System

Abstract

To provide a system for enabling a user to easily consult with a patent attorney at a low cost, and for facilitating the progress management of patent procedures, and for improving the business efficiencies of the patent attorney.SOLUTION: The system includes means for receiving user authorization information and obtaining user profile information, means for passing consultation content received from the user to a text analysis engine and extracting important keywords and context, means for generating appropriate information by a AI model based on the extracted keywords and context, and providing the information to the user, means for transferring the information to a specialist in a specialized field and obtaining an additional answer when a question from the user is advanced and cannot be solved by AI alone, means for tracking the user's progress of a specific procedure and reminding important deadlines and tasks, and means for allowing the specialist to give reward points by adding useful information to an answer database of the AI.SELECTED DRAWING: Figure 1

Description

[Technical Field]

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

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

[0003] [Patent Document 1] Japanese Patent Publication No. 2022-180282 Summary of the Invention [Problem to be solved by the invention]

[0004] The patent system is difficult for the general public to understand, and traditional consultations with patent attorneys are expensive, with unclear pricing and compatibility with patent attorneys. There have also been cases where information is biased toward patent attorneys. This can complicate the patent application process and hinder patent acquisition. Furthermore, the lack of progress management and reminder functions for patent procedures puts users at risk of forgetting important deadlines and tasks. Furthermore, there are insufficient support tools for patent attorneys to improve their work efficiency. [Means for solving the problem]

[0005] The present invention solves the above-mentioned problems by providing a system including the following means. Specifically, the system includes a means for receiving user authentication information and acquiring user profile information, a means for passing the inquiry received from the user to a text analysis engine and extracting important keywords and context, and a means for an AI model to generate appropriate information based on the extracted keywords and context and provide it to the user. The system also includes a means for forwarding information to a specialist in a specialized field to obtain additional answers when the user's question is difficult and cannot be resolved by AI alone, a means for tracking the user's progress in a specific procedure and reminding them of important deadlines and tasks, and a means for experts to award reward points for adding useful information to the AI's answer database. The system also includes a means for sending reminder notifications to users when a submission deadline approaches and a means for experts to use the reward points they have earned to place advertisements in related fields. This allows users to easily consult with patent attorneys at low cost, facilitating progress management of patent procedures and improving the work efficiency of patent attorneys.

[0006] "User Credentials" refers to a user's identification and password used to log into a system.

[0007] "Profile information" refers to information that indicates the attributes of a user, such as personal information and history information about the user.

[0008] "Consultation content" refers to text information in which a user describes questions or doubts about patents.

[0009] A "text analysis engine" refers to software that analyzes input text and extracts important keywords and context.

[0010] "Keywords" refer to words or phrases that are particularly important in expressing the user's question or inquiry.

[0011] "Context" refers to information that indicates the meaning and relevance of keywords within the overall content of a user's consultation.

[0012] An "AI model" refers to a computational model that uses artificial intelligence to solve a specific problem, in this case providing information about patents or analyzing consultation content.

[0013] "Subject matter expert" refers to a human expert (e.g., a patent attorney) with patent-related knowledge and experience.

[0014] "Progress tracking" refers to monitoring procedures initiated by a user and checking their completion status.

[0015] "Deadline" refers to the deadline for each step in the patent process.

[0016] "Remind" refers to a feature that notifies users when a specific task or deadline is approaching.

[0017] "Reward points" refer to units of reward that patent attorneys can earn by providing useful information to the system.

[0018] "Option to display advertisements" refers to the ability to display advertisements in related fields by using earned reward points. [Brief explanation of the drawings]

[0019] [Figure 1] 1 is a conceptual diagram showing an example of the configuration of a data processing system according to a first embodiment. [Figure 2] 1 is a conceptual diagram showing an example of main functions of a data processing device and a smart device according to a first embodiment. [Figure 3] FIG. 10 is a conceptual diagram showing an example of the configuration of a data processing system according to a second embodiment. [Figure 4] FIG. 10 is a conceptual diagram showing an example of main functions of a data processing device and smart glasses according to a second embodiment. [Figure 5]FIG. 10 is a conceptual diagram showing an example of the configuration of a data processing system according to a third embodiment. [Figure 6] FIG. 11 is a conceptual diagram showing an example of main functions of a data processing device and a headset-type terminal according to a third embodiment. [Figure 7] FIG. 10 is a conceptual diagram showing an example of the configuration of a data processing system according to a fourth embodiment. [Figure 8] FIG. 10 is a conceptual diagram showing an example of main functions of a data processing device and a robot according to a fourth embodiment. [Figure 9] 1 shows an emotion map onto which multiple emotions are mapped. [Figure 10] 1 shows an emotion map onto which multiple emotions are mapped. [Figure 11] FIG. 3 is a sequence diagram showing a processing flow of the data processing system according to the first embodiment. [Figure 12] FIG. 10 is a sequence diagram showing the flow of processing in the data processing system in Application Example 1. [Figure 13] FIG. 10 is a sequence diagram showing the flow of processing in the data processing system according to the second embodiment when an emotion engine is combined. [Figure 14] FIG. 10 is a sequence diagram showing the flow of processing in the data processing system in Application Example 2 when an emotion engine is combined. DETAILED DESCRIPTION OF THE INVENTION

[0020] An example of an embodiment of a system according to the technology of the present disclosure will be described below with reference to the accompanying drawings.

[0021] First, the terms used in the following description will be explained.

[0022] In the following embodiments, a coded processor (hereinafter simply referred to as a "processor") may be a single arithmetic device or a combination of multiple arithmetic devices. Furthermore, a processor may be a single type of arithmetic device or a combination of multiple types of arithmetic devices. Examples of arithmetic devices include a CPU (Central Processing Unit), a GPU (Graphics Processing Unit), a GPGPU (General-Purpose computing on Graphics Processing Units), and an APU (Accelerated Processing Unit).

[0023] In the following embodiments, a coded RAM (Random Access Memory) is a memory in which information is temporarily stored and is used as a working memory by a processor.

[0024] In the following embodiments, the coded storage is one or more non-volatile storage devices that store various programs, various parameters, etc. Examples of non-volatile storage devices include flash memory (SSD (Solid State Drive)), magnetic disks (e.g., hard disks), and magnetic tapes.

[0025] In the following embodiments, a communication I / F (Interface) with a symbol is an interface including a communication processor, an antenna, etc. The communication I / F controls communication between multiple computers. Examples of communication standards applied to the communication I / F include wireless communication standards including 5G (5th Generation Mobile Communication System), Wi-Fi (registered trademark), Bluetooth (registered trademark), etc.

[0026] In the following embodiments, "A and / or B" is synonymous with "at least one of A and B." In other words, "A and / or B" means that it may be only A, only B, or a combination of A and B. Furthermore, in this specification, the same concept as "A and / or B" is also applied when three or more things are expressed connected by "and / or."

[0027] [First embodiment]

[0028] FIG. 1 shows an example of the configuration of a data processing system 10 according to the first embodiment.

[0029] 1, a 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.

[0030] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 is an example of a "computer" according to the technology of the present disclosure. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, the RAM 30, and the storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[0031] The smart device 14 includes a computer 36, a reception device 38, an output device 40, a camera 42, and a communication I / F 44. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The reception device 38, the output device 40, and the camera 42 are also connected to the bus 52.

[0032] The reception device 38 includes a touch panel 38A, a microphone 38B, and the like, and receives user input. The touch panel 38A detects contact with an indicator (for example, a pen or a finger) to receive user input by the touch of the indicator. The microphone 38B detects the user's voice to receive user input by voice. The control unit 46A transmits data indicating the user input received by the touch panel 38A and the 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.

[0033] 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 of expression that the user 20 can perceive (for example, audio and / or text). The display 40A displays visible information such as text and images in accordance with instructions from the processor 46. The speaker 40B outputs audio in accordance with instructions from the processor 46. The camera 42 is a compact digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor.

[0034] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 control the exchange of various information between the processor 46 and the processor 28 via the network 54.

[0035] FIG. 2 shows an example of the main functions of the data processing device 12 and the smart device 14.

[0036] 2, in the data processing device 12, a specific process 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" according to the technology of the present 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 process 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.

[0037] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[0038] In the smart device 14, the processor 46 performs the reception output process. The storage 50 stores a reception output program 60. The reception output program 60 is used in conjunction with the 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 process is realized by the processor 46 operating as the control unit 46A in accordance with the reception output program 60 executed on the RAM 48.

[0039] Next, a description will be given of the specific processing performed by the specific processing unit 290 of the data processing device 12. In the following description, the data processing device 12 will be referred to as a "server" and the smart device 14 will be referred to as a "terminal."

[0040] The "patent attorney AI" system of the present invention is a system that allows users to easily consult about patent procedures and supports the efficient progress of the patent application process. Below, we will generate a program for the system and explain its processing in natural language.

[0041] Explanation of program processing

[0042] User authentication and basic information entry

[0043] 1. Device: The user launches the "Patent Attorney AI" application and enters authentication information (username, password) on the login screen.

[0044] 2. Server: Receives the user's authentication information and authenticates the login by checking it against the database. If authentication is successful, obtains the user's profile information and sends it to the device.

[0045] 3. Terminal: After successful authentication, the dashboard screen is displayed and a form is provided where the user can enter the details of their consultation.

[0046] Enter the consultation details

[0047] 1. Terminal: The user enters information into a text box for entering questions or inquiries about patents.

[0048] 2. Terminal: Sends the consultation details entered by the user to the server.

[0049] 3. Server: Passes the received consultation content to a text analysis engine to extract important keywords and context.

[0050] AI-based initial diagnosis and information provision

[0051] 1. Server: Based on the extracted keywords and context, the AI ​​model generates appropriate information (e.g., patent application steps and required documents).

[0052] 2. Server: Formats the generated information and sends it to the user.

[0053] 3. Terminal: Displays the results of the initial diagnosis and related information to the user, such as "specific steps for patent application" and "list of required documents."

[0054] Collaboration with patent attorneys

[0055] 1. Server: If the user's question is too advanced to be resolved by AI alone, the server transfers the consultation to an expert (e.g., a patent attorney).

[0056] 2. Patent attorney: The expert reviews the user's consultation and enters any necessary additional information or advice.

[0057] 3. Server: Receives additional information from the expert and forwards it to the user.

[0058] 4. Terminal: Additional information from the patent attorney is displayed to the user, allowing them to receive specific advice.

[0059] Procedure progress reminders

[0060] 1. Server: When a user initiates a specific patent procedure, it tracks its progress, for example, managing specific submission deadlines and tasks.

[0061] 2. Server: Set a reminder when the submission deadline approaches.

[0062] 3. On the device: A deadline reminder is sent to the user. For example, a notification saying, "Your patent application is due in one week."

[0063] 4. User: Based on the reminder notification, you can take the following actions:

[0064] Reward Points Management

[0065] 1. Patent attorneys: Experts add useful information to the AI's database of answers.

[0066] 2. Server: Reviews the submitted information, incorporates it into a database, and awards reward points to patent attorneys based on the usefulness of the information.

[0067] 3. Patent attorneys: You can choose to use the reward points you have earned to advertise in related fields.

[0068] Specific examples

[0069] Example 1: New invention consultation

[0070] 1. A user has come up with a new invention but does not know the procedure for filing a patent application.

[0071] 2. Using your device, log in to your account and enter a summary of your invention.

[0072] 3. The server analyzes the information entered and provides the user with basic application procedures and necessary documents.

[0073] 4. The user enters an additional, more specific question, asking, "Is this invention likely to be patentable?"

[0074] 5. The server researches similar cases from the past, generates a response, and obtains additional responses from patent attorneys if necessary.

[0075] 6. The answer is displayed on the device so the user knows the next step.

[0076] Example 2: Reminder function

[0077] 1. A user initiates the patent application process and a specific submission deadline is set.

[0078] 2. The server tracks this information and sets reminders when deadlines approach.

[0079] 3. A reminder notification will be sent to the device one week before the submission deadline, helping users to remember to complete important tasks.

[0080] This will enable the "Patent Attorney AI" system to support complex patent-related procedures, enabling both users and patent attorneys to carry out their work efficiently.

[0081] The processing flow will be explained below.

[0082] User authentication and basic information entry

[0083] Step 1:

[0084] Terminal: The user launches the "Patent Attorney AI" application and enters authentication information (username, password) on the login screen.

[0085] Step 2:

[0086] Server: Receives the user's authentication information and authenticates the login against a database.

[0087] Step 3:

[0088] Server: If authentication is successful, obtain the user's profile information and send it to the terminal.

[0089] Step 4:

[0090] Terminal: After successful authentication, the dashboard screen is displayed and a form is provided where the user can enter the details of their consultation.

[0091] Enter the consultation details

[0092] Step 1:

[0093] Terminal: The user enters information into a text box for entering questions or consultations about patents.

[0094] Step 2:

[0095] Terminal: Sends the consultation details entered by the user to the server.

[0096] Step 3:

[0097] Server: Passes the received consultation content to a text analysis engine to extract important keywords and context.

[0098] AI-based initial diagnosis and information provision

[0099] Step 1:

[0100] Server: Based on the extracted keywords and context, the AI ​​model generates appropriate information (e.g., patent application steps and required documents).

[0101] Step 2:

[0102] Server: Formats the generated information and sends it to the user.

[0103] Step 3:

[0104] Terminal: Displays the results of the initial diagnosis and related information to the user, such as "specific steps for patent application" and "list of required documents."

[0105] Collaboration with patent attorneys

[0106] Step 1:

[0107] Server: If the user's question is too advanced to be resolved by AI alone, the server transfers the consultation to an expert (e.g., a patent attorney).

[0108] Step 2:

[0109] Patent attorney: The expert reviews the user's consultation and enters any necessary additional information or advice.

[0110] Step 3:

[0111] Server: Receives additional information from the expert and forwards it to the user.

[0112] Step 4:

[0113] Terminal: Additional information from the patent attorney is displayed to the user, allowing them to receive specific advice.

[0114] Procedure progress reminders

[0115] Step 1:

[0116] Server: Tracks the progress of specific patent proceedings initiated by users, for example, managing specific submission deadlines and tasks.

[0117] Step 2:

[0118] Server: Set reminders when the submission deadline approaches.

[0119] Step 3:

[0120] Terminal: A deadline reminder is sent to the user. For example, a notification saying, "Your patent application is due in one week."

[0121] Step 4:

[0122] User: Based on the reminder notification, you can take the following actions:

[0123] Reward Points Management

[0124] Step 1:

[0125] Patent attorneys: Experts provide input to add useful information to the AI's database of answers.

[0126] Step 2:

[0127] Server: Reviews the submitted information and incorporates it into a database. Based on the usefulness of the information, reward points are awarded to patent attorneys.

[0128] Step 3:

[0129] Patent attorneys: You can choose to use your earned reward points to advertise in related fields.

[0130] Example 1

[0131] Next, a description will be given of Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the smart device 14 will be referred to as a "terminal."

[0132] In conventional patent procedures, users often required a great deal of time and effort to gather appropriate information. Furthermore, when expert assistance was required, the process of finding the appropriate expert was complicated, and managing the progress of the procedure was also time-consuming. Furthermore, the lack of a mechanism for effectively incorporating the knowledge of experts sometimes led to a decline in the quality of service provided to users. To solve these issues, a patent procedure support system that users can easily access is needed.

[0133] The specific processing by the specific processing unit 290 of the data processing device 12 in the first embodiment is realized by the following means.

[0134] In this invention, the server includes a means for receiving user authentication information and acquiring user identification information, a means for passing the consultation content received from the user to a text analysis means and extracting important features and background information, and a means for a generative AI model to generate appropriate data based on the extracted features and background information and provide it to the user. This allows users to efficiently obtain information about patent procedures and receive appropriate expert assistance. Furthermore, by tracking the progress of the procedures and notifying users of important deadlines and tasks, users can prevent delays in the procedures. Furthermore, by effectively incorporating expert knowledge into the system, the quality of service can be improved.

[0135] "User authentication information" is information used to identify a user and access a system, and typically consists of a username and password.

[0136] "User identification information" is data including the user's personal information and account information, and is used to identify the user.

[0137] "Consultation content" refers to questions or problems that users input into the system regarding patent procedures.

[0138] "Text analysis means" refers to technology for extracting important features and contextual information from received text data, and typically uses Natural Language Processing (NLP) techniques.

[0139] "Features" are important points or keywords extracted based on specific data or information.

[0140] "Background information" is data that provides context or relevance for particular data or information.

[0141] A "generative AI model" is a model that uses artificial intelligence technology to generate specific data or information, and makes predictions or judgments based on past data.

[0142] "Appropriate data" refers to information that is useful to the user and is generated based on the content of the user's consultation and background information.

[0143] An "expert" is a professional with advanced knowledge and experience in patent procedures.

[0144] A "response" is an additional answer or advice that an expert provides to a user in response to a question that the user has asked.

[0145] "Progress" refers to information that indicates the current state or progress of a particular procedure that a user is performing.

[0146] "Critical deadlines" are important filing or task deadlines in patent prosecution.

[0147] "Notifications" are messages sent by the system to inform users of important information or deadlines.

[0148] "Reward points" are points awarded to experts for providing useful information to the system, and serve as a reward for the experts.

[0149] "Promotion" refers to advertising or promotion in a related field using reward points acquired by an expert.

[0150] The system of this invention aims to efficiently support users in consulting about patent procedures. The system comprehensively performs a range of functions, from obtaining user authentication information, analyzing the text of the consultation, generating answers using AI, collaborating with experts, managing progress, and providing reward points.

[0151] Hardware and Software Configuration

[0152] Server: A high-performance computer equipped with a database management system and an analytical engine including an AI model. Specific software used for the generative AI model is natural language processing technology (e.g., Transformers and BERT).

[0153] Device: Using an internet browser or dedicated application on a device accessible to the user (smartphone, tablet, computer, etc.).

[0154] Database: A relational database (e.g., MySQL, PostgreSQL) for storing data such as user authentication information, profiles, text consultations, and expert responses.

[0155] System Operation

[0156] 1. User Authentication

[0157] The user logs into their account from a terminal and enters their username and password.

[0158] The server receives the entered authentication information and checks it against a database to obtain the user's identity.

[0159] 2. Input and analysis of consultation details

[0160] The user inputs questions or inquiries about patents into the terminal.

[0161] The terminal transmits the inputted contents to the server.

[0162] The server passes the received consultation content to a text analysis means to extract important features and background information.

[0163] 3. Early diagnosis and information provision using AI

[0164] The server uses the generative AI model to generate appropriate data based on the extracted features and background information.

[0165] The server provides the generated information to the user.

[0166] 4. Collaboration with experts

[0167] If a user's inquiry is too complex for the AI ​​model to handle on its own, the server will forward the information to an expert.

[0168] The expert checks the user's consultation and inputs an additional response.

[0169] The server forwards the expert's response to the user.

[0170] 5. Progress Management

[0171] The server tracks the user's progress in completing specific procedures and manages important deadlines and tasks.

[0172] A notification is sent to the device when the submission deadline approaches.

[0173] 6. Reward Points Management

[0174] Experts add useful information to the generative AI model's answer database.

[0175] The server reviews the provided information and awards reward points to the expert.

[0176] Professionals have the option to use earned reward points to post advertisements in related fields.

[0177] Specific examples

[0178] Example 1: New invention consultation

[0179] 1. If a user comes up with a new invention and does not know how to apply for a patent, they can log in to their account using their device and enter a summary of their invention.

[0180] 2. The server analyzes the information entered and generates information on basic application procedures and required documents.

[0181] 3. The user types a follow-up question: "Is this invention likely to be patented?"

[0182] 4. The server investigates similar cases from the past, generates an answer, and obtains additional answers from experts if necessary.

[0183] 5. The answer is displayed on the device so the user knows the next step.

[0184] Specific prompt examples:

[0185] I have come up with a new invention, but I don't know how to apply for a patent. Can you tell me if there is a high chance that this invention will be patented?

[0186] Example 2: Reminder function

[0187] 1. A user initiates the patent application process and sets a specific submission deadline.

[0188] 2. The server tracks this information and sets reminders when deadlines approach.

[0189] 3. A reminder will be sent to your device one week before the submission deadline.

[0190] 4. The user receives a reminder notification and proceeds with the next action.

[0191] Specific prompt examples:

[0192] Please inform us that the deadline for submitting patent applications is one week away.

[0193] As described above, this system supports complex patent-related procedures, enabling both users and experts to carry out their work efficiently.

[0194] The flow of the identification process in the first embodiment will be described with reference to FIG.

[0195] Step 1:

[0196] The user launches the "Patent Attorney AI" application and enters their username and password. A username and password are required as input. The device sends this authentication information to the server. The server compares the received input data with a database and verifies whether authentication is successful. If authentication is successful, the user's profile information is returned to the device as output.

[0197] Step 2:

[0198] After successful authentication, the terminal displays the user's dashboard screen and provides a form for the user to enter the consultation details. Input includes the user's questions about patents and the details of the consultation. The user enters the consultation details and clicks the "Send" button. The terminal then sends the entered consultation details to the server.

[0199] Step 3:

[0200] The server passes the received consultation content to the text analysis means. The input includes the text data of the consultation content entered by the user. The server uses the text analysis means to extract important features and background information. Specifically, it uses natural language processing (NLP) technology to analyze the text and extract important keywords and context. The output is the extracted features and background information.

[0201] Step 4:

[0202] The server passes the extracted features and background information as input to a generative AI model. The generative AI model generates appropriate data based on the input data. Specifically, the generative AI model compares the data with past data and generates appropriate patent procedure steps and necessary document information based on the consultation content. The generated information is obtained as output.

[0203] Step 5:

[0204] The server formats the generated information and sends it to the device to be served to the user. The input includes the information generated by the generative AI model. The server formats this into an easy-to-read format and serves it to the user. The output is the formatted information sent to the device.

[0205] Step 6:

[0206] The terminal displays the information received from the server on the user's dashboard, which includes formatted information received from the server as input, allowing the user to review the patent application procedures and required documents.

[0207] Step 7:

[0208] If the user's inquiry is too advanced for the generative AI model alone to handle, the server forwards the information to an expert. The input includes the user's advanced inquiry content, which the server forwards to the expert. The expert then inputs an additional response based on this and returns it to the server. The output is the expert's response.

[0209] Step 8:

[0210] The server receives the expert's response and sends it to the terminal for presentation to the user. The input includes the response data from the expert, which the server converts into a user-friendly format and sends to the terminal. The output is the formatted response sent to the terminal.

[0211] Step 9:

[0212] The device displays the expert's response on a dashboard, where the user can see specific advice. The input includes the expert's response data. The user can then take further action based on the response.

[0213] Step 10:

[0214] The server tracks the progress of a specific patent procedure when a user initiates it. The input includes the user's initiation information. The server tracks the progress and manages important deadlines and tasks. The output is progress data.

[0215] Step 11:

[0216] The server sets reminders when a specific deadline approaches and sends notifications to the user's device. The input includes progress data and deadline information. The server generates a reminder notification based on this and sends it to the device. The output is a reminder notification.

[0217] Step 12:

[0218] The device displays a reminder notification to the user, helping the user remember to proceed with important tasks. The input includes the reminder notification received from the server. The user checks the notification and takes the next action.

[0219] Step 13:

[0220] Experts add useful information to the generative AI model's answer database. The input includes information based on the expert's knowledge and experience. The expert enters the information into the system, which is received by the server. This adds useful information to the database. The output is updated information.

[0221] Step 14:

[0222] The server awards reward points to the expert based on the provided information. The input includes information provided by the expert. The server evaluates this information, calculates and awards reward points. The output is reward points.

[0223] Step 15:

[0224] The expert uses the reward points he / she has acquired to select an option to post an advertisement in a related field. The input includes the reward points he / she has acquired and the advertisement content. The expert selects the option, and the server performs the procedure to post the advertisement. The output is the advertisement.

[0225] (Application example 1)

[0226] Next, a description will be given of Application Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the smart device 14 will be referred to as a "terminal."

[0227] While support systems existed to help users efficiently advance patent procedures, they were limited in their ability to provide appropriate information in real time in response to user inquiries and questions. Furthermore, there was no system that provided patent-related knowledge through educational content and offered expert support, making it difficult for users to understand the progress of their procedures and take appropriate action.

[0228] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 1 is realized by the following means.

[0229] In this invention, the server includes: means for receiving user authentication information and acquiring user profile information; means for passing the inquiry received from the user to a text analysis engine and extracting important keywords and context; means for an AI model to generate appropriate information based on the extracted keywords and context and provide it to the user; means for forwarding the information to a specialist in a specialized field to obtain additional answers if the user's question is difficult and cannot be solved by AI alone; means for tracking the user's progress in a specific procedure and reminding the user of important deadlines and tasks; means for experts to add useful information to the AI's answer database and award reward points; means for streaming educational content viewable on electronic devices; and means for analyzing viewers' questions in real time and providing related information. This allows users to deepen their knowledge of patent procedures and receive expert assistance at the appropriate time.

[0230] "User authentication information" is information for identifying a specific user and verifying access rights.

[0231] "Profile information" refers to basic attribute information about a user and personal data within the system.

[0232] "Consultation content" refers to questions or matters of consultation that a user inputs into the system regarding patent procedures.

[0233] A "text analysis engine" is software that extracts important keywords and context from input text data.

[0234] An "AI model" is a data processing algorithm that uses artificial intelligence to solve a specific problem.

[0235] An "expert" is a person with advanced knowledge and skills in a particular field who provides advice and information to users.

[0236] "Progress" is information that indicates the current stage of a particular task or project.

[0237] A "deadline" refers to the deadline for completing a particular task or procedure.

[0238] A "task" refers to a specific task or action that needs to be completed.

[0239] A "remind" is a notification that reminds a user of a specific action or deadline.

[0240] "Reward Points" are a form of reward given to Experts for useful activities they perform on the system.

[0241] "Educational content" refers to learning materials created to teach specific knowledge or skills.

[0242] "Streaming distribution" is a method of continuously transmitting and viewing audio and video in real time over the Internet.

[0243] "Real-time" refers to the instantaneous processing of data and exchange of information.

[0244] As an embodiment of the present invention, a patent procedure support system is realized as follows: Specific programs and processing methods of this system are described below.

[0245] System configuration and hardware / software used

[0246] Hardware and software used

[0247] Hardware: Smartphones, head-mounted displays, servers

[0248] Software: Mobile applications (iOS / Android), server-side AI models, text analysis engines, streaming platforms (e.g., AWS Elemental Media Services)

[0249] System processing explanation

[0250] User authentication and basic information entry

[0251] The device launches the "Patent Attorney AI Course" application. The user enters authentication information (username, password) on the login screen. The server receives the user's authentication information and authenticates it by checking it against a database. If authentication is successful, the server obtains the user's profile information and sends it to the device. After successful authentication, the device displays the main dashboard screen, and the user can select the course they wish to view.

[0252] Lecture viewing and interaction

[0253] The user selects a course from their device and presses the play button to watch the video course. The server streams the video data of the selected course. While watching the video course, the user enters their question or inquiry into a text box on the screen. The server passes the input inquiry to a text analysis engine (e.g., AWS Comprehend) to extract important keywords and context.

[0254] Early diagnosis and information provision using AI models

[0255] The server uses an AI model (e.g., OpenAI GPT-4) to generate relevant information based on the extracted keywords and context. The generated information is then formatted and sent to the user. The device then displays the information generated by the AI ​​model to the user, helping them further understand the information.

[0256] Collaboration with experts (patent attorneys)

[0257] When a user enters an advanced question, the server forwards the information to a specific expert (patent attorney). The expert uses a dedicated dashboard to check the user's question and enters an answer. The server receives additional information from the expert and forwards it back to the user. The terminal displays the information from the expert and helps the user take appropriate action.

[0258] Reminder function

[0259] The server tracks the user's progress in completing a specific procedure. When a specific deadline approaches, the server sets a reminder and sends a notification to the user. The notification is sent to the user's device using Firebase Cloud Messaging. The user can then take the next action after receiving the reminder notification.

[0260] Examples and prompts

[0261] Example 1: Initial diagnosis

[0262] User Input: "Is this invention likely to be patented?"

[0263] AI response: "The user has entered a summary of their invention. Input: ____. Based on this, please assess the likelihood of obtaining a patent."

[0264] Example 2: Feedback on a specific task

[0265] User Input: "What documents do I need to file a patent application?"

[0266] AI response: "The user asked about the documents required for a patent application. Please provide details below."

[0267] The patent procedure support system implemented in this way allows users to deepen their knowledge of patent procedures and receive expert support at the appropriate time.

[0268] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[0269] Step 1:

[0270] The user launches the "Patent Attorney AI Course" application and enters authentication information (username, password) on the login screen. The entered authentication information is sent to the server. The server receives the authentication information and authenticates the user by checking it against a database. During this process, the server obtains the user's profile information and sends a successful authentication result to the terminal.

[0271] Input: Username, Password

[0272] Data processing: User authentication by database matching

[0273] Output: Authentication success / failure result, user profile information

[0274] Specific operation: The server searches for user information in a database (e.g., AWS RDS), and if there is a match, it sends a flag indicating successful authentication to the terminal.

[0275] Step 2:

[0276] After successful authentication, the device displays the main dashboard screen. The user selects the course they wish to view from the dashboard. This course selection data is sent to the server.

[0277] Input: User-input course selection

[0278] Data processing: Send the selected course ID to the server

[0279] Output: Course selection data to the server

[0280] Specific behavior: The user interface displays a list of courses, and the user selects one by touch.

[0281] Step 3:

[0282] The server retrieves the video data of the course selected by the user and prepares it for streaming. The server then sends the video data to the device via streaming (e.g., AWS Elemental Media Services).

[0283] Input: Selected course ID

[0284] Data processing: Acquisition of video data and streaming distribution

[0285] Output: Sending video data via streaming

[0286] Specific operation: The server generates a URL for the video data and passes it to the streaming service.

[0287] Step 4:

[0288] The device plays the video lecture, and the user watches the video. While watching, the user enters questions or inquiries into a text box on the screen. This input data is sent to the server.

[0289] Input: User-entered consultation details

[0290] Data processing: Receiving and sending text data

[0291] Output: Send consultation text to server

[0292] Specific operation: The user interface provides a text input field and sends the input to the server using a submit button.

[0293] Step 5:

[0294] The server passes the consultation content received from the user to a text analysis engine (e.g., AWS Comprehend) to extract important keywords and context.

[0295] Input: Consultation content text

[0296] Data processing: Keyword extraction through text analysis

[0297] Output: Extracted keywords and context

[0298] Specific operation: The server sends text data to the API of the text analysis engine and receives the analysis results.

[0299] Step 6:

[0300] The server uses an AI model (e.g., OpenAI GPT-4) to generate relevant information based on the extracted keywords and context, formats the information, and sends it to the user.

[0301] Input: Keywords, Context

[0302] Data processing: Information generation using AI models

[0303] Output: Sending relevant formatted information

[0304] Specific operation: The server calls the AI ​​model, formats the generated information in HTML or JSON format, and sends it to the device.

[0305] Step 7:

[0306] The device displays information from the AI ​​model to the user, who then asks additional questions or takes action based on the information.

[0307] Input: Formatted and relevant information from the AI ​​model

[0308] Data processing: UI display of information

[0309] Output: Displaying information to the user

[0310] What it does: The user interface displays the generated information in rich text and card format, encouraging further interaction.

[0311] Step 8:

[0312] When a user enters a complex question, the server transfers the question to an expert, who then uses a dedicated dashboard to review the question and enter additional answers. The server then receives the information from the expert and sends it to the user.

[0313] Input: Advanced Question Content

[0314] Data processing: forwarding to experts and receiving answers

[0315] Output: User submission of additional information from the expert

[0316] Specific operation: The server displays the question on the expert's dashboard, receives the answer entered by the expert, and returns it to the user.

[0317] Step 9:

[0318] The server tracks the user's progress in completing specific procedures, sets reminders when specific deadlines for submissions are approaching, and sends notifications to the user.

[0319] Input: Procedural progress data, submission deadline

[0320] Data processing: generating and sending notification content

[0321] Output: Reminder notification sent to user

[0322] Specific operation: The server obtains deadline information from the progress management system, generates a reminder notification, and sends it to the user's device via Firebase Cloud Messaging.

[0323] Step 10:

[0324] The user receives a reminder notification and takes the next action, such as checking submitted documents or consulting an expert for further assistance.

[0325] Input: Reminder notification

[0326] Data manipulation: Performing user actions

[0327] Output: The following procedure is completed

[0328] Specific behavior: The user clicks on the link in the notification, and a screen appears on the device to take the next step or to consult with an expert.

[0329] Furthermore, an emotion engine that estimates the user's emotion may be combined. That is, the identification processing unit 290 may estimate the user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

[0330] The "Patent Attorney AI" system of the present invention is a system that allows users to easily consult about patent procedures and supports the efficient progress of the patent application process. Furthermore, it aims to improve the user experience by incorporating an emotion engine that recognizes and responds to the user's emotions. Below, we will generate a program for the system and explain its processing in natural language.

[0331] Explanation of program processing

[0332] User authentication and basic information entry

[0333] 1. Device: The user launches the "Patent Attorney AI" application and enters authentication information (username, password) on the login screen.

[0334] 2. Server: Receives the user's authentication information and authenticates the login by checking it against a database.

[0335] 3. Server: If authentication is successful, obtain the user's profile information and send it to the device.

[0336] 4. Terminal: After successful authentication, the dashboard screen is displayed and a form is provided where the user can enter the details of their consultation.

[0337] Enter the consultation details

[0338] 1. Terminal: The user enters information into a text box for entering questions or inquiries about patents.

[0339] 2. Terminal: Sends the consultation details entered by the user to the server.

[0340] 3. Server: The received consultation content is passed to a text analysis engine to extract important keywords and context.

[0341] AI-based initial diagnosis and information provision

[0342] 1. Server: Based on the extracted keywords and context, the AI ​​model generates appropriate information (e.g., patent application steps and required documents).

[0343] 2. Server: Formats the generated information and sends it to the user.

[0344] 3. Terminal: Displays the results of the initial diagnosis and related information to the user, such as "specific steps for patent application" and "list of required documents."

[0345] Emotion recognition and response using emotion engine

[0346] 1. Device: The emotion engine analyzes the user's text input and behavior and recognizes emotions.

[0347] 2. Server: Adjust the information provided and the tone of the response based on the emotions recognized by the emotion engine.

[0348] 3. Terminal: For example, if the user is perceived as confused, additional support information is displayed in a gentle tone.

[0349] Collaboration with patent attorneys

[0350] 1. Server: If the user's question is too advanced to be resolved by AI alone, the server transfers the consultation to an expert (e.g., a patent attorney).

[0351] 2. Patent attorney: The expert reviews the user's consultation and enters any necessary additional information or advice.

[0352] 3. Server: Receives additional information from the expert and forwards it to the user.

[0353] 4. Terminal: Additional information from the patent attorney is displayed to the user, allowing them to receive specific advice.

[0354] Procedure progress reminders

[0355] 1. Server: When a user initiates a specific patent procedure, it tracks its progress, for example, managing specific submission deadlines and tasks.

[0356] 2. Server: Set a reminder when the submission deadline approaches.

[0357] 3. On the device: A deadline reminder is sent to the user. For example, a notification saying, "Your patent application is due in one week."

[0358] 4. User: Based on the reminder notification, you can take the following actions:

[0359] Reward Points Management

[0360] 1. Patent attorneys: Experts provide input to add useful information to the AI's database of answers.

[0361] 2. Server: Reviews the submitted information, incorporates it into a database, and awards reward points to patent attorneys based on the usefulness of the information.

[0362] 3. Patent attorneys: You can choose to use the reward points you have earned to advertise in related fields.

[0363] Specific examples

[0364] Example 1: New invention consultation

[0365] 1. A user has come up with a new invention but does not know the procedure for filing a patent application.

[0366] 2. Using your device, log in to your account and enter a summary of your invention.

[0367] 3. The server analyzes the information entered and provides the user with basic application procedures and necessary documents.

[0368] 4. The emotion engine detects anxiety from the user's input and displays a more polite help message.

[0369] 5. The user enters an additional, more specific question, asking, "Is this invention likely to be patentable?"

[0370] 6. The server researches similar cases from the past, generates a response, and obtains additional responses from patent attorneys if necessary.

[0371] 7. The answer is displayed on the device so the user knows the next step.

[0372] Example 2: Reminder function

[0373] 1. A user initiates the patent application process and a specific submission deadline is set.

[0374] 2. The server tracks this information and sets reminders when deadlines approach.

[0375] 3. A reminder notification will be sent to the device one week before the submission deadline, helping users to remember to complete important tasks.

[0376] This allows the "Patent Attorney AI" system to support complex patent-related procedures, enabling both users and experts to carry out their work efficiently. Furthermore, the emotion engine improves the user experience, allowing particularly troubled users to receive appropriate support.

[0377] The processing flow will be explained below.

[0378] Processing of the "Patent Attorney AI" system including an emotion engine

[0379] User authentication and basic information entry

[0380] Step 1:

[0381] Terminal: The user launches the "Patent Attorney AI" application and enters authentication information (username, password) on the login screen.

[0382] Step 2:

[0383] Server: Receives the user's authentication information and authenticates the login against a database.

[0384] Step 3:

[0385] Server: If authentication is successful, obtain the user's profile information and send it to the terminal.

[0386] Step 4:

[0387] Terminal: After successful authentication, the dashboard screen is displayed and a form is provided where the user can enter the details of their consultation.

[0388] Enter the consultation details

[0389] Step 1:

[0390] Terminal: The user enters information into a text box for entering questions or consultations about patents.

[0391] Step 2:

[0392] Terminal: Sends the consultation details entered by the user to the server.

[0393] Step 3:

[0394] Server: Passes the received consultation content to a text analysis engine to extract important keywords and context.

[0395] AI-based initial diagnosis and information provision

[0396] Step 1:

[0397] Server: Based on the extracted keywords and context, the AI ​​model generates appropriate information (e.g., patent application steps and required documents).

[0398] Step 2:

[0399] Server: Formats the generated information and sends it to the user.

[0400] Step 3:

[0401] Terminal: Displays the results of the initial diagnosis and related information to the user, such as "specific steps for patent application" and "list of required documents."

[0402] Emotion recognition and response using emotion engine

[0403] Step 1:

[0404] Device: The emotion engine analyzes the user's text input and behavior to recognize emotions.

[0405] Step 2:

[0406] Server: Adjusts the information provided and the tone of the response based on the emotions recognized by the emotion engine.

[0407] Step 3:

[0408] Terminal: For example, if the user is perceived as confused, additional support information is displayed in a gentle tone.

[0409] Collaboration with patent attorneys

[0410] Step 1:

[0411] Server: If the user's question is too advanced to be resolved by AI alone, the server transfers the consultation to an expert (e.g., a patent attorney).

[0412] Step 2:

[0413] Patent attorney: The expert reviews the user's consultation and enters any necessary additional information or advice.

[0414] Step 3:

[0415] Server: Receives additional information from the expert and forwards it to the user.

[0416] Step 4:

[0417] Terminal: Additional information from the patent attorney is displayed to the user, allowing them to receive specific advice.

[0418] Procedure progress reminders

[0419] Step 1:

[0420] Server: Tracks the progress of specific patent proceedings initiated by users, for example, managing specific submission deadlines and tasks.

[0421] Step 2:

[0422] Server: Set reminders and start timers when the submission deadline approaches.

[0423] Step 3:

[0424] Terminal: A deadline reminder is sent to the user. For example, a notification saying, "Your patent application is due in one week."

[0425] Step 4:

[0426] User: Based on the reminder notification, you can take the following actions:

[0427] Reward Points Management

[0428] Step 1:

[0429] Patent attorneys: Experts provide input to add useful information to the AI's database of answers.

[0430] Step 2:

[0431] Server: Reviews the submitted information and incorporates it into a database. Based on the usefulness of the information, reward points are awarded to patent attorneys.

[0432] Step 3:

[0433] Patent attorneys: You can choose to use your earned reward points to advertise in related fields.

[0434] Example 2

[0435] Next, a description will be given of Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the smart device 14 will be referred to as a "terminal."

[0436] Traditional patent procedures are extremely complicated, making it difficult for individual inventors to smoothly navigate the patent application process. During the patent acquisition process, users often overlook important deadlines or fail to obtain appropriate information. Furthermore, due to a lack of appropriate responses based on the user's emotional state, they are prone to feeling anxious and confused during the patent process. Furthermore, many questions are difficult and often cannot be resolved by artificial intelligence alone, requiring expert support. Smooth collaboration is crucial in these situations. Conventional systems are unable to adequately address these issues.

[0437] The identification process by the identification processing unit 290 of the data processing device 12 in Example 2 is realized by the following means. In this invention, the server includes: means for receiving user authentication information and acquiring user profile information; means for passing the consultation content received from the user to a text analysis engine and extracting important keywords and context; means for an AI model to generate appropriate information based on the extracted keywords and context and provide it to the user; means for forwarding information to a specialized expert and obtaining additional answers when the user's question is difficult and cannot be solved by AI alone; means for tracking the user's progress in a specific procedure and reminding them of important deadlines and tasks; means for the expert to add useful information to the AI's answer database and award reward points; means for analyzing the user's text input and behavior, and for an emotion engine to recognize emotions and adjust responses; and means for formatting the consultation content and additional answers from the expert and forwarding them to the user. This allows users to avoid missing important deadlines in patent prosecution, obtain appropriate information, and receive support tailored to their emotional state. Furthermore, AI and experts work together effectively to provide users with high-quality information and support. As a result, the entire patent process can proceed efficiently and user-friendly.

[0438] "User authentication information" refers to the information a user needs to log into a system, and typically includes a username and password.

[0439] "Profile Information" means data including a user's personal information and account information, which is used to identify the user and provide services.

[0440] "Consultation content" refers to questions or matters of consultation that a user submits to the system regarding a patent application.

[0441] A "text analysis engine" is a software engine that uses natural language processing technology to extract important keywords and context from input text data.

[0442] An "artificial intelligence model" refers to an algorithm or system that uses machine learning or deep learning to generate appropriate information from data.

[0443] "Subject matter experts" refer to professionals with in-depth knowledge and experience in patents and intellectual property, typically including patent attorneys and patent lawyers.

[0444] "Progress" refers to information indicating how far a user has progressed in the patent process.

[0445] "Deadline" refers to the deadline for each task or submission in the patent process.

[0446] "Remind" is a feature that notifies users so they don't forget important deadlines or tasks.

[0447] "Reward points" refer to incentives awarded to experts when they provide useful information, and can be used for specific benefits or advertising within the system.

[0448] An "emotion engine" is a software engine that analyzes a user's text input and behavior to recognize the user's emotional state.

[0449] The present invention is a support system for users to smoothly proceed with patent procedures, and specific embodiments thereof are described below.

[0450] The system mainly consists of a user terminal, a central server, and the "Patent Attorney AI" application that constitutes the user interface. Terminals can be ordinary PCs, smartphones, tablets, etc., and the server functions as a central processing unit including a database.

[0451] User authentication and basic information entry

[0452] 1. Terminal: The user launches the "Patent Attorney AI" application and enters authentication information (username, password) on the login screen. The entered information is securely transmitted to the server using the SSL / TLS protocol.

[0453] 2. Server: Receives the authentication information and authenticates the login by checking it against a database (e.g., MySQL or PostgreSQL). If authentication is successful, it obtains the user's profile information and sends it to the terminal in JSON format.

[0454] 3. Terminal: If authentication is successful, the dashboard screen will be displayed and a form will be provided where the user can enter the details of their consultation.

[0455] Input and analysis of consultation details

[0456] 1. Terminal: When the user enters a question or inquiry about a patent and clicks the send button, the information is sent to the server in JSON format.

[0457] 2. Server: The received consultation content is passed to a natural language processing (NLP) engine (e.g., SpaCy or NLTK) to extract important keywords and context. The extracted results are stored in a database for further processing.

[0458] Early diagnosis and information provision

[0459] 1. Server: Input the extracted keywords and context into an artificial intelligence model (e.g., GPT-3 or BERT) to generate appropriate information.

[0460] Example prompt: "Generate a list of required application procedures and documents based on the patent question entered by the user."

[0461] 2. Server: Formats the information generated by the AI ​​model and prepares it for delivery to the user. The formatted information is sent to the device in JSON format.

[0462] 3. Terminal: Displays the initial diagnosis results and provided information to the user. For example, the "steps for patent application" and "list of required documents" are displayed.

[0463] Emotion recognition and response using emotion engine

[0464] 1. Terminal: Analyzes the user's text input and behavior (e.g., typing speed and click operations) in real time and passes the results to an emotion engine (e.g., IBM Watson or Aylien).

[0465] 2. Server: Based on the emotion engine's recognition of the user's emotions, the server adjusts the tone of the information and responses it provides. For example, if the server recognizes that the user is confused, it provides additional support information in a gentler tone.

[0466] 3. Device: Display messages and information tailored to the user's emotions. For example, polite guidance such as "If you need help, please see here" is displayed.

[0467] Collaboration with experts

[0468] 1. Server: When a complex question is received that cannot be resolved by artificial intelligence alone, the content is forwarded to an expert (e.g., a patent attorney).

[0469] 2. Patent attorney: The expert logs in to a dedicated portal, reviews the user's question, and enters any additional information or specific advice required.

[0470] 3. Server: Receives additional responses from the patent attorney, formats them, and sends them to the terminal.

[0471] 4. Terminal: Experts provide information and give users specific advice on next steps.

[0472] Procedure progress reminders

[0473] 1. Server: When a user initiates a specific patent procedure, its progress is tracked in a database. Deadlines for each task are managed.

[0474] 2. Server: When the submission deadline approaches, a reminder is generated and sent to the device.

[0475] 3. Terminal: A reminder notification is displayed to the user, for example, a message saying, "The deadline for submitting your patent application is one week away." The user receives the notification and can take appropriate action.

[0476] Reward Points Management

[0477] 1. Patent attorneys: Experts add useful information to the AI's database of answers.

[0478] 2. Server: Automatically reviews the submitted information, evaluates its quality and usefulness, and awards reward points to the attorney if the evaluation is high.

[0479] 3. Patent Attorneys: You will be given the option to use your earned reward points to advertise in related fields.

[0480] This system allows users to carry out patent procedures themselves while receiving appropriate support and proceeding efficiently. In particular, support from the emotion engine and smooth collaboration with experts contribute to an improved user experience.

[0481] The flow of the identification process in the second embodiment will be described with reference to FIG.

[0482] Step 1:

[0483] Terminal: The user launches the "Patent Attorney AI" application and enters authentication information (username, password) on the login screen. This becomes the input data. When the user presses the "Login" button, the entered authentication information is sent to the server using the SSL / TLS protocol. The output is encrypted authentication information sent to the server.

[0484] Step 2:

[0485] Server: Receives authentication information and performs authentication by checking it against a database (e.g., MySQL or PostgreSQL). This check involves data calculations to confirm whether the username and password match. If authentication is successful, retrieves the user's profile information from the database and sends it to the terminal as output data in JSON format. As output, JSON data containing the user's profile information is sent to the terminal.

[0486] Step 3:

[0487] Terminal: After successful authentication, the terminal displays a dashboard screen and provides a form where the user can enter their consultation details. This includes input fields such as "Question," "Invention summary," and "Contact information." The input fields are displayed to the user as output data.

[0488] Step 4:

[0489] Terminal: When a user enters a question or consultation about a patent and clicks the send button, the entered information is sent to the server in JSON format. The input data is the user's question or consultation content, and the output data is JSON format data containing that content.

[0490] Step 5:

[0491] Server: Passes the received consultation content to a natural language processing (NLP) engine (e.g., SpaCy or NLTK) to extract important keywords and context. The input data is the consultation content sent by the user, and important keywords and context are extracted through data processing by the NLP engine. The output data is a list of the extracted keywords and context. The extraction results are stored in a database.

[0492] Step 6:

[0493] Server: Inputs the extracted keywords and context into an artificial intelligence model (e.g., GPT-3 or BERT) to generate appropriate information. The input data are the extracted keywords and context, and the artificial intelligence model calculates the data to generate information related to the user's question. Example prompt: "Based on the patent question entered by the user, please generate a list of the necessary application procedures and documents." The output data is the generated answer information.

[0494] Step 7:

[0495] Server: Formats the information generated by the AI ​​model and presents it to the user. This formatting involves processing the data to generate formatted documents that explain technical terms and specific procedures in an easy-to-understand manner. The formatted data is sent to the terminal in JSON format. The output data is formatted information.

[0496] Step 8:

[0497] Terminal: Displays the initial diagnosis results and provided information to the user. This includes "patent application steps" and "list of required documents." Output data is the specific information displayed on the user's screen.

[0498] Step 9:

[0499] Terminal: Analyzes the user's text input and behavior (e.g., typing speed and click operations) in real time and passes them to an emotion engine (e.g., IBM Watson or Aylien). The input data is the user's behavior data, and emotions are identified through data calculations by the emotion engine. The output data is the result of the emotion identification.

[0500] Step 10:

[0501] Server: The emotion engine adjusts the tone of the information and responses it provides based on the emotions it recognizes. For example, if it recognizes that the user is confused, it generates additional support information in a gentler tone. At this time, data processing involves adding modifications to the template sentence according to the user's emotional state. The output data is the adjusted message.

[0502] Step 11:

[0503] Terminal: Displays messages and information tailored to the user's emotions. For example, polite guidance such as "If you have any questions, please see here" is displayed. The output data is a message tailored to the user's emotions.

[0504] Step 12:

[0505] Server: When a user receives a complex question that cannot be resolved by AI alone, the server transfers the question to an expert (e.g., a patent attorney). The input data is the content of the complex question, and the transferred data is the specific question information. The expert logs into a dedicated portal, checks the user's question, and enters any necessary additional information or specific advice.

[0506] Step 13:

[0507] Server: Receives additional responses from patent attorneys, formats them, and sends them to the terminal. The input data is the content of the patent attorney's response, and the output data is the formatted additional response information.

[0508] Step 14:

[0509] Terminal: Information from experts is provided and users can get specific advice. The output data is specific advice that is displayed on the user's screen.

[0510] Step 15:

[0511] Server: When a user starts a specific patent procedure, its progress is tracked in a database. The input data is the user's procedure start information, and the progress information is updated in the database through tracking. The output data is the progress information.

[0512] Step 16:

[0513] Server: When the deadline approaches, a reminder is generated and sent to the terminal. The input data is progress information, and the output data is the reminder notification.

[0514] Step 17:

[0515] Terminal: A reminder notification is displayed to the user, for example, a message saying "The deadline for submitting patent application documents is one week away." The output data is the reminder notification.

[0516] Step 18:

[0517] User: Receives a reminder and takes the next step to meet the deadline. The input data is the reminder and the output data is the user's next action.

[0518] Step 19:

[0519] Patent attorney: An expert adds useful information to the AI's response database. The input data is the newly added information, and the output data is the information stored in the database.

[0520] Step 20:

[0521] Server: Automatically reviews the provided information and evaluates its quality and usefulness. If the evaluation is high, reward points are awarded to the patent attorney. The input data is the information to be evaluated, and the output data is the awarded reward points.

[0522] Step 21:

[0523] Patent attorneys: They are given the option to use their earned reward points to place advertisements in related fields. The input data is the reward points, and the output data is the use of the advertisement placement option.

[0524] (Application example 2)

[0525] Next, a description will be given of Application Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the smart device 14 will be referred to as a "terminal."

[0526] In modern factories, new technologies and processes are frequently proposed, but the process of managing them and filing patent applications is extremely complex, time-consuming, and costly. In particular, the need to quickly evaluate proposed technologies on-site and determine their patentability is particularly challenging, but traditional methods are too labor-intensive. In addition, factory workers often lack adequate support and feedback on their proposals, which can lead to demotivation. Furthermore, tracking the progress of patent applications and managing important deadlines presents challenges.

[0527] The specific processing by the specific processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means. In this invention, the server includes: means for receiving user authentication information and acquiring user profile information; means for passing the inquiry received from the user to a text analysis engine and extracting important keywords and context; means for an AI model to generate appropriate information based on the extracted keywords and context and provide it to the user; means for forwarding information to a specialized expert in a specialized field to obtain additional answers if the user's question is difficult and cannot be solved by AI alone; means for tracking the progress of the user's specific procedures and reminding the user of important deadlines and tasks; means for the expert to add useful information to the AI's answer database and award reward points; means for receiving suggestions via voice input, converting the speech to text, and analyzing the content as a possible patent application; means for recognizing emotions from voice and facial expressions and adjusting the tone of the information provided based on the emotions; and means for tracking the progress of patent application proposals and sending reminders. This enables efficient management of new technology and process proposals in factories and rapid and accurate patent application procedures.

[0528] "User authentication information" means information used to identify a user and verify the user's access rights to access a system.

[0529] "Profile information" is data containing basic information about each user that identifies the individual user.

[0530] "Consultation content" is information detailing the question or problem that the user wants to solve through the system.

[0531] A "text analysis engine" is software or an algorithm that extracts meaning and important keywords from input text data and performs analysis.

[0532] "Keywords" are particularly important words or phrases in the content of a consultation, and are the subject of search and analysis.

[0533] "Context" is information about the context or situation in which a keyword is used, which clarifies the meaning of the keyword.

[0534] An "AI model" is an artificial intelligence algorithm or machine learning model that generates appropriate information or answers based on analyzed data.

[0535] "Subject matter experts" are individuals with advanced knowledge and experience in a particular field who provide additional assistance for problems that cannot be solved by AI.

[0536] "Progress" is information that indicates the progress of a particular task or project.

[0537] A "deadline" is the date by which a particular task or procedure must be completed.

[0538] A "task" is a specific job or operation that a user must accomplish.

[0539] "Reward points" are points that are awarded to experts for adding useful information to the database, which allow the experts to earn rewards or use certain options.

[0540] "Voice input" refers to a method in which a user inputs information by voice using a voice recognition device such as a microphone.

[0541] "Text conversion" is the process of converting audio data into text data.

[0542] "Patentability" is an indicator used to evaluate the likelihood that a new technology or invention will be granted a patent.

[0543] "Recognizing emotions from voice and facial expressions" is a technology that analyzes a user's voice and facial expressions to determine what emotions the user is feeling.

[0544] "Adjusting tone" means changing the tone or presentation of the information provided based on the user's emotional state.

[0545] "Sending reminders" means notifying users so that they do not forget important tasks or deadlines.

[0546] The system for implementing this invention uses the following hardware and software: The hardware includes smart glasses and microphones used by users, and a server. The software includes Google Cloud Natural Language API, GCP AutoML, AWS Rekognition, Slack API, and ICS (Internet Calendar Scheduling).

[0547] Obtaining user credentials and viewing profile information

[0548] The device receives user authentication information through the smart glasses and sends it to the server. The server compares the information with a database and authenticates the user. If authentication is successful, the server obtains the user's profile information and sends it to the device. After successful authentication, the device displays a dashboard screen and provides a form where the user can enter their consultation details.

[0549] Voice input and analysis of proposals

[0550] Users can voice-input suggestions for new technologies or process improvements through the smart glasses. The device converts the voice data into text and sends it to a server. The server then uses the Google Cloud Natural Language API to analyze the text and extract important keywords and context.

[0551] example:

[0552] Proposal: "Could production efficiency be improved by introducing a new robot arm control method?"

[0553] Extracted keywords: "Robot arm control method" and "Production efficiency"

[0554] Information generation using AI models

[0555] The server uses GCP's AutoML to perform an initial diagnosis of patentability based on the extracted keywords and context. As a result of the initial diagnosis, relevant information is provided to the user, including information on patent application procedures and similar existing technologies.

[0556] example:

[0557] Prompt: "Is this improvement proposal related to an existing patent?"

[0558] Prompt: "What procedure is needed next?"

[0559] Emotion recognition from speech and facial expressions

[0560] The device sends the user's voice and facial expression data to the server, which then uses AWS Rekognition to recognize emotions. Based on the recognized emotions, the server adjusts the tone of the information provided and displays appropriate support messages to the user. For example, if the server detects that the user is confused, it will provide more detailed explanations or additional information.

[0561] Collaboration with experts in the field

[0562] For more advanced suggestions that cannot be resolved using the information provided by the AI ​​model alone, the server uses a Slack API to forward them to a subject matter expert, who then sends additional answers and assistance to the server, which then displays them on the device.

[0563] example:

[0564] Prompt: "What documents do I need to create this idea?"

[0565] Proposal progress tracking and reminders

[0566] The server uses ICS to track the progress of proposals and manage deadlines and tasks. When important deadlines approach, reminders are automatically sent to the device, allowing users to efficiently progress through the patent application process without forgetting deadlines.

[0567] example:

[0568] Prompt: "The submission deadline is approaching. Don't forget to complete the process."

[0569] The system will enable efficient management of new technology and process proposals in factories, and ensure that patent application procedures are carried out quickly and accurately. Furthermore, emotion recognition capabilities will improve the user experience, enabling appropriate support to be provided to users who are particularly challenged.

[0570] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[0571] Step 1:

[0572] A user uses smart glasses and authenticates using voice commands. The device receives the user authentication information (e.g., username and password) and sends it to the server. The server checks this information against a database to confirm authentication and returns the result to the device. This allows the user to log in to the system and gain access rights.

[0573] input:

[0574] User authentication information (username, password)

[0575] output:

[0576] Authentication result (success or failure)

[0577] Specific behavior:

[0578] User enters username and password into smart glasses using voice commands

[0579] The device sends authentication information to the server and receives the authentication result.

[0580] Step 2:

[0581] If the server is successful in the authentication, it retrieves the user's profile information from the database and sends it to the terminal. The terminal then displays a dashboard screen and provides a form in which the user can enter the details of their consultation.

[0582] input:

[0583] Authentication result (success)

[0584] output:

[0585] Profile Information

[0586] Specific behavior:

[0587] The server searches the database to retrieve the user's profile information

[0588] Send profile information to device and display dashboard

[0589] Step 3:

[0590] Users can input suggestions for new technologies or process improvements by voice through smart glasses. The device converts this voice data into text and sends it to a server. The server then uses the Google Cloud Natural Language API to analyze the text and extract important keywords and context.

[0591] input:

[0592] Voice data (user suggestions)

[0593] output:

[0594] Extracted keywords and context

[0595] Specific behavior:

[0596] The device converts the voice data into text data

[0597] The server passes the text data to the Google Cloud Natural Language API to extract keywords and context.

[0598] Step 4:

[0599] The server uses GCP's AutoML to perform an initial diagnosis of patentability based on the extracted keywords and context. As a result of the initial diagnosis, it generates relevant information (e.g., patent application procedures, whether there are similar technologies, etc.) and sends it to the terminal.

[0600] input:

[0601] Extracted keywords and context

[0602] output:

[0603] Initial diagnosis results (related information)

[0604] Specific behavior:

[0605] The server inputs keywords and context into GCP's AutoML to perform an initial diagnosis.

[0606] Format the initial diagnostic results and send them to the device

[0607] Step 5:

[0608] The device displays the initial diagnosis results received from the server to the user. At the same time, it sends the user's voice and facial expressions to the server, which uses AWS Rekognition to recognize the user's emotions.

[0609] input:

[0610] Voice and facial expression data

[0611] Initial diagnosis results

[0612] output:

[0613] Recognized emotions

[0614] Adjusted information tone

[0615] Specific behavior:

[0616] The device displays the initial diagnosis results to the user

[0617] Voice and facial expression data is sent to the server

[0618] The server analyzes emotions using AWS Rekognition and adjusts the tone

[0619] Step 6:

[0620] The server adjusts the tone of the information based on the user's emotions to provide more appropriate support messages to the user. For example, if the user is confused, it displays more detailed explanations and additional information.

[0621] input:

[0622] Recognized emotions

[0623] output:

[0624] Tailored support messages

[0625] Specific behavior:

[0626] The server analyzes the perceived emotion and adjusts the tone appropriately

[0627] Supports message formatting and sending to device

[0628] Step 7:

[0629] If the user's suggestion is too advanced for the AI ​​alone to solve, the server uses the Slack API to forward the information to a subject matter expert, who then sends additional answers and assistance to the server, which then displays them on the device.

[0630] input:

[0631] Advanced proposal content

[0632] output:

[0633] Additional answers from experts

[0634] Specific behavior:

[0635] The server transfers the information to the expert using the Slack API

[0636] Receive answers from experts and send them to your device

[0637] Step 8:

[0638] The server uses ICS to track the progress of proposals and manage deadlines and tasks, automatically sending reminders to devices as important deadlines approach.

[0639] input:

[0640] Proposal progress

[0641] Deadline

[0642] output:

[0643] Reminder notifications

[0644] Specific behavior:

[0645] The server records the progress of the proposal in a database

[0646] Send reminders using ICS based on deadlines

[0647] 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 a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the 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.

[0648] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (Internet Search<URL: https: / / openai.com / blog / chatgpt> ), Gemini (Internet search <url: https: gemini.google.com ?hl="ja">) and other generation AIs. The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating voice, text data indicating text, and image data indicating an image is also input. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt, and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[0649] In the above embodiment, an example in which the specific process is performed by the data processing device 12 has been given, but the technology of the present disclosure is not limited to this, and the specific process may be performed by the smart device 14.

[0650] [Second embodiment]

[0651] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0652] 3, the data processing system 210 includes the data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0653] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 is an example of a "computer" according to the technology of the present disclosure. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, the RAM 30, and the storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[0654] The smart glasses 214 include a computer 36, a microphone 238, a speaker 240, a camera 42, and a communication I / F 44. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, and the camera 42 are also connected to the bus 52.

[0655] The microphone 238 receives instructions and the like from the user 20 by receiving voice uttered by the user 20. The microphone 238 captures the voice uttered by the user 20, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio in accordance with instructions from the processor 46.

[0656] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the surroundings of user 20 (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0657] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0658] Fig. 4 shows an example of the main functions of the data processing device 12 and the smart glasses 214. As shown in Fig. 4, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0659] The specific processing program 56 is an example of a "program" according to the technology of the present 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.

[0660] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[0661] In the smart glasses 214, the reception output process is performed by the processor 46. A reception output program 60 is stored in the storage 50. 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 process is realized by the processor 46 operating as the control unit 46A in accordance with the reception output program 60 executed on the RAM 48.

[0662] Next, a description will be given of the identification process performed by the identification processing unit 290 of the data processing device 12. 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."

[0663] The "patent attorney AI" system of the present invention is a system that allows users to easily consult about patent procedures and supports the efficient progress of the patent application process. Below, we will generate a program for the system and explain its processing in natural language.

[0664] Explanation of program processing

[0665] User authentication and basic information entry

[0666] 1. Device: The user launches the "Patent Attorney AI" application and enters authentication information (username, password) on the login screen.

[0667] 2. Server: Receives the user's authentication information and authenticates the login by checking it against the database. If authentication is successful, obtains the user's profile information and sends it to the device.

[0668] 3. Terminal: After successful authentication, the dashboard screen is displayed and a form is provided where the user can enter the details of their consultation.

[0669] Enter the consultation details

[0670] 1. Terminal: The user enters information into a text box for entering questions or inquiries about patents.

[0671] 2. Terminal: Sends the consultation details entered by the user to the server.

[0672] 3. Server: Passes the received consultation content to a text analysis engine to extract important keywords and context.

[0673] AI-based initial diagnosis and information provision

[0674] 1. Server: Based on the extracted keywords and context, the AI ​​model generates appropriate information (e.g., patent application steps and required documents).

[0675] 2. Server: Formats the generated information and sends it to the user.

[0676] 3. Terminal: Displays the results of the initial diagnosis and related information to the user, such as "specific steps for patent application" and "list of required documents."

[0677] Collaboration with patent attorneys

[0678] 1. Server: If the user's question is too advanced to be resolved by AI alone, the server transfers the consultation to an expert (e.g., a patent attorney).

[0679] 2. Patent attorney: The expert reviews the user's consultation and enters any necessary additional information or advice.

[0680] 3. Server: Receives additional information from the expert and forwards it to the user.

[0681] 4. Terminal: Additional information from the patent attorney is displayed to the user, allowing them to receive specific advice.

[0682] Procedure progress reminders

[0683] 1. Server: When a user initiates a specific patent procedure, it tracks its progress, for example, managing specific submission deadlines and tasks.

[0684] 2. Server: Set a reminder when the submission deadline approaches.

[0685] 3. On the device: A deadline reminder is sent to the user. For example, a notification saying, "Your patent application is due in one week."

[0686] 4. User: Based on the reminder notification, you can take the following actions:

[0687] Reward Points Management

[0688] 1. Patent attorneys: Experts add useful information to the AI's database of answers.

[0689] 2. Server: Reviews the submitted information, incorporates it into a database, and awards reward points to patent attorneys based on the usefulness of the information.

[0690] 3. Patent attorneys: You can choose to use the reward points you have earned to advertise in related fields.

[0691] Specific examples

[0692] Example 1: New invention consultation

[0693] 1. A user has come up with a new invention but does not know the procedure for filing a patent application.

[0694] 2. Using your device, log in to your account and enter a summary of your invention.

[0695] 3. The server analyzes the information entered and provides the user with basic application procedures and necessary documents.

[0696] 4. The user enters an additional, more specific question, asking, "Is this invention likely to be patentable?"

[0697] 5. The server researches similar cases from the past, generates a response, and obtains additional responses from patent attorneys if necessary.

[0698] 6. The answer is displayed on the device so the user knows the next step.

[0699] Example 2: Reminder function

[0700] 1. A user initiates the patent application process and a specific submission deadline is set.

[0701] 2. The server tracks this information and sets reminders when deadlines approach.

[0702] 3. A reminder notification will be sent to the device one week before the submission deadline, helping users to remember to complete important tasks.

[0703] This will enable the "Patent Attorney AI" system to support complex patent-related procedures, enabling both users and patent attorneys to carry out their work efficiently.

[0704] The processing flow will be explained below.

[0705] User authentication and basic information entry

[0706] Step 1:

[0707] Terminal: The user launches the "Patent Attorney AI" application and enters authentication information (username, password) on the login screen.

[0708] Step 2:

[0709] Server: Receives the user's authentication information and authenticates the login against a database.

[0710] Step 3:

[0711] Server: If authentication is successful, obtain the user's profile information and send it to the terminal.

[0712] Step 4:

[0713] Terminal: After successful authentication, the dashboard screen is displayed and a form is provided where the user can enter the details of their consultation.

[0714] Enter the consultation details

[0715] Step 1:

[0716] Terminal: The user enters information into a text box for entering questions or consultations about patents.

[0717] Step 2:

[0718] Terminal: Sends the consultation details entered by the user to the server.

[0719] Step 3:

[0720] Server: Passes the received consultation content to a text analysis engine to extract important keywords and context.

[0721] AI-based initial diagnosis and information provision

[0722] Step 1:

[0723] Server: Based on the extracted keywords and context, the AI ​​model generates appropriate information (e.g., patent application steps and required documents).

[0724] Step 2:

[0725] Server: Formats the generated information and sends it to the user.

[0726] Step 3:

[0727] Terminal: Displays the results of the initial diagnosis and related information to the user, such as "specific steps for patent application" and "list of required documents."

[0728] Collaboration with patent attorneys

[0729] Step 1:

[0730] Server: If the user's question is too advanced to be resolved by AI alone, the server transfers the consultation to an expert (e.g., a patent attorney).

[0731] Step 2:

[0732] Patent attorney: The expert reviews the user's consultation and enters any necessary additional information or advice.

[0733] Step 3:

[0734] Server: Receives additional information from the expert and forwards it to the user.

[0735] Step 4:

[0736] Terminal: Additional information from the patent attorney is displayed to the user, allowing them to receive specific advice.

[0737] Procedure progress reminders

[0738] Step 1:

[0739] Server: Tracks the progress of specific patent proceedings initiated by users, for example, managing specific submission deadlines and tasks.

[0740] Step 2:

[0741] Server: Set reminders when the submission deadline approaches.

[0742] Step 3:

[0743] Terminal: A deadline reminder is sent to the user. For example, a notification saying, "Your patent application is due in one week."

[0744] Step 4:

[0745] User: Based on the reminder notification, you can take the following actions:

[0746] Reward Points Management

[0747] Step 1:

[0748] Patent attorneys: Experts provide input to add useful information to the AI's database of answers.

[0749] Step 2:

[0750] Server: Reviews the submitted information and incorporates it into a database. Based on the usefulness of the information, reward points are awarded to patent attorneys.

[0751] Step 3:

[0752] Patent attorneys: You can choose to use your earned reward points to advertise in related fields.

[0753] Example 1

[0754] Next, a description will be given of Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the smart glasses 214 will be referred to as a "terminal."

[0755] In conventional patent procedures, users often required a great deal of time and effort to gather appropriate information. Furthermore, when expert assistance was required, the process of finding the appropriate expert was complicated, and managing the progress of the procedure was also time-consuming. Furthermore, the lack of a mechanism for effectively incorporating the knowledge of experts sometimes led to a decline in the quality of service provided to users. To solve these issues, a patent procedure support system that users can easily access is needed.

[0756] The specific processing by the specific processing unit 290 of the data processing device 12 in the first embodiment is realized by the following means.

[0757] In this invention, the server includes a means for receiving user authentication information and acquiring user identification information, a means for passing the consultation content received from the user to a text analysis means and extracting important features and background information, and a means for a generative AI model to generate appropriate data based on the extracted features and background information and provide it to the user. This allows users to efficiently obtain information about patent procedures and receive appropriate expert assistance. Furthermore, by tracking the progress of the procedures and notifying users of important deadlines and tasks, users can prevent delays in the procedures. Furthermore, by effectively incorporating expert knowledge into the system, the quality of service can be improved.

[0758] "User authentication information" is information used to identify a user and access a system, and typically consists of a username and password.

[0759] "User identification information" is data including the user's personal information and account information, and is used to identify the user.

[0760] "Consultation content" refers to questions or problems that users input into the system regarding patent procedures.

[0761] "Text analysis means" refers to technology for extracting important features and contextual information from received text data, and typically uses Natural Language Processing (NLP) techniques.

[0762] "Features" are important points or keywords extracted based on specific data or information.

[0763] "Background information" is data that provides context or relevance for particular data or information.

[0764] A "generative AI model" is a model that uses artificial intelligence technology to generate specific data or information, and makes predictions or judgments based on past data.

[0765] "Appropriate data" refers to information that is useful to the user and is generated based on the content of the user's consultation and background information.

[0766] An "expert" is a professional with advanced knowledge and experience in patent procedures.

[0767] A "response" is an additional answer or advice that an expert provides to a user in response to a question that the user has asked.

[0768] "Progress" refers to information that indicates the current state or progress of a particular procedure that a user is performing.

[0769] "Critical deadlines" are important filing or task deadlines in patent prosecution.

[0770] "Notifications" are messages sent by the system to inform users of important information or deadlines.

[0771] "Reward points" are points awarded to experts for providing useful information to the system, and serve as a reward for the experts.

[0772] "Promotion" refers to advertising or promotion in a related field using reward points acquired by an expert.

[0773] The system of this invention aims to efficiently support users in consulting about patent procedures. The system comprehensively performs a range of functions, from obtaining user authentication information, analyzing the text of the consultation, generating answers using AI, collaborating with experts, managing progress, and providing reward points.

[0774] Hardware and Software Configuration

[0775] Server: A high-performance computer equipped with a database management system and an analytical engine including an AI model. Specific software used for the generative AI model is natural language processing technology (e.g., Transformers and BERT).

[0776] Device: Using an internet browser or dedicated application on a device accessible to the user (smartphone, tablet, computer, etc.).

[0777] Database: A relational database (e.g., MySQL, PostgreSQL) for storing data such as user authentication information, profiles, text consultations, and expert responses.

[0778] System Operation

[0779] 1. User Authentication

[0780] The user logs into their account from a terminal and enters their username and password.

[0781] The server receives the entered authentication information and checks it against a database to obtain the user's identity.

[0782] 2. Input and analysis of consultation details

[0783] The user inputs questions or inquiries about patents into the terminal.

[0784] The terminal transmits the inputted contents to the server.

[0785] The server passes the received consultation content to a text analysis means to extract important features and background information.

[0786] 3. Early diagnosis and information provision using AI

[0787] The server uses the generative AI model to generate appropriate data based on the extracted features and background information.

[0788] The server provides the generated information to the user.

[0789] 4. Collaboration with experts

[0790] If a user's inquiry is too complex for the AI ​​model to handle on its own, the server will forward the information to an expert.

[0791] The expert checks the user's consultation and inputs an additional response.

[0792] The server forwards the expert's response to the user.

[0793] 5. Progress Management

[0794] The server tracks the user's progress in completing specific procedures and manages important deadlines and tasks.

[0795] A notification is sent to the device when the submission deadline approaches.

[0796] 6. Reward Points Management

[0797] Experts add useful information to the generative AI model's answer database.

[0798] The server reviews the provided information and awards reward points to the expert.

[0799] Professionals have the option to use earned reward points to post advertisements in related fields.

[0800] Specific examples

[0801] Example 1: New invention consultation

[0802] 1. If a user comes up with a new invention and does not know how to apply for a patent, they can log in to their account using their device and enter a summary of their invention.

[0803] 2. The server analyzes the information entered and generates information on basic application procedures and required documents.

[0804] 3. The user types a follow-up question: "Is this invention likely to be patented?"

[0805] 4. The server investigates similar cases from the past, generates an answer, and obtains additional answers from experts if necessary.

[0806] 5. The answer is displayed on the device so the user knows the next step.

[0807] Specific prompt examples:

[0808] I have come up with a new invention, but I don't know how to apply for a patent. Can you tell me if there is a high chance that this invention will be patented?

[0809] Example 2: Reminder function

[0810] 1. A user initiates the patent application process and sets a specific submission deadline.

[0811] 2. The server tracks this information and sets reminders when deadlines approach.

[0812] 3. A reminder will be sent to your device one week before the submission deadline.

[0813] 4. The user receives a reminder notification and proceeds with the next action.

[0814] Specific prompt examples:

[0815] Please inform us that the deadline for submitting patent applications is one week away.

[0816] As described above, this system supports complex patent-related procedures, enabling both users and experts to carry out their work efficiently.

[0817] The flow of the identification process in the first embodiment will be described with reference to FIG.

[0818] Step 1:

[0819] The user launches the "Patent Attorney AI" application and enters their username and password. A username and password are required as input. The device sends this authentication information to the server. The server compares the received input data with a database and verifies whether authentication is successful. If authentication is successful, the user's profile information is returned to the device as output.

[0820] Step 2:

[0821] After successful authentication, the terminal displays the user's dashboard screen and provides a form for the user to enter the consultation details. Input includes the user's questions about patents and the details of the consultation. The user enters the consultation details and clicks the "Send" button. The terminal then sends the entered consultation details to the server.

[0822] Step 3:

[0823] The server passes the received consultation content to the text analysis means. The input includes the text data of the consultation content entered by the user. The server uses the text analysis means to extract important features and background information. Specifically, it uses natural language processing (NLP) technology to analyze the text and extract important keywords and context. The output is the extracted features and background information.

[0824] Step 4:

[0825] The server passes the extracted features and background information as input to a generative AI model. The generative AI model generates appropriate data based on the input data. Specifically, the generative AI model compares the data with past data and generates appropriate patent procedure steps and necessary document information based on the consultation content. The generated information is obtained as output.

[0826] Step 5:

[0827] The server formats the generated information and sends it to the device to be served to the user. The input includes the information generated by the generative AI model. The server formats this into an easy-to-read format and serves it to the user. The output is the formatted information sent to the device.

[0828] Step 6:

[0829] The terminal displays the information received from the server on the user's dashboard, which includes formatted information received from the server as input, allowing the user to review the patent application procedures and required documents.

[0830] Step 7:

[0831] If the user's inquiry is too advanced for the generative AI model alone to handle, the server forwards the information to an expert. The input includes the user's advanced inquiry content, which the server forwards to the expert. The expert then inputs an additional response based on this and returns it to the server. The output is the expert's response.

[0832] Step 8:

[0833] The server receives the expert's response and sends it to the terminal for presentation to the user. The input includes the response data from the expert, which the server converts into a user-friendly format and sends to the terminal. The output is the formatted response sent to the terminal.

[0834] Step 9:

[0835] The device displays the expert's response on a dashboard, where the user can see specific advice. The input includes the expert's response data. The user can then take further action based on the response.

[0836] Step 10:

[0837] The server tracks the progress of a specific patent procedure when a user initiates it. The input includes the user's initiation information. The server tracks the progress and manages important deadlines and tasks. The output is progress data.

[0838] Step 11:

[0839] The server sets reminders when a specific deadline approaches and sends notifications to the user's device. The input includes progress data and deadline information. The server generates a reminder notification based on this and sends it to the device. The output is a reminder notification.

[0840] Step 12:

[0841] The device displays a reminder notification to the user, helping the user remember to proceed with important tasks. The input includes the reminder notification received from the server. The user checks the notification and takes the next action.

[0842] Step 13:

[0843] Experts add useful information to the generative AI model's answer database. The input includes information based on the expert's knowledge and experience. The expert enters the information into the system, which is received by the server. This adds useful information to the database. The output is updated information.

[0844] Step 14:

[0845] The server awards reward points to the expert based on the provided information. The input includes information provided by the expert. The server evaluates this information, calculates and awards reward points. The output is reward points.

[0846] Step 15:

[0847] The expert uses the reward points he / she has acquired to select an option to post an advertisement in a related field. The input includes the reward points he / she has acquired and the advertisement content. The expert selects the option, and the server performs the procedure to post the advertisement. The output is the advertisement.

[0848] (Application example 1)

[0849] Next, a description will be given of Application Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the smart glasses 214 will be referred to as a "terminal."

[0850] While support systems existed to help users efficiently advance patent procedures, they were limited in their ability to provide appropriate information in real time in response to user inquiries and questions. Furthermore, there was no system that provided patent-related knowledge through educational content and offered expert support, making it difficult for users to understand the progress of their procedures and take appropriate action.

[0851] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 1 is realized by the following means.

[0852] In this invention, the server includes: means for receiving user authentication information and acquiring user profile information; means for passing the inquiry received from the user to a text analysis engine and extracting important keywords and context; means for an AI model to generate appropriate information based on the extracted keywords and context and provide it to the user; means for forwarding the information to a specialist in a specialized field to obtain additional answers if the user's question is difficult and cannot be solved by AI alone; means for tracking the user's progress in a specific procedure and reminding the user of important deadlines and tasks; means for experts to add useful information to the AI's answer database and award reward points; means for streaming educational content viewable on electronic devices; and means for analyzing viewers' questions in real time and providing related information. This allows users to deepen their knowledge of patent procedures and receive expert assistance at the appropriate time.

[0853] "User authentication information" is information for identifying a specific user and verifying access rights.

[0854] "Profile information" refers to basic attribute information about a user and personal data within the system.

[0855] "Consultation content" refers to questions or matters of consultation that a user inputs into the system regarding patent procedures.

[0856] A "text analysis engine" is software that extracts important keywords and context from input text data.

[0857] An "AI model" is a data processing algorithm that uses artificial intelligence to solve a specific problem.

[0858] An "expert" is a person with advanced knowledge and skills in a particular field who provides advice and information to users.

[0859] "Progress" is information that indicates the current stage of a particular task or project.

[0860] A "deadline" refers to the deadline for completing a particular task or procedure.

[0861] A "task" refers to a specific task or action that needs to be completed.

[0862] A "remind" is a notification that reminds a user of a specific action or deadline.

[0863] "Reward Points" are a form of reward given to Experts for useful activities they perform on the system.

[0864] "Educational content" refers to learning materials created to teach specific knowledge or skills.

[0865] "Streaming distribution" is a method of continuously transmitting and viewing audio and video in real time over the Internet.

[0866] "Real-time" refers to the instantaneous processing of data and exchange of information.

[0867] As an embodiment of the present invention, a patent procedure support system is realized as follows: Specific programs and processing methods of this system are described below.

[0868] System configuration and hardware / software used

[0869] Hardware and software used

[0870] Hardware: Smartphones, head-mounted displays, servers

[0871] Software: Mobile applications (iOS / Android), server-side AI models, text analysis engines, streaming platforms (e.g., AWS Elemental Media Services)

[0872] System processing explanation

[0873] User authentication and basic information entry

[0874] The device launches the "Patent Attorney AI Course" application. The user enters authentication information (username, password) on the login screen. The server receives the user's authentication information and authenticates it by checking it against a database. If authentication is successful, the server obtains the user's profile information and sends it to the device. After successful authentication, the device displays the main dashboard screen, and the user can select the course they wish to view.

[0875] Lecture viewing and interaction

[0876] The user selects a course from their device and presses the play button to watch the video course. The server streams the video data of the selected course. While watching the video course, the user enters their question or inquiry into a text box on the screen. The server passes the input inquiry to a text analysis engine (e.g., AWS Comprehend) to extract important keywords and context.

[0877] Early diagnosis and information provision using AI models

[0878] The server uses an AI model (e.g., OpenAI GPT-4) to generate relevant information based on the extracted keywords and context. The generated information is then formatted and sent to the user. The device then displays the information generated by the AI ​​model to the user, helping them further understand the information.

[0879] Collaboration with experts (patent attorneys)

[0880] When a user enters an advanced question, the server forwards the information to a specific expert (patent attorney). The expert uses a dedicated dashboard to check the user's question and enters an answer. The server receives additional information from the expert and forwards it back to the user. The terminal displays the information from the expert and helps the user take appropriate action.

[0881] Reminder function

[0882] The server tracks the user's progress in completing a specific procedure. When a specific deadline approaches, the server sets a reminder and sends a notification to the user. The notification is sent to the user's device using Firebase Cloud Messaging. The user can then take the next action after receiving the reminder notification.

[0883] Examples and prompts

[0884] Example 1: Initial diagnosis

[0885] User Input: "Is this invention likely to be patented?"

[0886] AI response: "The user has entered a summary of their invention. Input: ____. Based on this, please assess the likelihood of obtaining a patent."

[0887] Example 2: Feedback on a specific task

[0888] User Input: "What documents do I need to file a patent application?"

[0889] AI response: "The user asked about the documents required for a patent application. Please provide details below."

[0890] The patent procedure support system implemented in this way allows users to deepen their knowledge of patent procedures and receive expert support at the appropriate time.

[0891] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[0892] Step 1:

[0893] The user launches the "Patent Attorney AI Course" application and enters authentication information (username, password) on the login screen. The entered authentication information is sent to the server. The server receives the authentication information and authenticates the user by checking it against a database. During this process, the server obtains the user's profile information and sends a successful authentication result to the terminal.

[0894] Input: Username, Password

[0895] Data processing: User authentication by database matching

[0896] Output: Authentication success / failure result, user profile information

[0897] Specific operation: The server searches for user information in a database (e.g., AWS RDS), and if there is a match, it sends a flag indicating successful authentication to the terminal.

[0898] Step 2:

[0899] After successful authentication, the device displays the main dashboard screen. The user selects the course they wish to view from the dashboard. This course selection data is sent to the server.

[0900] Input: User-input course selection

[0901] Data processing: Send the selected course ID to the server

[0902] Output: Course selection data to the server

[0903] Specific behavior: The user interface displays a list of courses, and the user selects one by touch.

[0904] Step 3:

[0905] The server retrieves the video data of the course selected by the user and prepares it for streaming. The server then sends the video data to the device via streaming (e.g., AWS Elemental Media Services).

[0906] Input: Selected course ID

[0907] Data processing: Acquisition of video data and streaming distribution

[0908] Output: Sending video data via streaming

[0909] Specific operation: The server generates a URL for the video data and passes it to the streaming service.

[0910] Step 4:

[0911] The device plays the video lecture, and the user watches the video. While watching, the user enters questions or inquiries into a text box on the screen. This input data is sent to the server.

[0912] Input: User-entered consultation details

[0913] Data processing: Receiving and sending text data

[0914] Output: Send consultation text to server

[0915] Specific operation: The user interface provides a text input field and sends the input to the server using a submit button.

[0916] Step 5:

[0917] The server passes the consultation content received from the user to a text analysis engine (e.g., AWS Comprehend) to extract important keywords and context.

[0918] Input: Consultation content text

[0919] Data processing: Keyword extraction through text analysis

[0920] Output: Extracted keywords and context

[0921] Specific operation: The server sends text data to the API of the text analysis engine and receives the analysis results.

[0922] Step 6:

[0923] The server uses an AI model (e.g., OpenAI GPT-4) to generate relevant information based on the extracted keywords and context, formats the information, and sends it to the user.

[0924] Input: Keywords, Context

[0925] Data processing: Information generation using AI models

[0926] Output: Sending relevant formatted information

[0927] Specific operation: The server calls the AI ​​model, formats the generated information in HTML or JSON format, and sends it to the device.

[0928] Step 7:

[0929] The device displays information from the AI ​​model to the user, who then asks additional questions or takes action based on the information.

[0930] Input: Formatted and relevant information from the AI ​​model

[0931] Data processing: UI display of information

[0932] Output: Displaying information to the user

[0933] What it does: The user interface displays the generated information in rich text and card format, encouraging further interaction.

[0934] Step 8:

[0935] When a user enters a complex question, the server transfers the question to an expert, who then uses a dedicated dashboard to review the question and enter additional answers. The server then receives the information from the expert and sends it to the user.

[0936] Input: Advanced Question Content

[0937] Data processing: forwarding to experts and receiving answers

[0938] Output: User submission of additional information from the expert

[0939] Specific operation: The server displays the question on the expert's dashboard, receives the answer entered by the expert, and returns it to the user.

[0940] Step 9:

[0941] The server tracks the user's progress in completing specific procedures, sets reminders when specific deadlines for submissions are approaching, and sends notifications to the user.

[0942] Input: Procedural progress data, submission deadline

[0943] Data processing: generating and sending notification content

[0944] Output: Reminder notification sent to user

[0945] Specific operation: The server obtains deadline information from the progress management system, generates a reminder notification, and sends it to the user's device via Firebase Cloud Messaging.

[0946] Step 10:

[0947] The user receives a reminder notification and takes the next action, such as checking submitted documents or consulting an expert for further assistance.

[0948] Input: Reminder notification

[0949] Data manipulation: Performing user actions

[0950] Output: The following procedure is completed

[0951] Specific behavior: The user clicks on the link in the notification, and a screen appears on the device to take the next step or to consult with an expert.

[0952] Furthermore, an emotion engine that estimates the user's emotion may be further combined. That is, the identification processing unit 290 may estimate the user's emotion using the emotion identification model 59, and perform identification processing using the user's emotion.

[0953] The "Patent Attorney AI" system of the present invention is a system that allows users to easily consult about patent procedures and supports the efficient progress of the patent application process. Furthermore, it aims to improve the user experience by incorporating an emotion engine that recognizes and responds to the user's emotions. Below, we will generate a program for the system and explain its processing in natural language.

[0954] Explanation of program processing

[0955] User authentication and basic information entry

[0956] 1. Device: The user launches the "Patent Attorney AI" application and enters authentication information (username, password) on the login screen.

[0957] 2. Server: Receives the user's authentication information and authenticates the login by checking it against a database.

[0958] 3. Server: If authentication is successful, obtain the user's profile information and send it to the device.

[0959] 4. Terminal: After successful authentication, the dashboard screen is displayed and a form is provided where the user can enter the details of their consultation.

[0960] Enter the consultation details

[0961] 1. Terminal: The user enters information into a text box for entering questions or inquiries about patents.

[0962] 2. Terminal: Sends the consultation details entered by the user to the server.

[0963] 3. Server: The received consultation content is passed to a text analysis engine to extract important keywords and context.

[0964] AI-based initial diagnosis and information provision

[0965] 1. Server: Based on the extracted keywords and context, the AI ​​model generates appropriate information (e.g., patent application steps and required documents).

[0966] 2. Server: Formats the generated information and sends it to the user.

[0967] 3. Terminal: Displays the results of the initial diagnosis and related information to the user, such as "specific steps for patent application" and "list of required documents."

[0968] Emotion recognition and response using emotion engine

[0969] 1. Device: The emotion engine analyzes the user's text input and behavior and recognizes emotions.

[0970] 2. Server: Adjust the information provided and the tone of the response based on the emotions recognized by the emotion engine.

[0971] 3. Terminal: For example, if the user is perceived as confused, additional support information is displayed in a gentle tone.

[0972] Collaboration with patent attorneys

[0973] 1. Server: If the user's question is too advanced to be resolved by AI alone, the server transfers the consultation to an expert (e.g., a patent attorney).

[0974] 2. Patent attorney: The expert reviews the user's consultation and enters any necessary additional information or advice.

[0975] 3. Server: Receives additional information from the expert and forwards it to the user.

[0976] 4. Terminal: Additional information from the patent attorney is displayed to the user, allowing them to receive specific advice.

[0977] Procedure progress reminders

[0978] 1. Server: When a user initiates a specific patent procedure, it tracks its progress, for example, managing specific submission deadlines and tasks.

[0979] 2. Server: Set a reminder when the submission deadline approaches.

[0980] 3. On the device: A deadline reminder is sent to the user. For example, a notification saying, "Your patent application is due in one week."

[0981] 4. User: Based on the reminder notification, you can take the following actions:

[0982] Reward Points Management

[0983] 1. Patent attorneys: Experts provide input to add useful information to the AI's database of answers.

[0984] 2. Server: Reviews the submitted information, incorporates it into a database, and awards reward points to patent attorneys based on the usefulness of the information.

[0985] 3. Patent attorneys: You can choose to use the reward points you have earned to advertise in related fields.

[0986] Specific examples

[0987] Example 1: New invention consultation

[0988] 1. A user has come up with a new invention but does not know the procedure for filing a patent application.

[0989] 2. Using your device, log in to your account and enter a summary of your invention.

[0990] 3. The server analyzes the information entered and provides the user with basic application procedures and necessary documents.

[0991] 4. The emotion engine detects anxiety from the user's input and displays a more polite help message.

[0992] 5. The user enters an additional, more specific question, asking, "Is this invention likely to be patentable?"

[0993] 6. The server researches similar cases from the past, generates a response, and obtains additional responses from patent attorneys if necessary.

[0994] 7. The answer is displayed on the device so the user knows the next step.

[0995] Example 2: Reminder function

[0996] 1. A user initiates the patent application process and a specific submission deadline is set.

[0997] 2. The server tracks this information and sets reminders when deadlines approach.

[0998] 3. A reminder notification will be sent to the device one week before the submission deadline, helping users to remember to complete important tasks.

[0999] This allows the "Patent Attorney AI" system to support complex patent-related procedures, enabling both users and experts to carry out their work efficiently. Furthermore, the emotion engine improves the user experience, allowing particularly troubled users to receive appropriate support.

[1000] The processing flow will be explained below.

[1001] Processing of the "Patent Attorney AI" system including an emotion engine

[1002] User authentication and basic information entry

[1003] Step 1:

[1004] Terminal: The user launches the "Patent Attorney AI" application and enters authentication information (username, password) on the login screen.

[1005] Step 2:

[1006] Server: Receives the user's authentication information and authenticates the login against a database.

[1007] Step 3:

[1008] Server: If authentication is successful, obtain the user's profile information and send it to the terminal.

[1009] Step 4:

[1010] Terminal: After successful authentication, the dashboard screen is displayed and a form is provided where the user can enter the details of their consultation.

[1011] Enter the consultation details

[1012] Step 1:

[1013] Terminal: The user enters information into a text box for entering questions or consultations about patents.

[1014] Step 2:

[1015] Terminal: Sends the consultation details entered by the user to the server.

[1016] Step 3:

[1017] Server: Passes the received consultation content to a text analysis engine to extract important keywords and context.

[1018] AI-based initial diagnosis and information provision

[1019] Step 1:

[1020] Server: Based on the extracted keywords and context, the AI ​​model generates appropriate information (e.g., patent application steps and required documents).

[1021] Step 2:

[1022] Server: Formats the generated information and sends it to the user.

[1023] Step 3:

[1024] Terminal: Displays the results of the initial diagnosis and related information to the user, such as "specific steps for patent application" and "list of required documents."

[1025] Emotion recognition and response using emotion engine

[1026] Step 1:

[1027] Device: The emotion engine analyzes the user's text input and behavior to recognize emotions.

[1028] Step 2:

[1029] Server: Adjusts the information provided and the tone of the response based on the emotions recognized by the emotion engine.

[1030] Step 3:

[1031] Terminal: For example, if the user is perceived as confused, additional support information is displayed in a gentle tone.

[1032] Collaboration with patent attorneys

[1033] Step 1:

[1034] Server: If the user's question is too advanced to be resolved by AI alone, the server transfers the consultation to an expert (e.g., a patent attorney).

[1035] Step 2:

[1036] Patent attorney: The expert reviews the user's consultation and enters any necessary additional information or advice.

[1037] Step 3:

[1038] Server: Receives additional information from the expert and forwards it to the user.

[1039] Step 4:

[1040] Terminal: Additional information from the patent attorney is displayed to the user, allowing them to receive specific advice.

[1041] Procedure progress reminders

[1042] Step 1:

[1043] Server: Tracks the progress of specific patent proceedings initiated by users, for example, managing specific submission deadlines and tasks.

[1044] Step 2:

[1045] Server: Set reminders and start timers when the submission deadline approaches.

[1046] Step 3:

[1047] Terminal: A deadline reminder is sent to the user. For example, a notification saying, "Your patent application is due in one week."

[1048] Step 4:

[1049] User: Based on the reminder notification, you can take the following actions:

[1050] Reward Points Management

[1051] Step 1:

[1052] Patent attorneys: Experts provide input to add useful information to the AI's database of answers.

[1053] Step 2:

[1054] Server: Reviews the submitted information and incorporates it into a database. Based on the usefulness of the information, reward points are awarded to patent attorneys.

[1055] Step 3:

[1056] Patent attorneys: You can choose to use your earned reward points to advertise in related fields.

[1057] Example 2

[1058] Next, a description will be given of Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the smart glasses 214 will be referred to as a "terminal."

[1059] Traditional patent procedures are extremely complicated, making it difficult for individual inventors to smoothly navigate the patent application process. During the patent acquisition process, users often overlook important deadlines or fail to obtain appropriate information. Furthermore, due to a lack of appropriate responses based on the user's emotional state, they are prone to feeling anxious and confused during the patent process. Furthermore, many questions are difficult and often cannot be resolved by artificial intelligence alone, requiring expert support. Smooth collaboration is crucial in these situations. Conventional systems are unable to adequately address these issues.

[1060] The identification process by the identification processing unit 290 of the data processing device 12 in Example 2 is realized by the following means. In this invention, the server includes: means for receiving user authentication information and acquiring user profile information; means for passing the consultation content received from the user to a text analysis engine and extracting important keywords and context; means for an AI model to generate appropriate information based on the extracted keywords and context and provide it to the user; means for forwarding information to a specialized expert and obtaining additional answers when the user's question is difficult and cannot be solved by AI alone; means for tracking the user's progress in a specific procedure and reminding them of important deadlines and tasks; means for the expert to add useful information to the AI's answer database and award reward points; means for analyzing the user's text input and behavior, and for an emotion engine to recognize emotions and adjust responses; and means for formatting the consultation content and additional answers from the expert and forwarding them to the user. This allows users to avoid missing important deadlines in patent prosecution, obtain appropriate information, and receive support tailored to their emotional state. Furthermore, AI and experts work together effectively to provide users with high-quality information and support. As a result, the entire patent process can proceed efficiently and user-friendly.

[1061] "User authentication information" refers to the information a user needs to log into a system, and typically includes a username and password.

[1062] "Profile Information" means data including a user's personal information and account information, which is used to identify the user and provide services.

[1063] "Consultation content" refers to questions or matters of consultation that a user submits to the system regarding a patent application.

[1064] A "text analysis engine" is a software engine that uses natural language processing technology to extract important keywords and context from input text data.

[1065] An "artificial intelligence model" refers to an algorithm or system that uses machine learning or deep learning to generate appropriate information from data.

[1066] "Subject matter experts" refer to professionals with in-depth knowledge and experience in patents and intellectual property, typically including patent attorneys and patent lawyers.

[1067] "Progress" refers to information indicating how far a user has progressed in the patent process.

[1068] "Deadline" refers to the deadline for each task or submission in the patent process.

[1069] "Remind" is a feature that notifies users so they don't forget important deadlines or tasks.

[1070] "Reward points" refer to incentives awarded to experts when they provide useful information, and can be used for specific benefits or advertising within the system.

[1071] An "emotion engine" is a software engine that analyzes a user's text input and behavior to recognize the user's emotional state.

[1072] The present invention is a support system for users to smoothly proceed with patent procedures, and specific embodiments thereof are described below.

[1073] The system mainly consists of a user terminal, a central server, and the "Patent Attorney AI" application that constitutes the user interface. Terminals can be ordinary PCs, smartphones, tablets, etc., and the server functions as a central processing unit including a database.

[1074] User authentication and basic information entry

[1075] 1. Terminal: The user launches the "Patent Attorney AI" application and enters authentication information (username, password) on the login screen. The entered information is securely transmitted to the server using the SSL / TLS protocol.

[1076] 2. Server: Receives the authentication information and authenticates the login by checking it against a database (e.g., MySQL or PostgreSQL). If authentication is successful, it obtains the user's profile information and sends it to the terminal in JSON format.

[1077] 3. Terminal: If authentication is successful, the dashboard screen will be displayed and a form will be provided where the user can enter the details of their consultation.

[1078] Input and analysis of consultation details

[1079] 1. Terminal: When the user enters a question or inquiry about a patent and clicks the send button, the information is sent to the server in JSON format.

[1080] 2. Server: The received consultation content is passed to a natural language processing (NLP) engine (e.g., SpaCy or NLTK) to extract important keywords and context. The extracted results are stored in a database for further processing.

[1081] Early diagnosis and information provision

[1082] 1. Server: Input the extracted keywords and context into an artificial intelligence model (e.g., GPT-3 or BERT) to generate appropriate information.

[1083] Example prompt: "Generate a list of required application procedures and documents based on the patent question entered by the user."

[1084] 2. Server: Formats the information generated by the AI ​​model and prepares it for delivery to the user. The formatted information is sent to the device in JSON format.

[1085] 3. Terminal: Displays the initial diagnosis results and provided information to the user. For example, the "steps for patent application" and "list of required documents" are displayed.

[1086] Emotion recognition and response using emotion engine

[1087] 1. Terminal: Analyzes the user's text input and behavior (e.g., typing speed and click operations) in real time and passes the results to an emotion engine (e.g., IBM Watson or Aylien).

[1088] 2. Server: Based on the emotion engine's recognition of the user's emotions, the server adjusts the tone of the information and responses it provides. For example, if the server recognizes that the user is confused, it provides additional support information in a gentler tone.

[1089] 3. Device: Display messages and information tailored to the user's emotions. For example, polite guidance such as "If you need help, please see here" is displayed.

[1090] Collaboration with experts

[1091] 1. Server: When a complex question is received that cannot be resolved by artificial intelligence alone, the content is forwarded to an expert (e.g., a patent attorney).

[1092] 2. Patent attorney: The expert logs in to a dedicated portal, reviews the user's question, and enters any additional information or specific advice required.

[1093] 3. Server: Receives additional responses from the patent attorney, formats them, and sends them to the terminal.

[1094] 4. Terminal: Experts provide information and give users specific advice on next steps.

[1095] Procedure progress reminders

[1096] 1. Server: When a user initiates a specific patent procedure, its progress is tracked in a database. Deadlines for each task are managed.

[1097] 2. Server: When the submission deadline approaches, a reminder is generated and sent to the device.

[1098] 3. Terminal: A reminder notification is displayed to the user, for example, a message saying, "The deadline for submitting your patent application is one week away." The user receives the notification and can take appropriate action.

[1099] Reward Points Management

[1100] 1. Patent attorneys: Experts add useful information to the AI's database of answers.

[1101] 2. Server: Automatically reviews the submitted information, evaluates its quality and usefulness, and awards reward points to the attorney if the evaluation is high.

[1102] 3. Patent Attorneys: You will be given the option to use your earned reward points to advertise in related fields.

[1103] This system allows users to carry out patent procedures themselves while receiving appropriate support and proceeding efficiently. In particular, support from the emotion engine and smooth collaboration with experts contribute to an improved user experience.

[1104] The flow of the identification process in the second embodiment will be described with reference to FIG.

[1105] Step 1:

[1106] Terminal: The user launches the "Patent Attorney AI" application and enters authentication information (username, password) on the login screen. This becomes the input data. When the user presses the "Login" button, the entered authentication information is sent to the server using the SSL / TLS protocol. The output is encrypted authentication information sent to the server.

[1107] Step 2:

[1108] Server: Receives authentication information and performs authentication by checking it against a database (e.g., MySQL or PostgreSQL). This check involves data calculations to confirm whether the username and password match. If authentication is successful, retrieves the user's profile information from the database and sends it to the terminal as output data in JSON format. As output, JSON data containing the user's profile information is sent to the terminal.

[1109] Step 3:

[1110] Terminal: After successful authentication, the terminal displays a dashboard screen and provides a form where the user can enter their consultation details. This includes input fields such as "Question," "Invention summary," and "Contact information." The input fields are displayed to the user as output data.

[1111] Step 4:

[1112] Terminal: When a user enters a question or consultation about a patent and clicks the send button, the entered information is sent to the server in JSON format. The input data is the user's question or consultation content, and the output data is JSON format data containing that content.

[1113] Step 5:

[1114] Server: Passes the received consultation content to a natural language processing (NLP) engine (e.g., SpaCy or NLTK) to extract important keywords and context. The input data is the consultation content sent by the user, and important keywords and context are extracted through data processing by the NLP engine. The output data is a list of the extracted keywords and context. The extraction results are stored in a database.

[1115] Step 6:

[1116] Server: Inputs the extracted keywords and context into an artificial intelligence model (e.g., GPT-3 or BERT) to generate appropriate information. The input data are the extracted keywords and context, and the artificial intelligence model calculates the data to generate information related to the user's question. Example prompt: "Based on the patent question entered by the user, please generate a list of the necessary application procedures and documents." The output data is the generated answer information.

[1117] Step 7:

[1118] Server: Formats the information generated by the AI ​​model and presents it to the user. This formatting involves processing the data to generate formatted documents that explain technical terms and specific procedures in an easy-to-understand manner. The formatted data is sent to the terminal in JSON format. The output data is formatted information.

[1119] Step 8:

[1120] Terminal: Displays the initial diagnosis results and provided information to the user. This includes "patent application steps" and "list of required documents." Output data is the specific information displayed on the user's screen.

[1121] Step 9:

[1122] Terminal: Analyzes the user's text input and behavior (e.g., typing speed and click operations) in real time and passes them to an emotion engine (e.g., IBM Watson or Aylien). The input data is the user's behavior data, and emotions are identified through data calculations by the emotion engine. The output data is the result of the emotion identification.

[1123] Step 10:

[1124] Server: The emotion engine adjusts the tone of the information and responses it provides based on the emotions it recognizes. For example, if it recognizes that the user is confused, it generates additional support information in a gentler tone. At this time, data processing involves adding modifications to the template sentence according to the user's emotional state. The output data is the adjusted message.

[1125] Step 11:

[1126] Terminal: Displays messages and information tailored to the user's emotions. For example, polite guidance such as "If you have any questions, please see here" is displayed. The output data is a message tailored to the user's emotions.

[1127] Step 12:

[1128] Server: When a user receives a complex question that cannot be resolved by AI alone, the server transfers the question to an expert (e.g., a patent attorney). The input data is the content of the complex question, and the transferred data is the specific question information. The expert logs into a dedicated portal, checks the user's question, and enters any necessary additional information or specific advice.

[1129] Step 13:

[1130] Server: Receives additional responses from patent attorneys, formats them, and sends them to the terminal. The input data is the content of the patent attorney's response, and the output data is the formatted additional response information.

[1131] Step 14:

[1132] Terminal: Information from experts is provided and users can get specific advice. The output data is specific advice that is displayed on the user's screen.

[1133] Step 15:

[1134] Server: When a user starts a specific patent procedure, its progress is tracked in a database. The input data is the user's procedure start information, and the progress information is updated in the database through tracking. The output data is the progress information.

[1135] Step 16:

[1136] Server: When the deadline approaches, a reminder is generated and sent to the terminal. The input data is progress information, and the output data is the reminder notification.

[1137] Step 17:

[1138] Terminal: A reminder notification is displayed to the user, for example, a message saying "The deadline for submitting patent application documents is one week away." The output data is the reminder notification.

[1139] Step 18:

[1140] User: Receives a reminder and takes the next step to meet the deadline. The input data is the reminder and the output data is the user's next action.

[1141] Step 19:

[1142] Patent attorney: An expert adds useful information to the AI's response database. The input data is the newly added information, and the output data is the information stored in the database.

[1143] Step 20:

[1144] Server: Automatically reviews the provided information and evaluates its quality and usefulness. If the evaluation is high, reward points are awarded to the patent attorney. The input data is the information to be evaluated, and the output data is the awarded reward points.

[1145] Step 21:

[1146] Patent attorneys: They are given the option to use their earned reward points to place advertisements in related fields. The input data is the reward points, and the output data is the use of the advertisement placement option.

[1147] (Application example 2)

[1148] Next, a description will be given of Application Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the smart glasses 214 will be referred to as a "terminal."

[1149] In modern factories, new technologies and processes are frequently proposed, but the process of managing them and filing patent applications is extremely complex, time-consuming, and costly. In particular, the need to quickly evaluate proposed technologies on-site and determine their patentability is particularly challenging, but traditional methods are too labor-intensive. In addition, factory workers often lack adequate support and feedback on their proposals, which can lead to demotivation. Furthermore, tracking the progress of patent applications and managing important deadlines presents challenges.

[1150] The specific processing by the specific processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means. In this invention, the server includes: means for receiving user authentication information and acquiring user profile information; means for passing the inquiry received from the user to a text analysis engine and extracting important keywords and context; means for an AI model to generate appropriate information based on the extracted keywords and context and provide it to the user; means for forwarding information to a specialized expert in a specialized field to obtain additional answers if the user's question is difficult and cannot be solved by AI alone; means for tracking the progress of the user's specific procedures and reminding the user of important deadlines and tasks; means for the expert to add useful information to the AI's answer database and award reward points; means for receiving suggestions via voice input, converting the speech to text, and analyzing the content as a possible patent application; means for recognizing emotions from voice and facial expressions and adjusting the tone of the information provided based on the emotions; and means for tracking the progress of patent application proposals and sending reminders. This enables efficient management of new technology and process proposals in factories and rapid and accurate patent application procedures.

[1151] "User authentication information" means information used to identify a user and verify the user's access rights to access a system.

[1152] "Profile information" is data containing basic information about each user that identifies the individual user.

[1153] "Consultation content" is information detailing the question or problem that the user wants to solve through the system.

[1154] A "text analysis engine" is software or an algorithm that extracts meaning and important keywords from input text data and performs analysis.

[1155] "Keywords" are particularly important words or phrases in the content of a consultation, and are the subject of search and analysis.

[1156] "Context" is information about the context or situation in which a keyword is used, which clarifies the meaning of the keyword.

[1157] An "AI model" is an artificial intelligence algorithm or machine learning model that generates appropriate information or answers based on analyzed data.

[1158] "Subject matter experts" are individuals with advanced knowledge and experience in a particular field who provide additional assistance for problems that cannot be solved by AI.

[1159] "Progress" is information that indicates the progress of a particular task or project.

[1160] A "deadline" is the date by which a particular task or procedure must be completed.

[1161] A "task" is a specific job or operation that a user must accomplish.

[1162] "Reward points" are points that are awarded to experts for adding useful information to the database, which allow the experts to earn rewards or use certain options.

[1163] "Voice input" refers to a method in which a user inputs information by voice using a voice recognition device such as a microphone.

[1164] "Text conversion" is the process of converting audio data into text data.

[1165] "Patentability" is an indicator used to evaluate the likelihood that a new technology or invention will be granted a patent.

[1166] "Recognizing emotions from voice and facial expressions" is a technology that analyzes a user's voice and facial expressions to determine what emotions the user is feeling.

[1167] "Adjusting tone" means changing the tone or presentation of the information provided based on the user's emotional state.

[1168] "Sending reminders" means notifying users so that they do not forget important tasks or deadlines.

[1169] The system for implementing this invention uses the following hardware and software: The hardware includes smart glasses and microphones used by users, and a server. The software includes Google Cloud Natural Language API, GCP AutoML, AWS Rekognition, Slack API, and ICS (Internet Calendar Scheduling).

[1170] Obtaining user credentials and viewing profile information

[1171] The device receives user authentication information through the smart glasses and sends it to the server. The server compares the information with a database and authenticates the user. If authentication is successful, the server obtains the user's profile information and sends it to the device. After successful authentication, the device displays a dashboard screen and provides a form where the user can enter their consultation details.

[1172] Voice input and analysis of proposals

[1173] Users can voice-input suggestions for new technologies or process improvements through the smart glasses. The device converts the voice data into text and sends it to a server. The server then uses the Google Cloud Natural Language API to analyze the text and extract important keywords and context.

[1174] example:

[1175] Proposal: "Could production efficiency be improved by introducing a new robot arm control method?"

[1176] Extracted keywords: "Robot arm control method" and "Production efficiency"

[1177] Information generation using AI models

[1178] The server uses GCP's AutoML to perform an initial diagnosis of patentability based on the extracted keywords and context. As a result of the initial diagnosis, relevant information is provided to the user, including information on patent application procedures and similar existing technologies.

[1179] example:

[1180] Prompt: "Is this improvement proposal related to an existing patent?"

[1181] Prompt: "What procedure is needed next?"

[1182] Emotion recognition from speech and facial expressions

[1183] The device sends the user's voice and facial expression data to the server, which then uses AWS Rekognition to recognize emotions. Based on the recognized emotions, the server adjusts the tone of the information provided and displays appropriate support messages to the user. For example, if the server detects that the user is confused, it will provide more detailed explanations or additional information.

[1184] Collaboration with experts in the field

[1185] For more advanced suggestions that cannot be resolved using the information provided by the AI ​​model alone, the server uses a Slack API to forward them to a subject matter expert, who then sends additional answers and assistance to the server, which then displays them on the device.

[1186] example:

[1187] Prompt: "What documents do I need to create this idea?"

[1188] Proposal progress tracking and reminders

[1189] The server uses ICS to track the progress of proposals and manage deadlines and tasks. When important deadlines approach, reminders are automatically sent to the device, allowing users to efficiently progress through the patent application process without forgetting deadlines.

[1190] example:

[1191] Prompt: "The submission deadline is approaching. Don't forget to complete the process."

[1192] The system will enable efficient management of new technology and process proposals in factories, and ensure that patent application procedures are carried out quickly and accurately. Furthermore, emotion recognition capabilities will improve the user experience, enabling appropriate support to be provided to users who are particularly challenged.

[1193] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[1194] Step 1:

[1195] A user uses smart glasses and authenticates using voice commands. The device receives the user authentication information (e.g., username and password) and sends it to the server. The server checks this information against a database to confirm authentication and returns the result to the device. This allows the user to log in to the system and gain access rights.

[1196] input:

[1197] User authentication information (username, password)

[1198] output:

[1199] Authentication result (success or failure)

[1200] Specific behavior:

[1201] User enters username and password into smart glasses using voice commands

[1202] The device sends authentication information to the server and receives the authentication result.

[1203] Step 2:

[1204] If the server is successful in the authentication, it retrieves the user's profile information from the database and sends it to the terminal. The terminal then displays a dashboard screen and provides a form in which the user can enter the details of their consultation.

[1205] input:

[1206] Authentication result (success)

[1207] output:

[1208] Profile Information

[1209] Specific behavior:

[1210] The server searches the database to retrieve the user's profile information

[1211] Send profile information to device and display dashboard

[1212] Step 3:

[1213] Users can input suggestions for new technologies or process improvements by voice through smart glasses. The device converts this voice data into text and sends it to a server. The server then uses the Google Cloud Natural Language API to analyze the text and extract important keywords and context.

[1214] input:

[1215] Voice data (user suggestions)

[1216] output:

[1217] Extracted keywords and context

[1218] Specific behavior:

[1219] The device converts the voice data into text data

[1220] The server passes the text data to the Google Cloud Natural Language API to extract keywords and context.

[1221] Step 4:

[1222] The server uses GCP's AutoML to perform an initial diagnosis of patentability based on the extracted keywords and context. As a result of the initial diagnosis, it generates relevant information (e.g., patent application procedures, whether there are similar technologies, etc.) and sends it to the terminal.

[1223] input:

[1224] Extracted keywords and context

[1225] output:

[1226] Initial diagnosis results (related information)

[1227] Specific behavior:

[1228] The server inputs keywords and context into GCP's AutoML to perform an initial diagnosis.

[1229] Format the initial diagnostic results and send them to the device

[1230] Step 5:

[1231] The device displays the initial diagnosis results received from the server to the user. At the same time, it sends the user's voice and facial expressions to the server, which uses AWS Rekognition to recognize the user's emotions.

[1232] input:

[1233] Voice and facial expression data

[1234] Initial diagnosis results

[1235] output:

[1236] Recognized emotions

[1237] Adjusted information tone

[1238] Specific behavior:

[1239] The device displays the initial diagnosis results to the user

[1240] Voice and facial expression data is sent to the server

[1241] The server analyzes emotions using AWS Rekognition and adjusts the tone

[1242] Step 6:

[1243] The server adjusts the tone of the information based on the user's emotions to provide more appropriate support messages to the user. For example, if the user is confused, it displays more detailed explanations and additional information.

[1244] input:

[1245] Recognized emotions

[1246] output:

[1247] Tailored support messages

[1248] Specific behavior:

[1249] The server analyzes the perceived emotion and adjusts the tone appropriately

[1250] Supports message formatting and sending to device

[1251] Step 7:

[1252] If the user's suggestion is too advanced for the AI ​​alone to solve, the server uses the Slack API to forward the information to a subject matter expert, who then sends additional answers and assistance to the server, which then displays them on the device.

[1253] input:

[1254] Advanced proposal content

[1255] output:

[1256] Additional answers from experts

[1257] Specific behavior:

[1258] The server transfers the information to the expert using the Slack API

[1259] Receive answers from experts and send them to your device

[1260] Step 8:

[1261] The server uses ICS to track the progress of proposals and manage deadlines and tasks, automatically sending reminders to devices as important deadlines approach.

[1262] input:

[1263] Proposal progress

[1264] Deadline

[1265] output:

[1266] Reminder notifications

[1267] Specific behavior:

[1268] The server records the progress of the proposal in a database

[1269] Send reminders using ICS based on deadlines

[1270] 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 a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[1271] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (Internet Search<URL: https: / / openai.com / blog / chatgpt> ), Gemini (Internet search <url: https: gemini.google.com ?hl="ja">) and other generation AIs. The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating voice, text data indicating text, and image data indicating an image is also input. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt, and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[1272] In the above embodiment, an example in which the specific processing is performed by the data processing device 12 has been given, but the technology of the present disclosure is not limited to this, and the specific processing may be performed by the smart glasses 214.

[1273] [Third embodiment]

[1274] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[1275] 5, the data processing system 310 includes the data processing device 12 and a headset terminal 314. An example of the data processing device 12 is a server.

[1276] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 is an example of a "computer" according to the technology of the present disclosure. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, the RAM 30, and the storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[1277] The headset type terminal 314 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication I / F 44, and a display 343. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, the camera 42, and the display 343 are also connected to the bus 52.

[1278] The microphone 238 receives instructions and the like from the user 20 by receiving voice uttered by the user 20. The microphone 238 captures the voice uttered by the user 20, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio in accordance with instructions from the processor 46.

[1279] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the surroundings of user 20 (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[1280] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[1281] Fig. 6 shows an example of the main functions of the data processing device 12 and the headset type terminal 314. As shown in Fig. 6, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[1282] The specific processing program 56 is an example of a "program" according to the technology of the present 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.

[1283] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[1284] In the headset type terminal 314, a reception output process is performed by the processor 46. A reception output program 60 is stored in the storage 50. 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 process is realized by the processor 46 operating as the control unit 46A in accordance with the reception output program 60 executed on the RAM 48.

[1285] Next, a description will be given of the identification process performed by the identification processing unit 290 of the data processing device 12. 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."

[1286] The "patent attorney AI" system of the present invention is a system that allows users to easily consult about patent procedures and supports the efficient progress of the patent application process. Below, we will generate a program for the system and explain its processing in natural language.

[1287] Explanation of program processing

[1288] User authentication and basic information entry

[1289] 1. Device: The user launches the "Patent Attorney AI" application and enters authentication information (username, password) on the login screen.

[1290] 2. Server: Receives the user's authentication information and authenticates the login by checking it against the database. If authentication is successful, obtains the user's profile information and sends it to the device.

[1291] 3. Terminal: After successful authentication, the dashboard screen is displayed and a form is provided where the user can enter the details of their consultation.

[1292] Enter the consultation details

[1293] 1. Terminal: The user enters information into a text box for entering questions or inquiries about patents.

[1294] 2. Terminal: Sends the consultation details entered by the user to the server.

[1295] 3. Server: Passes the received consultation content to a text analysis engine to extract important keywords and context.

[1296] AI-based initial diagnosis and information provision

[1297] 1. Server: Based on the extracted keywords and context, the AI ​​model generates appropriate information (e.g., patent application steps and required documents).

[1298] 2. Server: Formats the generated information and sends it to the user.

[1299] 3. Terminal: Displays the results of the initial diagnosis and related information to the user, such as "specific steps for patent application" and "list of required documents."

[1300] Collaboration with patent attorneys

[1301] 1. Server: If the user's question is too advanced to be resolved by AI alone, the server transfers the consultation to an expert (e.g., a patent attorney).

[1302] 2. Patent attorney: The expert reviews the user's consultation and enters any necessary additional information or advice.

[1303] 3. Server: Receives additional information from the expert and forwards it to the user.

[1304] 4. Terminal: Additional information from the patent attorney is displayed to the user, allowing them to receive specific advice.

[1305] Procedure progress reminders

[1306] 1. Server: When a user initiates a specific patent procedure, it tracks its progress, for example, managing specific submission deadlines and tasks.

[1307] 2. Server: Set a reminder when the submission deadline approaches.

[1308] 3. On the device: A deadline reminder is sent to the user. For example, a notification saying, "Your patent application is due in one week."

[1309] 4. User: Based on the reminder notification, you can take the following actions:

[1310] Reward Points Management

[1311] 1. Patent attorneys: Experts add useful information to the AI's database of answers.

[1312] 2. Server: Reviews the submitted information, incorporates it into a database, and awards reward points to patent attorneys based on the usefulness of the information.

[1313] 3. Patent attorneys: You can choose to use the reward points you have earned to advertise in related fields.

[1314] Specific examples

[1315] Example 1: New invention consultation

[1316] 1. A user has come up with a new invention but does not know the procedure for filing a patent application.

[1317] 2. Using your device, log in to your account and enter a summary of your invention.

[1318] 3. The server analyzes the information entered and provides the user with basic application procedures and necessary documents.

[1319] 4. The user enters an additional, more specific question, asking, "Is this invention likely to be patentable?"

[1320] 5. The server researches similar cases from the past, generates a response, and obtains additional responses from patent attorneys if necessary.

[1321] 6. The answer is displayed on the device so the user knows the next step.

[1322] Example 2: Reminder function

[1323] 1. A user initiates the patent application process and a specific submission deadline is set.

[1324] 2. The server tracks this information and sets reminders when deadlines approach.

[1325] 3. A reminder notification will be sent to the device one week before the submission deadline, helping users to remember to complete important tasks.

[1326] This will enable the "Patent Attorney AI" system to support complex patent-related procedures, enabling both users and patent attorneys to carry out their work efficiently.

[1327] The processing flow will be explained below.

[1328] User authentication and basic information entry

[1329] Step 1:

[1330] Terminal: The user launches the "Patent Attorney AI" application and enters authentication information (username, password) on the login screen.

[1331] Step 2:

[1332] Server: Receives the user's authentication information and authenticates the login against a database.

[1333] Step 3:

[1334] Server: If authentication is successful, obtain the user's profile information and send it to the terminal.

[1335] Step 4:

[1336] Terminal: After successful authentication, the dashboard screen is displayed and a form is provided where the user can enter the details of their consultation.

[1337] Enter the consultation details

[1338] Step 1:

[1339] Terminal: The user enters information into a text box for entering questions or consultations about patents.

[1340] Step 2:

[1341] Terminal: Sends the consultation details entered by the user to the server.

[1342] Step 3:

[1343] Server: Passes the received consultation content to a text analysis engine to extract important keywords and context.

[1344] AI-based initial diagnosis and information provision

[1345] Step 1:

[1346] Server: Based on the extracted keywords and context, the AI ​​model generates appropriate information (e.g., patent application steps and required documents).

[1347] Step 2:

[1348] Server: Formats the generated information and sends it to the user.

[1349] Step 3:

[1350] Terminal: Displays the results of the initial diagnosis and related information to the user, such as "specific steps for patent application" and "list of required documents."

[1351] Collaboration with patent attorneys

[1352] Step 1:

[1353] Server: If the user's question is too advanced to be resolved by AI alone, the server transfers the consultation to an expert (e.g., a patent attorney).

[1354] Step 2:

[1355] Patent attorney: The expert reviews the user's consultation and enters any necessary additional information or advice.

[1356] Step 3:

[1357] Server: Receives additional information from the expert and forwards it to the user.

[1358] Step 4:

[1359] Terminal: Additional information from the patent attorney is displayed to the user, allowing them to receive specific advice.

[1360] Procedure progress reminders

[1361] Step 1:

[1362] Server: Tracks the progress of specific patent proceedings initiated by users, for example, managing specific submission deadlines and tasks.

[1363] Step 2:

[1364] Server: Set reminders when the submission deadline approaches.

[1365] Step 3:

[1366] Terminal: A deadline reminder is sent to the user. For example, a notification saying, "Your patent application is due in one week."

[1367] Step 4:

[1368] User: Based on the reminder notification, you can take the following actions:

[1369] Reward Points Management

[1370] Step 1:

[1371] Patent attorneys: Experts provide input to add useful information to the AI's database of answers.

[1372] Step 2:

[1373] Server: Reviews the submitted information and incorporates it into a database. Based on the usefulness of the information, reward points are awarded to patent attorneys.

[1374] Step 3:

[1375] Patent attorneys: You can choose to use your earned reward points to advertise in related fields.

[1376] Example 1

[1377] Next, a description will be given of Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the headset type terminal 314 will be referred to as a "terminal."

[1378] In conventional patent procedures, users often required a great deal of time and effort to gather appropriate information. Furthermore, when expert assistance was required, the process of finding the appropriate expert was complicated, and managing the progress of the procedure was also time-consuming. Furthermore, the lack of a mechanism for effectively incorporating the knowledge of experts sometimes led to a decline in the quality of service provided to users. To solve these issues, a patent procedure support system that users can easily access is needed.

[1379] The specific processing by the specific processing unit 290 of the data processing device 12 in the first embodiment is realized by the following means.

[1380] In this invention, the server includes a means for receiving user authentication information and acquiring user identification information, a means for passing the consultation content received from the user to a text analysis means and extracting important features and background information, and a means for a generative AI model to generate appropriate data based on the extracted features and background information and provide it to the user. This allows users to efficiently obtain information about patent procedures and receive appropriate expert assistance. Furthermore, by tracking the progress of the procedures and notifying users of important deadlines and tasks, users can prevent delays in the procedures. Furthermore, by effectively incorporating expert knowledge into the system, the quality of service can be improved.

[1381] "User authentication information" is information used to identify a user and access a system, and typically consists of a username and password.

[1382] "User identification information" is data including the user's personal information and account information, and is used to identify the user.

[1383] "Consultation content" refers to questions or problems that users input into the system regarding patent procedures.

[1384] "Text analysis means" refers to technology for extracting important features and contextual information from received text data, and typically uses Natural Language Processing (NLP) techniques.

[1385] "Features" are important points or keywords extracted based on specific data or information.

[1386] "Background information" is data that provides context or relevance for particular data or information.

[1387] A "generative AI model" is a model that uses artificial intelligence technology to generate specific data or information, and makes predictions or judgments based on past data.

[1388] "Appropriate data" refers to information that is useful to the user and is generated based on the content of the user's consultation and background information.

[1389] An "expert" is a professional with advanced knowledge and experience in patent procedures.

[1390] A "response" is an additional answer or advice that an expert provides to a user in response to a question that the user has asked.

[1391] "Progress" refers to information that indicates the current state or progress of a particular procedure that a user is performing.

[1392] "Critical deadlines" are important filing or task deadlines in patent prosecution.

[1393] "Notifications" are messages sent by the system to inform users of important information or deadlines.

[1394] "Reward points" are points awarded to experts for providing useful information to the system, and serve as a reward for the experts.

[1395] "Promotion" refers to advertising or promotion in a related field using reward points acquired by an expert.

[1396] The system of this invention aims to efficiently support users in consulting about patent procedures. The system comprehensively performs a range of functions, from obtaining user authentication information, analyzing the text of the consultation, generating answers using AI, collaborating with experts, managing progress, and providing reward points.

[1397] Hardware and Software Configuration

[1398] Server: A high-performance computer equipped with a database management system and an analytical engine including an AI model. Specific software used for the generative AI model is natural language processing technology (e.g., Transformers and BERT).

[1399] Device: Using an internet browser or dedicated application on a device accessible to the user (smartphone, tablet, computer, etc.).

[1400] Database: A relational database (e.g., MySQL, PostgreSQL) for storing data such as user authentication information, profiles, text consultations, and expert responses.

[1401] System Operation

[1402] 1. User Authentication

[1403] The user logs into their account from a terminal and enters their username and password.

[1404] The server receives the entered authentication information and checks it against a database to obtain the user's identity.

[1405] 2. Input and analysis of consultation details

[1406] The user inputs questions or inquiries about patents into the terminal.

[1407] The terminal transmits the inputted contents to the server.

[1408] The server passes the received consultation content to a text analysis means to extract important features and background information.

[1409] 3. Early diagnosis and information provision using AI

[1410] The server uses the generative AI model to generate appropriate data based on the extracted features and background information.

[1411] The server provides the generated information to the user.

[1412] 4. Collaboration with experts

[1413] If a user's inquiry is too complex for the AI ​​model to handle on its own, the server will forward the information to an expert.

[1414] The expert checks the user's consultation and inputs an additional response.

[1415] The server forwards the expert's response to the user.

[1416] 5. Progress Management

[1417] The server tracks the user's progress in completing specific procedures and manages important deadlines and tasks.

[1418] A notification is sent to the device when the submission deadline approaches.

[1419] 6. Reward Points Management

[1420] Experts add useful information to the generative AI model's answer database.

[1421] The server reviews the provided information and awards reward points to the expert.

[1422] Professionals have the option to use earned reward points to post advertisements in related fields.

[1423] Specific examples

[1424] Example 1: New invention consultation

[1425] 1. If a user comes up with a new invention and does not know how to apply for a patent, they can log in to their account using their device and enter a summary of their invention.

[1426] 2. The server analyzes the information entered and generates information on basic application procedures and required documents.

[1427] 3. The user types a follow-up question: "Is this invention likely to be patented?"

[1428] 4. The server investigates similar cases from the past, generates an answer, and obtains additional answers from experts if necessary.

[1429] 5. The answer is displayed on the device so the user knows the next step.

[1430] Specific prompt examples:

[1431] I have come up with a new invention, but I don't know how to apply for a patent. Can you tell me if there is a high chance that this invention will be patented?

[1432] Example 2: Reminder function

[1433] 1. A user initiates the patent application process and sets a specific submission deadline.

[1434] 2. The server tracks this information and sets reminders when deadlines approach.

[1435] 3. A reminder will be sent to your device one week before the submission deadline.

[1436] 4. The user receives a reminder notification and proceeds with the next action.

[1437] Specific prompt examples:

[1438] Please inform us that the deadline for submitting patent applications is one week away.

[1439] As described above, this system supports complex patent-related procedures, enabling both users and experts to carry out their work efficiently.

[1440] The flow of the identification process in the first embodiment will be described with reference to FIG.

[1441] Step 1:

[1442] The user launches the "Patent Attorney AI" application and enters their username and password. A username and password are required as input. The device sends this authentication information to the server. The server compares the received input data with a database and verifies whether authentication is successful. If authentication is successful, the user's profile information is returned to the device as output.

[1443] Step 2:

[1444] After successful authentication, the terminal displays the user's dashboard screen and provides a form for the user to enter the consultation details. Input includes the user's questions about patents and the details of the consultation. The user enters the consultation details and clicks the "Send" button. The terminal then sends the entered consultation details to the server.

[1445] Step 3:

[1446] The server passes the received consultation content to the text analysis means. The input includes the text data of the consultation content entered by the user. The server uses the text analysis means to extract important features and background information. Specifically, it uses natural language processing (NLP) technology to analyze the text and extract important keywords and context. The output is the extracted features and background information.

[1447] Step 4:

[1448] The server passes the extracted features and background information as input to a generative AI model. The generative AI model generates appropriate data based on the input data. Specifically, the generative AI model compares the data with past data and generates appropriate patent procedure steps and necessary document information based on the consultation content. The generated information is obtained as output.

[1449] Step 5:

[1450] The server formats the generated information and sends it to the device to be served to the user. The input includes the information generated by the generative AI model. The server formats this into an easy-to-read format and serves it to the user. The output is the formatted information sent to the device.

[1451] Step 6:

[1452] The terminal displays the information received from the server on the user's dashboard, which includes formatted information received from the server as input, allowing the user to review the patent application procedures and required documents.

[1453] Step 7:

[1454] If the user's inquiry is too advanced for the generative AI model alone to handle, the server forwards the information to an expert. The input includes the user's advanced inquiry content, which the server forwards to the expert. The expert then inputs an additional response based on this and returns it to the server. The output is the expert's response.

[1455] Step 8:

[1456] The server receives the expert's response and sends it to the terminal for presentation to the user. The input includes the response data from the expert, which the server converts into a user-friendly format and sends to the terminal. The output is the formatted response sent to the terminal.

[1457] Step 9:

[1458] The device displays the expert's response on a dashboard, where the user can see specific advice. The input includes the expert's response data. The user can then take further action based on the response.

[1459] Step 10:

[1460] The server tracks the progress of a specific patent procedure when a user initiates it. The input includes the user's initiation information. The server tracks the progress and manages important deadlines and tasks. The output is progress data.

[1461] Step 11:

[1462] The server sets reminders when a specific deadline approaches and sends notifications to the user's device. The input includes progress data and deadline information. The server generates a reminder notification based on this and sends it to the device. The output is a reminder notification.

[1463] Step 12:

[1464] The device displays a reminder notification to the user, helping the user remember to proceed with important tasks. The input includes the reminder notification received from the server. The user checks the notification and takes the next action.

[1465] Step 13:

[1466] Experts add useful information to the generative AI model's answer database. The input includes information based on the expert's knowledge and experience. The expert enters the information into the system, which is received by the server. This adds useful information to the database. The output is updated information.

[1467] Step 14:

[1468] The server awards reward points to the expert based on the provided information. The input includes information provided by the expert. The server evaluates this information, calculates and awards reward points. The output is reward points.

[1469] Step 15:

[1470] The expert uses the reward points he / she has acquired to select an option to post an advertisement in a related field. The input includes the reward points he / she has acquired and the advertisement content. The expert selects the option, and the server performs the procedure to post the advertisement. The output is the advertisement.

[1471] (Application example 1)

[1472] Next, a description will be given of Application Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the headset type terminal 314 will be referred to as a "terminal."

[1473] While support systems existed to help users efficiently advance patent procedures, they were limited in their ability to provide appropriate information in real time in response to user inquiries and questions. Furthermore, there was no system that provided patent-related knowledge through educational content and offered expert support, making it difficult for users to understand the progress of their procedures and take appropriate action.

[1474] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 1 is realized by the following means.

[1475] In this invention, the server includes: means for receiving user authentication information and acquiring user profile information; means for passing the inquiry received from the user to a text analysis engine and extracting important keywords and context; means for an AI model to generate appropriate information based on the extracted keywords and context and provide it to the user; means for forwarding the information to a specialist in a specialized field to obtain additional answers if the user's question is difficult and cannot be solved by AI alone; means for tracking the user's progress in a specific procedure and reminding the user of important deadlines and tasks; means for experts to add useful information to the AI's answer database and award reward points; means for streaming educational content viewable on electronic devices; and means for analyzing viewers' questions in real time and providing related information. This allows users to deepen their knowledge of patent procedures and receive expert assistance at the appropriate time.

[1476] "User authentication information" is information for identifying a specific user and verifying access rights.

[1477] "Profile information" refers to basic attribute information about a user and personal data within the system.

[1478] "Consultation content" refers to questions or matters of consultation that a user inputs into the system regarding patent procedures.

[1479] A "text analysis engine" is software that extracts important keywords and context from input text data.

[1480] An "AI model" is a data processing algorithm that uses artificial intelligence to solve a specific problem.

[1481] An "expert" is a person with advanced knowledge and skills in a particular field who provides advice and information to users.

[1482] "Progress" is information that indicates the current stage of a particular task or project.

[1483] A "deadline" refers to the deadline for completing a particular task or procedure.

[1484] A "task" refers to a specific task or action that needs to be completed.

[1485] A "remind" is a notification that reminds a user of a specific action or deadline.

[1486] "Reward Points" are a form of reward given to Experts for useful activities they perform on the system.

[1487] "Educational content" refers to learning materials created to teach specific knowledge or skills.

[1488] "Streaming distribution" is a method of continuously transmitting and viewing audio and video in real time over the Internet.

[1489] "Real-time" refers to the instantaneous processing of data and exchange of information.

[1490] As an embodiment of the present invention, a patent procedure support system is realized as follows: Specific programs and processing methods of this system are described below.

[1491] System configuration and hardware / software used

[1492] Hardware and software used

[1493] Hardware: Smartphones, head-mounted displays, servers

[1494] Software: Mobile applications (iOS / Android), server-side AI models, text analysis engines, streaming platforms (e.g., AWS Elemental Media Services)

[1495] System processing explanation

[1496] User authentication and basic information entry

[1497] The device launches the "Patent Attorney AI Course" application. The user enters authentication information (username, password) on the login screen. The server receives the user's authentication information and authenticates it by checking it against a database. If authentication is successful, the server obtains the user's profile information and sends it to the device. After successful authentication, the device displays the main dashboard screen, and the user can select the course they wish to view.

[1498] Lecture viewing and interaction

[1499] The user selects a course from their device and presses the play button to watch the video course. The server streams the video data of the selected course. While watching the video course, the user enters their question or inquiry into a text box on the screen. The server passes the input inquiry to a text analysis engine (e.g., AWS Comprehend) to extract important keywords and context.

[1500] Early diagnosis and information provision using AI models

[1501] The server uses an AI model (e.g., OpenAI GPT-4) to generate relevant information based on the extracted keywords and context. The generated information is then formatted and sent to the user. The device then displays the information generated by the AI ​​model to the user, helping them further understand the information.

[1502] Collaboration with experts (patent attorneys)

[1503] When a user enters an advanced question, the server forwards the information to a specific expert (patent attorney). The expert uses a dedicated dashboard to check the user's question and enters an answer. The server receives additional information from the expert and forwards it back to the user. The terminal displays the information from the expert and helps the user take appropriate action.

[1504] Reminder function

[1505] The server tracks the user's progress in completing a specific procedure. When a specific deadline approaches, the server sets a reminder and sends a notification to the user. The notification is sent to the user's device using Firebase Cloud Messaging. The user can then take the next action after receiving the reminder notification.

[1506] Examples and prompts

[1507] Example 1: Initial diagnosis

[1508] User Input: "Is this invention likely to be patented?"

[1509] AI response: "The user has entered a summary of their invention. Input: ____. Based on this, please assess the likelihood of obtaining a patent."

[1510] Example 2: Feedback on a specific task

[1511] User Input: "What documents do I need to file a patent application?"

[1512] AI response: "The user asked about the documents required for a patent application. Please provide details below."

[1513] The patent procedure support system implemented in this way allows users to deepen their knowledge of patent procedures and receive expert support at the appropriate time.

[1514] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[1515] Step 1:

[1516] The user launches the "Patent Attorney AI Course" application and enters authentication information (username, password) on the login screen. The entered authentication information is sent to the server. The server receives the authentication information and authenticates the user by checking it against a database. During this process, the server obtains the user's profile information and sends a successful authentication result to the terminal.

[1517] Input: Username, Password

[1518] Data processing: User authentication by database matching

[1519] Output: Authentication success / failure result, user profile information

[1520] Specific operation: The server searches for user information in a database (e.g., AWS RDS), and if there is a match, it sends a flag indicating successful authentication to the terminal.

[1521] Step 2:

[1522] After successful authentication, the device displays the main dashboard screen. The user selects the course they wish to view from the dashboard. This course selection data is sent to the server.

[1523] Input: User-input course selection

[1524] Data processing: Send the selected course ID to the server

[1525] Output: Course selection data to the server

[1526] Specific behavior: The user interface displays a list of courses, and the user selects one by touch.

[1527] Step 3:

[1528] The server retrieves the video data of the course selected by the user and prepares it for streaming. The server then sends the video data to the device via streaming (e.g., AWS Elemental Media Services).

[1529] Input: Selected course ID

[1530] Data processing: Acquisition of video data and streaming distribution

[1531] Output: Sending video data via streaming

[1532] Specific operation: The server generates a URL for the video data and passes it to the streaming service.

[1533] Step 4:

[1534] The device plays the video lecture, and the user watches the video. While watching, the user enters questions or inquiries into a text box on the screen. This input data is sent to the server.

[1535] Input: User-entered consultation details

[1536] Data processing: Receiving and sending text data

[1537] Output: Send consultation text to server

[1538] Specific operation: The user interface provides a text input field and sends the input to the server using a submit button.

[1539] Step 5:

[1540] The server passes the consultation content received from the user to a text analysis engine (e.g., AWS Comprehend) to extract important keywords and context.

[1541] Input: Consultation content text

[1542] Data processing: Keyword extraction through text analysis

[1543] Output: Extracted keywords and context

[1544] Specific operation: The server sends text data to the API of the text analysis engine and receives the analysis results.

[1545] Step 6:

[1546] The server uses an AI model (e.g., OpenAI GPT-4) to generate relevant information based on the extracted keywords and context, formats the information, and sends it to the user.

[1547] Input: Keywords, Context

[1548] Data processing: Information generation using AI models

[1549] Output: Sending relevant formatted information

[1550] Specific operation: The server calls the AI ​​model, formats the generated information in HTML or JSON format, and sends it to the device.

[1551] Step 7:

[1552] The device displays information from the AI ​​model to the user, who then asks additional questions or takes action based on the information.

[1553] Input: Formatted and relevant information from the AI ​​model

[1554] Data processing: UI display of information

[1555] Output: Displaying information to the user

[1556] What it does: The user interface displays the generated information in rich text and card format, encouraging further interaction.

[1557] Step 8:

[1558] When a user enters a complex question, the server transfers the question to an expert, who then uses a dedicated dashboard to review the question and enter additional answers. The server then receives the information from the expert and sends it to the user.

[1559] Input: Advanced Question Content

[1560] Data processing: forwarding to experts and receiving answers

[1561] Output: User submission of additional information from the expert

[1562] Specific operation: The server displays the question on the expert's dashboard, receives the answer entered by the expert, and returns it to the user.

[1563] Step 9:

[1564] The server tracks the user's progress in completing specific procedures, sets reminders when specific deadlines for submissions are approaching, and sends notifications to the user.

[1565] Input: Procedural progress data, submission deadline

[1566] Data processing: generating and sending notification content

[1567] Output: Reminder notification sent to user

[1568] Specific operation: The server obtains deadline information from the progress management system, generates a reminder notification, and sends it to the user's device via Firebase Cloud Messaging.

[1569] Step 10:

[1570] The user receives a reminder notification and takes the next action, such as checking submitted documents or consulting an expert for further assistance.

[1571] Input: Reminder notification

[1572] Data manipulation: Performing user actions

[1573] Output: The following procedure is completed

[1574] Specific behavior: The user clicks on the link in the notification, and a screen appears on the device to take the next step or to consult with an expert.

[1575] Furthermore, an emotion engine that estimates the user's emotion may be further combined. That is, the identification processing unit 290 may estimate the user's emotion using the emotion identification model 59, and perform identification processing using the user's emotion.

[1576] The "Patent Attorney AI" system of the present invention is a system that allows users to easily consult about patent procedures and supports the efficient progress of the patent application process. Furthermore, it aims to improve the user experience by incorporating an emotion engine that recognizes and responds to the user's emotions. Below, we will generate a program for the system and explain its processing in natural language.

[1577] Explanation of program processing

[1578] User authentication and basic information entry

[1579] 1. Device: The user launches the "Patent Attorney AI" application and enters authentication information (username, password) on the login screen.

[1580] 2. Server: Receives the user's authentication information and authenticates the login by checking it against a database.

[1581] 3. Server: If authentication is successful, obtain the user's profile information and send it to the device.

[1582] 4. Terminal: After successful authentication, the dashboard screen is displayed and a form is provided where the user can enter the details of their consultation.

[1583] Enter the consultation details

[1584] 1. Terminal: The user enters information into a text box for entering questions or inquiries about patents.

[1585] 2. Terminal: Sends the consultation details entered by the user to the server.

[1586] 3. Server: The received consultation content is passed to a text analysis engine to extract important keywords and context.

[1587] AI-based initial diagnosis and information provision

[1588] 1. Server: Based on the extracted keywords and context, the AI ​​model generates appropriate information (e.g., patent application steps and required documents).

[1589] 2. Server: Formats the generated information and sends it to the user.

[1590] 3. Terminal: Displays the results of the initial diagnosis and related information to the user, such as "specific steps for patent application" and "list of required documents."

[1591] Emotion recognition and response using emotion engine

[1592] 1. Device: The emotion engine analyzes the user's text input and behavior and recognizes emotions.

[1593] 2. Server: Adjust the information provided and the tone of the response based on the emotions recognized by the emotion engine.

[1594] 3. Terminal: For example, if the user is perceived as confused, additional support information is displayed in a gentle tone.

[1595] Collaboration with patent attorneys

[1596] 1. Server: If the user's question is too advanced to be resolved by AI alone, the server transfers the consultation to an expert (e.g., a patent attorney).

[1597] 2. Patent attorney: The expert reviews the user's consultation and enters any necessary additional information or advice.

[1598] 3. Server: Receives additional information from the expert and forwards it to the user.

[1599] 4. Terminal: Additional information from the patent attorney is displayed to the user, allowing them to receive specific advice.

[1600] Procedure progress reminders

[1601] 1. Server: When a user initiates a specific patent procedure, it tracks its progress, for example, managing specific submission deadlines and tasks.

[1602] 2. Server: Set a reminder when the submission deadline approaches.

[1603] 3. On the device: A deadline reminder is sent to the user. For example, a notification saying, "Your patent application is due in one week."

[1604] 4. User: Based on the reminder notification, you can take the following actions:

[1605] Reward Points Management

[1606] 1. Patent attorneys: Experts provide input to add useful information to the AI's database of answers.

[1607] 2. Server: Reviews the submitted information, incorporates it into a database, and awards reward points to patent attorneys based on the usefulness of the information.

[1608] 3. Patent attorneys: You can choose to use the reward points you have earned to advertise in related fields.

[1609] Specific examples

[1610] Example 1: New invention consultation

[1611] 1. A user has come up with a new invention but does not know the procedure for filing a patent application.

[1612] 2. Using your device, log in to your account and enter a summary of your invention.

[1613] 3. The server analyzes the information entered and provides the user with basic application procedures and necessary documents.

[1614] 4. The emotion engine detects anxiety from the user's input and displays a more polite help message.

[1615] 5. The user enters an additional, more specific question, asking, "Is this invention likely to be patentable?"

[1616] 6. The server researches similar cases from the past, generates a response, and obtains additional responses from patent attorneys if necessary.

[1617] 7. The answer is displayed on the device so the user knows the next step.

[1618] Example 2: Reminder function

[1619] 1. A user initiates the patent application process and a specific submission deadline is set.

[1620] 2. The server tracks this information and sets reminders when deadlines approach.

[1621] 3. A reminder notification will be sent to the device one week before the submission deadline, helping users to remember to complete important tasks.

[1622] This allows the "Patent Attorney AI" system to support complex patent-related procedures, enabling both users and experts to carry out their work efficiently. Furthermore, the emotion engine improves the user experience, allowing particularly troubled users to receive appropriate support.

[1623] The processing flow will be explained below.

[1624] Processing of the "Patent Attorney AI" system including an emotion engine

[1625] User authentication and basic information entry

[1626] Step 1:

[1627] Terminal: The user launches the "Patent Attorney AI" application and enters authentication information (username, password) on the login screen.

[1628] Step 2:

[1629] Server: Receives the user's authentication information and authenticates the login against a database.

[1630] Step 3:

[1631] Server: If authentication is successful, obtain the user's profile information and send it to the terminal.

[1632] Step 4:

[1633] Terminal: After successful authentication, the dashboard screen is displayed and a form is provided where the user can enter the details of their consultation.

[1634] Enter the consultation details

[1635] Step 1:

[1636] Terminal: The user enters information into a text box for entering questions or consultations about patents.

[1637] Step 2:

[1638] Terminal: Sends the consultation details entered by the user to the server.

[1639] Step 3:

[1640] Server: Passes the received consultation content to a text analysis engine to extract important keywords and context.

[1641] AI-based initial diagnosis and information provision

[1642] Step 1:

[1643] Server: Based on the extracted keywords and context, the AI ​​model generates appropriate information (e.g., patent application steps and required documents).

[1644] Step 2:

[1645] Server: Formats the generated information and sends it to the user.

[1646] Step 3:

[1647] Terminal: Displays the results of the initial diagnosis and related information to the user, such as "specific steps for patent application" and "list of required documents."

[1648] Emotion recognition and response using emotion engine

[1649] Step 1:

[1650] Device: The emotion engine analyzes the user's text input and behavior to recognize emotions.

[1651] Step 2:

[1652] Server: Adjusts the information provided and the tone of the response based on the emotions recognized by the emotion engine.

[1653] Step 3:

[1654] Terminal: For example, if the user is perceived as confused, additional support information is displayed in a gentle tone.

[1655] Collaboration with patent attorneys

[1656] Step 1:

[1657] Server: If the user's question is too advanced to be resolved by AI alone, the server transfers the consultation to an expert (e.g., a patent attorney).

[1658] Step 2:

[1659] Patent attorney: The expert reviews the user's consultation and enters any necessary additional information or advice.

[1660] Step 3:

[1661] Server: Receives additional information from the expert and forwards it to the user.

[1662] Step 4:

[1663] Terminal: Additional information from the patent attorney is displayed to the user, allowing them to receive specific advice.

[1664] Procedure progress reminders

[1665] Step 1:

[1666] Server: Tracks the progress of specific patent proceedings initiated by users, for example, managing specific submission deadlines and tasks.

[1667] Step 2:

[1668] Server: Set reminders and start timers when the submission deadline approaches.

[1669] Step 3:

[1670] Terminal: A deadline reminder is sent to the user. For example, a notification saying, "Your patent application is due in one week."

[1671] Step 4:

[1672] User: Based on the reminder notification, you can take the following actions:

[1673] Reward Points Management

[1674] Step 1:

[1675] Patent attorneys: Experts provide input to add useful information to the AI's database of answers.

[1676] Step 2:

[1677] Server: Reviews the submitted information and incorporates it into a database. Based on the usefulness of the information, reward points are awarded to patent attorneys.

[1678] Step 3:

[1679] Patent attorneys: You can choose to use your earned reward points to advertise in related fields.

[1680] Example 2

[1681] Next, a description will be given of Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the headset type terminal 314 will be referred to as a "terminal."

[1682] Traditional patent procedures are extremely complicated, making it difficult for individual inventors to smoothly navigate the patent application process. During the patent acquisition process, users often overlook important deadlines or fail to obtain appropriate information. Furthermore, due to a lack of appropriate responses based on the user's emotional state, they are prone to feeling anxious and confused during the patent process. Furthermore, many questions are difficult and often cannot be resolved by artificial intelligence alone, requiring expert support. Smooth collaboration is crucial in these situations. Conventional systems are unable to adequately address these issues.

[1683] The identification process by the identification processing unit 290 of the data processing device 12 in Example 2 is realized by the following means. In this invention, the server includes: means for receiving user authentication information and acquiring user profile information; means for passing the consultation content received from the user to a text analysis engine and extracting important keywords and context; means for an AI model to generate appropriate information based on the extracted keywords and context and provide it to the user; means for forwarding information to a specialized expert and obtaining additional answers when the user's question is difficult and cannot be solved by AI alone; means for tracking the user's progress in a specific procedure and reminding them of important deadlines and tasks; means for the expert to add useful information to the AI's answer database and award reward points; means for analyzing the user's text input and behavior, and for an emotion engine to recognize emotions and adjust responses; and means for formatting the consultation content and additional answers from the expert and forwarding them to the user. This allows users to avoid missing important deadlines in patent prosecution, obtain appropriate information, and receive support tailored to their emotional state. Furthermore, AI and experts work together effectively to provide users with high-quality information and support. As a result, the entire patent process can proceed efficiently and user-friendly.

[1684] "User authentication information" refers to the information a user needs to log into a system, and typically includes a username and password.

[1685] "Profile Information" means data including a user's personal information and account information, which is used to identify the user and provide services.

[1686] "Consultation content" refers to questions or matters of consultation that a user submits to the system regarding a patent application.

[1687] A "text analysis engine" is a software engine that uses natural language processing technology to extract important keywords and context from input text data.

[1688] An "artificial intelligence model" refers to an algorithm or system that uses machine learning or deep learning to generate appropriate information from data.

[1689] "Subject matter experts" refer to professionals with in-depth knowledge and experience in patents and intellectual property, typically including patent attorneys and patent lawyers.

[1690] "Progress" refers to information indicating how far a user has progressed in the patent process.

[1691] "Deadline" refers to the deadline for each task or submission in the patent process.

[1692] "Remind" is a feature that notifies users so they don't forget important deadlines or tasks.

[1693] "Reward points" refer to incentives awarded to experts when they provide useful information, and can be used for specific benefits or advertising within the system.

[1694] An "emotion engine" is a software engine that analyzes a user's text input and behavior to recognize the user's emotional state.

[1695] The present invention is a support system for users to smoothly proceed with patent procedures, and specific embodiments thereof are described below.

[1696] The system mainly consists of a user terminal, a central server, and the "Patent Attorney AI" application that constitutes the user interface. Terminals can be ordinary PCs, smartphones, tablets, etc., and the server functions as a central processing unit including a database.

[1697] User authentication and basic information entry

[1698] 1. Terminal: The user launches the "Patent Attorney AI" application and enters authentication information (username, password) on the login screen. The entered information is securely transmitted to the server using the SSL / TLS protocol.

[1699] 2. Server: Receives the authentication information and authenticates the login by checking it against a database (e.g., MySQL or PostgreSQL). If authentication is successful, it obtains the user's profile information and sends it to the terminal in JSON format.

[1700] 3. Terminal: If authentication is successful, the dashboard screen will be displayed and a form will be provided where the user can enter the details of their consultation.

[1701] Input and analysis of consultation details

[1702] 1. Terminal: When the user enters a question or inquiry about a patent and clicks the send button, the information is sent to the server in JSON format.

[1703] 2. Server: The received consultation content is passed to a natural language processing (NLP) engine (e.g., SpaCy or NLTK) to extract important keywords and context. The extracted results are stored in a database for further processing.

[1704] Early diagnosis and information provision

[1705] 1. Server: Input the extracted keywords and context into an artificial intelligence model (e.g., GPT-3 or BERT) to generate appropriate information.

[1706] Example prompt: "Generate a list of required application procedures and documents based on the patent question entered by the user."

[1707] 2. Server: Formats the information generated by the AI ​​model and prepares it for delivery to the user. The formatted information is sent to the device in JSON format.

[1708] 3. Terminal: Displays the initial diagnosis results and provided information to the user. For example, the "steps for patent application" and "list of required documents" are displayed.

[1709] Emotion recognition and response using emotion engine

[1710] 1. Terminal: Analyzes the user's text input and behavior (e.g., typing speed and click operations) in real time and passes the results to an emotion engine (e.g., IBM Watson or Aylien).

[1711] 2. Server: Based on the emotion engine's recognition of the user's emotions, the server adjusts the tone of the information and responses it provides. For example, if the server recognizes that the user is confused, it provides additional support information in a gentler tone.

[1712] 3. Device: Display messages and information tailored to the user's emotions. For example, polite guidance such as "If you need help, please see here" is displayed.

[1713] Collaboration with experts

[1714] 1. Server: When a complex question is received that cannot be resolved by artificial intelligence alone, the content is forwarded to an expert (e.g., a patent attorney).

[1715] 2. Patent attorney: The expert logs in to a dedicated portal, reviews the user's question, and enters any additional information or specific advice required.

[1716] 3. Server: Receives additional responses from the patent attorney, formats them, and sends them to the terminal.

[1717] 4. Terminal: Experts provide information and give users specific advice on next steps.

[1718] Procedure progress reminders

[1719] 1. Server: When a user initiates a specific patent procedure, its progress is tracked in a database. Deadlines for each task are managed.

[1720] 2. Server: When the submission deadline approaches, a reminder is generated and sent to the device.

[1721] 3. Terminal: A reminder notification is displayed to the user, for example, a message saying, "The deadline for submitting your patent application is one week away." The user receives the notification and can take appropriate action.

[1722] Reward Points Management

[1723] 1. Patent attorneys: Experts add useful information to the AI's database of answers.

[1724] 2. Server: Automatically reviews the submitted information, evaluates its quality and usefulness, and awards reward points to the attorney if the evaluation is high.

[1725] 3. Patent Attorneys: You will be given the option to use your earned reward points to advertise in related fields.

[1726] This system allows users to carry out patent procedures themselves while receiving appropriate support and proceeding efficiently. In particular, support from the emotion engine and smooth collaboration with experts contribute to an improved user experience.

[1727] The flow of the identification process in the second embodiment will be described with reference to FIG.

[1728] Step 1:

[1729] Terminal: The user launches the "Patent Attorney AI" application and enters authentication information (username, password) on the login screen. This becomes the input data. When the user presses the "Login" button, the entered authentication information is sent to the server using the SSL / TLS protocol. The output is encrypted authentication information sent to the server.

[1730] Step 2:

[1731] Server: Receives authentication information and performs authentication by checking it against a database (e.g., MySQL or PostgreSQL). This check involves data calculations to confirm whether the username and password match. If authentication is successful, retrieves the user's profile information from the database and sends it to the terminal as output data in JSON format. As output, JSON data containing the user's profile information is sent to the terminal.

[1732] Step 3:

[1733] Terminal: After successful authentication, the terminal displays a dashboard screen and provides a form where the user can enter their consultation details. This includes input fields such as "Question," "Invention summary," and "Contact information." The input fields are displayed to the user as output data.

[1734] Step 4:

[1735] Terminal: When a user enters a question or consultation about a patent and clicks the send button, the entered information is sent to the server in JSON format. The input data is the user's question or consultation content, and the output data is JSON format data containing that content.

[1736] Step 5:

[1737] Server: Passes the received consultation content to a natural language processing (NLP) engine (e.g., SpaCy or NLTK) to extract important keywords and context. The input data is the consultation content sent by the user, and important keywords and context are extracted through data processing by the NLP engine. The output data is a list of the extracted keywords and context. The extraction results are stored in a database.

[1738] Step 6:

[1739] Server: Inputs the extracted keywords and context into an artificial intelligence model (e.g., GPT-3 or BERT) to generate appropriate information. The input data are the extracted keywords and context, and the artificial intelligence model calculates the data to generate information related to the user's question. Example prompt: "Based on the patent question entered by the user, please generate a list of the necessary application procedures and documents." The output data is the generated answer information.

[1740] Step 7:

[1741] Server: Formats the information generated by the AI ​​model and presents it to the user. This formatting involves processing the data to generate formatted documents that explain technical terms and specific procedures in an easy-to-understand manner. The formatted data is sent to the terminal in JSON format. The output data is formatted information.

[1742] Step 8:

[1743] Terminal: Displays the initial diagnosis results and provided information to the user. This includes "patent application steps" and "list of required documents." Output data is the specific information displayed on the user's screen.

[1744] Step 9:

[1745] Terminal: Analyzes the user's text input and behavior (e.g., typing speed and click operations) in real time and passes them to an emotion engine (e.g., IBM Watson or Aylien). The input data is the user's behavior data, and emotions are identified through data calculations by the emotion engine. The output data is the result of the emotion identification.

[1746] Step 10:

[1747] Server: The emotion engine adjusts the tone of the information and responses it provides based on the emotions it recognizes. For example, if it recognizes that the user is confused, it generates additional support information in a gentler tone. At this time, data processing involves adding modifications to the template sentence according to the user's emotional state. The output data is the adjusted message.

[1748] Step 11:

[1749] Terminal: Displays messages and information tailored to the user's emotions. For example, polite guidance such as "If you have any questions, please see here" is displayed. The output data is a message tailored to the user's emotions.

[1750] Step 12:

[1751] Server: When a user receives a complex question that cannot be resolved by AI alone, the server transfers the question to an expert (e.g., a patent attorney). The input data is the content of the complex question, and the transferred data is the specific question information. The expert logs into a dedicated portal, checks the user's question, and enters any necessary additional information or specific advice.

[1752] Step 13:

[1753] Server: Receives additional responses from patent attorneys, formats them, and sends them to the terminal. The input data is the content of the patent attorney's response, and the output data is the formatted additional response information.

[1754] Step 14:

[1755] Terminal: Information from experts is provided and users can get specific advice. The output data is specific advice that is displayed on the user's screen.

[1756] Step 15:

[1757] Server: When a user starts a specific patent procedure, its progress is tracked in a database. The input data is the user's procedure start information, and the progress information is updated in the database through tracking. The output data is the progress information.

[1758] Step 16:

[1759] Server: When the deadline approaches, a reminder is generated and sent to the terminal. The input data is progress information, and the output data is the reminder notification.

[1760] Step 17:

[1761] Terminal: A reminder notification is displayed to the user, for example, a message saying "The deadline for submitting patent application documents is one week away." The output data is the reminder notification.

[1762] Step 18:

[1763] User: Receives a reminder and takes the next step to meet the deadline. The input data is the reminder and the output data is the user's next action.

[1764] Step 19:

[1765] Patent attorney: An expert adds useful information to the AI's response database. The input data is the newly added information, and the output data is the information stored in the database.

[1766] Step 20:

[1767] Server: Automatically reviews the provided information and evaluates its quality and usefulness. If the evaluation is high, reward points are awarded to the patent attorney. The input data is the information to be evaluated, and the output data is the awarded reward points.

[1768] Step 21:

[1769] Patent attorneys: They are given the option to use their earned reward points to place advertisements in related fields. The input data is the reward points, and the output data is the use of the advertisement placement option.

[1770] (Application example 2)

[1771] Next, a description will be given of Application Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the headset type terminal 314 will be referred to as a "terminal."

[1772] In modern factories, new technologies and processes are frequently proposed, but the process of managing them and filing patent applications is extremely complex, time-consuming, and costly. In particular, the need to quickly evaluate proposed technologies on-site and determine their patentability is particularly challenging, but traditional methods are too labor-intensive. In addition, factory workers often lack adequate support and feedback on their proposals, which can lead to demotivation. Furthermore, tracking the progress of patent applications and managing important deadlines presents challenges.

[1773] The specific processing by the specific processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means. In this invention, the server includes: means for receiving user authentication information and acquiring user profile information; means for passing the inquiry received from the user to a text analysis engine and extracting important keywords and context; means for an AI model to generate appropriate information based on the extracted keywords and context and provide it to the user; means for forwarding information to a specialized expert in a specialized field to obtain additional answers if the user's question is difficult and cannot be solved by AI alone; means for tracking the progress of the user's specific procedures and reminding the user of important deadlines and tasks; means for the expert to add useful information to the AI's answer database and award reward points; means for receiving suggestions via voice input, converting the speech to text, and analyzing the content as a possible patent application; means for recognizing emotions from voice and facial expressions and adjusting the tone of the information provided based on the emotions; and means for tracking the progress of patent application proposals and sending reminders. This enables efficient management of new technology and process proposals in factories and rapid and accurate patent application procedures.

[1774] "User authentication information" means information used to identify a user and verify the user's access rights to access a system.

[1775] "Profile information" is data containing basic information about each user that identifies the individual user.

[1776] "Consultation content" is information detailing the question or problem that the user wants to solve through the system.

[1777] A "text analysis engine" is software or an algorithm that extracts meaning and important keywords from input text data and performs analysis.

[1778] "Keywords" are particularly important words or phrases in the content of a consultation, and are the subject of search and analysis.

[1779] "Context" is information about the context or situation in which a keyword is used, which clarifies the meaning of the keyword.

[1780] An "AI model" is an artificial intelligence algorithm or machine learning model that generates appropriate information or answers based on analyzed data.

[1781] "Subject matter experts" are individuals with advanced knowledge and experience in a particular field who provide additional assistance for problems that cannot be solved by AI.

[1782] "Progress" is information that indicates the progress of a particular task or project.

[1783] A "deadline" is the date by which a particular task or procedure must be completed.

[1784] A "task" is a specific job or operation that a user must accomplish.

[1785] "Reward points" are points that are awarded to experts for adding useful information to the database, which allow the experts to earn rewards or use certain options.

[1786] "Voice input" refers to a method in which a user inputs information by voice using a voice recognition device such as a microphone.

[1787] "Text conversion" is the process of converting audio data into text data.

[1788] "Patentability" is an indicator used to evaluate the likelihood that a new technology or invention will be granted a patent.

[1789] "Recognizing emotions from voice and facial expressions" is a technology that analyzes a user's voice and facial expressions to determine what emotions the user is feeling.

[1790] "Adjusting tone" means changing the tone or presentation of the information provided based on the user's emotional state.

[1791] "Sending reminders" means notifying users so that they do not forget important tasks or deadlines.

[1792] The system for implementing this invention uses the following hardware and software: The hardware includes smart glasses and microphones used by users, and a server. The software includes Google Cloud Natural Language API, GCP AutoML, AWS Rekognition, Slack API, and ICS (Internet Calendar Scheduling).

[1793] Obtaining user credentials and viewing profile information

[1794] The device receives user authentication information through the smart glasses and sends it to the server. The server compares the information with a database and authenticates the user. If authentication is successful, the server obtains the user's profile information and sends it to the device. After successful authentication, the device displays a dashboard screen and provides a form where the user can enter their consultation details.

[1795] Voice input and analysis of proposals

[1796] Users can voice-input suggestions for new technologies or process improvements through the smart glasses. The device converts the voice data into text and sends it to a server. The server then uses the Google Cloud Natural Language API to analyze the text and extract important keywords and context.

[1797] example:

[1798] Proposal: "Could production efficiency be improved by introducing a new robot arm control method?"

[1799] Extracted keywords: "Robot arm control method" and "Production efficiency"

[1800] Information generation using AI models

[1801] The server uses GCP's AutoML to perform an initial diagnosis of patentability based on the extracted keywords and context. As a result of the initial diagnosis, relevant information is provided to the user, including information on patent application procedures and similar existing technologies.

[1802] example:

[1803] Prompt: "Is this improvement proposal related to an existing patent?"

[1804] Prompt: "What procedure is needed next?"

[1805] Emotion recognition from speech and facial expressions

[1806] The device sends the user's voice and facial expression data to the server, which then uses AWS Rekognition to recognize emotions. Based on the recognized emotions, the server adjusts the tone of the information provided and displays appropriate support messages to the user. For example, if the server detects that the user is confused, it will provide more detailed explanations or additional information.

[1807] Collaboration with experts in the field

[1808] For more advanced suggestions that cannot be resolved using the information provided by the AI ​​model alone, the server uses a Slack API to forward them to a subject matter expert, who then sends additional answers and assistance to the server, which then displays them on the device.

[1809] example:

[1810] Prompt: "What documents do I need to create this idea?"

[1811] Proposal progress tracking and reminders

[1812] The server uses ICS to track the progress of proposals and manage deadlines and tasks. When important deadlines approach, reminders are automatically sent to the device, allowing users to efficiently progress through the patent application process without forgetting deadlines.

[1813] example:

[1814] Prompt: "The submission deadline is approaching. Don't forget to complete the process."

[1815] The system will enable efficient management of new technology and process proposals in factories, and ensure that patent application procedures are carried out quickly and accurately. Furthermore, emotion recognition capabilities will improve the user experience, enabling appropriate support to be provided to users who are particularly challenged.

[1816] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[1817] Step 1:

[1818] A user uses smart glasses and authenticates using voice commands. The device receives the user authentication information (e.g., username and password) and sends it to the server. The server checks this information against a database to confirm authentication and returns the result to the device. This allows the user to log in to the system and gain access rights.

[1819] input:

[1820] User authentication information (username, password)

[1821] output:

[1822] Authentication result (success or failure)

[1823] Specific behavior:

[1824] User enters username and password into smart glasses using voice commands

[1825] The device sends authentication information to the server and receives the authentication result.

[1826] Step 2:

[1827] If the server is successful in the authentication, it retrieves the user's profile information from the database and sends it to the terminal. The terminal then displays a dashboard screen and provides a form in which the user can enter the details of their consultation.

[1828] input:

[1829] Authentication result (success)

[1830] output:

[1831] Profile Information

[1832] Specific behavior:

[1833] The server searches the database to retrieve the user's profile information

[1834] Send profile information to device and display dashboard

[1835] Step 3:

[1836] Users can input suggestions for new technologies or process improvements by voice through smart glasses. The device converts this voice data into text and sends it to a server. The server then uses the Google Cloud Natural Language API to analyze the text and extract important keywords and context.

[1837] input:

[1838] Voice data (user suggestions)

[1839] output:

[1840] Extracted keywords and context

[1841] Specific behavior:

[1842] The device converts the voice data into text data

[1843] The server passes the text data to the Google Cloud Natural Language API to extract keywords and context.

[1844] Step 4:

[1845] The server uses GCP's AutoML to perform an initial diagnosis of patentability based on the extracted keywords and context. As a result of the initial diagnosis, it generates relevant information (e.g., patent application procedures, whether there are similar technologies, etc.) and sends it to the terminal.

[1846] input:

[1847] Extracted keywords and context

[1848] output:

[1849] Initial diagnosis results (related information)

[1850] Specific behavior:

[1851] The server inputs keywords and context into GCP's AutoML to perform an initial diagnosis.

[1852] Format the initial diagnostic results and send them to the device

[1853] Step 5:

[1854] The device displays the initial diagnosis results received from the server to the user. At the same time, it sends the user's voice and facial expressions to the server, which uses AWS Rekognition to recognize the user's emotions.

[1855] input:

[1856] Voice and facial expression data

[1857] Initial diagnosis results

[1858] output:

[1859] Recognized emotions

[1860] Adjusted information tone

[1861] Specific behavior:

[1862] The device displays the initial diagnosis results to the user

[1863] Voice and facial expression data is sent to the server

[1864] The server analyzes emotions using AWS Rekognition and adjusts the tone

[1865] Step 6:

[1866] The server adjusts the tone of the information based on the user's emotions to provide more appropriate support messages to the user. For example, if the user is confused, it displays more detailed explanations and additional information.

[1867] input:

[1868] Recognized emotions

[1869] output:

[1870] Tailored support messages

[1871] Specific behavior:

[1872] The server analyzes the perceived emotion and adjusts the tone appropriately

[1873] Supports message formatting and sending to device

[1874] Step 7:

[1875] If the user's suggestion is too advanced for the AI ​​alone to solve, the server uses the Slack API to forward the information to a subject matter expert, who then sends additional answers and assistance to the server, which then displays them on the device.

[1876] input:

[1877] Advanced proposal content

[1878] output:

[1879] Additional answers from experts

[1880] Specific behavior:

[1881] The server transfers the information to the expert using the Slack API

[1882] Receive answers from experts and send them to your device

[1883] Step 8:

[1884] The server uses ICS to track the progress of proposals and manage deadlines and tasks, automatically sending reminders to devices as important deadlines approach.

[1885] input:

[1886] Proposal progress

[1887] Deadline

[1888] output:

[1889] Reminder notifications

[1890] Specific behavior:

[1891] The server records the progress of the proposal in a database

[1892] Send reminders using ICS based on deadlines

[1893] The specific processing unit 290 transmits the result of the specific processing to the headset type terminal 314. In the headset type terminal 314, the control unit 46A causes the speaker 240 and the display 343 to output the result of the specific processing. The microphone 238 acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[1894] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (Internet Search<URL: https: / / openai.com / blog / chatgpt> ), Gemini (Internet search <url: https: gemini.google.com ?hl="ja">) and other generation AIs. The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating voice, text data indicating text, and image data indicating an image is also input. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt, and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[1895] In the above embodiment, an example was given in which the specific processing is performed by the data processing device 12, but the technology of the present disclosure is not limited to this, and the specific processing may be performed by the headset type terminal 314.

[1896] [Fourth embodiment]

[1897] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[1898] 7, a 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.

[1899] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 is an example of a "computer" according to the technology of the present disclosure. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, the RAM 30, and the storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[1900] The robot 414 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication I / F 44, and a control target 443. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, the camera 42, and the control target 443 are also connected to the bus 52.

[1901] The microphone 238 receives instructions and the like from the user 20 by receiving voice uttered by the user 20. The microphone 238 captures the voice uttered by the user 20, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio in accordance with instructions from the processor 46.

[1902] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the surroundings of user 20 (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[1903] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[1904] The control object 443 includes a display device, LEDs in the eyes, and motors for driving 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 emotions of the robot 414 can be expressed by controlling these motors. In addition, the facial expressions of the robot 414 can also be expressed by controlling the light emission state of the LEDs in the eyes of the robot 414.

[1905] Fig. 8 shows an example of the main functions of the data processing device 12 and the robot 414. As shown in Fig. 8, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[1906] The specific processing program 56 is an example of a "program" according to the technology of the present 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.

[1907] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[1908] In the robot 414, the processor 46 performs the reception output process. A reception output program 60 is stored in the storage 50. 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 process is realized by the processor 46 operating as the control unit 46A in accordance with the reception output program 60 executed on the RAM 48.

[1909] Next, a description will be given of the specific processing performed by the specific processing unit 290 of the data processing device 12. In the following description, the data processing device 12 will be referred to as a "server" and the robot 414 will be referred to as a "terminal."

[1910] The "patent attorney AI" system of the present invention is a system that allows users to easily consult about patent procedures and supports the efficient progress of the patent application process. Below, we will generate a program for the system and explain its processing in natural language.

[1911] Explanation of program processing

[1912] User authentication and basic information entry

[1913] 1. Device: The user launches the "Patent Attorney AI" application and enters authentication information (username, password) on the login screen.

[1914] 2. Server: Receives the user's authentication information and authenticates the login by checking it against the database. If authentication is successful, obtains the user's profile information and sends it to the device.

[1915] 3. Terminal: After successful authentication, the dashboard screen is displayed and a form is provided where the user can enter the details of their consultation.

[1916] Enter the consultation details

[1917] 1. Terminal: The user enters information into a text box for entering questions or inquiries about patents.

[1918] 2. Terminal: Sends the consultation details entered by the user to the server.

[1919] 3. Server: Passes the received consultation content to a text analysis engine to extract important keywords and context.

[1920] AI-based initial diagnosis and information provision

[1921] 1. Server: Based on the extracted keywords and context, the AI ​​model generates appropriate information (e.g., patent application steps and required documents).

[1922] 2. Server: Formats the generated information and sends it to the user.

[1923] 3. Terminal: Displays the results of the initial diagnosis and related information to the user, such as "specific steps for patent application" and "list of required documents."

[1924] Collaboration with patent attorneys

[1925] 1. Server: If the user's question is too advanced to be resolved by AI alone, the server transfers the consultation to an expert (e.g., a patent attorney).

[1926] 2. Patent attorney: The expert reviews the user's consultation and enters any necessary additional information or advice.

[1927] 3. Server: Receives additional information from the expert and forwards it to the user.

[1928] 4. Terminal: Additional information from the patent attorney is displayed to the user, allowing them to receive specific advice.

[1929] Procedure progress reminders

[1930] 1. Server: When a user initiates a specific patent procedure, it tracks its progress, for example, managing specific submission deadlines and tasks.

[1931] 2. Server: Set a reminder when the submission deadline approaches.

[1932] 3. On the device: A deadline reminder is sent to the user. For example, a notification saying, "Your patent application is due in one week."

[1933] 4. User: Based on the reminder notification, you can take the following actions:

[1934] Reward Points Management

[1935] 1. Patent attorneys: Experts add useful information to the AI's database of answers.

[1936] 2. Server: Reviews the submitted information, incorporates it into a database, and awards reward points to patent attorneys based on the usefulness of the information.

[1937] 3. Patent attorneys: You can choose to use the reward points you have earned to advertise in related fields.

[1938] Specific examples

[1939] Example 1: New invention consultation

[1940] 1. A user has come up with a new invention but does not know the procedure for filing a patent application.

[1941] 2. Using your device, log in to your account and enter a summary of your invention.

[1942] 3. The server analyzes the information entered and provides the user with basic application procedures and necessary documents.

[1943] 4. The user enters an additional, more specific question, asking, "Is this invention likely to be patentable?"

[1944] 5. The server researches similar cases from the past, generates a response, and obtains additional responses from patent attorneys if necessary.

[1945] 6. The answer is displayed on the device so the user knows the next step.

[1946] Example 2: Reminder function

[1947] 1. A user initiates the patent application process and a specific submission deadline is set.

[1948] 2. The server tracks this information and sets reminders when deadlines approach.

[1949] 3. A reminder notification will be sent to the device one week before the submission deadline, helping users to remember to complete important tasks.

[1950] This will enable the "Patent Attorney AI" system to support complex patent-related procedures, enabling both users and patent attorneys to carry out their work efficiently.

[1951] The processing flow will be explained below.

[1952] User authentication and basic information entry

[1953] Step 1:

[1954] Terminal: The user launches the "Patent Attorney AI" application and enters authentication information (username, password) on the login screen.

[1955] Step 2:

[1956] Server: Receives the user's authentication information and authenticates the login against a database.

[1957] Step 3:

[1958] Server: If authentication is successful, obtain the user's profile information and send it to the terminal.

[1959] Step 4:

[1960] Terminal: After successful authentication, the dashboard screen is displayed and a form is provided where the user can enter the details of their consultation.

[1961] Enter the consultation details

[1962] Step 1:

[1963] Terminal: The user enters information into a text box for entering questions or consultations about patents.

[1964] Step 2:

[1965] Terminal: Sends the consultation details entered by the user to the server.

[1966] Step 3:

[1967] Server: Passes the received consultation content to a text analysis engine to extract important keywords and context.

[1968] AI-based initial diagnosis and information provision

[1969] Step 1:

[1970] Server: Based on the extracted keywords and context, the AI ​​model generates appropriate information (e.g., patent application steps and required documents).

[1971] Step 2:

[1972] Server: Formats the generated information and sends it to the user.

[1973] Step 3:

[1974] Terminal: Displays the results of the initial diagnosis and related information to the user, such as "specific steps for patent application" and "list of required documents."

[1975] Collaboration with patent attorneys

[1976] Step 1:

[1977] Server: If the user's question is too advanced to be resolved by AI alone, the server transfers the consultation to an expert (e.g., a patent attorney).

[1978] Step 2:

[1979] Patent attorney: The expert reviews the user's consultation and enters any necessary additional information or advice.

[1980] Step 3:

[1981] Server: Receives additional information from the expert and forwards it to the user.

[1982] Step 4:

[1983] Terminal: Additional information from the patent attorney is displayed to the user, allowing them to receive specific advice.

[1984] Procedure progress reminders

[1985] Step 1:

[1986] Server: Tracks the progress of specific patent proceedings initiated by users, for example, managing specific submission deadlines and tasks.

[1987] Step 2:

[1988] Server: Set reminders when the submission deadline approaches.

[1989] Step 3:

[1990] Terminal: A deadline reminder is sent to the user. For example, a notification saying, "Your patent application is due in one week."

[1991] Step 4:

[1992] User: Based on the reminder notification, you can take the following actions:

[1993] Reward Points Management

[1994] Step 1:

[1995] Patent attorneys: Experts provide input to add useful information to the AI's database of answers.

[1996] Step 2:

[1997] Server: Reviews the submitted information and incorporates it into a database. Based on the usefulness of the information, reward points are awarded to patent attorneys.

[1998] Step 3:

[1999] Patent attorneys: You can choose to use your earned reward points to advertise in related fields.

[2000] Example 1

[2001] Next, a description will be given of Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the robot 414 will be referred to as a "terminal."

[2002] In conventional patent procedures, users often required a great deal of time and effort to gather appropriate information. Furthermore, when expert assistance was required, the process of finding the appropriate expert was complicated, and managing the progress of the procedure was also time-consuming. Furthermore, the lack of a mechanism for effectively incorporating the knowledge of experts sometimes led to a decline in the quality of service provided to users. To solve these issues, a patent procedure support system that users can easily access is needed.

[2003] The specific processing by the specific processing unit 290 of the data processing device 12 in the first embodiment is realized by the following means.

[2004] In this invention, the server includes a means for receiving user authentication information and acquiring user identification information, a means for passing the consultation content received from the user to a text analysis means and extracting important features and background information, and a means for a generative AI model to generate appropriate data based on the extracted features and background information and provide it to the user. This allows users to efficiently obtain information about patent procedures and receive appropriate expert assistance. Furthermore, by tracking the progress of the procedures and notifying users of important deadlines and tasks, users can prevent delays in the procedures. Furthermore, by effectively incorporating expert knowledge into the system, the quality of service can be improved.

[2005] "User authentication information" is information used to identify a user and access a system, and typically consists of a username and password.

[2006] "User identification information" is data including the user's personal information and account information, and is used to identify the user.

[2007] "Consultation content" refers to questions or problems that users input into the system regarding patent procedures.

[2008] "Text analysis means" refers to technology for extracting important features and contextual information from received text data, and typically uses Natural Language Processing (NLP) techniques.

[2009] "Features" are important points or keywords extracted based on specific data or information.

[2010] "Background information" is data that provides context or relevance for particular data or information.

[2011] A "generative AI model" is a model that uses artificial intelligence technology to generate specific data or information, and makes predictions or judgments based on past data.

[2012] "Appropriate data" refers to information that is useful to the user and is generated based on the content of the user's consultation and background information.

[2013] An "expert" is a professional with advanced knowledge and experience in patent procedures.

[2014] A "response" is an additional answer or advice that an expert provides to a user in response to a question that the user has asked.

[2015] "Progress" refers to information that indicates the current state or progress of a particular procedure that a user is performing.

[2016] "Critical deadlines" are important filing or task deadlines in patent prosecution.

[2017] "Notifications" are messages sent by the system to inform users of important information or deadlines.

[2018] "Reward points" are points awarded to experts for providing useful information to the system, and serve as a reward for the experts.

[2019] "Promotion" refers to advertising or promotion in a related field using reward points acquired by an expert.

[2020] The system of this invention aims to efficiently support users in consulting about patent procedures. The system comprehensively performs a range of functions, from obtaining user authentication information, analyzing the text of the consultation, generating answers using AI, collaborating with experts, managing progress, and providing reward points.

[2021] Hardware and Software Configuration

[2022] Server: A high-performance computer equipped with a database management system and an analytical engine including an AI model. Specific software used for the generative AI model is natural language processing technology (e.g., Transformers and BERT).

[2023] Device: Using an internet browser or dedicated application on a device accessible to the user (smartphone, tablet, computer, etc.).

[2024] Database: A relational database (e.g., MySQL, PostgreSQL) for storing data such as user authentication information, profiles, text consultations, and expert responses.

[2025] System Operation

[2026] 1. User Authentication

[2027] The user logs into their account from a terminal and enters their username and password.

[2028] The server receives the entered authentication information and checks it against a database to obtain the user's identity.

[2029] 2. Input and analysis of consultation details

[2030] The user inputs questions or inquiries about patents into the terminal.

[2031] The terminal transmits the inputted contents to the server.

[2032] The server passes the received consultation content to a text analysis means to extract important features and background information.

[2033] 3. Early diagnosis and information provision using AI

[2034] The server uses the generative AI model to generate appropriate data based on the extracted features and background information.

[2035] The server provides the generated information to the user.

[2036] 4. Collaboration with experts

[2037] If a user's inquiry is too complex for the AI ​​model to handle on its own, the server will forward the information to an expert.

[2038] The expert checks the user's consultation and inputs an additional response.

[2039] The server forwards the expert's response to the user.

[2040] 5. Progress Management

[2041] The server tracks the user's progress in completing specific procedures and manages important deadlines and tasks.

[2042] A notification is sent to the device when the submission deadline approaches.

[2043] 6. Reward Points Management

[2044] Experts add useful information to the generative AI model's answer database.

[2045] The server reviews the provided information and awards reward points to the expert.

[2046] Professionals have the option to use earned reward points to post advertisements in related fields.

[2047] Specific examples

[2048] Example 1: New invention consultation

[2049] 1. If a user comes up with a new invention and does not know how to apply for a patent, they can log in to their account using their device and enter a summary of their invention.

[2050] 2. The server analyzes the information entered and generates information on basic application procedures and required documents.

[2051] 3. The user types a follow-up question: "Is this invention likely to be patented?"

[2052] 4. The server investigates similar cases from the past, generates an answer, and obtains additional answers from experts if necessary.

[2053] 5. The answer is displayed on the device so the user knows the next step.

[2054] Specific prompt examples:

[2055] I have come up with a new invention, but I don't know how to apply for a patent. Can you tell me if there is a high chance that this invention will be patented?

[2056] Example 2: Reminder function

[2057] 1. A user initiates the patent application process and sets a specific submission deadline.

[2058] 2. The server tracks this information and sets reminders when deadlines approach.

[2059] 3. A reminder will be sent to your device one week before the submission deadline.

[2060] 4. The user receives a reminder notification and proceeds with the next action.

[2061] Specific prompt examples:

[2062] Please inform us that the deadline for submitting patent applications is one week away.

[2063] As described above, this system supports complex patent-related procedures, enabling both users and experts to carry out their work efficiently.

[2064] The flow of the identification process in the first embodiment will be described with reference to FIG.

[2065] Step 1:

[2066] The user launches the "Patent Attorney AI" application and enters their username and password. A username and password are required as input. The device sends this authentication information to the server. The server compares the received input data with a database and verifies whether authentication is successful. If authentication is successful, the user's profile information is returned to the device as output.

[2067] Step 2:

[2068] After successful authentication, the terminal displays the user's dashboard screen and provides a form for the user to enter the consultation details. Input includes the user's questions about patents and the details of the consultation. The user enters the consultation details and clicks the "Send" button. The terminal then sends the entered consultation details to the server.

[2069] Step 3:

[2070] The server passes the received consultation content to the text analysis means. The input includes the text data of the consultation content entered by the user. The server uses the text analysis means to extract important features and background information. Specifically, it uses natural language processing (NLP) technology to analyze the text and extract important keywords and context. The output is the extracted features and background information.

[2071] Step 4:

[2072] The server passes the extracted features and background information as input to a generative AI model. The generative AI model generates appropriate data based on the input data. Specifically, the generative AI model compares the data with past data and generates appropriate patent procedure steps and necessary document information based on the consultation content. The generated information is obtained as output.

[2073] Step 5:

[2074] The server formats the generated information and sends it to the device to be served to the user. The input includes the information generated by the generative AI model. The server formats this into an easy-to-read format and serves it to the user. The output is the formatted information sent to the device.

[2075] Step 6:

[2076] The terminal displays the information received from the server on the user's dashboard, which includes formatted information received from the server as input, allowing the user to review the patent application procedures and required documents.

[2077] Step 7:

[2078] If the user's inquiry is too advanced for the generative AI model alone to handle, the server forwards the information to an expert. The input includes the user's advanced inquiry content, which the server forwards to the expert. The expert then inputs an additional response based on this and returns it to the server. The output is the expert's response.

[2079] Step 8:

[2080] The server receives the expert's response and sends it to the terminal for presentation to the user. The input includes the response data from the expert, which the server converts into a user-friendly format and sends to the terminal. The output is the formatted response sent to the terminal.

[2081] Step 9:

[2082] The device displays the expert's response on a dashboard, where the user can see specific advice. The input includes the expert's response data. The user can then take further action based on the response.

[2083] Step 10:

[2084] The server tracks the progress of a specific patent procedure when a user initiates it. The input includes the user's initiation information. The server tracks the progress and manages important deadlines and tasks. The output is progress data.

[2085] Step 11:

[2086] The server sets reminders when a specific deadline approaches and sends notifications to the user's device. The input includes progress data and deadline information. The server generates a reminder notification based on this and sends it to the device. The output is a reminder notification.

[2087] Step 12:

[2088] The device displays a reminder notification to the user, helping the user remember to proceed with important tasks. The input includes the reminder notification received from the server. The user checks the notification and takes the next action.

[2089] Step 13:

[2090] Experts add useful information to the generative AI model's answer database. The input includes information based on the expert's knowledge and experience. The expert enters the information into the system, which is received by the server. This adds useful information to the database. The output is updated information.

[2091] Step 14:

[2092] The server awards reward points to the expert based on the provided information. The input includes information provided by the expert. The server evaluates this information, calculates and awards reward points. The output is reward points.

[2093] Step 15:

[2094] The expert uses the reward points he / she has acquired to select an option to post an advertisement in a related field. The input includes the reward points he / she has acquired and the advertisement content. The expert selects the option, and the server performs the procedure to post the advertisement. The output is the advertisement.

[2095] (Application example 1)

[2096] Next, a description will be given of Application Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the robot 414 will be referred to as a "terminal."

[2097] While support systems existed to help users efficiently advance patent procedures, they were limited in their ability to provide appropriate information in real time in response to user inquiries and questions. Furthermore, there was no system that provided patent-related knowledge through educational content and offered expert support, making it difficult for users to understand the progress of their procedures and take appropriate action.

[2098] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 1 is realized by the following means.

[2099] In this invention, the server includes: means for receiving user authentication information and acquiring user profile information; means for passing the inquiry received from the user to a text analysis engine and extracting important keywords and context; means for an AI model to generate appropriate information based on the extracted keywords and context and provide it to the user; means for forwarding the information to a specialist in a specialized field to obtain additional answers if the user's question is difficult and cannot be solved by AI alone; means for tracking the user's progress in a specific procedure and reminding the user of important deadlines and tasks; means for experts to add useful information to the AI's answer database and award reward points; means for streaming educational content viewable on electronic devices; and means for analyzing viewers' questions in real time and providing related information. This allows users to deepen their knowledge of patent procedures and receive expert assistance at the appropriate time.

[2100] "User authentication information" is information for identifying a specific user and verifying access rights.

[2101] "Profile information" refers to basic attribute information about a user and personal data within the system.

[2102] "Consultation content" refers to questions or matters of consultation that a user inputs into the system regarding patent procedures.

[2103] A "text analysis engine" is software that extracts important keywords and context from input text data.

[2104] An "AI model" is a data processing algorithm that uses artificial intelligence to solve a specific problem.

[2105] An "expert" is a person with advanced knowledge and skills in a particular field who provides advice and information to users.

[2106] "Progress" is information that indicates the current stage of a particular task or project.

[2107] A "deadline" refers to the deadline for completing a particular task or procedure.

[2108] A "task" refers to a specific task or action that needs to be completed.

[2109] A "remind" is a notification that reminds a user of a specific action or deadline.

[2110] "Reward Points" are a form of reward given to Experts for useful activities they perform on the system.

[2111] "Educational content" refers to learning materials created to teach specific knowledge or skills.

[2112] "Streaming distribution" is a method of continuously transmitting and viewing audio and video in real time over the Internet.

[2113] "Real-time" refers to the instantaneous processing of data and exchange of information.

[2114] As an embodiment of the present invention, a patent procedure support system is realized as follows: Specific programs and processing methods of this system are described below.

[2115] System configuration and hardware / software used

[2116] Hardware and software used

[2117] Hardware: Smartphones, head-mounted displays, servers

[2118] Software: Mobile applications (iOS / Android), server-side AI models, text analysis engines, streaming platforms (e.g., AWS Elemental Media Services)

[2119] System processing explanation

[2120] User authentication and basic information entry

[2121] The device launches the "Patent Attorney AI Course" application. The user enters authentication information (username, password) on the login screen. The server receives the user's authentication information and authenticates it by checking it against a database. If authentication is successful, the server obtains the user's profile information and sends it to the device. After successful authentication, the device displays the main dashboard screen, and the user can select the course they wish to view.

[2122] Lecture viewing and interaction

[2123] The user selects a course from their device and presses the play button to watch the video course. The server streams the video data of the selected course. While watching the video course, the user enters their question or inquiry into a text box on the screen. The server passes the input inquiry to a text analysis engine (e.g., AWS Comprehend) to extract important keywords and context.

[2124] Early diagnosis and information provision using AI models

[2125] The server uses an AI model (e.g., OpenAI GPT-4) to generate relevant information based on the extracted keywords and context. The generated information is then formatted and sent to the user. The device then displays the information generated by the AI ​​model to the user, helping them further understand the information.

[2126] Collaboration with experts (patent attorneys)

[2127] When a user enters an advanced question, the server forwards the information to a specific expert (patent attorney). The expert uses a dedicated dashboard to check the user's question and enters an answer. The server receives additional information from the expert and forwards it back to the user. The terminal displays the information from the expert and helps the user take appropriate action.

[2128] Reminder function

[2129] The server tracks the user's progress in completing a specific procedure. When a specific deadline approaches, the server sets a reminder and sends a notification to the user. The notification is sent to the user's device using Firebase Cloud Messaging. The user can then take the next action after receiving the reminder notification.

[2130] Examples and prompts

[2131] Example 1: Initial diagnosis

[2132] User Input: "Is this invention likely to be patented?"

[2133] AI response: "The user has entered a summary of their invention. Input: ____. Based on this, please assess the likelihood of obtaining a patent."

[2134] Example 2: Feedback on a specific task

[2135] User Input: "What documents do I need to file a patent application?"

[2136] AI response: "The user asked about the documents required for a patent application. Please provide details below."

[2137] The patent procedure support system implemented in this way allows users to deepen their knowledge of patent procedures and receive expert support at the appropriate time.

[2138] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[2139] Step 1:

[2140] The user launches the "Patent Attorney AI Course" application and enters authentication information (username, password) on the login screen. The entered authentication information is sent to the server. The server receives the authentication information and authenticates the user by checking it against a database. During this process, the server obtains the user's profile information and sends a successful authentication result to the terminal.

[2141] Input: Username, Password

[2142] Data processing: User authentication by database matching

[2143] Output: Authentication success / failure result, user profile information

[2144] Specific operation: The server searches for user information in a database (e.g., AWS RDS), and if there is a match, it sends a flag indicating successful authentication to the terminal.

[2145] Step 2:

[2146] After successful authentication, the device displays the main dashboard screen. The user selects the course they wish to view from the dashboard. This course selection data is sent to the server.

[2147] Input: User-input course selection

[2148] Data processing: Send the selected course ID to the server

[2149] Output: Course selection data to the server

[2150] Specific behavior: The user interface displays a list of courses, and the user selects one by touch.

[2151] Step 3:

[2152] The server retrieves the video data of the course selected by the user and prepares it for streaming. The server then sends the video data to the device via streaming (e.g., AWS Elemental Media Services).

[2153] Input: Selected course ID

[2154] Data processing: Acquisition of video data and streaming distribution

[2155] Output: Sending video data via streaming

[2156] Specific operation: The server generates a URL for the video data and passes it to the streaming service.

[2157] Step 4:

[2158] The device plays the video lecture, and the user watches the video. While watching, the user enters questions or inquiries into a text box on the screen. This input data is sent to the server.

[2159] Input: User-entered consultation details

[2160] Data processing: Receiving and sending text data

[2161] Output: Send consultation text to server

[2162] Specific operation: The user interface provides a text input field and sends the input to the server using a submit button.

[2163] Step 5:

[2164] The server passes the consultation content received from the user to a text analysis engine (e.g., AWS Comprehend) to extract important keywords and context.

[2165] Input: Consultation content text

[2166] Data processing: Keyword extraction through text analysis

[2167] Output: Extracted keywords and context

[2168] Specific operation: The server sends text data to the API of the text analysis engine and receives the analysis results.

[2169] Step 6:

[2170] The server uses an AI model (e.g., OpenAI GPT-4) to generate relevant information based on the extracted keywords and context, formats the information, and sends it to the user.

[2171] Input: Keywords, Context

[2172] Data processing: Information generation using AI models

[2173] Output: Sending relevant formatted information

[2174] Specific operation: The server calls the AI ​​model, formats the generated information in HTML or JSON format, and sends it to the device.

[2175] Step 7:

[2176] The device displays information from the AI ​​model to the user, who then asks additional questions or takes action based on the information.

[2177] Input: Formatted and relevant information from the AI ​​model

[2178] Data processing: UI display of information

[2179] Output: Displaying information to the user

[2180] What it does: The user interface displays the generated information in rich text and card format, encouraging further interaction.

[2181] Step 8:

[2182] When a user enters a complex question, the server transfers the question to an expert, who then uses a dedicated dashboard to review the question and enter additional answers. The server then receives the information from the expert and sends it to the user.

[2183] Input: Advanced Question Content

[2184] Data processing: forwarding to experts and receiving answers

[2185] Output: User submission of additional information from the expert

[2186] Specific operation: The server displays the question on the expert's dashboard, receives the answer entered by the expert, and returns it to the user.

[2187] Step 9:

[2188] The server tracks the user's progress in completing specific procedures, sets reminders when specific deadlines for submissions are approaching, and sends notifications to the user.

[2189] Input: Procedural progress data, submission deadline

[2190] Data processing: generating and sending notification content

[2191] Output: Reminder notification sent to user

[2192] Specific operation: The server obtains deadline information from the progress management system, generates a reminder notification, and sends it to the user's device via Firebase Cloud Messaging.

[2193] Step 10:

[2194] The user receives a reminder notification and takes the next action, such as checking submitted documents or consulting an expert for further assistance.

[2195] Input: Reminder notification

[2196] Data manipulation: Performing user actions

[2197] Output: The following procedure is completed

[2198] Specific behavior: The user clicks on the link in the notification, and a screen appears on the device to take the next step or to consult with an expert.

[2199] Furthermore, an emotion engine that estimates the user's emotion may be further combined. That is, the identification processing unit 290 may estimate the user's emotion using the emotion identification model 59, and perform identification processing using the user's emotion.

[2200] The "Patent Attorney AI" system of the present invention is a system that allows users to easily consult about patent procedures and supports the efficient progress of the patent application process. Furthermore, it aims to improve the user experience by incorporating an emotion engine that recognizes and responds to the user's emotions. Below, we will generate a program for the system and explain its processing in natural language.

[2201] Explanation of program processing

[2202] User authentication and basic information entry

[2203] 1. Device: The user launches the "Patent Attorney AI" application and enters authentication information (username, password) on the login screen.

[2204] 2. Server: Receives the user's authentication information and authenticates the login by checking it against a database.

[2205] 3. Server: If authentication is successful, obtain the user's profile information and send it to the device.

[2206] 4. Terminal: After successful authentication, the dashboard screen is displayed and a form is provided where the user can enter the details of their consultation.

[2207] Enter the consultation details

[2208] 1. Terminal: The user enters information into a text box for entering questions or inquiries about patents.

[2209] 2. Terminal: Sends the consultation details entered by the user to the server.

[2210] 3. Server: The received consultation content is passed to a text analysis engine to extract important keywords and context.

[2211] AI-based initial diagnosis and information provision

[2212] 1. Server: Based on the extracted keywords and context, the AI ​​model generates appropriate information (e.g., patent application steps and required documents).

[2213] 2. Server: Formats the generated information and sends it to the user.

[2214] 3. Terminal: Displays the results of the initial diagnosis and related information to the user, such as "specific steps for patent application" and "list of required documents."

[2215] Emotion recognition and response using emotion engine

[2216] 1. Device: The emotion engine analyzes the user's text input and behavior and recognizes emotions.

[2217] 2. Server: Adjust the information provided and the tone of the response based on the emotions recognized by the emotion engine.

[2218] 3. Terminal: For example, if the user is perceived as confused, additional support information is displayed in a gentle tone. ...

Claims

1. means for receiving user authentication information and obtaining user profile information; A means of passing the consultation content received from the user to a text analysis engine and extracting important keywords and context; Based on the extracted keywords and context, the AI ​​model generates appropriate information and provides it to the user. If a user's question is too advanced for AI alone to resolve, a means of transferring the information to a subject matter expert to obtain additional answers is provided. A way to track the user's progress on specific procedures and remind them of important deadlines and tasks; A system that includes a means for experts to award reward points by adding useful information to the AI's answer database.

2. 2. The system according to claim 1, further comprising means for sending a reminder notice to the user when the submission deadline approaches.

3. 2. The system of claim 1, further comprising means for providing an option for experts to use their acquired reward points to place advertisements in related fields.

Images