specific technical problem.

[0561] "Means for recognizing emotional states and adjusting technical concepts accordingly" refers to a function that judges emotions from the user's facial expressions and voice, and provides optimized technical suggestions according to that emotional state.

[0562] "Proposed means" refers to methods and processes for presenting generated technical concepts to users and supporting their understanding and decision-making.

[0563] This system consists primarily of a user, a terminal, a server, and an emotion engine. When a user inputs a new patent idea via the terminal, that data is sent to the server. The server incorporates a natural language processing model that generates technical concepts based on the input idea.

[0564] The server first retrieves relevant existing technical information from a database of information sources. Based on this data, it generates new technical concepts, adjusting the process to avoid using existing information. A generative AI model is used at this stage.

[0565] Furthermore, the server uses an emotion engine to analyze the user's current emotional state. This analysis is performed by sensing voice and facial expression data through the interface provided by the terminal and recognizing the emotional state. Based on the recognition results, the server adjusts the technical concept to the optimal form according to the user's emotions and then proposes it to the user.

[0566] As a specific example of its use, if a user is considering new ideas regarding energy efficiency, the server, upon receiving the query, will retrieve relevant existing technical information and, if the user is in a positive emotional state, generate and present more complex and technically advanced proposals.

[0567] Examples of prompt messages are as follows:

[0568] "A user is brainstorming ideas for new features in home appliances. Based on the sentiment analysis results, please suggest some ideas. The user's expression is smiling."

[0569] This system allows users to receive more personalized suggestions that take their emotional state into account, enabling them to efficiently generate new patent ideas.

[0570] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0571] Step 1:

[0572] The user inputs a new idea into the device. The device then sends the input data to the server. Voice input and text input are possible, and this becomes the basic data for the user's idea.

[0573] Step 2:

[0574] The server searches its information source database for relevant existing information based on the received data. Input: Idea data received from the user. Output: Relevant existing information. The server uses this information to prepare to generate new technical concepts.

[0575] Step 3:

[0576] The server's natural language processing model generates new technological concepts while avoiding existing information. Input: Existing data and user ideas. Output: New technological concepts. The server uses its generative AI model to make novel proposals that meet the user's requirements.

[0577] Step 4:

[0578] The device records the user's voice and facial expressions and sends them to the server. This generates user emotion data, which can then be analyzed by an emotion engine. Input: User's voice and facial expression data. Output: Emotional state.

[0579] Step 5:

[0580] The server uses an emotion engine to analyze the user's emotional state. Input: Voice and facial expression data. Output: User's emotional state. The server adjusts its suggestions based on this emotional state.

[0581] Step 6:

[0582] The server proposes a refined technical concept to the user. Input: New technical concept and the user's emotional state. Output: The refined technical concept presented to the user. This final proposal is presented in the form that best suits the user.

[0583] 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.

[0584] Data generation model 58 is a type of so-called generative AI (Artificial Intelligence). An 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.

[0585] 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.

[0586] [Fourth Embodiment]

[0587] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[0588] 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.

[0589] 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).

[0590] 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.

[0591] 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.

[0592] 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).

[0593] 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.

[0594] 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.

[0595] 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.

[0596] 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.

[0597] 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.

[0598] 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.

[0599] 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".

[0600] This invention is a system for efficiently generating patent ideas, combining a natural language processing model with patent database search. Specific embodiments are described below.

[0601] This system consists primarily of a user, a terminal, and a server. The user uses the terminal to input ideas and technical concepts. The terminal sends this information to the server, which then searches a patent database to retrieve existing patent information.

[0602] A natural language processing model on the server generates new technological concepts while avoiding existing patent information. This generation process evaluates novelty based on the input ideas, considering context and relevance. The generated technological concepts are then sent from the server to the terminal and presented to the user.

[0603] Furthermore, by uploading business documents provided by the user from their terminal to the server, a natural language processing model analyzes the content of the documents. This automatically generates technical concepts based on the documents and proposes them to the user. As a result, the user can obtain many technical concepts without any effort.

[0604] For example, if a user is thinking of a new digital pen technology concept, they input an outline of it into the device. The server receives the input, searches the patent database, and retrieves relevant technical information. A natural language processing model avoids existing digital pen patent information and devises a new proposal, such as a technology that enhances AI-powered handwriting recognition, and presents it to the user. Through this process, users can efficiently obtain new patent ideas.

[0605] The following describes the processing flow.

[0606] Step 1:

[0607] The user uses a device to input ideas or keywords related to patents. The device receives this input and prepares to send the data to the server.

[0608] Step 2:

[0609] The terminal converts the user's input into a predetermined format and sends it to the server. The server receives the data.

[0610] Step 3:

[0611] The server connects to the patent database and searches for existing patent information based on the entered keywords. The server finds relevant patents and collects the information.

[0612] Step 4:

[0613] The server passes existing patent information it has acquired to a natural language processing model. The model analyzes the data and generates new, non-competitive technology concepts.

[0614] Step 5:

[0615] The server converts the newly generated technological concept into a form that is easy for the user to understand and sends it to the terminal.

[0616] Step 6:

[0617] The terminal displays new technology concepts received from the server to the user. The user reviews the proposals and considers the possibilities of new ideas.

[0618] Step 7:

[0619] Users can optionally upload work documents to their devices and send them to the server. The server receives the documents and uses a natural language processing model to analyze their content.

[0620] Step 8:

[0621] Based on the analysis of the data, the server automatically generates a new patent proposal and presents it to the user again via the terminal.

[0622] (Example 1)

[0623] 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".

[0624] The problem this invention aims to solve is to efficiently and effectively create novel technical concepts and propose them to users based on diverse technical information. Furthermore, it aims to increase the likelihood of obtaining patents by generating technical concepts based on materials provided by users while avoiding existing patent information.

[0625] 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.

[0626] In this invention, the server includes means for generating a technical concept based on input information using a natural language processing model, means for generating a highly novel technical concept based on relevant information obtained from a database, and means for analyzing relevant data based on the obtained information and presenting the generated technical concept to the user. This makes it possible for the user to efficiently acquire new patent ideas and consider their patentability.

[0627] A "natural language processing model" is an artificial intelligence technology that analyzes digital text data, understands its meaning from its context, and generates appropriate technical concepts.

[0628] A "technical concept" is a conceptual explanation or proposal regarding a specific idea or technology, and includes innovative ideas that possess novelty and usefulness.

[0629] A "database" is an information management system in which patent information and technical information are systematically stored, and data can be quickly searched and retrieved as needed.

[0630] "Novelty" refers to the ability to propose value that is differentiated from others by possessing unique characteristics and elements not found in existing technologies or information.

[0631] "Analysis" is the process of thoroughly investigating and breaking down information and data to derive useful knowledge and conclusions from it.

[0632] "Related data" refers to information that is related to user input or information obtained from databases, and is useful for generating and evaluating technical concepts.

[0633] This invention is a system that enables the efficient generation of new technological concepts and mainly consists of three components: a server, a terminal, and a user. The user inputs technological ideas and concepts into the terminal and sends them to the server. The server is equipped with a natural language processing model and generates new technological concepts based on information obtained from the database and the user's input information. Furthermore, the server analyzes the materials provided by the user and uses the analysis results to generate technological concepts.

[0634] The server uses a natural language processing model to analyze input technical ideas and evaluate the novelty and inventiveness of the technical concepts. The generated technical concepts are proposed to the user, based on the principle of avoiding existing patent information obtained from patent databases. This allows the user to quickly and easily obtain new patent ideas.

[0635] The terminal has the functionality to receive user input information in digital format and upload it to the server. For example, if a user inputs "I want to think of a new digital pen technology concept" as a prompt, the server will search a patent database, analyze relevant patent information, and generate a new technology proposal while considering existing information. A concrete example of a prompt might be a question like, "How can this technology be further developed?"

[0636] In short, this system allows users to efficiently increase their chances of obtaining patents by providing ideas through a terminal, and the server generating and proposing new technologies based on those ideas. Furthermore, the accuracy and effectiveness of the entire system can be enhanced by utilizing natural language processing models and database search technologies.

[0637] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0638] Step 1:

[0639] Users input new technical ideas and concepts through their terminals. The input is in text format and can include specific technical requirements and an outline of the idea. This input is converted into digital data when sent to the server.

[0640] Step 2:

[0641] The terminal encrypts user input data and sends it to the server while maintaining security. This process ensures that user ideas are safely delivered to the server. The input is the user's ideas, and the output is the secure transfer of data to the server.

[0642] Step 3:

[0643] The server analyzes the received digital data and searches the patent database. This operation retrieves existing patent information related to the input data. The retrieved information is stored in the server's data cache. The input is the user's idea, and the output is the relevant patent information.

[0644] Step 4:

[0645] The server uses a natural language processing model to compare existing patent information in the data cache with the user's input ideas and generate new technological concepts. This model evaluates novelty based on the input information and generates new technological proposals as output.

[0646] Step 5:

[0647] The server re-analyzes the generated new technology concept to verify its practicality and patentability. This includes evaluating its inventiveness by comparing it with existing information in the database. The input is the generated technology concept, and the output is the evaluated technology proposal.

[0648] Step 6:

[0649] The server sends evaluated technical proposals to the terminal and presents them to the user. The user reviews the proposals through the terminal and decides whether to further develop their own technical ideas. The input is the evaluated technical proposals, and the output is the information presented to the user.

[0650] Step 7:

[0651] If necessary, users upload business documents from their terminals to the server. The server analyzes the content of the documents and generates new, relevant technical concepts. Through this process, the input documents become the input for the technical concepts, and new proposals are generated as output.

[0652] This entire process allows users to quickly and safely generate new patent ideas and proceed with development based on them.

[0653] (Application Example 1)

[0654] 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".

[0655] In the modern era, efficiently generating and realizing ideas for mechanical devices tailored to individualized lifestyles and home environments is a major challenge. Behind this challenge lies the need to discover original technological concepts while avoiding existing patent information. In particular, developing innovative mechanical devices that address specific needs in daily life at home is crucial for enriching users' lives.

[0656] 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.

[0657] In this invention, the server includes means for generating technical concepts based on input ideas using a natural language processing model, means for retrieving existing patent information related to the input technical concepts from a patent database, means for generating new technical concepts that circumvent existing patent information, and means for generating concepts of unique mechanical devices based on specific needs within the home. This enables users to efficiently obtain ideas for unique mechanical devices suitable for their homes.

[0658] A "natural language processing model" is an algorithm that analyzes text data, understands the meaning and context of language, and generates new information and ideas.

[0659] A "technological concept" refers to a new idea, method, or structure that can be implemented in a specific technological field.

[0660] A "patent database" is an information system that collects and provides searchable information on past and present patents.

[0661] "Means of obtaining existing patent information" refers to the process of searching for relevant technical information and documents from patent databases and utilizing their contents.

[0662] "Means for generating new technological concepts that circumvent existing patent information" refers to methods for creating novel technological ideas that do not infringe on known technologies.

[0663] "A means of generating the concept of unique mechanical devices based on specific needs within the home" refers to the process of developing original mechanical device ideas tailored to individual home environments and daily life.

[0664] "Means of proposal" refers to methods for effectively presenting generated technical concepts and ideas to users.

[0665] The system that realizes this invention is structured around three elements: a server, a terminal, and a user.

[0666] First, the user inputs ideas and technical concepts on a device such as a smartphone. The device then transmits this information to a server. The server first retrieves existing patent information related to the input technical concept using a patent database.

[0667] Next, a natural language processing model installed on the server (specifically, a model using the Transformers library) is used to generate new technological concepts based on the acquired patent information. In this process, various data analysis methods are used to avoid existing patents and create original technological ideas.

[0668] Furthermore, this system develops unique mechanical device concepts tailored to specific needs within the home. In other words, users can materialize ideas for mechanical devices suited to their lifestyle and environment with the assistance of natural language processing models.

[0669] The generated technical concepts are sent from the server to the terminal and presented to the user. For example, if a user has an idea for a "breakfast preparation robot," the server receives this idea and generates a new technical proposal—for instance, a concept for a robot with new functions that correspond to existing coffee makers and toasters. In this way, users can efficiently obtain innovative technical ideas that meet their household needs.

[0670] Examples of prompt statements are as follows:

[0671] "User: I have a new idea for a breakfast preparation robot. I want a robot that pours coffee and toasts bread. Are there any new technological concepts related to this functionality?"

[0672] Based on this user input, the system will propose appropriate new technologies.

[0673] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0674] Step 1:

[0675] Users input ideas and technical concepts into a device such as a smartphone. The input data is saved on the device as text data in natural language format. This data includes the functions and features of specific mechanical devices conceived by the user.

[0676] Step 2:

[0677] The terminal sends the entered text data to the server. Internet communication is used for data transmission, ensuring rapid data transfer from the terminal to the server.

[0678] Step 3:

[0679] The server forms a query to search the patent database based on the received data. The search query includes keywords related to the user's idea, which are used to retrieve existing patent information from the database.

[0680] Step 4:

[0681] A server retrieves relevant existing patent information from a patent database and analyzes the data using a natural language processing model. The analysis uses the Transformer library to evaluate context and relevance for generating new technological concepts that circumvent existing patents.

[0682] Step 5:

[0683] The server generates unique technological concepts based on specific needs within the home. Here, a natural language processing model uses user input data and information obtained from a patent database to form specific and novel proposals.

[0684] Step 6:

[0685] The generated new technology concept is sent from the server to the terminal. The user can receive and review this proposal on the terminal. The data presented here plays a crucial role in confirming whether the newly generated technology concept reliably meets the user's needs.

[0686] Step 7:

[0687] Users can work on specific projects and development based on the technical concepts generated through their devices. At this stage, they examine the alignment between the proposed technology and real-world needs, and work to form the final idea.

[0688] 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.

[0689] This invention combines a patent idea generation system with an emotion engine, aiming to optimize the proposed technical concepts according to the user's emotional state. Specific embodiments are described below.

[0690] This system consists primarily of a user, a terminal, a server, and an emotion engine. When a user uses the terminal to consult about a patent idea, the terminal sends the information to the server. The server searches the patent database and retrieves existing relevant patent information.

[0691] The natural language processing model embedded in the server generates new technological concepts that avoid the acquired patent information. Here, the server utilizes an emotion engine to recognize the user's emotional state. The emotion engine determines the emotional state through analysis of the user's input methods and responses, and adjusts the suggested content based on that information.

[0692] For example, if a user is experiencing stress during the idea generation process, the emotion engine will detect this, and the server will adjust the suggestions to be simpler and easier to understand. Conversely, if the user is relaxed, the system will optimize based on their emotional state, such as by increasing the number of innovative and complex suggestions.

[0693] As a concrete example, suppose a user is thinking about a new communication device technology. Based on the idea entered from the terminal, the server searches a patent database and analyzes relevant patents. While a natural language processing model generates a new technology to circumvent these, an emotion engine analyzes the user's reaction. For example, if the user smiles and says, "This idea might be great," the emotion engine receives this positive feedback and makes a proposal that includes detailed technical specifications. This entire process enables personalized technology proposals that respond to the user's emotions.

[0694] This system is an innovative solution that reduces the burden on users and generates patent ideas efficiently and effectively.

[0695] The following describes the processing flow.

[0696] Step 1:

[0697] The user enters their patent idea inquiry into a terminal. The terminal receives the input data and prepares to send it to the server.

[0698] Step 2:

[0699] The terminal formats the input data and sends it to the server. The server receives the data.

[0700] Step 3:

[0701] The server connects to a patent database and searches for existing patent information related to the entered idea. It retrieves the relevant patent information and saves it as data.

[0702] Step 4:

[0703] The server uses a natural language processing model to generate new technology concepts that circumvent existing patent information obtained. The generated technology concepts are then saved.

[0704] Step 5:

[0705] The server uses an emotion engine to analyze the user's input methods and responses to recognize their emotional state. For example, the emotion engine determines the user's emotions based on the speed at which the user operates the device and the linguistic characteristics of their input.

[0706] Step 6:

[0707] The server adjusts the technical concepts it generates based on information from the emotion engine, according to the user's emotional state. For example, if the user is stressed, the server simplifies the technical concepts to make them easier to understand. Conversely, if positive emotions are recognized, it adds more detailed suggestions.

[0708] Step 7:

[0709] The server sends the revised technical concept to the terminal. The terminal receives it and displays it to the user. The user reviews the proposal and provides feedback.

[0710] Step 8:

[0711] Based on user feedback, the server works with an emotion engine to generate further optimized technical concepts and repeats a cycle of making new suggestions.

[0712] (Example 2)

[0713] 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".

[0714] Conventional technology concept generation systems make technical suggestions without considering the user's emotional state, resulting in uniform suggestions that are unaffected by changes in the user's emotions, such as stress or relaxation. This makes it difficult to generate optimal suggestions tailored to the user's situation and to create creative technology concepts.

[0715] 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.

[0716] In this invention, the server includes means for generating technical concepts based on input ideas using a natural language processing algorithm, means for acquiring existing technical information related to the input technical concepts from a database, and means for recognizing the user's emotional state using an emotion analysis algorithm and adjusting the new technical concepts. This enables optimal suggestions and efficient generation of technical concepts that are tailored to the user's emotional state.

[0717] A "natural language processing algorithm" is a technology that enables computers to understand, analyze, and generate human language.

[0718] A "technical concept" refers to an idea or plan aimed at innovation or improvement in a specific technological field.

[0719] A "database" is a collection of information designed to allow for the efficient searching and retrieval of large amounts of data.

[0720] An "emotion analysis algorithm" is a technology for detecting and analyzing a user's emotional state, inferring emotions based on input data and the user's responses.

[0721] A "user" is someone who operates this system and receives technical suggestions.

[0722] This invention is a system composed of three main elements: a user, a terminal, and a server, designed to support the creation of patentable ideas. The user inputs technical ideas using the terminal, and this information is sent to the server as prompt messages. These prompt messages are analyzed by a natural language processing algorithm and used to generate new technical concepts.

[0723] The server accesses a database that stores existing technical information and retrieves existing technical information related to the ideas provided by the user. This retrieved information serves as important foundational data in the subsequent process of generating technical concepts. The sentiment analysis algorithm built into the server evaluates the user's emotional state and adjusts the technical suggestions to the user so that they are optimized according to the user's emotions.

[0724] As a concrete example, consider a scenario where a user is brainstorming ideas for new communication technologies. The user inputs a specific technology idea on their device, and this information is sent to a server. The server retrieves relevant technology information from its database and uses a natural language processing algorithm to construct a new technology concept based on this information. Simultaneously, a sentiment analysis algorithm analyzes the user's response and adjusts the proposed solution. For example, if the user gives a positive response such as "This idea might be great," the server will provide a proposal that includes detailed technology specifications.

[0725] An example of a prompt message would be, "I'm thinking of ideas for new communication technologies. Please tell me how to reduce stress and get innovative suggestions."

[0726] This system is an innovative means of providing users with higher-quality, personalized technology suggestions by combining generative AI models with sentiment analysis. Users can receive optimized suggestions tailored to their emotions, supporting the efficient generation of patent ideas.

[0727] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0728] Step 1:

[0729] The user uses a terminal to input prompts related to their patent idea. The terminal formats the input prompts as text data and sends them to the server. The input includes the technical field and keywords the user is considering, and the output is sent to the server as text data.

[0730] Step 2:

[0731] The server receives a prompt message sent from the terminal. Based on this data, the server uses a natural language processing algorithm to begin an initial analysis of the technical concept. The input is text data that serves as the prompt message, and the output generates summarized technical keywords and concepts.

[0732] Step 3:

[0733] The server uses the generated technical keywords to query its internal database and retrieve relevant existing technical information. The input is the parsed keywords, and the output is a list of relevant technical information or a technical summary. By executing database queries, information is collected quickly and accurately.

[0734] Step 4:

[0735] The server uses natural language processing algorithms to generate new technical concepts based on the acquired existing technical information. In this step, the existing input information and keywords from the prompt are integrated to output novel technical concepts while avoiding existing technologies.

[0736] Step 5:

[0737] The sentiment analysis algorithm built into the server analyzes the writing style of prompts and user interactions to evaluate the user's emotional state. Using prompts and response data obtained from the user interface as input, it generates the current emotional state as output. This allows for the optimization of suggested content.

[0738] Step 6:

[0739] The server optimizes new technical concepts based on the results of sentiment analysis and generates adjusted proposals. Because adjustments are made taking emotional states into account, for example, if the user is experiencing stress, a clear and simple technical proposal will be provided. An optimized technical proposal based on the input emotional data is generated as output.

[0740] Step 7:

[0741] The server sends the finalized technical proposal to the terminal and provides it to the user. The terminal receives this proposal and displays it to the user. The input is an optimized technical concept, and the output is a technical proposal presented to the user in an easy-to-understand format. This allows the user to use the proposal as a reference for further creative activities.

[0742] (Application Example 2)

[0743] 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".

[0744] When generating patent ideas, users often face complex technical information and large amounts of patent data, leading to stress. Furthermore, effectively developing ideas requires optimized suggestions tailored to the user's emotional state. However, current systems struggle to provide flexible technical suggestions that respond to user emotions and circumstances, failing to fully address individual user needs.

[0745] 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.

[0746] In this invention, the server includes means for generating technical concepts based on input ideas using a natural language processing model, means for acquiring existing information related to the input technical concepts from an information source database, means for generating new technical concepts that avoid the existing information, means for recognizing the user's emotional state and adjusting the technical concepts accordingly, and means for proposing the adjusted new technical concepts to the user. This enables more personalized technical suggestions that are tailored to the user's emotional state.

[0747] A "natural language processing model" is an artificial intelligence technology that enables computers to analyze and generate human language by understanding and generating text data.

[0748] A "source information database" is a digital repository that stores and makes searchable a variety of existing technical data, including patent information.

[0749] A "technical concept" is a new technical idea or component devised to solve a specific technical problem.

[0750] "Means for recognizing emotional states and adjusting technical concepts accordingly" refers to a function that judges emotions from the user's facial expressions and voice, and provides optimized technical suggestions according to that emotional state.

[0751] "Proposed means" refers to methods and processes for presenting generated technical concepts to users and supporting their understanding and decision-making.

[0752] This system consists primarily of a user, a terminal, a server, and an emotion engine. When a user inputs a new patent idea via the terminal, that data is sent to the server. The server incorporates a natural language processing model that generates technical concepts based on the input idea.

[0753] The server first retrieves relevant existing technical information from a database of information sources. Based on this data, it generates new technical concepts, adjusting the process to avoid using existing information. A generative AI model is used at this stage.

[0754] Furthermore, the server uses an emotion engine to analyze the user's current emotional state. This analysis is performed by sensing voice and facial expression data through the interface provided by the terminal and recognizing the emotional state. Based on the recognition results, the server adjusts the technical concept to the optimal form according to the user's emotions and then proposes it to the user.

[0755] As a specific example of its use, if a user is considering new ideas regarding energy efficiency, the server, upon receiving the query, will retrieve relevant existing technical information and, if the user is in a positive emotional state, generate and present more complex and technically advanced proposals.

[0756] Examples of prompt messages are as follows:

[0757] "A user is brainstorming ideas for new features in home appliances. Based on the sentiment analysis results, please suggest some ideas. The user's expression is smiling."

[0758] This system allows users to receive more personalized suggestions that take their emotional state into account, enabling them to efficiently generate new patent ideas.

[0759] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0760] Step 1:

[0761] The user inputs a new idea into the device. The device then sends the input data to the server. Voice input and text input are possible, and this becomes the basic data for the user's idea.

[0762] Step 2:

[0763] The server searches its information source database for relevant existing information based on the received data. Input: Idea data received from the user. Output: Relevant existing information. The server uses this information to prepare to generate new technical concepts.

[0764] Step 3:

[0765] The server's natural language processing model generates new technological concepts while avoiding existing information. Input: Existing data and user ideas. Output: New technological concepts. The server uses its generative AI model to make novel proposals that meet the user's requirements.

[0766] Step 4:

[0767] The device records the user's voice and facial expressions and sends them to the server. This generates user emotion data, which can then be analyzed by an emotion engine. Input: User's voice and facial expression data. Output: Emotional state.

[0768] Step 5:

[0769] The server uses an emotion engine to analyze the user's emotional state. Input: Voice and facial expression data. Output: User's emotional state. The server adjusts its suggestions based on this emotional state.

[0770] Step 6:

[0771] The server proposes a refined technical concept to the user. Input: New technical concept and the user's emotional state. Output: The refined technical concept presented to the user. This final proposal is presented in the form that best suits the user.

[0772] 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.

[0773] Data generation model 58 is a type of so-called generative AI (Artificial Intelligence). An 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.

[0774] 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 robot 414.

[0775] 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.

[0776] 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.

[0777] 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.

[0778] 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.

[0779] 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.

[0780] 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."

[0781] 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.

[0782] 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.

[0783] 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.

[0784] 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.

[0785] 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.

[0786] 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.

[0787] 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.

[0788] 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.

[0789] 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.

[0790] 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.

[0791] 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.

[0792] 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.

[0793] The following is further disclosed regarding the embodiments described above.

[0794] (Claim 1)

[0795] A means for generating technical concepts based on input ideas using a natural language processing model,

[0796] A means for obtaining existing patent information related to a technical concept entered from a patent database,

[0797] Means for generating a new technical concept that avoids the aforementioned existing patent information,

[0798] A system including means for proposing the aforementioned new technological concept to a user.

[0799] (Claim 2)

[0800] The system according to claim 1, further comprising means for analyzing materials provided by a user and automatically generating a technical concept based on the results of the analysis.

[0801] (Claim 3)

[0802] The system according to claim 1, further comprising means for evaluating the inventiveness of an technical concept using patent information obtained from the aforementioned patent database.

[0803] "Example 1"

[0804] (Claim 1)

[0805] A means for generating technical concepts based on input information using a natural language processing model,

[0806] A means for generating highly novel technological concepts based on relevant information obtained from a database,

[0807] A means of analyzing related data based on acquired information and presenting the generated technology concept to the user,

[0808] A means for users to input data in digital format and send it to a server,

[0809] A system that includes means for transmitting input data while maintaining its security.

[0810] (Claim 2)

[0811] The system according to claim 1, which analyzes user-provided materials and generates new technical concepts from the analyzed content.

[0812] (Claim 3)

[0813] The system according to claim 1, which includes means for obtaining patent information from a database and examining the inventiveness of a technical concept using this information as an evaluation criterion.

[0814] "Application Example 1"

[0815] (Claim 1)

[0816] A means for generating technical concepts based on input ideas using a natural language processing model,

[0817] A means for obtaining existing patent information related to a technical concept entered from a patent database,

[0818] Means for generating a new technical concept that avoids the aforementioned existing patent information,

[0819] A means of generating the concept of a unique mechanical device based on specific needs within the home,

[0820] A system including means for proposing the aforementioned new technological concept to a user.

[0821] (Claim 2)

[0822] The system according to claim 1, further comprising means for analyzing materials provided by a user and automatically generating a technical concept based on the results of the analysis.

[0823] (Claim 3)

[0824] The system according to claim 1, further comprising means for evaluating the inventiveness of an technical concept using patent information obtained from the aforementioned patent database.

[0825] "Example 2 of combining an emotion engine"

[0826] (Claim 1)

[0827] A means of generating technical concepts based on input ideas using natural language processing algorithms,

[0828] A means of obtaining existing technical information related to a technical concept entered from a database,

[0829] A means for creating a new technical concept that avoids the aforementioned existing technical information,

[0830] A means for recognizing the user's emotional state using an emotion analysis algorithm and adjusting the aforementioned new technological concept,

[0831] Means for providing the aforementioned adjusted new technical concept to the user,

[0832] A system that includes this.

[0833] (Claim 2)

[0834] The system according to claim 1, further comprising means for analyzing information provided by a user and automatically generating technical concepts based on the results of the analysis.

[0835] (Claim 3)

[0836] The system according to claim 1, further comprising means for evaluating the inventiveness of a technical concept using technical information obtained from the aforementioned database.

[0837] "Application example 2 when combining with an emotional engine"

[0838] (Claim 1)

[0839] A means for generating technical concepts based on input ideas using a natural language processing model,

[0840] A means of obtaining existing information related to a technical concept entered from an information source database,

[0841] Means for generating a new technical concept that avoids the aforementioned existing information,

[0842] A means of recognizing the user's emotional state and adjusting the technical concept accordingly,

[0843] A system that includes means for proposing a refined new technological concept to the user.

[0844] (Claim 2)

[0845] The system according to claim 1, further comprising means for analyzing materials provided by a user and automatically generating a technical concept based on the results of the analysis.

[0846] (Claim 3)

[0847] The system according to claim 1, further comprising means for evaluating the inventiveness of a technical concept using information obtained from the aforementioned information source database. [Explanation of Symbols]

[0848] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Devices 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robots< / url:> < / url:> < / url:> < / url:>

Claims

1. A means for generating technical concepts based on input ideas using a natural language processing model, A means for obtaining existing patent information related to a technical concept entered from a patent database, Means for generating a new technical concept that avoids the aforementioned existing patent information, A system including means for proposing the aforementioned new technological concept to a user.

2. The system according to claim 1, further comprising means for analyzing materials provided by a user and automatically generating a technical concept based on the results of the analysis.

3. The system according to claim 1, further comprising means for evaluating the inventiveness of an technical concept using patent information obtained from the aforementioned patent database.

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