system

Abstract

Provide a system. 【Solution means】 Means for collecting information from each department within the organization, Means for integrating and preprocessing the collected information, Means for analyzing the relevance between operations from the integrated information, Means for formulating the theme of collaboration and group composition based on the analysis results, Means for presenting the formulated theme of collaboration and supporting consensus formation between regions, Means for managing and monitoring the progress of collaborative activities after consensus, Means for transmitting the monitoring results to relevant parties in real time to enable rapid response, Means for making optimal collaboration proposals to efficiently utilize the functions of the city and improve public benefits, A system including the above.

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] Due to insufficient communication between departments within an enterprise and inefficient business operations, there are problems that the overall business efficiency and innovation opportunities are hindered. Also, the potential for collaboration is missed, and waste of resources occurs as each department conducts service or product development individually. It is required to improve such a situation and optimize the entire organization.

Means for Solving the Problems

[0005] To solve this problem, the present invention proposes a system that collects business data from each business unit within a company, and integrates and preprocesses the collected data. Furthermore, it analyzes the relationships between projects from the integrated data and formulates collaborative themes and team compositions based on the analysis results. This provides a process for presenting collaborative themes and supporting consensus building between departments. It also includes means for managing and monitoring the progress of collaborative projects after agreement is reached, thereby realizing efficient collaboration.

[0006] "Within a company" refers to the environment within a company's organization where various departments and employees work towards a common goal.

[0007] A "business unit" is an organizational unit within a company that is responsible for specific tasks or projects, and is a department with specific performance targets.

[0008] "Business data" refers to records of information related to business activities within a company, including information about projects, customers, and progress.

[0009] "Integration" is the process of combining multiple data sets into a single, consistent format, and is done to maintain data integrity.

[0010] "Preprocessing" refers to the process of standardizing data formats and removing unnecessary data prior to data analysis, with the aim of improving data quality and streamlining the analysis.

[0011] "Relationship analysis" is the process of identifying and understanding the relationships and patterns that exist between different datasets.

[0012] A "collaboration theme" refers to a theme or issue that different business units or members should work on together, and its purpose is to set clear goals.

[0013] A "team composition proposal" is a suggestion for a team composed of the most suitable members for a specific project or task.

[0014] "Project management" refers to the systematic execution of various activities necessary to ensure the success of a project, from planning and execution to monitoring progress.

[0015] "Monitoring" is an observational activity that tracks the progress of a project or task, enabling a quick response when problems arise.

[0016] "Efficient collaboration" refers to cooperative activities aimed at achieving goals by effectively coordinating with each department and member, and making the most of limited resources. [Brief explanation of the drawing]

[0017] [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]It is a sequence diagram showing the processing flow of the data processing system in Embodiment 1. [Figure 12] It is a sequence diagram showing the processing flow of the data processing system in Application Example 1. [Figure 13] It is a sequence diagram showing the processing flow of the data processing system in Embodiment 2 when the emotion engine is combined. [Figure 14] It is a sequence diagram showing the processing flow of the data processing system in Application Example 2 when the emotion engine is combined.

Mode for Carrying Out the Invention

[0018] 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.

[0019] First, the terms used in the following description will be explained.

[0020] 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.

[0021] 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.

[0022] In the following embodiments, the signed storage is one or more non-volatile storage devices that store various programs and various parameters. Examples of non-volatile storage devices include flash memory (SSD (Solid State Drive)), magnetic disks (e.g., hard disks), or magnetic tapes.

[0023] 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).

[0024] 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."

[0025] [First Embodiment]

[0026] Figure 1 shows an example of the configuration of the data processing system 10 according to the first embodiment.

[0027] 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.

[0028] 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).

[0029] 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.

[0030] 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.

[0031] 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.

[0032] 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.

[0033] Figure 2 shows an example of the main functions of the data processing device 12 and the smart device 14.

[0034] 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.

[0035] 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.

[0036] 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.

[0037] 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".

[0038] This invention relates to an AI agent system that collects business data from various business units within a company, integrates and preprocesses it, and analyzes the relationships between projects. The server first extracts business-related data from each business unit's database. This data includes customer information, product development progress, sales status, and so on.

[0039] The server standardizes the format of the collected data and supplements any missing data. It also improves data quality by removing duplicate information. Then, an AI agent analyzes the data to consider the relationships between projects and the possibilities for collaboration. This makes it possible to detect projects from different departments that target the same customer.

[0040] Based on the analysis results, the AI ​​agent suggests collaborative themes between departments that are expected to yield the highest level of cooperation. For example, it might recommend that the sales and development departments jointly plan new products based on customer needs. This suggestion is then presented to the managers of each department via their respective terminals.

[0041] The user (administrator) reviews and considers the collaboration proposal displayed on their terminal. If they agree to the proposal, the system automatically prepares the project management tool and assigns roles and tasks to the relevant members. This enables a smooth project launch.

[0042] After the project begins, the server monitors the progress of the collaborative project in real time. If progress is behind schedule or a critical problem arises, stakeholders are notified with an alert. This enables a quick response and improves the probability of project success.

[0043] These systems facilitate efficient collaboration within companies and promote innovation by eliminating "invisible barriers."

[0044] The following describes the processing flow.

[0045] Step 1:

[0046] The server collects necessary business data from each business unit's database. This includes project details, customer information, and progress status. The server automatically sets a data collection schedule and retrieves updated information periodically.

[0047] Step 2:

[0048] The server integrates and preprocesses the collected data. Specifically, it standardizes data formats, removes duplicate data, and imputes missing data. This generates a clean dataset suitable for analysis.

[0049] Step 3:

[0050] The server uses an AI agent to analyze the relationships between pre-processed data. It clusters similarities between projects and common customer targets to highlight potential collaborations.

[0051] Step 4:

[0052] Based on the analysis results, the AI ​​agent identifies departments where collaboration would be effective and formulates collaboration themes. The proposals include specific project objectives and expected benefits.

[0053] Step 5:

[0054] Collaboration proposals from the AI ​​agent are notified to the administrators (users) of each department via the terminal. Administrators can review the proposals and provide comments and feedback.

[0055] Step 6:

[0056] Once a user agrees to a collaboration proposal, the server configures the project management tools. It assigns roles and tasks, preparing the project for launch.

[0057] Step 7:

[0058] During project execution, the server monitors progress and provides real-time status updates. In the event of significant delays or problems, the server sends alerts to relevant parties to prompt a quick response.

[0059] Step 8:

[0060] After the collaborative project is completed, the server evaluates the results and generates feedback. This allows for identification of areas for improvement and success factors for future projects.

[0061] (Example 1)

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

[0063] When various organizational units within a company operate independently, overlapping activities and a lack of coordination can occur, hindering efficient resource utilization and rapid decision-making. Therefore, a system is needed to effectively collect, analyze, and promote collaboration among different organizations.

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

[0065] In this invention, the server includes means for collecting business information from each organizational unit of a company, means for integrating and pre-processing the collected information into a consistent format, and means for analyzing the relationships between activities from the consistent information. This reduces overlapping activities between organizations, strengthens collaboration, and enables efficient resource allocation and rapid project initiation.

[0066] "Organizational units within a company" refer to departments or divisions within a company that have different functions and roles, and these are units that operate independently.

[0067] "Business information" refers to data related to the activities within a company, including customer information, product development progress, and sales status.

[0068] "A consistent format" refers to a state where data collected from multiple different sources is integrated and all data is presented in a unified format.

[0069] "Preprocessing" refers to the process of preparing raw data to be analyzable, and includes processes such as standardizing data formats, removing duplicate information, and supplementing missing data.

[0070] "Relevance between activities" refers to the common elements and mutual influences between different projects or departments, and is an important indicator when exploring possibilities for collaboration based on this.

[0071] In this invention, the server uses a database management system and APIs to acquire data in order to collect business information from each organizational unit of a company. Specifically, this includes data such as customer information, product development progress, and sales status. This data is acquired using a dedicated data extraction tool during the collection phase, and then the information is integrated and preprocessed.

[0072] The server uses ETL (Extract, Transform, Load) tools to standardize data formats, remove duplicate data, and fill in missing information using predictive models. Examples of specific ETL tools include Talend and Apache® NiFi, but the server is not limited to these.

[0073] Next, the server uses a generative AI model to analyze the data, which has been formatted into a consistent format. The purpose of the analysis is to evaluate the relationships between activities and identify potential opportunities for collaboration in each project.

[0074] Based on the analysis results, the AI ​​agent proposes the most effective collaboration themes. This allows administrators to receive clear visual suggestions via their devices. The generated suggestions may include recommendations for the sales and development departments to jointly plan a new product.

[0075] Furthermore, the administrator, as a user, reviews the collaboration proposal presented on their device, and if they agree to the proposal, the project management tool is automatically launched. This assigns roles and tasks to the relevant members, ensuring a smooth project launch.

[0076] Furthermore, once the project begins, the server monitors progress in real time and sends alerts if progress is delayed or if critical issues arise. This feature ensures that the project stays on schedule and allows all involved members to respond promptly.

[0077] As a concrete example, suppose a new product development project for a new customer faces the challenge of a shortage of personnel with specific skills among the project members. This system can address this challenge by suggesting members with similar skills from other internal projects and facilitating collaboration.

[0078] An example of a prompt for a generated AI model is, "Please provide the analysis and suggestions necessary to maximize synergy in a project involving multiple departments within an organization." This prompt serves as input information to appropriately analyze and suggest collaboration opportunities that the system seeks.

[0079] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0080] Step 1:

[0081] The server collects business information from databases of each organizational unit. Inputs include customer information, product development progress, and sales data obtained through APIs and database management systems. The server stores this information in storage for the ETL process. The output is a set of raw data provided in various formats.

[0082] Step 2:

[0083] The server integrates the collected data into a consistent format and preprocesses it. The raw data obtained in Step 1 is used as input. The server first uses ETL tools to standardize the format, for example, by converting character codes and normalizing field names. Then, it identifies and removes duplicate data and uses a predictive model to fill in any missing information. The output is a cleansed, high-quality dataset.

[0084] Step 3:

[0085] The server analyzes processed data using a generative AI model. The input is a formatted dataset. During the analysis process, the AI ​​model is applied to evaluate the relevance between activities and the potential for collaboration. Specifically, it identifies patterns such as similarity in customer targets and past collaboration between departments. The output includes relevance scores for each project and collaboration suggestions.

[0086] Step 4:

[0087] The AI ​​agent proposes collaboration themes based on the analysis results and presents them to managers in each department via the terminal. Inputs include the relevance score and collaboration proposals generated in step 3. The AI ​​agent creates a visual proposal in a dashboard format and displays it on the terminal. For example, it might request a joint new product plan between the sales and development departments and propose it to relevant stakeholders. The output includes evaluation and decision-making by the managers.

[0088] Step 5:

[0089] The user (administrator) evaluates the presented collaboration proposals and decides whether to accept them. The input consists of the proposal content displayed on the terminal and related analytical information. If the administrator approves the proposal, the system launches the project management tool and proceeds with project preparation. Specifically, roles and tasks are automatically assigned to the relevant members. The output is that the project preparation status is complete and ready to proceed.

[0090] Step 6:

[0091] The server monitors project progress in real time and sends alerts to stakeholders as needed. Inputs include project progress data and risk assessment information. The server analyzes this data and generates appropriate alerts if progress is delayed or critical issues arise. Specific actions include push notifications, emails, and warning displays on a dashboard. Outputs include providing immediate feedback to the project team, enabling quick responses.

[0092] (Application Example 1)

[0093] 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."

[0094] In modern urban management, various departments with diverse roles function independently, making it difficult for them to cooperate in improving public services. As a result, inefficient operations occur, and the convenience of citizens is not sufficiently improved. In particular, a lack of coordination between departments in critical areas such as transportation and energy management is a factor that degrades the overall functionality of the city.

[0095] 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.

[0096] In this invention, the server includes means for collecting information from each department within the organization, means for integrating and pre-processing the collected information, and means for analyzing the relationships between tasks from the integrated information. This makes it possible to efficiently operate urban functions by utilizing the information held by each department and to propose optimal collaborative solutions to improve public benefits.

[0097] An "organization" is a group composed of multiple departments or divisions that share a common goal and cooperate with each other to achieve it.

[0098] "Information" refers to various data and knowledge collected from different departments within an organization, which are useful for improving urban management.

[0099] "Integration" is the process of combining multiple pieces of information with different forms and structures into one and converting them into a common format.

[0100] "Preprocessing" is the process of removing duplicates and missing data from collected information to improve its quality and facilitate data analysis.

[0101] "Work" refers to a series of activities or projects carried out using information to achieve a specific objective.

[0102] "Relevance" refers to the relationship between different tasks or projects, indicating the extent to which they influence each other or have potential for collaboration.

[0103] A "proposal" is a guideline for themes and action plans that each department within an organization should work on collaboratively, based on the analysis results.

[0104] "Monitoring" refers to the means of constantly checking the status of ongoing collaborative activities and taking appropriate instructions or measures as needed.

[0105] "Benefits" refer to the convenience and advantages that citizens and users can enjoy, and are expected as urban services improve.

[0106] The system for realizing this invention mainly includes the following configuration: The server collects information from each department within the organization and integrates and preprocesses that information. Specifically, the server uses Python to cleanse and integrate the data and uses Pandas to standardize the information format. The information is then duplicated and its quality is improved.

[0107] Next, the server uses an AI agent for analysis. The AI ​​agent uses TENSORFLOW® to analyze the relationships between tasks from the integrated information. This allows it to suggest themes and action plans that will make collaboration between different departments most effective.

[0108] The proposal is presented to the user's device. Users primarily use smartphones or smart glasses to review the proposal, and once agreement is reached, the system automatically begins managing and monitoring the collaborative activities. Progress and related alerts are notified to the user in real time, and specific examples such as energy management and traffic volume adjustment are provided.

[0109] For example, in response to an expected surge in traffic during a weekend event, a collaborative proposal might be made such as, "The Traffic Management Department and the Energy Management Department propose increasing the operating hours of electric buses and making adjustments to alleviate congestion."

[0110] Example of a prompt:

[0111] "Please propose interdepartmental cooperation measures to alleviate traffic congestion within the smart city."

[0112] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0113] Step 1:

[0114] The server collects information from each department within the organization. As input, it accesses a database containing departmental operational data, retrieving data such as customer information, product development progress, and sales status. The output is the collected raw data, which is stored for subsequent processing.

[0115] Step 2:

[0116] The server integrates and preprocesses the collected information. This step uses Pandas to standardize the information format and eliminate duplicate data. Specifically, it standardizes data with different formats and removes noise and inconsistencies through data cleansing. The input is the collected raw data, and the output is the improved, integrated data.

[0117] Step 3:

[0118] The server analyzes the relationships between tasks from the integrated data. Here, TensorFlow is used to extract correlations between data using a generative AI model. The input is pre-processed integrated data, and the output is the relationship analysis result. This result indicates areas where interdepartmental collaboration is expected.

[0119] Step 4:

[0120] The server proposes collaboration themes based on the analysis results. In this step, it sends collaboration proposals to the user terminal based on insights obtained from the generated AI model. Specifically, it identifies departments where efficient cooperation can be expected and presents concrete measures. The input is the relevance analysis results, and the output is a notification message as a proposal.

[0121] Step 5:

[0122] Users review proposals via their terminals and reach agreements as needed. Once an agreement is reached, the system automatically initiates management and monitoring processes. The user's actions involve reviewing and confirming the proposals, and the output is the result of the agreement.

[0123] Step 6:

[0124] The server monitors the progress of ongoing collaborative activities and notifies users of relevant alerts in real time. It continuously monitors progress and provides prompt notifications when important events occur. The input is collaborative activity progress data, and the output is alert notifications to users.

[0125] 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.

[0126] This invention combines an emotion engine with a system that collects, integrates, and preprocesses business data from various business units within a company. The emotion engine recognizes the user's emotions and can provide feedback that takes the user's emotional state into account when building consensus between departments or monitoring collaborative projects.

[0127] Specifically, the server first collects the necessary data from each business unit's database, then integrates and preprocesses it. During this process, data formats are standardized, and duplicate data is removed. Next, an AI agent analyzes the preprocessed data to identify relationships between projects and explore possibilities for collaboration.

[0128] Based on the analysis results, collaboration themes and team compositions are formulated, and the proposals are notified to the managers of each business unit using a terminal. The managers, who are also users, review the collaboration proposals presented on the terminal and make decisions based on the results of the emotion engine's analysis of emotional data. The emotion engine recognizes the emotional state in real time through the user's text input and voice data, and provides situation-appropriate feedback to the managers.

[0129] If a user agrees to a collaboration proposal, the server provides project management tools and assigns roles and tasks to the relevant members. Throughout the collaborative project, the server monitors progress, and an emotion engine detects the emotions of project participants. If participants are experiencing stress or decreased motivation, the server provides appropriate feedback to address these issues.

[0130] In this way, systems that incorporate an emotion engine go beyond simply analyzing business data; they function as a powerful tool to support effective communication for collaboration and enable efficient organizational operation.

[0131] The following describes the processing flow.

[0132] Step 1:

[0133] The server extracts business data from each business unit's database. This includes project information, customer data, progress records, and more. Data retrieval is performed periodically, and the system is configured to always maintain the most up-to-date information.

[0134] Step 2:

[0135] The server integrates the collected data, standardizes the format, and removes duplicate data. Furthermore, it uses machine learning models to preprocess the data and generate a clean dataset that forms the basis for analysis.

[0136] Step 3:

[0137] An AI agent analyzes pre-processed data to explore the relationships between projects. The server identifies projects where collaboration between business units is possible and evaluates the interests of each project.

[0138] Step 4:

[0139] Based on the analysis results, the AI ​​agent formulates collaboration themes and proposed team structures. The server then notifies the managers of each business unit of these formulations and makes specific proposals regarding the collaboration.

[0140] Step 5:

[0141] Through the device, the user (administrator) receives collaboration proposals and reviews them in conjunction with the sentiment data provided by the sentiment engine. The sentiment engine analyzes nuances derived from text and audio to support the administrator's decision-making.

[0142] Step 6:

[0143] If user consent is obtained, the server will configure the project management tools and assign the necessary roles and tasks to stakeholders. Project schedules and resource allocation will also be automatically optimized.

[0144] Step 7:

[0145] After the collaborative project begins, the server monitors progress, and the emotion engine regularly checks participants' stress levels and motivation. It provides feedback and suggests improvements as needed to help the project succeed.

[0146] Step 8:

[0147] Once a project is complete, the server evaluates the results, uses the data gathered by the emotion engine to compile feedback, and proposes improvements for future projects. This process facilitates smoother collaboration within the company.

[0148] (Example 2)

[0149] 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".

[0150] In modern companies, each department often possesses its own unique operational information, and there is a lack of systems to integrate and collaborate on this information. Furthermore, a problem arises in inter-departmental consensus building and project progress, where effective feedback that considers the feelings of stakeholders is often not provided. This leads to concerns that the efficiency and quality of communication in cross-departmental collaborative activities will decline, hindering overall organizational productivity. This invention aims to solve these problems.

[0151] 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.

[0152] In this invention, the server includes means for collecting business information from each department within an enterprise, means for integrating and pre-processing the collected information, means for analyzing the relationships between activities from the integrated information, and means for analyzing emotional information and providing feedback to stakeholders based on this information. This enables effective information integration and communication support to facilitate collaborative activities between departments.

[0153] A "company" is a group of legal entities or sole proprietorships that operate systematically toward a common goal.

[0154] A "department" is an organizational unit within a company that is responsible for specific tasks or functions.

[0155] "Business information" refers to data and knowledge that companies and departments generate and acquire in the course of their daily activities and operations.

[0156] "Integration" refers to bringing together information from different sources and formats in a consistent and coherent manner.

[0157] "Preprocessing" refers to the processing steps, such as standardizing the format or correcting / deleting inappropriate data, that are carried out prior to the analysis and use of information.

[0158] "Emotional information" refers to information expressed through human emotions, and can be obtained from text, audio, facial expressions, and other sources.

[0159] "Feedback" refers to information or reactions provided based on the results obtained from a particular action or process, with the aim of making improvements or corrections.

[0160] "Collaborative activities" refer to joint work undertaken by multiple departments or individuals, sharing resources and knowledge to achieve a common goal.

[0161] "Relevance" is a concept that refers to the degree of interrelationship or connection between different pieces of information or elements.

[0162] "Collection" is the act of selecting and compiling necessary information from specified sources.

[0163] To implement this invention, the system operates according to the following procedure. First, the server collects business information from information sources in each department within the company. This process includes extracting data from databases built for each department. The server then consolidates this data in one place and integrates and preprocesses it. This preprocessing involves standardizing the information format and removing duplicate data. SQL database management systems and data integration tools can be used for these processes.

[0164] Next, the server uses an AI agent to analyze the relationships between the integrated information. This AI agent leverages machine learning algorithms to analyze data correlations and identify activities that should be coordinated. Data analysis libraries using programming languages ​​such as Python and R are suitable for relationship analysis.

[0165] Based on the analysis results, the server automatically formulates optimal collaboration themes and personnel configurations. The formulated proposals are notified to the managers of each department via terminals. When reviewing the proposals on the terminals, managers refer to the feedback provided by the sentiment engine. This sentiment engine extracts and analyzes emotional information from the user's text and voice to provide appropriate feedback in real time.

[0166] As a specific scenario, when a user (administrator) receives a plan proposal, they can use a prompt message for the generative AI model such as, "What emotional data should be considered when the sales and development departments jointly develop a new product?" This helps ensure that appropriate decisions are made based on emotional information, supporting the efficient progress of the project.

[0167] Ultimately, the server uses project management tools to assign clear roles and tasks to participants, ensuring the smooth execution of agreed-upon collaborative activities. Furthermore, throughout the project, the server continuously monitors participants' emotional states and provides situation-sensitive feedback to improve organizational productivity and communication quality.

[0168] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0169] Step 1:

[0170] The server accesses information sources within each department of the company and collects business information. It requires connection information for each department's database as input. The server issues SQL queries to extract the necessary data and stores it in integrated storage. The output is a set of collected raw data.

[0171] Step 2:

[0172] The server integrates and preprocesses the collected raw data. The raw data collected in step 1 is used as input. This data is converted to a unified format, and duplicate data is removed. This process uses data cleansing tools and scripts. The output is clean data in a unified format.

[0173] Step 3:

[0174] The server uses an AI agent to analyze pre-processed data. The input is the clean data obtained in step 2. The AI ​​agent uses machine learning algorithms to analyze the relationships between the data and identify potential collaborative projects and themes. The output is a list of highly relevant projects.

[0175] Step 4:

[0176] The server formulates collaborative themes and team compositions based on the analysis results. The input is the analysis results from step 3. A generative AI model is used to describe the compositions in natural language. The output is a document outlining the collaborative themes and compositions.

[0177] Step 5:

[0178] The terminal notifies the administrators of each department of the collaboration proposal sent from the server. The input is the proposal document created in step 4. The terminal uses its notification function to quickly inform the administrators asynchronously. The output is the proposal content displayed on the terminal.

[0179] Step 6:

[0180] The administrator, acting as the user, reviews the suggestions on a terminal and provides feedback through the emotion engine. The input consists of text and voice information entered by the administrator on the terminal. The emotion engine analyzes the input data and evaluates the user's emotions. The output is the analyzed emotional state and the feedback based on it.

[0181] Step 7:

[0182] The server configures a project management tool to correspond to the agreed-upon collaborative activities. The input is the agreed-upon content obtained in step 6. The server assigns tasks and sets the schedule for the project. The output is the tasks and schedule visualized on the project management tool.

[0183] Step 8:

[0184] The server continuously monitors participants' emotional states throughout the collaborative activity. The input is participant response data collected during the project. The emotion engine analyzes this data to detect changes in stress and motivation. The output is situation-specific feedback and suggestions for improvement.

[0185] (Application Example 2)

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

[0187] Collaborative activities between departments within a company often fail to proceed efficiently due to miscommunication and emotional disagreements. In particular, neglecting the emotional state of participants can lead to project delays and decreased motivation. Furthermore, the lack of mechanisms to identify and address participant dissatisfaction and stress early in the project is also a problem.

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

[0189] In this invention, the server includes means for collecting business information from each department within the company, means for integrating and pre-processing the collected information, and means for recognizing the emotional state of users and providing feedback that takes the emotional state into account during the progress of agreement and collaborative activities. This enables effective communication between departments and allows for the efficient progress of projects through feedback that takes into account the emotional state of participants.

[0190] "Business information" refers to data on activities collected from various departments within a company, including specific facts and statistical information related to the performance of duties.

[0191] "Unifying information formats" refers to the process of converting business information provided in different formats into a consistent format, enabling data compatibility and usability.

[0192] "Removing duplicate information" is the process of removing duplicated information from a database, and is done to maintain the continuous consistency and accuracy of the data.

[0193] "Relationships between activities" is a concept that shows how different projects and tasks influence each other, and it is important for promoting efficient resource allocation and collaboration.

[0194] A "collaboration theme" refers to the subject or objective of an activity undertaken jointly by multiple departments or organizations, and is formulated based on specific strategic goals.

[0195] A "group structure plan" is an organizational arrangement plan that clarifies the roles and responsibilities of each individual participating in collaborative activities, and is planned to ensure the smooth implementation of those activities.

[0196] "Emotional state" refers to the internal emotional state that activity participants or users feel at a particular point in time, and is an important factor that influences the progress of the activity and their motivation to participate.

[0197] "Feedback" refers to advice and guidance provided based on the activity status and results, and is intended to improve the project and promote the growth of participants.

[0198] This invention is a system that effectively promotes interdepartmental collaboration by collecting business information from each department within a company and integrating and preprocessing that information. By incorporating an emotion engine, this system provides real-time feedback that takes into account the emotional state of participants during the progress of a project.

[0199] The server first collects business information from various departments scattered throughout the company. The collected information is then integrated after preprocessing, which includes standardizing data formats and removing duplicate information. Based on the integrated information, an AI agent analyzes the relationships between activities and automatically formulates collaborative themes and proposed organizational structures. In this process, a cloud-based database management system and data processing engines using Python and Node.js are utilized.

[0200] The established collaboration themes are notified to the managers of each department via the terminal. Managers review the information displayed on the terminal based on feedback on emotional states analyzed by the emotion engine, and then decide on specific actions. The emotion engine uses the Google® Cloud Speech-to-Text API to analyze emotions from voice and text data. As decision-making takes place in each phase, the system analyzes the emotional states of participants and provides feedback according to changes in stress and motivation.

[0201] A concrete example is a public library design project led by a local community. Residents submit their opinions through a dedicated app, collecting emotional data that facilitates discussion and project improvements. This application allows for the development of negotiation strategies tailored to the emotional tendencies of the residents.

[0202] Examples of prompt statements are as follows:

[0203] "Based on the sentiment data obtained from residents involved in the 'Design of a Public Library' project, please propose what changes can be made to improve overall resident satisfaction."

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

[0205] Step 1:

[0206] The server collects business information from each department. This information includes project activity details and participant lists. The collected data is entered into a database and managed centrally.

[0207] Step 2:

[0208] The server integrates and preprocesses the collected business information. Here, data formats are standardized and duplicate information is removed. Specifically, a Python script is used to perform data cleaning, and the results of the cleansing process are saved to the integrated database.

[0209] Step 3:

[0210] The server analyzes the relationships between activities using pre-processed information. The AI ​​agent calculates the correlation between projects included in the data using machine learning algorithms and inputs the results into the generative AI model.

[0211] Step 4:

[0212] The server formulates collaboration themes and proposed organizational structures based on the results analyzed by the generative AI model. These themes and structures are generated by the generative AI model using prompt messages and output to the terminal.

[0213] Step 5:

[0214] The terminal notifies the managers of each department of the formulated collaboration themes and proposed organizational structures. The managers then review the information displayed on the terminal and decide on the project implementation policy.

[0215] Step 6:

[0216] Users review the suggestions displayed on their device and make decisions based on the feedback provided by the sentiment engine. The sentiment engine uses the Google Cloud Speech-to-Text API to convert the user's voice or text into sentiment data and provide feedback in real time.

[0217] Step 7:

[0218] The server monitors the progress of collaborative activities and detects participants' emotional states as needed. Participant emotional data is used for feedback, and specific advice regarding stress and decreased motivation is output as feedback to the terminal.

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

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

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

[0222] [Second Embodiment]

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

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

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

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

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

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

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

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

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

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

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

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

[0235] This invention relates to an AI agent system that collects business data from various business units within a company, integrates and preprocesses it, and analyzes the relationships between projects. The server first extracts business-related data from each business unit's database. This data includes customer information, product development progress, sales status, and so on.

[0236] The server standardizes the format of the collected data and supplements any missing data. It also improves data quality by removing duplicate information. Then, an AI agent analyzes the data to consider the relationships between projects and the possibilities for collaboration. This makes it possible to detect projects from different departments that target the same customer.

[0237] Based on the analysis results, the AI ​​agent suggests collaborative themes between departments that are expected to yield the highest level of cooperation. For example, it might recommend that the sales and development departments jointly plan new products based on customer needs. This suggestion is then presented to the managers of each department via their respective terminals.

[0238] The user (administrator) reviews and considers the collaboration proposal displayed on their terminal. If they agree to the proposal, the system automatically prepares the project management tool and assigns roles and tasks to the relevant members. This enables a smooth project launch.

[0239] After the project begins, the server monitors the progress of the collaborative project in real time. If progress is behind schedule or a critical problem arises, stakeholders are notified with an alert. This enables a quick response and improves the probability of project success.

[0240] These systems facilitate efficient collaboration within companies and promote innovation by eliminating "invisible barriers."

[0241] The following describes the processing flow.

[0242] Step 1:

[0243] The server collects necessary business data from each business unit's database. This includes project details, customer information, and progress status. The server automatically sets a data collection schedule and retrieves updated information periodically.

[0244] Step 2:

[0245] The server integrates and preprocesses the collected data. Specifically, it standardizes data formats, removes duplicate data, and imputes missing data. This generates a clean dataset suitable for analysis.

[0246] Step 3:

[0247] The server uses an AI agent to analyze the relationships between pre-processed data. It clusters similarities between projects and common customer targets to highlight potential collaborations.

[0248] Step 4:

[0249] Based on the analysis results, the AI ​​agent identifies departments where collaboration would be effective and formulates collaboration themes. The proposals include specific project objectives and expected benefits.

[0250] Step 5:

[0251] Collaboration proposals from the AI ​​agent are notified to the administrators (users) of each department via the terminal. Administrators can review the proposals and provide comments and feedback.

[0252] Step 6:

[0253] Once a user agrees to a collaboration proposal, the server configures the project management tools. It assigns roles and tasks, preparing the project for launch.

[0254] Step 7:

[0255] During project execution, the server monitors progress and provides real-time status updates. In the event of significant delays or problems, the server sends alerts to relevant parties to prompt a quick response.

[0256] Step 8:

[0257] After the collaborative project is completed, the server evaluates the results and generates feedback. This allows for identification of areas for improvement and success factors for future projects.

[0258] (Example 1)

[0259] 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."

[0260] When various organizational units within a company operate independently, overlapping activities and a lack of coordination can occur, hindering efficient resource utilization and rapid decision-making. Therefore, a system is needed to effectively collect, analyze, and promote collaboration among different organizations.

[0261] 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.

[0262] In this invention, the server includes means for collecting business information from each organizational unit of a company, means for integrating and pre-processing the collected information into a consistent format, and means for analyzing the relationships between activities from the consistent information. This reduces overlapping activities between organizations, strengthens collaboration, and enables efficient resource allocation and rapid project initiation.

[0263] "Organizational units within a company" refer to departments or divisions within a company that have different functions and roles, and these are units that operate independently.

[0264] "Business information" refers to data related to the activities within a company, including customer information, product development progress, and sales status.

[0265] "A consistent format" refers to a state where data collected from multiple different sources is integrated and all data is presented in a unified format.

[0266] "Preprocessing" refers to the process of preparing raw data to be analyzable, and includes processes such as standardizing data formats, removing duplicate information, and supplementing missing data.

[0267] "Relevance between activities" refers to the common elements and mutual influences between different projects or departments, and is an important indicator when exploring possibilities for collaboration based on this.

[0268] In this invention, the server uses a database management system and APIs to acquire data in order to collect business information from each organizational unit of a company. Specifically, this includes data such as customer information, product development progress, and sales status. This data is acquired using a dedicated data extraction tool during the collection phase, and then the information is integrated and preprocessed.

[0269] The server uses ETL (Extract, Transform, Load) tools to standardize data formats, remove duplicate data, and fill in missing information using predictive models. Examples of specific ETL tools include Talend and Apache NiFi, but the system is not limited to these.

[0270] Next, the server uses a generative AI model to analyze the data, which has been formatted into a consistent format. The purpose of the analysis is to evaluate the relationships between activities and identify potential opportunities for collaboration in each project.

[0271] Based on the analysis results, the AI ​​agent proposes the most effective collaboration themes. This allows administrators to receive clear visual suggestions via their devices. The generated suggestions may include recommendations for the sales and development departments to jointly plan a new product.

[0272] Furthermore, the administrator, as a user, reviews the collaboration proposal presented on their device, and if they agree to the proposal, the project management tool is automatically launched. This assigns roles and tasks to the relevant members, ensuring a smooth project launch.

[0273] Furthermore, once the project begins, the server monitors progress in real time and sends alerts if progress is delayed or if critical issues arise. This feature ensures that the project stays on schedule and allows all involved members to respond promptly.

[0274] As a concrete example, suppose a new product development project for a new customer faces the challenge of a shortage of personnel with specific skills among the project members. This system can address this challenge by suggesting members with similar skills from other internal projects and facilitating collaboration.

[0275] An example of a prompt for a generated AI model is, "Please provide the analysis and suggestions necessary to maximize synergy in a project involving multiple departments within an organization." This prompt serves as input information to appropriately analyze and suggest collaboration opportunities that the system seeks.

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

[0277] Step 1:

[0278] The server collects business information from databases of each organizational unit. Inputs include customer information, product development progress, and sales data obtained through APIs and database management systems. The server stores this information in storage for the ETL process. The output is a set of raw data provided in various formats.

[0279] Step 2:

[0280] The server integrates the collected data into a consistent format and preprocesses them. As input, the raw data obtained in Step 1 is used. The server first utilizes an ETL tool to unify the format, for example, performing character encoding conversion and field name normalization. Then, it identifies and deletes duplicate data, and for missing information, it uses a prediction model to supplement it. As output, a cleansed high-quality dataset is generated.

[0281] Step 3:

[0282] The server analyzes the processed data using a generative AI model. As input, the dataset that has been formatted is used. In the analysis process, the AI model is applied, and the relevance between activities and the potential for collaboration are evaluated. Specifically, for example, patterns such as the similarity of customer targets and the past cooperation performance between departments are identified. As output, the relevance scores and collaboration proposals for each project are generated.

[0283] Step 4:

[0284] The AI agent proposes collaboration themes based on the analysis results and presents them to the managers of each department through the terminal. As input, there are the relevance scores and collaboration proposals generated in Step 3. The AI agent creates a visual proposal in the form of a dashboard and displays it on the terminal. As a specific operation, for example, a joint new product planning by the sales department and the development department is required and proposed to the relevant stakeholders. As output, evaluations and decisions by the managers are obtained.

[0285] Step 5:

[0286] The administrator, who is the user, evaluates the presented collaboration proposal and decides whether to adopt it. As input, there is the content of the proposal presented on the terminal and the related analysis information. When the administrator approves the proposal, the system launches a project management tool and proceeds with the project preparation. Specifically, the roles and tasks are automatically assigned to the relevant members. As output, the project preparation is completed and the project becomes viable.

[0287] Step 6:

[0288] The server monitors the progress of the project in real time and sends alerts to the relevant parties as needed. As input, it includes the project progress data and risk assessment information. The server analyzes these data and generates appropriate alerts when the progress is delayed or important problems occur. Specific actions include push notifications, emails, and warning displays on the dashboard. As output, immediate feedback is provided to the project team, enabling prompt response.

[0289] (Application Example 1)

[0290] Next, Application Example 1 will be described. In the following description, the data processing device 12 is referred to as the "server", and the smart glasses 214 are referred to as the "terminal".

[0291] In modern urban operations, since each department with diverse roles functions independently, it is difficult to cooperate to improve public services. As a result, there are problems such as inefficient operations and insufficient improvement in citizens' convenience. In particular, the lack of cooperation between departments in important fields such as traffic and energy management is a factor that reduces the overall function of the city.

[0292] The specific processing by the specific processing unit 290 of the data processing device 12 in Application Example 1 is realized by the following means.

[0293] In this invention, the server includes means for collecting information from each department within the organization, means for integrating and pre-processing the collected information, and means for analyzing the relationships between tasks from the integrated information. This makes it possible to efficiently operate urban functions by utilizing the information held by each department and to propose optimal collaborative solutions to improve public benefits.

[0294] An "organization" is a group composed of multiple departments or divisions that share a common goal and cooperate with each other to achieve it.

[0295] "Information" refers to various data and knowledge collected from different departments within an organization, which are useful for improving urban management.

[0296] "Integration" is the process of combining multiple pieces of information with different forms and structures into one and converting them into a common format.

[0297] "Preprocessing" is the process of removing duplicates and missing data from collected information to improve its quality and facilitate data analysis.

[0298] "Work" refers to a series of activities or projects carried out using information to achieve a specific objective.

[0299] "Relevance" refers to the relationship between different tasks or projects, indicating the extent to which they influence each other or have potential for collaboration.

[0300] A "proposal" is a guideline for themes and action plans that each department within an organization should work on collaboratively, based on the analysis results.

[0301] "Monitoring" refers to the means of constantly checking the status of ongoing collaborative activities and taking appropriate instructions or measures as needed.

[0302] "Benefits" refer to the convenience and advantages that citizens and users can enjoy, and are expected as urban services improve.

[0303] The system for realizing this invention mainly includes the following components. The server collects information from each department within the organization and integrates and preprocesses this information. Specifically, the server uses Python to perform data cleaning and integration, and uses Pandas to unify the information format. Duplicates in the information are eliminated, and the quality is improved.

[0304] Subsequently, the server uses an AI agent for analysis. The AI agent uses TensorFlow to analyze the relevance between operations from the integrated information. Thereby, it proposes themes and action plans that make the cooperation between different departments most effective.

[0305] The proposal is presented to the user's terminal. The user mainly uses a smartphone or smart glasses to confirm the proposal. After consensus formation, the system automatically starts the management and monitoring of collaborative activities. Progress and related alerts are notified to the user in real time, and specific examples such as energy management and traffic volume adjustment are presented.

[0306] For example, in response to the expected sharp increase in traffic volume during weekend events, a collaborative proposal such as "The traffic management department and the energy management department propose to increase the operating time of electric buses and make adjustments to relieve congestion." is made.

[0307] Example of prompt text:

[0308] "Please propose a cooperation plan between departments for resolving traffic congestion within the smart city."

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

[0310] Step 1:

[0311] The server collects information from each department within the organization. As input, it accesses a database containing departmental operational data, retrieving data such as customer information, product development progress, and sales status. The output is the collected raw data, which is stored for subsequent processing.

[0312] Step 2:

[0313] The server integrates and preprocesses the collected information. This step uses Pandas to standardize the information format and eliminate duplicate data. Specifically, it standardizes data with different formats and removes noise and inconsistencies through data cleansing. The input is the collected raw data, and the output is the improved, integrated data.

[0314] Step 3:

[0315] The server analyzes the relationships between tasks from the integrated data. Here, TensorFlow is used to extract correlations between data using a generative AI model. The input is pre-processed integrated data, and the output is the relationship analysis result. This result indicates areas where interdepartmental collaboration is expected.

[0316] Step 4:

[0317] The server proposes collaboration themes based on the analysis results. In this step, it sends collaboration proposals to the user terminal based on insights obtained from the generated AI model. Specifically, it identifies departments where efficient cooperation can be expected and presents concrete measures. The input is the relevance analysis results, and the output is a notification message as a proposal.

[0318] Step 5:

[0319] Users review proposals via their terminals and reach agreements as needed. Once an agreement is reached, the system automatically initiates management and monitoring processes. The user's actions involve reviewing and confirming the proposals, and the output is the result of the agreement.

[0320] Step 6:

[0321] The server monitors the progress of ongoing collaborative activities and notifies users of relevant alerts in real time. It continuously monitors progress and provides prompt notifications when important events occur. The input is collaborative activity progress data, and the output is alert notifications to users.

[0322] 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.

[0323] This invention combines an emotion engine with a system that collects, integrates, and preprocesses business data from various business units within a company. The emotion engine recognizes the user's emotions and can provide feedback that takes the user's emotional state into account when building consensus between departments or monitoring collaborative projects.

[0324] Specifically, the server first collects the necessary data from each business unit's database, then integrates and preprocesses it. During this process, data formats are standardized, and duplicate data is removed. Next, an AI agent analyzes the preprocessed data to identify relationships between projects and explore possibilities for collaboration.

[0325] Based on the analysis results, collaboration themes and team compositions are formulated, and the proposals are notified to the managers of each business unit using a terminal. The managers, who are also users, review the collaboration proposals presented on the terminal and make decisions based on the results of the emotion engine's analysis of emotional data. The emotion engine recognizes the emotional state in real time through the user's text input and voice data, and provides situation-appropriate feedback to the managers.

[0326] If a user agrees to a collaboration proposal, the server provides project management tools and assigns roles and tasks to the relevant members. Throughout the collaborative project, the server monitors progress, and an emotion engine detects the emotions of project participants. If participants are experiencing stress or decreased motivation, the server provides appropriate feedback to address these issues.

[0327] In this way, systems that incorporate an emotion engine go beyond simply analyzing business data; they function as a powerful tool to support effective communication for collaboration and enable efficient organizational operation.

[0328] The following describes the processing flow.

[0329] Step 1:

[0330] The server extracts business data from each business unit's database. This includes project information, customer data, progress records, and more. Data retrieval is performed periodically, and the system is configured to always maintain the most up-to-date information.

[0331] Step 2:

[0332] The server integrates the collected data, standardizes the format, and removes duplicate data. Furthermore, it uses machine learning models to preprocess the data and generate a clean dataset that forms the basis for analysis.

[0333] Step 3:

[0334] An AI agent analyzes pre-processed data to explore the relationships between projects. The server identifies projects where collaboration between business units is possible and evaluates the interests of each project.

[0335] Step 4:

[0336] Based on the analysis results, the AI ​​agent formulates collaboration themes and proposed team structures. The server then notifies the managers of each business unit of these formulations and makes specific proposals regarding the collaboration.

[0337] Step 5:

[0338] Through the device, the user (administrator) receives collaboration proposals and reviews them in conjunction with the sentiment data provided by the sentiment engine. The sentiment engine analyzes nuances derived from text and audio to support the administrator's decision-making.

[0339] Step 6:

[0340] If user consent is obtained, the server will configure the project management tools and assign the necessary roles and tasks to stakeholders. Project schedules and resource allocation will also be automatically optimized.

[0341] Step 7:

[0342] After the collaborative project begins, the server monitors progress, and the emotion engine regularly checks participants' stress levels and motivation. It provides feedback and suggests improvements as needed to help the project succeed.

[0343] Step 8:

[0344] Once a project is complete, the server evaluates the results, uses the data gathered by the emotion engine to compile feedback, and proposes improvements for future projects. This process facilitates smoother collaboration within the company.

[0345] (Example 2)

[0346] 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".

[0347] In modern companies, each department often possesses its own unique operational information, and there is a lack of systems to integrate and collaborate on this information. Furthermore, a problem arises in inter-departmental consensus building and project progress, where effective feedback that considers the feelings of stakeholders is often not provided. This leads to concerns that the efficiency and quality of communication in cross-departmental collaborative activities will decline, hindering overall organizational productivity. This invention aims to solve these problems.

[0348] 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.

[0349] In this invention, the server includes means for collecting business information from each department within an enterprise, means for integrating and pre-processing the collected information, means for analyzing the relationships between activities from the integrated information, and means for analyzing emotional information and providing feedback to stakeholders based on this information. This enables effective information integration and communication support to facilitate collaborative activities between departments.

[0350] A "company" is a group of legal entities or sole proprietorships that operate systematically toward a common goal.

[0351] A "department" is an organizational unit within a company that is responsible for specific tasks or functions.

[0352] "Business information" refers to data and knowledge that companies and departments generate and acquire in the course of their daily activities and operations.

[0353] "Integration" refers to bringing together information from different sources and formats in a consistent and coherent manner.

[0354] "Preprocessing" refers to the processing steps, such as standardizing the format or correcting / deleting inappropriate data, that are carried out prior to the analysis and use of information.

[0355] "Emotional information" refers to information expressed through human emotions, and can be obtained from text, audio, facial expressions, and other sources.

[0356] "Feedback" refers to information or reactions provided based on the results obtained from a particular action or process, with the aim of making improvements or corrections.

[0357] "Collaborative activities" refer to joint work undertaken by multiple departments or individuals, sharing resources and knowledge to achieve a common goal.

[0358] "Relevance" is a concept that refers to the degree of interrelationship or connection between different pieces of information or elements.

[0359] "Collection" is the act of selecting and compiling necessary information from specified sources.

[0360] To implement this invention, the system operates according to the following procedure. First, the server collects business information from information sources in each department within the company. This process includes extracting data from databases built for each department. The server then consolidates this data in one place and integrates and preprocesses it. This preprocessing involves standardizing the information format and removing duplicate data. SQL database management systems and data integration tools can be used for these processes.

[0361] Next, the server uses an AI agent to analyze the relationships between the integrated information. This AI agent leverages machine learning algorithms to analyze data correlations and identify activities that should be coordinated. Data analysis libraries using programming languages ​​such as Python and R are suitable for relationship analysis.

[0362] Based on the analysis results, the server automatically formulates optimal collaboration themes and personnel configurations. The formulated proposals are notified to the managers of each department via terminals. When reviewing the proposals on the terminals, managers refer to the feedback provided by the sentiment engine. This sentiment engine extracts and analyzes emotional information from the user's text and voice to provide appropriate feedback in real time.

[0363] As a specific scenario, when a user (administrator) receives a plan proposal, they can use a prompt message for the generative AI model such as, "What emotional data should be considered when the sales and development departments jointly develop a new product?" This helps ensure that appropriate decisions are made based on emotional information, supporting the efficient progress of the project.

[0364] Ultimately, the server uses project management tools to assign clear roles and tasks to participants, ensuring the smooth execution of agreed-upon collaborative activities. Furthermore, throughout the project, the server continuously monitors participants' emotional states and provides situation-sensitive feedback to improve organizational productivity and communication quality.

[0365] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0366] Step 1:

[0367] The server accesses information sources within each department of the company and collects business information. It requires connection information for each department's database as input. The server issues SQL queries to extract the necessary data and stores it in integrated storage. The output is a set of collected raw data.

[0368] Step 2:

[0369] The server integrates and preprocesses the collected raw data. The raw data collected in step 1 is used as input. This data is converted to a unified format, and duplicate data is removed. This process uses data cleansing tools and scripts. The output is clean data in a unified format.

[0370] Step 3:

[0371] The server uses an AI agent to analyze pre-processed data. The input is the clean data obtained in step 2. The AI ​​agent uses machine learning algorithms to analyze the relationships between the data and identify potential collaborative projects and themes. The output is a list of highly relevant projects.

[0372] Step 4:

[0373] The server formulates collaborative themes and team compositions based on the analysis results. The input is the analysis results from step 3. A generative AI model is used to describe the compositions in natural language. The output is a document outlining the collaborative themes and compositions.

[0374] Step 5:

[0375] The terminal notifies the administrators of each department of the collaboration proposal sent from the server. The input is the proposal document created in step 4. The terminal uses its notification function to quickly inform the administrators asynchronously. The output is the proposal content displayed on the terminal.

[0376] Step 6:

[0377] The administrator, acting as the user, reviews the suggestions on a terminal and provides feedback through the emotion engine. The input consists of text and voice information entered by the administrator on the terminal. The emotion engine analyzes the input data and evaluates the user's emotions. The output is the analyzed emotional state and the feedback based on it.

[0378] Step 7:

[0379] The server configures a project management tool to correspond to the agreed-upon collaborative activities. The input is the agreed-upon content obtained in step 6. The server assigns tasks and sets the schedule for the project. The output is the tasks and schedule visualized on the project management tool.

[0380] Step 8:

[0381] The server continuously monitors participants' emotional states throughout the collaborative activity. The input is participant response data collected during the project. The emotion engine analyzes this data to detect changes in stress and motivation. The output is situation-specific feedback and suggestions for improvement.

[0382] (Application Example 2)

[0383] 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 as the "terminal".

[0384] Collaborative activities between departments within a company often fail to proceed efficiently due to miscommunication and emotional disagreements. In particular, neglecting the emotional state of participants can lead to project delays and decreased motivation. Furthermore, the lack of mechanisms to identify and address participant dissatisfaction and stress early in the project is also a problem.

[0385] 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.

[0386] In this invention, the server includes means for collecting business information from each department within the company, means for integrating and pre-processing the collected information, and means for recognizing the emotional state of users and providing feedback that takes the emotional state into account during the progress of agreement and collaborative activities. This enables effective communication between departments and allows for the efficient progress of projects through feedback that takes into account the emotional state of participants.

[0387] "Business information" refers to data on activities collected from various departments within a company, including specific facts and statistical information related to the performance of duties.

[0388] "Unifying information formats" refers to the process of converting business information provided in different formats into a consistent format, enabling data compatibility and usability.

[0389] "Removing duplicate information" is the process of removing duplicated information from a database, and is done to maintain the continuous consistency and accuracy of the data.

[0390] "Relationships between activities" is a concept that shows how different projects and tasks influence each other, and it is important for promoting efficient resource allocation and collaboration.

[0391] A "collaboration theme" refers to the subject or objective of an activity undertaken jointly by multiple departments or organizations, and is formulated based on specific strategic goals.

[0392] A "group structure plan" is an organizational arrangement plan that clarifies the roles and responsibilities of each individual participating in collaborative activities, and is planned to ensure the smooth implementation of those activities.

[0393] "Emotional state" refers to the internal emotional state that activity participants or users feel at a particular point in time, and is an important factor that influences the progress of the activity and their motivation to participate.

[0394] "Feedback" refers to advice and guidance provided based on the activity status and results, and is intended to improve the project and promote the growth of participants.

[0395] This invention is a system that effectively promotes interdepartmental collaboration by collecting business information from each department within a company and integrating and preprocessing that information. By incorporating an emotion engine, this system provides real-time feedback that takes into account the emotional state of participants during the progress of a project.

[0396] The server first collects business information from various departments scattered throughout the company. The collected information is then integrated after preprocessing, which includes standardizing data formats and removing duplicate information. Based on the integrated information, an AI agent analyzes the relationships between activities and automatically formulates collaborative themes and proposed organizational structures. In this process, a cloud-based database management system and data processing engines using Python and Node.js are utilized.

[0397] The established collaboration themes are notified to the managers of each department via the terminal. Managers review the information displayed on the terminal based on feedback on emotional states analyzed by the emotion engine, and then decide on specific actions. The emotion engine uses the Google Cloud Speech-to-Text API to analyze emotions from voice and text data. As decision-making takes place in each phase, the system analyzes the emotional states of participants and provides feedback according to changes in stress and motivation.

[0398] A concrete example is a public library design project led by a local community. Residents submit their opinions through a dedicated app, collecting emotional data that facilitates discussion and project improvements. This application allows for the development of negotiation strategies tailored to the emotional tendencies of the residents.

[0399] Examples of prompt statements are as follows:

[0400] "Based on the sentiment data obtained from residents involved in the 'Design of a Public Library' project, please propose what changes can be made to improve overall resident satisfaction."

[0401] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0402] Step 1:

[0403] The server collects business information from each department. This information includes project activity details and participant lists. The collected data is entered into a database and managed centrally.

[0404] Step 2:

[0405] The server integrates and preprocesses the collected business information. Here, data formats are standardized and duplicate information is removed. Specifically, a Python script is used to perform data cleaning, and the results of the cleansing process are saved to the integrated database.

[0406] Step 3:

[0407] The server analyzes the relationships between activities using pre-processed information. The AI ​​agent calculates the correlation between projects included in the data using machine learning algorithms and inputs the results into the generative AI model.

[0408] Step 4:

[0409] The server formulates collaboration themes and proposed organizational structures based on the results analyzed by the generative AI model. These themes and structures are generated by the generative AI model using prompt messages and output to the terminal.

[0410] Step 5:

[0411] The terminal notifies the managers of each department of the formulated collaboration themes and proposed organizational structures. The managers then review the information displayed on the terminal and decide on the project implementation policy.

[0412] Step 6:

[0413] Users review the suggestions displayed on their device and make decisions based on the feedback provided by the sentiment engine. The sentiment engine uses the Google Cloud Speech-to-Text API to convert the user's voice or text into sentiment data and provide feedback in real time.

[0414] Step 7:

[0415] The server monitors the progress of collaborative activities and detects participants' emotional states as needed. Participant emotional data is used for feedback, and specific advice regarding stress and decreased motivation is output as feedback to the terminal.

[0416] 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.

[0417] Data generation model 58 is a type of so-called generative AI (Artificial Intelligence). One example of data generation model 58 is ChatGPT (Internet search<URL: https: / / openai.com / blog / chatgpt> ), Gemini (Internet search) <url: https: gemini.google.com ?hl="ja">Examples of generative AI include the following. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and with inference data such as audio data representing speech, text data representing text, and image data representing images. The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference results in data formats such as audio data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[0418] 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.

[0419] [Third Embodiment]

[0420] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[0421] 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.

[0422] 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).

[0423] 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.

[0424] 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.

[0425] 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).

[0426] 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.

[0427] 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.

[0428] 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.

[0429] 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.

[0430] 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.

[0431] 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".

[0432] This invention relates to an AI agent system that collects business data from various business units within a company, integrates and preprocesses it, and analyzes the relationships between projects. The server first extracts business-related data from each business unit's database. This data includes customer information, product development progress, sales status, and so on.

[0433] The server standardizes the format of the collected data and supplements any missing data. It also improves data quality by removing duplicate information. Then, an AI agent analyzes the data to consider the relationships between projects and the possibilities for collaboration. This makes it possible to detect projects from different departments that target the same customer.

[0434] Based on the analysis results, the AI ​​agent suggests collaborative themes between departments that are expected to yield the highest level of cooperation. For example, it might recommend that the sales and development departments jointly plan new products based on customer needs. This suggestion is then presented to the managers of each department via their respective terminals.

[0435] The user (administrator) reviews and considers the collaboration proposal displayed on their terminal. If they agree to the proposal, the system automatically prepares the project management tool and assigns roles and tasks to the relevant members. This enables a smooth project launch.

[0436] After the project begins, the server monitors the progress of the collaborative project in real time. If progress is behind schedule or a critical problem arises, stakeholders are notified with an alert. This enables a quick response and improves the probability of project success.

[0437] These systems facilitate efficient collaboration within companies and promote innovation by eliminating "invisible barriers."

[0438] The following describes the processing flow.

[0439] Step 1:

[0440] The server collects necessary business data from each business unit's database. This includes project details, customer information, and progress status. The server automatically sets a data collection schedule and retrieves updated information periodically.

[0441] Step 2:

[0442] The server integrates and preprocesses the collected data. Specifically, it standardizes data formats, removes duplicate data, and imputes missing data. This generates a clean dataset suitable for analysis.

[0443] Step 3:

[0444] The server uses an AI agent to analyze the relationships between pre-processed data. It clusters similarities between projects and common customer targets to highlight potential collaborations.

[0445] Step 4:

[0446] Based on the analysis results, the AI ​​agent identifies departments where collaboration would be effective and formulates collaboration themes. The proposals include specific project objectives and expected benefits.

[0447] Step 5:

[0448] Collaboration proposals from the AI ​​agent are notified to the administrators (users) of each department via the terminal. Administrators can review the proposals and provide comments and feedback.

[0449] Step 6:

[0450] Once a user agrees to a collaboration proposal, the server configures the project management tools. It assigns roles and tasks, preparing the project for launch.

[0451] Step 7:

[0452] During project execution, the server monitors progress and provides real-time status updates. In the event of significant delays or problems, the server sends alerts to relevant parties to prompt a quick response.

[0453] Step 8:

[0454] After the collaborative project is completed, the server evaluates the results and generates feedback. This allows for identification of areas for improvement and success factors for future projects.

[0455] (Example 1)

[0456] 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."

[0457] When various organizational units within a company operate independently, overlapping activities and a lack of coordination can occur, hindering efficient resource utilization and rapid decision-making. Therefore, a system is needed to effectively collect, analyze, and promote collaboration among different organizations.

[0458] 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.

[0459] In this invention, the server includes means for collecting business information from each organizational unit of a company, means for integrating and pre-processing the collected information into a consistent format, and means for analyzing the relationships between activities from the consistent information. This reduces overlapping activities between organizations, strengthens collaboration, and enables efficient resource allocation and rapid project initiation.

[0460] "Organizational units within a company" refer to departments or divisions within a company that have different functions and roles, and these are units that operate independently.

[0461] "Business information" refers to data related to the activities within a company, including customer information, product development progress, and sales status.

[0462] "A consistent format" refers to a state where data collected from multiple different sources is integrated and all data is presented in a unified format.

[0463] "Preprocessing" refers to the process of preparing raw data to be analyzable, and includes processes such as standardizing data formats, removing duplicate information, and supplementing missing data.

[0464] "Relevance between activities" refers to the common elements and mutual influences between different projects or departments, and is an important indicator when exploring possibilities for collaboration based on this.

[0465] In this invention, the server uses a database management system and APIs to acquire data in order to collect business information from each organizational unit of a company. Specifically, this includes data such as customer information, product development progress, and sales status. This data is acquired using a dedicated data extraction tool during the collection phase, and then the information is integrated and preprocessed.

[0466] The server uses ETL (Extract, Transform, Load) tools to standardize data formats, remove duplicate data, and fill in missing information using predictive models. Examples of specific ETL tools include Talend and Apache NiFi, but the system is not limited to these.

[0467] Next, the server uses a generative AI model to analyze the data, which has been formatted into a consistent format. The purpose of the analysis is to evaluate the relationships between activities and identify potential opportunities for collaboration in each project.

[0468] Based on the analysis results, the AI ​​agent proposes the most effective collaboration themes. This allows administrators to receive clear visual suggestions via their devices. The generated suggestions may include recommendations for the sales and development departments to jointly plan a new product.

[0469] Furthermore, the administrator, as a user, reviews the collaboration proposal presented on their device, and if they agree to the proposal, the project management tool is automatically launched. This assigns roles and tasks to the relevant members, ensuring a smooth project launch.

[0470] Furthermore, once the project begins, the server monitors progress in real time and sends alerts if progress is delayed or if critical issues arise. This feature ensures that the project stays on schedule and allows all involved members to respond promptly.

[0471] As a concrete example, suppose a new product development project for a new customer faces the challenge of a shortage of personnel with specific skills among the project members. This system can address this challenge by suggesting members with similar skills from other internal projects and facilitating collaboration.

[0472] An example of a prompt for a generated AI model is, "Please provide the analysis and suggestions necessary to maximize synergy in a project involving multiple departments within an organization." This prompt serves as input information to appropriately analyze and suggest collaboration opportunities that the system seeks.

[0473] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0474] Step 1:

[0475] The server collects business information from databases of each organizational unit. Inputs include customer information, product development progress, and sales data obtained through APIs and database management systems. The server stores this information in storage for the ETL process. The output is a set of raw data provided in various formats.

[0476] Step 2:

[0477] The server integrates the collected data into a consistent format and preprocesses it. The raw data obtained in Step 1 is used as input. The server first uses ETL tools to standardize the format, for example, by converting character codes and normalizing field names. Then, it identifies and removes duplicate data and uses a predictive model to fill in any missing information. The output is a cleansed, high-quality dataset.

[0478] Step 3:

[0479] The server analyzes processed data using a generative AI model. The input is a formatted dataset. During the analysis process, the AI ​​model is applied to evaluate the relevance between activities and the potential for collaboration. Specifically, it identifies patterns such as similarity in customer targets and past collaboration between departments. The output includes relevance scores for each project and collaboration suggestions.

[0480] Step 4:

[0481] The AI ​​agent proposes collaboration themes based on the analysis results and presents them to managers in each department via the terminal. Inputs include the relevance score and collaboration proposals generated in step 3. The AI ​​agent creates a visual proposal in a dashboard format and displays it on the terminal. For example, it might request a joint new product plan between the sales and development departments and propose it to relevant stakeholders. The output includes evaluation and decision-making by the managers.

[0482] Step 5:

[0483] The user (administrator) evaluates the presented collaboration proposals and decides whether to accept them. The input consists of the proposal content displayed on the terminal and related analytical information. If the administrator approves the proposal, the system launches the project management tool and proceeds with project preparation. Specifically, roles and tasks are automatically assigned to the relevant members. The output is that the project preparation status is complete and ready to proceed.

[0484] Step 6:

[0485] The server monitors project progress in real time and sends alerts to stakeholders as needed. Inputs include project progress data and risk assessment information. The server analyzes this data and generates appropriate alerts if progress is delayed or critical issues arise. Specific actions include push notifications, emails, and warning displays on a dashboard. Outputs include providing immediate feedback to the project team, enabling quick responses.

[0486] (Application Example 1)

[0487] 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."

[0488] In modern urban management, various departments with diverse roles function independently, making it difficult for them to cooperate in improving public services. As a result, inefficient operations occur, and the convenience of citizens is not sufficiently improved. In particular, a lack of coordination between departments in critical areas such as transportation and energy management is a factor that degrades the overall functionality of the city.

[0489] 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.

[0490] In this invention, the server includes means for collecting information from each department within the organization, means for integrating and pre-processing the collected information, and means for analyzing the relationships between tasks from the integrated information. This makes it possible to efficiently operate urban functions by utilizing the information held by each department and to propose optimal collaborative solutions to improve public benefits.

[0491] An "organization" is a group composed of multiple departments or divisions that share a common goal and cooperate with each other to achieve it.

[0492] "Information" refers to various data and knowledge collected from different departments within an organization, which are useful for improving urban management.

[0493] "Integration" is the process of combining multiple pieces of information with different forms and structures into one and converting them into a common format.

[0494] "Preprocessing" is the process of removing duplicates and missing data from collected information to improve its quality and facilitate data analysis.

[0495] "Work" refers to a series of activities or projects carried out using information to achieve a specific objective.

[0496] "Relevance" refers to the relationship between different tasks or projects, indicating the extent to which they influence each other or have potential for collaboration.

[0497] A "proposal" is a guideline for themes and action plans that each department within an organization should work on collaboratively, based on the analysis results.

[0498] "Monitoring" refers to the means of constantly checking the status of ongoing collaborative activities and taking appropriate instructions or measures as needed.

[0499] "Benefits" refer to the convenience and advantages that citizens and users can enjoy, and are expected as urban services improve.

[0500] The system for realizing this invention mainly includes the following configuration: The server collects information from each department within the organization and integrates and preprocesses that information. Specifically, the server uses Python to cleanse and integrate the data and uses Pandas to standardize the information format. The information is then duplicated and its quality is improved.

[0501] Next, the server uses an AI agent for analysis. The AI ​​agent uses TensorFlow to analyze the relationships between tasks from the integrated information. This allows it to suggest themes and action plans that will make collaboration between different departments most effective.

[0502] The proposal is presented to the user's device. Users primarily use smartphones or smart glasses to review the proposal, and once agreement is reached, the system automatically begins managing and monitoring the collaborative activities. Progress and related alerts are notified to the user in real time, and specific examples such as energy management and traffic volume adjustment are provided.

[0503] For example, in response to an expected surge in traffic during a weekend event, a collaborative proposal might be made such as, "The Traffic Management Department and the Energy Management Department propose increasing the operating hours of electric buses and making adjustments to alleviate congestion."

[0504] Example of a prompt:

[0505] "Please propose interdepartmental cooperation measures to alleviate traffic congestion within the smart city."

[0506] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0507] Step 1:

[0508] The server collects information from each department within the organization. As input, it accesses a database containing departmental operational data, retrieving data such as customer information, product development progress, and sales status. The output is the collected raw data, which is stored for subsequent processing.

[0509] Step 2:

[0510] The server integrates and preprocesses the collected information. This step uses Pandas to standardize the information format and eliminate duplicate data. Specifically, it standardizes data with different formats and removes noise and inconsistencies through data cleansing. The input is the collected raw data, and the output is the improved, integrated data.

[0511] Step 3:

[0512] The server analyzes the relationships between tasks from the integrated data. Here, TensorFlow is used to extract correlations between data using a generative AI model. The input is pre-processed integrated data, and the output is the relationship analysis result. This result indicates areas where interdepartmental collaboration is expected.

[0513] Step 4:

[0514] The server proposes collaboration themes based on the analysis results. In this step, it sends collaboration proposals to the user terminal based on insights obtained from the generated AI model. Specifically, it identifies departments where efficient cooperation can be expected and presents concrete measures. The input is the relevance analysis results, and the output is a notification message as a proposal.

[0515] Step 5:

[0516] Users review proposals via their terminals and reach agreements as needed. Once an agreement is reached, the system automatically initiates management and monitoring processes. The user's actions involve reviewing and confirming the proposals, and the output is the result of the agreement.

[0517] Step 6:

[0518] The server monitors the progress of ongoing collaborative activities and notifies users of relevant alerts in real time. It continuously monitors progress and provides prompt notifications when important events occur. The input is collaborative activity progress data, and the output is alert notifications to users.

[0519] 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.

[0520] This invention combines an emotion engine with a system that collects, integrates, and preprocesses business data from various business units within a company. The emotion engine recognizes the user's emotions and can provide feedback that takes the user's emotional state into account when building consensus between departments or monitoring collaborative projects.

[0521] Specifically, the server first collects the necessary data from each business unit's database, then integrates and preprocesses it. During this process, data formats are standardized, and duplicate data is removed. Next, an AI agent analyzes the preprocessed data to identify relationships between projects and explore possibilities for collaboration.

[0522] Based on the analysis results, collaboration themes and team compositions are formulated, and the proposals are notified to the managers of each business unit using a terminal. The managers, who are also users, review the collaboration proposals presented on the terminal and make decisions based on the results of the emotion engine's analysis of emotional data. The emotion engine recognizes the emotional state in real time through the user's text input and voice data, and provides situation-appropriate feedback to the managers.

[0523] If a user agrees to a collaboration proposal, the server provides project management tools and assigns roles and tasks to the relevant members. Throughout the collaborative project, the server monitors progress, and an emotion engine detects the emotions of project participants. If participants are experiencing stress or decreased motivation, the server provides appropriate feedback to address these issues.

[0524] In this way, systems that incorporate an emotion engine go beyond simply analyzing business data; they function as a powerful tool to support effective communication for collaboration and enable efficient organizational operation.

[0525] The following describes the processing flow.

[0526] Step 1:

[0527] The server extracts business data from each business unit's database. This includes project information, customer data, progress records, and more. Data retrieval is performed periodically, and the system is configured to always maintain the most up-to-date information.

[0528] Step 2:

[0529] The server integrates the collected data, standardizes the format, and removes duplicate data. Furthermore, it uses machine learning models to preprocess the data and generate a clean dataset that forms the basis for analysis.

[0530] Step 3:

[0531] An AI agent analyzes pre-processed data to explore the relationships between projects. The server identifies projects where collaboration between business units is possible and evaluates the interests of each project.

[0532] Step 4:

[0533] Based on the analysis results, the AI ​​agent formulates collaboration themes and proposed team structures. The server then notifies the managers of each business unit of these formulations and makes specific proposals regarding the collaboration.

[0534] Step 5:

[0535] Through the device, the user (administrator) receives collaboration proposals and reviews them in conjunction with the sentiment data provided by the sentiment engine. The sentiment engine analyzes nuances derived from text and audio to support the administrator's decision-making.

[0536] Step 6:

[0537] If user consent is obtained, the server will configure the project management tools and assign the necessary roles and tasks to stakeholders. Project schedules and resource allocation will also be automatically optimized.

[0538] Step 7:

[0539] After the collaborative project begins, the server monitors progress, and the emotion engine regularly checks participants' stress levels and motivation. It provides feedback and suggests improvements as needed to help the project succeed.

[0540] Step 8:

[0541] Once a project is complete, the server evaluates the results, uses the data gathered by the emotion engine to compile feedback, and proposes improvements for future projects. This process facilitates smoother collaboration within the company.

[0542] (Example 2)

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

[0544] In modern companies, each department often possesses its own unique operational information, and there is a lack of systems to integrate and collaborate on this information. Furthermore, a problem arises in inter-departmental consensus building and project progress, where effective feedback that considers the feelings of stakeholders is often not provided. This leads to concerns that the efficiency and quality of communication in cross-departmental collaborative activities will decline, hindering overall organizational productivity. This invention aims to solve these problems.

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

[0546] In this invention, the server includes means for collecting business information from each department within an enterprise, means for integrating and pre-processing the collected information, means for analyzing the relationships between activities from the integrated information, and means for analyzing emotional information and providing feedback to stakeholders based on this information. This enables effective information integration and communication support to facilitate collaborative activities between departments.

[0547] A "company" is a group of legal entities or sole proprietorships that operate systematically toward a common goal.

[0548] A "department" is an organizational unit within a company that is responsible for specific tasks or functions.

[0549] "Business information" refers to data and knowledge that companies and departments generate and acquire in the course of their daily activities and operations.

[0550] "Integration" refers to bringing together information from different sources and formats in a consistent and coherent manner.

[0551] "Preprocessing" refers to the processing steps, such as standardizing the format or correcting / deleting inappropriate data, that are carried out prior to the analysis and use of information.

[0552] "Emotional information" refers to information expressed through human emotions, and can be obtained from text, audio, facial expressions, and other sources.

[0553] "Feedback" refers to information or reactions provided based on the results obtained from a particular action or process, with the aim of making improvements or corrections.

[0554] "Collaborative activities" refer to joint work undertaken by multiple departments or individuals, sharing resources and knowledge to achieve a common goal.

[0555] "Relevance" is a concept that refers to the degree of interrelationship or connection between different pieces of information or elements.

[0556] "Collection" is the act of selecting and compiling necessary information from specified sources.

[0557] To implement this invention, the system operates according to the following procedure. First, the server collects business information from information sources in each department within the company. This process includes extracting data from databases built for each department. The server then consolidates this data in one place and integrates and preprocesses it. This preprocessing involves standardizing the information format and removing duplicate data. SQL database management systems and data integration tools can be used for these processes.

[0558] Next, the server uses an AI agent to analyze the relationships between the integrated information. This AI agent leverages machine learning algorithms to analyze data correlations and identify activities that should be coordinated. Data analysis libraries using programming languages ​​such as Python and R are suitable for relationship analysis.

[0559] Based on the analysis results, the server automatically formulates optimal collaboration themes and personnel configurations. The formulated proposals are notified to the managers of each department via terminals. When reviewing the proposals on the terminals, managers refer to the feedback provided by the sentiment engine. This sentiment engine extracts and analyzes emotional information from the user's text and voice to provide appropriate feedback in real time.

[0560] As a specific scenario, when a user (administrator) receives a plan proposal, they can use a prompt message for the generative AI model such as, "What emotional data should be considered when the sales and development departments jointly develop a new product?" This helps ensure that appropriate decisions are made based on emotional information, supporting the efficient progress of the project.

[0561] Ultimately, the server uses project management tools to assign clear roles and tasks to participants, ensuring the smooth execution of agreed-upon collaborative activities. Furthermore, throughout the project, the server continuously monitors participants' emotional states and provides situation-sensitive feedback to improve organizational productivity and communication quality.

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

[0563] Step 1:

[0564] The server accesses information sources within each department of the company and collects business information. It requires connection information for each department's database as input. The server issues SQL queries to extract the necessary data and stores it in integrated storage. The output is a set of collected raw data.

[0565] Step 2:

[0566] The server integrates and preprocesses the collected raw data. The raw data collected in step 1 is used as input. This data is converted to a unified format, and duplicate data is removed. This process uses data cleansing tools and scripts. The output is clean data in a unified format.

[0567] Step 3:

[0568] The server uses an AI agent to analyze pre-processed data. The input is the clean data obtained in step 2. The AI ​​agent uses machine learning algorithms to analyze the relationships between the data and identify potential collaborative projects and themes. The output is a list of highly relevant projects.

[0569] Step 4:

[0570] The server formulates collaborative themes and team compositions based on the analysis results. The input is the analysis results from step 3. A generative AI model is used to describe the compositions in natural language. The output is a document outlining the collaborative themes and compositions.

[0571] Step 5:

[0572] The terminal notifies the administrators of each department of the collaboration proposal sent from the server. The input is the proposal document created in step 4. The terminal uses its notification function to quickly inform the administrators asynchronously. The output is the proposal content displayed on the terminal.

[0573] Step 6:

[0574] The administrator, acting as the user, reviews the suggestions on a terminal and provides feedback through the emotion engine. The input consists of text and voice information entered by the administrator on the terminal. The emotion engine analyzes the input data and evaluates the user's emotions. The output is the analyzed emotional state and the feedback based on it.

[0575] Step 7:

[0576] The server configures a project management tool to correspond to the agreed-upon collaborative activities. The input is the agreed-upon content obtained in step 6. The server assigns tasks and sets the schedule for the project. The output is the tasks and schedule visualized on the project management tool.

[0577] Step 8:

[0578] The server continuously monitors participants' emotional states throughout the collaborative activity. The input is participant response data collected during the project. The emotion engine analyzes this data to detect changes in stress and motivation. The output is situation-specific feedback and suggestions for improvement.

[0579] (Application Example 2)

[0580] 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."

[0581] Collaborative activities between departments within a company often fail to proceed efficiently due to miscommunication and emotional disagreements. In particular, neglecting the emotional state of participants can lead to project delays and decreased motivation. Furthermore, the lack of mechanisms to identify and address participant dissatisfaction and stress early in the project is also a problem.

[0582] 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.

[0583] In this invention, the server includes means for collecting business information from each department within the company, means for integrating and pre-processing the collected information, and means for recognizing the emotional state of users and providing feedback that takes the emotional state into account during the progress of agreement and collaborative activities. This enables effective communication between departments and allows for the efficient progress of projects through feedback that takes into account the emotional state of participants.

[0584] "Business information" refers to data on activities collected from various departments within a company, including specific facts and statistical information related to the performance of duties.

[0585] "Unifying information formats" refers to the process of converting business information provided in different formats into a consistent format, enabling data compatibility and usability.

[0586] "Removing duplicate information" is the process of removing duplicated information from a database, and is done to maintain the continuous consistency and accuracy of the data.

[0587] "Relationships between activities" is a concept that shows how different projects and tasks influence each other, and it is important for promoting efficient resource allocation and collaboration.

[0588] A "collaboration theme" refers to the subject or objective of an activity undertaken jointly by multiple departments or organizations, and is formulated based on specific strategic goals.

[0589] A "group structure plan" is an organizational arrangement plan that clarifies the roles and responsibilities of each individual participating in collaborative activities, and is planned to ensure the smooth implementation of those activities.

[0590] "Emotional state" refers to the internal emotional state that activity participants or users feel at a particular point in time, and is an important factor that influences the progress of the activity and their motivation to participate.

[0591] "Feedback" refers to advice and guidance provided based on the activity status and results, and is intended to improve the project and promote the growth of participants.

[0592] This invention is a system that effectively promotes interdepartmental collaboration by collecting business information from each department within a company and integrating and preprocessing that information. By incorporating an emotion engine, this system provides real-time feedback that takes into account the emotional state of participants during the progress of a project.

[0593] The server first collects business information from various departments scattered throughout the company. The collected information is then integrated after preprocessing, which includes standardizing data formats and removing duplicate information. Based on the integrated information, an AI agent analyzes the relationships between activities and automatically formulates collaborative themes and proposed organizational structures. In this process, a cloud-based database management system and data processing engines using Python and Node.js are utilized.

[0594] The established collaboration themes are notified to the managers of each department via the terminal. Managers review the information displayed on the terminal based on feedback on emotional states analyzed by the emotion engine, and then decide on specific actions. The emotion engine uses the Google Cloud Speech-to-Text API to analyze emotions from voice and text data. As decision-making takes place in each phase, the system analyzes the emotional states of participants and provides feedback according to changes in stress and motivation.

[0595] A concrete example is a public library design project led by a local community. Residents submit their opinions through a dedicated app, collecting emotional data that facilitates discussion and project improvements. This application allows for the development of negotiation strategies tailored to the emotional tendencies of the residents.

[0596] Examples of prompt statements are as follows:

[0597] "Based on the sentiment data obtained from residents involved in the 'Design of a Public Library' project, please propose what changes can be made to improve overall resident satisfaction."

[0598] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0599] Step 1:

[0600] The server collects business information from each department. This information includes project activity details and participant lists. The collected data is entered into a database and managed centrally.

[0601] Step 2:

[0602] The server integrates and preprocesses the collected business information. Here, data formats are standardized and duplicate information is removed. Specifically, a Python script is used to perform data cleaning, and the results of the cleansing process are saved to the integrated database.

[0603] Step 3:

[0604] The server analyzes the relationships between activities using pre-processed information. The AI ​​agent calculates the correlation between projects included in the data using machine learning algorithms and inputs the results into the generative AI model.

[0605] Step 4:

[0606] The server formulates collaboration themes and proposed organizational structures based on the results analyzed by the generative AI model. These themes and structures are generated by the generative AI model using prompt messages and output to the terminal.

[0607] Step 5:

[0608] The terminal notifies the managers of each department of the formulated collaboration themes and proposed organizational structures. The managers then review the information displayed on the terminal and decide on the project implementation policy.

[0609] Step 6:

[0610] Users review the suggestions displayed on their device and make decisions based on the feedback provided by the sentiment engine. The sentiment engine uses the Google Cloud Speech-to-Text API to convert the user's voice or text into sentiment data and provide feedback in real time.

[0611] Step 7:

[0612] The server monitors the progress of collaborative activities and detects participants' emotional states as needed. Participant emotional data is used for feedback, and specific advice regarding stress and decreased motivation is output as feedback to the terminal.

[0613] 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.

[0614] Data generation model 58 is a type of so-called generative AI (Artificial Intelligence). One example of data generation model 58 is ChatGPT (Internet search<URL: https: / / openai.com / blog / chatgpt> ), Gemini (Internet search) <url: https: gemini.google.com ?hl="ja">Examples of generative AI include the following. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and with inference data such as audio data representing speech, text data representing text, and image data representing images. The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference results in data formats such as audio data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[0615] 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.

[0616] [Fourth Embodiment]

[0617] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[0618] 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.

[0619] 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).

[0620] 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.

[0621] 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.

[0622] 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).

[0623] 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.

[0624] 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.

[0625] 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.

[0626] 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.

[0627] 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.

[0628] 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.

[0629] 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".

[0630] This invention relates to an AI agent system that collects business data from various business units within a company, integrates and preprocesses it, and analyzes the relationships between projects. The server first extracts business-related data from each business unit's database. This data includes customer information, product development progress, sales status, and so on.

[0631] The server standardizes the format of the collected data and supplements any missing data. It also improves data quality by removing duplicate information. Then, an AI agent analyzes the data to consider the relationships between projects and the possibilities for collaboration. This makes it possible to detect projects from different departments that target the same customer.

[0632] Based on the analysis results, the AI ​​agent suggests collaborative themes between departments that are expected to yield the highest level of cooperation. For example, it might recommend that the sales and development departments jointly plan new products based on customer needs. This suggestion is then presented to the managers of each department via their respective terminals.

[0633] The user (administrator) reviews and considers the collaboration proposal displayed on their terminal. If they agree to the proposal, the system automatically prepares the project management tool and assigns roles and tasks to the relevant members. This enables a smooth project launch.

[0634] After the project begins, the server monitors the progress of the collaborative project in real time. If progress is behind schedule or a critical problem arises, stakeholders are notified with an alert. This enables a quick response and improves the probability of project success.

[0635] These systems facilitate efficient collaboration within companies and promote innovation by eliminating "invisible barriers."

[0636] The following describes the processing flow.

[0637] Step 1:

[0638] The server collects necessary business data from each business unit's database. This includes project details, customer information, and progress status. The server automatically sets a data collection schedule and retrieves updated information periodically.

[0639] Step 2:

[0640] The server integrates and preprocesses the collected data. Specifically, it standardizes data formats, removes duplicate data, and imputes missing data. This generates a clean dataset suitable for analysis.

[0641] Step 3:

[0642] The server uses an AI agent to analyze the relationships between pre-processed data. It clusters similarities between projects and common customer targets to highlight potential collaborations.

[0643] Step 4:

[0644] Based on the analysis results, the AI ​​agent identifies departments where collaboration would be effective and formulates collaboration themes. The proposals include specific project objectives and expected benefits.

[0645] Step 5:

[0646] Collaboration proposals from the AI ​​agent are notified to the administrators (users) of each department via the terminal. Administrators can review the proposals and provide comments and feedback.

[0647] Step 6:

[0648] Once a user agrees to a collaboration proposal, the server configures the project management tools. It assigns roles and tasks, preparing the project for launch.

[0649] Step 7:

[0650] During project execution, the server monitors progress and provides real-time status updates. In the event of significant delays or problems, the server sends alerts to relevant parties to prompt a quick response.

[0651] Step 8:

[0652] After the collaborative project is completed, the server evaluates the results and generates feedback. This allows for identification of areas for improvement and success factors for future projects.

[0653] (Example 1)

[0654] 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".

[0655] When various organizational units within a company operate independently, overlapping activities and a lack of coordination can occur, hindering efficient resource utilization and rapid decision-making. Therefore, a system is needed to effectively collect, analyze, and promote collaboration among different organizations.

[0656] 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.

[0657] In this invention, the server includes means for collecting business information from each organizational unit of a company, means for integrating and pre-processing the collected information into a consistent format, and means for analyzing the relationships between activities from the consistent information. This reduces overlapping activities between organizations, strengthens collaboration, and enables efficient resource allocation and rapid project initiation.

[0658] "Organizational units within a company" refer to departments or divisions within a company that have different functions and roles, and these are units that operate independently.

[0659] "Business information" refers to data related to the activities within a company, including customer information, product development progress, and sales status.

[0660] "A consistent format" refers to a state where data collected from multiple different sources is integrated and all data is presented in a unified format.

[0661] "Preprocessing" refers to the process of preparing raw data to be analyzable, and includes processes such as standardizing data formats, removing duplicate information, and supplementing missing data.

[0662] "Relevance between activities" refers to the common elements and mutual influences between different projects or departments, and is an important indicator when exploring possibilities for collaboration based on this.

[0663] In this invention, the server uses a database management system and APIs to acquire data in order to collect business information from each organizational unit of a company. Specifically, this includes data such as customer information, product development progress, and sales status. This data is acquired using a dedicated data extraction tool during the collection phase, and then the information is integrated and preprocessed.

[0664] The server uses ETL (Extract, Transform, Load) tools to standardize data formats, remove duplicate data, and fill in missing information using predictive models. Examples of specific ETL tools include Talend and Apache NiFi, but the system is not limited to these.

[0665] Next, the server uses a generative AI model to analyze the data, which has been formatted into a consistent format. The purpose of the analysis is to evaluate the relationships between activities and identify potential opportunities for collaboration in each project.

[0666] Based on the analysis results, the AI ​​agent proposes the most effective collaboration themes. This allows administrators to receive clear visual suggestions via their devices. The generated suggestions may include recommendations for the sales and development departments to jointly plan a new product.

[0667] Furthermore, the administrator, as a user, reviews the collaboration proposal presented on their device, and if they agree to the proposal, the project management tool is automatically launched. This assigns roles and tasks to the relevant members, ensuring a smooth project launch.

[0668] Furthermore, once the project begins, the server monitors progress in real time and sends alerts if progress is delayed or if critical issues arise. This feature ensures that the project stays on schedule and allows all involved members to respond promptly.

[0669] As a concrete example, suppose a new product development project for a new customer faces the challenge of a shortage of personnel with specific skills among the project members. This system can address this challenge by suggesting members with similar skills from other internal projects and facilitating collaboration.

[0670] An example of a prompt for a generated AI model is, "Please provide the analysis and suggestions necessary to maximize synergy in a project involving multiple departments within an organization." This prompt serves as input information to appropriately analyze and suggest collaboration opportunities that the system seeks.

[0671] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0672] Step 1:

[0673] The server collects business information from databases of each organizational unit. Inputs include customer information, product development progress, and sales data obtained through APIs and database management systems. The server stores this information in storage for the ETL process. The output is a set of raw data provided in various formats.

[0674] Step 2:

[0675] The server integrates the collected data into a consistent format and preprocesses it. The raw data obtained in Step 1 is used as input. The server first uses ETL tools to standardize the format, for example, by converting character codes and normalizing field names. Then, it identifies and removes duplicate data and uses a predictive model to fill in any missing information. The output is a cleansed, high-quality dataset.

[0676] Step 3:

[0677] The server analyzes processed data using a generative AI model. The input is a formatted dataset. During the analysis process, the AI ​​model is applied to evaluate the relevance between activities and the potential for collaboration. Specifically, it identifies patterns such as similarity in customer targets and past collaboration between departments. The output includes relevance scores for each project and collaboration suggestions.

[0678] Step 4:

[0679] The AI ​​agent proposes collaboration themes based on the analysis results and presents them to managers in each department via the terminal. Inputs include the relevance score and collaboration proposals generated in step 3. The AI ​​agent creates a visual proposal in a dashboard format and displays it on the terminal. For example, it might request a joint new product plan between the sales and development departments and propose it to relevant stakeholders. The output includes evaluation and decision-making by the managers.

[0680] Step 5:

[0681] The user (administrator) evaluates the presented collaboration proposals and decides whether to accept them. The input consists of the proposal content displayed on the terminal and related analytical information. If the administrator approves the proposal, the system launches the project management tool and proceeds with project preparation. Specifically, roles and tasks are automatically assigned to the relevant members. The output is that the project preparation status is complete and ready to proceed.

[0682] Step 6:

[0683] The server monitors project progress in real time and sends alerts to stakeholders as needed. Inputs include project progress data and risk assessment information. The server analyzes this data and generates appropriate alerts if progress is delayed or critical issues arise. Specific actions include push notifications, emails, and warning displays on a dashboard. Outputs include providing immediate feedback to the project team, enabling quick responses.

[0684] (Application Example 1)

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

[0686] In modern urban management, various departments with diverse roles function independently, making it difficult for them to cooperate in improving public services. As a result, inefficient operations occur, and the convenience of citizens is not sufficiently improved. In particular, a lack of coordination between departments in critical areas such as transportation and energy management is a factor that degrades the overall functionality of the city.

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

[0688] In this invention, the server includes means for collecting information from each department within the organization, means for integrating and pre-processing the collected information, and means for analyzing the relationships between tasks from the integrated information. This makes it possible to efficiently operate urban functions by utilizing the information held by each department and to propose optimal collaborative solutions to improve public benefits.

[0689] An "organization" is a group composed of multiple departments or divisions that share a common goal and cooperate with each other to achieve it.

[0690] "Information" refers to various data and knowledge collected from different departments within an organization, which are useful for improving urban management.

[0691] "Integration" is the process of combining multiple pieces of information with different forms and structures into one and converting them into a common format.

[0692] "Preprocessing" is the process of removing duplicates and missing data from collected information to improve its quality and facilitate data analysis.

[0693] "Work" refers to a series of activities or projects carried out using information to achieve a specific objective.

[0694] "Relevance" refers to the relationship between different tasks or projects, indicating the extent to which they influence each other or have potential for collaboration.

[0695] A "proposal" is a guideline for themes and action plans that each department within an organization should work on collaboratively, based on the analysis results.

[0696] "Monitoring" refers to the means of constantly checking the status of ongoing collaborative activities and taking appropriate instructions or measures as needed.

[0697] "Benefits" refer to the convenience and advantages that citizens and users can enjoy, and are expected as urban services improve.

[0698] The system for realizing this invention mainly includes the following configuration: The server collects information from each department within the organization and integrates and preprocesses that information. Specifically, the server uses Python to cleanse and integrate the data and uses Pandas to standardize the information format. The information is then duplicated and its quality is improved.

[0699] Next, the server uses an AI agent for analysis. The AI ​​agent uses TensorFlow to analyze the relationships between tasks from the integrated information. This allows it to suggest themes and action plans that will make collaboration between different departments most effective.

[0700] The proposal is presented to the user's device. Users primarily use smartphones or smart glasses to review the proposal, and once agreement is reached, the system automatically begins managing and monitoring the collaborative activities. Progress and related alerts are notified to the user in real time, and specific examples such as energy management and traffic volume adjustment are provided.

[0701] For example, in response to an expected surge in traffic during a weekend event, a collaborative proposal might be made such as, "The Traffic Management Department and the Energy Management Department propose increasing the operating hours of electric buses and making adjustments to alleviate congestion."

[0702] Example of a prompt:

[0703] "Please propose interdepartmental cooperation measures to alleviate traffic congestion within the smart city."

[0704] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0705] Step 1:

[0706] The server collects information from each department within the organization. As input, it accesses a database containing departmental operational data, retrieving data such as customer information, product development progress, and sales status. The output is the collected raw data, which is stored for subsequent processing.

[0707] Step 2:

[0708] The server integrates and preprocesses the collected information. This step uses Pandas to standardize the information format and eliminate duplicate data. Specifically, it standardizes data with different formats and removes noise and inconsistencies through data cleansing. The input is the collected raw data, and the output is the improved, integrated data.

[0709] Step 3:

[0710] The server analyzes the relationships between tasks from the integrated data. Here, TensorFlow is used to extract correlations between data using a generative AI model. The input is pre-processed integrated data, and the output is the relationship analysis result. This result indicates areas where interdepartmental collaboration is expected.

[0711] Step 4:

[0712] The server proposes collaboration themes based on the analysis results. In this step, it sends collaboration proposals to the user terminal based on insights obtained from the generated AI model. Specifically, it identifies departments where efficient cooperation can be expected and presents concrete measures. The input is the relevance analysis results, and the output is a notification message as a proposal.

[0713] Step 5:

[0714] Users review proposals via their terminals and reach agreements as needed. Once an agreement is reached, the system automatically initiates management and monitoring processes. The user's actions involve reviewing and confirming the proposals, and the output is the result of the agreement.

[0715] Step 6:

[0716] The server monitors the progress of ongoing collaborative activities and notifies users of relevant alerts in real time. It continuously monitors progress and provides prompt notifications when important events occur. The input is collaborative activity progress data, and the output is alert notifications to users.

[0717] 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.

[0718] This invention combines an emotion engine with a system that collects, integrates, and preprocesses business data from various business units within a company. The emotion engine recognizes the user's emotions and can provide feedback that takes the user's emotional state into account when building consensus between departments or monitoring collaborative projects.

[0719] Specifically, the server first collects the necessary data from each business unit's database, then integrates and preprocesses it. During this process, data formats are standardized, and duplicate data is removed. Next, an AI agent analyzes the preprocessed data to identify relationships between projects and explore possibilities for collaboration.

[0720] Based on the analysis results, collaboration themes and team compositions are formulated, and the proposals are notified to the managers of each business unit using a terminal. The managers, who are also users, review the collaboration proposals presented on the terminal and make decisions based on the results of the emotion engine's analysis of emotional data. The emotion engine recognizes the emotional state in real time through the user's text input and voice data, and provides situation-appropriate feedback to the managers.

[0721] If a user agrees to a collaboration proposal, the server provides project management tools and assigns roles and tasks to the relevant members. Throughout the collaborative project, the server monitors progress, and an emotion engine detects the emotions of project participants. If participants are experiencing stress or decreased motivation, the server provides appropriate feedback to address these issues.

[0722] In this way, systems that incorporate an emotion engine go beyond simply analyzing business data; they function as a powerful tool to support effective communication for collaboration and enable efficient organizational operation.

[0723] The following describes the processing flow.

[0724] Step 1:

[0725] The server extracts business data from each business unit's database. This includes project information, customer data, progress records, and more. Data retrieval is performed periodically, and the system is configured to always maintain the most up-to-date information.

[0726] Step 2:

[0727] The server integrates the collected data, standardizes the format, and removes duplicate data. Furthermore, it uses machine learning models to preprocess the data and generate a clean dataset that forms the basis for analysis.

[0728] Step 3:

[0729] An AI agent analyzes pre-processed data to explore the relationships between projects. The server identifies projects where collaboration between business units is possible and evaluates the interests of each project.

[0730] Step 4:

[0731] Based on the analysis results, the AI ​​agent formulates collaboration themes and proposed team structures. The server then notifies the managers of each business unit of these formulations and makes specific proposals regarding the collaboration.

[0732] Step 5:

[0733] Through the device, the user (administrator) receives collaboration proposals and reviews them in conjunction with the sentiment data provided by the sentiment engine. The sentiment engine analyzes nuances derived from text and audio to support the administrator's decision-making.

[0734] Step 6:

[0735] If user consent is obtained, the server will configure the project management tools and assign the necessary roles and tasks to stakeholders. Project schedules and resource allocation will also be automatically optimized.

[0736] Step 7:

[0737] After the collaborative project begins, the server monitors progress, and the emotion engine regularly checks participants' stress levels and motivation. It provides feedback and suggests improvements as needed to help the project succeed.

[0738] Step 8:

[0739] Once a project is complete, the server evaluates the results, uses the data gathered by the emotion engine to compile feedback, and proposes improvements for future projects. This process facilitates smoother collaboration within the company.

[0740] (Example 2)

[0741] 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".

[0742] In modern companies, each department often possesses its own unique operational information, and there is a lack of systems to integrate and collaborate on this information. Furthermore, a problem arises in inter-departmental consensus building and project progress, where effective feedback that considers the feelings of stakeholders is often not provided. This leads to concerns that the efficiency and quality of communication in cross-departmental collaborative activities will decline, hindering overall organizational productivity. This invention aims to solve these problems.

[0743] 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.

[0744] In this invention, the server includes means for collecting business information from each department within an enterprise, means for integrating and pre-processing the collected information, means for analyzing the relationships between activities from the integrated information, and means for analyzing emotional information and providing feedback to stakeholders based on this information. This enables effective information integration and communication support to facilitate collaborative activities between departments.

[0745] A "company" is a group of legal entities or sole proprietorships that operate systematically toward a common goal.

[0746] A "department" is an organizational unit within a company that is responsible for specific tasks or functions.

[0747] "Business information" refers to data and knowledge that companies and departments generate and acquire in the course of their daily activities and operations.

[0748] "Integration" refers to bringing together information from different sources and formats in a consistent and coherent manner.

[0749] "Preprocessing" refers to the processing steps, such as standardizing the format or correcting / deleting inappropriate data, that are carried out prior to the analysis and use of information.

[0750] "Emotional information" refers to information expressed through human emotions, and can be obtained from text, audio, facial expressions, and other sources.

[0751] "Feedback" refers to information or reactions provided based on the results obtained from a particular action or process, with the aim of making improvements or corrections.

[0752] "Collaborative activities" refer to joint work undertaken by multiple departments or individuals, sharing resources and knowledge to achieve a common goal.

[0753] "Relevance" is a concept that refers to the degree of interrelationship or connection between different pieces of information or elements.

[0754] "Collection" is the act of selecting and compiling necessary information from specified sources.

[0755] To implement this invention, the system operates according to the following procedure. First, the server collects business information from information sources in each department within the company. This process includes extracting data from databases built for each department. The server then consolidates this data in one place and integrates and preprocesses it. This preprocessing involves standardizing the information format and removing duplicate data. SQL database management systems and data integration tools can be used for these processes.

[0756] Next, the server uses an AI agent to analyze the relationships between the integrated information. This AI agent leverages machine learning algorithms to analyze data correlations and identify activities that should be coordinated. Data analysis libraries using programming languages ​​such as Python and R are suitable for relationship analysis.

[0757] Based on the analysis results, the server automatically formulates optimal collaboration themes and personnel configurations. The formulated proposals are notified to the managers of each department via terminals. When reviewing the proposals on the terminals, managers refer to the feedback provided by the sentiment engine. This sentiment engine extracts and analyzes emotional information from the user's text and voice to provide appropriate feedback in real time.

[0758] As a specific scenario, when a user (administrator) receives a plan proposal, they can use a prompt message for the generative AI model such as, "What emotional data should be considered when the sales and development departments jointly develop a new product?" This helps ensure that appropriate decisions are made based on emotional information, supporting the efficient progress of the project.

[0759] Ultimately, the server uses project management tools to assign clear roles and tasks to participants, ensuring the smooth execution of agreed-upon collaborative activities. Furthermore, throughout the project, the server continuously monitors participants' emotional states and provides situation-sensitive feedback to improve organizational productivity and communication quality.

[0760] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0761] Step 1:

[0762] The server accesses information sources within each department of the company and collects business information. It requires connection information for each department's database as input. The server issues SQL queries to extract the necessary data and stores it in integrated storage. The output is a set of collected raw data.

[0763] Step 2:

[0764] The server integrates and preprocesses the collected raw data. The raw data collected in step 1 is used as input. This data is converted to a unified format, and duplicate data is removed. This process uses data cleansing tools and scripts. The output is clean data in a unified format.

[0765] Step 3:

[0766] The server uses an AI agent to analyze pre-processed data. The input is the clean data obtained in step 2. The AI ​​agent uses machine learning algorithms to analyze the relationships between the data and identify potential collaborative projects and themes. The output is a list of highly relevant projects.

[0767] Step 4:

[0768] The server formulates collaborative themes and team compositions based on the analysis results. The input is the analysis results from step 3. A generative AI model is used to describe the compositions in natural language. The output is a document outlining the collaborative themes and compositions.

[0769] Step 5:

[0770] The terminal notifies the administrators of each department of the collaboration proposal sent from the server. The input is the proposal document created in step 4. The terminal uses its notification function to quickly inform the administrators asynchronously. The output is the proposal content displayed on the terminal.

[0771] Step 6:

[0772] The administrator, acting as the user, reviews the suggestions on a terminal and provides feedback through the emotion engine. The input consists of text and voice information entered by the administrator on the terminal. The emotion engine analyzes the input data and evaluates the user's emotions. The output is the analyzed emotional state and the feedback based on it.

[0773] Step 7:

[0774] The server configures a project management tool to correspond to the agreed-upon collaborative activities. The input is the agreed-upon content obtained in step 6. The server assigns tasks and sets the schedule for the project. The output is the tasks and schedule visualized on the project management tool.

[0775] Step 8:

[0776] The server continuously monitors participants' emotional states throughout the collaborative activity. The input is participant response data collected during the project. The emotion engine analyzes this data to detect changes in stress and motivation. The output is situation-specific feedback and suggestions for improvement.

[0777] (Application Example 2)

[0778] 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".

[0779] Collaborative activities between departments within a company often fail to proceed efficiently due to miscommunication and emotional disagreements. In particular, neglecting the emotional state of participants can lead to project delays and decreased motivation. Furthermore, the lack of mechanisms to identify and address participant dissatisfaction and stress early in the project is also a problem.

[0780] 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.

[0781] In this invention, the server includes means for collecting business information from each department within the company, means for integrating and pre-processing the collected information, and means for recognizing the emotional state of users and providing feedback that takes the emotional state into account during the progress of agreement and collaborative activities. This enables effective communication between departments and allows for the efficient progress of projects through feedback that takes into account the emotional state of participants.

[0782] "Business information" refers to data on activities collected from various departments within a company, including specific facts and statistical information related to the performance of duties.

[0783] "Unifying information formats" refers to the process of converting business information provided in different formats into a consistent format, enabling data compatibility and usability.

[0784] "Removing duplicate information" is the process of removing duplicated information from a database, and is done to maintain the continuous consistency and accuracy of the data.

[0785] "Relationships between activities" is a concept that shows how different projects and tasks influence each other, and it is important for promoting efficient resource allocation and collaboration.

[0786] A "collaboration theme" refers to the subject or objective of an activity undertaken jointly by multiple departments or organizations, and is formulated based on specific strategic goals.

[0787] A "group structure plan" is an organizational arrangement plan that clarifies the roles and responsibilities of each individual participating in collaborative activities, and is planned to ensure the smooth implementation of those activities.

[0788] "Emotional state" refers to the internal emotional state that activity participants or users feel at a particular point in time, and is an important factor that influences the progress of the activity and their motivation to participate.

[0789] "Feedback" refers to advice and guidance provided based on the activity status and results, and is intended to improve the project and promote the growth of participants.

[0790] This invention is a system that effectively promotes interdepartmental collaboration by collecting business information from each department within a company and integrating and preprocessing that information. By incorporating an emotion engine, this system provides real-time feedback that takes into account the emotional state of participants during the progress of a project.

[0791] The server first collects business information from various departments scattered throughout the company. The collected information is then integrated after preprocessing, which includes standardizing data formats and removing duplicate information. Based on the integrated information, an AI agent analyzes the relationships between activities and automatically formulates collaborative themes and proposed organizational structures. In this process, a cloud-based database management system and data processing engines using Python and Node.js are utilized.

[0792] The established collaboration themes are notified to the managers of each department via the terminal. Managers review the information displayed on the terminal based on feedback on emotional states analyzed by the emotion engine, and then decide on specific actions. The emotion engine uses the Google Cloud Speech-to-Text API to analyze emotions from voice and text data. As decision-making takes place in each phase, the system analyzes the emotional states of participants and provides feedback according to changes in stress and motivation.

[0793] A concrete example is a public library design project led by a local community. Residents submit their opinions through a dedicated app, collecting emotional data that facilitates discussion and project improvements. This application allows for the development of negotiation strategies tailored to the emotional tendencies of the residents.

[0794] Examples of prompt statements are as follows:

[0795] "Based on the sentiment data obtained from residents involved in the 'Design of a Public Library' project, please propose what changes can be made to improve overall resident satisfaction."

[0796] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0797] Step 1:

[0798] The server collects business information from each department. This information includes project activity details and participant lists. The collected data is entered into a database and managed centrally.

[0799] Step 2:

[0800] The server integrates and preprocesses the collected business information. Here, data formats are standardized and duplicate information is removed. Specifically, a Python script is used to perform data cleaning, and the results of the cleansing process are saved to the integrated database.

[0801] Step 3:

[0802] The server analyzes the relationships between activities using pre-processed information. The AI ​​agent calculates the correlation between projects included in the data using machine learning algorithms and inputs the results into the generative AI model.

[0803] Step 4:

[0804] The server formulates collaboration themes and proposed organizational structures based on the results analyzed by the generative AI model. These themes and structures are generated by the generative AI model using prompt messages and output to the terminal.

[0805] Step 5:

[0806] The terminal notifies the managers of each department of the formulated collaboration themes and proposed organizational structures. The managers then review the information displayed on the terminal and decide on the project implementation policy.

[0807] Step 6:

[0808] Users review the suggestions displayed on their device and make decisions based on the feedback provided by the sentiment engine. The sentiment engine uses the Google Cloud Speech-to-Text API to convert the user's voice or text into sentiment data and provide feedback in real time.

[0809] Step 7:

[0810] The server monitors the progress of collaborative activities and detects participants' emotional states as needed. Participant emotional data is used for feedback, and specific advice regarding stress and decreased motivation is output as feedback to the terminal.

[0811] 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.

[0812] Data generation model 58 is a type of so-called generative AI (Artificial Intelligence). One example of data generation model 58 is ChatGPT (Internet search<URL: https: / / openai.com / blog / chatgpt> ), Gemini (Internet search) <url: https: gemini.google.com ?hl="ja">Examples of generative AI include the following. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and with inference data such as audio data representing speech, text data representing text, and image data representing images. The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference results in data formats such as audio data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[0813] In the above embodiment, an example was given in which specific processing is performed by the data processing device 12, but the technology of this disclosure is not limited thereto, and the specific processing may also be performed by the robot 414.

[0814] 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.

[0815] 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. In the upper and lower directions of the concentric circles, emotions that are generally generated from reactions occurring in the brain and induced by situational judgment are located. Also, the upper side of the concentric circles is where "pleasant" emotions are located, and the lower side is where "unpleasant" emotions are located. In this way, 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.

[0816] 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.

[0817] 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.

[0818] 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.

[0819] 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."

[0820] 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.

[0821] 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.

[0822] 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.

[0823] 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.

[0824] 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.

[0825] 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.

[0826] 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.

[0827] 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.

[0828] 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.

[0829] 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.

[0830] 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.

[0831] 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.

[0832] The following is further disclosed regarding the embodiments described above.

[0833] (Claim 1)

[0834] Methods for collecting business data from each business unit within a company,

[0835] Means for integrating and preprocessing the collected data,

[0836] A means of analyzing the relationships between projects from integrated data,

[0837] A means of formulating collaborative themes and team compositions based on the analysis results,

[0838] Presenting formulated collaborative themes and providing means to support consensus building between departments,

[0839] A means of managing and monitoring the progress of collaborative projects after an agreement has been reached,

[0840] A system that includes this.

[0841] (Claim 2)

[0842] The system according to claim 1, wherein the means for integrating and preprocessing performs data format unification and duplicate data removal.

[0843] (Claim 3)

[0844] The system according to claim 1, wherein the means for monitoring progress is to notify stakeholders of the progress of the collaborative project and provide feedback.

[0845] "Example 1"

[0846] (Claim 1)

[0847] Means of collecting business information from each organizational unit of a company,

[0848] Means for integrating and preprocessing collected information into a consistent format,

[0849] A means of analyzing the relationships between activities from consistent information,

[0850] A means of creating a joint theme and proposed members based on the analysis results,

[0851] A means of presenting the collaborative theme that has been created and facilitating consensus building among the organizations,

[0852] Means for managing and monitoring the progress of joint activities after the agreement,

[0853] A system that includes this.

[0854] (Claim 2)

[0855] The system according to claim 1, wherein the means for integrating and preprocessing performs the unification of information formats and the removal of redundant information.

[0856] (Claim 3)

[0857] The system according to claim 1, wherein the means for monitoring progress reports the progress of the joint activity to the relevant parties and provides their opinions.

[0858] "Application Example 1"

[0859] (Claim 1)

[0860] Means of collecting information from each department within the organization,

[0861] Means for integrating and preprocessing the collected information,

[0862] A means of analyzing the relationships between tasks from integrated information,

[0863] A means of formulating themes and organizational structures for collaboration based on the analysis results,

[0864] Presenting formulated collaboration themes and providing means to support consensus building between domains,

[0865] Means for managing and monitoring the progress of collaborative activities after the agreement,

[0866] A means to communicate monitoring results to relevant parties in real time, enabling a rapid response,

[0867] A means of proposing optimal collaborations to efficiently operate urban functions and improve public convenience,

[0868] A system that includes this.

[0869] (Claim 2)

[0870] The system according to claim 1, wherein the means for integrating and preprocessing performs the unification of information formats and the elimination of redundant information.

[0871] (Claim 3)

[0872] The system according to claim 1, wherein the means for monitoring the progress notifies relevant parties of the progress of the collaborative activity and provides feedback.

[0873] "Example 2 of combining an emotion engine"

[0874] (Claim 1)

[0875] Means of collecting business information from various departments within a company,

[0876] Means for integrating and preprocessing the collected information,

[0877] A means of analyzing the relationships between activities from integrated information,

[0878] A means of formulating collaborative themes and group composition proposals based on the analysis results,

[0879] Presenting formulated cooperation themes and providing means to support consensus building between departments,

[0880] Means for managing and monitoring the progress of collaborative activities after an agreement,

[0881] A means of analyzing emotional information and providing feedback to stakeholders based on this information,

[0882] A system that includes this.

[0883] (Claim 2)

[0884] The system according to claim 1, wherein the means for integrating and preprocessing performs the unification of information formats and the removal of duplicate information.

[0885] (Claim 3)

[0886] The system according to claim 1, wherein the means for monitoring progress notifies stakeholders of the progress of the collaborative activity and provides feedback based on emotional information.

[0887] "Application example 2 when combining with an emotional engine"

[0888] (Claim 1)

[0889] Means of collecting business information from various departments within a company,

[0890] Means for integrating and preprocessing the collected information,

[0891] A means of analyzing the relationships between activities from integrated information,

[0892] A means of formulating collaborative themes and proposed organizational structures based on the analysis results,

[0893] Presenting formulated collaborative themes and providing means to support consensus building between departments,

[0894] A means of recognizing the emotional state of users and providing feedback that takes their emotional state into account during the progress of agreement and collaborative activities,

[0895] Means for managing and monitoring the progress of collaborative activities after an agreement has been reached,

[0896] A means of providing feedback to activity participants based on emotional data,

[0897] A system that includes this.

[0898] (Claim 2)

[0899] The system according to claim 1, wherein the means for integrating and preprocessing performs the unification of information formats and the removal of duplicate information.

[0900] (Claim 3)

[0901] The system according to claim 1, wherein the means for monitoring progress notifies stakeholders of the progress of collaborative activities and also provides feedback that takes into account their emotional state. [Explanation of symbols]

[0902] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Devices 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robots< / url:> < / url:> < / url:> < / url:>

Claims

1. Means of collecting information from each department within the organization, Means for integrating and preprocessing the collected information, A means of analyzing the relationships between tasks from integrated information, A means of formulating themes and organizational structures for collaboration based on the analysis results, Presenting formulated collaboration themes and providing means to support consensus building between domains, Means for managing and monitoring the progress of collaborative activities after the agreement, A means to communicate monitoring results to relevant parties in real time, enabling a rapid response, A means of proposing optimal collaborations to efficiently operate urban functions and improve public convenience, A system that includes this.

2. The system according to claim 1, wherein the means for integration and preprocessing unifies the information format and eliminates duplicate information.

3. The system according to claim 1, wherein the means for monitoring the progress notifies relevant parties of the progress of the collaborative activities and provides feedback.

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