system

Abstract

We provide the system. [Solution] A means of collecting data on tasks performed by multiple departments within an organization, The collected data is analyzed to identify opportunities for collaboration between different departments, A means of notifying the relevant person within the organization of the identified collaboration proposal, Means to support the formation of agreement on collaborative themes, A means of managing the progress of agreed-upon collaborative projects, A system that includes this.

Description

Technical Field

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

Background Art

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

Prior Art Documents

Patent Documents

[0003]

Patent Document 1

Summary of the Invention

Problems to be Solved by the Invention

[0004] In an organization, there may be a problem that the barriers between departments limit business possibilities. Specifically, there may be a lack of information sharing and collaboration between departments, resulting in the duplication of the development of similar services, or the inability to achieve a comprehensive approach and insufficient customer response. Such a situation hinders the opportunity for cooperation among personnel with different perspectives and specialties, and consequently becomes a factor inhibiting the innovation of the entire organization. There is a need to provide an effective method for solving this problem.

Means for Solving the Problems

[0005] This invention provides a system for promoting interdepartmental collaboration within an organization. First, it uses an information processing device to collect data on the work performed by each department within the organization. Next, it analyzes the collected data to identify potential collaboration opportunities between different departments, thereby clarifying the possibilities for collaboration. Subsequently, it notifies the relevant personnel within the organization of the identified collaboration proposals, supporting consensus building among the relevant departments. Furthermore, it manages the progress of collaborative projects implemented based on the agreements and, when necessary, shares information in real time using project management tools, thereby realizing a system that maximizes the effectiveness of collaboration.

[0006] An "information processing device" refers to a computer system or equipment that constitutes a part of such a system, used for collecting, analyzing, and managing data.

[0007] A "department" refers to a unit within an organization that is responsible for a specific task or function, and includes teams and groups.

[0008] "Data" refers to records in numerical, text, or other forms that constitute information related to a department or business.

[0009] "Analysis" refers to the process of finding meaning or patterns within data, and often involves the use of statistical methods and algorithms.

[0010] "Collaboration" refers to different departments or organizations working together on projects or tasks.

[0011] "Proposal" refers to presenting specific actions or plans based on the results of an analysis.

[0012] "Notification" refers to the act of informing a user of information from a system, or to the information itself.

[0013] "Agreement" refers to a situation where multiple departments or individuals reach a common understanding regarding a particular plan or action.

[0014] A "project" refers to a series of activities planned to achieve a specific goal or the entire set of such activities.

[0015] "Management" refers to the act of supervising the progress of a project or business operations and maintaining an effective process towards the goals.

[0016] "Real-time" refers to the situation where processing and updating are carried out almost simultaneously when an event occurs.

Brief Explanation of Drawings

[0017] [Figure 1] It is a conceptual diagram showing an example of the configuration of a data processing system according to the first embodiment. [Figure 2] It is a conceptual diagram showing an example of the main functions of a data processing device and a smart device according to the first embodiment. [Figure 3] It is a conceptual diagram showing an example of the configuration of a data processing system according to the second embodiment. [Figure 4] It is a conceptual diagram showing an example of the main functions of a data processing device and smart glasses according to the second embodiment. [Figure 5] It is a conceptual diagram showing an example of the configuration of a data processing system according to the third embodiment. [Figure 6] It is a conceptual diagram showing an example of the main functions of a data processing device and a headset-type terminal according to the third embodiment. [Figure 7] It is a conceptual diagram showing an example of the configuration of a data processing system according to the fourth embodiment. [Figure 8] It is a conceptual diagram showing an example of the main functions of a data processing device and a robot according to the fourth embodiment. [Figure 9] It shows an emotion map to which a plurality of emotions are mapped. [Figure 10] It shows an emotion map to which a plurality of emotions are mapped. [Figure 11] It is a sequence diagram showing the processing flow of the data processing system in Example 1. [Figure 12]It is a sequence diagram showing the processing flow of the data processing system in Application Example 1. [Figure 13] It is a sequence diagram showing the processing flow of the data processing system in Example 2 when combined with an emotion engine. [Figure 14] It is a sequence diagram showing the processing flow of the data processing system in Application Example 2 when combined with an emotion engine.

Mode for Carrying Out the Invention

[0018] Hereinafter, an example of an embodiment of the system according to the technology of the present disclosure will be described with reference to the accompanying drawings.

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

[0020] In the following embodiments, the labeled 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 labeled 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] The collaboration-promoting system of the present invention is a network system that includes a server that functions as an information processing device and terminals that serve as an interface with users. This system identifies potential collaboration opportunities by collecting and analyzing data related to the work performed by each department within an organization.

[0039] The server first retrieves the necessary data from each department's project management system and resource management database. This data includes project progress, resources used, and related work tasks. The server then integrates this data and formats it into a format that is easy to analyze.

[0040] Next, the server uses AI algorithms to analyze the data and identify potential for interdepartmental collaboration. The analysis utilizes text mining and machine learning techniques to identify departments with similar projects or complementary resources. For example, it can link a new product being planned by the product development department with other departments possessing the necessary supporting technologies.

[0041] Once processing is complete, the server notifies the terminal of the proposed collaboration. The terminal then displays the collaboration proposal to the managers of each department. The managers, as users, review the proposal, decide whether to start the collaborative project based on its content, and make any necessary planning adjustments.

[0042] Furthermore, the server monitors the progress of collaborative projects through project management tools, centralizing resource allocation and schedule management. This allows for real-time monitoring of project status and rapid sharing of necessary information with stakeholders. Schedule revisions and resource reallocations can also be automatically performed to maintain appropriate progress.

[0043] For example, when the product development department and the marketing department begin collaborating in connection with the development of a new product, the server facilitates communication between the departments and provides timely information for jointly formulating a promotional strategy. In this way, the present invention strengthens mutual cooperation within the organization and promotes innovation.

[0044] The following describes the processing flow.

[0045] Step 1:

[0046] The server accesses project management and resource management databases used by various departments within the organization to collect relevant business data. This includes information on project progress, resources used, and related tasks.

[0047] Step 2:

[0048] The server integrates and cleans the collected data, converting it into a consistent format. This process eliminates data duplication and inconsistencies, making it easier to analyze.

[0049] Step 3:

[0050] The server uses AI models to analyze data and identify potential collaboration opportunities across multiple departments. Here, text mining and machine learning algorithms are applied to find similar and complementary projects and resources.

[0051] Step 4:

[0052] The server creates a collaboration proposal based on the analysis results and sends the details to the terminal. This includes which departments should work together and specific collaboration themes.

[0053] Step 5:

[0054] The terminal notifies managers in each department of collaboration proposals received from the server and displays them on the screen. Managers review the proposals and make decisions regarding the implementation of collaborative projects.

[0055] Step 6:

[0056] The user, who is also a manager, forms agreements regarding the collaborative project and adjusts the necessary plans and team composition. This leads to the development of concrete action plans and schedules.

[0057] Step 7:

[0058] The server supports the progress of collaborative projects by utilizing project management tools. This includes real-time progress reporting, resource allocation coordination, and schedule management.

[0059] Step 8:

[0060] The server monitors the project's progress, makes adjustments as needed when issues arise, and provides necessary feedback to stakeholders.

[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 departments within an organization operate in isolation, potential complementary projects and resources go unutilized, and the synergistic effects of collaboration are not fully realized. Therefore, there is a need for a system that effectively identifies opportunities for interdepartmental collaboration and supports their realization.

[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 information on activities performed by multiple organizational units through a data processing system, means for formatting the collected information and converting it into an analyzable format, and means for analyzing the converted information using an AI algorithm to identify organizational units with similar activities or complementary resources. This makes it possible to discover potential opportunities for collaboration within the organization and to promote collaboration efficiently and effectively.

[0066] A "data processing system" is a computer system designed to collect, format, and analyze activity information within an organization.

[0067] An "organizational unit" refers to a department or team that independently carries out specific tasks or activities.

[0068] An "AI algorithm" is a computational method that uses machine learning techniques to analyze data and identify patterns and relationships.

[0069] An "activity management tool" is software used to monitor the progress and resource allocation of collaborative activities and to share information.

[0070] "Real-time" refers to a state where processing and data sharing occur instantly without delay.

[0071] "Generative AI technology" refers to technology in which AI generates new content or suggestions from a given prompt.

[0072] A "prompt message" is a sentence containing instructions or questions that are entered into an AI to perform a specific task.

[0073] The embodiments for carrying out the invention will now be described. This invention provides a system to support more effective collaboration among multiple departments within an organization. Specific examples of the invention are shown below.

[0074] The server, acting as a data processing system, accesses project management and resource management databases used by each department. It uses APIs and database connections to collect information from these databases, such as project progress, resources used, and related business tasks. The collected information is then normalized and cleansed, and converted into a format suitable for analysis.

[0075] The server analyzes the formatted data using AI algorithms. Leveraging text mining techniques, it extracts key items from the collected information and uses machine learning models to identify departments with similar projects or complementary resources. Based on the AI ​​analysis, the server identifies potential collaboration opportunities and notifies the terminals accordingly.

[0076] The terminal displays collaboration proposals received from the server to managers in each department. Managers review the presented information and initiate collaborative projects or make appropriate adjustments to the plan. Based on the information provided by the system, the managers in each department, as users, efficiently coordinate and optimize resources during project progress.

[0077] As a concrete example, in a new product development project, the server can identify synergies between the product development and marketing departments and provide information to enable both departments to collaborate in formulating a promotional strategy. In this way, it is possible to promote mutual cooperation within the organization and improve operational efficiency.

[0078] When utilizing generative AI technology, we will use the following text as an example of a prompt: "Please propose a strategy to optimize interdepartmental collaboration." This will allow the AI ​​to generate specific collaboration strategies and provide suggestions tailored to the organization's needs.

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

[0080] Step 1:

[0081] The server accesses project management and resource management databases used by various departments within the organization. Inputs include project progress, resource usage, and work task information. Specifically, it collects this information through APIs and database connections and converts it into a structured format. This allows for the collection of foundational data necessary to discover collaboration opportunities.

[0082] Step 2:

[0083] The server formats the collected data and converts it into a format that is easy to analyze. The input is the data obtained in Step 1. At this stage, data normalization and cleansing are performed to remove redundant information and unnecessary data, preparing it for analysis. The data is converted into text format or tabular format, which allows the AI ​​to perform analysis efficiently.

[0084] Step 3:

[0085] The server analyzes the formatted data using an AI algorithm. The input is the data processed in step 2. Here, text mining techniques are used to extract important keywords, and a machine learning model is used to identify departments with similar projects or complementary resources. The output is a list of relevant departments and projects as potential collaboration opportunities.

[0086] Step 4:

[0087] The server notifies the terminal of the potential for collaboration identified through the analysis. The input is the output result from step 3. Specifically, it compiles the information as a collaboration proposal and prepares it for immediate transmission to the department manager. This notification causes the collaboration proposal to be displayed on the terminal.

[0088] Step 5:

[0089] The terminal displays received collaboration proposals to the user, who is a manager. The input is the content of the collaboration proposal sent from the server. The terminal designs the screen to present the proposal clearly to the manager. The user manager reviews this, decides whether to start the collaboration project, and proceeds with any necessary plan adjustments.

[0090] Step 6:

[0091] The server monitors ongoing collaborative projects using project management tools. Inputs include project progress data and resource allocation information. The server updates this information in real time and automatically adjusts schedules and reallocates resources as needed, supporting the efficient progress of projects.

[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 organizations such as factories, each department often operates independently, resulting in underutilization of opportunities for inter-departmental collaboration and hindering efficient project progress. Furthermore, even after collaboration begins, insufficient optimization of project progress and adequate feedback on results make inter-departmental coordination a challenge.

[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 business data within the organization using an information processing device, means for analyzing the collected data to identify opportunities for collaboration, and means for notifying the person in charge of the collaboration proposal. This enables strengthened inter-departmental collaboration and improved project efficiency. Furthermore, by utilizing a generative AI model to generate predictive suggestions for collaboration opportunities and prompt messages, further business improvements can be achieved.

[0097] An "information processing device" is a computer system that has the function of collecting and analyzing business data, and plays a role in identifying and proposing opportunities for collaboration.

[0098] A "mobile communication terminal" is a portable communication device such as a smartphone or tablet, used to receive notifications about collaboration opportunities and project management information.

[0099] A "generative AI model" is an artificial intelligence system embedded in an information processing device that uses machine learning and data analysis to generate predictions and prompts for collaboration opportunities.

[0100] A "project management tool" is software used to monitor project progress and share information with stakeholders in real time, supporting efficient project execution within an organization.

[0101] To implement this invention, it is necessary to build an advanced information processing system. The server functions as a primary data aggregation point and analysis engine. Specifically, the server uses a RESTful API to acquire data such as resources and project progress from various departments within the organization. After the acquired data is stored in a database, a machine learning model is built using Python and TENSORFLOW® to analyze the possibilities of collaboration.

[0102] The generated collaboration proposals are notified to mobile communication devices via Firebase. These devices, such as smartphones and tablets, function as an interface to display proposals and notifications to users and facilitate important communication. The generation AI model generates data-driven prompts in real time.

[0103] For example, if the manufacturing department develops a new production process, the server may analyze data related to the new process and suggest collaboration with the design department. A prompt message such as "Suggest which departments we should collaborate with to implement the manufacturing department's new process improvement proposal" is sent to the mobile communication terminal. This prompt message allows the user to immediately understand the need for collaboration and quickly take the next steps.

[0104] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0105] Step 1:

[0106] The server retrieves business data from various departments within the organization. Specifically, it collects data from project management systems and resource management databases via RESTful APIs. The input is data on departmental progress and resource status, and the output is an integrated dataset containing this information. This dataset is formatted to provide the information necessary for subsequent analysis.

[0107] Step 2:

[0108] The server stores the collected data in a database and then performs preprocessing using a Python script. This involves cleaning and formatting the data to convert it into a format suitable for analysis by the AI ​​model. It also performs actions such as removing duplicate data and imputing missing values. The input is an integrated dataset, and the output is preprocessed data for analysis.

[0109] Step 3:

[0110] The server uses TensorFlow to perform data analysis with an AI model. It uses a generative AI model to identify potential collaboration opportunities from the data. The input is pre-processed data for analysis, and the output is a proposed list of collaboration opportunities. This list includes information indicating which departments are capable of collaborating.

[0111] Step 4:

[0112] The server uses Firebase to notify each department's terminal of the collaboration proposals it has generated. The input is a list of collaboration proposals, and the output is a notification message sent to the mobile communication terminal. The content displayed on the terminal includes specific instructions on how the relevant department should collaborate.

[0113] Step 5:

[0114] Based on the prompt displayed on the terminal, the user confirms the need for collaboration, gathers the relevant departments, and begins working together. Specific actions include setting up meetings and coordinating project schedules. The prompt displayed is, "Suggest which departments should we collaborate with to implement the manufacturing department's new process improvement proposal." The input is the prompt, and the output is an action plan for initiating collaboration.

[0115] 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.

[0116] This invention is a system that combines AI-based data analysis with an emotion engine that recognizes user emotions, effectively promoting collaboration between departments within an organization. This system includes a server as an information processing device and a terminal as an interface.

[0117] The server collects and integrates operational data from multiple departments within the organization. This provides data on project progress, resource usage, and related tasks. This data is then analyzed by AI algorithms to identify potential collaboration opportunities between different departments.

[0118] The server creates a collaboration proposal based on the analysis results and notifies the terminal. The terminal displays this notification to the managers of each department. A key feature of this invention is the use of an emotion engine. The emotion engine analyzes the emotions of the user who receives the proposal through the terminal and optimizes the content of the proposal and the timing of the notification based on the user's reaction. For example, it determines whether the user who sees the proposal is interested or dissatisfied and automatically provides appropriate feedback and supplementary information.

[0119] The user, a manager, decides to implement the collaborative project based on the proposal and makes the necessary adjustments. The server manages the progress of the agreed-upon project and shares information in real time using project management tools. It also analyzes user sentiment data during the project and provides useful feedback to stakeholders.

[0120] For example, when a collaboration proposal is made for the development of a new product, the emotion engine evaluates the level of interest in the project based on the initial reaction of the development manager who receives the proposal. Based on that level of interest, it then provides detailed information regarding specific action plans and resource allocation, supporting the smooth progress of the project.

[0121] In this way, the system of the present invention not only promotes interdepartmental collaboration and accelerates innovation within the organization, but also achieves more flexible and effective project management by taking user emotions into consideration.

[0122] The following describes the processing flow.

[0123] Step 1:

[0124] The server accesses the organization's project management and resource management databases to collect operational data from each department. This data includes project progress, resource usage, and related tasks.

[0125] Step 2:

[0126] The server integrates the collected data and converts it into a format that is easy to analyze. This process eliminates redundant information while maintaining data integrity.

[0127] Step 3:

[0128] The server uses AI algorithms to analyze integrated data and identify potential collaboration opportunities between different departments. The analysis employs machine learning and pattern recognition techniques to find departments with complementary resources or similar targets.

[0129] Step 4:

[0130] The server generates collaboration proposals based on identified collaboration opportunities and sends them as notifications to the terminal. These proposals include specific collaboration themes and project outlines.

[0131] Step 5:

[0132] The terminal displays collaboration proposals received from the server to managers in each department. Simultaneously, it activates an emotion engine to analyze the emotions of managers who view the proposals in real time.

[0133] Step 6:

[0134] The user, a manager, reviews the collaboration proposals presented on the device. The device uses an emotion engine to analyze the manager's reaction and detect the user's interests and concerns.

[0135] Step 7:

[0136] Based on the analysis results of the sentiment engine, the server updates suggestions to be more appropriate and provides supplementary information as needed. This information is optimized to suit the user's situation.

[0137] Step 8:

[0138] Users consider implementing collaborative projects based on updated information and feedback, and form the necessary agreements.

[0139] Step 9:

[0140] The server registers the details of agreed-upon collaborative projects in the project management tool and tracks and manages their progress. Project progress is shared with stakeholders in real time.

[0141] Step 10:

[0142] The server monitors user sentiment throughout the project, analyzes the feedback received, and evaluates each phase of the project. This result is then used to identify areas for improvement when proposing future collaborations.

[0143] (Example 2)

[0144] 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".

[0145] Modern organizations require efficient collaboration between different departments, but opportunities for collaboration can be missed due to information fragmentation and a lack of communication. Furthermore, a lack of appropriate feedback based on the feelings of those receiving proposals can negatively impact the quality and progress of collaboration. Therefore, there is a need for a system that automatically identifies opportunities for interdepartmental collaboration and provides proposals and feedback that take stakeholders' feelings into account, thereby facilitating smooth project progress.

[0146] 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.

[0147] In this invention, the server includes means for collecting information on tasks performed by multiple departments within an organization, means for analyzing the collected information and identifying possibilities for collaboration between different departments, means for transmitting the identified collaboration proposals to relevant parties within the organization, and means for evaluating the emotional state of the information recipients and dynamically adjusting the content of the proposals. This enables the rapid and accurate identification of opportunities for collaboration between departments and supports collaboration while taking into account the emotions of the parties involved.

[0148] An "information processing device" is a hardware or software system that integrates and analyzes data collected within an organization, and identifies and notifies users of opportunities for collaboration.

[0149] "Business information" refers to data related to the work performed by multiple departments within an organization, including information such as project progress, task assignments, and resource usage.

[0150] "Potential for collaboration" refers to proposals or opportunities resulting from an assessment of the likelihood that different departments can work together to create new value.

[0151] A "proposal" is a specific plan or proposal for collaboration generated based on the analysis results, and is information that is communicated to stakeholders within the organization.

[0152] "Emotional state" refers to the mental response exhibited by the information recipient upon receiving a proposal, and is evaluated by an emotion analysis device.

[0153] This invention is a system that uses a server as an information processing device and a terminal as an interface device. The server is responsible for collecting and integrating business information from each department within the organization and analyzing it using AI algorithms. Specifically, it collects project progress and resource usage information via the organization's database system and performs data analysis using scikit-learn, a Python machine learning library. This identifies possibilities for collaboration between different departments.

[0154] The server automatically generates identified collaboration proposals and sends them to relevant parties within the organization. These generated proposals are typically displayed on devices through project management tools and communication platforms. When managers and other stakeholders receive the proposals, the devices evaluate their emotional state using sentiment analysis technology. This sentiment analysis utilizes Google Cloud's Vision API and speech recognition technology.

[0155] The user, a manager, decides on collaborative projects based on the proposals and manages their progress by entering the information into a project management tool via the server. The server also records the project progress in real time on an information processing device and provides necessary feedback to stakeholders involved in the project.

[0156] For example, in the development of a new product, the system proposes a project that the development and marketing departments should undertake together and notifies the managers of each department. Based on the level of interest indicated by the managers who receive the notification, the system provides information on specific project details and resource allocation. This ensures that the project progresses smoothly and improves the overall efficiency of the organization.

[0157] An example of a prompt message for a generative AI model is: "Based on the initial response shown by the development manager, propose the optimal way to proceed with the new product development project." This allows for flexible feedback based on sentiment analysis, which is expected to increase the success rate of collaborative projects.

[0158] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0159] Step 1:

[0160] The server collects operational information from each department within the organization and integrates it into a database. This collected data includes project progress, task assignments, and resource usage. It receives operational information from each department as input and outputs it as an integrated dataset. Specifically, it centralizes information using a database system.

[0161] Step 2:

[0162] The server applies AI algorithms to an integrated dataset to analyze potential collaboration opportunities. The input is integrated business information, and the output generates a list of potential interdepartmental collaboration opportunities. Python machine learning libraries are used for data processing, including clustering and pattern recognition.

[0163] Step 3:

[0164] The server creates collaboration proposals based on the collaboration opportunities identified through analysis and sends them to the relevant parties. The input is a list of collaboration opportunities, and the output is a specific collaboration proposal. Specifically, a generative AI model is used to construct a proposal in natural language, and notifications are distributed through communication tools.

[0165] Step 4:

[0166] The device collects and analyzes emotional data from users who receive notifications. The input is the user's reaction, and the output is an emotional state report. Specifically, it uses a camera and microphone to record the user's facial expressions and voice, and then analyzes them with emotion analysis software.

[0167] Step 5:

[0168] The server adjusts suggestions based on the emotional state report and provides necessary feedback to the user. The input is the emotional state report, and the output is the adjusted suggestions and feedback. Its operation includes modifying suggestions based on emotions and optimizing notification timing.

[0169] Step 6:

[0170] The user, a manager, decides to implement the collaborative project based on the proposal and enters it into the project management tool. The input is the adjusted proposal, and the output is the project plan and resource assignment. Specifically, tasks are organized and progress is tracked using the project management platform.

[0171] Step 7:

[0172] The server continuously analyzes sentiment data obtained from users during the project and provides real-time feedback. Input is continuous sentiment data, and output is suggestions for project improvements and support information. это обеспечивает оптимизацию управления проектами на основе эмоциональных реакций.

[0173] (Application Example 2)

[0174] 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".

[0175] In modern organizations, interdepartmental collaboration is becoming increasingly important, but efficiently identifying and properly managing opportunities for collaboration between different departments is challenging. Furthermore, it is necessary to accurately understand the responses of those involved to proposed collaboration projects and flexibly optimize the content and progress of those proposals based on that understanding. However, existing systems currently lack sufficient advanced collaboration management capabilities, hindering efficient project execution.

[0176] 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.

[0177] In this invention, the server includes means for collecting data on tasks performed by multiple departments within an organization using an information processing device, means for analyzing the collected data and identifying opportunities for collaboration between different departments, means for notifying relevant personnel within the organization of the identified collaboration proposals, means for analyzing the user's response to the notification and optimizing the proposal content using an engine, and means for providing feedback to the user based on the analysis results using a terminal. This makes it possible to efficiently build cooperative relationships between departments and manage collaborative projects more flexibly and effectively.

[0178] An "information processing device" is a device that manages the entire system for collecting large amounts of business data generated within an organization and performing various analyses on it.

[0179] "Means of collection" refers to methods and functions for accurately and efficiently collecting business data between departments.

[0180] "Means of analysis" refers to the function of using collected data to identify possibilities for collaboration not only within a single department but also between different departments.

[0181] A "means of notification" refers to a mechanism for informing relevant parties within an organization about a identified collaboration proposal.

[0182] "Means of supporting consensus building" refers to a function that facilitates the approval of proposed collaboration plans within the organization.

[0183] "Means of managing progress" refers to the function of constantly monitoring the progress of agreed-upon collaborative projects and making appropriate adjustments.

[0184] The term "engine" refers to a set of algorithms used to analyze user feedback and optimize notification content based on that response.

[0185] A "terminal" is an interface device that a user can directly interact with, and is a device used for displaying and inputting information.

[0186] A "means of providing feedback" refers to a system for providing further suggestions and additional information in a timely manner, based on user reactions.

[0187] This invention is an information processing system for effectively promoting collaboration between departments. A server collects business data from each department within an organization and analyzes the data using a dedicated AI analysis engine. This analysis identifies opportunities for collaboration between different departments. The server generates specific collaboration proposals and notifies terminals via the network. The terminals display the proposals to the relevant personnel and receive feedback from users.

[0188] The emotion engine operates on the device, analyzing user responses in real time to optimize the content of suggestions and the timing of notifications. For example, if a user shows interest in a suggestion, the emotion engine analyzes this response and provides additional feedback and information tailored to the user's interests. This enables the smooth progress of collaborative projects.

[0189] As a concrete example, consider a new product development project. The server analyzes sales data and market analysis data and proposes collaboration between the research and sales departments. Project management tools are used to share the progress of each department in real time. If the user reviews the proposal and shows interest, the emotion engine supports the project's success by providing additional market data and development schedules. In this way, resources within the organization are effectively utilized, and innovation is accelerated.

[0190] An example of a prompt to input into a generative AI model is, "Analyze the user's response to this suggestion and determine what additional information should be provided."

[0191] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0192] Step 1:

[0193] The server collects business data from each department within the organization. It takes data indicating the work content and progress of each department as input. This data is centrally collected through an interface and converted into a standardized format, enabling subsequent analysis.

[0194] Step 2:

[0195] The server sends collected data to an AI analysis engine to identify opportunities for collaboration between different departments. It uses standardized business data as input and leverages data mining and pattern recognition technologies. The output generates a list of promising project candidates, forming proposals to encourage interdepartmental cooperation.

[0196] Step 3:

[0197] The server generates proposals for identified collaborative projects and notifies terminals. The input is a list of project candidates generated by an AI analysis engine. The proposals are notified to the relevant departments and personnel in an appropriate message format. This output serves as the foundation for ensuring that the proposals reach and are taken into consideration by users.

[0198] Step 4:

[0199] The terminal receives suggestions and displays them to the user. It receives suggestion notifications sent from the server as input and aggregates and displays them through the user interface. The user reviews the suggestions and provides feedback if they are interested or have an opinion. This feedback information is output and the process proceeds to the next sentiment analysis step.

[0200] Step 5:

[0201] The device activates an emotion engine to analyze user feedback. The input is response data submitted by the user. Natural language processing technology is used to identify the user's emotional state and response attributes. Based on the output information obtained from this analysis, the system optimizes the content of suggestions and the timing of notifications.

[0202] Step 6:

[0203] The server receives the analysis results from the emotion engine, optimizes the suggestions, and generates feedback. It references the analyzed emotion data as input and updates any necessary additional information and details. The output is feedback information that is re-notified to the user, with adjustments made to deepen their understanding of the suggestions and facilitate collaborative implementation.

[0204] 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.

[0205] 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.

[0206] 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.

[0207] [Second Embodiment]

[0208] Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment.

[0209] 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.

[0210] 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).

[0211] 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.

[0212] 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.

[0213] 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).

[0214] 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.

[0215] 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.

[0216] 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.

[0217] 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.

[0218] 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.

[0219] 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".

[0220] The collaboration-promoting system of the present invention is a network system that includes a server that functions as an information processing device and terminals that serve as an interface with users. This system identifies potential collaboration opportunities by collecting and analyzing data related to the work performed by each department within an organization.

[0221] The server first retrieves the necessary data from each department's project management system and resource management database. This data includes project progress, resources used, and related work tasks. The server then integrates this data and formats it into a format that is easy to analyze.

[0222] Next, the server uses AI algorithms to analyze the data and identify potential for interdepartmental collaboration. The analysis utilizes text mining and machine learning techniques to identify departments with similar projects or complementary resources. For example, it can link a new product being planned by the product development department with other departments possessing the necessary supporting technologies.

[0223] Once processing is complete, the server notifies the terminal of the proposed collaboration. The terminal then displays the collaboration proposal to the managers of each department. The managers, as users, review the proposal, decide whether to start the collaborative project based on its content, and make any necessary planning adjustments.

[0224] Furthermore, the server monitors the progress of collaborative projects through project management tools, centralizing resource allocation and schedule management. This allows for real-time monitoring of project status and rapid sharing of necessary information with stakeholders. Schedule revisions and resource reallocations can also be automatically performed to maintain appropriate progress.

[0225] For example, when the product development department and the marketing department begin collaborating in connection with the development of a new product, the server facilitates communication between the departments and provides timely information for jointly formulating a promotional strategy. In this way, the present invention strengthens mutual cooperation within the organization and promotes innovation.

[0226] The following describes the processing flow.

[0227] Step 1:

[0228] The server accesses project management and resource management databases used by various departments within the organization to collect relevant business data. This includes information on project progress, resources used, and related tasks.

[0229] Step 2:

[0230] The server integrates and cleans the collected data, converting it into a consistent format. This process eliminates data duplication and inconsistencies, making it easier to analyze.

[0231] Step 3:

[0232] The server uses AI models to analyze data and identify potential collaboration opportunities across multiple departments. Here, text mining and machine learning algorithms are applied to find similar and complementary projects and resources.

[0233] Step 4:

[0234] The server creates a collaboration proposal based on the analysis results and sends the details to the terminal. This includes which departments should work together and specific collaboration themes.

[0235] Step 5:

[0236] The terminal notifies managers in each department of collaboration proposals received from the server and displays them on the screen. Managers review the proposals and make decisions regarding the implementation of collaborative projects.

[0237] Step 6:

[0238] The user, who is also a manager, forms agreements regarding the collaborative project and adjusts the necessary plans and team composition. This leads to the development of concrete action plans and schedules.

[0239] Step 7:

[0240] The server supports the progress of collaborative projects by utilizing project management tools. This includes real-time progress reporting, resource allocation coordination, and schedule management.

[0241] Step 8:

[0242] The server monitors the project's progress, makes adjustments as needed when issues arise, and provides necessary feedback to stakeholders.

[0243] (Example 1)

[0244] 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."

[0245] When departments within an organization operate in isolation, potential complementary projects and resources go unutilized, and the synergistic effects of collaboration are not fully realized. Therefore, there is a need for a system that effectively identifies opportunities for interdepartmental collaboration and supports their realization.

[0246] 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.

[0247] In this invention, the server includes means for collecting information on activities performed by multiple organizational units through a data processing system, means for formatting the collected information and converting it into an analyzable format, and means for analyzing the converted information using an AI algorithm to identify organizational units with similar activities or complementary resources. This makes it possible to discover potential opportunities for collaboration within the organization and to promote collaboration efficiently and effectively.

[0248] A "data processing system" is a computer system designed to collect, format, and analyze activity information within an organization.

[0249] An "organizational unit" refers to a department or team that independently carries out specific tasks or activities.

[0250] An "AI algorithm" is a computational method that uses machine learning techniques to analyze data and identify patterns and relationships.

[0251] An "activity management tool" is software used to monitor the progress and resource allocation of collaborative activities and to share information.

[0252] "Real-time" refers to a state where processing and data sharing occur instantly without delay.

[0253] "Generative AI technology" refers to technology in which AI generates new content or suggestions from a given prompt.

[0254] A "prompt message" is a sentence containing instructions or questions that are entered into an AI to perform a specific task.

[0255] The embodiments for carrying out the invention will now be described. This invention provides a system to support more effective collaboration among multiple departments within an organization. Specific examples of the invention are shown below.

[0256] The server, acting as a data processing system, accesses project management and resource management databases used by each department. It uses APIs and database connections to collect information from these databases, such as project progress, resources used, and related business tasks. The collected information is then normalized and cleansed, and converted into a format suitable for analysis.

[0257] The server analyzes the formatted data using AI algorithms. Leveraging text mining techniques, it extracts key items from the collected information and uses machine learning models to identify departments with similar projects or complementary resources. Based on the AI ​​analysis, the server identifies potential collaboration opportunities and notifies the terminals accordingly.

[0258] The terminal displays collaboration proposals received from the server to managers in each department. Managers review the presented information and initiate collaborative projects or make appropriate adjustments to the plan. Based on the information provided by the system, the managers in each department, as users, efficiently coordinate and optimize resources during project progress.

[0259] As a concrete example, in a new product development project, the server can identify synergies between the product development and marketing departments and provide information to enable both departments to collaborate in formulating a promotional strategy. In this way, it is possible to promote mutual cooperation within the organization and improve operational efficiency.

[0260] When utilizing generative AI technology, we will use the following text as an example of a prompt: "Please propose a strategy to optimize interdepartmental collaboration." This will allow the AI ​​to generate specific collaboration strategies and provide suggestions tailored to the organization's needs.

[0261] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0262] Step 1:

[0263] The server accesses project management and resource management databases used by various departments within the organization. Inputs include project progress, resource usage, and work task information. Specifically, it collects this information through APIs and database connections and converts it into a structured format. This allows for the collection of foundational data necessary to discover collaboration opportunities.

[0264] Step 2:

[0265] The server formats the collected data and converts it into a format that is easy to analyze. The input is the data obtained in Step 1. At this stage, data normalization and cleansing are performed to remove redundant information and unnecessary data, preparing it for analysis. The data is converted into text format or tabular format, which allows the AI ​​to perform analysis efficiently.

[0266] Step 3:

[0267] The server analyzes the formatted data using an AI algorithm. The input is the data processed in step 2. Here, text mining techniques are used to extract important keywords, and a machine learning model is used to identify departments with similar projects or complementary resources. The output is a list of relevant departments and projects as potential collaboration opportunities.

[0268] Step 4:

[0269] The server notifies the terminal of the potential for collaboration identified through the analysis. The input is the output result from step 3. Specifically, it compiles the information as a collaboration proposal and prepares it for immediate transmission to the department manager. This notification causes the collaboration proposal to be displayed on the terminal.

[0270] Step 5:

[0271] The terminal displays received collaboration proposals to the user, who is a manager. The input is the content of the collaboration proposal sent from the server. The terminal designs the screen to present the proposal clearly to the manager. The user manager reviews this, decides whether to start the collaboration project, and proceeds with any necessary plan adjustments.

[0272] Step 6:

[0273] The server monitors ongoing collaborative projects using project management tools. Inputs include project progress data and resource allocation information. The server updates this information in real time and automatically adjusts schedules and reallocates resources as needed, supporting the efficient progress of projects.

[0274] (Application Example 1)

[0275] Next, we will explain Application Example 1. In the following explanation, the data processing device 12 will be referred to as the "server," and the smart glasses 214 will be referred to as the "terminal."

[0276] In organizations such as factories, each department often operates independently, resulting in underutilization of opportunities for inter-departmental collaboration and hindering efficient project progress. Furthermore, even after collaboration begins, insufficient optimization of project progress and adequate feedback on results make inter-departmental coordination a challenge.

[0277] 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.

[0278] In this invention, the server includes means for collecting business data within the organization using an information processing device, means for analyzing the collected data to identify opportunities for collaboration, and means for notifying the person in charge of the collaboration proposal. This enables strengthened inter-departmental collaboration and improved project efficiency. Furthermore, by utilizing a generative AI model to generate predictive suggestions for collaboration opportunities and prompt messages, further business improvements can be achieved.

[0279] An "information processing device" is a computer system that has the function of collecting and analyzing business data, and plays a role in identifying and proposing opportunities for collaboration.

[0280] A "mobile communication terminal" is a portable communication device such as a smartphone or a tablet, and is a device for receiving notifications of collaboration opportunities and project management information.

[0281] A "generative AI model" is an algorithm that uses machine learning and data analysis to generate predictions and prompt texts for collaboration opportunities, and is an artificial intelligence system incorporated into an information processing device.

[0282] A "project management tool" is software for monitoring the progress of a project and sharing information with relevant parties in real time, and supports efficient project execution within an organization.

[0283] To implement this invention, it is necessary to build an advanced information processing system. The server functions as the main data aggregation point and analysis engine. Specifically, the server uses a RESTful API to obtain data such as resources and project progress status from each department within the organization. The obtained data is saved in a database, and then a machine learning model is built using Python and TensorFlow to analyze the potential for collaboration.

[0284] The generated collaboration proposals are notified to the mobile communication terminal through Firebase. The terminal is a smartphone or a tablet, displays proposals and notifications to the user, and functions as an interface for smoothly advancing important communications. The generative AI model generates prompt texts based on data in real time.

[0285] As a specific example, if the manufacturing department develops a new production process, the server may analyze the data related to the new process and propose the possibility of collaborating with the design department. A prompt text such as "Propose which departments should collaborate to execute the manufacturing department's new process improvement proposal" is sent to the mobile communication terminal. With this prompt text, the user can immediately understand the need for collaboration and quickly take the next step.

[0286] The flow of the specific process in Application Example 1 will be described with reference to FIG. 12.

[0287] Step 1:

[0288] The server obtains business data from each department within the organization. Specifically, it collects data from the project management system and the resource management database through the RESTful API. The input is data related to the progress status and resource status of the department, and the output is an integrated dataset containing this information. This dataset has a unified format to provide the information necessary for subsequent analysis.

[0289] Step 2:

[0290] After saving the collected data in the database, the server performs preprocessing using Python scripts. At this time, it performs data cleaning and shaping, and converts it into a format suitable for analysis by the AI model. It also performs operations such as removing duplicate data and filling in missing values. The input is the integrated dataset, and the output is the preprocessed data for analysis.

[0291] Step 3:

[0292] The server performs data analysis using an AI model by utilizing TensorFlow. It identifies potential collaboration opportunities from the data using the generated AI model. At this time, the input is the preprocessed data for analysis, and the output is a list of proposals indicating collaboration opportunities. This list contains information indicating which departments can collaborate.

[0293] Step 4:

[0294] The server notifies each department's terminal of the generated collaboration proposals using Firebase. The input is the list of collaboration proposals, and the output is a notification message to the mobile communication terminal. The content displayed on the terminal includes specific instructions on how the corresponding department should collaborate.

[0295] Step 5:

[0296] Based on the prompt displayed on the terminal, the user confirms the need for collaboration, gathers the relevant departments, and begins working together. Specific actions include setting up meetings and coordinating project schedules. The prompt displayed is, "Suggest which departments should we collaborate with to implement the manufacturing department's new process improvement proposal." The input is the prompt, and the output is an action plan for initiating collaboration.

[0297] 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.

[0298] This invention is a system that combines AI-based data analysis with an emotion engine that recognizes user emotions, effectively promoting collaboration between departments within an organization. This system includes a server as an information processing device and a terminal as an interface.

[0299] The server collects and integrates operational data from multiple departments within the organization. This provides data on project progress, resource usage, and related tasks. This data is then analyzed by AI algorithms to identify potential collaboration opportunities between different departments.

[0300] The server creates a collaboration proposal based on the analysis results and notifies the terminal. The terminal displays this notification to the managers of each department. A key feature of this invention is the use of an emotion engine. The emotion engine analyzes the emotions of the user who receives the proposal through the terminal and optimizes the content of the proposal and the timing of the notification based on the user's reaction. For example, it determines whether the user who sees the proposal is interested or dissatisfied and automatically provides appropriate feedback and supplementary information.

[0301] The management staff, who is the user, decides to implement the collaborative project based on the proposal and makes necessary adjustments. The server manages the progress of the agreed project and shares information in real time using the project management tool. In addition, it analyzes the emotional data of users during the project progress and provides highly convenient feedback to the relevant parties.

[0302] For example, when a collaborative proposal is made in the development of a new product, the emotion engine evaluates the degree of interest in the project from the initial reaction of the head of the development department who received the proposal. Then, according to the degree of interest, it presents details regarding specific action plans and resource allocations, and provides support to smoothly progress the project.

[0303] In this way, the system of the present invention not only promotes collaboration between departments and accelerates innovation within the organization, but also realizes more flexible and effective project management by considering the emotions of users.

[0304] The following describes the processing flow.

[0305] Step 1:

[0306] The server accesses the project management system and resource management database within the organization and collects the business data of each department. The data collected includes the progress of the project, the resources used, and related tasks.

[0307] Step 2:

[0308] The server integrates the collected data and converts it into a format that is easy to analyze. In this process, redundant information is eliminated while maintaining the consistency of the data.

[0309] Step 3:

[0310] The server uses AI algorithms to analyze integrated data and identify potential collaboration opportunities between different departments. The analysis employs machine learning and pattern recognition techniques to find departments with complementary resources or similar targets.

[0311] Step 4:

[0312] The server generates collaboration proposals based on identified collaboration opportunities and sends them as notifications to the terminal. These proposals include specific collaboration themes and project outlines.

[0313] Step 5:

[0314] The terminal displays collaboration proposals received from the server to managers in each department. Simultaneously, it activates an emotion engine to analyze the emotions of managers who view the proposals in real time.

[0315] Step 6:

[0316] The user, a manager, reviews the collaboration proposals presented on the device. The device uses an emotion engine to analyze the manager's reaction and detect the user's interests and concerns.

[0317] Step 7:

[0318] Based on the analysis results of the sentiment engine, the server updates suggestions to be more appropriate and provides supplementary information as needed. This information is optimized to suit the user's situation.

[0319] Step 8:

[0320] Users consider implementing collaborative projects based on updated information and feedback, and form the necessary agreements.

[0321] Step 9:

[0322] The server registers the details of agreed-upon collaborative projects in the project management tool and tracks and manages their progress. Project progress is shared with stakeholders in real time.

[0323] Step 10:

[0324] The server monitors user sentiment throughout the project, analyzes the feedback received, and evaluates each phase of the project. This result is then used to identify areas for improvement when proposing future collaborations.

[0325] (Example 2)

[0326] 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".

[0327] Modern organizations require efficient collaboration between different departments, but opportunities for collaboration can be missed due to information fragmentation and a lack of communication. Furthermore, a lack of appropriate feedback based on the feelings of those receiving proposals can negatively impact the quality and progress of collaboration. Therefore, there is a need for a system that automatically identifies opportunities for interdepartmental collaboration and provides proposals and feedback that take stakeholders' feelings into account, thereby facilitating smooth project progress.

[0328] 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.

[0329] In this invention, the server includes means for collecting information on tasks performed by multiple departments within an organization, means for analyzing the collected information and identifying possibilities for collaboration between different departments, means for transmitting the identified collaboration proposals to relevant parties within the organization, and means for evaluating the emotional state of the information recipients and dynamically adjusting the content of the proposals. This enables the rapid and accurate identification of opportunities for collaboration between departments and supports collaboration while taking into account the emotions of the parties involved.

[0330] An "information processing device" is a hardware or software system that integrates and analyzes data collected within an organization, and identifies and notifies users of opportunities for collaboration.

[0331] "Business information" refers to data related to the work performed by multiple departments within an organization, including information such as project progress, task assignments, and resource usage.

[0332] "Potential for collaboration" refers to proposals or opportunities resulting from an assessment of the likelihood that different departments can work together to create new value.

[0333] A "proposal" is a specific plan or proposal for collaboration generated based on the analysis results, and is information that is communicated to stakeholders within the organization.

[0334] "Emotional state" refers to the mental response exhibited by the information recipient upon receiving a proposal, and is evaluated by an emotion analysis device.

[0335] This invention is a system that uses a server as an information processing device and a terminal as an interface device. The server is responsible for collecting and integrating business information from each department within the organization and analyzing it using AI algorithms. Specifically, it collects project progress and resource usage information via the organization's database system and performs data analysis using scikit-learn, a Python machine learning library. This identifies possibilities for collaboration between different departments.

[0336] The server automatically generates identified collaboration proposals and sends them to relevant stakeholders within the organization. These generated proposals are typically displayed on devices through project management tools and communication platforms. When managers and other stakeholders receive the proposals, the devices use sentiment analysis technology to assess their emotional state. This sentiment analysis utilizes Google Cloud's Vision API and speech recognition technology.

[0337] The user, a manager, decides on collaborative projects based on the proposals and manages their progress by entering the information into a project management tool via the server. The server also records the project progress in real time on an information processing device and provides necessary feedback to stakeholders involved in the project.

[0338] For example, in the development of a new product, the system proposes a project that the development and marketing departments should undertake together and notifies the managers of each department. Based on the level of interest indicated by the managers who receive the notification, the system provides information on specific project details and resource allocation. This ensures that the project progresses smoothly and improves the overall efficiency of the organization.

[0339] An example of a prompt message for a generative AI model is: "Based on the initial response shown by the development manager, propose the optimal way to proceed with the new product development project." This allows for flexible feedback based on sentiment analysis, which is expected to increase the success rate of collaborative projects.

[0340] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0341] Step 1:

[0342] The server collects operational information from each department within the organization and integrates it into a database. This collected data includes project progress, task assignments, and resource usage. It receives operational information from each department as input and outputs it as an integrated dataset. Specifically, it centralizes information using a database system.

[0343] Step 2:

[0344] The server applies AI algorithms to an integrated dataset to analyze potential collaboration opportunities. The input is integrated business information, and the output generates a list of potential interdepartmental collaboration opportunities. Python machine learning libraries are used for data processing, including clustering and pattern recognition.

[0345] Step 3:

[0346] The server creates collaboration proposals based on the collaboration opportunities identified through analysis and sends them to the relevant parties. The input is a list of collaboration opportunities, and the output is a specific collaboration proposal. Specifically, a generative AI model is used to construct a proposal in natural language, and notifications are distributed through communication tools.

[0347] Step 4:

[0348] The device collects and analyzes emotional data from users who receive notifications. The input is the user's reaction, and the output is an emotional state report. Specifically, it uses a camera and microphone to record the user's facial expressions and voice, and then analyzes them with emotion analysis software.

[0349] Step 5:

[0350] The server adjusts suggestions based on the emotional state report and provides necessary feedback to the user. The input is the emotional state report, and the output is the adjusted suggestions and feedback. Its operation includes modifying suggestions based on emotions and optimizing notification timing.

[0351] Step 6:

[0352] The user, a manager, decides to implement the collaborative project based on the proposal and enters it into the project management tool. The input is the adjusted proposal, and the output is the project plan and resource assignment. Specifically, tasks are organized and progress is tracked using the project management platform.

[0353] Step 7:

[0354] The server continuously analyzes sentiment data obtained from users during the project and provides real-time feedback. Input is continuous sentiment data, and output is suggestions for project improvements and support information. это обеспечивает оптимизацию управления проектами на основе эмоциональных реакций.

[0355] (Application Example 2)

[0356] 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".

[0357] In modern organizations, interdepartmental collaboration is becoming increasingly important, but efficiently identifying and properly managing opportunities for collaboration between different departments is challenging. Furthermore, it is necessary to accurately understand the responses of those involved to proposed collaboration projects and flexibly optimize the content and progress of those proposals based on that understanding. However, existing systems currently lack sufficient advanced collaboration management capabilities, hindering efficient project execution.

[0358] 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.

[0359] In this invention, the server includes means for collecting data on tasks performed by multiple departments within an organization using an information processing device, means for analyzing the collected data and identifying opportunities for collaboration between different departments, means for notifying relevant personnel within the organization of the identified collaboration proposals, means for analyzing the user's response to the notification and optimizing the proposal content using an engine, and means for providing feedback to the user based on the analysis results using a terminal. This makes it possible to efficiently build cooperative relationships between departments and manage collaborative projects more flexibly and effectively.

[0360] An "information processing device" is a device that manages the entire system for collecting large amounts of business data generated within an organization and performing various analyses on it.

[0361] "Means of collection" refers to methods and functions for accurately and efficiently collecting business data between departments.

[0362] "Means of analysis" refers to the function of using collected data to identify possibilities for collaboration not only within a single department but also between different departments.

[0363] A "means of notification" refers to a mechanism for informing relevant parties within an organization about a identified collaboration proposal.

[0364] "Means of supporting consensus building" refers to a function that facilitates the approval of proposed collaboration plans within the organization.

[0365] "Means of managing progress" refers to the function of constantly monitoring the progress of agreed-upon collaborative projects and making appropriate adjustments.

[0366] The term "engine" refers to a set of algorithms used to analyze user feedback and optimize notification content based on that response.

[0367] A "terminal" is an interface device that a user can directly interact with, and is a device used for displaying and inputting information.

[0368] A "means of providing feedback" refers to a system for providing further suggestions and additional information in a timely manner, based on user reactions.

[0369] This invention is an information processing system for effectively promoting collaboration between departments. A server collects business data from each department within an organization and analyzes the data using a dedicated AI analysis engine. This analysis identifies opportunities for collaboration between different departments. The server generates specific collaboration proposals and notifies terminals via the network. The terminals display the proposals to the relevant personnel and receive feedback from users.

[0370] The emotion engine operates on the device, analyzing user responses in real time to optimize the content of suggestions and the timing of notifications. For example, if a user shows interest in a suggestion, the emotion engine analyzes this response and provides additional feedback and information tailored to the user's interests. This enables the smooth progress of collaborative projects.

[0371] As a concrete example, consider a new product development project. The server analyzes sales data and market analysis data and proposes collaboration between the research and sales departments. Project management tools are used to share the progress of each department in real time. If the user reviews the proposal and shows interest, the emotion engine supports the project's success by providing additional market data and development schedules. In this way, resources within the organization are effectively utilized, and innovation is accelerated.

[0372] An example of a prompt to input into a generative AI model is, "Analyze the user's response to this suggestion and determine what additional information should be provided."

[0373] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0374] Step 1:

[0375] The server collects business data from each department within the organization. It takes data indicating the work content and progress of each department as input. This data is centrally collected through an interface and converted into a standardized format, enabling subsequent analysis.

[0376] Step 2:

[0377] The server sends collected data to an AI analysis engine to identify opportunities for collaboration between different departments. It uses standardized business data as input and leverages data mining and pattern recognition technologies. The output generates a list of promising project candidates, forming proposals to encourage interdepartmental cooperation.

[0378] Step 3:

[0379] The server generates proposals for identified collaborative projects and notifies terminals. The input is a list of project candidates generated by an AI analysis engine. The proposals are notified to the relevant departments and personnel in an appropriate message format. This output serves as the foundation for ensuring that the proposals reach and are taken into consideration by users.

[0380] Step 4:

[0381] The terminal receives suggestions and displays them to the user. It receives suggestion notifications sent from the server as input and aggregates and displays them through the user interface. The user reviews the suggestions and provides feedback if they are interested or have an opinion. This feedback information is output and the process proceeds to the next sentiment analysis step.

[0382] Step 5:

[0383] The device activates an emotion engine to analyze user feedback. The input is response data submitted by the user. Natural language processing technology is used to identify the user's emotional state and response attributes. Based on the output information obtained from this analysis, the system optimizes the content of suggestions and the timing of notifications.

[0384] Step 6:

[0385] The server receives the analysis results from the emotion engine, optimizes the suggestions, and generates feedback. It references the analyzed emotion data as input and updates any necessary additional information and details. The output is feedback information that is re-notified to the user, with adjustments made to deepen their understanding of the suggestions and facilitate collaborative implementation.

[0386] 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.

[0387] 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.

[0388] 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.

[0389] [Third Embodiment]

[0390] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[0391] 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.

[0392] 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).

[0393] 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.

[0394] 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.

[0395] 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).

[0396] 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.

[0397] 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.

[0398] 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.

[0399] 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.

[0400] 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.

[0401] 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".

[0402] The collaboration-promoting system of the present invention is a network system that includes a server that functions as an information processing device and terminals that serve as an interface with users. This system identifies potential collaboration opportunities by collecting and analyzing data related to the work performed by each department within an organization.

[0403] The server first retrieves the necessary data from each department's project management system and resource management database. This data includes project progress, resources used, and related work tasks. The server then integrates this data and formats it into a format that is easy to analyze.

[0404] Next, the server uses AI algorithms to analyze the data and identify potential for interdepartmental collaboration. The analysis utilizes text mining and machine learning techniques to identify departments with similar projects or complementary resources. For example, it can link a new product being planned by the product development department with other departments possessing the necessary supporting technologies.

[0405] Once processing is complete, the server notifies the terminal of the proposed collaboration. The terminal then displays the collaboration proposal to the managers of each department. The managers, as users, review the proposal, decide whether to start the collaborative project based on its content, and make any necessary planning adjustments.

[0406] Furthermore, the server monitors the progress of collaborative projects through project management tools, centralizing resource allocation and schedule management. This allows for real-time monitoring of project status and rapid sharing of necessary information with stakeholders. Schedule revisions and resource reallocations can also be automatically performed to maintain appropriate progress.

[0407] For example, when the product development department and the marketing department begin collaborating in connection with the development of a new product, the server facilitates communication between the departments and provides timely information for jointly formulating a promotional strategy. In this way, the present invention strengthens mutual cooperation within the organization and promotes innovation.

[0408] The following describes the processing flow.

[0409] Step 1:

[0410] The server accesses project management and resource management databases used by various departments within the organization to collect relevant business data. This includes information on project progress, resources used, and related tasks.

[0411] Step 2:

[0412] The server integrates and cleans the collected data, converting it into a consistent format. This process eliminates data duplication and inconsistencies, making it easier to analyze.

[0413] Step 3:

[0414] The server uses AI models to analyze data and identify potential collaboration opportunities across multiple departments. Here, text mining and machine learning algorithms are applied to find similar and complementary projects and resources.

[0415] Step 4:

[0416] The server creates a collaboration proposal based on the analysis results and sends the details to the terminal. This includes which departments should work together and specific collaboration themes.

[0417] Step 5:

[0418] The terminal notifies managers in each department of collaboration proposals received from the server and displays them on the screen. Managers review the proposals and make decisions regarding the implementation of collaborative projects.

[0419] Step 6:

[0420] The user, who is also a manager, forms agreements regarding the collaborative project and adjusts the necessary plans and team composition. This leads to the development of concrete action plans and schedules.

[0421] Step 7:

[0422] The server supports the progress of collaborative projects by utilizing project management tools. This includes real-time progress reporting, resource allocation coordination, and schedule management.

[0423] Step 8:

[0424] The server monitors the project's progress, makes adjustments as needed when issues arise, and provides necessary feedback to stakeholders.

[0425] (Example 1)

[0426] 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."

[0427] When departments within an organization operate in isolation, potential complementary projects and resources go unutilized, and the synergistic effects of collaboration are not fully realized. Therefore, there is a need for a system that effectively identifies opportunities for interdepartmental collaboration and supports their realization.

[0428] 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.

[0429] In this invention, the server includes means for collecting information on activities performed by multiple organizational units through a data processing system, means for formatting the collected information and converting it into an analyzable format, and means for analyzing the converted information using an AI algorithm to identify organizational units with similar activities or complementary resources. This makes it possible to discover potential opportunities for collaboration within the organization and to promote collaboration efficiently and effectively.

[0430] A "data processing system" is a computer system designed to collect, format, and analyze activity information within an organization.

[0431] An "organizational unit" refers to a department or team that independently carries out specific tasks or activities.

[0432] An "AI algorithm" is a computational method that uses machine learning techniques to analyze data and identify patterns and relationships.

[0433] An "activity management tool" is software used to monitor the progress and resource allocation of collaborative activities and to share information.

[0434] "Real-time" refers to a state where processing and data sharing occur instantly without delay.

[0435] "Generative AI technology" refers to technology in which AI generates new content or suggestions from a given prompt.

[0436] A "prompt message" is a sentence containing instructions or questions that are entered into an AI to perform a specific task.

[0437] The embodiments for carrying out the invention will now be described. This invention provides a system to support more effective collaboration among multiple departments within an organization. Specific examples of the invention are shown below.

[0438] The server, acting as a data processing system, accesses project management and resource management databases used by each department. It uses APIs and database connections to collect information from these databases, such as project progress, resources used, and related business tasks. The collected information is then normalized and cleansed, and converted into a format suitable for analysis.

[0439] The server analyzes the formatted data using AI algorithms. Leveraging text mining techniques, it extracts key items from the collected information and uses machine learning models to identify departments with similar projects or complementary resources. Based on the AI ​​analysis, the server identifies potential collaboration opportunities and notifies the terminals accordingly.

[0440] The terminal displays collaboration proposals received from the server to managers in each department. Managers review the presented information and initiate collaborative projects or make appropriate adjustments to the plan. Based on the information provided by the system, the managers in each department, as users, efficiently coordinate and optimize resources during project progress.

[0441] As a concrete example, in a new product development project, the server can identify synergies between the product development and marketing departments and provide information to enable both departments to collaborate in formulating a promotional strategy. In this way, it is possible to promote mutual cooperation within the organization and improve operational efficiency.

[0442] When utilizing generative AI technology, we will use the following text as an example of a prompt: "Please propose a strategy to optimize interdepartmental collaboration." This will allow the AI ​​to generate specific collaboration strategies and provide suggestions tailored to the organization's needs.

[0443] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0444] Step 1:

[0445] The server accesses project management and resource management databases used by various departments within the organization. Inputs include project progress, resource usage, and work task information. Specifically, it collects this information through APIs and database connections and converts it into a structured format. This allows for the collection of foundational data necessary to discover collaboration opportunities.

[0446] Step 2:

[0447] The server formats the collected data and converts it into a format that is easy to analyze. The input is the data obtained in Step 1. At this stage, data normalization and cleansing are performed to remove redundant information and unnecessary data, preparing it for analysis. The data is converted into text format or tabular format, which allows the AI ​​to perform analysis efficiently.

[0448] Step 3:

[0449] The server analyzes the formatted data using an AI algorithm. The input is the data processed in step 2. Here, text mining techniques are used to extract important keywords, and a machine learning model is used to identify departments with similar projects or complementary resources. The output is a list of relevant departments and projects as potential collaboration opportunities.

[0450] Step 4:

[0451] The server notifies the terminal of the potential for collaboration identified through the analysis. The input is the output result from step 3. Specifically, it compiles the information as a collaboration proposal and prepares it for immediate transmission to the department manager. This notification causes the collaboration proposal to be displayed on the terminal.

[0452] Step 5:

[0453] The terminal displays received collaboration proposals to the user, who is a manager. The input is the content of the collaboration proposal sent from the server. The terminal designs the screen to present the proposal clearly to the manager. The user manager reviews this, decides whether to start the collaboration project, and proceeds with any necessary plan adjustments.

[0454] Step 6:

[0455] The server monitors ongoing collaborative projects using project management tools. Inputs include project progress data and resource allocation information. The server updates this information in real time and automatically adjusts schedules and reallocates resources as needed, supporting the efficient progress of projects.

[0456] (Application Example 1)

[0457] 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."

[0458] In organizations such as factories, each department often operates independently, resulting in underutilization of opportunities for inter-departmental collaboration and hindering efficient project progress. Furthermore, even after collaboration begins, insufficient optimization of project progress and adequate feedback on results make inter-departmental coordination a challenge.

[0459] 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.

[0460] In this invention, the server includes means for collecting business data within the organization using an information processing device, means for analyzing the collected data to identify opportunities for collaboration, and means for notifying the person in charge of the collaboration proposal. This enables strengthened inter-departmental collaboration and improved project efficiency. Furthermore, by utilizing a generative AI model to generate predictive suggestions for collaboration opportunities and prompt messages, further business improvements can be achieved.

[0461] An "information processing device" is a computer system that has the function of collecting and analyzing business data, and plays a role in identifying and proposing opportunities for collaboration.

[0462] A "mobile communication terminal" is a portable communication device such as a smartphone or tablet, used to receive notifications about collaboration opportunities and project management information.

[0463] A "generative AI model" is an artificial intelligence system embedded in an information processing device that uses machine learning and data analysis to generate predictions and prompts for collaboration opportunities.

[0464] A "project management tool" is software used to monitor project progress and share information with stakeholders in real time, supporting efficient project execution within an organization.

[0465] To implement this invention, it is necessary to build an advanced information processing system. The server functions as a primary data aggregation point and analysis engine. Specifically, the server uses a RESTful API to retrieve data such as resources and project progress from various departments within the organization. After the obtained data is stored in a database, a machine learning model is built using Python and TensorFlow to analyze the possibilities of collaboration.

[0466] The generated collaboration proposals are notified to mobile communication devices via Firebase. These devices, such as smartphones and tablets, function as an interface to display proposals and notifications to users and facilitate important communication. The generation AI model generates data-driven prompts in real time.

[0467] For example, if the manufacturing department develops a new production process, the server may analyze data related to the new process and suggest collaboration with the design department. A prompt message such as "Suggest which departments we should collaborate with to implement the manufacturing department's new process improvement proposal" is sent to the mobile communication terminal. This prompt message allows the user to immediately understand the need for collaboration and quickly take the next steps.

[0468] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0469] Step 1:

[0470] The server retrieves business data from various departments within the organization. Specifically, it collects data from project management systems and resource management databases via RESTful APIs. The input is data on departmental progress and resource status, and the output is an integrated dataset containing this information. This dataset is formatted to provide the information necessary for subsequent analysis.

[0471] Step 2:

[0472] The server stores the collected data in a database and then performs preprocessing using a Python script. This involves cleaning and formatting the data to convert it into a format suitable for analysis by the AI ​​model. It also performs actions such as removing duplicate data and imputing missing values. The input is an integrated dataset, and the output is preprocessed data for analysis.

[0473] Step 3:

[0474] The server uses TensorFlow to perform data analysis with an AI model. It uses a generative AI model to identify potential collaboration opportunities from the data. The input is pre-processed data for analysis, and the output is a proposed list of collaboration opportunities. This list includes information indicating which departments are capable of collaborating.

[0475] Step 4:

[0476] The server uses Firebase to notify each department's terminal of the collaboration proposals it has generated. The input is a list of collaboration proposals, and the output is a notification message sent to the mobile communication terminal. The content displayed on the terminal includes specific instructions on how the relevant department should collaborate.

[0477] Step 5:

[0478] Based on the prompt displayed on the terminal, the user confirms the need for collaboration, gathers the relevant departments, and begins working together. Specific actions include setting up meetings and coordinating project schedules. The prompt displayed is, "Suggest which departments should we collaborate with to implement the manufacturing department's new process improvement proposal." The input is the prompt, and the output is an action plan for initiating collaboration.

[0479] 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.

[0480] This invention is a system that combines AI-based data analysis with an emotion engine that recognizes user emotions, effectively promoting collaboration between departments within an organization. This system includes a server as an information processing device and a terminal as an interface.

[0481] The server collects and integrates operational data from multiple departments within the organization. This provides data on project progress, resource usage, and related tasks. This data is then analyzed by AI algorithms to identify potential collaboration opportunities between different departments.

[0482] The server creates a collaboration proposal based on the analysis results and notifies the terminal. The terminal displays this notification to the managers of each department. A key feature of this invention is the use of an emotion engine. The emotion engine analyzes the emotions of the user who receives the proposal through the terminal and optimizes the content of the proposal and the timing of the notification based on the user's reaction. For example, it determines whether the user who sees the proposal is interested or dissatisfied and automatically provides appropriate feedback and supplementary information.

[0483] The user, a manager, decides to implement the collaborative project based on the proposal and makes the necessary adjustments. The server manages the progress of the agreed-upon project and shares information in real time using project management tools. It also analyzes user sentiment data during the project and provides useful feedback to stakeholders.

[0484] For example, when a collaboration proposal is made for the development of a new product, the emotion engine evaluates the level of interest in the project based on the initial reaction of the development manager who receives the proposal. Based on that level of interest, it then provides detailed information regarding specific action plans and resource allocation, supporting the smooth progress of the project.

[0485] In this way, the system of the present invention not only promotes interdepartmental collaboration and accelerates innovation within the organization, but also achieves more flexible and effective project management by taking user emotions into consideration.

[0486] The following describes the processing flow.

[0487] Step 1:

[0488] The server accesses the organization's project management and resource management databases to collect operational data from each department. This data includes project progress, resource usage, and related tasks.

[0489] Step 2:

[0490] The server integrates the collected data and converts it into a format that is easy to analyze. This process eliminates redundant information while maintaining data integrity.

[0491] Step 3:

[0492] The server uses AI algorithms to analyze integrated data and identify potential collaboration opportunities between different departments. The analysis employs machine learning and pattern recognition techniques to find departments with complementary resources or similar targets.

[0493] Step 4:

[0494] The server generates collaboration proposals based on identified collaboration opportunities and sends them as notifications to the terminal. These proposals include specific collaboration themes and project outlines.

[0495] Step 5:

[0496] The terminal displays collaboration proposals received from the server to managers in each department. Simultaneously, it activates an emotion engine to analyze the emotions of managers who view the proposals in real time.

[0497] Step 6:

[0498] The user, a manager, reviews the collaboration proposals presented on the device. The device uses an emotion engine to analyze the manager's reaction and detect the user's interests and concerns.

[0499] Step 7:

[0500] Based on the analysis results of the sentiment engine, the server updates suggestions to be more appropriate and provides supplementary information as needed. This information is optimized to suit the user's situation.

[0501] Step 8:

[0502] Users consider implementing collaborative projects based on updated information and feedback, and form the necessary agreements.

[0503] Step 9:

[0504] The server registers the details of agreed-upon collaborative projects in the project management tool and tracks and manages their progress. Project progress is shared with stakeholders in real time.

[0505] Step 10:

[0506] The server monitors user sentiment throughout the project, analyzes the feedback received, and evaluates each phase of the project. This result is then used to identify areas for improvement when proposing future collaborations.

[0507] (Example 2)

[0508] 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."

[0509] Modern organizations require efficient collaboration between different departments, but opportunities for collaboration can be missed due to information fragmentation and a lack of communication. Furthermore, a lack of appropriate feedback based on the feelings of those receiving proposals can negatively impact the quality and progress of collaboration. Therefore, there is a need for a system that automatically identifies opportunities for interdepartmental collaboration and provides proposals and feedback that take stakeholders' feelings into account, thereby facilitating smooth project progress.

[0510] 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.

[0511] In this invention, the server includes means for collecting information on tasks performed by multiple departments within an organization, means for analyzing the collected information and identifying possibilities for collaboration between different departments, means for transmitting the identified collaboration proposals to relevant parties within the organization, and means for evaluating the emotional state of the information recipients and dynamically adjusting the content of the proposals. This enables the rapid and accurate identification of opportunities for collaboration between departments and supports collaboration while taking into account the emotions of the parties involved.

[0512] An "information processing device" is a hardware or software system that integrates and analyzes data collected within an organization, and identifies and notifies users of opportunities for collaboration.

[0513] "Business information" refers to data related to the work performed by multiple departments within an organization, including information such as project progress, task assignments, and resource usage.

[0514] "Potential for collaboration" refers to proposals or opportunities resulting from an assessment of the likelihood that different departments can work together to create new value.

[0515] A "proposal" is a specific plan or proposal for collaboration generated based on the analysis results, and is information that is communicated to stakeholders within the organization.

[0516] "Emotional state" refers to the mental response exhibited by the information recipient upon receiving a proposal, and is evaluated by an emotion analysis device.

[0517] This invention is a system that uses a server as an information processing device and a terminal as an interface device. The server is responsible for collecting and integrating business information from each department within the organization and analyzing it using AI algorithms. Specifically, it collects project progress and resource usage information via the organization's database system and performs data analysis using scikit-learn, a Python machine learning library. This identifies possibilities for collaboration between different departments.

[0518] The server automatically generates identified collaboration proposals and sends them to relevant stakeholders within the organization. These generated proposals are typically displayed on devices through project management tools and communication platforms. When managers and other stakeholders receive the proposals, the devices use sentiment analysis technology to assess their emotional state. This sentiment analysis utilizes Google Cloud's Vision API and speech recognition technology.

[0519] The user, a manager, decides on collaborative projects based on the proposals and manages their progress by entering the information into a project management tool via the server. The server also records the project progress in real time on an information processing device and provides necessary feedback to stakeholders involved in the project.

[0520] For example, in the development of a new product, the system proposes a project that the development and marketing departments should undertake together and notifies the managers of each department. Based on the level of interest indicated by the managers who receive the notification, the system provides information on specific project details and resource allocation. This ensures that the project progresses smoothly and improves the overall efficiency of the organization.

[0521] An example of a prompt message for a generative AI model is: "Based on the initial response shown by the development manager, propose the optimal way to proceed with the new product development project." This allows for flexible feedback based on sentiment analysis, which is expected to increase the success rate of collaborative projects.

[0522] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0523] Step 1:

[0524] The server collects operational information from each department within the organization and integrates it into a database. This collected data includes project progress, task assignments, and resource usage. It receives operational information from each department as input and outputs it as an integrated dataset. Specifically, it centralizes information using a database system.

[0525] Step 2:

[0526] The server applies AI algorithms to an integrated dataset to analyze potential collaboration opportunities. The input is integrated business information, and the output generates a list of potential interdepartmental collaboration opportunities. Python machine learning libraries are used for data processing, including clustering and pattern recognition.

[0527] Step 3:

[0528] The server creates collaboration proposals based on the collaboration opportunities identified through analysis and sends them to the relevant parties. The input is a list of collaboration opportunities, and the output is a specific collaboration proposal. Specifically, a generative AI model is used to construct a proposal in natural language, and notifications are distributed through communication tools.

[0529] Step 4:

[0530] The device collects and analyzes emotional data from users who receive notifications. The input is the user's reaction, and the output is an emotional state report. Specifically, it uses a camera and microphone to record the user's facial expressions and voice, and then analyzes them with emotion analysis software.

[0531] Step 5:

[0532] The server adjusts suggestions based on the emotional state report and provides necessary feedback to the user. The input is the emotional state report, and the output is the adjusted suggestions and feedback. Its operation includes modifying suggestions based on emotions and optimizing notification timing.

[0533] Step 6:

[0534] The user, a manager, decides to implement the collaborative project based on the proposal and enters it into the project management tool. The input is the adjusted proposal, and the output is the project plan and resource assignment. Specifically, tasks are organized and progress is tracked using the project management platform.

[0535] Step 7:

[0536] The server continuously analyzes sentiment data obtained from users during the project and provides real-time feedback. Input is continuous sentiment data, and output is suggestions for project improvements and support information. это обеспечивает оптимизацию управления проектами на основе эмоциональных реакций.

[0537] (Application Example 2)

[0538] 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."

[0539] In modern organizations, interdepartmental collaboration is becoming increasingly important, but efficiently identifying and properly managing opportunities for collaboration between different departments is challenging. Furthermore, it is necessary to accurately understand the responses of those involved to proposed collaboration projects and flexibly optimize the content and progress of those proposals based on that understanding. However, existing systems currently lack sufficient advanced collaboration management capabilities, hindering efficient project execution.

[0540] 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.

[0541] In this invention, the server includes means for collecting data on tasks performed by multiple departments within an organization using an information processing device, means for analyzing the collected data and identifying opportunities for collaboration between different departments, means for notifying relevant personnel within the organization of the identified collaboration proposals, means for analyzing the user's response to the notification and optimizing the proposal content using an engine, and means for providing feedback to the user based on the analysis results using a terminal. This makes it possible to efficiently build cooperative relationships between departments and manage collaborative projects more flexibly and effectively.

[0542] An "information processing device" is a device that manages the entire system for collecting large amounts of business data generated within an organization and performing various analyses on it.

[0543] "Means of collection" refers to methods and functions for accurately and efficiently collecting business data between departments.

[0544] "Means of analysis" refers to the function of using collected data to identify possibilities for collaboration not only within a single department but also between different departments.

[0545] A "means of notification" refers to a mechanism for informing relevant parties within an organization about a identified collaboration proposal.

[0546] "Means of supporting consensus building" refers to a function that facilitates the approval of proposed collaboration plans within the organization.

[0547] "Means of managing progress" refers to the function of constantly monitoring the progress of agreed-upon collaborative projects and making appropriate adjustments.

[0548] The term "engine" refers to a set of algorithms used to analyze user feedback and optimize notification content based on that response.

[0549] A "terminal" is an interface device that a user can directly interact with, and is a device used for displaying and inputting information.

[0550] A "means of providing feedback" refers to a system for providing further suggestions and additional information in a timely manner, based on user reactions.

[0551] This invention is an information processing system for effectively promoting collaboration between departments. A server collects business data from each department within an organization and analyzes the data using a dedicated AI analysis engine. This analysis identifies opportunities for collaboration between different departments. The server generates specific collaboration proposals and notifies terminals via the network. The terminals display the proposals to the relevant personnel and receive feedback from users.

[0552] The emotion engine operates on the device, analyzing user responses in real time to optimize the content of suggestions and the timing of notifications. For example, if a user shows interest in a suggestion, the emotion engine analyzes this response and provides additional feedback and information tailored to the user's interests. This enables the smooth progress of collaborative projects.

[0553] As a concrete example, consider a new product development project. The server analyzes sales data and market analysis data and proposes collaboration between the research and sales departments. Project management tools are used to share the progress of each department in real time. If the user reviews the proposal and shows interest, the emotion engine supports the project's success by providing additional market data and development schedules. In this way, resources within the organization are effectively utilized, and innovation is accelerated.

[0554] An example of a prompt to input into a generative AI model is, "Analyze the user's response to this suggestion and determine what additional information should be provided."

[0555] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0556] Step 1:

[0557] The server collects business data from each department within the organization. It takes data indicating the work content and progress of each department as input. This data is centrally collected through an interface and converted into a standardized format, enabling subsequent analysis.

[0558] Step 2:

[0559] The server sends collected data to an AI analysis engine to identify opportunities for collaboration between different departments. It uses standardized business data as input and leverages data mining and pattern recognition technologies. The output generates a list of promising project candidates, forming proposals to encourage interdepartmental cooperation.

[0560] Step 3:

[0561] The server generates proposals for identified collaborative projects and notifies terminals. The input is a list of project candidates generated by an AI analysis engine. The proposals are notified to the relevant departments and personnel in an appropriate message format. This output serves as the foundation for ensuring that the proposals reach and are taken into consideration by users.

[0562] Step 4:

[0563] The terminal receives suggestions and displays them to the user. It receives suggestion notifications sent from the server as input and aggregates and displays them through the user interface. The user reviews the suggestions and provides feedback if they are interested or have an opinion. This feedback information is output and the process proceeds to the next sentiment analysis step.

[0564] Step 5:

[0565] The device activates an emotion engine to analyze user feedback. The input is response data submitted by the user. Natural language processing technology is used to identify the user's emotional state and response attributes. Based on the output information obtained from this analysis, the system optimizes the content of suggestions and the timing of notifications.

[0566] Step 6:

[0567] The server receives the analysis results from the emotion engine, optimizes the suggestions, and generates feedback. It references the analyzed emotion data as input and updates any necessary additional information and details. The output is feedback information that is re-notified to the user, with adjustments made to deepen their understanding of the suggestions and facilitate collaborative implementation.

[0568] 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.

[0569] 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.

[0570] 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.

[0571] [Fourth Embodiment]

[0572] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[0573] 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.

[0574] 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).

[0575] 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.

[0576] 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.

[0577] 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).

[0578] 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.

[0579] 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.

[0580] 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.

[0581] 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.

[0582] 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.

[0583] 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.

[0584] 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".

[0585] The collaboration-promoting system of the present invention is a network system that includes a server that functions as an information processing device and terminals that serve as an interface with users. This system identifies potential collaboration opportunities by collecting and analyzing data related to the work performed by each department within an organization.

[0586] The server first retrieves the necessary data from each department's project management system and resource management database. This data includes project progress, resources used, and related work tasks. The server then integrates this data and formats it into a format that is easy to analyze.

[0587] Next, the server uses AI algorithms to analyze the data and identify potential for interdepartmental collaboration. The analysis utilizes text mining and machine learning techniques to identify departments with similar projects or complementary resources. For example, it can link a new product being planned by the product development department with other departments possessing the necessary supporting technologies.

[0588] Once processing is complete, the server notifies the terminal of the proposed collaboration. The terminal then displays the collaboration proposal to the managers of each department. The managers, as users, review the proposal, decide whether to start the collaborative project based on its content, and make any necessary planning adjustments.

[0589] Furthermore, the server monitors the progress of collaborative projects through project management tools, centralizing resource allocation and schedule management. This allows for real-time monitoring of project status and rapid sharing of necessary information with stakeholders. Schedule revisions and resource reallocations can also be automatically performed to maintain appropriate progress.

[0590] For example, when the product development department and the marketing department begin collaborating in connection with the development of a new product, the server facilitates communication between the departments and provides timely information for jointly formulating a promotional strategy. In this way, the present invention strengthens mutual cooperation within the organization and promotes innovation.

[0591] The following describes the processing flow.

[0592] Step 1:

[0593] The server accesses project management and resource management databases used by various departments within the organization to collect relevant business data. This includes information on project progress, resources used, and related tasks.

[0594] Step 2:

[0595] The server integrates and cleans the collected data, converting it into a consistent format. This process eliminates data duplication and inconsistencies, making it easier to analyze.

[0596] Step 3:

[0597] The server uses AI models to analyze data and identify potential collaboration opportunities across multiple departments. Here, text mining and machine learning algorithms are applied to find similar and complementary projects and resources.

[0598] Step 4:

[0599] The server creates a collaboration proposal based on the analysis results and sends the details to the terminal. This includes which departments should work together and specific collaboration themes.

[0600] Step 5:

[0601] The terminal notifies managers in each department of collaboration proposals received from the server and displays them on the screen. Managers review the proposals and make decisions regarding the implementation of collaborative projects.

[0602] Step 6:

[0603] The user, who is also a manager, forms agreements regarding the collaborative project and adjusts the necessary plans and team composition. This leads to the development of concrete action plans and schedules.

[0604] Step 7:

[0605] The server supports the progress of collaborative projects by utilizing project management tools. This includes real-time progress reporting, resource allocation coordination, and schedule management.

[0606] Step 8:

[0607] The server monitors the project's progress, makes adjustments as needed when issues arise, and provides necessary feedback to stakeholders.

[0608] (Example 1)

[0609] 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".

[0610] When departments within an organization operate in isolation, potential complementary projects and resources go unutilized, and the synergistic effects of collaboration are not fully realized. Therefore, there is a need for a system that effectively identifies opportunities for interdepartmental collaboration and supports their realization.

[0611] 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.

[0612] In this invention, the server includes means for collecting information on activities performed by multiple organizational units through a data processing system, means for formatting the collected information and converting it into an analyzable format, and means for analyzing the converted information using an AI algorithm to identify organizational units with similar activities or complementary resources. This makes it possible to discover potential opportunities for collaboration within the organization and to promote collaboration efficiently and effectively.

[0613] A "data processing system" is a computer system designed to collect, format, and analyze activity information within an organization.

[0614] An "organizational unit" refers to a department or team that independently carries out specific tasks or activities.

[0615] An "AI algorithm" is a computational method that uses machine learning techniques to analyze data and identify patterns and relationships.

[0616] An "activity management tool" is software used to monitor the progress and resource allocation of collaborative activities and to share information.

[0617] "Real-time" refers to a state where processing and data sharing occur instantly without delay.

[0618] "Generative AI technology" refers to technology in which AI generates new content or suggestions from a given prompt.

[0619] A "prompt message" is a sentence containing instructions or questions that are entered into an AI to perform a specific task.

[0620] The embodiments for carrying out the invention will now be described. This invention provides a system to support more effective collaboration among multiple departments within an organization. Specific examples of the invention are shown below.

[0621] The server, acting as a data processing system, accesses project management and resource management databases used by each department. It uses APIs and database connections to collect information from these databases, such as project progress, resources used, and related business tasks. The collected information is then normalized and cleansed, and converted into a format suitable for analysis.

[0622] The server analyzes the formatted data using AI algorithms. Leveraging text mining techniques, it extracts key items from the collected information and uses machine learning models to identify departments with similar projects or complementary resources. Based on the AI ​​analysis, the server identifies potential collaboration opportunities and notifies the terminals accordingly.

[0623] The terminal displays collaboration proposals received from the server to managers in each department. Managers review the presented information and initiate collaborative projects or make appropriate adjustments to the plan. Based on the information provided by the system, the managers in each department, as users, efficiently coordinate and optimize resources during project progress.

[0624] As a concrete example, in a new product development project, the server can identify synergies between the product development and marketing departments and provide information to enable both departments to collaborate in formulating a promotional strategy. In this way, it is possible to promote mutual cooperation within the organization and improve operational efficiency.

[0625] When utilizing generative AI technology, we will use the following text as an example of a prompt: "Please propose a strategy to optimize interdepartmental collaboration." This will allow the AI ​​to generate specific collaboration strategies and provide suggestions tailored to the organization's needs.

[0626] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0627] Step 1:

[0628] The server accesses project management and resource management databases used by various departments within the organization. Inputs include project progress, resource usage, and work task information. Specifically, it collects this information through APIs and database connections and converts it into a structured format. This allows for the collection of foundational data necessary to discover collaboration opportunities.

[0629] Step 2:

[0630] The server formats the collected data and converts it into a format that is easy to analyze. The input is the data obtained in Step 1. At this stage, data normalization and cleansing are performed to remove redundant information and unnecessary data, preparing it for analysis. The data is converted into text format or tabular format, which allows the AI ​​to perform analysis efficiently.

[0631] Step 3:

[0632] The server analyzes the formatted data using an AI algorithm. The input is the data processed in step 2. Here, text mining techniques are used to extract important keywords, and a machine learning model is used to identify departments with similar projects or complementary resources. The output is a list of relevant departments and projects as potential collaboration opportunities.

[0633] Step 4:

[0634] The server notifies the terminal of the potential for collaboration identified through the analysis. The input is the output result from step 3. Specifically, it compiles the information as a collaboration proposal and prepares it for immediate transmission to the department manager. This notification causes the collaboration proposal to be displayed on the terminal.

[0635] Step 5:

[0636] The terminal displays received collaboration proposals to the user, who is a manager. The input is the content of the collaboration proposal sent from the server. The terminal designs the screen to present the proposal clearly to the manager. The user manager reviews this, decides whether to start the collaboration project, and proceeds with any necessary plan adjustments.

[0637] Step 6:

[0638] The server monitors ongoing collaborative projects using project management tools. Inputs include project progress data and resource allocation information. The server updates this information in real time and automatically adjusts schedules and reallocates resources as needed, supporting the efficient progress of projects.

[0639] (Application Example 1)

[0640] 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".

[0641] In organizations such as factories, each department often operates independently, resulting in underutilization of opportunities for inter-departmental collaboration and hindering efficient project progress. Furthermore, even after collaboration begins, insufficient optimization of project progress and adequate feedback on results make inter-departmental coordination a challenge.

[0642] 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.

[0643] In this invention, the server includes means for collecting business data within the organization using an information processing device, means for analyzing the collected data to identify opportunities for collaboration, and means for notifying the person in charge of the collaboration proposal. This enables strengthened inter-departmental collaboration and improved project efficiency. Furthermore, by utilizing a generative AI model to generate predictive suggestions for collaboration opportunities and prompt messages, further business improvements can be achieved.

[0644] An "information processing device" is a computer system that has the function of collecting and analyzing business data, and plays a role in identifying and proposing opportunities for collaboration.

[0645] A "mobile communication terminal" is a portable communication device such as a smartphone or tablet, used to receive notifications about collaboration opportunities and project management information.

[0646] A "generative AI model" is an artificial intelligence system embedded in an information processing device that uses machine learning and data analysis to generate predictions and prompts for collaboration opportunities.

[0647] A "project management tool" is software used to monitor project progress and share information with stakeholders in real time, supporting efficient project execution within an organization.

[0648] To implement this invention, it is necessary to build an advanced information processing system. The server functions as a primary data aggregation point and analysis engine. Specifically, the server uses a RESTful API to retrieve data such as resources and project progress from various departments within the organization. After the obtained data is stored in a database, a machine learning model is built using Python and TensorFlow to analyze the possibilities of collaboration.

[0649] The generated collaboration proposals are notified to mobile communication devices via Firebase. These devices, such as smartphones and tablets, function as an interface to display proposals and notifications to users and facilitate important communication. The generation AI model generates data-driven prompts in real time.

[0650] For example, if the manufacturing department develops a new production process, the server may analyze data related to the new process and suggest collaboration with the design department. A prompt message such as "Suggest which departments we should collaborate with to implement the manufacturing department's new process improvement proposal" is sent to the mobile communication terminal. This prompt message allows the user to immediately understand the need for collaboration and quickly take the next steps.

[0651] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0652] Step 1:

[0653] The server retrieves business data from various departments within the organization. Specifically, it collects data from project management systems and resource management databases via RESTful APIs. The input is data on departmental progress and resource status, and the output is an integrated dataset containing this information. This dataset is formatted to provide the information necessary for subsequent analysis.

[0654] Step 2:

[0655] The server stores the collected data in a database and then performs preprocessing using a Python script. This involves cleaning and formatting the data to convert it into a format suitable for analysis by the AI ​​model. It also performs actions such as removing duplicate data and imputing missing values. The input is an integrated dataset, and the output is preprocessed data for analysis.

[0656] Step 3:

[0657] The server uses TensorFlow to perform data analysis with an AI model. It uses a generative AI model to identify potential collaboration opportunities from the data. The input is pre-processed data for analysis, and the output is a proposed list of collaboration opportunities. This list includes information indicating which departments are capable of collaborating.

[0658] Step 4:

[0659] The server uses Firebase to notify each department's terminal of the collaboration proposals it has generated. The input is a list of collaboration proposals, and the output is a notification message sent to the mobile communication terminal. The content displayed on the terminal includes specific instructions on how the relevant department should collaborate.

[0660] Step 5:

[0661] Based on the prompt displayed on the terminal, the user confirms the need for collaboration, gathers the relevant departments, and begins working together. Specific actions include setting up meetings and coordinating project schedules. The prompt displayed is, "Suggest which departments should we collaborate with to implement the manufacturing department's new process improvement proposal." The input is the prompt, and the output is an action plan for initiating collaboration.

[0662] 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.

[0663] This invention is a system that combines AI-based data analysis with an emotion engine that recognizes user emotions, effectively promoting collaboration between departments within an organization. This system includes a server as an information processing device and a terminal as an interface.

[0664] The server collects and integrates operational data from multiple departments within the organization. This provides data on project progress, resource usage, and related tasks. This data is then analyzed by AI algorithms to identify potential collaboration opportunities between different departments.

[0665] The server creates a collaboration proposal based on the analysis results and notifies the terminal. The terminal displays this notification to the managers of each department. A key feature of this invention is the use of an emotion engine. The emotion engine analyzes the emotions of the user who receives the proposal through the terminal and optimizes the content of the proposal and the timing of the notification based on the user's reaction. For example, it determines whether the user who sees the proposal is interested or dissatisfied and automatically provides appropriate feedback and supplementary information.

[0666] The user, a manager, decides to implement the collaborative project based on the proposal and makes the necessary adjustments. The server manages the progress of the agreed-upon project and shares information in real time using project management tools. It also analyzes user sentiment data during the project and provides useful feedback to stakeholders.

[0667] For example, when a collaboration proposal is made for the development of a new product, the emotion engine evaluates the level of interest in the project based on the initial reaction of the development manager who receives the proposal. Based on that level of interest, it then provides detailed information regarding specific action plans and resource allocation, supporting the smooth progress of the project.

[0668] In this way, the system of the present invention not only promotes interdepartmental collaboration and accelerates innovation within the organization, but also achieves more flexible and effective project management by taking user emotions into consideration.

[0669] The following describes the processing flow.

[0670] Step 1:

[0671] The server accesses the organization's project management and resource management databases to collect operational data from each department. This data includes project progress, resource usage, and related tasks.

[0672] Step 2:

[0673] The server integrates the collected data and converts it into a format that is easy to analyze. This process eliminates redundant information while maintaining data integrity.

[0674] Step 3:

[0675] The server uses AI algorithms to analyze integrated data and identify potential collaboration opportunities between different departments. The analysis employs machine learning and pattern recognition techniques to find departments with complementary resources or similar targets.

[0676] Step 4:

[0677] The server generates collaboration proposals based on identified collaboration opportunities and sends them as notifications to the terminal. These proposals include specific collaboration themes and project outlines.

[0678] Step 5:

[0679] The terminal displays collaboration proposals received from the server to managers in each department. Simultaneously, it activates an emotion engine to analyze the emotions of managers who view the proposals in real time.

[0680] Step 6:

[0681] The user, a manager, reviews the collaboration proposals presented on the device. The device uses an emotion engine to analyze the manager's reaction and detect the user's interests and concerns.

[0682] Step 7:

[0683] Based on the analysis results of the sentiment engine, the server updates suggestions to be more appropriate and provides supplementary information as needed. This information is optimized to suit the user's situation.

[0684] Step 8:

[0685] Users consider implementing collaborative projects based on updated information and feedback, and form the necessary agreements.

[0686] Step 9:

[0687] The server registers the details of agreed-upon collaborative projects in the project management tool and tracks and manages their progress. Project progress is shared with stakeholders in real time.

[0688] Step 10:

[0689] The server monitors user sentiment throughout the project, analyzes the feedback received, and evaluates each phase of the project. This result is then used to identify areas for improvement when proposing future collaborations.

[0690] (Example 2)

[0691] 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".

[0692] Modern organizations require efficient collaboration between different departments, but opportunities for collaboration can be missed due to information fragmentation and a lack of communication. Furthermore, a lack of appropriate feedback based on the feelings of those receiving proposals can negatively impact the quality and progress of collaboration. Therefore, there is a need for a system that automatically identifies opportunities for interdepartmental collaboration and provides proposals and feedback that take stakeholders' feelings into account, thereby facilitating smooth project progress.

[0693] 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.

[0694] In this invention, the server includes means for collecting information on tasks performed by multiple departments within an organization, means for analyzing the collected information and identifying possibilities for collaboration between different departments, means for transmitting the identified collaboration proposals to relevant parties within the organization, and means for evaluating the emotional state of the information recipients and dynamically adjusting the content of the proposals. This enables the rapid and accurate identification of opportunities for collaboration between departments and supports collaboration while taking into account the emotions of the parties involved.

[0695] An "information processing device" is a hardware or software system that integrates and analyzes data collected within an organization, and identifies and notifies users of opportunities for collaboration.

[0696] "Business information" refers to data related to the work performed by multiple departments within an organization, including information such as project progress, task assignments, and resource usage.

[0697] "Potential for collaboration" refers to proposals or opportunities resulting from an assessment of the likelihood that different departments can work together to create new value.

[0698] A "proposal" is a specific plan or proposal for collaboration generated based on the analysis results, and is information that is communicated to stakeholders within the organization.

[0699] "Emotional state" refers to the mental response exhibited by the information recipient upon receiving a proposal, and is evaluated by an emotion analysis device.

[0700] This invention is a system that uses a server as an information processing device and a terminal as an interface device. The server is responsible for collecting and integrating business information from each department within the organization and analyzing it using AI algorithms. Specifically, it collects project progress and resource usage information via the organization's database system and performs data analysis using scikit-learn, a Python machine learning library. This identifies possibilities for collaboration between different departments.

[0701] The server automatically generates identified collaboration proposals and sends them to relevant stakeholders within the organization. These generated proposals are typically displayed on devices through project management tools and communication platforms. When managers and other stakeholders receive the proposals, the devices use sentiment analysis technology to assess their emotional state. This sentiment analysis utilizes Google Cloud's Vision API and speech recognition technology.

[0702] The user, a manager, decides on collaborative projects based on the proposals and manages their progress by entering the information into a project management tool via the server. The server also records the project progress in real time on an information processing device and provides necessary feedback to stakeholders involved in the project.

[0703] For example, in the development of a new product, the system proposes a project that the development and marketing departments should undertake together and notifies the managers of each department. Based on the level of interest indicated by the managers who receive the notification, the system provides information on specific project details and resource allocation. This ensures that the project progresses smoothly and improves the overall efficiency of the organization.

[0704] An example of a prompt message for a generative AI model is: "Based on the initial response shown by the development manager, propose the optimal way to proceed with the new product development project." This allows for flexible feedback based on sentiment analysis, which is expected to increase the success rate of collaborative projects.

[0705] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0706] Step 1:

[0707] The server collects operational information from each department within the organization and integrates it into a database. This collected data includes project progress, task assignments, and resource usage. It receives operational information from each department as input and outputs it as an integrated dataset. Specifically, it centralizes information using a database system.

[0708] Step 2:

[0709] The server applies AI algorithms to an integrated dataset to analyze potential collaboration opportunities. The input is integrated business information, and the output generates a list of potential interdepartmental collaboration opportunities. Python machine learning libraries are used for data processing, including clustering and pattern recognition.

[0710] Step 3:

[0711] The server creates collaboration proposals based on the collaboration opportunities identified through analysis and sends them to the relevant parties. The input is a list of collaboration opportunities, and the output is a specific collaboration proposal. Specifically, a generative AI model is used to construct a proposal in natural language, and notifications are distributed through communication tools.

[0712] Step 4:

[0713] The device collects and analyzes emotional data from users who receive notifications. The input is the user's reaction, and the output is an emotional state report. Specifically, it uses a camera and microphone to record the user's facial expressions and voice, and then analyzes them with emotion analysis software.

[0714] Step 5:

[0715] The server adjusts suggestions based on the emotional state report and provides necessary feedback to the user. The input is the emotional state report, and the output is the adjusted suggestions and feedback. Its operation includes modifying suggestions based on emotions and optimizing notification timing.

[0716] Step 6:

[0717] The user, a manager, decides to implement the collaborative project based on the proposal and enters it into the project management tool. The input is the adjusted proposal, and the output is the project plan and resource assignment. Specifically, tasks are organized and progress is tracked using the project management platform.

[0718] Step 7:

[0719] The server continuously analyzes sentiment data obtained from users during the project and provides real-time feedback. Input is continuous sentiment data, and output is suggestions for project improvements and support information. это обеспечивает оптимизацию управления проектами на основе эмоциональных реакций.

[0720] (Application Example 2)

[0721] 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".

[0722] In modern organizations, interdepartmental collaboration is becoming increasingly important, but efficiently identifying and properly managing opportunities for collaboration between different departments is challenging. Furthermore, it is necessary to accurately understand the responses of those involved to proposed collaboration projects and flexibly optimize the content and progress of those proposals based on that understanding. However, existing systems currently lack sufficient advanced collaboration management capabilities, hindering efficient project execution.

[0723] 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.

[0724] In this invention, the server includes means for collecting data on tasks performed by multiple departments within an organization using an information processing device, means for analyzing the collected data and identifying opportunities for collaboration between different departments, means for notifying relevant personnel within the organization of the identified collaboration proposals, means for analyzing the user's response to the notification and optimizing the proposal content using an engine, and means for providing feedback to the user based on the analysis results using a terminal. This makes it possible to efficiently build cooperative relationships between departments and manage collaborative projects more flexibly and effectively.

[0725] An "information processing device" is a device that manages the entire system for collecting large amounts of business data generated within an organization and performing various analyses on it.

[0726] "Means of collection" refers to methods and functions for accurately and efficiently collecting business data between departments.

[0727] "Means of analysis" refers to the function of using collected data to identify possibilities for collaboration not only within a single department but also between different departments.

[0728] A "means of notification" refers to a mechanism for informing relevant parties within an organization about a identified collaboration proposal.

[0729] "Means of supporting consensus building" refers to a function that facilitates the approval of proposed collaboration plans within the organization.

[0730] "Means of managing progress" refers to the function of constantly monitoring the progress of agreed-upon collaborative projects and making appropriate adjustments.

[0731] The term "engine" refers to a set of algorithms used to analyze user feedback and optimize notification content based on that response.

[0732] A "terminal" is an interface device that a user can directly interact with, and is a device used for displaying and inputting information.

[0733] A "means of providing feedback" refers to a system for providing further suggestions and additional information in a timely manner, based on user reactions.

[0734] This invention is an information processing system for effectively promoting collaboration between departments. A server collects business data from each department within an organization and analyzes the data using a dedicated AI analysis engine. This analysis identifies opportunities for collaboration between different departments. The server generates specific collaboration proposals and notifies terminals via the network. The terminals display the proposals to the relevant personnel and receive feedback from users.

[0735] The emotion engine operates on the device, analyzing user responses in real time to optimize the content of suggestions and the timing of notifications. For example, if a user shows interest in a suggestion, the emotion engine analyzes this response and provides additional feedback and information tailored to the user's interests. This enables the smooth progress of collaborative projects.

[0736] As a concrete example, consider a new product development project. The server analyzes sales data and market analysis data and proposes collaboration between the research and sales departments. Project management tools are used to share the progress of each department in real time. If the user reviews the proposal and shows interest, the emotion engine supports the project's success by providing additional market data and development schedules. In this way, resources within the organization are effectively utilized, and innovation is accelerated.

[0737] An example of a prompt to input into a generative AI model is, "Analyze the user's response to this suggestion and determine what additional information should be provided."

[0738] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0739] Step 1:

[0740] The server collects business data from each department within the organization. It takes data indicating the work content and progress of each department as input. This data is centrally collected through an interface and converted into a standardized format, enabling subsequent analysis.

[0741] Step 2:

[0742] The server sends collected data to an AI analysis engine to identify opportunities for collaboration between different departments. It uses standardized business data as input and leverages data mining and pattern recognition technologies. The output generates a list of promising project candidates, forming proposals to encourage interdepartmental cooperation.

[0743] Step 3:

[0744] The server generates proposals for identified collaborative projects and notifies terminals. The input is a list of project candidates generated by an AI analysis engine. The proposals are notified to the relevant departments and personnel in an appropriate message format. This output serves as the foundation for ensuring that the proposals reach and are taken into consideration by users.

[0745] Step 4:

[0746] The terminal receives suggestions and displays them to the user. It receives suggestion notifications sent from the server as input and aggregates and displays them through the user interface. The user reviews the suggestions and provides feedback if they are interested or have an opinion. This feedback information is output and the process proceeds to the next sentiment analysis step.

[0747] Step 5:

[0748] The device activates an emotion engine to analyze user feedback. The input is response data submitted by the user. Natural language processing technology is used to identify the user's emotional state and response attributes. Based on the output information obtained from this analysis, the system optimizes the content of suggestions and the timing of notifications.

[0749] Step 6:

[0750] The server receives the analysis results from the emotion engine, optimizes the suggestions, and generates feedback. It references the analyzed emotion data as input and updates any necessary additional information and details. The output is feedback information that is re-notified to the user, with adjustments made to deepen their understanding of the suggestions and facilitate collaborative implementation.

[0751] 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.

[0752] 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.

[0753] In the above embodiment, an example was given in which the specific processing is performed by the data processing device 12, but the technology of this disclosure is not limited thereto, and the specific processing may also be performed by the robot 414.

[0754] 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.

[0755] Figure 9 shows an emotion map 400 in which multiple emotions are mapped. In the emotion map 400, emotions are arranged in concentric circles radiating from the center. The closer to the center of the concentric circles, the more primitive the emotions are located. Further out of the concentric circles, emotions representing states and actions arising from mental states are located. Emotion is a concept that includes feelings and mental states. On the left side of the concentric circles, emotions that are generally generated from reactions occurring in the brain are located. On the right side of the concentric circles, emotions that are generally induced by situational judgment are located. Above and below the concentric circles, emotions that are generally generated from reactions occurring in the brain and induced by situational judgment are located. In addition, the emotion of "pleasure" is located on the upper side of the concentric circles, and the emotion of "displeasure" is located on the lower side. Thus, in the emotion map 400, multiple emotions are mapped based on the structure in which emotions arise, and emotions that are likely to occur simultaneously are mapped close together.

[0756] 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.

[0757] 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.

[0758] 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.

[0759] 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."

[0760] 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.

[0761] 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.

[0762] 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.

[0763] 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.

[0764] 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.

[0765] 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.

[0766] 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.

[0767] 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.

[0768] 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.

[0769] 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.

[0770] 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.

[0771] 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.

[0772] The following is further disclosed regarding the embodiments described above.

[0773] (Claim 1)

[0774] Information processing equipment provides a means for collecting data on tasks performed by multiple departments within an organization,

[0775] The aforementioned information processing device provides means for analyzing collected data and identifying opportunities for collaboration between different departments,

[0776] The aforementioned information processing device provides a means for notifying the person in charge within the organization of the identified collaboration proposal,

[0777] The aforementioned information processing device provides a means for supporting the formation of agreement on collaborative themes,

[0778] The aforementioned information processing device provides a means for managing the progress of the agreed-upon collaborative project,

[0779] A system that includes this.

[0780] (Claim 2)

[0781] The system according to claim 1, further comprising means for evaluating the results of a collaborative project and generating feedback for future collaborations using the information processing device.

[0782] (Claim 3)

[0783] The system according to claim 1, further comprising means for sharing information on collaborative projects in real time using a project management tool via the information processing device.

[0784] "Example 1"

[0785] (Claim 1)

[0786] The data processing system provides a means for collecting information on activities performed by multiple organizational units,

[0787] The aforementioned data processing system includes means for formatting the collected information and converting it into an analyzable format,

[0788] The aforementioned data processing system analyzes the transformed information using an AI algorithm to identify organizational units with similar activities or complementary resources,

[0789] The aforementioned data processing system provides means for notifying identified opportunities for cooperation,

[0790] The aforementioned data processing system provides means to support the adjustment of plans based on the notified cooperation proposals,

[0791] The aforementioned data processing system provides a means for monitoring the progress of collaborative activities and sharing information in real time using an activity management tool.

[0792] A system that includes this.

[0793] (Claim 2)

[0794] The system according to claim 1, further comprising means for evaluating the results of collaborative activities and generating feedback for future collaborations using the data processing system.

[0795] (Claim 3)

[0796] The system according to claim 1, further comprising means for proposing a cooperative strategy using prompt sentences by utilizing generation AI technology through the data processing system.

[0797] "Application Example 1"

[0798] (Claim 1)

[0799] Information processing equipment provides a means for collecting data on tasks performed by multiple departments within an organization,

[0800] The aforementioned information processing device provides means for analyzing collected data and identifying opportunities for collaboration between different departments,

[0801] The aforementioned information processing device provides a means for notifying the person in charge within the organization of the identified collaboration proposal,

[0802] The aforementioned information processing device provides a means for supporting the formation of agreement on collaborative themes,

[0803] The aforementioned information processing device provides a means for managing the progress of the agreed-upon collaborative project,

[0804] The aforementioned information processing device provides a means to notify mobile communication terminals of collaboration opportunities and streamline collaborative projects,

[0805] A means for generating predictive suggestions of collaboration opportunities using a generative AI model and generating prompt sentences,

[0806] A system that includes this.

[0807] (Claim 2)

[0808] The system according to claim 1, further comprising means for evaluating the results of a collaborative project and generating feedback for future collaborations using the information processing device.

[0809] (Claim 3)

[0810] The system according to claim 1, further comprising means for using a project management tool to share information on collaborative projects in real time and optimize the operation of factory equipment using the information processing device.

[0811] "Example 2 of combining an emotion engine"

[0812] (Claim 1)

[0813] Information processing equipment provides a means of collecting information on tasks performed by multiple departments within an organization,

[0814] The aforementioned information processing device provides means for analyzing the collected information and identifying possibilities for collaboration between different departments,

[0815] The aforementioned information processing device provides means for transmitting the identified collaboration proposal to relevant parties within the organization,

[0816] The aforementioned information processing device provides a means to support agreement on collaborative themes,

[0817] The aforementioned information processing device provides means for controlling the progress of agreed-upon collaborative activities,

[0818] The information processing device includes an emotion analysis device that evaluates the emotional state of the information recipient and dynamically adjusts the content of the proposal.

[0819] A system that includes this.

[0820] (Claim 2)

[0821] The system according to claim 1, further comprising means for evaluating the results of collaborative activities and generating feedback for future collaborations using the information processing device.

[0822] (Claim 3)

[0823] The system according to claim 1, further comprising means for providing a project management function for sharing information on collaborative activities in real time using the information processing device.

[0824] "Application example 2 when combining with an emotional engine"

[0825] (Claim 1)

[0826] Information processing equipment provides a means for collecting data on tasks performed by multiple departments within an organization,

[0827] The aforementioned information processing device provides means for analyzing collected data and identifying opportunities for collaboration between different departments,

[0828] The aforementioned information processing device provides a means for notifying the person in charge within the organization of the identified collaboration proposal,

[0829] The aforementioned information processing device provides a means for supporting the formation of agreement on collaborative themes,

[0830] The aforementioned information processing device provides a means for managing the progress of the agreed-upon collaborative project,

[0831] The engine analyzes the user's response to the notification and optimizes the suggested content.

[0832] Depending on the device, there are means to provide feedback to the user based on the analysis results,

[0833] A system that includes this.

[0834] (Claim 2)

[0835] The system according to claim 1, further comprising means for evaluating the results of a collaborative project and generating feedback for future collaborations using the information processing device.

[0836] (Claim 3)

[0837] The system according to claim 1, further comprising means for sharing information on collaborative projects in real time using a project management tool via the information processing device. [Explanation of symbols]

[0838] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Devices 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robots< / url:> < / url:> < / url:> < / url:>

Claims

1. A means of collecting data on tasks performed by multiple departments within an organization, The collected data is analyzed to identify opportunities for collaboration between different departments, A means of notifying the relevant person within the organization of the identified collaboration proposal, Means to support the formation of agreement on collaborative themes, A means of managing the progress of agreed-upon collaborative projects, A system that includes this.

2. The system according to claim 1, further comprising means for evaluating the results of a collaborative project and generating feedback for future collaborations.

3. The system according to claim 1, further comprising means for sharing information on collaborative projects in real time using a project management tool.

Images