system

Abstract

We provide the system. [Solution] A means for inputting the user's area of ​​expertise and generating profile data, A means for matching collaborators from different fields of expertise based on the aforementioned profile data, One method involves using an agent that has the ability to integrate and translate information from different specialized fields. A means for generating and presenting new ideas based on the aforementioned integrated information, A means of providing a real-time collaborative work environment, A system that includes this.

Description

Technical Field

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

Background Art

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

Prior Art Documents

Patent Documents

[0003]

Patent Document 1

Summary of the Invention

Problems to be Solved by the Invention

[0004] In modern complex and diversified social problems and technological developments, cooperation between different specialized fields is essential, but differences in language and technical terms, physical distance, and the difficulty of efficient matching are barriers. Therefore, an effective platform is required for experts in different fields to collaborate smoothly and create new solutions.

Means for Solving the Problems

[0005] This invention provides a means for generating expert information entered by users from different fields as profiles and matching them with appropriate collaborators based on those profiles. Furthermore, it uses an agent that integrates and translates information from different fields to generate and present new ideas to users. In addition, by providing a real-time collaborative work environment and building a system that supports idea evaluation and project progress management, it promotes interdisciplinary collaboration and creates new value.

[0006] "User expertise information" refers to data related to the knowledge and experience possessed by individual users, and forms the basis for profile generation.

[0007] "Profile data" refers to a profile created based on the user's area of ​​expertise, and is used as input for the matching algorithm.

[0008] "Means of matching collaborators from different fields of expertise" refers to methods or systems for analyzing user profile data and identifying the most suitable collaboration partners.

[0009] An "agent with the ability to integrate and translate information" refers to an automated system that analyzes specialized knowledge acquired from different fields, translates it to achieve a common understanding, and integrates it.

[0010] "Means for generating and presenting new ideas" refers to technologies that use computer algorithms to generate new ideas based on integrated information and then present them to users.

[0011] "Means of providing a real-time collaborative work environment" refers to an online platform or tool that enables physically separated users to communicate, share data, and collaborate on tasks simultaneously. [Brief explanation of the drawing]

[0012] [Figure 1] This is a conceptual diagram showing an example of the configuration of a data processing system according to the first embodiment. [Figure 2] This is a conceptual diagram showing an example of the essential functions of a data processing device and a smart device according to the first embodiment. [Figure 3] This is a conceptual diagram showing an example of the configuration of a data processing system according to the second embodiment. [Figure 4] This is a conceptual diagram showing an example of the main functions of a data processing device and smart glasses according to the second embodiment. [Figure 5] This is a conceptual diagram showing an example of the configuration of a data processing system according to the third embodiment. [Figure 6] This is a conceptual diagram showing an example of the main functions of a data processing device and a headset-type terminal according to the third embodiment. [Figure 7] This is a conceptual diagram showing an example of the configuration of a data processing system according to the fourth embodiment. [Figure 8] This is a conceptual diagram showing an example of the main functions of a data processing device and a robot according to the fourth embodiment. [Figure 9] This shows an emotion map where multiple emotions are mapped. [Figure 10] This shows an emotion map where multiple emotions are mapped. [Figure 11] This is a sequence diagram showing the processing flow of the data processing system in Example 1. [Figure 12] This is a sequence diagram showing the processing flow of the data processing system in Application Example 1. [Figure 13] This is a sequence diagram showing the processing flow of the data processing system in Example 2, which incorporates an emotion engine. [Figure 14] This is a sequence diagram showing the processing flow of the data processing system in Application Example 2, which combines an emotion engine. [Modes for carrying out the invention]

[0013] Hereinafter, an example of an embodiment of a system according to the technology of the present disclosure will be described with reference to the accompanying drawings.

[0014] First, the terms used in the following description will be explained.

[0015] In the following embodiments, the numbered processor (hereinafter simply referred to as "processor") may be a single arithmetic unit or a combination of multiple arithmetic units. Also, the processor may be a single type of arithmetic unit or a combination of multiple types of arithmetic units. Examples of arithmetic units include a CPU (Central Processing Unit), a GPU (Graphics Processing Unit), a GPGPU (General-Purpose computing on Graphics Processing Units), an APU (Accelerated Processing Unit), and the like.

[0016] In the following embodiments, the numbered RAM (Random Access Memory) is a memory in which information is temporarily stored and is used as a work memory by the processor.

[0017] In the following embodiments, the numbered storage is one or more non-volatile storage devices that store various programs and various parameters, etc. Examples of non-volatile storage devices include flash memory (SSD (Solid State Drive)), magnetic disks (e.g., hard disks), or magnetic tapes, etc.

[0018] In the following embodiments, the signed communication interface (I / F) is an interface that includes a communication processor and an antenna, etc. The communication interface manages communication between multiple computers. Examples of communication standards applicable to the communication interface include wireless communication standards such as 5G (5th Generation Mobile Communication System), Wi-Fi (registered trademark), or Bluetooth (registered trademark).

[0019] In the following embodiments, "A and / or B" is synonymous with "at least one of A and B." That is, "A and / or B" means that it may be A alone, or B alone, or a combination of A and B. Furthermore, in this specification, the same concept as "A and / or B" applies when expressing three or more things linked by "and / or."

[0020] [First Embodiment]

[0021] Figure 1 shows an example of the configuration of the data processing system 10 according to the first embodiment.

[0022] As shown in Figure 1, the data processing system 10 includes a data processing device 12 and a smart device 14. An example of the data processing device 12 is a server.

[0023] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 is an example of a "computer" related to the technology of this disclosure. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[0024] The smart device 14 comprises a computer 36, a reception device 38, an output device 40, a camera 42, and a communication interface 44. The computer 36 comprises a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The reception device 38, output device 40, and camera 42 are also connected to the bus 52.

[0025] The reception device 38 is equipped with a touch panel 38A and a microphone 38B, etc., and receives user input. The touch panel 38A receives user input by detecting contact with an object (e.g., a pen or finger). The microphone 38B receives user input by detecting the user's voice. The control unit 46A transmits data indicating the user input received by the touch panel 38A and microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the data indicating the user input.

[0026] The output device 40 includes a display 40A and a speaker 40B, and presents data to the user 20 by outputting the data in a form perceptible to the user 20 (e.g., audio and / or text). The display 40A displays visible information such as text and images according to instructions from the processor 46. The speaker 40B outputs audio according to instructions from the processor 46. The camera 42 is a small digital camera equipped with an optical system such as a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor.

[0027] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various types of information between processor 46 and processor 28 via network 54.

[0028] Figure 2 shows an example of the main functions of the data processing device 12 and the smart device 14.

[0029] As shown in Figure 2, in the data processing device 12, a specific processing is performed by the processor 28. A specific processing program 56 is stored in the storage 32. The specific processing program 56 is an example of a "program" related to the technology of this disclosure. The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0030] The storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[0031] In the smart device 14, the processor 46 performs the reception output processing. The storage 50 stores the reception output program 60. The reception output program 60 is used in conjunction with a specific processing program 56 by the data processing system 10. The processor 46 reads the reception output program 60 from the storage 50 and executes the read reception output program 60 on the RAM 48. The reception output processing is realized by the processor 46 operating as a control unit 46A according to the reception output program 60 executed on the RAM 48.

[0032] Next, the specific processing performed by the specific processing unit 290 of the data processing device 12 will be described. In the following description, the data processing device 12 will be referred to as the "server" and the smart device 14 as the "terminal".

[0033] The system based on this invention aims to enable users from different specialized fields to collaborate effectively and create new solutions. Specific embodiments are shown below.

[0034] "Collection of user information and generation of profiles"

[0035] Users input their area of ​​expertise, skills, and research interests through their terminal. The server generates profile data for each user based on this information and stores it in a database. For example, if a researcher in the medical field has a project idea related to biomedical engineering, they can input the details of that idea, enabling them to collaborate with experts in related fields.

[0036] "Matching experts from different fields"

[0037] The server analyzes the generated profile data and runs algorithms to identify users from different fields with similar interests. This allows each user to find suitable collaborators. For example, if a big data analytics expert wants to contribute to an environmental science project, the matching system will select a suitable project owner.

[0038] "Integration and Translation of Knowledge"

[0039] The server sends information about users from different professional fields who have decided to collaborate to an AI agent. The AI ​​agent analyzes this information, translates and integrates it to reach a common understanding, and supports smooth communication between users with different backgrounds.

[0040] Idea Generation and Presentation

[0041] When a user submits an initial project idea, the device sends it to the server. An AI agent on the server compares it with a database of relevant research to generate new ideas and suggest the best solution for the user's needs. This result is then presented to the user via the device, and feedback is collected.

[0042] "Providing a real-time collaborative work environment"

[0043] The server provides an online collaborative platform to enable real-time communication between users. This allows users to utilize features such as chat, video conferencing, and file sharing, enabling collaboration beyond geographical constraints. For example, it becomes easier for project teams scattered around the world to simultaneously edit documents and work collaboratively. In this way, the present invention facilitates effective co-creation among experts from different fields, accelerating the creation of new value.

[0044] The following describes the processing flow.

[0045] Step 1:

[0046] The user uses a terminal to input their area of ​​expertise, skills, and research interests. The terminal temporarily stores this information and sends it to the server.

[0047] Step 2:

[0048] The server analyzes the received user information and generates profile data for each user. This profile data is stored in a database for use in the matching process.

[0049] Step 3:

[0050] The server executes an algorithm to search for commonalities and complementary information among users based on accumulated profile data. Based on these results, the server generates a list of optimal collaborators and notifies the user.

[0051] Step 4:

[0052] The user selects their preferred collaborator from the presented list. The device then sends the selection result back to the server.

[0053] Step 5:

[0054] The server transmits information from collaborators with different areas of expertise to an AI agent. The AI ​​agent analyzes this information and translates and integrates it to reach a common understanding.

[0055] Step 6:

[0056] The user inputs project ideas into their device and sends them to the server. The server's AI agent generates and evaluates new ideas based on past research and data related to the project.

[0057] Step 7:

[0058] The server sends the evaluation results of the generated ideas to the user, providing feedback on the feasibility of the ideas and areas for improvement.

[0059] Step 8:

[0060] The server establishes a real-time communication platform to enable effective collaboration among users. This platform includes chat, video conferencing, and file sharing capabilities.

[0061] Step 9:

[0062] Users will use the provided platform to share documents in real time and collaborate while monitoring project progress.

[0063] (Example 1)

[0064] Next, we will describe Example 1. In the following description, the data processing device 12 will be referred to as the "server," and the smart device 14 will be referred to as the "terminal."

[0065] When users from different professional fields collaborate to create new concepts, it is difficult to build effective cooperative relationships and fully utilize each individual's expertise. Furthermore, there is a lack of appropriate means to integrate information from different fields and facilitate smooth communication among users. This situation contributes to the creation of innovative solutions among diverse experts.

[0066] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 1 is realized by the following means.

[0067] In this invention, the server includes means for inputting information about the user's area of ​​expertise using an information processing device and generating user profile data; means for executing an algorithm for identifying and matching collaborators from different areas of expertise based on the profile data; and means for using an intelligent agent that has the function of integrating and translating information from different areas of expertise. This enables the establishment of effective collaborative relationships and smooth communication between users from different areas of expertise.

[0068] An "information processing device" is a device used for inputting, processing, storing, and outputting information, and mainly refers to computers and their peripherals.

[0069] "User" refers to an individual or organization that accesses the system using an information processing device and inputs their own information.

[0070] "Specialized field" refers to a specific area of ​​expertise that possesses particular knowledge and skills, and represents the field to which a user's specialized knowledge and skills belong.

[0071] "Profile data" refers to a dataset that comprehensively compiles information about a user's areas of expertise, skills, interests, and other relevant details.

[0072] An "algorithm" refers to a set of rules that define the steps or computational flow required to solve a specific problem.

[0073] An "intelligent agent" refers to a software program or system that automatically makes decisions and takes actions based on the information it is given.

[0074] "Information integration" refers to the process of unifying data and information obtained from different sources so that they can be handled in a unified manner.

[0075] "Translation" refers to the process of replacing specific technical terms or concepts with terms or concepts from a different language or field.

[0076] The system based on this invention provides an effective information processing environment for users from diverse professional fields to collaborate and create new concepts.

[0077] Users input information about their areas of expertise, skills, and interests using a terminal. This information is transmitted to the server via an information processing device. The server analyzes this information and performs processing to generate user profile data. The profile data is stored in a database.

[0078] The server runs an algorithm to match users from different areas of expertise based on their profile data. Specifically, it uses a generative AI model to identify highly similar users. This algorithm makes it possible to connect specialists from different fields who share a common interest in a particular topic.

[0079] Furthermore, the server uses intelligent agents to integrate information from different specialized fields and translate it into an easily understandable format. These intelligent agents combine machine learning and natural language processing technologies to enable smooth communication between users. This translation function allows users with diverse backgrounds to collaborate and communicate efficiently.

[0080] When a user inputs a new project idea via their device, an AI agent on the server searches the database for relevant information and generates a new concept. The generated idea is then presented to the user via the device, and feedback is collected. This feedback is used to further improve the idea.

[0081] As a concrete example, consider a scenario where software developers in different countries collaborate on developing a new application in a remote work environment. This system matches suitable partners based on their skills and interests, and supports understanding through a common language. An example of a prompt for the generated AI model is: "Suggest ways for software developers in different countries to collaborate effectively. Describe specific steps and tools to use to advance a common project."

[0082] In this way, the system provides a comprehensive information processing environment that enables users from different specialized fields to collaborate and create new value.

[0083] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0084] Step 1:

[0085] Users use a terminal to input information about their areas of expertise, skills, and interests. The entered information is converted into a standardized data format and sent to an information processing device. Specifically, an input form is provided, the data is formatted according to the user's input, and then sent to the server.

[0086] Step 2:

[0087] The server analyzes standardized user information received from the terminal and generates profile data. This analysis includes categorizing expertise and skills, and tagging topics of interest. This creates a comprehensive profile for each user, which is then stored in the database. The output is the user's profile data.

[0088] Step 3:

[0089] The server executes an algorithm to identify users from different professional fields who have similar interests and complementary skills, based on the generated profile data. The input is profile data, and the algorithm performs a matching process to generate a list of the most suitable collaborators as output. Specifically, this involves database queries and the application of machine learning algorithms.

[0090] Step 4:

[0091] The server transmits information between identified users to an intelligent agent. This agent generates integrated information to facilitate translation of technical terms and common understanding. The input information is data between identified profiles, and the output is integrated and translated information. This process involves the use of natural language processing techniques.

[0092] Step 5:

[0093] The user inputs a new project idea into a device and sends it to the server. The server uses an AI agent to analyze the input idea and compare it with information in a relevant database. It generates the most suitable new idea and presents it to the user as feedback through the device. The input is the user's idea, and the output is an AI-generated suggestion.

[0094] Step 6:

[0095] The server provides an online collaborative work platform to support real-time communication between users. Users can conduct video conferences, chat, and edit documents in real time via their terminals. In this environment, communication data is input, and collaborative work is performed as output through the provided interaction tools. Specific operations include the use of communication infrastructure and the operation of the user interface.

[0096] (Application Example 1)

[0097] Next, we will explain Application Example 1. In the following explanation, the data processing device 12 will be referred to as the "server," and the smart device 14 will be referred to as the "terminal."

[0098] When experts from different fields collaborate to contribute to smart city projects, problems arise such as communication gaps between these fields and difficulty in finding appropriate collaborators. Furthermore, there is a need to optimize contributions to the project and quickly generate and present effective strategies.

[0099] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 1 is realized by the following means.

[0100] In this invention, the server includes means for inputting user role field information and generating profile data, means for matching collaborators from different role fields based on the profile data, and means for using a processing device that has the function of integrating and translating information from different role fields. This enables optimal collaborator matching across role fields, as well as smooth integration and translation of information. As a result, participants involved in smart city projects can contribute according to their role fields and efficiently generate and present new strategies.

[0101] A "user" is an individual or group that uses the system to input role field information and is matched with collaborators.

[0102] A "role area" refers to a specific field or topic in which a user specializes, and the system classifies users based on this.

[0103] "Profile data" is data generated based on role field information entered by the user, and it indicates the user's characteristics and interests.

[0104] "Matching" refers to the process of connecting collaborators from different roles and fields to facilitate efficient collaboration within a project.

[0105] A "processing device" is a device that has the function of integrating and translating information from different fields of function, and thus facilitates smooth communication.

[0106] "New strategies" refer to new solutions or ideas generated based on integrated information that facilitate the progress of the project.

[0107] A "participant" is an individual or organization that contributes to a smart city project through the system based on their assigned role.

[0108] "Policy evaluation" refers to measuring the validity and effectiveness of newly generated policies and providing feedback on them.

[0109] "Progress management" refers to activities aimed at tracking the progress of a project and supporting the optimization of the plan.

[0110] This system connects experts from different fields of expertise and supports collaboration to contribute to smart city projects. The system begins with users accessing a server using their smartphone or computer and entering information about their field of expertise. The server then generates profile data based on this information and stores it in a database.

[0111] Next, the server analyzes the generated profile data and runs an algorithm that effectively matches users from different role areas. This quickly identifies relevant collaborators and facilitates communication between users. Information from different role areas is integrated by a processing unit within the server, and an AI agent translates it to help create a shared understanding.

[0112] When a user proposes a new initiative or idea, the server matches it against a relevant knowledge base and generates the most suitable solution. As a result, new solutions are presented to the user, and improvements are encouraged through evaluation. The real-time communication platform helps users collaborate efficiently beyond geographical constraints, enabling project progress management and plan optimization.

[0113] A concrete example of this system is the collaboration between medical professionals and engineers in a smart city project focused on health management. Medical professionals can provide ideas for analyzing patient data, and engineers can propose the optimal techniques for that analysis, enabling them to jointly develop new solutions.

[0114] An example of a prompt for a generated AI model is, "Please list the profiles of the necessary experts to help develop effective healthcare solutions in smart cities." This helps users quickly find suitable collaborators for their project.

[0115] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0116] Step 1:

[0117] The user enters role area information into the terminal. The terminal formats this information and sends it to the server as profile data. The server stores the received profile data in a database and converts this data into a parseable format.

[0118] Step 2:

[0119] The server runs an algorithm to effectively match collaborators from different role areas based on profile data stored in the database. This algorithm identifies the optimal pair of collaborators based on similar interests and projects and generates matching results.

[0120] Step 3:

[0121] The processing unit transmits information about the matched collaborators to an AI agent. The AI ​​agent integrates this information and translates it to enable a common understanding between different role areas. The translated information is used to facilitate smooth communication between users.

[0122] Step 4:

[0123] New ideas proposed by users are sent to a server via their device. Within the server, an AI agent compares these ideas with relevant knowledge bases and generates new strategies. These generated strategies are then presented to the user, and feedback is collected for evaluation.

[0124] Step 5:

[0125] The server provides an online collaborative platform that facilitates real-time communication between users. This includes chat, video conferencing, and file sharing features, allowing users to collaborate across geographical limitations. Within this environment, project progress is managed and plans are optimized.

[0126] Furthermore, an emotion engine that estimates the user's emotions may be incorporated. That is, the identification processing unit 290 may use the emotion identification model 59 to estimate the user's emotions and perform identification processing using the user's emotions.

[0127] This invention is a cross-disciplinary collaborative support system equipped with an emotion engine that recognizes user emotions, and a specific embodiment thereof is shown below.

[0128] "Recognition of user emotions"

[0129] When users input specialized information or project-related information through their terminal, the emotion engine analyzes the user's emotional state from the input data and speech. The server receives the voice and text data and uses an emotion analysis algorithm to identify the user's emotional state. For example, if a user is feeling anxious about the progress of a project, this emotional state can be recognized.

[0130] "Emotion-Based Feedback"

[0131] Based on the recognized emotional information, the server provides feedback designed to motivate the user. This includes sending positive confirmation messages and showcasing project success stories. For example, if the server determines that a user has negative feelings towards the project, it can automatically send an encouraging message to that user.

[0132] Project optimization based on emotional information

[0133] The server monitors the emotional state of all project members in real time to make appropriate adjustments to the project plan. This includes reassessing workload distribution and progress speed to optimize the plan for smooth progress. For example, if the server detects that multiple members are experiencing stress, it will suggest rescheduling their work.

[0134] "Collaborator matching that takes emotions into consideration"

[0135] The system combines user profile data and emotional information to select more appropriate collaborators. Based on the analysis results received from the server, the terminal builds optimal collaborative relationships between users with different areas of expertise. For example, if a user is feeling anxious in the early stages of a project, the server can recommend an experienced mentor collaborator to that user.

[0136] In this way, the present invention realizes a collaboration system that takes user emotions into consideration and provides an environment in which experts from different fields can work together efficiently and smoothly. This system is expected to accelerate the creation of new value and improve the results of each project.

[0137] The following describes the processing flow.

[0138] Step 1:

[0139] Users input specialized information and project ideas through their devices. These devices then transmit this information to a server. In addition, text and voice data from user input and communication are collected for sentiment analysis.

[0140] Step 2:

[0141] The server generates a user profile using the received data. Simultaneously, it uses an emotion engine to analyze emotional states from text and voice data. The emotion engine employs natural language processing and speech emotion recognition technologies.

[0142] Step 3:

[0143] The server adds emotional information to the user's profile based on the analyzed emotions. This enhanced profile is stored in a database and used in subsequent processes.

[0144] Step 4:

[0145] The server selects the most suitable collaborator from among experts in different fields, based on profile data and associated emotional information. The collaborator recommendations are optimized by an algorithm that takes the user's emotional state into account.

[0146] Step 5:

[0147] The terminal receives a list of professionals sent from the server and presents it to the user. The user can select collaborators from the list, and this selection is shared back to the server from the terminal.

[0148] Step 6:

[0149] After the project begins, the device sends real-time sentiment data to the server as it interacts with the data entered by the user. The server continuously processes this data and monitors the project's progress.

[0150] Step 7:

[0151] Based on emotional information collected in real time, the server sends feedback and encouraging messages to users as needed. It also proposes specific measures if the project plan needs to be readjusted.

[0152] Step 8:

[0153] The server analyzes the emotional state of all members and optimizes the entire project. It reallocates resources and introduces additional collaborators as needed.

[0154] Step 9:

[0155] Users communicate with collaborators in real time, incorporating feedback and suggestions to advance the project. Emotional information consistently serves as a crucial indicator for project success.

[0156] (Example 2)

[0157] Next, we will describe Example 2. In the following description, the data processing device 12 will be referred to as the "server" and the smart device 14 as the "terminal".

[0158] The problem that this invention aims to solve is the difficulty in making appropriate adjustments based on the emotional state of members and the progress of the project when building effective collaborative relationships between different areas of expertise. Conventional systems make it difficult to analyze emotional information in real time and to select the optimal collaborators considering each member's contribution to the project, which can hinder the smooth progress of the project.

[0159] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 2 is realized by the following means.

[0160] In this invention, the server includes means for acquiring user expertise information as input data and generating profile data, means for combining the profile data with an emotion analysis algorithm to match collaborators from different areas of expertise, and means for analyzing voice and text data and using a generative AI model to identify emotional information. This enables the provision of an optimal collaborative environment based on the user's emotions and efficient adjustment of project plans.

[0161] "Specialized field information" refers to data related to a user's job duties and expertise, and is a fundamental component of the user's profile.

[0162] "Profile data" is a dataset that compiles attribute information such as a user's area of ​​expertise and activity history, and serves as the basis for selecting collaborators and performing sentiment analysis.

[0163] An "emotion analysis algorithm" is a computational method or model that uses data input by the user to identify their emotional state, and is used to objectively evaluate the user's emotional state.

[0164] A "generative AI model" is an artificial intelligence system that learns from large amounts of data and generates adaptive outputs to perform specific tasks. In this invention, it is used for identifying emotions and generating feedback messages.

[0165] A "collaborator" is an individual or organization with other areas of expertise who works together with the user on a project, providing different specialized knowledge and skills.

[0166] A "feedback message" is a message generated based on the results of sentiment analysis and the current status of the project, in order to increase user motivation or indicate the direction of the project.

[0167] A "project adjustment proposal" refers to changes in the plan or reallocation of tasks suggested based on the project's progress and the emotional state of the participating members, and is intended to help the project move forward efficiently.

[0168] The system of the present invention provides various functions to efficiently support project progress based on user input data. This system primarily uses specialized domain information and project-related text and audio data entered by the user via a terminal.

[0169] Users input information related to their projects through the terminal. This input data includes, for example, text-based instructions or audio recordings of meetings. The terminal converts this data into a format that can be used for text analysis and audio analysis.

[0170] The server receives text and voice data transmitted from the terminal and performs sentiment analysis using a generative AI model. The sentiment analysis algorithm uses natural language processing and voice sentiment analysis to identify the user's emotional state. This analysis makes it possible to evaluate the user's feelings towards the progress of the project in real time.

[0171] Based on the analyzed emotional information, the server generates and provides appropriate feedback messages to the user. This feedback is created using prompts generated by a generative AI model, aiming to improve the user's emotional state and increase their motivation. For example, a prompt such as, "To alleviate anxiety about the current project, please provide an overview of a similar project that was previously successful," can be used to provide the user with helpful information.

[0172] Furthermore, the server aggregates the emotional states of all project members and proposes ways to optimize the project's progress plan. In this process, it can also recommend the most suitable collaborators to improve cooperation among members. For example, if a user is feeling anxious in the early stages of a project, recommending an experienced mentor can help increase the project's success rate.

[0173] In this way, the present invention provides a practical means for achieving efficient project management by providing optimal feedback and collaboration that takes user emotions into consideration in complex project environments.

[0174] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0175] Step 1:

[0176] Users input specialized information and project-related data into the terminal. This input data includes text information and meeting audio recordings. The terminal receives this information and prepares it for the next processing step; if it is text data, it is used as is, and if it is audio data, it is converted to text using speech recognition technology.

[0177] Step 2:

[0178] The device sends the received text data to the server. The transmitted data becomes input for analyzing the user's emotional state. The server first uses a generative AI model to preprocess the text data using natural language processing algorithms. This preprocessing involves noise removal and extraction of keywords that express emotions.

[0179] Step 3:

[0180] The server inputs pre-processed data into an emotion analysis algorithm to identify the user's emotional state. Here, a generative AI model is used to analyze the data, considering the frequency and context of emotion-indicating words from a vast dictionary database. As a result, the user's current emotional state is output, which is then used to generate subsequent feedback.

[0181] Step 4:

[0182] Based on the analysis results, the server generates feedback messages using a generation AI model while referring to prompts. Specifically, it creates customized encouraging messages to improve the user's emotional state and advice to move the project forward. An example of a prompt is, "If the user's emotions are negative, analyze the cause and generate an appropriate encouraging message." This feedback is intended to improve the user's motivation and stabilize their emotions.

[0183] Step 5:

[0184] The device displays feedback messages received from the server to the user. This includes on-screen message displays and email notifications. Furthermore, the device accepts additional feedback and comments from the user, collecting information to include in future updates.

[0185] Step 6:

[0186] The server aggregates emotional data from all project members and proposes progress optimizations based on a project management algorithm. Using the analyzed emotional states, it suggests adjusting the workload for members experiencing stress and sharing success stories for areas where motivation is needed. The proposed results regarding the project's progress are generated as output and will be discussed at the next project meeting.

[0187] (Application Example 2)

[0188] Next, we will explain application example 2. In the following explanation, the data processing device 12 will be referred to as the "server," and the smart device 14 will be referred to as the "terminal."

[0189] The objective of this invention is to efficiently facilitate smooth information sharing and collaborative generation of novel ideas among collaborators from different professional fields. In particular, it is necessary to optimize project progress by considering the user's emotional state when planning work and selecting collaborators. Furthermore, it is essential to provide an efficient and collaborative work environment by offering feedback based on emotional states.

[0190] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means.

[0191] In this invention, the server includes means for using an emotion analysis engine to recognize the user's emotional state, means for optimizing work plans and collaborator selection based on emotional information, and means for providing a real-time collaborative activity environment. This enables flexible and efficient project management that reflects the user's emotional state.

[0192] A "user" is an individual or group that uses a system to input information in order to achieve a specific purpose.

[0193] "Specialized field information" refers to information that includes knowledge and skills related to the specific field to which the user belongs.

[0194] "Attribute data" refers to data that represents the characteristics of an individual or group, generated based on information such as the user's area of ​​expertise.

[0195] A "collaborator" is an individual or group selected to work together in different areas of expertise.

[0196] A "proxy program" is software that has the function of adjusting and translating different information to assist the user.

[0197] "Ideas" are new ideas or concepts generated based on integrated information.

[0198] An "emotion analysis engine" is a system component that analyzes and recognizes a user's emotional state based on their statements and actions.

[0199] "Emotional information" refers to data about a user's emotional state obtained by an emotion analysis engine.

[0200] A "work plan" is a schedule and procedure formulated to effectively advance a project or task.

[0201] A "collaborative environment" refers to an environment or platform provided to enable multiple users to work together smoothly.

[0202] In one embodiment of this invention, the system includes a function to input the user's area of ​​expertise and generate attribute data. The server executes a process to select collaborators from different areas of expertise based on the generated attribute data. The collaborator selection process incorporates an emotion analysis engine that analyzes the user's emotional state, and takes emotional information into consideration to select the most suitable collaborators and create a work plan.

[0203] The server also uses Google® Cloud Speech-to-Text API and Google Cloud Natural Language API to analyze user speech and input text. The resulting sentiment information is used to provide a real-time collaborative environment. In this environment, proxy programs coordinate information from different areas of expertise, enabling smooth information sharing among users.

[0204] A typical use case is when a user inputs "This task is so difficult, there's no end in sight" into the system. The server transcribes the statement into text, and its sentiment analysis engine determines that the user is experiencing stress. The system can then provide feedback to the user such as, "We recommend you take a short break." This enables flexible project management that takes the user's emotional state into consideration, and provides a collaborative work environment.

[0205] As an example of prompts for a generative AI model, in response to the input "What is an appropriate feedback message when a worker is feeling stressed?", an example response could be "Your health is our top priority. Why don't you take a 10-minute refresh break?". Such prompts enable communication that takes emotions into consideration.

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

[0207] Step 1:

[0208] The user inputs specialized domain information through a terminal. The input information is stored as attribute data. The server uses this attribute data to prepare for the next stage of processing.

[0209] Step 2:

[0210] The server receives user voice and text data. Voice data is obtained using a microphone and converted to text using the Google Cloud Speech-to-Text API. To obtain emotional information from the text input, the Google Cloud Natural Language API is used to analyze the user's emotional state. The analyzed emotional state is stored for subsequent processing.

[0211] Step 3:

[0212] Based on the emotional information obtained, the server creates a work plan and selects collaborators based on the user's emotional state. In this process, the emotion analysis engine detects emotional states such as stress and anxiety, and makes necessary adjustments to optimize the project's progress. The adjusted plan and information on selected collaborators are generated and recorded.

[0213] Step 4:

[0214] The server generates appropriate feedback for the user based on emotional information and tailored plans. This generated feedback is designed to motivate the user and is sent from the server to the terminal for the user to understand.

[0215] Step 5:

[0216] The terminal displays feedback received from the server to the user. For example, if the terminal determines that the user is "stressed" about the project, it can display a message such as "We recommend you take a short break." This allows the user to adjust their work based on the feedback.

[0217] The specific processing unit 290 transmits the result of the specific processing to the smart device 14. In the smart device 14, the control unit 46A causes the output device 40 to output the result of the specific processing. The microphone 38B acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0218] Data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of data generation model 58 is ChatGPT (registered trademark) (Internet search).<URL: https: / / openai.com / blog / chatgpt> ), Gemini (registered trademark) (Internet search) <url: https: gemini.google.com ?hl="ja">Examples of generative AI include the following. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and with inference data such as audio data representing speech, text data representing text, and image data representing images. The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference results in data formats such as audio data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[0219] In the above embodiment, an example was given in which specific processing is performed by the data processing device 12, but the technology of this disclosure is not limited thereto, and the specific processing may also be performed by the smart device 14.

[0220] [Second Embodiment]

[0221] Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment.

[0222] As shown in Figure 3, the data processing system 210 includes a data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0223] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 is an example of a "computer" related to the technology of this disclosure. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[0224] The smart glasses 214 include a computer 36, a microphone 238, a speaker 240, a camera 42, and a communication interface 44. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, and camera 42 are also connected to the bus 52.

[0225] The microphone 238 receives voice signals from the user 20 and receives instructions from the user 20. The microphone 238 captures the voice signals from the user 20, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0226] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the area around the user 20 (for example, an imaging range defined by a field of view equivalent to the width of a typical healthy person's field of vision).

[0227] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0228] Figure 4 shows an example of the main functions of the data processing device 12 and the smart glasses 214. As shown in Figure 4, the data processing device 12 performs specific processing using the processor 28. The storage 32 stores the specific processing program 56.

[0229] The specific processing program 56 is an example of a "program" relating to the technology of this disclosure. The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0230] The storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[0231] In the smart glasses 214, the processor 46 performs the reception output processing. The storage 50 stores the reception output program 60. The processor 46 reads the reception output program 60 from the storage 50 and executes the read reception output program 60 on the RAM 48. The reception output processing is realized by the processor 46 operating as a control unit 46A according to the reception output program 60 executed on the RAM 48.

[0232] Next, the identification processing performed by the identification processing unit 290 of the data processing device 12 will be described. In the following description, the data processing device 12 will be referred to as the "server" and the smart glasses 214 will be referred to as the "terminal".

[0233] The system based on this invention aims to enable users from different specialized fields to collaborate effectively and create new solutions. Specific embodiments are shown below.

[0234] "Collection of user information and generation of profiles"

[0235] Users input their area of ​​expertise, skills, and research interests through their terminal. The server generates profile data for each user based on this information and stores it in a database. For example, if a researcher in the medical field has a project idea related to biomedical engineering, they can input the details of that idea, enabling them to collaborate with experts in related fields.

[0236] "Matching experts from different fields"

[0237] The server analyzes the generated profile data and runs algorithms to identify users from different fields with similar interests. This allows each user to find suitable collaborators. For example, if a big data analytics expert wants to contribute to an environmental science project, the matching system will select a suitable project owner.

[0238] "Integration and Translation of Knowledge"

[0239] The server sends information about users from different professional fields who have decided to collaborate to an AI agent. The AI ​​agent analyzes this information, translates and integrates it to reach a common understanding, and supports smooth communication between users with different backgrounds.

[0240] Idea Generation and Presentation

[0241] When a user submits an initial project idea, the device sends it to the server. An AI agent on the server compares it with a database of relevant research to generate new ideas and suggest the best solution for the user's needs. This result is then presented to the user via the device, and feedback is collected.

[0242] "Providing a real-time collaborative work environment"

[0243] The server provides an online collaborative platform to enable real-time communication between users. This allows users to utilize features such as chat, video conferencing, and file sharing, enabling collaboration beyond geographical constraints. For example, it becomes easier for project teams scattered around the world to simultaneously edit documents and work collaboratively. In this way, the present invention facilitates effective co-creation among experts from different fields, accelerating the creation of new value.

[0244] The following describes the processing flow.

[0245] Step 1:

[0246] The user uses a terminal to input their area of ​​expertise, skills, and research interests. The terminal temporarily stores this information and sends it to the server.

[0247] Step 2:

[0248] The server analyzes the received user information and generates profile data for each user. This profile data is stored in a database for use in the matching process.

[0249] Step 3:

[0250] The server executes an algorithm to search for commonalities and complementary information among users based on accumulated profile data. Based on these results, the server generates a list of optimal collaborators and notifies the user.

[0251] Step 4:

[0252] The user selects their preferred collaborator from the presented list. The device then sends the selection result back to the server.

[0253] Step 5:

[0254] The server transmits information from collaborators with different areas of expertise to an AI agent. The AI ​​agent analyzes this information and translates and integrates it to reach a common understanding.

[0255] Step 6:

[0256] The user inputs project ideas into their device and sends them to the server. The server's AI agent generates and evaluates new ideas based on past research and data related to the project.

[0257] Step 7:

[0258] The server sends the evaluation results of the generated ideas to the user, providing feedback on the feasibility of the ideas and areas for improvement.

[0259] Step 8:

[0260] The server establishes a real-time communication platform to enable effective collaboration among users. This platform includes chat, video conferencing, and file sharing capabilities.

[0261] Step 9:

[0262] Users will use the provided platform to share documents in real time and collaborate while monitoring project progress.

[0263] (Example 1)

[0264] Next, we will describe Example 1. In the following description, the data processing device 12 will be referred to as the "server," and the smart glasses 214 will be referred to as the "terminal."

[0265] When users from different professional fields collaborate to create new concepts, it is difficult to build effective cooperative relationships and fully utilize each individual's expertise. Furthermore, there is a lack of appropriate means to integrate information from different fields and facilitate smooth communication among users. This situation contributes to the creation of innovative solutions among diverse experts.

[0266] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 1 is realized by the following means.

[0267] In this invention, the server includes means for inputting information about the user's area of ​​expertise using an information processing device and generating user profile data; means for executing an algorithm for identifying and matching collaborators from different areas of expertise based on the profile data; and means for using an intelligent agent that has the function of integrating and translating information from different areas of expertise. This enables the establishment of effective collaborative relationships and smooth communication between users from different areas of expertise.

[0268] An "information processing device" is a device used for inputting, processing, storing, and outputting information, and mainly refers to computers and their peripherals.

[0269] "User" refers to an individual or organization that accesses the system using an information processing device and inputs their own information.

[0270] "Specialized field" refers to a specific area of ​​expertise that possesses particular knowledge and skills, and represents the field to which a user's specialized knowledge and skills belong.

[0271] "Profile data" refers to a dataset that comprehensively compiles information about a user's areas of expertise, skills, interests, and other relevant details.

[0272] An "algorithm" refers to a set of rules that define the steps or computational flow required to solve a specific problem.

[0273] An "intelligent agent" refers to a software program or system that automatically makes decisions and takes actions based on the information it is given.

[0274] "Information integration" refers to the process of unifying data and information obtained from different sources so that they can be handled in a unified manner.

[0275] "Translation" refers to the process of replacing specific technical terms or concepts with terms or concepts from a different language or field.

[0276] The system based on this invention provides an effective information processing environment for users from diverse professional fields to collaborate and create new concepts.

[0277] Users input information about their areas of expertise, skills, and interests using a terminal. This information is transmitted to the server via an information processing device. The server analyzes this information and performs processing to generate user profile data. The profile data is stored in a database.

[0278] The server runs an algorithm to match users from different areas of expertise based on their profile data. Specifically, it uses a generative AI model to identify highly similar users. This algorithm makes it possible to connect specialists from different fields who share a common interest in a particular topic.

[0279] Furthermore, the server uses intelligent agents to integrate information from different specialized fields and translate it into an easily understandable format. These intelligent agents combine machine learning and natural language processing technologies to enable smooth communication between users. This translation function allows users with diverse backgrounds to collaborate and communicate efficiently.

[0280] When a user inputs a new project idea from a terminal, the AI agent in the server searches for relevant information from the database and generates new concepts. The generated ideas are presented to the user via the terminal and feedback is collected. This feedback is utilized for further idea improvement.

[0281] As a specific example, consider a situation where software developers in different countries in a remote work environment cooperate in the development of a new application. This system matches appropriate partners based on their skills and interests and supports understanding in a common language. An example of a prompt sentence for the generative AI model is, "Please propose a method for software developers in different countries to effectively cooperate with each other. Explain specific steps and tools to be used for proceeding with a common project."

[0282] In this way, the system provides an integrated information processing environment for users in different specialized fields to cooperate with each other and create new value.

[0283] The flow of specific processing in Example 1 will be described using FIG. 11.

[0284] Step 1:

[0285] The user uses the terminal to input information regarding specialized fields, skills, and interests. The input information is converted into a standardized data format and transmitted to the information processing device. As a specific operation, an input form is provided, and the data is formatted according to the user's input content and transmitted to the server.

[0286] Step 2:

[0287] The server analyzes standardized user information received from the terminal and generates profile data. This analysis includes categorizing expertise and skills, and tagging topics of interest. This creates a comprehensive profile for each user, which is then stored in the database. The output is the user's profile data.

[0288] Step 3:

[0289] The server executes an algorithm to identify users from different professional fields who have similar interests and complementary skills, based on the generated profile data. The input is profile data, and the algorithm performs a matching process to generate a list of the most suitable collaborators as output. Specifically, this involves database queries and the application of machine learning algorithms.

[0290] Step 4:

[0291] The server transmits information between identified users to an intelligent agent. This agent generates integrated information to facilitate translation of technical terms and common understanding. The input information is data between identified profiles, and the output is integrated and translated information. This process involves the use of natural language processing techniques.

[0292] Step 5:

[0293] The user inputs a new project idea into a device and sends it to the server. The server uses an AI agent to analyze the input idea and compare it with information in a relevant database. It generates the most suitable new idea and presents it to the user as feedback through the device. The input is the user's idea, and the output is an AI-generated suggestion.

[0294] Step 6:

[0295] The server provides an online collaborative work platform to support real-time communication between users. Users can conduct video conferences, chat, and edit documents in real time via their terminals. In this environment, communication data is input, and collaborative work is performed as output through the provided interaction tools. Specific operations include the use of communication infrastructure and the operation of the user interface.

[0296] (Application Example 1)

[0297] Next, we will explain Application Example 1. In the following explanation, the data processing device 12 will be referred to as the "server," and the smart glasses 214 will be referred to as the "terminal."

[0298] When experts from different fields collaborate to contribute to smart city projects, problems arise such as communication gaps between these fields and difficulty in finding appropriate collaborators. Furthermore, there is a need to optimize contributions to the project and quickly generate and present effective strategies.

[0299] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 1 is realized by the following means.

[0300] In this invention, the server includes means for inputting user role field information and generating profile data, means for matching collaborators from different role fields based on the profile data, and means for using a processing device that has the function of integrating and translating information from different role fields. This enables optimal collaborator matching across role fields, as well as smooth integration and translation of information. As a result, participants involved in smart city projects can contribute according to their role fields and efficiently generate and present new strategies.

[0301] A "user" is an individual or group that uses the system to input role field information and is matched with collaborators.

[0302] The "role field" refers to a specific field or theme that a user specializes in, and the system classifies users based on this.

[0303] "Profile data" is data generated based on the role field information input by the user, indicating the characteristics and interests of the user.

[0304] "Matching" is a process of appropriately linking collaborators in different role fields to promote efficient cooperation in a project.

[0305] The "processing device" is a device that has the function of integrating and translating information in different role fields and helps with smooth communication.

[0306] The "new strategy" refers to new solutions or ideas generated based on the integrated information, which promotes the progress of the project.

[0307] A "participant" is an individual or group related to a smart city project who contributes based on their role field through the system.

[0308] "Evaluation of the strategy" is to measure the validity and effectiveness of the generated new strategy and provide feedback.

[0309] "Management of progress" is an activity to track the progress of a project and support the optimization of the plan.

[0310] This system connects experts in different role fields and supports collaboration to contribute to smart city projects. This system starts with users accessing the server using a smartphone or computer and inputting information about their role field. The server generates profile data based on this information and stores it in the database.

[0311] Next, the server analyzes the generated profile data and runs an algorithm that effectively matches users from different role areas. This quickly identifies relevant collaborators and facilitates communication between users. Information from different role areas is integrated by a processing unit within the server, and an AI agent translates it to help create a shared understanding.

[0312] When a user proposes a new initiative or idea, the server matches it against a relevant knowledge base and generates the most suitable solution. As a result, new solutions are presented to the user, and improvements are encouraged through evaluation. The real-time communication platform helps users collaborate efficiently beyond geographical constraints, enabling project progress management and plan optimization.

[0313] A concrete example of this system is the collaboration between medical professionals and engineers in a smart city project focused on health management. Medical professionals can provide ideas for analyzing patient data, and engineers can propose the optimal techniques for that analysis, enabling them to jointly develop new solutions.

[0314] An example of a prompt for a generated AI model is, "Please list the profiles of the necessary experts to help develop effective healthcare solutions in smart cities." This helps users quickly find suitable collaborators for their project.

[0315] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0316] Step 1:

[0317] The user enters role area information into the terminal. The terminal formats this information and sends it to the server as profile data. The server stores the received profile data in a database and converts this data into a parseable format.

[0318] Step 2:

[0319] The server runs an algorithm to effectively match collaborators from different role areas based on profile data stored in the database. This algorithm identifies the optimal pair of collaborators based on similar interests and projects and generates matching results.

[0320] Step 3:

[0321] The processing unit transmits information about the matched collaborators to an AI agent. The AI ​​agent integrates this information and translates it to enable a common understanding between different role areas. The translated information is used to facilitate smooth communication between users.

[0322] Step 4:

[0323] New ideas proposed by users are sent to a server via their device. Within the server, an AI agent compares these ideas with relevant knowledge bases and generates new strategies. These generated strategies are then presented to the user, and feedback is collected for evaluation.

[0324] Step 5:

[0325] The server provides an online collaborative platform that facilitates real-time communication between users. This includes chat, video conferencing, and file sharing features, allowing users to collaborate across geographical limitations. Within this environment, project progress is managed and plans are optimized.

[0326] Furthermore, an emotion engine that estimates the user's emotions may be incorporated. That is, the identification processing unit 290 may use the emotion identification model 59 to estimate the user's emotions and perform identification processing using the user's emotions.

[0327] This invention is a cross-disciplinary collaborative support system equipped with an emotion engine that recognizes user emotions, and a specific embodiment thereof is shown below.

[0328] "Recognition of user emotions"

[0329] When users input specialized information or project-related information through their terminal, the emotion engine analyzes the user's emotional state from the input data and speech. The server receives the voice and text data and uses an emotion analysis algorithm to identify the user's emotional state. For example, if a user is feeling anxious about the progress of a project, this emotional state can be recognized.

[0330] "Emotion-Based Feedback"

[0331] Based on the recognized emotional information, the server provides feedback designed to motivate the user. This includes sending positive confirmation messages and showcasing project success stories. For example, if the server determines that a user has negative feelings towards the project, it can automatically send an encouraging message to that user.

[0332] Project optimization based on emotional information

[0333] The server monitors the emotional state of all project members in real time to make appropriate adjustments to the project plan. This includes reassessing workload distribution and progress speed to optimize the plan for smooth progress. For example, if the server detects that multiple members are experiencing stress, it will suggest rescheduling their work.

[0334] "Collaborator matching that takes emotions into consideration"

[0335] The system combines user profile data and emotional information to select more appropriate collaborators. Based on the analysis results received from the server, the terminal builds optimal collaborative relationships between users with different areas of expertise. For example, if a user is feeling anxious in the early stages of a project, the server can recommend an experienced mentor collaborator to that user.

[0336] In this way, the present invention realizes a collaboration system that takes user emotions into consideration and provides an environment in which experts from different fields can work together efficiently and smoothly. This system is expected to accelerate the creation of new value and improve the results of each project.

[0337] The following describes the processing flow.

[0338] Step 1:

[0339] Users input specialized information and project ideas through their devices. These devices then transmit this information to a server. In addition, text and voice data from user input and communication are collected for sentiment analysis.

[0340] Step 2:

[0341] The server generates a user profile using the received data. Simultaneously, it uses an emotion engine to analyze emotional states from text and voice data. The emotion engine employs natural language processing and speech emotion recognition technologies.

[0342] Step 3:

[0343] The server adds emotional information to the user's profile based on the analyzed emotions. This enhanced profile is stored in a database and used in subsequent processes.

[0344] Step 4:

[0345] The server selects the most suitable collaborator from among experts in different fields, based on profile data and associated emotional information. The collaborator recommendations are optimized by an algorithm that takes the user's emotional state into account.

[0346] Step 5:

[0347] The terminal receives a list of professionals sent from the server and presents it to the user. The user can select collaborators from the list, and this selection is shared back to the server from the terminal.

[0348] Step 6:

[0349] After the project begins, the device sends real-time sentiment data to the server as it interacts with the data entered by the user. The server continuously processes this data and monitors the project's progress.

[0350] Step 7:

[0351] Based on emotional information collected in real time, the server sends feedback and encouraging messages to users as needed. It also proposes specific measures if the project plan needs to be readjusted.

[0352] Step 8:

[0353] The server analyzes the emotional state of all members and optimizes the entire project. It reallocates resources and introduces additional collaborators as needed.

[0354] Step 9:

[0355] Users communicate with collaborators in real time, incorporating feedback and suggestions to advance the project. Emotional information consistently serves as a crucial indicator for project success.

[0356] (Example 2)

[0357] Next, we will describe Example 2. In the following description, the data processing device 12 will be referred to as the "server" and the smart glasses 214 will be referred to as the "terminal".

[0358] The problem that this invention aims to solve is the difficulty in making appropriate adjustments based on the emotional state of members and the progress of the project when building effective collaborative relationships between different areas of expertise. Conventional systems make it difficult to analyze emotional information in real time and to select the optimal collaborators considering each member's contribution to the project, which can hinder the smooth progress of the project.

[0359] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 2 is realized by the following means.

[0360] In this invention, the server includes means for acquiring user expertise information as input data and generating profile data, means for combining the profile data with an emotion analysis algorithm to match collaborators from different areas of expertise, and means for analyzing voice and text data and using a generative AI model to identify emotional information. This enables the provision of an optimal collaborative environment based on the user's emotions and efficient adjustment of project plans.

[0361] "Specialized field information" refers to data related to a user's job duties and expertise, and is a fundamental component of the user's profile.

[0362] "Profile data" is a dataset that compiles attribute information such as a user's area of ​​expertise and activity history, and serves as the basis for selecting collaborators and performing sentiment analysis.

[0363] An "emotion analysis algorithm" is a computational method or model that uses data input by the user to identify their emotional state, and is used to objectively evaluate the user's emotional state.

[0364] A "generative AI model" is an artificial intelligence system that learns from large amounts of data and generates adaptive outputs to perform specific tasks. In this invention, it is used for identifying emotions and generating feedback messages.

[0365] A "collaborator" is an individual or organization with other areas of expertise who works together with the user on a project, providing different specialized knowledge and skills.

[0366] A "feedback message" is a message generated based on the results of sentiment analysis and the current status of the project, in order to increase user motivation or indicate the direction of the project.

[0367] A "project adjustment proposal" refers to changes in the plan or reallocation of tasks suggested based on the project's progress and the emotional state of the participating members, and is intended to help the project move forward efficiently.

[0368] The system of the present invention provides various functions to efficiently support project progress based on user input data. This system primarily uses specialized domain information and project-related text and audio data entered by the user via a terminal.

[0369] Users input information related to their projects through the terminal. This input data includes, for example, text-based instructions or audio recordings of meetings. The terminal converts this data into a format that can be used for text analysis and audio analysis.

[0370] The server receives text and voice data transmitted from the terminal and performs sentiment analysis using a generative AI model. The sentiment analysis algorithm uses natural language processing and voice sentiment analysis to identify the user's emotional state. This analysis makes it possible to evaluate the user's feelings towards the progress of the project in real time.

[0371] Based on the analyzed emotional information, the server generates and provides appropriate feedback messages to the user. This feedback is created using prompts generated by a generative AI model, aiming to improve the user's emotional state and increase their motivation. For example, a prompt such as, "To alleviate anxiety about the current project, please provide an overview of a similar project that was previously successful," can be used to provide the user with helpful information.

[0372] Furthermore, the server aggregates the emotional states of all project members and proposes ways to optimize the project's progress plan. In this process, it can also recommend the most suitable collaborators to improve cooperation among members. For example, if a user is feeling anxious in the early stages of a project, recommending an experienced mentor can help increase the project's success rate.

[0373] In this way, the present invention provides a practical means for achieving efficient project management by providing optimal feedback and collaboration that takes user emotions into consideration in complex project environments.

[0374] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0375] Step 1:

[0376] Users input specialized information and project-related data into the terminal. This input data includes text information and meeting audio recordings. The terminal receives this information and prepares it for the next processing step; if it is text data, it is used as is, and if it is audio data, it is converted to text using speech recognition technology.

[0377] Step 2:

[0378] The device sends the received text data to the server. The transmitted data becomes input for analyzing the user's emotional state. The server first uses a generative AI model to preprocess the text data using natural language processing algorithms. This preprocessing involves noise removal and extraction of keywords that express emotions.

[0379] Step 3:

[0380] The server inputs pre-processed data into an emotion analysis algorithm to identify the user's emotional state. Here, a generative AI model is used to analyze the data, considering the frequency and context of emotion-indicating words from a vast dictionary database. As a result, the user's current emotional state is output, which is then used to generate subsequent feedback.

[0381] Step 4:

[0382] Based on the analysis results, the server generates feedback messages using a generation AI model while referring to prompts. Specifically, it creates customized encouraging messages to improve the user's emotional state and advice to move the project forward. An example of a prompt is, "If the user's emotions are negative, analyze the cause and generate an appropriate encouraging message." This feedback is intended to improve the user's motivation and stabilize their emotions.

[0383] Step 5:

[0384] The device displays feedback messages received from the server to the user. This includes on-screen message displays and email notifications. Furthermore, the device accepts additional feedback and comments from the user, collecting information to include in future updates.

[0385] Step 6:

[0386] The server aggregates emotional data from all project members and proposes progress optimizations based on a project management algorithm. Using the analyzed emotional states, it suggests adjusting the workload for members experiencing stress and sharing success stories for areas where motivation is needed. The proposed results regarding the project's progress are generated as output and will be discussed at the next project meeting.

[0387] (Application Example 2)

[0388] Next, we will explain application example 2. In the following explanation, the data processing device 12 will be referred to as the "server," and the smart glasses 214 will be referred to as the "terminal."

[0389] The objective of this invention is to efficiently facilitate smooth information sharing and collaborative generation of novel ideas among collaborators from different professional fields. In particular, it is necessary to optimize project progress by considering the user's emotional state when planning work and selecting collaborators. Furthermore, it is essential to provide an efficient and collaborative work environment by offering feedback based on emotional states.

[0390] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means.

[0391] In this invention, the server includes means for using an emotion analysis engine to recognize the user's emotional state, means for optimizing work plans and collaborator selection based on emotional information, and means for providing a real-time collaborative activity environment. This enables flexible and efficient project management that reflects the user's emotional state.

[0392] A "user" is an individual or group that uses a system to input information in order to achieve a specific purpose.

[0393] "Specialized field information" refers to information that includes knowledge and skills related to the specific field to which the user belongs.

[0394] "Attribute data" refers to data that represents the characteristics of an individual or group, generated based on information such as the user's area of ​​expertise.

[0395] A "collaborator" is an individual or group selected to work together in different areas of expertise.

[0396] A "proxy program" is software that has the function of adjusting and translating different information to assist the user.

[0397] "Ideas" are new ideas or concepts generated based on integrated information.

[0398] An "emotion analysis engine" is a system component that analyzes and recognizes a user's emotional state based on their statements and actions.

[0399] "Emotional information" refers to data about a user's emotional state obtained by an emotion analysis engine.

[0400] A "work plan" is a schedule and procedure formulated to effectively advance a project or task.

[0401] A "collaborative environment" refers to an environment or platform provided to enable multiple users to work together smoothly.

[0402] In one embodiment of this invention, the system includes a function to input the user's area of ​​expertise and generate attribute data. The server executes a process to select collaborators from different areas of expertise based on the generated attribute data. The collaborator selection process incorporates an emotion analysis engine that analyzes the user's emotional state, and takes emotional information into consideration to select the most suitable collaborators and create a work plan.

[0403] The server also uses the Google Cloud Speech-to-Text API and Google Cloud Natural Language API to analyze user speech and input text. The resulting sentiment information is used to provide a real-time collaborative environment. In this environment, proxy programs coordinate information from different areas of expertise, enabling smooth information sharing among users.

[0404] A typical use case is when a user inputs "This task is so difficult, there's no end in sight" into the system. The server transcribes the statement into text, and its sentiment analysis engine determines that the user is experiencing stress. The system can then provide feedback to the user such as, "We recommend you take a short break." This enables flexible project management that takes the user's emotional state into consideration, and provides a collaborative work environment.

[0405] As an example of prompts for a generative AI model, in response to the input "What is an appropriate feedback message when a worker is feeling stressed?", an example response could be "Your health is our top priority. Why don't you take a 10-minute refresh break?". Such prompts enable communication that takes emotions into consideration.

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

[0407] Step 1:

[0408] The user inputs specialized domain information through a terminal. The input information is stored as attribute data. The server uses this attribute data to prepare for the next stage of processing.

[0409] Step 2:

[0410] The server receives user voice and text data. Voice data is obtained using a microphone and converted to text using the Google Cloud Speech-to-Text API. To obtain emotional information from the text input, the Google Cloud Natural Language API is used to analyze the user's emotional state. The analyzed emotional state is stored for subsequent processing.

[0411] Step 3:

[0412] Based on the emotional information obtained, the server creates a work plan and selects collaborators based on the user's emotional state. In this process, the emotion analysis engine detects emotional states such as stress and anxiety, and makes necessary adjustments to optimize the project's progress. The adjusted plan and information on selected collaborators are generated and recorded.

[0413] Step 4:

[0414] The server generates appropriate feedback for the user based on emotional information and tailored plans. This generated feedback is designed to motivate the user and is sent from the server to the terminal for the user to understand.

[0415] Step 5:

[0416] The terminal displays feedback received from the server to the user. For example, if the terminal determines that the user is "stressed" about the project, it can display a message such as "We recommend you take a short break." This allows the user to adjust their work based on the feedback.

[0417] The specific processing unit 290 transmits the result of the specific processing to the smart glasses 214. In the smart glasses 214, the control unit 46A causes the speaker 240 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0418] Data generation model 58 is a type of so-called generative AI (Artificial Intelligence). An example of data generation model 58 is ChatGPT (Internet Search<URL: https: / / openai.com / blog / chatgpt> ), Gemini (Internet search) <url: https: gemini.google.com ?hl="ja">Examples of generative AI include the following. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and with inference data such as audio data representing speech, text data representing text, and image data representing images. The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference results in data formats such as audio data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[0419] In the above embodiment, an example was given in which specific processing is performed by the data processing device 12, but the technology of this disclosure is not limited thereto, and the specific processing may also be performed by the smart glasses 214.

[0420] [Third Embodiment]

[0421] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[0422] As shown in Figure 5, the data processing system 310 includes a data processing device 12 and a headset terminal 314. An example of the data processing device 12 is a server.

[0423] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 is an example of a "computer" related to the technology of this disclosure. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[0424] The headset terminal 314 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication interface 44, and a display 343. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, camera 42, and display 343 are also connected to the bus 52.

[0425] The microphone 238 receives voice signals from the user 20 and receives instructions from the user 20. The microphone 238 captures the voice signals from the user 20, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0426] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the area around the user 20 (for example, an imaging range defined by a field of view equivalent to the width of a typical healthy person's field of vision).

[0427] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0428] Figure 6 shows an example of the main functions of the data processing device 12 and the headset terminal 314. As shown in Figure 6, the data processing device 12 performs specific processing using the processor 28. The storage 32 stores the specific processing program 56.

[0429] The specific processing program 56 is an example of a "program" relating to the technology of this disclosure. The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0430] The storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[0431] In the headset terminal 314, the processor 46 performs the reception output processing. The storage 50 stores the reception output program 60. The processor 46 reads the reception output program 60 from the storage 50 and executes the read reception output program 60 on the RAM 48. The reception output processing is realized by the processor 46 operating as a control unit 46A according to the reception output program 60 executed on the RAM 48.

[0432] Next, the specific processing performed by the specific processing unit 290 of the data processing device 12 will be described. In the following description, the data processing device 12 will be referred to as the "server" and the headset terminal 314 will be referred to as the "terminal".

[0433] The system based on this invention aims to enable users from different specialized fields to collaborate effectively and create new solutions. Specific embodiments are shown below.

[0434] "Collection of user information and generation of profiles"

[0435] Users input their area of ​​expertise, skills, and research interests through their terminal. The server generates profile data for each user based on this information and stores it in a database. For example, if a researcher in the medical field has a project idea related to biomedical engineering, they can input the details of that idea, enabling them to collaborate with experts in related fields.

[0436] "Matching experts from different fields"

[0437] The server analyzes the generated profile data and runs algorithms to identify users from different fields with similar interests. This allows each user to find suitable collaborators. For example, if a big data analytics expert wants to contribute to an environmental science project, the matching system will select a suitable project owner.

[0438] "Integration and Translation of Knowledge"

[0439] The server sends information about users from different professional fields who have decided to collaborate to an AI agent. The AI ​​agent analyzes this information, translates and integrates it to reach a common understanding, and supports smooth communication between users with different backgrounds.

[0440] Idea Generation and Presentation

[0441] When a user submits an initial project idea, the device sends it to the server. An AI agent on the server compares it with a database of relevant research to generate new ideas and suggest the best solution for the user's needs. This result is then presented to the user via the device, and feedback is collected.

[0442] "Providing a real-time collaborative work environment"

[0443] The server provides an online collaborative platform to enable real-time communication between users. This allows users to utilize features such as chat, video conferencing, and file sharing, enabling collaboration beyond geographical constraints. For example, it becomes easier for project teams scattered around the world to simultaneously edit documents and work collaboratively. In this way, the present invention facilitates effective co-creation among experts from different fields, accelerating the creation of new value.

[0444] The following describes the processing flow.

[0445] Step 1:

[0446] The user uses a terminal to input their area of ​​expertise, skills, and research interests. The terminal temporarily stores this information and sends it to the server.

[0447] Step 2:

[0448] The server analyzes the received user information and generates profile data for each user. This profile data is stored in a database for use in the matching process.

[0449] Step 3:

[0450] The server executes an algorithm to search for commonalities and complementary information among users based on accumulated profile data. Based on these results, the server generates a list of optimal collaborators and notifies the user.

[0451] Step 4:

[0452] The user selects their preferred collaborator from the presented list. The device then sends the selection result back to the server.

[0453] Step 5:

[0454] The server transmits information from collaborators with different areas of expertise to an AI agent. The AI ​​agent analyzes this information and translates and integrates it to reach a common understanding.

[0455] Step 6:

[0456] The user inputs project ideas into their device and sends them to the server. The server's AI agent generates and evaluates new ideas based on past research and data related to the project.

[0457] Step 7:

[0458] The server sends the evaluation results of the generated ideas to the user, providing feedback on the feasibility of the ideas and areas for improvement.

[0459] Step 8:

[0460] The server establishes a real-time communication platform to enable effective collaboration among users. This platform includes chat, video conferencing, and file sharing capabilities.

[0461] Step 9:

[0462] Users will use the provided platform to share documents in real time and collaborate while monitoring project progress.

[0463] (Example 1)

[0464] Next, we will describe Example 1. In the following description, the data processing device 12 will be referred to as the "server," and the headset-type terminal 314 will be referred to as the "terminal."

[0465] When users from different professional fields collaborate to create new concepts, it is difficult to build effective cooperative relationships and fully utilize each individual's expertise. Furthermore, there is a lack of appropriate means to integrate information from different fields and facilitate smooth communication among users. This situation contributes to the creation of innovative solutions among diverse experts.

[0466] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 1 is realized by the following means.

[0467] In this invention, the server includes means for inputting information about the user's area of ​​expertise using an information processing device and generating user profile data; means for executing an algorithm for identifying and matching collaborators from different areas of expertise based on the profile data; and means for using an intelligent agent that has the function of integrating and translating information from different areas of expertise. This enables the establishment of effective collaborative relationships and smooth communication between users from different areas of expertise.

[0468] An "information processing device" is a device used for inputting, processing, storing, and outputting information, and mainly refers to computers and their peripherals.

[0469] "User" refers to an individual or organization that accesses the system using an information processing device and inputs their own information.

[0470] "Specialized field" refers to a specific area of ​​expertise that possesses particular knowledge and skills, and represents the field to which a user's specialized knowledge and skills belong.

[0471] "Profile data" refers to a dataset that comprehensively compiles information about a user's areas of expertise, skills, interests, and other relevant details.

[0472] An "algorithm" refers to a set of rules that define the steps or computational flow required to solve a specific problem.

[0473] An "intelligent agent" refers to a software program or system that automatically makes decisions and takes actions based on the information it is given.

[0474] "Information integration" refers to the process of unifying data and information obtained from different sources so that they can be handled in a unified manner.

[0475] "Translation" refers to the process of replacing specific technical terms or concepts with terms or concepts from a different language or field.

[0476] The system based on this invention provides an effective information processing environment for users from diverse professional fields to collaborate and create new concepts.

[0477] Users input information about their areas of expertise, skills, and interests using a terminal. This information is transmitted to the server via an information processing device. The server analyzes this information and performs processing to generate user profile data. The profile data is stored in a database.

[0478] The server runs an algorithm to match users from different areas of expertise based on their profile data. Specifically, it uses a generative AI model to identify highly similar users. This algorithm makes it possible to connect specialists from different fields who share a common interest in a particular topic.

[0479] Furthermore, the server uses intelligent agents to integrate information from different specialized fields and translate it into an easily understandable format. These intelligent agents combine machine learning and natural language processing technologies to enable smooth communication between users. This translation function allows users with diverse backgrounds to collaborate and communicate efficiently.

[0480] When a user inputs a new project idea via their device, an AI agent on the server searches the database for relevant information and generates a new concept. The generated idea is then presented to the user via the device, and feedback is collected. This feedback is used to further improve the idea.

[0481] As a concrete example, consider a scenario where software developers in different countries collaborate on developing a new application in a remote work environment. This system matches suitable partners based on their skills and interests, and supports understanding through a common language. An example of a prompt for the generated AI model is: "Suggest ways for software developers in different countries to collaborate effectively. Describe specific steps and tools to use to advance a common project."

[0482] In this way, the system provides a comprehensive information processing environment that enables users from different specialized fields to collaborate and create new value.

[0483] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0484] Step 1:

[0485] Users use a terminal to input information about their areas of expertise, skills, and interests. The entered information is converted into a standardized data format and sent to an information processing device. Specifically, an input form is provided, the data is formatted according to the user's input, and then sent to the server.

[0486] Step 2:

[0487] The server analyzes standardized user information received from the terminal and generates profile data. This analysis includes categorizing expertise and skills, and tagging topics of interest. This creates a comprehensive profile for each user, which is then stored in the database. The output is the user's profile data.

[0488] Step 3:

[0489] The server executes an algorithm to identify users from different professional fields who have similar interests and complementary skills, based on the generated profile data. The input is profile data, and the algorithm performs a matching process to generate a list of the most suitable collaborators as output. Specifically, this involves database queries and the application of machine learning algorithms.

[0490] Step 4:

[0491] The server transmits information between identified users to an intelligent agent. This agent generates integrated information to facilitate translation of technical terms and common understanding. The input information is data between identified profiles, and the output is integrated and translated information. This process involves the use of natural language processing techniques.

[0492] Step 5:

[0493] The user inputs a new project idea into a device and sends it to the server. The server uses an AI agent to analyze the input idea and compare it with information in a relevant database. It generates the most suitable new idea and presents it to the user as feedback through the device. The input is the user's idea, and the output is an AI-generated suggestion.

[0494] Step 6:

[0495] The server provides an online collaborative work platform to support real-time communication between users. Users can conduct video conferences, chat, and edit documents in real time via their terminals. In this environment, communication data is input, and collaborative work is performed as output through the provided interaction tools. Specific operations include the use of communication infrastructure and the operation of the user interface.

[0496] (Application Example 1)

[0497] Next, we will explain Application Example 1. In the following explanation, the data processing device 12 will be referred to as the "server," and the headset-type terminal 314 will be referred to as the "terminal."

[0498] When experts from different fields collaborate to contribute to smart city projects, problems arise such as communication gaps between these fields and difficulty in finding appropriate collaborators. Furthermore, there is a need to optimize contributions to the project and quickly generate and present effective strategies.

[0499] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 1 is realized by the following means.

[0500] In this invention, the server includes means for inputting user role field information and generating profile data, means for matching collaborators from different role fields based on the profile data, and means for using a processing device that has the function of integrating and translating information from different role fields. This enables optimal collaborator matching across role fields, as well as smooth integration and translation of information. As a result, participants involved in smart city projects can contribute according to their role fields and efficiently generate and present new strategies.

[0501] A "user" is an individual or group that uses the system to input role field information and is matched with collaborators.

[0502] A "role area" refers to a specific field or topic in which a user specializes, and the system classifies users based on this.

[0503] "Profile data" is data generated based on role field information entered by the user, and it indicates the user's characteristics and interests.

[0504] "Matching" refers to the process of connecting collaborators from different roles and fields to facilitate efficient collaboration within a project.

[0505] A "processing device" is a device that has the function of integrating and translating information from different fields of function, and thus facilitates smooth communication.

[0506] "New strategies" refer to new solutions or ideas generated based on integrated information that facilitate the progress of the project.

[0507] A "participant" is an individual or organization that contributes to a smart city project through the system based on their assigned role.

[0508] "Policy evaluation" refers to measuring the validity and effectiveness of newly generated policies and providing feedback on them.

[0509] "Progress management" refers to activities aimed at tracking the progress of a project and supporting the optimization of the plan.

[0510] This system connects experts from different fields of expertise and supports collaboration to contribute to smart city projects. The system begins with users accessing a server using their smartphone or computer and entering information about their field of expertise. The server then generates profile data based on this information and stores it in a database.

[0511] Next, the server analyzes the generated profile data and runs an algorithm that effectively matches users from different role areas. This quickly identifies relevant collaborators and facilitates communication between users. Information from different role areas is integrated by a processing unit within the server, and an AI agent translates it to help create a shared understanding.

[0512] When a user proposes a new initiative or idea, the server matches it against a relevant knowledge base and generates the most suitable solution. As a result, new solutions are presented to the user, and improvements are encouraged through evaluation. The real-time communication platform helps users collaborate efficiently beyond geographical constraints, enabling project progress management and plan optimization.

[0513] A concrete example of this system is the collaboration between medical professionals and engineers in a smart city project focused on health management. Medical professionals can provide ideas for analyzing patient data, and engineers can propose the optimal techniques for that analysis, enabling them to jointly develop new solutions.

[0514] An example of a prompt for a generated AI model is, "Please list the profiles of the necessary experts to help develop effective healthcare solutions in smart cities." This helps users quickly find suitable collaborators for their project.

[0515] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0516] Step 1:

[0517] The user enters role area information into the terminal. The terminal formats this information and sends it to the server as profile data. The server stores the received profile data in a database and converts this data into a parseable format.

[0518] Step 2:

[0519] The server runs an algorithm to effectively match collaborators from different role areas based on profile data stored in the database. This algorithm identifies the optimal pair of collaborators based on similar interests and projects and generates matching results.

[0520] Step 3:

[0521] The processing unit transmits information about the matched collaborators to an AI agent. The AI ​​agent integrates this information and translates it to enable a common understanding between different role areas. The translated information is used to facilitate smooth communication between users.

[0522] Step 4:

[0523] New ideas proposed by users are sent to a server via their device. Within the server, an AI agent compares these ideas with relevant knowledge bases and generates new strategies. These generated strategies are then presented to the user, and feedback is collected for evaluation.

[0524] Step 5:

[0525] The server provides an online collaborative platform that facilitates real-time communication between users. This includes chat, video conferencing, and file sharing features, allowing users to collaborate across geographical limitations. Within this environment, project progress is managed and plans are optimized.

[0526] Furthermore, an emotion engine that estimates the user's emotions may be incorporated. That is, the identification processing unit 290 may use the emotion identification model 59 to estimate the user's emotions and perform identification processing using the user's emotions.

[0527] This invention is a cross-disciplinary collaborative support system equipped with an emotion engine that recognizes user emotions, and a specific embodiment thereof is shown below.

[0528] "Recognition of user emotions"

[0529] When users input specialized information or project-related information through their terminal, the emotion engine analyzes the user's emotional state from the input data and speech. The server receives the voice and text data and uses an emotion analysis algorithm to identify the user's emotional state. For example, if a user is feeling anxious about the progress of a project, this emotional state can be recognized.

[0530] "Emotion-Based Feedback"

[0531] Based on the recognized emotional information, the server provides feedback designed to motivate the user. This includes sending positive confirmation messages and showcasing project success stories. For example, if the server determines that a user has negative feelings towards the project, it can automatically send an encouraging message to that user.

[0532] Project optimization based on emotional information

[0533] The server monitors the emotional state of all project members in real time to make appropriate adjustments to the project plan. This includes reassessing workload distribution and progress speed to optimize the plan for smooth progress. For example, if the server detects that multiple members are experiencing stress, it will suggest rescheduling their work.

[0534] "Collaborator matching that takes emotions into consideration"

[0535] The system combines user profile data and emotional information to select more appropriate collaborators. Based on the analysis results received from the server, the terminal builds optimal collaborative relationships between users with different areas of expertise. For example, if a user is feeling anxious in the early stages of a project, the server can recommend an experienced mentor collaborator to that user.

[0536] In this way, the present invention realizes a collaboration system that takes user emotions into consideration and provides an environment in which experts from different fields can work together efficiently and smoothly. This system is expected to accelerate the creation of new value and improve the results of each project.

[0537] The following describes the processing flow.

[0538] Step 1:

[0539] Users input specialized information and project ideas through their devices. These devices then transmit this information to a server. In addition, text and voice data from user input and communication are collected for sentiment analysis.

[0540] Step 2:

[0541] The server generates a user profile using the received data. Simultaneously, it uses an emotion engine to analyze emotional states from text and voice data. The emotion engine employs natural language processing and speech emotion recognition technologies.

[0542] Step 3:

[0543] The server adds emotional information to the user's profile based on the analyzed emotions. This enhanced profile is stored in a database and used in subsequent processes.

[0544] Step 4:

[0545] The server selects the most suitable collaborator from among experts in different fields, based on profile data and associated emotional information. The collaborator recommendations are optimized by an algorithm that takes the user's emotional state into account.

[0546] Step 5:

[0547] The terminal receives a list of professionals sent from the server and presents it to the user. The user can select collaborators from the list, and this selection is shared back to the server from the terminal.

[0548] Step 6:

[0549] After the project begins, the device sends real-time sentiment data to the server as it interacts with the data entered by the user. The server continuously processes this data and monitors the project's progress.

[0550] Step 7:

[0551] Based on emotional information collected in real time, the server sends feedback and encouraging messages to users as needed. It also proposes specific measures if the project plan needs to be readjusted.

[0552] Step 8:

[0553] The server analyzes the emotional state of all members and optimizes the entire project. It reallocates resources and introduces additional collaborators as needed.

[0554] Step 9:

[0555] Users communicate with collaborators in real time, incorporating feedback and suggestions to advance the project. Emotional information consistently serves as a crucial indicator for project success.

[0556] (Example 2)

[0557] Next, we will describe Example 2. In the following description, the data processing device 12 will be referred to as the "server," and the headset-type terminal 314 will be referred to as the "terminal."

[0558] The problem that this invention aims to solve is the difficulty in making appropriate adjustments based on the emotional state of members and the progress of the project when building effective collaborative relationships between different areas of expertise. Conventional systems make it difficult to analyze emotional information in real time and to select the optimal collaborators considering each member's contribution to the project, which can hinder the smooth progress of the project.

[0559] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 2 is realized by the following means.

[0560] In this invention, the server includes means for acquiring user expertise information as input data and generating profile data, means for combining the profile data with an emotion analysis algorithm to match collaborators from different areas of expertise, and means for analyzing voice and text data and using a generative AI model to identify emotional information. This enables the provision of an optimal collaborative environment based on the user's emotions and efficient adjustment of project plans.

[0561] "Specialized field information" refers to data related to a user's job duties and expertise, and is a fundamental component of the user's profile.

[0562] "Profile data" is a dataset that compiles attribute information such as a user's area of ​​expertise and activity history, and serves as the basis for selecting collaborators and performing sentiment analysis.

[0563] An "emotion analysis algorithm" is a computational method or model that uses data input by the user to identify their emotional state, and is used to objectively evaluate the user's emotional state.

[0564] A "generative AI model" is an artificial intelligence system that learns from large amounts of data and generates adaptive outputs to perform specific tasks. In this invention, it is used for identifying emotions and generating feedback messages.

[0565] A "collaborator" is an individual or organization with other areas of expertise who works together with the user on a project, providing different specialized knowledge and skills.

[0566] A "feedback message" is a message generated based on the results of sentiment analysis and the current status of the project, in order to increase user motivation or indicate the direction of the project.

[0567] A "project adjustment proposal" refers to changes in the plan or reallocation of tasks suggested based on the project's progress and the emotional state of the participating members, and is intended to help the project move forward efficiently.

[0568] The system of the present invention provides various functions to efficiently support project progress based on user input data. This system primarily uses specialized domain information and project-related text and audio data entered by the user via a terminal.

[0569] Users input information related to their projects through the terminal. This input data includes, for example, text-based instructions or audio recordings of meetings. The terminal converts this data into a format that can be used for text analysis and audio analysis.

[0570] The server receives text and voice data transmitted from the terminal and performs sentiment analysis using a generative AI model. The sentiment analysis algorithm uses natural language processing and voice sentiment analysis to identify the user's emotional state. This analysis makes it possible to evaluate the user's feelings towards the progress of the project in real time.

[0571] Based on the analyzed emotional information, the server generates and provides appropriate feedback messages to the user. This feedback is created using prompts generated by a generative AI model, aiming to improve the user's emotional state and increase their motivation. For example, a prompt such as, "To alleviate anxiety about the current project, please provide an overview of a similar project that was previously successful," can be used to provide the user with helpful information.

[0572] Furthermore, the server aggregates the emotional states of all project members and proposes ways to optimize the project's progress plan. In this process, it can also recommend the most suitable collaborators to improve cooperation among members. For example, if a user is feeling anxious in the early stages of a project, recommending an experienced mentor can help increase the project's success rate.

[0573] In this way, the present invention provides a practical means for achieving efficient project management by providing optimal feedback and collaboration that takes user emotions into consideration in complex project environments.

[0574] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0575] Step 1:

[0576] Users input specialized information and project-related data into the terminal. This input data includes text information and meeting audio recordings. The terminal receives this information and prepares it for the next processing step; if it is text data, it is used as is, and if it is audio data, it is converted to text using speech recognition technology.

[0577] Step 2:

[0578] The device sends the received text data to the server. The transmitted data becomes input for analyzing the user's emotional state. The server first uses a generative AI model to preprocess the text data using natural language processing algorithms. This preprocessing involves noise removal and extraction of keywords that express emotions.

[0579] Step 3:

[0580] The server inputs pre-processed data into an emotion analysis algorithm to identify the user's emotional state. Here, a generative AI model is used to analyze the data, considering the frequency and context of emotion-indicating words from a vast dictionary database. As a result, the user's current emotional state is output, which is then used to generate subsequent feedback.

[0581] Step 4:

[0582] Based on the analysis results, the server generates feedback messages using a generation AI model while referring to prompts. Specifically, it creates customized encouraging messages to improve the user's emotional state and advice to move the project forward. An example of a prompt is, "If the user's emotions are negative, analyze the cause and generate an appropriate encouraging message." This feedback is intended to improve the user's motivation and stabilize their emotions.

[0583] Step 5:

[0584] The device displays feedback messages received from the server to the user. This includes on-screen message displays and email notifications. Furthermore, the device accepts additional feedback and comments from the user, collecting information to include in future updates.

[0585] Step 6:

[0586] The server aggregates emotional data from all project members and proposes progress optimizations based on a project management algorithm. Using the analyzed emotional states, it suggests adjusting the workload for members experiencing stress and sharing success stories for areas where motivation is needed. The proposed results regarding the project's progress are generated as output and will be discussed at the next project meeting.

[0587] (Application Example 2)

[0588] Next, we will explain application example 2. In the following explanation, the data processing device 12 will be referred to as the "server," and the headset-type terminal 314 will be referred to as the "terminal."

[0589] The objective of this invention is to efficiently facilitate smooth information sharing and collaborative generation of novel ideas among collaborators from different professional fields. In particular, it is necessary to optimize project progress by considering the user's emotional state when planning work and selecting collaborators. Furthermore, it is essential to provide an efficient and collaborative work environment by offering feedback based on emotional states.

[0590] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means.

[0591] In this invention, the server includes means for using an emotion analysis engine to recognize the user's emotional state, means for optimizing work plans and collaborator selection based on emotional information, and means for providing a real-time collaborative activity environment. This enables flexible and efficient project management that reflects the user's emotional state.

[0592] A "user" is an individual or group that uses a system to input information in order to achieve a specific purpose.

[0593] "Specialized field information" refers to information that includes knowledge and skills related to the specific field to which the user belongs.

[0594] "Attribute data" refers to data that represents the characteristics of an individual or group, generated based on information such as the user's area of ​​expertise.

[0595] A "collaborator" is an individual or group selected to work together in different areas of expertise.

[0596] A "proxy program" is software that has the function of adjusting and translating different information to assist the user.

[0597] "Ideas" are new ideas or concepts generated based on integrated information.

[0598] An "emotion analysis engine" is a system component that analyzes and recognizes a user's emotional state based on their statements and actions.

[0599] "Emotional information" refers to data about a user's emotional state obtained by an emotion analysis engine.

[0600] A "work plan" is a schedule and procedure formulated to effectively advance a project or task.

[0601] A "collaborative environment" refers to an environment or platform provided to enable multiple users to work together smoothly.

[0602] In one embodiment of this invention, the system includes a function to input the user's area of ​​expertise and generate attribute data. The server executes a process to select collaborators from different areas of expertise based on the generated attribute data. The collaborator selection process incorporates an emotion analysis engine that analyzes the user's emotional state, and takes emotional information into consideration to select the most suitable collaborators and create a work plan.

[0603] The server also uses the Google Cloud Speech-to-Text API and Google Cloud Natural Language API to analyze user speech and input text. The resulting sentiment information is used to provide a real-time collaborative environment. In this environment, proxy programs coordinate information from different areas of expertise, enabling smooth information sharing among users.

[0604] A typical use case is when a user inputs "This task is so difficult, there's no end in sight" into the system. The server transcribes the statement into text, and its sentiment analysis engine determines that the user is experiencing stress. The system can then provide feedback to the user such as, "We recommend you take a short break." This enables flexible project management that takes the user's emotional state into consideration, and provides a collaborative work environment.

[0605] As an example of prompts for a generative AI model, in response to the input "What is an appropriate feedback message when a worker is feeling stressed?", an example response could be "Your health is our top priority. Why don't you take a 10-minute refresh break?". Such prompts enable communication that takes emotions into consideration.

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

[0607] Step 1:

[0608] The user inputs specialized domain information through a terminal. The input information is stored as attribute data. The server uses this attribute data to prepare for the next stage of processing.

[0609] Step 2:

[0610] The server receives user voice and text data. Voice data is obtained using a microphone and converted to text using the Google Cloud Speech-to-Text API. To obtain emotional information from the text input, the Google Cloud Natural Language API is used to analyze the user's emotional state. The analyzed emotional state is stored for subsequent processing.

[0611] Step 3:

[0612] Based on the emotional information obtained, the server creates a work plan and selects collaborators based on the user's emotional state. In this process, the emotion analysis engine detects emotional states such as stress and anxiety, and makes necessary adjustments to optimize the project's progress. The adjusted plan and information on selected collaborators are generated and recorded.

[0613] Step 4:

[0614] The server generates appropriate feedback for the user based on emotional information and tailored plans. This generated feedback is designed to motivate the user and is sent from the server to the terminal for the user to understand.

[0615] Step 5:

[0616] The terminal displays feedback received from the server to the user. For example, if the terminal determines that the user is "stressed" about the project, it can display a message such as "We recommend you take a short break." This allows the user to adjust their work based on the feedback.

[0617] The specific processing unit 290 transmits the result of the specific processing to the headset terminal 314. In the headset terminal 314, the control unit 46A causes the speaker 240 and display 343 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0618] Data generation model 58 is a type of so-called generative AI (Artificial Intelligence). An example of data generation model 58 is ChatGPT (Internet Search<URL: https: / / openai.com / blog / chatgpt> ), Gemini (Internet search) <url: https: gemini.google.com ?hl="ja">Examples of generative AI include the following. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and with inference data such as audio data representing speech, text data representing text, and image data representing images. The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference results in data formats such as audio data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[0619] In the above embodiment, an example was given in which specific processing is performed by the data processing device 12, but the technology of this disclosure is not limited thereto, and specific processing may also be performed by the headset terminal 314.

[0620] [Fourth Embodiment]

[0621] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[0622] As shown in Figure 7, the data processing system 410 includes a data processing device 12 and a robot 414. An example of the data processing device 12 is a server.

[0623] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 is an example of a "computer" related to the technology of this disclosure. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[0624] The robot 414 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication interface 44, and a controlled object 443. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, camera 42, and controlled object 443 are also connected to the bus 52.

[0625] The microphone 238 receives voice signals from the user 20 and receives instructions from the user 20. The microphone 238 captures the voice signals from the user 20, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0626] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the area around the user 20 (for example, an imaging range defined by a field of view equivalent to the width of a typical healthy person's field of vision).

[0627] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0628] The controlled object 443 includes a display device, LEDs in the eyes, and motors that drive the arms, hands, and feet. The posture and gestures of the robot 414 are controlled by controlling the motors of the arms, hands, and feet. Some of the robot 414's emotions can be expressed by controlling these motors. Furthermore, the robot 414's facial expressions can also be expressed by controlling the illumination state of the LEDs in its eyes.

[0629] Figure 8 shows an example of the main functions of the data processing device 12 and the robot 414. As shown in Figure 8, the data processing device 12 performs specific processing using the processor 28. The storage 32 stores the specific processing program 56.

[0630] The specific processing program 56 is an example of a "program" relating to the technology of this disclosure. The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0631] The storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[0632] In robot 414, the processor 46 performs the reception output processing. The storage 50 stores the reception output program 60. The processor 46 reads the reception output program 60 from the storage 50 and executes the read reception output program 60 on the RAM 48. The reception output processing is realized by the processor 46 operating as a control unit 46A according to the reception output program 60 executed on the RAM 48.

[0633] Next, the specific processing performed by the specific processing unit 290 of the data processing device 12 will be described. In the following description, the data processing device 12 will be referred to as the "server" and the robot 414 as the "terminal".

[0634] The system based on this invention aims to enable users from different specialized fields to collaborate effectively and create new solutions. Specific embodiments are shown below.

[0635] "Collection of user information and generation of profiles"

[0636] Users input their area of ​​expertise, skills, and research interests through their terminal. The server generates profile data for each user based on this information and stores it in a database. For example, if a researcher in the medical field has a project idea related to biomedical engineering, they can input the details of that idea, enabling them to collaborate with experts in related fields.

[0637] "Matching experts from different fields"

[0638] The server analyzes the generated profile data and runs algorithms to identify users from different fields with similar interests. This allows each user to find suitable collaborators. For example, if a big data analytics expert wants to contribute to an environmental science project, the matching system will select a suitable project owner.

[0639] "Integration and Translation of Knowledge"

[0640] The server sends information about users from different professional fields who have decided to collaborate to an AI agent. The AI ​​agent analyzes this information, translates and integrates it to reach a common understanding, and supports smooth communication between users with different backgrounds.

[0641] Idea Generation and Presentation

[0642] When a user submits an initial project idea, the device sends it to the server. An AI agent on the server compares it with a database of relevant research to generate new ideas and suggest the best solution for the user's needs. This result is then presented to the user via the device, and feedback is collected.

[0643] "Providing a real-time collaborative work environment"

[0644] The server provides an online collaborative platform to enable real-time communication between users. This allows users to utilize features such as chat, video conferencing, and file sharing, enabling collaboration beyond geographical constraints. For example, it becomes easier for project teams scattered around the world to simultaneously edit documents and work collaboratively. In this way, the present invention facilitates effective co-creation among experts from different fields, accelerating the creation of new value.

[0645] The following describes the processing flow.

[0646] Step 1:

[0647] The user uses a terminal to input their area of ​​expertise, skills, and research interests. The terminal temporarily stores this information and sends it to the server.

[0648] Step 2:

[0649] The server analyzes the received user information and generates profile data for each user. This profile data is stored in a database for use in the matching process.

[0650] Step 3:

[0651] The server executes an algorithm to search for commonalities and complementary information among users based on accumulated profile data. Based on these results, the server generates a list of optimal collaborators and notifies the user.

[0652] Step 4:

[0653] The user selects their preferred collaborator from the presented list. The device then sends the selection result back to the server.

[0654] Step 5:

[0655] The server transmits information from collaborators with different areas of expertise to an AI agent. The AI ​​agent analyzes this information and translates and integrates it to reach a common understanding.

[0656] Step 6:

[0657] The user inputs project ideas into their device and sends them to the server. The server's AI agent generates and evaluates new ideas based on past research and data related to the project.

[0658] Step 7:

[0659] The server sends the evaluation results of the generated ideas to the user, providing feedback on the feasibility of the ideas and areas for improvement.

[0660] Step 8:

[0661] The server establishes a real-time communication platform to enable effective collaboration among users. This platform includes chat, video conferencing, and file sharing capabilities.

[0662] Step 9:

[0663] Users will use the provided platform to share documents in real time and collaborate while monitoring project progress.

[0664] (Example 1)

[0665] Next, we will describe Example 1. In the following description, the data processing device 12 will be referred to as the "server" and the robot 414 as the "terminal".

[0666] When users from different professional fields collaborate to create new concepts, it is difficult to build effective cooperative relationships and fully utilize each individual's expertise. Furthermore, there is a lack of appropriate means to integrate information from different fields and facilitate smooth communication among users. This situation contributes to the creation of innovative solutions among diverse experts.

[0667] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 1 is realized by the following means.

[0668] In this invention, the server includes means for inputting information about the user's area of ​​expertise using an information processing device and generating user profile data; means for executing an algorithm for identifying and matching collaborators from different areas of expertise based on the profile data; and means for using an intelligent agent that has the function of integrating and translating information from different areas of expertise. This enables the establishment of effective collaborative relationships and smooth communication between users from different areas of expertise.

[0669] An "information processing device" is a device used for inputting, processing, storing, and outputting information, and mainly refers to computers and their peripherals.

[0670] "User" refers to an individual or organization that accesses the system using an information processing device and inputs their own information.

[0671] "Specialized field" refers to a specific area of ​​expertise that possesses particular knowledge and skills, and represents the field to which a user's specialized knowledge and skills belong.

[0672] "Profile data" refers to a dataset that comprehensively compiles information about a user's areas of expertise, skills, interests, and other relevant details.

[0673] An "algorithm" refers to a set of rules that define the steps or computational flow required to solve a specific problem.

[0674] An "intelligent agent" refers to a software program or system that automatically makes decisions and takes actions based on the information it is given.

[0675] "Information integration" refers to the process of unifying data and information obtained from different sources so that they can be handled in a unified manner.

[0676] "Translation" refers to the process of replacing specific technical terms or concepts with terms or concepts from a different language or field.

[0677] The system based on this invention provides an effective information processing environment for users from diverse professional fields to collaborate and create new concepts.

[0678] Users input information about their areas of expertise, skills, and interests using a terminal. This information is transmitted to the server via an information processing device. The server analyzes this information and performs processing to generate user profile data. The profile data is stored in a database.

[0679] The server runs an algorithm to match users from different areas of expertise based on their profile data. Specifically, it uses a generative AI model to identify highly similar users. This algorithm makes it possible to connect specialists from different fields who share a common interest in a particular topic.

[0680] Furthermore, the server uses intelligent agents to integrate information from different specialized fields and translate it into an easily understandable format. These intelligent agents combine machine learning and natural language processing technologies to enable smooth communication between users. This translation function allows users with diverse backgrounds to collaborate and communicate efficiently.

[0681] When a user inputs a new project idea via their device, an AI agent on the server searches the database for relevant information and generates a new concept. The generated idea is then presented to the user via the device, and feedback is collected. This feedback is used to further improve the idea.

[0682] As a concrete example, consider a scenario where software developers in different countries collaborate on developing a new application in a remote work environment. This system matches suitable partners based on their skills and interests, and supports understanding through a common language. An example of a prompt for the generated AI model is: "Suggest ways for software developers in different countries to collaborate effectively. Describe specific steps and tools to use to advance a common project."

[0683] In this way, the system provides a comprehensive information processing environment that enables users from different specialized fields to collaborate and create new value.

[0684] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0685] Step 1:

[0686] Users use a terminal to input information about their areas of expertise, skills, and interests. The entered information is converted into a standardized data format and sent to an information processing device. Specifically, an input form is provided, the data is formatted according to the user's input, and then sent to the server.

[0687] Step 2:

[0688] The server analyzes standardized user information received from the terminal and generates profile data. This analysis includes categorizing expertise and skills, and tagging topics of interest. This creates a comprehensive profile for each user, which is then stored in the database. The output is the user's profile data.

[0689] Step 3:

[0690] The server executes an algorithm to identify users from different professional fields who have similar interests and complementary skills, based on the generated profile data. The input is profile data, and the algorithm performs a matching process to generate a list of the most suitable collaborators as output. Specifically, this involves database queries and the application of machine learning algorithms.

[0691] Step 4:

[0692] The server transmits information between identified users to an intelligent agent. This agent generates integrated information to facilitate translation of technical terms and common understanding. The input information is data between identified profiles, and the output is integrated and translated information. This process involves the use of natural language processing techniques.

[0693] Step 5:

[0694] The user inputs a new project idea into a device and sends it to the server. The server uses an AI agent to analyze the input idea and compare it with information in a relevant database. It generates the most suitable new idea and presents it to the user as feedback through the device. The input is the user's idea, and the output is an AI-generated suggestion.

[0695] Step 6:

[0696] The server provides an online collaborative work platform to support real-time communication between users. Users can conduct video conferences, chat, and edit documents in real time via their terminals. In this environment, communication data is input, and collaborative work is performed as output through the provided interaction tools. Specific operations include the use of communication infrastructure and the operation of the user interface.

[0697] (Application Example 1)

[0698] Next, we will explain Application Example 1. In the following explanation, the data processing device 12 will be referred to as the "server" and the robot 414 as the "terminal".

[0699] When experts from different fields collaborate to contribute to smart city projects, problems arise such as communication gaps between these fields and difficulty in finding appropriate collaborators. Furthermore, there is a need to optimize contributions to the project and quickly generate and present effective strategies.

[0700] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 1 is realized by the following means.

[0701] In this invention, the server includes means for inputting user role field information and generating profile data, means for matching collaborators from different role fields based on the profile data, and means for using a processing device that has the function of integrating and translating information from different role fields. This enables optimal collaborator matching across role fields, as well as smooth integration and translation of information. As a result, participants involved in smart city projects can contribute according to their role fields and efficiently generate and present new strategies.

[0702] A "user" is an individual or group that uses the system to input role field information and is matched with collaborators.

[0703] A "role area" refers to a specific field or topic in which a user specializes, and the system classifies users based on this.

[0704] "Profile data" is data generated based on role field information entered by the user, and it indicates the user's characteristics and interests.

[0705] "Matching" refers to the process of connecting collaborators from different roles and fields to facilitate efficient collaboration within a project.

[0706] A "processing device" is a device that has the function of integrating and translating information from different fields of function, and thus facilitates smooth communication.

[0707] "New strategies" refer to new solutions or ideas generated based on integrated information that facilitate the progress of the project.

[0708] A "participant" is an individual or organization that contributes to a smart city project through the system based on their assigned role.

[0709] "Policy evaluation" refers to measuring the validity and effectiveness of newly generated policies and providing feedback on them.

[0710] "Progress management" refers to activities aimed at tracking the progress of a project and supporting the optimization of the plan.

[0711] This system connects experts from different fields of expertise and supports collaboration to contribute to smart city projects. The system begins with users accessing a server using their smartphone or computer and entering information about their field of expertise. The server then generates profile data based on this information and stores it in a database.

[0712] Next, the server analyzes the generated profile data and runs an algorithm that effectively matches users from different role areas. This quickly identifies relevant collaborators and facilitates communication between users. Information from different role areas is integrated by a processing unit within the server, and an AI agent translates it to help create a shared understanding.

[0713] When a user proposes a new initiative or idea, the server matches it against a relevant knowledge base and generates the most suitable solution. As a result, new solutions are presented to the user, and improvements are encouraged through evaluation. The real-time communication platform helps users collaborate efficiently beyond geographical constraints, enabling project progress management and plan optimization.

[0714] A concrete example of this system is the collaboration between medical professionals and engineers in a smart city project focused on health management. Medical professionals can provide ideas for analyzing patient data, and engineers can propose the optimal techniques for that analysis, enabling them to jointly develop new solutions.

[0715] An example of a prompt for a generated AI model is, "Please list the profiles of the necessary experts to help develop effective healthcare solutions in smart cities." This helps users quickly find suitable collaborators for their project.

[0716] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0717] Step 1:

[0718] The user enters role area information into the terminal. The terminal formats this information and sends it to the server as profile data. The server stores the received profile data in a database and converts this data into a parseable format.

[0719] Step 2:

[0720] The server runs an algorithm to effectively match collaborators from different role areas based on profile data stored in the database. This algorithm identifies the optimal pair of collaborators based on similar interests and projects and generates matching results.

[0721] Step 3:

[0722] The processing unit transmits information about the matched collaborators to an AI agent. The AI ​​agent integrates this information and translates it to enable a common understanding between different role areas. The translated information is used to facilitate smooth communication between users.

[0723] Step 4:

[0724] New ideas proposed by users are sent to a server via their device. Within the server, an AI agent compares these ideas with relevant knowledge bases and generates new strategies. These generated strategies are then presented to the user, and feedback is collected for evaluation.

[0725] Step 5:

[0726] The server provides an online collaborative platform that facilitates real-time communication between users. This includes chat, video conferencing, and file sharing features, allowing users to collaborate across geographical limitations. Within this environment, project progress is managed and plans are optimized.

[0727] Furthermore, an emotion engine that estimates the user's emotions may be incorporated. That is, the identification processing unit 290 may use the emotion identification model 59 to estimate the user's emotions and perform identification processing using the user's emotions.

[0728] This invention is a cross-disciplinary collaborative support system equipped with an emotion engine that recognizes user emotions, and a specific embodiment thereof is shown below.

[0729] "Recognition of user emotions"

[0730] When users input specialized information or project-related information through their terminal, the emotion engine analyzes the user's emotional state from the input data and speech. The server receives the voice and text data and uses an emotion analysis algorithm to identify the user's emotional state. For example, if a user is feeling anxious about the progress of a project, this emotional state can be recognized.

[0731] "Emotion-Based Feedback"

[0732] Based on the recognized emotional information, the server provides feedback designed to motivate the user. This includes sending positive confirmation messages and showcasing project success stories. For example, if the server determines that a user has negative feelings towards the project, it can automatically send an encouraging message to that user.

[0733] Project optimization based on emotional information

[0734] The server monitors the emotional state of all project members in real time to make appropriate adjustments to the project plan. This includes reassessing workload distribution and progress speed to optimize the plan for smooth progress. For example, if the server detects that multiple members are experiencing stress, it will suggest rescheduling their work.

[0735] "Collaborator matching that takes emotions into consideration"

[0736] The system combines user profile data and emotional information to select more appropriate collaborators. Based on the analysis results received from the server, the terminal builds optimal collaborative relationships between users with different areas of expertise. For example, if a user is feeling anxious in the early stages of a project, the server can recommend an experienced mentor collaborator to that user.

[0737] In this way, the present invention realizes a collaboration system that takes user emotions into consideration and provides an environment in which experts from different fields can work together efficiently and smoothly. This system is expected to accelerate the creation of new value and improve the results of each project.

[0738] The following describes the processing flow.

[0739] Step 1:

[0740] Users input specialized information and project ideas through their devices. These devices then transmit this information to a server. In addition, text and voice data from user input and communication are collected for sentiment analysis.

[0741] Step 2:

[0742] The server generates a user profile using the received data. Simultaneously, it uses an emotion engine to analyze emotional states from text and voice data. The emotion engine employs natural language processing and speech emotion recognition technologies.

[0743] Step 3:

[0744] The server adds emotional information to the user's profile based on the analyzed emotions. This enhanced profile is stored in a database and used in subsequent processes.

[0745] Step 4:

[0746] The server selects the most suitable collaborator from among experts in different fields, based on profile data and associated emotional information. The collaborator recommendations are optimized by an algorithm that takes the user's emotional state into account.

[0747] Step 5:

[0748] The terminal receives a list of professionals sent from the server and presents it to the user. The user can select collaborators from the list, and this selection is shared back to the server from the terminal.

[0749] Step 6:

[0750] After the project begins, the device sends real-time sentiment data to the server as it interacts with the data entered by the user. The server continuously processes this data and monitors the project's progress.

[0751] Step 7:

[0752] Based on emotional information collected in real time, the server sends feedback and encouraging messages to users as needed. It also proposes specific measures if the project plan needs to be readjusted.

[0753] Step 8:

[0754] The server analyzes the emotional state of all members and optimizes the entire project. It reallocates resources and introduces additional collaborators as needed.

[0755] Step 9:

[0756] Users communicate with collaborators in real time, incorporating feedback and suggestions to advance the project. Emotional information consistently serves as a crucial indicator for project success.

[0757] (Example 2)

[0758] Next, we will describe Example 2. In the following description, the data processing device 12 will be referred to as the "server" and the robot 414 as the "terminal".

[0759] The problem that this invention aims to solve is the difficulty in making appropriate adjustments based on the emotional state of members and the progress of the project when building effective collaborative relationships between different areas of expertise. Conventional systems make it difficult to analyze emotional information in real time and to select the optimal collaborators considering each member's contribution to the project, which can hinder the smooth progress of the project.

[0760] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 2 is realized by the following means.

[0761] In this invention, the server includes means for acquiring user expertise information as input data and generating profile data, means for combining the profile data with an emotion analysis algorithm to match collaborators from different areas of expertise, and means for analyzing voice and text data and using a generative AI model to identify emotional information. This enables the provision of an optimal collaborative environment based on the user's emotions and efficient adjustment of project plans.

[0762] "Specialized field information" refers to data related to a user's job duties and expertise, and is a fundamental component of the user's profile.

[0763] "Profile data" is a dataset that compiles attribute information such as a user's area of ​​expertise and activity history, and serves as the basis for selecting collaborators and performing sentiment analysis.

[0764] An "emotion analysis algorithm" is a computational method or model that uses data input by the user to identify their emotional state, and is used to objectively evaluate the user's emotional state.

[0765] A "generative AI model" is an artificial intelligence system that learns from large amounts of data and generates adaptive outputs to perform specific tasks. In this invention, it is used for identifying emotions and generating feedback messages.

[0766] A "collaborator" is an individual or organization with other areas of expertise who works together with the user on a project, providing different specialized knowledge and skills.

[0767] A "feedback message" is a message generated based on the results of sentiment analysis and the current status of the project, in order to increase user motivation or indicate the direction of the project.

[0768] A "project adjustment proposal" refers to changes in the plan or reallocation of tasks suggested based on the project's progress and the emotional state of the participating members, and is intended to help the project move forward efficiently.

[0769] The system of the present invention provides various functions to efficiently support project progress based on user input data. This system primarily uses specialized domain information and project-related text and audio data entered by the user via a terminal.

[0770] Users input information related to their projects through the terminal. This input data includes, for example, text-based instructions or audio recordings of meetings. The terminal converts this data into a format that can be used for text analysis and audio analysis.

[0771] The server receives text and voice data transmitted from the terminal and performs sentiment analysis using a generative AI model. The sentiment analysis algorithm uses natural language processing and voice sentiment analysis to identify the user's emotional state. This analysis makes it possible to evaluate the user's feelings towards the progress of the project in real time.

[0772] Based on the analyzed emotional information, the server generates and provides appropriate feedback messages to the user. This feedback is created using prompts generated by a generative AI model, aiming to improve the user's emotional state and increase their motivation. For example, a prompt such as, "To alleviate anxiety about the current project, please provide an overview of a similar project that was previously successful," can be used to provide the user with helpful information.

[0773] Furthermore, the server aggregates the emotional states of all project members and proposes ways to optimize the project's progress plan. In this process, it can also recommend the most suitable collaborators to improve cooperation among members. For example, if a user is feeling anxious in the early stages of a project, recommending an experienced mentor can help increase the project's success rate.

[0774] In this way, the present invention provides a practical means for achieving efficient project management by providing optimal feedback and collaboration that takes user emotions into consideration in complex project environments.

[0775] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0776] Step 1:

[0777] Users input specialized information and project-related data into the terminal. This input data includes text information and meeting audio recordings. The terminal receives this information and prepares it for the next processing step; if it is text data, it is used as is, and if it is audio data, it is converted to text using speech recognition technology.

[0778] Step 2:

[0779] The device sends the received text data to the server. The transmitted data becomes input for analyzing the user's emotional state. The server first uses a generative AI model to preprocess the text data using natural language processing algorithms. This preprocessing involves noise removal and extraction of keywords that express emotions.

[0780] Step 3:

[0781] The server inputs pre-processed data into an emotion analysis algorithm to identify the user's emotional state. Here, a generative AI model is used to analyze the data, considering the frequency and context of emotion-indicating words from a vast dictionary database. As a result, the user's current emotional state is output, which is then used to generate subsequent feedback.

[0782] Step 4:

[0783] Based on the analysis results, the server generates feedback messages using a generation AI model while referring to prompts. Specifically, it creates customized encouraging messages to improve the user's emotional state and advice to move the project forward. An example of a prompt is, "If the user's emotions are negative, analyze the cause and generate an appropriate encouraging message." This feedback is intended to improve the user's motivation and stabilize their emotions.

[0784] Step 5:

[0785] The device displays feedback messages received from the server to the user. This includes on-screen message displays and email notifications. Furthermore, the device accepts additional feedback and comments from the user, collecting information to include in future updates.

[0786] Step 6:

[0787] The server aggregates emotional data from all project members and proposes progress optimizations based on a project management algorithm. Using the analyzed emotional states, it suggests adjusting the workload for members experiencing stress and sharing success stories for areas where motivation is needed. The proposed results regarding the project's progress are generated as output and will be discussed at the next project meeting.

[0788] (Application Example 2)

[0789] Next, we will explain application example 2. In the following explanation, the data processing device 12 will be referred to as the "server" and the robot 414 as the "terminal".

[0790] The objective of this invention is to efficiently facilitate smooth information sharing and collaborative generation of novel ideas among collaborators from different professional fields. In particular, it is necessary to optimize project progress by considering the user's emotional state when planning work and selecting collaborators. Furthermore, it is essential to provide an efficient and collaborative work environment by offering feedback based on emotional states.

[0791] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means.

[0792] In this invention, the server includes means for using an emotion analysis engine to recognize the user's emotional state, means for optimizing work plans and collaborator selection based on emotional information, and means for providing a real-time collaborative activity environment. This enables flexible and efficient project management that reflects the user's emotional state.

[0793] A "user" is an individual or group that uses a system to input information in order to achieve a specific purpose.

[0794] "Specialized field information" refers to information that includes knowledge and skills related to the specific field to which the user belongs.

[0795] "Attribute data" refers to data that represents the characteristics of an individual or group, generated based on information such as the user's area of ​​expertise.

[0796] A "collaborator" is an individual or group selected to work together in different areas of expertise.

[0797] A "proxy program" is software that has the function of adjusting and translating different information to assist the user.

[0798] "Ideas" are new ideas or concepts generated based on integrated information.

[0799] An "emotion analysis engine" is a system component that analyzes and recognizes a user's emotional state based on their statements and actions.

[0800] "Emotional information" refers to data about a user's emotional state obtained by an emotion analysis engine.

[0801] A "work plan" is a schedule and procedure formulated to effectively advance a project or task.

[0802] A "collaborative environment" refers to an environment or platform provided to enable multiple users to work together smoothly.

[0803] In one embodiment of this invention, the system includes a function to input the user's area of ​​expertise and generate attribute data. The server executes a process to select collaborators from different areas of expertise based on the generated attribute data. The collaborator selection process incorporates an emotion analysis engine that analyzes the user's emotional state, and takes emotional information into consideration to select the most suitable collaborators and create a work plan.

[0804] The server also uses the Google Cloud Speech-to-Text API and Google Cloud Natural Language API to analyze user speech and input text. The resulting sentiment information is used to provide a real-time collaborative environment. In this environment, proxy programs coordinate information from different areas of expertise, enabling smooth information sharing among users.

[0805] A typical use case is when a user inputs "This task is so difficult, there's no end in sight" into the system. The server transcribes the statement into text, and its sentiment analysis engine determines that the user is experiencing stress. The system can then provide feedback to the user such as, "We recommend you take a short break." This enables flexible project management that takes the user's emotional state into consideration, and provides a collaborative work environment.

[0806] As an example of prompts for a generative AI model, in response to the input "What is an appropriate feedback message when a worker is feeling stressed?", an example response could be "Your health is our top priority. Why don't you take a 10-minute refresh break?". Such prompts enable communication that takes emotions into consideration.

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

[0808] Step 1:

[0809] The user inputs specialized domain information through a terminal. The input information is stored as attribute data. The server uses this attribute data to prepare for the next stage of processing.

[0810] Step 2:

[0811] The server receives user voice and text data. Voice data is obtained using a microphone and converted to text using the Google Cloud Speech-to-Text API. To obtain emotional information from the text input, the Google Cloud Natural Language API is used to analyze the user's emotional state. The analyzed emotional state is stored for subsequent processing.

[0812] Step 3:

[0813] Based on the emotional information obtained, the server creates a work plan and selects collaborators based on the user's emotional state. In this process, the emotion analysis engine detects emotional states such as stress and anxiety, and makes necessary adjustments to optimize the project's progress. The adjusted plan and information on selected collaborators are generated and recorded.

[0814] Step 4:

[0815] The server generates appropriate feedback for the user based on emotional information and tailored plans. This generated feedback is designed to motivate the user and is sent from the server to the terminal for the user to understand.

[0816] Step 5:

[0817] The terminal displays feedback received from the server to the user. For example, if the terminal determines that the user is "stressed" about the project, it can display a message such as "We recommend you take a short break." This allows the user to adjust their work based on the feedback.

[0818] The specific processing unit 290 transmits the result of the specific processing to the robot 414. In the robot 414, the control unit 46A causes the speaker 240 and the controlled object 443 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0819] Data generation model 58 is a type of so-called generative AI (Artificial Intelligence). An example of data generation model 58 is ChatGPT (Internet Search<URL: https: / / openai.com / blog / chatgpt> ), Gemini (Internet search) <url: https: gemini.google.com ?hl="ja">Examples of generative AI include the following. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and with inference data such as audio data representing speech, text data representing text, and image data representing images. The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference results in data formats such as audio data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[0820] In the above embodiment, an example was given in which the specific processing is performed by the data processing device 12, but the technology of this disclosure is not limited thereto, and the specific processing may also be performed by the robot 414.

[0821] Furthermore, the emotion identification model 59, acting as an emotion engine, may determine the user's emotion according to a specific mapping. Specifically, the emotion identification model 59 may determine the user's emotion according to a specific mapping, which is an emotion map (see Figure 9). Similarly, the emotion identification model 59 may also determine the robot's emotion, and the identification processing unit 290 may perform identification processing using the robot's emotion.

[0822] Figure 9 shows an emotion map 400 in which multiple emotions are mapped. In the emotion map 400, emotions are arranged in concentric circles radiating from the center. The closer to the center of the concentric circles, the more primitive the emotions are located. Further out of the concentric circles, emotions representing states and actions arising from mental states are located. Emotion is a concept that includes feelings and mental states. On the left side of the concentric circles, emotions that are generally generated from reactions occurring in the brain are located. On the right side of the concentric circles, emotions that are generally induced by situational judgment are located. Above and below the concentric circles, emotions that are generally generated from reactions occurring in the brain and induced by situational judgment are located. In addition, the emotion of "pleasure" is located on the upper side of the concentric circles, and the emotion of "displeasure" is located on the lower side. Thus, in the emotion map 400, multiple emotions are mapped based on the structure in which emotions arise, and emotions that are likely to occur simultaneously are mapped close together.

[0823] These emotions are distributed at the 3 o'clock position on the Emotion Map 400, and usually fluctuate between feelings of security and anxiety. In the right half of the Emotion Map 400, situational awareness takes precedence over internal feelings, resulting in a calm impression.

[0824] The inside of the Emotion Map 400 represents inner thoughts, while the outside represents actions. Therefore, the further you go from the outside of the Emotion Map 400, the more visible (expressed in actions) your emotions become.

[0825] Here, human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, it results in discomfort, and when they approach the ideal, it results in pleasure. Similarly, in robots, cars, motorcycles, etc., emotions can be created based on various balances, such as posture and battery level. When these balances deviate from the ideal, it results in discomfort, and when they approach the ideal, it results in pleasure. The emotion map can be generated, for example, based on Dr. Mitsuyoshi's emotion map (Research on a system for analyzing brain physiological signals of speech emotion recognition and emotion, Tokushima University, doctoral dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map contains emotions belonging to a region called "response," where sensation is dominant. The right half of the emotion map contains emotions belonging to a region called "situation," where situational awareness is dominant.

[0826] The emotion map defines two emotions that promote learning. One is the emotion around the middle of the negative "repentance" and "reflection" on the situation side. In other words, it is when the robot experiences negative emotions such as "I never want to feel this way again" or "I don't want to be scolded again." The other is the emotion around the positive "desire" on the reaction side. In other words, it is when the robot has positive feelings such as "I want more" or "I want to know more."

[0827] The emotion identification model 59 inputs user input into a pre-trained neural network, obtains emotion values ​​representing each emotion shown in the emotion map 400, and determines the user's emotion. This neural network is pre-trained based on multiple training data sets, which are combinations of user input and emotion values ​​representing each emotion shown in the emotion map 400. Furthermore, this neural network is trained so that emotions located close together have similar values, as shown in the emotion map 900 in Figure 10. Figure 10 shows an example where multiple emotions such as "reassured," "calm," and "confident" have similar emotion values.

[0828] The above description primarily focuses on the functions of the data processing device 12 in relation to this disclosure. However, the system related to this disclosure is not necessarily implemented on a server. The system related to this disclosure may be implemented as a general information processing system. This disclosure may be implemented, for example, as a software program that runs on a personal computer or as an application that runs on a smartphone. The method related to this disclosure may be provided to users in SaaS (Software as a Service) format.

[0829] In the above embodiment, an example was given in which a specific process is performed by a single computer 22. However, the technology of this disclosure is not limited thereto, and a distributed processing of the specific process may be performed by multiple computers, including computer 22. For example, a data generation model 58 may be provided in an external device of the data processing device 12, and the external device may generate data according to the input data.

[0830] In the above embodiment, an example was given in which the specific processing program 56 is stored in the storage 32, but the technology of this disclosure is not limited thereto. For example, the specific processing program 56 may be stored in a portable, computer-readable, non-temporary storage medium such as a USB (Universal Serial Bus) memory. The specific processing program 56 stored in the non-temporary storage medium is installed in the computer 22 of the data processing device 12. The processor 28 executes specific processing according to the specific processing program 56.

[0831] Alternatively, the specific processing program 56 may be stored in a storage device such as a server connected to the data processing device 12 via the network 54, and the specific processing program 56 may be downloaded and installed on the computer 22 in response to a request from the data processing device 12.

[0832] Furthermore, it is not necessary to store the entirety of the specific processing program 56 in a storage device such as a server connected to the data processing device 12 via the network 54, or to store the entirety of the specific processing program 56 in the storage 32; it is acceptable to store only a portion of the specific processing program 56.

[0833] The following types of processors can be used as hardware resources to perform specific processing. Examples of processors include a CPU, a general-purpose processor that functions as a hardware resource to perform specific processing by executing software, i.e., a program. Other examples of processors include dedicated electrical circuits, such as FPGAs (Field-Programmable Gate Arrays), PLDs (Programmable Logic Devices), or ASICs (Application Specific Integrated Circuits), which have circuit configurations specifically designed to perform specific processing. All of these processors have built-in or connected memory, and all of them perform specific processing by using memory.

[0834] The hardware resource that performs a specific process may consist of one of these various processors, or it may consist of a combination of two or more processors of the same or different types (for example, a combination of multiple FPGAs, or a combination of a CPU and an FPGA). Alternatively, the hardware resource that performs a specific process may consist of a single processor.

[0835] Examples of configurations using a single processor include, firstly, a configuration in which one or more CPUs and software are combined to form a single processor, and this processor functions as a hardware resource that performs a specific process. Secondly, there is a configuration using a processor that realizes the functions of the entire system, including multiple hardware resources that perform a specific process, on a single IC chip, as exemplified by SoCs (System-on-a-chip). In this way, a specific process is realized using one or more of the above types of processors as hardware resources.

[0836] Furthermore, the hardware structure of these various processors can more specifically utilize electrical circuits that combine circuit elements such as semiconductor devices. Also, the specific processing described above is merely an example. Therefore, it goes without saying that unnecessary steps can be deleted, new steps added, or the processing order rearranged, as long as it does not deviate from the main purpose.

[0837] The descriptions and illustrations presented above are detailed explanations of the technical aspects of this disclosure and are merely examples of the technical aspects. For example, the above descriptions of the structure, function, operation, and effect are examples of the structure, function, operation, and effect of the technical aspects of this disclosure. Therefore, it goes without saying that you may delete unnecessary parts, add new elements, or replace elements in the descriptions and illustrations presented above, as long as you do not deviate from the essence of the technical aspects of this disclosure. Furthermore, in order to avoid confusion and facilitate understanding of the technical aspects of this disclosure, explanations of common technical knowledge and the like that do not require special explanation to enable the implementation of the technical aspects of this disclosure have been omitted from the descriptions and illustrations presented above.

[0838] All documents, patent applications, and technical standards described herein are incorporated by reference to the same extent as if each individual document, patent application, and technical standard were specifically and individually noted to be incorporated by reference.

[0839] The following is further disclosed regarding the embodiments described above.

[0840] (Claim 1)

[0841] A means of inputting user's area of ​​expertise information and generating profile data,

[0842] A means for matching collaborators from different fields of expertise based on the aforementioned profile data,

[0843] One method involves using an agent that has the ability to integrate and translate information from different specialized fields.

[0844] A means for generating and presenting new ideas based on the aforementioned integrated information,

[0845] A means of providing a real-time collaborative work environment,

[0846] A system that includes this.

[0847] (Claim 2)

[0848] The system according to claim 1, comprising means for evaluating the generated ideas and providing feedback to the user on the results.

[0849] (Claim 3)

[0850] The system according to claim 1, comprising means of using an agent to manage the progress of a project and optimize the plan.

[0851] "Example 1"

[0852] (Claim 1)

[0853] A means for inputting information about the user's area of ​​expertise using an information processing device and generating user profile data,

[0854] Means for executing an algorithm to identify and match collaborators from different areas of expertise based on the aforementioned profile data,

[0855] A method using an intelligent agent that has the function of integrating and translating information from different specialized fields,

[0856] A means of using an artificial intelligence device to generate and present a new concept based on the aforementioned integrated information,

[0857] A means of using communication equipment to provide a real-time collaborative work environment among diverse users,

[0858] An information processing system that includes this.

[0859] (Claim 2)

[0860] The information processing system according to claim 1, comprising means for evaluating a generated concept and presenting the evaluation results to the user and providing feedback.

[0861] (Claim 3)

[0862] The information processing system according to claim 1, comprising an intelligent agent for monitoring the progress of a work project and performing plan optimization.

[0863] "Application Example 1"

[0864] (Claim 1)

[0865] A means of inputting user role area information and generating profile data,

[0866] A means for matching collaborators in different role fields based on the aforementioned profile data,

[0867] A means of using a processing device that has the function of integrating and translating information from different fields of function,

[0868] A means for generating and presenting new strategies based on the aforementioned integrated information,

[0869] A means of providing a real-time collaborative work environment,

[0870] To facilitate collaboration across different roles and responsibilities, we need a means to identify information and propose the most relevant issues.

[0871] Means that enable participants involved in smart city projects to contribute according to their respective roles,

[0872] A system that includes this.

[0873] (Claim 2)

[0874] The system according to claim 1, further comprising means for evaluating the generated policies and providing feedback to the user on the results.

[0875] (Claim 3)

[0876] The system according to claim 1, comprising means of using a processing device for managing the progress of the design and optimizing the plan.

[0877] "Example 2 of combining an emotion engine"

[0878] (Claim 1)

[0879] A means of acquiring user expertise information as input data and generating profile data,

[0880] A means of matching collaborators from different professional fields by combining the aforementioned profile data with an emotion analysis algorithm,

[0881] A means of using a generative AI model to analyze speech and text data and identify emotional information,

[0882] A means of generating and providing feedback messages that enhance user motivation based on recognized emotional information,

[0883] A means of proposing real-time plan adjustments in accordance with the project's progress,

[0884] To optimize collaboration among users, we need a means to recommend appropriate collaborators based on the project's progress.

[0885] A system that includes this.

[0886] (Claim 2)

[0887] The system according to claim 1, comprising means for evaluating the generated feedback messages and project adjustment proposals and providing the results to the user.

[0888] (Claim 3)

[0889] The system according to claim 1, comprising means of using an agent that manages project progress along with emotional information and proposes work reallocation.

[0890] "Application example 2 when combining with an emotional engine"

[0891] (Claim 1)

[0892] A means of inputting user's area of ​​expertise information and generating attribute data,

[0893] A means for selecting collaborators from different areas of expertise based on the aforementioned attribute data,

[0894] One method involves using an agency program that has the function of coordinating and translating information from different specialized fields.

[0895] A means for generating and presenting new ideas based on the aforementioned adjusted information,

[0896] A means of providing a real-time collaborative activity environment,

[0897] A method using an emotion analysis engine that recognizes the user's emotional state,

[0898] A means of optimizing work plans and collaborator selection based on emotional information,

[0899] A system that includes this.

[0900] (Claim 2)

[0901] The system according to claim 1, comprising means for evaluating the generated ideas and providing feedback to the user on the results.

[0902] (Claim 3)

[0903] The system according to claim 1, comprising means of using a proxy program to manage the progress of a project and optimize the plan. [Explanation of Symbols]

[0904] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Devices 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robots< / url:> < / url:> < / url:> < / url:>

Claims

1. A means of inputting user's area of ​​expertise information and generating profile data, A means for matching collaborators from different fields of expertise based on the aforementioned profile data, One method involves using an agent that has the ability to integrate and translate information from different specialized fields. A means for generating and presenting new ideas based on the aforementioned integrated information, A means of providing a real-time collaborative work environment, A system that includes this.

2. The system according to claim 1, comprising means for evaluating the generated ideas and providing feedback to the user on the results.

3. The system according to claim 1, comprising means of using an agent to manage the progress of a project and optimize the plan.

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