System

Abstract

A system is provided.SOLUTION: A system comprising: terminals for collecting in-house information; a server for integrating the in-house information collected from the terminals; a AI unit for analyzing the in-house information integrated by the server and mapping an information distribution path; a gap detection unit for automatically detecting an information gap from the information distribution path generated by the AI unit; an optimizing suggestion unit for generating an optimizing suggestion for minimizing the information gap detected by the gap detection unit; and a report generation unit for reporting the suggestion generated by the optimizing suggestion unit.SELECTED DRAWING: Figure 1

Description

[Technical Field]

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

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

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

[0004] In modern companies, complex organizational structures and the widespread use of remote work have led to problems such as slow information flow and important information getting lost. This can delay project progress and prevent important decisions from being made properly. Furthermore, if information is not properly communicated to specific departments or individuals, miscommunication and duplication of work can occur, resulting in reduced productivity. Therefore, effective methods are needed to streamline information flow within organizations and improve information transparency. [Means for solving the problem]

[0005] The present invention solves the above problem by providing a system including terminal means for collecting internal company information, server means for integrating data collected from the terminal means, AI means for analyzing the data integrated by the server means and mapping information distribution routes, gap detection means for automatically detecting information gaps from the information distribution routes generated by the AI ​​means, optimization proposal means for generating optimization proposals to minimize the information gaps detected by the gap detection means, and report generation means for reporting the proposals generated by the optimization proposal means.

[0006] Specifically, a means for obtaining approval from users is incorporated to ensure transparency in data collection. In addition, the report generation means includes a dashboard means for visually presenting optimization proposals, allowing users to intuitively grasp information flows and gaps and make efficient decisions. This will improve the efficiency and transparency of information distribution within the company.

[0007] "Terminal means" refers to a device or software component that automatically collects data such as communication logs, emails, document sharing, and meeting contents with permission from the user.

[0008] The "server means" is a computer or system for unifying data collected from the terminal means and storing it in an integrated database.

[0009] "AI means" refers to software or algorithms that utilize artificial intelligence technologies such as natural language processing (NLP) to analyze integrated data and generate and visualize information distribution channels.

[0010] "Gap detection means" refers to a function or module that analyzes information distribution paths generated by AI means and automatically identifies gaps or delays in the distribution of information.

[0011] The "optimization proposal means" is a system component for generating specific proposals to minimize the information gaps detected by the gap detection means and improve information distribution.

[0012] The "report generation means" is a function or tool for creating a report to present information to the user based on the proposals and analysis results generated by the optimization proposal means.

[0013] The "dashboard means" is an interface that visually displays the reports generated by the report generating means and enables the user to intuitively grasp the flow of information and gaps. [Brief explanation of the drawings]

[0014] [Figure 1] 1 is a conceptual diagram showing an example of the configuration of a data processing system according to a first embodiment. [Figure 2] 1 is a conceptual diagram showing an example of main functions of a data processing device and a smart device according to a first embodiment. [Figure 3] FIG. 10 is a conceptual diagram showing an example of the configuration of a data processing system according to a second embodiment. [Figure 4] FIG. 10 is a conceptual diagram showing an example of main functions of a data processing device and smart glasses according to a second embodiment. [Figure 5] FIG. 10 is a conceptual diagram showing an example of the configuration of a data processing system according to a third embodiment. [Figure 6] FIG. 11 is a conceptual diagram showing an example of main functions of a data processing device and a headset-type terminal according to a third embodiment. [Figure 7] FIG. 10 is a conceptual diagram showing an example of the configuration of a data processing system according to a fourth embodiment. [Figure 8] FIG. 10 is a conceptual diagram showing an example of main functions of a data processing device and a robot according to a fourth embodiment. [Figure 9] 1 shows an emotion map onto which multiple emotions are mapped. [Figure 10]1 shows an emotion map onto which multiple emotions are mapped. [Figure 11] FIG. 3 is a sequence diagram showing a processing flow of the data processing system according to the first embodiment. [Figure 12] FIG. 10 is a sequence diagram showing the flow of processing in the data processing system in Application Example 1. [Figure 13] FIG. 10 is a sequence diagram showing the flow of processing in the data processing system according to the second embodiment when an emotion engine is combined. [Figure 14] FIG. 10 is a sequence diagram showing the flow of processing in the data processing system in Application Example 2 when an emotion engine is combined. DETAILED DESCRIPTION OF THE INVENTION

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

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

[0017] In the following embodiments, a coded processor (hereinafter simply referred to as a "processor") may be a single arithmetic device or a combination of multiple arithmetic devices. Furthermore, a processor may be a single type of arithmetic device or a combination of multiple types of arithmetic devices. Examples of arithmetic devices include a CPU (Central Processing Unit), a GPU (Graphics Processing Unit), a GPGPU (General-Purpose computing on Graphics Processing Units), and an APU (Accelerated Processing Unit).

[0018] In the following embodiments, a coded RAM (Random Access Memory) is a memory in which information is temporarily stored and is used as a working memory by a processor.

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

[0020] In the following embodiments, a communication I / F (Interface) with a symbol is an interface including a communication processor, an antenna, etc. The communication I / F controls communication between multiple computers. Examples of communication standards applied to the communication I / F include wireless communication standards including 5G (5th Generation Mobile Communication System), Wi-Fi (registered trademark), Bluetooth (registered trademark), etc.

[0021] In the following embodiments, "A and / or B" is synonymous with "at least one of A and B." In other words, "A and / or B" means that it may be only A, only B, or a combination of A and B. Furthermore, in this specification, the same concept as "A and / or B" is also applied when three or more things are expressed connected by "and / or."

[0022] [First embodiment]

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

[0024] 1, a data processing system 10 includes a data processing device 12 and a smart device 14. An example of the data processing device 12 is a server.

[0025] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 is an example of a "computer" according to the technology of the present disclosure. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, the RAM 30, and the storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[0026] The smart device 14 includes a computer 36, a reception device 38, an output device 40, a camera 42, and a communication I / F 44. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The reception device 38, the output device 40, and the camera 42 are also connected to the bus 52.

[0027] The reception device 38 includes a touch panel 38A, a microphone 38B, and the like, and receives user input. The touch panel 38A detects contact with an indicator (for example, a pen or a finger) to receive user input by the touch of the indicator. The microphone 38B detects the user's voice to receive user input by voice. The control unit 46A transmits data indicating the user input received by the touch panel 38A and the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the data indicating the user input.

[0028] The output device 40 includes a display 40A and a speaker 40B, and presents data to the user 20 by outputting the data in a form of expression that the user 20 can perceive (for example, audio and / or text). The display 40A displays visible information such as text and images in accordance with instructions from the processor 46. The speaker 40B outputs audio in accordance with instructions from the processor 46. The camera 42 is a compact digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor.

[0029] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 control the exchange of various information between the processor 46 and the processor 28 via the network 54.

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

[0031] 2, in the data processing device 12, a specific process is performed by the processor 28. A specific processing program 56 is stored in the storage 32. The specific processing program 56 is an example of a "program" according to the technology of the present disclosure. The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific process is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0032] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[0033] In the smart device 14, the processor 46 performs the reception output process. The storage 50 stores a reception output program 60. The reception output program 60 is used in conjunction with the specific processing program 56 by the data processing system 10. The processor 46 reads the reception output program 60 from the storage 50 and executes the read reception output program 60 on the RAM 48. The reception output process is realized by the processor 46 operating as the control unit 46A in accordance with the reception output program 60 executed on the RAM 48.

[0034] Next, a description will be given of the specific processing performed by the specific processing unit 290 of the data processing device 12. In the following description, the data processing device 12 will be referred to as a "server" and the smart device 14 will be referred to as a "terminal."

[0035] The present invention is a system for improving the efficiency of an information distribution process within a company, and is realized by the following means.

[0036] System configuration

[0037] The system includes a terminal means, a server means, an AI means, a gap detection means, an optimization suggestion means, and a report generation means.

[0038] Terminal means

[0039] The terminal means obtains permission from the user and automatically collects data such as internal communication logs, emails, document sharing, and meeting contents. This collection includes data from email servers, chat tools, document management systems, meeting recording systems, etc. The terminal means transmits the collected data to the server means.

[0040] Server Means

[0041] The server means integrates the data collected from the terminal means and stores it centrally. The server standardizes the format of each data and adds necessary metadata (e.g., department, individual, timestamp, etc.). This integrated data is later analyzed by the AI ​​means.

[0042] AI means

[0043] AI tools analyze the collected and integrated data to generate and visualize information distribution channels. AI tools use natural language processing (NLP) techniques to analyze text data and extract important keywords and themes. They also generate network diagrams to clarify how, when, and between which departments and individuals information is circulating.

[0044] Gap Detection Means

[0045] The gap detection method analyzes the information distribution path generated by the AI ​​method and detects gaps or delays in the distribution of information. This method identifies areas where information is not being properly transmitted based on specific conditions and issues alerts or warnings.

[0046] Optimization proposal method

[0047] The optimization suggestion means generates optimal improvement suggestions for minimizing the information gaps identified by the gap detection means, such as establishing an information sharing meeting, using a specific communication tool, or increasing the priority of specific information.

[0048] Report Generation Method

[0049] The report generation means creates a report to present information to the user based on the proposals generated by the optimization proposal means. The report is visually displayed through the dashboard means, allowing the user to intuitively grasp the flow of information and gaps. In addition, the report is notified to the user via email or chat tool as needed.

[0050] Explanation of program processing

[0051] The program of the above system operates as follows.

[0052] Data collection

[0053] The terminal means obtains permission from the user and collects various data, including obtaining emails from the email server, collecting chat tool logs, and obtaining file information from the document sharing system.

[0054] Data Integration

[0055] The server receives the data sent from the terminal and stores it in the integrated database. The server converts the data into a unified format, adds metadata, and stores it.

[0056] Data analysis

[0057] AI tools analyze the data stored on the server tools and use natural language processing technology to analyze the text data, thereby extracting important keywords and themes and mapping the distribution channels of information.

[0058] Gap Detection

[0059] The gap detection means analyzes the information distribution path generated by the AI ​​means and detects information omissions or delays. This means automatically identifies gaps based on specific conditions.

[0060] Optimization suggestions

[0061] An optimization suggestion means generates a suggestion for minimizing the information gap detected by the gap detection means, the suggestion being based on the data and including a specific measure for optimizing the information distribution.

[0062] Report Generation

[0063] The report generation means compiles the proposals generated by the optimization proposal means into a report and notifies the user. The report is visually displayed via the dashboard means, allowing the user to intuitively grasp the current state of information distribution and areas for improvement.

[0064] Specific examples

[0065] Example 1: Critical project information isn't reaching the remote team

[0066] The terminal means collects data from the project management tool and mail server. The server means integrates the data, and the AI ​​means analyzes the information distribution route. The gap detection means identifies when information is not reaching the remote team, and the optimization proposal means generates proposals to increase the priority of information sharing. The report generation means displays improvement proposals on a dashboard, where the user can confirm and implement the improvement measures.

[0067] Example 2: Resolving communication errors between departments

[0068] The terminal means collects logs from internal chat tools and data on meeting contents. The server means integrates this data, and the AI ​​means analyzes the flow of information. The gap detection means detects when information is not being communicated properly between specific departments, and the optimization proposal means proposes the establishment of regular information sharing meetings. The report generation means compiles the proposals into a report, which the user confirms and implements improvement measures.

[0069] As a result, the system of the present invention efficiently manages the distribution of information within a company, and realizes improved transparency and productivity.

[0070] The processing flow will be explained below.

[0071] Step 1:

[0072] The user performs initial setup to begin using the system. The user confirms the permission settings and grants access to individual data sources (email server, chat tool, document sharing system, conference system, etc.).

[0073] Step 2:

[0074] The device accesses data sources authorized by the user and collects various data. The device obtains email header information and text from the email server, extracts chat tool logs, and obtains file information and metadata (author, update date, etc.) from the document sharing system. It also collects recording data and meeting minutes from the conference system.

[0075] Step 3:

[0076] The device sends the collected data to the server, which temporarily stores the data, converts it into an appropriate format, and then sends the data to the server through the interface.

[0077] Step 4:

[0078] The server receives the data sent from the devices and centrally integrates it. The server unifies the data format and adds the necessary metadata (department, individual, timestamp, etc.).

[0079] Step 5:

[0080] The server saves the data in a unified database. The server stores the data in a unified format in the database and generates an index to enable quick access.

[0081] Step 6:

[0082] The server analyzes the data using AI means. The server uses natural language processing (NLP) techniques to analyze the text data and extract key keywords and themes.

[0083] Step 7:

[0084] The server generates information distribution routes and visualizes in the form of a network diagram how information is distributed between individual departments and individuals.

[0085] Step 8:

[0086] The server uses gap detection techniques to analyze the information flow and automatically detect missing or delayed information. The server identifies gaps based on specific conditions and sets relevant alerts.

[0087] Step 9:

[0088] The server uses an optimization proposal method to generate specific proposals to minimize the gap. The server compares past data and considers effective strategies and reflects them in the proposals.

[0089] Step 10:

[0090] The server uses a report generator to generate a report containing the analysis results and optimization suggestions, which is formatted in a visually appealing format and prepared for display on a dashboard.

[0091] Step 11:

[0092] The server notifies the user of the report. The server displays the report on the dashboard and sends notifications via email or chat tools as needed.

[0093] Step 12:

[0094] Users can check the report through the dashboard and implement the suggested improvements. Users make decisions based on the report's contents and proceed with the process of optimizing the flow of information within the company in accordance with the system's suggestions.

[0095] Example 1

[0096] Next, a description will be given of Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the smart device 14 will be referred to as a "terminal."

[0097] Improving the efficiency of information distribution within a company is extremely important for improving corporate productivity and transparency. However, with conventional systems, each process from information collection, integration, analysis, and optimization proposals is fragmented, making it difficult to efficiently manage it centrally. Furthermore, there is a high likelihood of miscommunication and misunderstandings due to insufficient automatic detection of information gaps and delays and the implementation of countermeasures. A comprehensive system that can solve these problems is needed.

[0098] The specific processing by the specific processing unit 290 of the data processing device 12 in the first embodiment is realized by the following means.

[0099] In this invention, the server includes terminal means for obtaining permission from users and collecting various types of data within the company, server means for receiving the data collected from the terminal means, converting it into a unified format, and centrally storing it, AI means for analyzing the data stored in the server means using natural language analysis technology and mapping information distribution routes, gap detection means for automatically detecting information gaps and delays from the information distribution routes generated by the AI ​​means, optimization proposal means for generating optimization proposals to minimize the information gaps detected by the gap detection means, and report generation means for compiling the proposals generated by the optimization proposal means into a report and notifying the user. This enables efficient and integrated management of the company's information distribution process, automatically detecting information gaps and delays, and proposing optimal improvement measures.

[0100] "Terminal means" refers to a device or system that obtains permission from a user and collects various data within a company.

[0101] "Server means" refers to a device or system that receives data collected from terminal means, converts it into a unified format, and stores it in a centralized manner.

[0102] "AI means" refers to devices or systems that have the function of analyzing data stored in server means using natural language analysis technology and mapping information distribution routes.

[0103] "Gap detection means" refers to a device or system that automatically detects information gaps or delays in information distribution channels generated by AI means.

[0104] The term "optimization proposal means" refers to a device or system that generates optimal proposals to minimize the information gaps detected by the gap detection means.

[0105] The "report generation means" refers to a device or system that compiles the proposals generated by the optimization proposal means into a report and notifies the user of the report.

[0106] "User" refers to a company employee or administrator who uses the system, grants permissions, or reviews reports.

[0107] "Data" refers to various information generated within the company, such as emails, chat logs, documents, and meeting recordings.

[0108] "Natural language analysis technology" refers to the technology of analyzing text data and extracting important keywords and themes.

[0109] "Information distribution route" refers to the route that indicates how, when, and between which departments and individuals information is distributed within a company.

[0110] "Metadata" is auxiliary information added to data, such as department, individual, timestamp, etc.

[0111] The present invention is a system for improving the efficiency of an in-house information distribution process. The system includes a terminal unit, a server unit, an AI unit, a gap detection unit, an optimization suggestion unit, and a report generation unit.

[0112] System configuration

[0113] Terminal means

[0114] The terminal means obtains permission from the user and automatically collects data such as internal communication logs, emails, document sharing, and meeting contents. This collection includes email servers (e.g., Microsoft Exchange), chat tools (e.g., Slack), document management systems (e.g., Google Drive), and meeting recording systems (e.g., Zoom). The terminal means transmits the collected data to the server means.

[0115] Server Means

[0116] The server means integrates the data collected from the terminal means and stores it centrally. The server standardizes the format of each piece of data and adds the necessary metadata (department, individual, timestamp, etc.). This integrated data is later analyzed by the AI ​​means. A cloud-based database system (e.g., Amazon RDS) is often used for the server means.

[0117] AI means

[0118] AI tools analyze collected and integrated data to generate and visualize information distribution channels. They use natural language processing (NLP) techniques to analyze text data and extract important keywords and themes. They also generate network diagrams to clarify how, when, and between which departments and individuals information is circulating. Examples of use include Google BERT and OpenAI's GPT model.

[0119] Gap Detection Means

[0120] The gap detection method analyzes the information distribution path generated by the AI ​​method and detects gaps or delays in the distribution of information. This method identifies areas where information is not being properly transmitted based on specific conditions and issues alerts or warnings.

[0121] Optimization proposal method

[0122] The optimization suggestion means generates optimal improvement suggestions to minimize the information gaps identified by the gap detection means. These suggestions include establishing an information sharing meeting, using a specific communication tool, raising the priority of specific information, etc. Specifically, it suggests prioritizing the notification of project information to the remote team and establishing regular information sharing meetings.

[0123] Report Generation Method

[0124] The report generation means creates a report to present information to the user based on the proposals generated by the optimization proposal means. The report is visually displayed through a dashboard means (e.g., Power BI) so that the user can intuitively grasp the flow of information and gaps. In addition, the report is notified to the user via email or chat tools as necessary.

[0125] Specific examples

[0126] Example 1: Critical project information isn't reaching the remote team

[0127] Based on the user's permission, the terminal means collects data from the project management tool and mail server. The server means integrates the data, and the AI ​​means analyzes the information distribution path. The gap detection means identifies when information has not reached the remote team, and the optimization proposal means generates proposals to increase the priority of information sharing. The report generation means displays improvement proposals on a dashboard, where the user can confirm and implement the improvement measures.

[0128] Example 2: Resolving communication errors between departments

[0129] The user uses the terminal means to collect data on the logs of the internal chat tool and meeting contents. The server means integrates this data, and the AI ​​means analyzes the flow of information. The gap detection means detects that information is not being communicated properly between specific departments, and the optimization proposal means proposes the establishment of regular information sharing meetings. The report generation means compiles the proposals into a report, which the user confirms and implements improvement measures.

[0130] As described above, this system efficiently manages the flow of information within a company, improving transparency and productivity.

[0131] Prompt Sentence Examples

[0132] "Proposing solutions when important project information isn't reaching a remote team"

[0133] "How to resolve communication errors between departments"

[0134] The present invention enables companies to optimize information distribution and improve business efficiency.

[0135] The flow of the identification process in the first embodiment will be described with reference to FIG.

[0136] Step 1:

[0137] The terminal means obtains permission from the user and collects various data from the company's email server, chat tool, document management system, and meeting recording system. Specifically, the terminal means connects to Microsoft Exchange to collect email data, collects chat logs using the Slack API, obtains file information using the Google Drive API, and obtains meeting recording data using the Zoom API. The collected data is temporarily stored in the terminal means in the form of raw data from each tool.

[0138] Input: User permission, API connection information for each tool

[0139] Output: Raw data collected from each tool

[0140] Step 2:

[0141] The server receives data collected from the terminal devices and converts it into a unified format. Specifically, it converts email content into text format, chat logs into JSON format, integrates document information into a common metadata format, and converts conference recording data into text. The converted data is then added with metadata such as sender, recipient, and timestamp, and is stored centrally in an integrated database.

[0142] Input: Raw data collected from each tool

[0143] Output: Data converted into a unified format, consolidated data with added metadata

[0144] Step 3:

[0145] The AI ​​tool analyzes the integrated data stored on the server. Specifically, it uses natural language processing technology to extract important keywords and themes from the text data. The AI ​​tool uses Google BERT and OpenAI's GPT model to analyze the content of emails and chat logs and identify relevant keywords and context. It also analyzes the content of documents and maps each piece of information as an information distribution channel.

[0146] Input: Data converted to a unified format, with added metadata

[0147] Output: Extracted keywords and themes, mapped information distribution channels

[0148] Step 4:

[0149] The gap detection method analyzes the information distribution channels generated by the AI ​​method and detects missing or delayed information. Specifically, it identifies which channels are not transmitting information based on specific pre-defined conditions (e.g., the time frame within which the required information should be released). An alert is generated about the detected gap, and the appropriate personnel are notified.

[0150] Input: Mapped information distribution channels, specific conditions

[0151] Output: Information gaps detected, alerts generated

[0152] Step 5:

[0153] The optimization suggestion unit generates suggestions to minimize information gaps detected by the gap detection unit, such as prioritizing communication of project information to remote teams, establishing regular information sharing meetings, recommending the use of specific communication tools, etc. The suggestions are generated based on the data and indicate specific areas for improvement.

[0154] Input: Detected information gaps

[0155] Output: Generated optimization proposals

[0156] Step 6:

[0157] The report generator compiles the optimization proposals into a report and notifies the user. Specifically, Power BI is used to create a dashboard that visually displays the proposals and the current status of information distribution. The report is then converted into PDF format and distributed to the user via email or chat tools.

[0158] Input: Generated optimization proposals

[0159] Output: Visual dashboards, PDF reports, and user notifications

[0160] Through the above steps, this system can efficiently manage the flow of information within a company, improving transparency and productivity.

[0161] (Application example 1)

[0162] Next, a description will be given of Application Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the smart device 14 will be referred to as a "terminal."

[0163] In the distribution of information within a factory, important information is often not communicated efficiently, which can have a negative impact on production efficiency and quality control. Therefore, there is a need for a system that can quickly identify which department or worker is experiencing delays or deficiencies and make appropriate suggestions. In addition, there is a need for a method that visually shows the optimization of information communication and allows workers to intuitively understand it.

[0164] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 1 is realized by the following means.

[0165] In this invention, the server includes device means for collecting in-house information, storage means for integrating data collected from the device means, machine learning means for analyzing the integrated data by the storage means and mapping information distribution routes, gap detection means for automatically detecting information gaps from the information distribution routes generated by the machine learning means, proposal generation means for generating optimization proposals to minimize the information gaps detected by the gap detection means, a visualization device for visually presenting the proposals generated by the proposal generation means, means for using smart glasses as the visualization device, means for wirelessly transmitting data collected by the smart glasses to the storage means, and specific means for optimizing the information distribution routes. This makes it possible to quickly identify delays or gaps in information distribution within a factory and propose and implement optimal information sharing measures.

[0166] "Internal information" is a general term for information relating to business, communication, and operations carried out within a company or organization.

[0167] "Device means" refers to terminal equipment and its interface for collecting and transmitting information.

[0168] "Storage means" refers to a storage system for centrally storing collected data and managing it in an accessible form as needed.

[0169] "Machine learning tools" refers to algorithms and software used to analyze collected data, find patterns and trends, and map information distribution channels.

[0170] "Gap detection means" refers to a mechanism for automatically identifying gaps (lack of or delay in) information distribution.

[0171] "Proposal generator for generating optimization proposals" refers to algorithms and systems for generating specific improvement proposals to minimize detected deficiencies.

[0172] "Visualization device" refers to a device and software for visually presenting the generated proposals and the current state of information distribution.

[0173] "Smart glasses" refers to a wearable device that visually displays collected data in real time and has the ability to interactively share information with the user.

[0174] "Wireless communication" refers to technologies and protocols for transmitting and receiving data without a physical connection.

[0175] "Information distribution route" refers to a route diagram that shows how, when, and between which departments and individuals information is distributed.

[0176] "Specific means" refers to methods that specifically demonstrate the actions and technologies necessary to achieve optimized information distribution.

[0177] The present invention is a system for improving the efficiency of information distribution within a factory. A method for implementing a program for the system that realizes this application example will be specifically described below.

[0178] Data collection

[0179] The smart glasses are used as terminals. They collect data such as communication logs, emails, shared documents, and work instructions between workers in the factory in real time. This data is sent to a server via wireless communication.

[0180] Data Integration

[0181] The server consolidates and centrally stores the data sent from the smart glasses. A database is used as the storage medium. The collected data is converted into a unified format and stored with metadata such as department, individual, and timestamp.

[0182] Data analysis

[0183] The server uses machine learning techniques to analyze the integrated data. Specifically, it uses natural language processing (NLP) techniques to extract important keywords and themes from the collected text data. It also generates a network diagram of information distribution routes. This process uses libraries such as "spaCy" and "NetworkX."

[0184] Gap Detection

[0185] The server analyzes the information distribution routes generated by the machine learning method and automatically detects information gaps (lack of information or delays) using the gap detection method, thereby identifying areas where information transmission to specific departments or individuals is stalled.

[0186] Generate optimization suggestions

[0187] The server uses a proposal generator to generate optimization proposals based on the detected gaps, which suggest specific improvements, such as changing the information sharing method or introducing a new communication method.

[0188] Reporting and Visualization

[0189] The report generation means displays the generated proposals on the visualization device. The visualization device, smart glasses, visually presents the proposals and the current status of information distribution to workers in real time in a dashboard format, allowing workers to intuitively understand problems and improvement measures and implement them.

[0190] Specific examples

[0191] For example, consider a scenario where critical information about a particular manufacturing process isn't being communicated to a remote quality control team. The server analyzes the data collected by the smart glasses and detects the gaps. In this case, the server suggests setting up an information-sharing meeting or introducing new communication methods. These suggestions are presented in real time through the smart glasses, allowing workers to immediately implement improvements.

[0192] Prompt Sentence Examples

[0193] "It's possible that information isn't reaching certain departments through this information distribution network. What communication methods would be effective in making improvements?"

[0194] This makes it possible to quickly identify delays or lack of information flow within the factory and propose and implement optimal information sharing methods.

[0195] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[0196] Step 1:

[0197] The smart glasses collect data such as communication logs, emails, shared documents, and work instructions between workers in the factory in real time and transmit it to a server via wireless communication.

[0198] Input: Worker communication data

[0199] Output: Data collected by the smart glasses and transmitted to the server via wireless communication.

[0200] Step 2:

[0201] The server receives the data sent from the smart glasses and stores it in a centralized storage device. The collected data is converted into a unified format and metadata such as department, individual, and timestamp are added.

[0202] Input: Raw data sent from smart glasses

[0203] Output: Data converted into a unified format and with metadata added

[0204] Step 3:

[0205] The server uses machine learning techniques to analyze the stored data. Specifically, it uses natural language processing (NLP) techniques to extract important keywords and themes from the collected text data. It also generates a network diagram of information distribution routes.

[0206] Input: Data converted into a unified format and with metadata added

[0207] Output: A network diagram of important keywords and themes and information distribution channels

[0208] Step 4:

[0209] The server analyzes the information distribution path generated by the machine learning means and automatically detects information gaps (lack or delay) using the gap detection means.

[0210] Input: Network diagram of information distribution channels

[0211] Output: Detected information gaps

[0212] Step 5:

[0213] The server uses a proposal generator to generate optimization proposals based on the detected gaps, which suggest specific improvements, such as changing the information sharing method or introducing a new communication method.

[0214] Input: Detected information gaps

[0215] Output: Optimization suggestions

[0216] Step 6:

[0217] The server compiles the generated optimization proposals into a report using a report generation means and displays it on the visualization device. The smart glasses as a visualization device visually present the proposals and the current status of information distribution to workers in real time in dashboard format, allowing workers to intuitively understand problems and improvement measures and implement them.

[0218] Input: Optimization proposal

[0219] Output: Reports and dashboards displayed on smart glasses

[0220] This process flow makes it possible to quickly identify delays or lack of information distribution within the factory and propose and implement optimal information sharing methods.

[0221] Furthermore, an emotion engine that estimates the user's emotion may be combined. That is, the identification processing unit 290 may estimate the user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

[0222] The present invention is a system for improving the efficiency of an in-house information distribution process and optimizing information transmission by recognizing user emotions, and is realized by the following means.

[0223] System configuration

[0224] The system includes a terminal means, a server means, an AI means, a gap detection means, an optimization suggestion means, a report generation means, and an emotion engine.

[0225] Terminal means

[0226] The terminal means obtains permission from the user and automatically collects data such as in-house communication logs, emails, document sharing, meeting contents, etc. The collected data is sent to the server means.

[0227] Server Means

[0228] The server unit integrates the data collected from the terminal unit and stores it in a centralized manner. The server unit unifies the format of each data and adds necessary metadata, allowing for efficient data analysis.

[0229] AI means

[0230] AI tools analyze the collected data and generate and visualize information distribution channels. They primarily use natural language processing (NLP) technology to analyze text data and extract important keywords and themes. They also visualize how information is distributed as a network diagram.

[0231] Gap Detection Means

[0232] The gap detection method analyzes the information distribution channels generated by the AI ​​method and automatically detects missing or delayed information. It identifies gaps based on specific conditions and issues alerts or warnings.

[0233] Optimization proposal method

[0234] The optimization suggestion means generates specific suggestions for minimizing the information gaps identified by the gap detection means, such as establishing an information sharing meeting, using a specific communication tool, or increasing the priority of specific information.

[0235] Report Generation Method

[0236] The report generation means creates a report for presenting information to the user based on the proposals generated by the optimization proposal means. This report is visually displayed, allowing the user to intuitively grasp the flow of information and gaps.

[0237] Emotion Engine

[0238] The emotion engine recognizes user emotions and uses them as part of data analysis and optimization proposals. The emotion engine extracts user emotional data from the content of emails and chats and generates proposals to appropriately adjust the method of information transmission. The data obtained by the emotion engine is also reflected in the report generation means, providing appropriate feedback to the user.

[0239] Explanation of program processing

[0240] The program of the above system operates as follows.

[0241] Data collection

[0242] With the user's permission, the terminal means collects various data, including emails from the mail server, chat tool logs, file information from the document sharing system, and conference recordings, all of which are collected and sent to the server means.

[0243] Data Integration

[0244] The server receives the data collected from the terminals, integrates it, standardizes the data format, adds necessary metadata, and stores it in a database.

[0245] Data analysis

[0246] AI means analyzes the data stored on the server means, analyzes the text data using natural language processing technology, extracts important keywords and themes, and generates information distribution channels.

[0247] Information Visualization

[0248] The server means visualizes the information distribution route as a network diagram, allowing the user to intuitively understand the flow of information.

[0249] Gap Detection

[0250] The gap detection tool analyzes information distribution channels and automatically detects missing or delayed information. It identifies gaps based on specific conditions and issues alerts or warnings.

[0251] Optimization suggestions

[0252] An optimization suggestion unit generates specific suggestions for minimizing the gap. The suggestions are based on data and indicate specific measures for optimizing information distribution.

[0253] Report Generation

[0254] The report generation means compiles the proposals generated by the optimization proposal means into a report and provides the information visually to the user, thereby enabling the user to easily grasp the current state of information distribution and areas for improvement.

[0255] Emotion analysis

[0256] The emotion engine analyzes the content of users' emails and chats to extract emotional data. This allows the system to reflect the user's emotions and optimize the way information is communicated. Furthermore, the emotional data is reflected in the report generation tool, providing more appropriate feedback.

[0257] Specific examples

[0258] Example 1: Critical project information isn't reaching the remote team

[0259] The terminal means collects data from the user's email and project management tool. The server means integrates the data, and the AI ​​means analyzes important project information and identifies when information has not reached the remote team. The gap detection means detects problems, and the optimization suggestion means suggests establishing an information sharing meeting for the remote team. The report generation means displays improvement suggestions on a dashboard for the user to confirm. The emotion engine analyzes the emotion data of the remote team and suggests appropriate ways to communicate information. As a result, the remote team receives the latest information, allowing the project to progress smoothly.

[0260] Example 2: Resolving communication errors between departments

[0261] The terminal means collects logs from internal chat tools and meeting contents. The server means integrates this data, and the AI ​​means analyzes it to generate an information flow. The gap detection means detects problems with information transmission between departments, and the optimization proposal means proposes the establishment of regular information sharing meetings. The report generation means compiles this into a report and provides it visually to the user. The emotion engine analyzes the emotion data from each department and proposes the optimal communication method. This series of processes reduces miscommunication between departments and achieves efficient information transmission.

[0262] By combining an emotion engine, the system of the present invention realizes optimization of information distribution and improvement of transparency, taking into account the emotions of users.

[0263] The processing flow will be explained below.

[0264] Step 1:

[0265] The user performs initial setup to begin using the system. The user confirms the permission settings and grants access to individual data sources (email server, chat tool, document sharing system, conference system, etc.).

[0266] Step 2:

[0267] The device accesses data sources authorized by the user and collects various data. The device obtains email header information and text from the email server, collects chat tool logs, and obtains file information and metadata (author, update date, etc.) from the document sharing system. It also collects recording data and meeting minutes from the conference system.

[0268] Step 3:

[0269] The device sends the collected data to the server, which temporarily stores the data, converts it into an appropriate format, and then sends the data to the server through the interface.

[0270] Step 4:

[0271] The server receives the data sent from the devices and centrally integrates it. The server unifies the data format and adds the necessary metadata (department, individual, timestamp, etc.).

[0272] Step 5:

[0273] The server saves the data in a unified database. The server stores the data in a unified format in the database and generates an index to enable quick access.

[0274] Step 6:

[0275] The emotion engine analyzes the user's emotions from the collected data. The emotion engine uses natural language processing (NLP) technology to extract user emotional data from the content of emails and chats.

[0276] Step 7:

[0277] The server analyzes the data using AI means. The server uses natural language processing (NLP) techniques to analyze the text data and extract key keywords and themes.

[0278] Step 8:

[0279] The server generates information distribution routes and visualizes in the form of a network diagram how information is distributed between individual departments and individuals.

[0280] Step 9:

[0281] The server uses gap detection techniques to analyze the information flow and automatically detect missing or delayed information. The server identifies gaps based on specific conditions and sets relevant alerts.

[0282] Step 10:

[0283] The server uses an optimization proposal method to generate specific proposals to minimize the gap. The server compares past data and considers effective strategies and reflects them in the proposals.

[0284] Step 11:

[0285] The emotion engine generates additional suggestions based on the user's emotion data to optimize the effectiveness of the proposed improvement measures. The emotion engine proposes an optimal information transmission method that reflects the emotion data.

[0286] Step 12:

[0287] The server uses a report generator to generate a report containing the analysis results and optimization suggestions, which is formatted in a visually appealing format and prepared for display on a dashboard.

[0288] Step 13:

[0289] The server notifies the user of the report. The server displays the report on the dashboard and sends notifications via email or chat tools as needed.

[0290] Step 14:

[0291] Users can check the report through the dashboard and implement the suggested improvements. Users make decisions based on the report's contents and proceed with the process of optimizing the flow of information within the company in accordance with the system's suggestions.

[0292] Specific examples

[0293] Example 1: Critical project information isn't reaching the remote team

[0294] Step 1

[0295] The user configures the system and allows access to the remote team's mail server and chat tools.

[0296] Step 2

[0297] The device collects emails, chat logs, and documents related to the project from these data sources.

[0298] Step 3

[0299] The data collected by the terminal is sent to the server, where it is temporarily stored and formatted.

[0300] Step 4

[0301] The server receives the data and centrally integrates it, adding metadata to the data.

[0302] Step 5

[0303] The server stores the data converted into a unified format in a database and generates an index for easy access.

[0304] Step 6

[0305] The emotion engine analyzes the emotional data of the remote team from the collected data.

[0306] Step 7

[0307] The server uses AI tools to analyze project-related data and extract keywords and themes.

[0308] Step 8

[0309] The server creates an information distribution channel and makes it visible that information is not reaching the remote team.

[0310] Step 9

[0311] The server uses gap detection to identify missing information for the remote team and set alerts.

[0312] Step 10

[0313] The server proposes an information sharing meeting with the remote team.

[0314] Step 11

[0315] The emotion engine suggests ways to optimize information sharing based on emotional data from remote teams.

[0316] Step 12

[0317] The server compiles these into a report and displays it to the user on a dashboard.

[0318] Step 13

[0319] The server notifies the user of the report via email or chat tool.

[0320] Step 14

[0321] The user reviews the report and takes action to establish the proposed information sharing meeting.

[0322] Example 2: Resolving communication errors between departments

[0323] Step 1

[0324] Users configure the system to allow access to chat tools and conferencing systems used between specific departments.

[0325] Step 2

[0326] The device collects chat logs and meeting recordings from these data sources.

[0327] Step 3

[0328] The data collected by the terminal is sent to the server, where it is temporarily stored and formatted.

[0329] Step 4

[0330] The server receives the data and centrally integrates it, adding metadata to the data.

[0331] Step 5

[0332] The server stores the data converted into a unified format in a database and generates an index for quick access.

[0333] Step 6

[0334] The emotion engine analyzes the emotion data between departments from the collected data.

[0335] Step 7

[0336] The server uses AI tools to analyze communication-related data and extract keywords and themes.

[0337] Step 8

[0338] The server generates information distribution channels and visualizes problems in communication between departments.

[0339] Step 9

[0340] The server uses gap detection means to identify areas where information is not being properly communicated and sets alerts.

[0341] Step 10

[0342] The server proposes the establishment of regular information sharing meetings.

[0343] Step 11

[0344] The emotion engine suggests optimal communication methods based on the emotional data of each department.

[0345] Step 12

[0346] The server compiles these into a report and displays it to the user on a dashboard.

[0347] Step 13

[0348] The server notifies the user of the report via email or chat tool.

[0349] Step 14

[0350] The user reviews the report and takes action to establish the proposed information sharing meeting.

[0351] In this way, the system of the present invention efficiently manages information distribution while taking into account the feelings of users, thereby improving transparency and productivity.

[0352] Example 2

[0353] Next, a description will be given of Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the smart device 14 will be referred to as a "terminal."

[0354] Conventional systems lack the means to streamline internal information distribution processes, resulting in inefficiencies in business operations due to lack of information and delays.In addition, information transmission is not optimized with consideration for user emotions, and specific measures to improve the quality of communication are needed.

[0355] The specific processing by the specific processing unit 290 of the data processing device 12 in the second embodiment is realized by the following means. In this invention, the server includes terminal means for collecting in-house information, server means for integrating the collected data, artificial intelligence means for analyzing the integrated data and mapping information distribution routes, gap detection means for automatically detecting information gaps from the generated information distribution routes, optimization proposal means for generating optimization proposals for minimizing the gaps, report generation means for reporting the generated proposals, and an emotion engine for analyzing user emotions. This makes it possible to improve the efficiency of the information distribution process and optimize information transmission taking user emotions into consideration.

[0356] "Terminal means" refers to a device that automatically collects company information with the user's permission.

[0357] "Server means" refers to a computer system for centrally storing and integrating data collected from terminal means.

[0358] "Artificial intelligence means" refers to algorithms and software for analyzing data integrated by the server means and mapping information distribution channels.

[0359] "Gap detection means" refers to a function that automatically detects a lack of or delay in information from the information distribution path generated by the artificial intelligence means.

[0360] The "optimization suggestion means" refers to a function that generates specific suggestions to minimize the information gaps detected by the gap detection means.

[0361] The "report generation means" refers to a function for creating documents and graphs for visually presenting information based on the proposals generated by the optimization proposal means.

[0362] An "emotion engine" refers to algorithms or software that analyzes users' emotions and optimizes the way information is communicated.

[0363] "Dashboard means" refers to a screen or interface that visually presents the reports generated by the report generation means and allows the user to intuitively grasp the information.

[0364] This invention is a system for optimizing information transmission by improving the efficiency of in-house information distribution processes and recognizing user emotions. The program for this system is configured using the following hardware and software.

[0365] System configuration

[0366] The system includes a terminal means, a server means, an artificial intelligence means, a gap detection means, an optimization suggestion means, a report generation means, and an emotion engine.

[0367] Terminal means

[0368] The terminal means obtains permission from the user and automatically collects data such as internal communication logs, emails, document sharing, and meeting contents. For example, this could be a computer or smartphone used by the user. The collected data is sent to the server means. A secure communication protocol (e.g., HTTPS) is used to send the data.

[0369] Server Means

[0370] The server means integrates the data collected from the terminal means and stores it in a centralized manner. For example, a high-performance server computer plays this role. The server standardizes the data format and converts it into, for example, JSON or XML format. It also adds necessary metadata (such as timestamp, sender, and destination) and stores it in a database.

[0371] Artificial Intelligence Tools

[0372] Artificial intelligence means placed within the server analyzes the stored data. Specifically, it uses natural language processing (NLP) technology to analyze text data and extract important keywords and themes. For example, machine learning models and deep learning models are used. It also analyzes email sending and receiving patterns to generate information distribution channels.

[0373] Gap Detection Means

[0374] The gap detection means analyzes the information distribution path generated by the artificial intelligence means and automatically detects missing or delayed information. It identifies gaps based on specific conditions (e.g., when information is not shared within a specific time) and issues an alert or warning to the user.

[0375] Optimization proposal method

[0376] The optimization suggestion means generates specific suggestions to address the problems identified by the gap detection means, such as establishing an information sharing meeting, using a specific communication tool, or raising the priority of information, thereby enabling users to optimize information distribution.

[0377] Report Generation Method

[0378] The report generation means creates a report to visually present information based on the proposals generated by the optimization proposal means. This report includes graphs and charts, allowing the user to intuitively understand the current state of information distribution and areas for improvement.

[0379] Emotion Engine

[0380] An emotion engine is an algorithm or software that analyzes a user's emotions and optimizes the method of communication. For example, it analyzes the content of a user's email or chat messages and extracts emotional data. This emotional data is reflected in the report generation tool and presented visually.

[0381] Specific examples

[0382] Example 1: Critical project information isn't reaching the remote team

[0383] The terminal means collects data from the user's email and project management tool. The server means integrates the data, and the artificial intelligence means analyzes the project information and identifies when important information has not reached the remote team. The gap detection means detects the problem, and the optimization suggestion means suggests establishing an information sharing meeting for the remote team. The report generation means displays improvement suggestions on a dashboard for the user to confirm. The emotion engine analyzes the emotion data of the remote team and suggests an appropriate method of communicating information. As a result, the remote team receives the latest information, and the project progresses smoothly.

[0384] Example 2: Resolving communication errors between departments

[0385] The terminal means collects logs from internal chat tools and the contents of meetings. The server means integrates this data, and the artificial intelligence means analyzes the flow of information. The gap detection means detects problems with information transmission between departments. The optimization proposal means proposes the establishment of regular information sharing meetings. The report generation means compiles this into a report and provides it visually to the user. The emotion engine analyzes the emotion data from each department and proposes the optimal communication method. This series of processes reduces miscommunication between departments and achieves efficient information transmission.

[0386] Prompt Sentence Examples

[0387] "Please extract emotional data from the content of this email."

[0388] "Collect and integrate data from your project management tools."

[0389] "Detect gaps in information flow between departments and generate optimization suggestions."

[0390] The system of the present invention uses a generative AI model to generate proposals in real time, significantly improving the user's work efficiency.

[0391] The flow of the identification process in the second embodiment will be described with reference to FIG.

[0392] Step 1: Data collection

[0393] With the user's permission, the device automatically collects data such as emails, chat logs, document sharing information, and meeting recordings. For example, the device connects to a mail server and obtains the latest email data. The collected data is sent from the device to the server via a secure communication protocol (e.g., HTTPS). The input is the permission information and various data obtained from the user, and the output is the integrated data transferred to the server.

[0394] Step 2: Data Integration

[0395] The server receives data sent from the terminal and stores it centrally. The server standardizes the format of the received data, converting it into, for example, JSON or XML format. It also adds necessary metadata (timestamp, sender, destination, etc.) and stores it in a database. The input is the raw data sent from the terminal, and the output is data stored in the database in a standardized format.

[0396] Step 3: Data analysis

[0397] The server passes the stored data to an artificial intelligence means, which uses natural language processing (NLP) techniques to analyze the text data and extract important keywords and themes. For example, it may extract specific keywords (e.g., "deadline" or "meeting") from the text of emails or chat logs. The input is the consolidated text data, and the output is the extracted keywords and themes.

[0398] Step 4: Visualize the information

[0399] The server visualizes the information distribution routes generated by the artificial intelligence means as a network diagram. When a user accesses the dashboard, the network diagram is displayed, allowing the user to intuitively understand important information flows and communication patterns. Specifically, nodes and edges are used to visually show who is sending information to whom. The input is the analysis results from the artificial intelligence means, and the output is visual information provided to the user.

[0400] Step 5: Gap detection

[0401] The gap detection means analyzes the generated information distribution channels and automatically detects missing or delayed information. It identifies gaps based on specific conditions (e.g., when information is not shared within a specific time frame) and issues alerts or warnings to users. The input is the generated information distribution channels, and the output is the identified gaps and alert information.

[0402] Step 6: Optimization suggestions

[0403] The optimization proposal means generates specific proposals to minimize the information gaps identified by the gap detection means. The proposals include measures such as establishing an information sharing meeting, using specific communication tools, and raising the priority of information. The input is the detected gap information, and the output is the generated optimization proposals.

[0404] Step 7: Generate Reports

[0405] The report generation means creates a report to visually present information based on the proposals generated by the optimization proposal means. The report includes graphs and charts, allowing the user to intuitively understand the current state of information distribution and areas for improvement. The input is the generated optimization proposals, and the output is the visually presented report.

[0406] Step 8: Sentiment Analysis

[0407] The emotion engine analyzes the content of the user's emails and chat messages and extracts emotional data. The emotional data is reflected in reports and suggests appropriate ways to communicate information. Specifically, if the user is under stress, the emotion engine suggests ways to alleviate the stress. The input is the content of the emails and chat messages, and the output is the extracted emotional data and suggestions.

[0408] (Application example 2)

[0409] Next, a description will be given of Application Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the smart device 14 will be referred to as a "terminal."

[0410] Conventional factory management systems lacked the means to effectively collect and analyze work data and worker conversation data, preventing them from fully optimizing work efficiency and information distribution. Furthermore, it was difficult to improve information transmission while taking into account the emotional state of workers, which created a risk of lower worker satisfaction and work efficiency. This has led to a demand for improvements to production processes and worker satisfaction.

[0411] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 2 is realized by the following means.

[0412] In this invention, the server includes terminal means for collecting in-house information, server means for integrating data collected from the terminal means, AI means for analyzing the integrated data by the server means and mapping information distribution routes, gap detection means for automatically detecting information gaps from the information distribution routes generated by the AI ​​means, optimization proposal means for generating optimization proposals to minimize the information gaps detected by the gap detection means, report generation means for reporting the proposals generated by the optimization proposal means, robot means for collecting work data and worker conversations via environmental sensors, cameras, and microphones in the factory, an emotion engine for analyzing the work data and worker conversation data and extracting emotional information, emotion data optimization proposal means for generating proposals to optimize information distribution feedback based on the emotional information, and data visualization means for reporting and visually displaying the proposals generated by the emotion data optimization proposal means. This enables optimization of information distribution and work efficiency in the factory and improvement of information communication taking into account the emotions of workers.

[0413] "Internal information" refers to data, knowledge, and communication records generated and collected within a company or organization.

[0414] "Terminal means" is a device for collecting information and transmitting it to server means.

[0415] The "server means" is a system for integrating and centrally managing collected data.

[0416] "AI means" refers to technologies for analyzing collected and integrated data and generating and visualizing information distribution channels.

[0417] The "gap detection means" is a mechanism for automatically detecting data loss or delay based on the generated information distribution path.

[0418] "Optimization suggestions" are techniques for generating specific suggestions to minimize detected information gaps.

[0419] A "report generator" is a system for visually presenting optimization suggestions and providing information to a user.

[0420] "Robotic means" refers to devices that collect work data and worker conversations through environmental sensors, cameras, and microphones within the factory.

[0421] The "emotion engine" is a technology for analyzing and extracting emotional data from worker conversations and feedback.

[0422] The "emotion data optimization proposal means" is a technology for generating proposals for optimizing feedback in information distribution by utilizing emotion data.

[0423] A "data visualization tool" is a tool for visually displaying generated proposals and providing information in a form that is easy for users to understand.

[0424] This invention is a system for realizing a "smart factory management system" that optimizes information distribution and work efficiency within a factory. This system is configured using the following hardware and software.

[0425] System configuration

[0426] Terminal means

[0427] The terminal means consists of robots equipped with environmental sensors, cameras, and microphones in the factory. These devices collect work data and worker conversations in real time.

[0428] Server Means

[0429] The server means uses a cloud server (e.g., AWS, Google Cloud) and a database (e.g., MySQL, PostgreSQL). The server centrally stores the collected data and standardizes the data format. Furthermore, it adds necessary metadata to support data analysis.

[0430] AI means

[0431] AI tools use natural language processing tools (e.g., NLTK, spaCy) and machine learning libraries (e.g., TensorFlow, PyTorch) to analyze the collected data, extract important keywords and themes from the text data, and generate and visualize information distribution channels.

[0432] Gap Detection Means

[0433] The gap detection method analyzes the information distribution channels generated by the AI ​​method to detect missing or delayed information. Based on specific conditions, it identifies gaps and issues alerts or warnings.

[0434] Optimization proposal method

[0435] The optimization proposal unit generates specific proposals for minimizing the information gaps identified by the gap detection unit, including detailed procedures for specific tasks and optimization of information distribution.

[0436] Report Generation Method

[0437] The report generation means creates a report based on the proposals generated by the optimization proposal means and the sentiment data optimization proposal means. The report is visually displayed using a data visualization tool (e.g., Tableau, Power BI).

[0438] Emotion Engine

[0439] The emotion engine uses emotion analysis tools (e.g., Microsoft Azure Text Analytics, IBM Watson Natural Language Understanding) to extract emotional data from worker conversations and feedback. Based on this data, it makes suggestions to optimize feedback for information distribution.

[0440] Robotic Means

[0441] The robotic means collects data on factory operations and conversations between workers and transmits the data to a server in real time. The robot is equipped with environmental sensors and cameras, and transmits the collected information to a cloud server for storage.

[0442] Specific examples

[0443] Example 1: When work efficiency declines

[0444] Robots in factories collect work data and conversations between workers. The server consolidates and analyzes this data and detects when work efficiency is declining at specific times. The optimization proposal tool proposes new work flows and adjusts maintenance schedules for specific machines. The report generation tool displays this visually and promptly informs workers of countermeasures.

[0445] Example prompts to input to the generative AI model

[0446] "Collect data on declines in work efficiency and identify the causes. Please provide specific suggestions for improvement."

[0447] This system combines these methods to optimize the flow of information within the factory, improving work efficiency and worker satisfaction.

[0448] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[0449] Step 1:

[0450] The terminal means (robots in the factory) collects work data and conversation data of workers using environmental sensors, cameras, and microphones. The collected data is sent to the server means in real time.

[0451] Input: Work data, worker conversation data

[0452] Output: Data sent to the server

[0453] Step 2:

[0454] The server unit centrally stores the data received from the terminal unit, unifies the data format, and stores the data in a database with necessary metadata added.

[0455] Input: Data sent from the terminal means

[0456] Output: Data saved in a unified format

[0457] Step 3:

[0458] AI tools analyze the stored data and use natural language processing tools (NLTK and spaCy) to extract important keywords and themes from the text data. Machine learning libraries (TensorFlow and PyTorch) are used to generate and visualize information distribution channels.

[0459] Input: Data stored in a unified format

[0460] Output: Visualized data of important keywords, themes, and information distribution channels

[0461] Step 4:

[0462] The gap detection means analyzes the information distribution channels generated by the AI ​​means and detects information omissions or delays. Based on this, it identifies gaps under specific conditions and issues alerts or warnings.

[0463] Input: Visualization data of information distribution channels

[0464] Output: Gap information, alerts, warnings

[0465] Step 5:

[0466] The optimization proposal means generates specific improvement proposals (detailed procedures for specific tasks or new information sharing methods) to minimize the information gaps identified by the gap detection means.

[0467] Input: Gap information

[0468] Output: Improvement suggestions

[0469] Step 6:

[0470] The emotion engine analyzes and extracts emotional information from worker conversations and feedback data using emotion analysis tools (Microsoft Azure Text Analytics and IBM Watson Natural Language Understanding). Based on the acquired emotional information, it makes suggestions to optimize feedback on information transmission.

[0471] Input: Worker conversation data, feedback data

[0472] Output: Emotional information, optimization suggestions based on emotional data

[0473] Step 7:

[0474] The report generation means creates a visual report using a data visualization tool (Tableau or Power BI) based on the proposals generated by the optimization proposal means and the sentiment data optimization proposal means, and displays the report to the user.

[0475] Input: Improvement suggestions, optimization suggestions based on sentiment data

[0476] Output: Visually displayed reports and dashboards

[0477] Step 8:

[0478] Users view visual reports and dashboards and take specific actions to improve work efficiency.

[0479] Input: Visual reports, dashboards

[0480] Output: Actions taken

[0481] The specific processing unit 290 transmits the result of the specific processing to the smart device 14. In the smart device 14, the control unit 46A causes the output device 40 to output the result of the specific processing. The microphone 38B acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0482] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (Internet Search<URL: https: / / openai.com / blog / chatgpt> ), Gemini (Internet search <url: https: gemini.google.com ?hl="ja">) and other generation AIs. The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating voice, text data indicating text, and image data indicating an image is also input. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt, and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[0483] In the above embodiment, an example in which the specific process is performed by the data processing device 12 has been given, but the technology of the present disclosure is not limited to this, and the specific process may be performed by the smart device 14.

[0484] [Second embodiment]

[0485] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0486] 3, the data processing system 210 includes the data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0487] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 is an example of a "computer" according to the technology of the present disclosure. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, the RAM 30, and the storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[0488] The smart glasses 214 include a computer 36, a microphone 238, a speaker 240, a camera 42, and a communication I / F 44. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, and the camera 42 are also connected to the bus 52.

[0489] The microphone 238 receives instructions and the like from the user 20 by receiving voice uttered by the user 20. The microphone 238 captures the voice uttered by the user 20, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio in accordance with instructions from the processor 46.

[0490] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the surroundings of user 20 (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0491] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0492] Fig. 4 shows an example of the main functions of the data processing device 12 and the smart glasses 214. As shown in Fig. 4, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0493] The specific processing program 56 is an example of a "program" according to the technology of the present disclosure. The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0494] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[0495] In the smart glasses 214, the reception output process is performed by the processor 46. A reception output program 60 is stored in the storage 50. The processor 46 reads the reception output program 60 from the storage 50 and executes the read reception output program 60 on the RAM 48. The reception output process is realized by the processor 46 operating as the control unit 46A in accordance with the reception output program 60 executed on the RAM 48.

[0496] Next, a description will be given of the identification process performed by the identification processing unit 290 of the data processing device 12. In the following description, the data processing device 12 will be referred to as the "server" and the smart glasses 214 will be referred to as the "terminal."

[0497] The present invention is a system for improving the efficiency of an information distribution process within a company, and is realized by the following means.

[0498] System configuration

[0499] The system includes a terminal means, a server means, an AI means, a gap detection means, an optimization suggestion means, and a report generation means.

[0500] Terminal means

[0501] The terminal means obtains permission from the user and automatically collects data such as internal communication logs, emails, document sharing, and meeting contents. This collection includes data from email servers, chat tools, document management systems, meeting recording systems, etc. The terminal means transmits the collected data to the server means.

[0502] Server Means

[0503] The server means integrates the data collected from the terminal means and stores it centrally. The server standardizes the format of each data and adds necessary metadata (e.g., department, individual, timestamp, etc.). This integrated data is later analyzed by the AI ​​means.

[0504] AI means

[0505] AI tools analyze the collected and integrated data to generate and visualize information distribution channels. AI tools use natural language processing (NLP) techniques to analyze text data and extract important keywords and themes. They also generate network diagrams to clarify how, when, and between which departments and individuals information is circulating.

[0506] Gap Detection Means

[0507] The gap detection method analyzes the information distribution path generated by the AI ​​method and detects gaps or delays in the distribution of information. This method identifies areas where information is not being properly transmitted based on specific conditions and issues alerts or warnings.

[0508] Optimization proposal method

[0509] The optimization suggestion means generates optimal improvement suggestions for minimizing the information gaps identified by the gap detection means, such as establishing an information sharing meeting, using a specific communication tool, or increasing the priority of specific information.

[0510] Report Generation Method

[0511] The report generation means creates a report to present information to the user based on the proposals generated by the optimization proposal means. The report is visually displayed through the dashboard means, allowing the user to intuitively grasp the flow of information and gaps. In addition, the report is notified to the user via email or chat tool as needed.

[0512] Explanation of program processing

[0513] The program of the above system operates as follows.

[0514] Data collection

[0515] The terminal means obtains permission from the user and collects various data, including obtaining emails from the email server, collecting chat tool logs, and obtaining file information from the document sharing system.

[0516] Data Integration

[0517] The server receives the data sent from the terminal and stores it in the integrated database. The server converts the data into a unified format, adds metadata, and stores it.

[0518] Data analysis

[0519] AI tools analyze the data stored on the server tools and use natural language processing technology to analyze the text data, thereby extracting important keywords and themes and mapping the distribution channels of information.

[0520] Gap Detection

[0521] The gap detection means analyzes the information distribution path generated by the AI ​​means and detects information omissions or delays. This means automatically identifies gaps based on specific conditions.

[0522] Optimization suggestions

[0523] An optimization suggestion means generates a suggestion for minimizing the information gap detected by the gap detection means, the suggestion being based on the data and including a specific measure for optimizing the information distribution.

[0524] Report Generation

[0525] The report generation means compiles the proposals generated by the optimization proposal means into a report and notifies the user. The report is visually displayed via the dashboard means, allowing the user to intuitively grasp the current state of information distribution and areas for improvement.

[0526] Specific examples

[0527] Example 1: Critical project information isn't reaching the remote team

[0528] The terminal means collects data from the project management tool and mail server. The server means integrates the data, and the AI ​​means analyzes the information distribution route. The gap detection means identifies when information is not reaching the remote team, and the optimization proposal means generates proposals to increase the priority of information sharing. The report generation means displays improvement proposals on a dashboard, where the user can confirm and implement the improvement measures.

[0529] Example 2: Resolving communication errors between departments

[0530] The terminal means collects logs from internal chat tools and data on meeting contents. The server means integrates this data, and the AI ​​means analyzes the flow of information. The gap detection means detects when information is not being communicated properly between specific departments, and the optimization proposal means proposes the establishment of regular information sharing meetings. The report generation means compiles the proposals into a report, which the user confirms and implements improvement measures.

[0531] As a result, the system of the present invention efficiently manages the distribution of information within a company, and realizes improved transparency and productivity.

[0532] The processing flow will be explained below.

[0533] Step 1:

[0534] The user performs initial setup to begin using the system. The user confirms the permission settings and grants access to individual data sources (email server, chat tool, document sharing system, conference system, etc.).

[0535] Step 2:

[0536] The device accesses data sources authorized by the user and collects various data. The device obtains email header information and text from the email server, extracts chat tool logs, and obtains file information and metadata (author, update date, etc.) from the document sharing system. It also collects recording data and meeting minutes from the conference system.

[0537] Step 3:

[0538] The device sends the collected data to the server, which temporarily stores the data, converts it into an appropriate format, and then sends the data to the server through the interface.

[0539] Step 4:

[0540] The server receives the data sent from the devices and centrally integrates it. The server unifies the data format and adds the necessary metadata (department, individual, timestamp, etc.).

[0541] Step 5:

[0542] The server saves the data in a unified database. The server stores the data in a unified format in the database and generates an index to enable quick access.

[0543] Step 6:

[0544] The server analyzes the data using AI means. The server uses natural language processing (NLP) techniques to analyze the text data and extract key keywords and themes.

[0545] Step 7:

[0546] The server generates information distribution routes and visualizes in the form of a network diagram how information is distributed between individual departments and individuals.

[0547] Step 8:

[0548] The server uses gap detection techniques to analyze the information flow and automatically detect missing or delayed information. The server identifies gaps based on specific conditions and sets relevant alerts.

[0549] Step 9:

[0550] The server uses an optimization proposal method to generate specific proposals to minimize the gap. The server compares past data and considers effective strategies and reflects them in the proposals.

[0551] Step 10:

[0552] The server uses a report generator to generate a report containing the analysis results and optimization suggestions, which is formatted in a visually appealing format and prepared for display on a dashboard.

[0553] Step 11:

[0554] The server notifies the user of the report. The server displays the report on the dashboard and sends notifications via email or chat tools as needed.

[0555] Step 12:

[0556] Users can check the report through the dashboard and implement the suggested improvements. Users make decisions based on the report's contents and proceed with the process of optimizing the flow of information within the company in accordance with the system's suggestions.

[0557] Example 1

[0558] Next, a description will be given of Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the smart glasses 214 will be referred to as a "terminal."

[0559] Improving the efficiency of information distribution within a company is extremely important for improving corporate productivity and transparency. However, with conventional systems, each process from information collection, integration, analysis, and optimization proposals is fragmented, making it difficult to efficiently manage it centrally. Furthermore, there is a high likelihood of miscommunication and misunderstandings due to insufficient automatic detection of information gaps and delays and the implementation of countermeasures. A comprehensive system that can solve these problems is needed.

[0560] The specific processing by the specific processing unit 290 of the data processing device 12 in the first embodiment is realized by the following means.

[0561] In this invention, the server includes terminal means for obtaining permission from users and collecting various types of data within the company, server means for receiving the data collected from the terminal means, converting it into a unified format, and centrally storing it, AI means for analyzing the data stored in the server means using natural language analysis technology and mapping information distribution routes, gap detection means for automatically detecting information gaps and delays from the information distribution routes generated by the AI ​​means, optimization proposal means for generating optimization proposals to minimize the information gaps detected by the gap detection means, and report generation means for compiling the proposals generated by the optimization proposal means into a report and notifying the user. This enables efficient and integrated management of the company's information distribution process, automatically detecting information gaps and delays, and proposing optimal improvement measures.

[0562] "Terminal means" refers to a device or system that obtains permission from a user and collects various data within a company.

[0563] "Server means" refers to a device or system that receives data collected from terminal means, converts it into a unified format, and stores it in a centralized manner.

[0564] "AI means" refers to devices or systems that have the function of analyzing data stored in server means using natural language analysis technology and mapping information distribution routes.

[0565] "Gap detection means" refers to a device or system that automatically detects information gaps or delays in information distribution channels generated by AI means.

[0566] The term "optimization proposal means" refers to a device or system that generates optimal proposals to minimize the information gaps detected by the gap detection means.

[0567] The "report generation means" refers to a device or system that compiles the proposals generated by the optimization proposal means into a report and notifies the user of the report.

[0568] "User" refers to a company employee or administrator who uses the system, grants permissions, or reviews reports.

[0569] "Data" refers to various information generated within the company, such as emails, chat logs, documents, and meeting recordings.

[0570] "Natural language analysis technology" refers to the technology of analyzing text data and extracting important keywords and themes.

[0571] "Information distribution route" refers to the route that indicates how, when, and between which departments and individuals information is distributed within a company.

[0572] "Metadata" is auxiliary information added to data, such as department, individual, timestamp, etc.

[0573] The present invention is a system for improving the efficiency of an in-house information distribution process. The system includes a terminal unit, a server unit, an AI unit, a gap detection unit, an optimization suggestion unit, and a report generation unit.

[0574] System configuration

[0575] Terminal means

[0576] The terminal means obtains permission from the user and automatically collects data such as internal communication logs, emails, document sharing, and meeting contents. This collection includes email servers (e.g., Microsoft Exchange), chat tools (e.g., Slack), document management systems (e.g., Google Drive), and meeting recording systems (e.g., Zoom). The terminal means transmits the collected data to the server means.

[0577] Server Means

[0578] The server means integrates the data collected from the terminal means and stores it centrally. The server standardizes the format of each piece of data and adds the necessary metadata (department, individual, timestamp, etc.). This integrated data is later analyzed by the AI ​​means. A cloud-based database system (e.g., Amazon RDS) is often used for the server means.

[0579] AI means

[0580] AI tools analyze collected and integrated data to generate and visualize information distribution channels. They use natural language processing (NLP) techniques to analyze text data and extract important keywords and themes. They also generate network diagrams to clarify how, when, and between which departments and individuals information is circulating. Examples of use include Google BERT and OpenAI's GPT model.

[0581] Gap Detection Means

[0582] The gap detection method analyzes the information distribution path generated by the AI ​​method and detects gaps or delays in the distribution of information. This method identifies areas where information is not being properly transmitted based on specific conditions and issues alerts or warnings.

[0583] Optimization proposal method

[0584] The optimization suggestion means generates optimal improvement suggestions to minimize the information gaps identified by the gap detection means. These suggestions include establishing an information sharing meeting, using a specific communication tool, raising the priority of specific information, etc. Specifically, it suggests prioritizing the notification of project information to the remote team and establishing regular information sharing meetings.

[0585] Report Generation Method

[0586] The report generation means creates a report to present information to the user based on the proposals generated by the optimization proposal means. The report is visually displayed through a dashboard means (e.g., Power BI) so that the user can intuitively grasp the flow of information and gaps. In addition, the report is notified to the user via email or chat tools as necessary.

[0587] Specific examples

[0588] Example 1: Critical project information isn't reaching the remote team

[0589] Based on the user's permission, the terminal means collects data from the project management tool and mail server. The server means integrates the data, and the AI ​​means analyzes the information distribution path. The gap detection means identifies when information has not reached the remote team, and the optimization proposal means generates proposals to increase the priority of information sharing. The report generation means displays improvement proposals on a dashboard, where the user can confirm and implement the improvement measures.

[0590] Example 2: Resolving communication errors between departments

[0591] The user uses the terminal means to collect data on the logs of the internal chat tool and meeting contents. The server means integrates this data, and the AI ​​means analyzes the flow of information. The gap detection means detects that information is not being communicated properly between specific departments, and the optimization proposal means proposes the establishment of regular information sharing meetings. The report generation means compiles the proposals into a report, which the user confirms and implements improvement measures.

[0592] As described above, this system efficiently manages the flow of information within a company, improving transparency and productivity.

[0593] Prompt Sentence Examples

[0594] "Proposing solutions when important project information isn't reaching a remote team"

[0595] "How to resolve communication errors between departments"

[0596] The present invention enables companies to optimize information distribution and improve business efficiency.

[0597] The flow of the identification process in the first embodiment will be described with reference to FIG.

[0598] Step 1:

[0599] The terminal means obtains permission from the user and collects various data from the company's email server, chat tool, document management system, and meeting recording system. Specifically, the terminal means connects to Microsoft Exchange to collect email data, collects chat logs using the Slack API, obtains file information using the Google Drive API, and obtains meeting recording data using the Zoom API. The collected data is temporarily stored in the terminal means in the form of raw data from each tool.

[0600] Input: User permission, API connection information for each tool

[0601] Output: Raw data collected from each tool

[0602] Step 2:

[0603] The server receives data collected from the terminal devices and converts it into a unified format. Specifically, it converts email content into text format, chat logs into JSON format, integrates document information into a common metadata format, and converts conference recording data into text. The converted data is then added with metadata such as sender, recipient, and timestamp, and is stored centrally in an integrated database.

[0604] Input: Raw data collected from each tool

[0605] Output: Data converted into a unified format, consolidated data with added metadata

[0606] Step 3:

[0607] The AI ​​tool analyzes the integrated data stored on the server. Specifically, it uses natural language processing technology to extract important keywords and themes from the text data. The AI ​​tool uses Google BERT and OpenAI's GPT model to analyze the content of emails and chat logs and identify relevant keywords and context. It also analyzes the content of documents and maps each piece of information as an information distribution channel.

[0608] Input: Data converted to a unified format, with added metadata

[0609] Output: Extracted keywords and themes, mapped information distribution channels

[0610] Step 4:

[0611] The gap detection method analyzes the information distribution channels generated by the AI ​​method and detects missing or delayed information. Specifically, it identifies which channels are not transmitting information based on specific pre-defined conditions (e.g., the time frame within which the required information should be released). An alert is generated about the detected gap, and the appropriate personnel are notified.

[0612] Input: Mapped information distribution channels, specific conditions

[0613] Output: Information gaps detected, alerts generated

[0614] Step 5:

[0615] The optimization suggestion unit generates suggestions to minimize information gaps detected by the gap detection unit, such as prioritizing communication of project information to remote teams, establishing regular information sharing meetings, recommending the use of specific communication tools, etc. The suggestions are generated based on the data and indicate specific areas for improvement.

[0616] Input: Detected information gaps

[0617] Output: Generated optimization proposals

[0618] Step 6:

[0619] The report generator compiles the optimization proposals into a report and notifies the user. Specifically, Power BI is used to create a dashboard that visually displays the proposals and the current status of information distribution. The report is then converted into PDF format and distributed to the user via email or chat tools.

[0620] Input: Generated optimization proposals

[0621] Output: Visual dashboards, PDF reports, and user notifications

[0622] Through the above steps, this system can efficiently manage the flow of information within a company, improving transparency and productivity.

[0623] (Application example 1)

[0624] Next, a description will be given of Application Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the smart glasses 214 will be referred to as a "terminal."

[0625] In the distribution of information within a factory, important information is often not communicated efficiently, which can have a negative impact on production efficiency and quality control. Therefore, there is a need for a system that can quickly identify which department or worker is experiencing delays or deficiencies and make appropriate suggestions. In addition, there is a need for a method that visually shows the optimization of information communication and allows workers to intuitively understand it.

[0626] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 1 is realized by the following means.

[0627] In this invention, the server includes device means for collecting in-house information, storage means for integrating data collected from the device means, machine learning means for analyzing the integrated data by the storage means and mapping information distribution routes, gap detection means for automatically detecting information gaps from the information distribution routes generated by the machine learning means, proposal generation means for generating optimization proposals to minimize the information gaps detected by the gap detection means, a visualization device for visually presenting the proposals generated by the proposal generation means, means for using smart glasses as the visualization device, means for wirelessly transmitting data collected by the smart glasses to the storage means, and specific means for optimizing the information distribution routes. This makes it possible to quickly identify delays or gaps in information distribution within a factory and propose and implement optimal information sharing measures.

[0628] "Internal information" is a general term for information relating to business, communication, and operations carried out within a company or organization.

[0629] "Device means" refers to terminal equipment and its interface for collecting and transmitting information.

[0630] "Storage means" refers to a storage system for centrally storing collected data and managing it in an accessible form as needed.

[0631] "Machine learning tools" refers to algorithms and software used to analyze collected data, find patterns and trends, and map information distribution channels.

[0632] "Gap detection means" refers to a mechanism for automatically identifying gaps (lack of or delay in) information distribution.

[0633] "Proposal generator for generating optimization proposals" refers to algorithms and systems for generating specific improvement proposals to minimize detected deficiencies.

[0634] "Visualization device" refers to a device and software for visually presenting the generated proposals and the current state of information distribution.

[0635] "Smart glasses" refers to a wearable device that visually displays collected data in real time and has the ability to interactively share information with the user.

[0636] "Wireless communication" refers to technologies and protocols for transmitting and receiving data without a physical connection.

[0637] "Information distribution route" refers to a route diagram that shows how, when, and between which departments and individuals information is distributed.

[0638] "Specific means" refers to methods that specifically demonstrate the actions and technologies necessary to achieve optimized information distribution.

[0639] The present invention is a system for improving the efficiency of information distribution within a factory. A method for implementing a program for the system that realizes this application example will be specifically described below.

[0640] Data collection

[0641] The smart glasses are used as terminals. They collect data such as communication logs, emails, shared documents, and work instructions between workers in the factory in real time. This data is sent to a server via wireless communication.

[0642] Data Integration

[0643] The server consolidates and centrally stores the data sent from the smart glasses. A database is used as the storage medium. The collected data is converted into a unified format and stored with metadata such as department, individual, and timestamp.

[0644] Data analysis

[0645] The server uses machine learning techniques to analyze the integrated data. Specifically, it uses natural language processing (NLP) techniques to extract important keywords and themes from the collected text data. It also generates a network diagram of information distribution routes. This process uses libraries such as "spaCy" and "NetworkX."

[0646] Gap Detection

[0647] The server analyzes the information distribution routes generated by the machine learning method and automatically detects information gaps (lack of information or delays) using the gap detection method, thereby identifying areas where information transmission to specific departments or individuals is stalled.

[0648] Generate optimization suggestions

[0649] The server uses a proposal generator to generate optimization proposals based on the detected gaps, which suggest specific improvements, such as changing the information sharing method or introducing a new communication method.

[0650] Reporting and Visualization

[0651] The report generation means displays the generated proposals on the visualization device. The visualization device, smart glasses, visually presents the proposals and the current status of information distribution to workers in real time in a dashboard format, allowing workers to intuitively understand problems and improvement measures and implement them.

[0652] Specific examples

[0653] For example, consider a scenario where critical information about a particular manufacturing process isn't being communicated to a remote quality control team. The server analyzes the data collected by the smart glasses and detects the gaps. In this case, the server suggests setting up an information-sharing meeting or introducing new communication methods. These suggestions are presented in real time through the smart glasses, allowing workers to immediately implement improvements.

[0654] Prompt Sentence Examples

[0655] "It's possible that information isn't reaching certain departments through this information distribution network. What communication methods would be effective in making improvements?"

[0656] This makes it possible to quickly identify delays or lack of information flow within the factory and propose and implement optimal information sharing methods.

[0657] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[0658] Step 1:

[0659] The smart glasses collect data such as communication logs, emails, shared documents, and work instructions between workers in the factory in real time and transmit it to a server via wireless communication.

[0660] Input: Worker communication data

[0661] Output: Data collected by the smart glasses and transmitted to the server via wireless communication.

[0662] Step 2:

[0663] The server receives the data sent from the smart glasses and stores it in a centralized storage device. The collected data is converted into a unified format and metadata such as department, individual, and timestamp are added.

[0664] Input: Raw data sent from smart glasses

[0665] Output: Data converted into a unified format and with metadata added

[0666] Step 3:

[0667] The server uses machine learning techniques to analyze the stored data. Specifically, it uses natural language processing (NLP) techniques to extract important keywords and themes from the collected text data. It also generates a network diagram of information distribution routes.

[0668] Input: Data converted into a unified format and with metadata added

[0669] Output: A network diagram of important keywords and themes and information distribution channels

[0670] Step 4:

[0671] The server analyzes the information distribution path generated by the machine learning means and automatically detects information gaps (lack or delay) using the gap detection means.

[0672] Input: Network diagram of information distribution channels

[0673] Output: Detected information gaps

[0674] Step 5:

[0675] The server uses a proposal generator to generate optimization proposals based on the detected gaps, which suggest specific improvements, such as changing the information sharing method or introducing a new communication method.

[0676] Input: Detected information gaps

[0677] Output: Optimization suggestions

[0678] Step 6:

[0679] The server compiles the generated optimization proposals into a report using a report generation means and displays it on the visualization device. The smart glasses as a visualization device visually present the proposals and the current status of information distribution to workers in real time in dashboard format, allowing workers to intuitively understand problems and improvement measures and implement them.

[0680] Input: Optimization proposal

[0681] Output: Reports and dashboards displayed on smart glasses

[0682] This process flow makes it possible to quickly identify delays or lack of information distribution within the factory and propose and implement optimal information sharing methods.

[0683] Furthermore, an emotion engine that estimates the user's emotion may be further combined. That is, the identification processing unit 290 may estimate the user's emotion using the emotion identification model 59, and perform identification processing using the user's emotion.

[0684] The present invention is a system for improving the efficiency of an in-house information distribution process and optimizing information transmission by recognizing user emotions, and is realized by the following means.

[0685] System configuration

[0686] The system includes a terminal means, a server means, an AI means, a gap detection means, an optimization suggestion means, a report generation means, and an emotion engine.

[0687] Terminal means

[0688] The terminal means obtains permission from the user and automatically collects data such as in-house communication logs, emails, document sharing, meeting contents, etc. The collected data is sent to the server means.

[0689] Server Means

[0690] The server unit integrates the data collected from the terminal unit and stores it in a centralized manner. The server unit unifies the format of each data and adds necessary metadata, allowing for efficient data analysis.

[0691] AI means

[0692] AI tools analyze the collected data and generate and visualize information distribution channels. They primarily use natural language processing (NLP) technology to analyze text data and extract important keywords and themes. They also visualize how information is distributed as a network diagram.

[0693] Gap Detection Means

[0694] The gap detection method analyzes the information distribution channels generated by the AI ​​method and automatically detects missing or delayed information. It identifies gaps based on specific conditions and issues alerts or warnings.

[0695] Optimization proposal method

[0696] The optimization suggestion means generates specific suggestions for minimizing the information gaps identified by the gap detection means, such as establishing an information sharing meeting, using a specific communication tool, or increasing the priority of specific information.

[0697] Report Generation Method

[0698] The report generation means creates a report for presenting information to the user based on the proposals generated by the optimization proposal means. This report is visually displayed, allowing the user to intuitively grasp the flow of information and gaps.

[0699] Emotion Engine

[0700] The emotion engine recognizes user emotions and uses them as part of data analysis and optimization proposals. The emotion engine extracts user emotional data from the content of emails and chats and generates proposals to appropriately adjust the method of information transmission. The data obtained by the emotion engine is also reflected in the report generation means, providing appropriate feedback to the user.

[0701] Explanation of program processing

[0702] The program of the above system operates as follows.

[0703] Data collection

[0704] With the user's permission, the terminal means collects various data, including emails from the mail server, chat tool logs, file information from the document sharing system, and conference recordings, all of which are collected and sent to the server means.

[0705] Data Integration

[0706] The server receives the data collected from the terminals, integrates it, standardizes the data format, adds necessary metadata, and stores it in a database.

[0707] Data analysis

[0708] AI means analyzes the data stored on the server means, analyzes the text data using natural language processing technology, extracts important keywords and themes, and generates information distribution channels.

[0709] Information Visualization

[0710] The server means visualizes the information distribution route as a network diagram, allowing the user to intuitively understand the flow of information.

[0711] Gap Detection

[0712] The gap detection tool analyzes information distribution channels and automatically detects missing or delayed information. It identifies gaps based on specific conditions and issues alerts or warnings.

[0713] Optimization suggestions

[0714] An optimization suggestion unit generates specific suggestions for minimizing the gap. The suggestions are based on data and indicate specific measures for optimizing information distribution.

[0715] Report Generation

[0716] The report generation means compiles the proposals generated by the optimization proposal means into a report and provides the information visually to the user, thereby enabling the user to easily grasp the current state of information distribution and areas for improvement.

[0717] Emotion analysis

[0718] The emotion engine analyzes the content of users' emails and chats to extract emotional data. This allows the system to reflect the user's emotions and optimize the way information is communicated. Furthermore, the emotional data is reflected in the report generation tool, providing more appropriate feedback.

[0719] Specific examples

[0720] Example 1: Critical project information isn't reaching the remote team

[0721] The terminal means collects data from the user's email and project management tool. The server means integrates the data, and the AI ​​means analyzes important project information and identifies when information has not reached the remote team. The gap detection means detects problems, and the optimization suggestion means suggests establishing an information sharing meeting for the remote team. The report generation means displays improvement suggestions on a dashboard for the user to confirm. The emotion engine analyzes the emotion data of the remote team and suggests appropriate ways to communicate information. As a result, the remote team receives the latest information, allowing the project to progress smoothly.

[0722] Example 2: Resolving communication errors between departments

[0723] The terminal means collects logs from internal chat tools and meeting contents. The server means integrates this data, and the AI ​​means analyzes it to generate an information flow. The gap detection means detects problems with information transmission between departments, and the optimization proposal means proposes the establishment of regular information sharing meetings. The report generation means compiles this into a report and provides it visually to the user. The emotion engine analyzes the emotion data from each department and proposes the optimal communication method. This series of processes reduces miscommunication between departments and achieves efficient information transmission.

[0724] By combining an emotion engine, the system of the present invention realizes optimization of information distribution and improvement of transparency, taking into account the emotions of users.

[0725] The processing flow will be explained below.

[0726] Step 1:

[0727] The user performs initial setup to begin using the system. The user confirms the permission settings and grants access to individual data sources (email server, chat tool, document sharing system, conference system, etc.).

[0728] Step 2:

[0729] The device accesses data sources authorized by the user and collects various data. The device obtains email header information and text from the email server, collects chat tool logs, and obtains file information and metadata (author, update date, etc.) from the document sharing system. It also collects recording data and meeting minutes from the conference system.

[0730] Step 3:

[0731] The device sends the collected data to the server, which temporarily stores the data, converts it into an appropriate format, and then sends the data to the server through the interface.

[0732] Step 4:

[0733] The server receives the data sent from the devices and centrally integrates it. The server unifies the data format and adds the necessary metadata (department, individual, timestamp, etc.).

[0734] Step 5:

[0735] The server saves the data in a unified database. The server stores the data in a unified format in the database and generates an index to enable quick access.

[0736] Step 6:

[0737] The emotion engine analyzes the user's emotions from the collected data. The emotion engine uses natural language processing (NLP) technology to extract user emotional data from the content of emails and chats.

[0738] Step 7:

[0739] The server analyzes the data using AI means. The server uses natural language processing (NLP) techniques to analyze the text data and extract key keywords and themes.

[0740] Step 8:

[0741] The server generates information distribution routes and visualizes in the form of a network diagram how information is distributed between individual departments and individuals.

[0742] Step 9:

[0743] The server uses gap detection techniques to analyze the information flow and automatically detect missing or delayed information. The server identifies gaps based on specific conditions and sets relevant alerts.

[0744] Step 10:

[0745] The server uses an optimization proposal method to generate specific proposals to minimize the gap. The server compares past data and considers effective strategies and reflects them in the proposals.

[0746] Step 11:

[0747] The emotion engine generates additional suggestions based on the user's emotion data to optimize the effectiveness of the proposed improvement measures. The emotion engine proposes an optimal information transmission method that reflects the emotion data.

[0748] Step 12:

[0749] The server uses a report generator to generate a report containing the analysis results and optimization suggestions, which is formatted in a visually appealing format and prepared for display on a dashboard.

[0750] Step 13:

[0751] The server notifies the user of the report. The server displays the report on the dashboard and sends notifications via email or chat tools as needed.

[0752] Step 14:

[0753] Users can check the report through the dashboard and implement the suggested improvements. Users make decisions based on the report's contents and proceed with the process of optimizing the flow of information within the company in accordance with the system's suggestions.

[0754] Specific examples

[0755] Example 1: Critical project information isn't reaching the remote team

[0756] Step 1

[0757] The user configures the system and allows access to the remote team's mail server and chat tools.

[0758] Step 2

[0759] The device collects emails, chat logs, and documents related to the project from these data sources.

[0760] Step 3

[0761] The data collected by the terminal is sent to the server, where it is temporarily stored and formatted.

[0762] Step 4

[0763] The server receives the data and centrally integrates it, adding metadata to the data.

[0764] Step 5

[0765] The server stores the data converted into a unified format in a database and generates an index for easy access.

[0766] Step 6

[0767] The emotion engine analyzes the emotional data of the remote team from the collected data.

[0768] Step 7

[0769] The server uses AI tools to analyze project-related data and extract keywords and themes.

[0770] Step 8

[0771] The server creates an information distribution channel and makes it visible that information is not reaching the remote team.

[0772] Step 9

[0773] The server uses gap detection to identify missing information for the remote team and set alerts.

[0774] Step 10

[0775] The server proposes an information sharing meeting with the remote team.

[0776] Step 11

[0777] The emotion engine suggests ways to optimize information sharing based on emotional data from remote teams.

[0778] Step 12

[0779] The server compiles these into a report and displays it to the user on a dashboard.

[0780] Step 13

[0781] The server notifies the user of the report via email or chat tool.

[0782] Step 14

[0783] The user reviews the report and takes action to establish the proposed information sharing meeting.

[0784] Example 2: Resolving communication errors between departments

[0785] Step 1

[0786] Users configure the system to allow access to chat tools and conferencing systems used between specific departments.

[0787] Step 2

[0788] The device collects chat logs and meeting recordings from these data sources.

[0789] Step 3

[0790] The data collected by the terminal is sent to the server, where it is temporarily stored and formatted.

[0791] Step 4

[0792] The server receives the data and centrally integrates it, adding metadata to the data.

[0793] Step 5

[0794] The server stores the data converted into a unified format in a database and generates an index for quick access.

[0795] Step 6

[0796] The emotion engine analyzes the emotion data between departments from the collected data.

[0797] Step 7

[0798] The server uses AI tools to analyze communication-related data and extract keywords and themes.

[0799] Step 8

[0800] The server generates information distribution channels and visualizes problems in communication between departments.

[0801] Step 9

[0802] The server uses gap detection means to identify areas where information is not being properly communicated and sets alerts.

[0803] Step 10

[0804] The server proposes the establishment of regular information sharing meetings.

[0805] Step 11

[0806] The emotion engine suggests optimal communication methods based on the emotional data of each department.

[0807] Step 12

[0808] The server compiles these into a report and displays it to the user on a dashboard.

[0809] Step 13

[0810] The server notifies the user of the report via email or chat tool.

[0811] Step 14

[0812] The user reviews the report and takes action to establish the proposed information sharing meeting.

[0813] In this way, the system of the present invention efficiently manages information distribution while taking into account the feelings of users, thereby improving transparency and productivity.

[0814] Example 2

[0815] Next, a description will be given of Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the smart glasses 214 will be referred to as a "terminal."

[0816] Conventional systems lack the means to streamline internal information distribution processes, resulting in inefficiencies in business operations due to lack of information and delays.In addition, information transmission is not optimized with consideration for user emotions, and specific measures to improve the quality of communication are needed.

[0817] The specific processing by the specific processing unit 290 of the data processing device 12 in the second embodiment is realized by the following means. In this invention, the server includes terminal means for collecting in-house information, server means for integrating the collected data, artificial intelligence means for analyzing the integrated data and mapping information distribution routes, gap detection means for automatically detecting information gaps from the generated information distribution routes, optimization proposal means for generating optimization proposals for minimizing the gaps, report generation means for reporting the generated proposals, and an emotion engine for analyzing user emotions. This makes it possible to improve the efficiency of the information distribution process and optimize information transmission taking user emotions into consideration.

[0818] "Terminal means" refers to a device that automatically collects company information with the user's permission.

[0819] "Server means" refers to a computer system for centrally storing and integrating data collected from terminal means.

[0820] "Artificial intelligence means" refers to algorithms and software for analyzing data integrated by the server means and mapping information distribution channels.

[0821] "Gap detection means" refers to a function that automatically detects a lack of or delay in information from the information distribution path generated by the artificial intelligence means.

[0822] The "optimization suggestion means" refers to a function that generates specific suggestions to minimize the information gaps detected by the gap detection means.

[0823] The "report generation means" refers to a function for creating documents and graphs for visually presenting information based on the proposals generated by the optimization proposal means.

[0824] An "emotion engine" refers to algorithms or software that analyzes users' emotions and optimizes the way information is communicated.

[0825] "Dashboard means" refers to a screen or interface that visually presents the reports generated by the report generation means and allows the user to intuitively grasp the information.

[0826] This invention is a system for optimizing information transmission by improving the efficiency of in-house information distribution processes and recognizing user emotions. The program for this system is configured using the following hardware and software.

[0827] System configuration

[0828] The system includes a terminal means, a server means, an artificial intelligence means, a gap detection means, an optimization suggestion means, a report generation means, and an emotion engine.

[0829] Terminal means

[0830] The terminal means obtains permission from the user and automatically collects data such as internal communication logs, emails, document sharing, and meeting contents. For example, this could be a computer or smartphone used by the user. The collected data is sent to the server means. A secure communication protocol (e.g., HTTPS) is used to send the data.

[0831] Server Means

[0832] The server means integrates the data collected from the terminal means and stores it in a centralized manner. For example, a high-performance server computer plays this role. The server standardizes the data format and converts it into, for example, JSON or XML format. It also adds necessary metadata (such as timestamp, sender, and destination) and stores it in a database.

[0833] Artificial Intelligence Tools

[0834] Artificial intelligence means placed within the server analyzes the stored data. Specifically, it uses natural language processing (NLP) technology to analyze text data and extract important keywords and themes. For example, machine learning models and deep learning models are used. It also analyzes email sending and receiving patterns to generate information distribution channels.

[0835] Gap Detection Means

[0836] The gap detection means analyzes the information distribution path generated by the artificial intelligence means and automatically detects missing or delayed information. It identifies gaps based on specific conditions (e.g., when information is not shared within a specific time) and issues an alert or warning to the user.

[0837] Optimization proposal method

[0838] The optimization suggestion means generates specific suggestions to address the problems identified by the gap detection means, such as establishing an information sharing meeting, using a specific communication tool, or raising the priority of information, thereby enabling users to optimize information distribution.

[0839] Report Generation Method

[0840] The report generation means creates a report to visually present information based on the proposals generated by the optimization proposal means. This report includes graphs and charts, allowing the user to intuitively understand the current state of information distribution and areas for improvement.

[0841] Emotion Engine

[0842] An emotion engine is an algorithm or software that analyzes a user's emotions and optimizes the method of communication. For example, it analyzes the content of a user's email or chat messages and extracts emotional data. This emotional data is reflected in the report generation tool and presented visually.

[0843] Specific examples

[0844] Example 1: Critical project information isn't reaching the remote team

[0845] The terminal means collects data from the user's email and project management tool. The server means integrates the data, and the artificial intelligence means analyzes the project information and identifies when important information has not reached the remote team. The gap detection means detects the problem, and the optimization suggestion means suggests establishing an information sharing meeting for the remote team. The report generation means displays improvement suggestions on a dashboard for the user to confirm. The emotion engine analyzes the emotion data of the remote team and suggests an appropriate method of communicating information. As a result, the remote team receives the latest information, and the project progresses smoothly.

[0846] Example 2: Resolving communication errors between departments

[0847] The terminal means collects logs from internal chat tools and the contents of meetings. The server means integrates this data, and the artificial intelligence means analyzes the flow of information. The gap detection means detects problems with information transmission between departments. The optimization proposal means proposes the establishment of regular information sharing meetings. The report generation means compiles this into a report and provides it visually to the user. The emotion engine analyzes the emotion data from each department and proposes the optimal communication method. This series of processes reduces miscommunication between departments and achieves efficient information transmission.

[0848] Prompt Sentence Examples

[0849] "Please extract emotional data from the content of this email."

[0850] "Collect and integrate data from your project management tools."

[0851] "Detect gaps in information flow between departments and generate optimization suggestions."

[0852] The system of the present invention uses a generative AI model to generate proposals in real time, significantly improving the user's work efficiency.

[0853] The flow of the identification process in the second embodiment will be described with reference to FIG.

[0854] Step 1: Data collection

[0855] With the user's permission, the device automatically collects data such as emails, chat logs, document sharing information, and meeting recordings. For example, the device connects to a mail server and obtains the latest email data. The collected data is sent from the device to the server via a secure communication protocol (e.g., HTTPS). The input is the permission information and various data obtained from the user, and the output is the integrated data transferred to the server.

[0856] Step 2: Data Integration

[0857] The server receives data sent from the terminal and stores it centrally. The server standardizes the format of the received data, converting it into, for example, JSON or XML format. It also adds necessary metadata (timestamp, sender, destination, etc.) and stores it in a database. The input is the raw data sent from the terminal, and the output is data stored in the database in a standardized format.

[0858] Step 3: Data analysis

[0859] The server passes the stored data to an artificial intelligence means, which uses natural language processing (NLP) techniques to analyze the text data and extract important keywords and themes. For example, it may extract specific keywords (e.g., "deadline" or "meeting") from the text of emails or chat logs. The input is the consolidated text data, and the output is the extracted keywords and themes.

[0860] Step 4: Visualize the information

[0861] The server visualizes the information distribution routes generated by the artificial intelligence means as a network diagram. When a user accesses the dashboard, the network diagram is displayed, allowing the user to intuitively understand important information flows and communication patterns. Specifically, nodes and edges are used to visually show who is sending information to whom. The input is the analysis results from the artificial intelligence means, and the output is visual information provided to the user.

[0862] Step 5: Gap detection

[0863] The gap detection means analyzes the generated information distribution channels and automatically detects missing or delayed information. It identifies gaps based on specific conditions (e.g., when information is not shared within a specific time frame) and issues alerts or warnings to users. The input is the generated information distribution channels, and the output is the identified gaps and alert information.

[0864] Step 6: Optimization suggestions

[0865] The optimization proposal means generates specific proposals to minimize the information gaps identified by the gap detection means. The proposals include measures such as establishing an information sharing meeting, using specific communication tools, and raising the priority of information. The input is the detected gap information, and the output is the generated optimization proposals.

[0866] Step 7: Generate Reports

[0867] The report generation means creates a report to visually present information based on the proposals generated by the optimization proposal means. The report includes graphs and charts, allowing the user to intuitively understand the current state of information distribution and areas for improvement. The input is the generated optimization proposals, and the output is the visually presented report.

[0868] Step 8: Sentiment Analysis

[0869] The emotion engine analyzes the content of the user's emails and chat messages and extracts emotional data. The emotional data is reflected in reports and suggests appropriate ways to communicate information. Specifically, if the user is under stress, the emotion engine suggests ways to alleviate the stress. The input is the content of the emails and chat messages, and the output is the extracted emotional data and suggestions.

[0870] (Application example 2)

[0871] Next, a description will be given of Application Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the smart glasses 214 will be referred to as a "terminal."

[0872] Conventional factory management systems lacked the means to effectively collect and analyze work data and worker conversation data, preventing them from fully optimizing work efficiency and information distribution. Furthermore, it was difficult to improve information transmission while taking into account the emotional state of workers, which created a risk of lower worker satisfaction and work efficiency. This has led to a demand for improvements to production processes and worker satisfaction.

[0873] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 2 is realized by the following means.

[0874] In this invention, the server includes terminal means for collecting in-house information, server means for integrating data collected from the terminal means, AI means for analyzing the integrated data by the server means and mapping information distribution routes, gap detection means for automatically detecting information gaps from the information distribution routes generated by the AI ​​means, optimization proposal means for generating optimization proposals to minimize the information gaps detected by the gap detection means, report generation means for reporting the proposals generated by the optimization proposal means, robot means for collecting work data and worker conversations via environmental sensors, cameras, and microphones in the factory, an emotion engine for analyzing the work data and worker conversation data and extracting emotional information, emotion data optimization proposal means for generating proposals to optimize information distribution feedback based on the emotional information, and data visualization means for reporting and visually displaying the proposals generated by the emotion data optimization proposal means. This enables optimization of information distribution and work efficiency in the factory and improvement of information communication taking into account the emotions of workers.

[0875] "Internal information" refers to data, knowledge, and communication records generated and collected within a company or organization.

[0876] "Terminal means" is a device for collecting information and transmitting it to server means.

[0877] The "server means" is a system for integrating and centrally managing collected data.

[0878] "AI means" refers to technologies for analyzing collected and integrated data and generating and visualizing information distribution channels.

[0879] The "gap detection means" is a mechanism for automatically detecting data loss or delay based on the generated information distribution path.

[0880] "Optimization suggestions" are techniques for generating specific suggestions to minimize detected information gaps.

[0881] A "report generator" is a system for visually presenting optimization suggestions and providing information to a user.

[0882] "Robotic means" refers to devices that collect work data and worker conversations through environmental sensors, cameras, and microphones within the factory.

[0883] The "emotion engine" is a technology for analyzing and extracting emotional data from worker conversations and feedback.

[0884] The "emotion data optimization proposal means" is a technology for generating proposals for optimizing feedback in information distribution by utilizing emotion data.

[0885] A "data visualization tool" is a tool for visually displaying generated proposals and providing information in a form that is easy for users to understand.

[0886] This invention is a system for realizing a "smart factory management system" that optimizes information distribution and work efficiency within a factory. This system is configured using the following hardware and software.

[0887] System configuration

[0888] Terminal means

[0889] The terminal means consists of robots equipped with environmental sensors, cameras, and microphones in the factory. These devices collect work data and worker conversations in real time.

[0890] Server Means

[0891] The server means uses a cloud server (e.g., AWS, Google Cloud) and a database (e.g., MySQL, PostgreSQL). The server centrally stores the collected data and standardizes the data format. Furthermore, it adds necessary metadata to support data analysis.

[0892] AI means

[0893] AI tools use natural language processing tools (e.g., NLTK, spaCy) and machine learning libraries (e.g., TensorFlow, PyTorch) to analyze the collected data, extract important keywords and themes from the text data, and generate and visualize information distribution channels.

[0894] Gap Detection Means

[0895] The gap detection method analyzes the information distribution channels generated by the AI ​​method to detect missing or delayed information. Based on specific conditions, it identifies gaps and issues alerts or warnings.

[0896] Optimization proposal method

[0897] The optimization proposal unit generates specific proposals for minimizing the information gaps identified by the gap detection unit, including detailed procedures for specific tasks and optimization of information distribution.

[0898] Report Generation Method

[0899] The report generation means creates a report based on the proposals generated by the optimization proposal means and the sentiment data optimization proposal means. The report is visually displayed using a data visualization tool (e.g., Tableau, Power BI).

[0900] Emotion Engine

[0901] The emotion engine uses emotion analysis tools (e.g., Microsoft Azure Text Analytics, IBM Watson Natural Language Understanding) to extract emotional data from worker conversations and feedback. Based on this data, it makes suggestions to optimize feedback for information distribution.

[0902] Robotic Means

[0903] The robotic means collects data on factory operations and conversations between workers and transmits the data to a server in real time. The robot is equipped with environmental sensors and cameras, and transmits the collected information to a cloud server for storage.

[0904] Specific examples

[0905] Example 1: When work efficiency declines

[0906] Robots in factories collect work data and conversations between workers. The server consolidates and analyzes this data and detects when work efficiency is declining at specific times. The optimization proposal tool proposes new work flows and adjusts maintenance schedules for specific machines. The report generation tool displays this visually and promptly informs workers of countermeasures.

[0907] Example prompts to input to the generative AI model

[0908] "Collect data on declines in work efficiency and identify the causes. Please provide specific suggestions for improvement."

[0909] This system combines these methods to optimize the flow of information within the factory, improving work efficiency and worker satisfaction.

[0910] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[0911] Step 1:

[0912] The terminal means (robots in the factory) collects work data and conversation data of workers using environmental sensors, cameras, and microphones. The collected data is sent to the server means in real time.

[0913] Input: Work data, worker conversation data

[0914] Output: Data sent to the server

[0915] Step 2:

[0916] The server unit centrally stores the data received from the terminal unit, unifies the data format, and stores the data in a database with necessary metadata added.

[0917] Input: Data sent from the terminal means

[0918] Output: Data saved in a unified format

[0919] Step 3:

[0920] AI tools analyze the stored data and use natural language processing tools (NLTK and spaCy) to extract important keywords and themes from the text data. Machine learning libraries (TensorFlow and PyTorch) are used to generate and visualize information distribution channels.

[0921] Input: Data stored in a unified format

[0922] Output: Visualized data of important keywords, themes, and information distribution channels

[0923] Step 4:

[0924] The gap detection means analyzes the information distribution channels generated by the AI ​​means and detects information omissions or delays. Based on this, it identifies gaps under specific conditions and issues alerts or warnings.

[0925] Input: Visualization data of information distribution channels

[0926] Output: Gap information, alerts, warnings

[0927] Step 5:

[0928] The optimization proposal means generates specific improvement proposals (detailed procedures for specific tasks or new information sharing methods) to minimize the information gaps identified by the gap detection means.

[0929] Input: Gap information

[0930] Output: Improvement suggestions

[0931] Step 6:

[0932] The emotion engine analyzes and extracts emotional information from worker conversations and feedback data using emotion analysis tools (Microsoft Azure Text Analytics and IBM Watson Natural Language Understanding). Based on the acquired emotional information, it makes suggestions to optimize feedback on information transmission.

[0933] Input: Worker conversation data, feedback data

[0934] Output: Emotional information, optimization suggestions based on emotional data

[0935] Step 7:

[0936] The report generation means creates a visual report using a data visualization tool (Tableau or Power BI) based on the proposals generated by the optimization proposal means and the sentiment data optimization proposal means, and displays the report to the user.

[0937] Input: Improvement suggestions, optimization suggestions based on sentiment data

[0938] Output: Visually displayed reports and dashboards

[0939] Step 8:

[0940] Users view visual reports and dashboards and take specific actions to improve work efficiency.

[0941] Input: Visual reports, dashboards

[0942] Output: Actions taken

[0943] The specific processing unit 290 transmits the result of the specific processing to the smart glasses 214. In the smart glasses 214, the control unit 46A causes the speaker 240 to output the result of the specific processing. The microphone 238 acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0944] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (Internet Search<URL: https: / / openai.com / blog / chatgpt> ), Gemini (Internet search <url: https: gemini.google.com ?hl="ja">) and other generation AIs. The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating voice, text data indicating text, and image data indicating an image is also input. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt, and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[0945] In the above embodiment, an example in which the specific processing is performed by the data processing device 12 has been given, but the technology of the present disclosure is not limited to this, and the specific processing may be performed by the smart glasses 214.

[0946] [Third embodiment]

[0947] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0948] 5, the data processing system 310 includes the data processing device 12 and a headset terminal 314. An example of the data processing device 12 is a server.

[0949] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 is an example of a "computer" according to the technology of the present disclosure. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, the RAM 30, and the storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[0950] The headset type terminal 314 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication I / F 44, and a display 343. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, the camera 42, and the display 343 are also connected to the bus 52.

[0951] The microphone 238 receives instructions and the like from the user 20 by receiving voice uttered by the user 20. The microphone 238 captures the voice uttered by the user 20, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio in accordance with instructions from the processor 46.

[0952] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the surroundings of user 20 (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0953] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0954] Fig. 6 shows an example of the main functions of the data processing device 12 and the headset type terminal 314. As shown in Fig. 6, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0955] The specific processing program 56 is an example of a "program" according to the technology of the present disclosure. The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0956] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[0957] In the headset type terminal 314, a reception output process is performed by the processor 46. A reception output program 60 is stored in the storage 50. The processor 46 reads the reception output program 60 from the storage 50 and executes the read reception output program 60 on the RAM 48. The reception output process is realized by the processor 46 operating as the control unit 46A in accordance with the reception output program 60 executed on the RAM 48.

[0958] Next, a description will be given of the identification process performed by the identification processing unit 290 of the data processing device 12. In the following description, the data processing device 12 will be referred to as the "server" and the headset type terminal 314 will be referred to as the "terminal."

[0959] The present invention is a system for improving the efficiency of an information distribution process within a company, and is realized by the following means.

[0960] System configuration

[0961] The system includes a terminal means, a server means, an AI means, a gap detection means, an optimization suggestion means, and a report generation means.

[0962] Terminal means

[0963] The terminal means obtains permission from the user and automatically collects data such as internal communication logs, emails, document sharing, and meeting contents. This collection includes data from email servers, chat tools, document management systems, meeting recording systems, etc. The terminal means transmits the collected data to the server means.

[0964] Server Means

[0965] The server means integrates the data collected from the terminal means and stores it centrally. The server standardizes the format of each data and adds necessary metadata (e.g., department, individual, timestamp, etc.). This integrated data is later analyzed by the AI ​​means.

[0966] AI means

[0967] AI tools analyze the collected and integrated data to generate and visualize information distribution channels. AI tools use natural language processing (NLP) techniques to analyze text data and extract important keywords and themes. They also generate network diagrams to clarify how, when, and between which departments and individuals information is circulating.

[0968] Gap Detection Means

[0969] The gap detection method analyzes the information distribution path generated by the AI ​​method and detects gaps or delays in the distribution of information. This method identifies areas where information is not being properly transmitted based on specific conditions and issues alerts or warnings.

[0970] Optimization proposal method

[0971] The optimization suggestion means generates optimal improvement suggestions for minimizing the information gaps identified by the gap detection means, such as establishing an information sharing meeting, using a specific communication tool, or increasing the priority of specific information.

[0972] Report Generation Method

[0973] The report generation means creates a report to present information to the user based on the proposals generated by the optimization proposal means. The report is visually displayed through the dashboard means, allowing the user to intuitively grasp the flow of information and gaps. In addition, the report is notified to the user via email or chat tool as needed.

[0974] Explanation of program processing

[0975] The program of the above system operates as follows.

[0976] Data collection

[0977] The terminal means obtains permission from the user and collects various data, including obtaining emails from the email server, collecting chat tool logs, and obtaining file information from the document sharing system.

[0978] Data Integration

[0979] The server receives the data sent from the terminal and stores it in the integrated database. The server converts the data into a unified format, adds metadata, and stores it.

[0980] Data analysis

[0981] AI tools analyze the data stored on the server tools and use natural language processing technology to analyze the text data, thereby extracting important keywords and themes and mapping the distribution channels of information.

[0982] Gap Detection

[0983] The gap detection means analyzes the information distribution path generated by the AI ​​means and detects information omissions or delays. This means automatically identifies gaps based on specific conditions.

[0984] Optimization suggestions

[0985] An optimization suggestion means generates a suggestion for minimizing the information gap detected by the gap detection means, the suggestion being based on the data and including a specific measure for optimizing the information distribution.

[0986] Report Generation

[0987] The report generation means compiles the proposals generated by the optimization proposal means into a report and notifies the user. The report is visually displayed via the dashboard means, allowing the user to intuitively grasp the current state of information distribution and areas for improvement.

[0988] Specific examples

[0989] Example 1: Critical project information isn't reaching the remote team

[0990] The terminal means collects data from the project management tool and mail server. The server means integrates the data, and the AI ​​means analyzes the information distribution route. The gap detection means identifies when information is not reaching the remote team, and the optimization proposal means generates proposals to increase the priority of information sharing. The report generation means displays improvement proposals on a dashboard, where the user can confirm and implement the improvement measures.

[0991] Example 2: Resolving communication errors between departments

[0992] The terminal means collects logs from internal chat tools and data on meeting contents. The server means integrates this data, and the AI ​​means analyzes the flow of information. The gap detection means detects when information is not being communicated properly between specific departments, and the optimization proposal means proposes the establishment of regular information sharing meetings. The report generation means compiles the proposals into a report, which the user confirms and implements improvement measures.

[0993] As a result, the system of the present invention efficiently manages the distribution of information within a company, and realizes improved transparency and productivity.

[0994] The processing flow will be explained below.

[0995] Step 1:

[0996] The user performs initial setup to begin using the system. The user confirms the permission settings and grants access to individual data sources (email server, chat tool, document sharing system, conference system, etc.).

[0997] Step 2:

[0998] The device accesses data sources authorized by the user and collects various data. The device obtains email header information and text from the email server, extracts chat tool logs, and obtains file information and metadata (author, update date, etc.) from the document sharing system. It also collects recording data and meeting minutes from the conference system.

[0999] Step 3:

[1000] The device sends the collected data to the server, which temporarily stores the data, converts it into an appropriate format, and then sends the data to the server through the interface.

[1001] Step 4:

[1002] The server receives the data sent from the devices and centrally integrates it. The server unifies the data format and adds the necessary metadata (department, individual, timestamp, etc.).

[1003] Step 5:

[1004] The server saves the data in a unified database. The server stores the data in a unified format in the database and generates an index to enable quick access.

[1005] Step 6:

[1006] The server analyzes the data using AI means. The server uses natural language processing (NLP) techniques to analyze the text data and extract key keywords and themes.

[1007] Step 7:

[1008] The server generates information distribution routes and visualizes in the form of a network diagram how information is distributed between individual departments and individuals.

[1009] Step 8:

[1010] The server uses gap detection techniques to analyze the information flow and automatically detect missing or delayed information. The server identifies gaps based on specific conditions and sets relevant alerts.

[1011] Step 9:

[1012] The server uses an optimization proposal method to generate specific proposals to minimize the gap. The server compares past data and considers effective strategies and reflects them in the proposals.

[1013] Step 10:

[1014] The server uses a report generator to generate a report containing the analysis results and optimization suggestions, which is formatted in a visually appealing format and prepared for display on a dashboard.

[1015] Step 11:

[1016] The server notifies the user of the report. The server displays the report on the dashboard and sends notifications via email or chat tools as needed.

[1017] Step 12:

[1018] Users can check the report through the dashboard and implement the suggested improvements. Users make decisions based on the report's contents and proceed with the process of optimizing the flow of information within the company in accordance with the system's suggestions.

[1019] Example 1

[1020] Next, a description will be given of Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the headset type terminal 314 will be referred to as a "terminal."

[1021] Improving the efficiency of information distribution within a company is extremely important for improving corporate productivity and transparency. However, with conventional systems, each process from information collection, integration, analysis, and optimization proposals is fragmented, making it difficult to efficiently manage it centrally. Furthermore, there is a high likelihood of miscommunication and misunderstandings due to insufficient automatic detection of information gaps and delays and the implementation of countermeasures. A comprehensive system that can solve these problems is needed.

[1022] The specific processing by the specific processing unit 290 of the data processing device 12 in the first embodiment is realized by the following means.

[1023] In this invention, the server includes terminal means for obtaining permission from users and collecting various types of data within the company, server means for receiving the data collected from the terminal means, converting it into a unified format, and centrally storing it, AI means for analyzing the data stored in the server means using natural language analysis technology and mapping information distribution routes, gap detection means for automatically detecting information gaps and delays from the information distribution routes generated by the AI ​​means, optimization proposal means for generating optimization proposals to minimize the information gaps detected by the gap detection means, and report generation means for compiling the proposals generated by the optimization proposal means into a report and notifying the user. This enables efficient and integrated management of the company's information distribution process, automatically detecting information gaps and delays, and proposing optimal improvement measures.

[1024] "Terminal means" refers to a device or system that obtains permission from a user and collects various data within a company.

[1025] "Server means" refers to a device or system that receives data collected from terminal means, converts it into a unified format, and stores it in a centralized manner.

[1026] "AI means" refers to devices or systems that have the function of analyzing data stored in server means using natural language analysis technology and mapping information distribution routes.

[1027] "Gap detection means" refers to a device or system that automatically detects information gaps or delays in information distribution channels generated by AI means.

[1028] The term "optimization proposal means" refers to a device or system that generates optimal proposals to minimize the information gaps detected by the gap detection means.

[1029] The "report generation means" refers to a device or system that compiles the proposals generated by the optimization proposal means into a report and notifies the user of the report.

[1030] "User" refers to a company employee or administrator who uses the system, grants permissions, or reviews reports.

[1031] "Data" refers to various information generated within the company, such as emails, chat logs, documents, and meeting recordings.

[1032] "Natural language analysis technology" refers to the technology of analyzing text data and extracting important keywords and themes.

[1033] "Information distribution route" refers to the route that indicates how, when, and between which departments and individuals information is distributed within a company.

[1034] "Metadata" is auxiliary information added to data, such as department, individual, timestamp, etc.

[1035] The present invention is a system for improving the efficiency of an in-house information distribution process. The system includes a terminal unit, a server unit, an AI unit, a gap detection unit, an optimization suggestion unit, and a report generation unit.

[1036] System configuration

[1037] Terminal means

[1038] The terminal means obtains permission from the user and automatically collects data such as internal communication logs, emails, document sharing, and meeting contents. This collection includes email servers (e.g., Microsoft Exchange), chat tools (e.g., Slack), document management systems (e.g., Google Drive), and meeting recording systems (e.g., Zoom). The terminal means transmits the collected data to the server means.

[1039] Server Means

[1040] The server means integrates the data collected from the terminal means and stores it centrally. The server standardizes the format of each piece of data and adds the necessary metadata (department, individual, timestamp, etc.). This integrated data is later analyzed by the AI ​​means. A cloud-based database system (e.g., Amazon RDS) is often used for the server means.

[1041] AI means

[1042] AI tools analyze collected and integrated data to generate and visualize information distribution channels. They use natural language processing (NLP) techniques to analyze text data and extract important keywords and themes. They also generate network diagrams to clarify how, when, and between which departments and individuals information is circulating. Examples of use include Google BERT and OpenAI's GPT model.

[1043] Gap Detection Means

[1044] The gap detection method analyzes the information distribution path generated by the AI ​​method and detects gaps or delays in the distribution of information. This method identifies areas where information is not being properly transmitted based on specific conditions and issues alerts or warnings.

[1045] Optimization proposal method

[1046] The optimization suggestion means generates optimal improvement suggestions to minimize the information gaps identified by the gap detection means. These suggestions include establishing an information sharing meeting, using a specific communication tool, raising the priority of specific information, etc. Specifically, it suggests prioritizing the notification of project information to the remote team and establishing regular information sharing meetings.

[1047] Report Generation Method

[1048] The report generation means creates a report to present information to the user based on the proposals generated by the optimization proposal means. The report is visually displayed through a dashboard means (e.g., Power BI) so that the user can intuitively grasp the flow of information and gaps. In addition, the report is notified to the user via email or chat tools as necessary.

[1049] Specific examples

[1050] Example 1: Critical project information isn't reaching the remote team

[1051] Based on the user's permission, the terminal means collects data from the project management tool and mail server. The server means integrates the data, and the AI ​​means analyzes the information distribution path. The gap detection means identifies when information has not reached the remote team, and the optimization proposal means generates proposals to increase the priority of information sharing. The report generation means displays improvement proposals on a dashboard, where the user can confirm and implement the improvement measures.

[1052] Example 2: Resolving communication errors between departments

[1053] The user uses the terminal means to collect data on the logs of the internal chat tool and meeting contents. The server means integrates this data, and the AI ​​means analyzes the flow of information. The gap detection means detects that information is not being communicated properly between specific departments, and the optimization proposal means proposes the establishment of regular information sharing meetings. The report generation means compiles the proposals into a report, which the user confirms and implements improvement measures.

[1054] As described above, this system efficiently manages the flow of information within a company, improving transparency and productivity.

[1055] Prompt Sentence Examples

[1056] "Proposing solutions when important project information isn't reaching a remote team"

[1057] "How to resolve communication errors between departments"

[1058] The present invention enables companies to optimize information distribution and improve business efficiency.

[1059] The flow of the identification process in the first embodiment will be described with reference to FIG.

[1060] Step 1:

[1061] The terminal means obtains permission from the user and collects various data from the company's email server, chat tool, document management system, and meeting recording system. Specifically, the terminal means connects to Microsoft Exchange to collect email data, collects chat logs using the Slack API, obtains file information using the Google Drive API, and obtains meeting recording data using the Zoom API. The collected data is temporarily stored in the terminal means in the form of raw data from each tool.

[1062] Input: User permission, API connection information for each tool

[1063] Output: Raw data collected from each tool

[1064] Step 2:

[1065] The server receives data collected from the terminal devices and converts it into a unified format. Specifically, it converts email content into text format, chat logs into JSON format, integrates document information into a common metadata format, and converts conference recording data into text. The converted data is then added with metadata such as sender, recipient, and timestamp, and is stored centrally in an integrated database.

[1066] Input: Raw data collected from each tool

[1067] Output: Data converted into a unified format, consolidated data with added metadata

[1068] Step 3:

[1069] The AI ​​tool analyzes the integrated data stored on the server. Specifically, it uses natural language processing technology to extract important keywords and themes from the text data. The AI ​​tool uses Google BERT and OpenAI's GPT model to analyze the content of emails and chat logs and identify relevant keywords and context. It also analyzes the content of documents and maps each piece of information as an information distribution channel.

[1070] Input: Data converted to a unified format, with added metadata

[1071] Output: Extracted keywords and themes, mapped information distribution channels

[1072] Step 4:

[1073] The gap detection method analyzes the information distribution channels generated by the AI ​​method and detects missing or delayed information. Specifically, it identifies which channels are not transmitting information based on specific pre-defined conditions (e.g., the time frame within which the required information should be released). An alert is generated about the detected gap, and the appropriate personnel are notified.

[1074] Input: Mapped information distribution channels, specific conditions

[1075] Output: Information gaps detected, alerts generated

[1076] Step 5:

[1077] The optimization suggestion unit generates suggestions to minimize information gaps detected by the gap detection unit, such as prioritizing communication of project information to remote teams, establishing regular information sharing meetings, recommending the use of specific communication tools, etc. The suggestions are generated based on the data and indicate specific areas for improvement.

[1078] Input: Detected information gaps

[1079] Output: Generated optimization proposals

[1080] Step 6:

[1081] The report generator compiles the optimization proposals into a report and notifies the user. Specifically, Power BI is used to create a dashboard that visually displays the proposals and the current status of information distribution. The report is then converted into PDF format and distributed to the user via email or chat tools.

[1082] Input: Generated optimization proposals

[1083] Output: Visual dashboards, PDF reports, and user notifications

[1084] Through the above steps, this system can efficiently manage the flow of information within a company, improving transparency and productivity.

[1085] (Application example 1)

[1086] Next, a description will be given of Application Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the headset type terminal 314 will be referred to as a "terminal."

[1087] In the distribution of information within a factory, important information is often not communicated efficiently, which can have a negative impact on production efficiency and quality control. Therefore, there is a need for a system that can quickly identify which department or worker is experiencing delays or deficiencies and make appropriate suggestions. In addition, there is a need for a method that visually shows the optimization of information communication and allows workers to intuitively understand it.

[1088] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 1 is realized by the following means.

[1089] In this invention, the server includes device means for collecting in-house information, storage means for integrating data collected from the device means, machine learning means for analyzing the integrated data by the storage means and mapping information distribution routes, gap detection means for automatically detecting information gaps from the information distribution routes generated by the machine learning means, proposal generation means for generating optimization proposals to minimize the information gaps detected by the gap detection means, a visualization device for visually presenting the proposals generated by the proposal generation means, means for using smart glasses as the visualization device, means for wirelessly transmitting data collected by the smart glasses to the storage means, and specific means for optimizing the information distribution routes. This makes it possible to quickly identify delays or gaps in information distribution within a factory and propose and implement optimal information sharing measures.

[1090] "Internal information" is a general term for information relating to business, communication, and operations carried out within a company or organization.

[1091] "Device means" refers to terminal equipment and its interface for collecting and transmitting information.

[1092] "Storage means" refers to a storage system for centrally storing collected data and managing it in an accessible form as needed.

[1093] "Machine learning tools" refers to algorithms and software used to analyze collected data, find patterns and trends, and map information distribution channels.

[1094] "Gap detection means" refers to a mechanism for automatically identifying gaps (lack of or delay in) information distribution.

[1095] "Proposal generator for generating optimization proposals" refers to algorithms and systems for generating specific improvement proposals to minimize detected deficiencies.

[1096] "Visualization device" refers to a device and software for visually presenting the generated proposals and the current state of information distribution.

[1097] "Smart glasses" refers to a wearable device that visually displays collected data in real time and has the ability to interactively share information with the user.

[1098] "Wireless communication" refers to technologies and protocols for transmitting and receiving data without a physical connection.

[1099] "Information distribution route" refers to a route diagram that shows how, when, and between which departments and individuals information is distributed.

[1100] "Specific means" refers to methods that specifically demonstrate the actions and technologies necessary to achieve optimized information distribution.

[1101] The present invention is a system for improving the efficiency of information distribution within a factory. A method for implementing a program for the system that realizes this application example will be specifically described below.

[1102] Data collection

[1103] The smart glasses are used as terminals. They collect data such as communication logs, emails, shared documents, and work instructions between workers in the factory in real time. This data is sent to a server via wireless communication.

[1104] Data Integration

[1105] The server consolidates and centrally stores the data sent from the smart glasses. A database is used as the storage medium. The collected data is converted into a unified format and stored with metadata such as department, individual, and timestamp.

[1106] Data analysis

[1107] The server uses machine learning techniques to analyze the integrated data. Specifically, it uses natural language processing (NLP) techniques to extract important keywords and themes from the collected text data. It also generates a network diagram of information distribution routes. This process uses libraries such as "spaCy" and "NetworkX."

[1108] Gap Detection

[1109] The server analyzes the information distribution routes generated by the machine learning method and automatically detects information gaps (lack of information or delays) using the gap detection method, thereby identifying areas where information transmission to specific departments or individuals is stalled.

[1110] Generate optimization suggestions

[1111] The server uses a proposal generator to generate optimization proposals based on the detected gaps, which suggest specific improvements, such as changing the information sharing method or introducing a new communication method.

[1112] Reporting and Visualization

[1113] The report generation means displays the generated proposals on the visualization device. The visualization device, smart glasses, visually presents the proposals and the current status of information distribution to workers in real time in a dashboard format, allowing workers to intuitively understand problems and improvement measures and implement them.

[1114] Specific examples

[1115] For example, consider a scenario where critical information about a particular manufacturing process isn't being communicated to a remote quality control team. The server analyzes the data collected by the smart glasses and detects the gaps. In this case, the server suggests setting up an information-sharing meeting or introducing new communication methods. These suggestions are presented in real time through the smart glasses, allowing workers to immediately implement improvements.

[1116] Prompt Sentence Examples

[1117] "It's possible that information isn't reaching certain departments through this information distribution network. What communication methods would be effective in making improvements?"

[1118] This makes it possible to quickly identify delays or lack of information flow within the factory and propose and implement optimal information sharing methods.

[1119] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[1120] Step 1:

[1121] The smart glasses collect data such as communication logs, emails, shared documents, and work instructions between workers in the factory in real time and transmit it to a server via wireless communication.

[1122] Input: Worker communication data

[1123] Output: Data collected by the smart glasses and transmitted to the server via wireless communication.

[1124] Step 2:

[1125] The server receives the data sent from the smart glasses and stores it in a centralized storage device. The collected data is converted into a unified format and metadata such as department, individual, and timestamp are added.

[1126] Input: Raw data sent from smart glasses

[1127] Output: Data converted into a unified format and with metadata added

[1128] Step 3:

[1129] The server uses machine learning techniques to analyze the stored data. Specifically, it uses natural language processing (NLP) techniques to extract important keywords and themes from the collected text data. It also generates a network diagram of information distribution routes.

[1130] Input: Data converted into a unified format and with metadata added

[1131] Output: A network diagram of important keywords and themes and information distribution channels

[1132] Step 4:

[1133] The server analyzes the information distribution path generated by the machine learning means and automatically detects information gaps (lack or delay) using the gap detection means.

[1134] Input: Network diagram of information distribution channels

[1135] Output: Detected information gaps

[1136] Step 5:

[1137] The server uses a proposal generator to generate optimization proposals based on the detected gaps, which suggest specific improvements, such as changing the information sharing method or introducing a new communication method.

[1138] Input: Detected information gaps

[1139] Output: Optimization suggestions

[1140] Step 6:

[1141] The server compiles the generated optimization proposals into a report using a report generation means and displays it on the visualization device. The smart glasses as a visualization device visually present the proposals and the current status of information distribution to workers in real time in dashboard format, allowing workers to intuitively understand problems and improvement measures and implement them.

[1142] Input: Optimization proposal

[1143] Output: Reports and dashboards displayed on smart glasses

[1144] This process flow makes it possible to quickly identify delays or lack of information distribution within the factory and propose and implement optimal information sharing methods.

[1145] Furthermore, an emotion engine that estimates the user's emotion may be further combined. That is, the identification processing unit 290 may estimate the user's emotion using the emotion identification model 59, and perform identification processing using the user's emotion.

[1146] The present invention is a system for improving the efficiency of an in-house information distribution process and optimizing information transmission by recognizing user emotions, and is realized by the following means.

[1147] System configuration

[1148] The system includes a terminal means, a server means, an AI means, a gap detection means, an optimization suggestion means, a report generation means, and an emotion engine.

[1149] Terminal means

[1150] The terminal means obtains permission from the user and automatically collects data such as in-house communication logs, emails, document sharing, meeting contents, etc. The collected data is sent to the server means.

[1151] Server Means

[1152] The server unit integrates the data collected from the terminal unit and stores it in a centralized manner. The server unit unifies the format of each data and adds necessary metadata, allowing for efficient data analysis.

[1153] AI means

[1154] AI tools analyze the collected data and generate and visualize information distribution channels. They primarily use natural language processing (NLP) technology to analyze text data and extract important keywords and themes. They also visualize how information is distributed as a network diagram.

[1155] Gap Detection Means

[1156] The gap detection method analyzes the information distribution channels generated by the AI ​​method and automatically detects missing or delayed information. It identifies gaps based on specific conditions and issues alerts or warnings.

[1157] Optimization proposal method

[1158] The optimization suggestion means generates specific suggestions for minimizing the information gaps identified by the gap detection means, such as establishing an information sharing meeting, using a specific communication tool, or increasing the priority of specific information.

[1159] Report Generation Method

[1160] The report generation means creates a report for presenting information to the user based on the proposals generated by the optimization proposal means. This report is visually displayed, allowing the user to intuitively grasp the flow of information and gaps.

[1161] Emotion Engine

[1162] The emotion engine recognizes user emotions and uses them as part of data analysis and optimization proposals. The emotion engine extracts user emotional data from the content of emails and chats and generates proposals to appropriately adjust the method of information transmission. The data obtained by the emotion engine is also reflected in the report generation means, providing appropriate feedback to the user.

[1163] Explanation of program processing

[1164] The program of the above system operates as follows.

[1165] Data collection

[1166] With the user's permission, the terminal means collects various data, including emails from the mail server, chat tool logs, file information from the document sharing system, and conference recordings, all of which are collected and sent to the server means.

[1167] Data Integration

[1168] The server receives the data collected from the terminals, integrates it, standardizes the data format, adds necessary metadata, and stores it in a database.

[1169] Data analysis

[1170] AI means analyzes the data stored on the server means, analyzes the text data using natural language processing technology, extracts important keywords and themes, and generates information distribution channels.

[1171] Information Visualization

[1172] The server means visualizes the information distribution route as a network diagram, allowing the user to intuitively understand the flow of information.

[1173] Gap Detection

[1174] The gap detection tool analyzes information distribution channels and automatically detects missing or delayed information. It identifies gaps based on specific conditions and issues alerts or warnings.

[1175] Optimization suggestions

[1176] An optimization suggestion unit generates specific suggestions for minimizing the gap. The suggestions are based on data and indicate specific measures for optimizing information distribution.

[1177] Report Generation

[1178] The report generation means compiles the proposals generated by the optimization proposal means into a report and provides the information visually to the user, thereby enabling the user to easily grasp the current state of information distribution and areas for improvement.

[1179] Emotion analysis

[1180] The emotion engine analyzes the content of users' emails and chats to extract emotional data. This allows the system to reflect the user's emotions and optimize the way information is communicated. Furthermore, the emotional data is reflected in the report generation tool, providing more appropriate feedback.

[1181] Specific examples

[1182] Example 1: Critical project information isn't reaching the remote team

[1183] The terminal means collects data from the user's email and project management tool. The server means integrates the data, and the AI ​​means analyzes important project information and identifies when information has not reached the remote team. The gap detection means detects problems, and the optimization suggestion means suggests establishing an information sharing meeting for the remote team. The report generation means displays improvement suggestions on a dashboard for the user to confirm. The emotion engine analyzes the emotion data of the remote team and suggests appropriate ways to communicate information. As a result, the remote team receives the latest information, allowing the project to progress smoothly.

[1184] Example 2: Resolving communication errors between departments

[1185] The terminal means collects logs from internal chat tools and meeting contents. The server means integrates this data, and the AI ​​means analyzes it to generate an information flow. The gap detection means detects problems with information transmission between departments, and the optimization proposal means proposes the establishment of regular information sharing meetings. The report generation means compiles this into a report and provides it visually to the user. The emotion engine analyzes the emotion data from each department and proposes the optimal communication method. This series of processes reduces miscommunication between departments and achieves efficient information transmission.

[1186] By combining an emotion engine, the system of the present invention realizes optimization of information distribution and improvement of transparency, taking into account the emotions of users.

[1187] The processing flow will be explained below.

[1188] Step 1:

[1189] The user performs initial setup to begin using the system. The user confirms the permission settings and grants access to individual data sources (email server, chat tool, document sharing system, conference system, etc.).

[1190] Step 2:

[1191] The device accesses data sources authorized by the user and collects various data. The device obtains email header information and text from the email server, collects chat tool logs, and obtains file information and metadata (author, update date, etc.) from the document sharing system. It also collects recording data and meeting minutes from the conference system.

[1192] Step 3:

[1193] The device sends the collected data to the server, which temporarily stores the data, converts it into an appropriate format, and then sends the data to the server through the interface.

[1194] Step 4:

[1195] The server receives the data sent from the devices and centrally integrates it. The server unifies the data format and adds the necessary metadata (department, individual, timestamp, etc.).

[1196] Step 5:

[1197] The server saves the data in a unified database. The server stores the data in a unified format in the database and generates an index to enable quick access.

[1198] Step 6:

[1199] The emotion engine analyzes the user's emotions from the collected data. The emotion engine uses natural language processing (NLP) technology to extract user emotional data from the content of emails and chats.

[1200] Step 7:

[1201] The server analyzes the data using AI means. The server uses natural language processing (NLP) techniques to analyze the text data and extract key keywords and themes.

[1202] Step 8:

[1203] The server generates information distribution routes and visualizes in the form of a network diagram how information is distributed between individual departments and individuals.

[1204] Step 9:

[1205] The server uses gap detection techniques to analyze the information flow and automatically detect missing or delayed information. The server identifies gaps based on specific conditions and sets relevant alerts.

[1206] Step 10:

[1207] The server uses an optimization proposal method to generate specific proposals to minimize the gap. The server compares past data and considers effective strategies and reflects them in the proposals.

[1208] Step 11:

[1209] The emotion engine generates additional suggestions based on the user's emotion data to optimize the effectiveness of the proposed improvement measures. The emotion engine proposes an optimal information transmission method that reflects the emotion data.

[1210] Step 12:

[1211] The server uses a report generator to generate a report containing the analysis results and optimization suggestions, which is formatted in a visually appealing format and prepared for display on a dashboard.

[1212] Step 13:

[1213] The server notifies the user of the report. The server displays the report on the dashboard and sends notifications via email or chat tools as needed.

[1214] Step 14:

[1215] Users can check the report through the dashboard and implement the suggested improvements. Users make decisions based on the report's contents and proceed with the process of optimizing the flow of information within the company in accordance with the system's suggestions.

[1216] Specific examples

[1217] Example 1: Critical project information isn't reaching the remote team

[1218] Step 1

[1219] The user configures the system and allows access to the remote team's mail server and chat tools.

[1220] Step 2

[1221] The device collects emails, chat logs, and documents related to the project from these data sources.

[1222] Step 3

[1223] The data collected by the terminal is sent to the server, where it is temporarily stored and formatted.

[1224] Step 4

[1225] The server receives the data and centrally integrates it, adding metadata to the data.

[1226] Step 5

[1227] The server stores the data converted into a unified format in a database and generates an index for easy access.

[1228] Step 6

[1229] The emotion engine analyzes the emotional data of the remote team from the collected data.

[1230] Step 7

[1231] The server uses AI tools to analyze project-related data and extract keywords and themes.

[1232] Step 8

[1233] The server creates an information distribution channel and makes it visible that information is not reaching the remote team.

[1234] Step 9

[1235] The server uses gap detection to identify missing information for the remote team and set alerts.

[1236] Step 10

[1237] The server proposes an information sharing meeting with the remote team.

[1238] Step 11

[1239] The emotion engine suggests ways to optimize information sharing based on emotional data from remote teams.

[1240] Step 12

[1241] The server compiles these into a report and displays it to the user on a dashboard.

[1242] Step 13

[1243] The server notifies the user of the report via email or chat tool.

[1244] Step 14

[1245] The user reviews the report and takes action to establish the proposed information sharing meeting.

[1246] Example 2: Resolving communication errors between departments

[1247] Step 1

[1248] Users configure the system to allow access to chat tools and conferencing systems used between specific departments.

[1249] Step 2

[1250] The device collects chat logs and meeting recordings from these data sources.

[1251] Step 3

[1252] The data collected by the terminal is sent to the server, where it is temporarily stored and formatted.

[1253] Step 4

[1254] The server receives the data and centrally integrates it, adding metadata to the data.

[1255] Step 5

[1256] The server stores the data converted into a unified format in a database and generates an index for quick access.

[1257] Step 6

[1258] The emotion engine analyzes the emotion data between departments from the collected data.

[1259] Step 7

[1260] The server uses AI tools to analyze communication-related data and extract keywords and themes.

[1261] Step 8

[1262] The server generates information distribution channels and visualizes problems in communication between departments.

[1263] Step 9

[1264] The server uses gap detection means to identify areas where information is not being properly communicated and sets alerts.

[1265] Step 10

[1266] The server proposes the establishment of regular information sharing meetings.

[1267] Step 11

[1268] The emotion engine suggests optimal communication methods based on the emotional data of each department.

[1269] Step 12

[1270] The server compiles these into a report and displays it to the user on a dashboard.

[1271] Step 13

[1272] The server notifies the user of the report via email or chat tool.

[1273] Step 14

[1274] The user reviews the report and takes action to establish the proposed information sharing meeting.

[1275] In this way, the system of the present invention efficiently manages information distribution while taking into account the feelings of users, thereby improving transparency and productivity.

[1276] Example 2

[1277] Next, a description will be given of Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the headset type terminal 314 will be referred to as a "terminal."

[1278] Conventional systems lack the means to streamline internal information distribution processes, resulting in inefficiencies in business operations due to lack of information and delays.In addition, information transmission is not optimized with consideration for user emotions, and specific measures to improve the quality of communication are needed.

[1279] The specific processing by the specific processing unit 290 of the data processing device 12 in the second embodiment is realized by the following means. In this invention, the server includes terminal means for collecting in-house information, server means for integrating the collected data, artificial intelligence means for analyzing the integrated data and mapping information distribution routes, gap detection means for automatically detecting information gaps from the generated information distribution routes, optimization proposal means for generating optimization proposals for minimizing the gaps, report generation means for reporting the generated proposals, and an emotion engine for analyzing user emotions. This makes it possible to improve the efficiency of the information distribution process and optimize information transmission taking user emotions into consideration.

[1280] "Terminal means" refers to a device that automatically collects company information with the user's permission.

[1281] "Server means" refers to a computer system for centrally storing and integrating data collected from terminal means.

[1282] "Artificial intelligence means" refers to algorithms and software for analyzing data integrated by the server means and mapping information distribution channels.

[1283] "Gap detection means" refers to a function that automatically detects a lack of or delay in information from the information distribution path generated by the artificial intelligence means.

[1284] The "optimization suggestion means" refers to a function that generates specific suggestions to minimize the information gaps detected by the gap detection means.

[1285] The "report generation means" refers to a function for creating documents and graphs for visually presenting information based on the proposals generated by the optimization proposal means.

[1286] An "emotion engine" refers to algorithms or software that analyzes users' emotions and optimizes the way information is communicated.

[1287] "Dashboard means" refers to a screen or interface that visually presents the reports generated by the report generation means and allows the user to intuitively grasp the information.

[1288] This invention is a system for optimizing information transmission by improving the efficiency of in-house information distribution processes and recognizing user emotions. The program for this system is configured using the following hardware and software.

[1289] System configuration

[1290] The system includes a terminal means, a server means, an artificial intelligence means, a gap detection means, an optimization suggestion means, a report generation means, and an emotion engine.

[1291] Terminal means

[1292] The terminal means obtains permission from the user and automatically collects data such as internal communication logs, emails, document sharing, and meeting contents. For example, this could be a computer or smartphone used by the user. The collected data is sent to the server means. A secure communication protocol (e.g., HTTPS) is used to send the data.

[1293] Server Means

[1294] The server means integrates the data collected from the terminal means and stores it in a centralized manner. For example, a high-performance server computer plays this role. The server standardizes the data format and converts it into, for example, JSON or XML format. It also adds necessary metadata (such as timestamp, sender, and destination) and stores it in a database.

[1295] Artificial Intelligence Tools

[1296] Artificial intelligence means placed within the server analyzes the stored data. Specifically, it uses natural language processing (NLP) technology to analyze text data and extract important keywords and themes. For example, machine learning models and deep learning models are used. It also analyzes email sending and receiving patterns to generate information distribution channels.

[1297] Gap Detection Means

[1298] The gap detection means analyzes the information distribution path generated by the artificial intelligence means and automatically detects missing or delayed information. It identifies gaps based on specific conditions (e.g., when information is not shared within a specific time) and issues an alert or warning to the user.

[1299] Optimization proposal method

[1300] The optimization suggestion means generates specific suggestions to address the problems identified by the gap detection means, such as establishing an information sharing meeting, using a specific communication tool, or raising the priority of information, thereby enabling users to optimize information distribution.

[1301] Report Generation Method

[1302] The report generation means creates a report to visually present information based on the proposals generated by the optimization proposal means. This report includes graphs and charts, allowing the user to intuitively understand the current state of information distribution and areas for improvement.

[1303] Emotion Engine

[1304] An emotion engine is an algorithm or software that analyzes a user's emotions and optimizes the method of communication. For example, it analyzes the content of a user's email or chat messages and extracts emotional data. This emotional data is reflected in the report generation tool and presented visually.

[1305] Specific examples

[1306] Example 1: Critical project information isn't reaching the remote team

[1307] The terminal means collects data from the user's email and project management tool. The server means integrates the data, and the artificial intelligence means analyzes the project information and identifies when important information has not reached the remote team. The gap detection means detects the problem, and the optimization suggestion means suggests establishing an information sharing meeting for the remote team. The report generation means displays improvement suggestions on a dashboard for the user to confirm. The emotion engine analyzes the emotion data of the remote team and suggests an appropriate method of communicating information. As a result, the remote team receives the latest information, and the project progresses smoothly.

[1308] Example 2: Resolving communication errors between departments

[1309] The terminal means collects logs from internal chat tools and the contents of meetings. The server means integrates this data, and the artificial intelligence means analyzes the flow of information. The gap detection means detects problems with information transmission between departments. The optimization proposal means proposes the establishment of regular information sharing meetings. The report generation means compiles this into a report and provides it visually to the user. The emotion engine analyzes the emotion data from each department and proposes the optimal communication method. This series of processes reduces miscommunication between departments and achieves efficient information transmission.

[1310] Prompt Sentence Examples

[1311] "Please extract emotional data from the content of this email."

[1312] "Collect and integrate data from your project management tools."

[1313] "Detect gaps in information flow between departments and generate optimization suggestions."

[1314] The system of the present invention uses a generative AI model to generate proposals in real time, significantly improving the user's work efficiency.

[1315] The flow of the identification process in the second embodiment will be described with reference to FIG.

[1316] Step 1: Data collection

[1317] With the user's permission, the device automatically collects data such as emails, chat logs, document sharing information, and meeting recordings. For example, the device connects to a mail server and obtains the latest email data. The collected data is sent from the device to the server via a secure communication protocol (e.g., HTTPS). The input is the permission information and various data obtained from the user, and the output is the integrated data transferred to the server.

[1318] Step 2: Data Integration

[1319] The server receives data sent from the terminal and stores it centrally. The server standardizes the format of the received data, converting it into, for example, JSON or XML format. It also adds necessary metadata (timestamp, sender, destination, etc.) and stores it in a database. The input is the raw data sent from the terminal, and the output is data stored in the database in a standardized format.

[1320] Step 3: Data analysis

[1321] The server passes the stored data to an artificial intelligence means, which uses natural language processing (NLP) techniques to analyze the text data and extract important keywords and themes. For example, it may extract specific keywords (e.g., "deadline" or "meeting") from the text of emails or chat logs. The input is the consolidated text data, and the output is the extracted keywords and themes.

[1322] Step 4: Visualize the information

[1323] The server visualizes the information distribution routes generated by the artificial intelligence means as a network diagram. When a user accesses the dashboard, the network diagram is displayed, allowing the user to intuitively understand important information flows and communication patterns. Specifically, nodes and edges are used to visually show who is sending information to whom. The input is the analysis results from the artificial intelligence means, and the output is visual information provided to the user.

[1324] Step 5: Gap detection

[1325] The gap detection means analyzes the generated information distribution channels and automatically detects missing or delayed information. It identifies gaps based on specific conditions (e.g., when information is not shared within a specific time frame) and issues alerts or warnings to users. The input is the generated information distribution channels, and the output is the identified gaps and alert information.

[1326] Step 6: Optimization suggestions

[1327] The optimization proposal means generates specific proposals to minimize the information gaps identified by the gap detection means. The proposals include measures such as establishing an information sharing meeting, using specific communication tools, and raising the priority of information. The input is the detected gap information, and the output is the generated optimization proposals.

[1328] Step 7: Generate Reports

[1329] The report generation means creates a report to visually present information based on the proposals generated by the optimization proposal means. The report includes graphs and charts, allowing the user to intuitively understand the current state of information distribution and areas for improvement. The input is the generated optimization proposals, and the output is the visually presented report.

[1330] Step 8: Sentiment Analysis

[1331] The emotion engine analyzes the content of the user's emails and chat messages and extracts emotional data. The emotional data is reflected in reports and suggests appropriate ways to communicate information. Specifically, if the user is under stress, the emotion engine suggests ways to alleviate the stress. The input is the content of the emails and chat messages, and the output is the extracted emotional data and suggestions.

[1332] (Application example 2)

[1333] Next, a description will be given of Application Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the headset type terminal 314 will be referred to as a "terminal."

[1334] Conventional factory management systems lacked the means to effectively collect and analyze work data and worker conversation data, preventing them from fully optimizing work efficiency and information distribution. Furthermore, it was difficult to improve information transmission while taking into account the emotional state of workers, which created a risk of lower worker satisfaction and work efficiency. This has led to a demand for improvements to production processes and worker satisfaction.

[1335] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 2 is realized by the following means.

[1336] In this invention, the server includes terminal means for collecting in-house information, server means for integrating data collected from the terminal means, AI means for analyzing the integrated data by the server means and mapping information distribution routes, gap detection means for automatically detecting information gaps from the information distribution routes generated by the AI ​​means, optimization proposal means for generating optimization proposals to minimize the information gaps detected by the gap detection means, report generation means for reporting the proposals generated by the optimization proposal means, robot means for collecting work data and worker conversations via environmental sensors, cameras, and microphones in the factory, an emotion engine for analyzing the work data and worker conversation data and extracting emotional information, emotion data optimization proposal means for generating proposals to optimize information distribution feedback based on the emotional information, and data visualization means for reporting and visually displaying the proposals generated by the emotion data optimization proposal means. This enables optimization of information distribution and work efficiency in the factory and improvement of information communication taking into account the emotions of workers.

[1337] "Internal information" refers to data, knowledge, and communication records generated and collected within a company or organization.

[1338] "Terminal means" is a device for collecting information and transmitting it to server means.

[1339] The "server means" is a system for integrating and centrally managing collected data.

[1340] "AI means" refers to technologies for analyzing collected and integrated data and generating and visualizing information distribution channels.

[1341] The "gap detection means" is a mechanism for automatically detecting data loss or delay based on the generated information distribution path.

[1342] "Optimization suggestions" are techniques for generating specific suggestions to minimize detected information gaps.

[1343] A "report generator" is a system for visually presenting optimization suggestions and providing information to a user.

[1344] "Robotic means" refers to devices that collect work data and worker conversations through environmental sensors, cameras, and microphones within the factory.

[1345] The "emotion engine" is a technology for analyzing and extracting emotional data from worker conversations and feedback.

[1346] The "emotion data optimization proposal means" is a technology for generating proposals for optimizing feedback in information distribution by utilizing emotion data.

[1347] A "data visualization tool" is a tool for visually displaying generated proposals and providing information in a form that is easy for users to understand.

[1348] This invention is a system for realizing a "smart factory management system" that optimizes information distribution and work efficiency within a factory. This system is configured using the following hardware and software.

[1349] System configuration

[1350] Terminal means

[1351] The terminal means consists of robots equipped with environmental sensors, cameras, and microphones in the factory. These devices collect work data and worker conversations in real time.

[1352] Server Means

[1353] The server means uses a cloud server (e.g., AWS, Google Cloud) and a database (e.g., MySQL, PostgreSQL). The server centrally stores the collected data and standardizes the data format. Furthermore, it adds necessary metadata to support data analysis.

[1354] AI means

[1355] AI tools use natural language processing tools (e.g., NLTK, spaCy) and machine learning libraries (e.g., TensorFlow, PyTorch) to analyze the collected data, extract important keywords and themes from the text data, and generate and visualize information distribution channels.

[1356] Gap Detection Means

[1357] The gap detection method analyzes the information distribution channels generated by the AI ​​method to detect missing or delayed information. Based on specific conditions, it identifies gaps and issues alerts or warnings.

[1358] Optimization proposal method

[1359] The optimization proposal unit generates specific proposals for minimizing the information gaps identified by the gap detection unit, including detailed procedures for specific tasks and optimization of information distribution.

[1360] Report Generation Method

[1361] The report generation means creates a report based on the proposals generated by the optimization proposal means and the sentiment data optimization proposal means. The report is visually displayed using a data visualization tool (e.g., Tableau, Power BI).

[1362] Emotion Engine

[1363] The emotion engine uses emotion analysis tools (e.g., Microsoft Azure Text Analytics, IBM Watson Natural Language Understanding) to extract emotional data from worker conversations and feedback. Based on this data, it makes suggestions to optimize feedback for information distribution.

[1364] Robotic Means

[1365] The robotic means collects data on factory operations and conversations between workers and transmits the data to a server in real time. The robot is equipped with environmental sensors and cameras, and transmits the collected information to a cloud server for storage.

[1366] Specific examples

[1367] Example 1: When work efficiency declines

[1368] Robots in factories collect work data and conversations between workers. The server consolidates and analyzes this data and detects when work efficiency is declining at specific times. The optimization proposal tool proposes new work flows and adjusts maintenance schedules for specific machines. The report generation tool displays this visually and promptly informs workers of countermeasures.

[1369] Example prompts to input to the generative AI model

[1370] "Collect data on declines in work efficiency and identify the causes. Please provide specific suggestions for improvement."

[1371] This system combines these methods to optimize the flow of information within the factory, improving work efficiency and worker satisfaction.

[1372] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[1373] Step 1:

[1374] The terminal means (robots in the factory) collects work data and conversation data of workers using environmental sensors, cameras, and microphones. The collected data is sent to the server means in real time.

[1375] Input: Work data, worker conversation data

[1376] Output: Data sent to the server

[1377] Step 2:

[1378] The server unit centrally stores the data received from the terminal unit, unifies the data format, and stores the data in a database with necessary metadata added.

[1379] Input: Data sent from the terminal means

[1380] Output: Data saved in a unified format

[1381] Step 3:

[1382] AI tools analyze the stored data and use natural language processing tools (NLTK and spaCy) to extract important keywords and themes from the text data. Machine learning libraries (TensorFlow and PyTorch) are used to generate and visualize information distribution channels.

[1383] Input: Data stored in a unified format

[1384] Output: Visualized data of important keywords, themes, and information distribution channels

[1385] Step 4:

[1386] The gap detection means analyzes the information distribution channels generated by the AI ​​means and detects information omissions or delays. Based on this, it identifies gaps under specific conditions and issues alerts or warnings.

[1387] Input: Visualization data of information distribution channels

[1388] Output: Gap information, alerts, warnings

[1389] Step 5:

[1390] The optimization proposal means generates specific improvement proposals (detailed procedures for specific tasks or new information sharing methods) to minimize the information gaps identified by the gap detection means.

[1391] Input: Gap information

[1392] Output: Improvement suggestions

[1393] Step 6:

[1394] The emotion engine analyzes and extracts emotional information from worker conversations and feedback data using emotion analysis tools (Microsoft Azure Text Analytics and IBM Watson Natural Language Understanding). Based on the acquired emotional information, it makes suggestions to optimize feedback on information transmission.

[1395] Input: Worker conversation data, feedback data

[1396] Output: Emotional information, optimization suggestions based on emotional data

[1397] Step 7:

[1398] The report generation means creates a visual report using a data visualization tool (Tableau or Power BI) based on the proposals generated by the optimization proposal means and the sentiment data optimization proposal means, and displays the report to the user.

[1399] Input: Improvement suggestions, optimization suggestions based on sentiment data

[1400] Output: Visually displayed reports and dashboards

[1401] Step 8:

[1402] Users view visual reports and dashboards and take specific actions to improve work efficiency.

[1403] Input: Visual reports, dashboards

[1404] Output: Actions taken

[1405] The specific processing unit 290 transmits the result of the specific processing to the headset type terminal 314. In the headset type terminal 314, the control unit 46A causes the speaker 240 and the display 343 to output the result of the specific processing. The microphone 238 acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[1406] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (Internet Search<URL: https: / / openai.com / blog / chatgpt> ), Gemini (Internet search <url: https: gemini.google.com ?hl="ja">) and other generation AIs. The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating voice, text data indicating text, and image data indicating an image is also input. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt, and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[1407] In the above embodiment, an example was given in which the specific processing is performed by the data processing device 12, but the technology of the present disclosure is not limited to this, and the specific processing may be performed by the headset type terminal 314.

[1408] [Fourth embodiment]

[1409] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[1410] 7, a data processing system 410 includes a data processing device 12 and a robot 414. An example of the data processing device 12 is a server.

[1411] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 is an example of a "computer" according to the technology of the present disclosure. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, the RAM 30, and the storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[1412] The robot 414 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication I / F 44, and a control target 443. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, the camera 42, and the control target 443 are also connected to the bus 52.

[1413] The microphone 238 receives instructions and the like from the user 20 by receiving voice uttered by the user 20. The microphone 238 captures the voice uttered by the user 20, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio in accordance with instructions from the processor 46.

[1414] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the surroundings of user 20 (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[1415] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[1416] The control object 443 includes a display device, LEDs in the eyes, and motors for driving the arms, hands, and feet. The posture and gestures of the robot 414 are controlled by controlling the motors of the arms, hands, and feet. Some of the emotions of the robot 414 can be expressed by controlling these motors. In addition, the facial expressions of the robot 414 can also be expressed by controlling the light emission state of the LEDs in the eyes of the robot 414.

[1417] Fig. 8 shows an example of the main functions of the data processing device 12 and the robot 414. As shown in Fig. 8, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[1418] The specific processing program 56 is an example of a "program" according to the technology of the present disclosure. The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[1419] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[1420] In the robot 414, the processor 46 performs the reception output process. A reception output program 60 is stored in the storage 50. The processor 46 reads the reception output program 60 from the storage 50 and executes the read reception output program 60 on the RAM 48. The reception output process is realized by the processor 46 operating as the control unit 46A in accordance with the reception output program 60 executed on the RAM 48.

[1421] Next, a description will be given of the specific processing performed by the specific processing unit 290 of the data processing device 12. In the following description, the data processing device 12 will be referred to as a "server" and the robot 414 will be referred to as a "terminal."

[1422] The present invention is a system for improving the efficiency of an information distribution process within a company, and is realized by the following means.

[1423] System configuration

[1424] The system includes a terminal means, a server means, an AI means, a gap detection means, an optimization suggestion means, and a report generation means.

[1425] Terminal means

[1426] The terminal means obtains permission from the user and automatically collects data such as internal communication logs, emails, document sharing, and meeting contents. This collection includes data from email servers, chat tools, document management systems, meeting recording systems, etc. The terminal means transmits the collected data to the server means.

[1427] Server Means

[1428] The server means integrates the data collected from the terminal means and stores it centrally. The server standardizes the format of each data and adds necessary metadata (e.g., department, individual, timestamp, etc.). This integrated data is later analyzed by the AI ​​means.

[1429] AI means

[1430] AI tools analyze the collected and integrated data to generate and visualize information distribution channels. AI tools use natural language processing (NLP) techniques to analyze text data and extract important keywords and themes. They also generate network diagrams to clarify how, when, and between which departments and individuals information is circulating.

[1431] Gap Detection Means

[1432] The gap detection method analyzes the information distribution path generated by the AI ​​method and detects gaps or delays in the distribution of information. This method identifies areas where information is not being properly transmitted based on specific conditions and issues alerts or warnings.

[1433] Optimization proposal method

[1434] The optimization suggestion means generates optimal improvement suggestions for minimizing the information gaps identified by the gap detection means, such as establishing an information sharing meeting, using a specific communication tool, or increasing the priority of specific information.

[1435] Report Generation Method

[1436] The report generation means creates a report to present information to the user based on the proposals generated by the optimization proposal means. The report is visually displayed through the dashboard means, allowing the user to intuitively grasp the flow of information and gaps. In addition, the report is notified to the user via email or chat tool as needed.

[1437] Explanation of program processing

[1438] The program of the above system operates as follows.

[1439] Data collection

[1440] The terminal means obtains permission from the user and collects various data, including obtaining emails from the email server, collecting chat tool logs, and obtaining file information from the document sharing system.

[1441] Data Integration

[1442] The server receives the data sent from the terminal and stores it in the integrated database. The server converts the data into a unified format, adds metadata, and stores it.

[1443] Data analysis

[1444] AI tools analyze the data stored on the server tools and use natural language processing technology to analyze the text data, thereby extracting important keywords and themes and mapping the distribution channels of information.

[1445] Gap Detection

[1446] The gap detection means analyzes the information distribution path generated by the AI ​​means and detects information omissions or delays. This means automatically identifies gaps based on specific conditions.

[1447] Optimization suggestions

[1448] An optimization suggestion means generates a suggestion for minimizing the information gap detected by the gap detection means, the suggestion being based on the data and including a specific measure for optimizing the information distribution.

[1449] Report Generation

[1450] The report generation means compiles the proposals generated by the optimization proposal means into a report and notifies the user. The report is visually displayed via the dashboard means, allowing the user to intuitively grasp the current state of information distribution and areas for improvement.

[1451] Specific examples

[1452] Example 1: Critical project information isn't reaching the remote team

[1453] The terminal means collects data from the project management tool and mail server. The server means integrates the data, and the AI ​​means analyzes the information distribution route. The gap detection means identifies when information is not reaching the remote team, and the optimization proposal means generates proposals to increase the priority of information sharing. The report generation means displays improvement proposals on a dashboard, where the user can confirm and implement the improvement measures.

[1454] Example 2: Resolving communication errors between departments

[1455] The terminal means collects logs from internal chat tools and data on meeting contents. The server means integrates this data, and the AI ​​means analyzes the flow of information. The gap detection means detects when information is not being communicated properly between specific departments, and the optimization proposal means proposes the establishment of regular information sharing meetings. The report generation means compiles the proposals into a report, which the user confirms and implements improvement measures.

[1456] As a result, the system of the present invention efficiently manages the distribution of information within a company, and realizes improved transparency and productivity.

[1457] The processing flow will be explained below.

[1458] Step 1:

[1459] The user performs initial setup to begin using the system. The user confirms the permission settings and grants access to individual data sources (email server, chat tool, document sharing system, conference system, etc.).

[1460] Step 2:

[1461] The device accesses data sources authorized by the user and collects various data. The device obtains email header information and text from the email server, extracts chat tool logs, and obtains file information and metadata (author, update date, etc.) from the document sharing system. It also collects recording data and meeting minutes from the conference system.

[1462] Step 3:

[1463] The device sends the collected data to the server, which temporarily stores the data, converts it into an appropriate format, and then sends the data to the server through the interface.

[1464] Step 4:

[1465] The server receives the data sent from the devices and centrally integrates it. The server unifies the data format and adds the necessary metadata (department, individual, timestamp, etc.).

[1466] Step 5:

[1467] The server saves the data in a unified database. The server stores the data in a unified format in the database and generates an index to enable quick access.

[1468] Step 6:

[1469] The server analyzes the data using AI means. The server uses natural language processing (NLP) techniques to analyze the text data and extract key keywords and themes.

[1470] Step 7:

[1471] The server generates information distribution routes and visualizes in the form of a network diagram how information is distributed between individual departments and individuals.

[1472] Step 8:

[1473] The server uses gap detection techniques to analyze the information flow and automatically detect missing or delayed information. The server identifies gaps based on specific conditions and sets relevant alerts.

[1474] Step 9:

[1475] The server uses an optimization proposal method to generate specific proposals to minimize the gap. The server compares past data and considers effective strategies and reflects them in the proposals.

[1476] Step 10:

[1477] The server uses a report generator to generate a report containing the analysis results and optimization suggestions, which is formatted in a visually appealing format and prepared for display on a dashboard.

[1478] Step 11:

[1479] The server notifies the user of the report. The server displays the report on the dashboard and sends notifications via email or chat tools as needed.

[1480] Step 12:

[1481] Users can check the report through the dashboard and implement the suggested improvements. Users make decisions based on the report's contents and proceed with the process of optimizing the flow of information within the company in accordance with the system's suggestions.

[1482] Example 1

[1483] Next, a description will be given of Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the robot 414 will be referred to as a "terminal."

[1484] Improving the efficiency of information distribution within a company is extremely important for improving corporate productivity and transparency. However, with conventional systems, each process from information collection, integration, analysis, and optimization proposals is fragmented, making it difficult to efficiently manage it centrally. Furthermore, there is a high likelihood of miscommunication and misunderstandings due to insufficient automatic detection of information gaps and delays and the implementation of countermeasures. A comprehensive system that can solve these problems is needed.

[1485] The specific processing by the specific processing unit 290 of the data processing device 12 in the first embodiment is realized by the following means.

[1486] In this invention, the server includes terminal means for obtaining permission from users and collecting various types of data within the company, server means for receiving the data collected from the terminal means, converting it into a unified format, and centrally storing it, AI means for analyzing the data stored in the server means using natural language analysis technology and mapping information distribution routes, gap detection means for automatically detecting information gaps and delays from the information distribution routes generated by the AI ​​means, optimization proposal means for generating optimization proposals to minimize the information gaps detected by the gap detection means, and report generation means for compiling the proposals generated by the optimization proposal means into a report and notifying the user. This enables efficient and integrated management of the company's information distribution process, automatically detecting information gaps and delays, and proposing optimal improvement measures.

[1487] "Terminal means" refers to a device or system that obtains permission from a user and collects various data within a company.

[1488] "Server means" refers to a device or system that receives data collected from terminal means, converts it into a unified format, and stores it in a centralized manner.

[1489] "AI means" refers to devices or systems that have the function of analyzing data stored in server means using natural language analysis technology and mapping information distribution routes.

[1490] "Gap detection means" refers to a device or system that automatically detects information gaps or delays in information distribution channels generated by AI means.

[1491] The term "optimization proposal means" refers to a device or system that generates optimal proposals to minimize the information gaps detected by the gap detection means.

[1492] The "report generation means" refers to a device or system that compiles the proposals generated by the optimization proposal means into a report and notifies the user of the report.

[1493] "User" refers to a company employee or administrator who uses the system, grants permissions, or reviews reports.

[1494] "Data" refers to various information generated within the company, such as emails, chat logs, documents, and meeting recordings.

[1495] "Natural language analysis technology" refers to the technology of analyzing text data and extracting important keywords and themes.

[1496] "Information distribution route" refers to the route that indicates how, when, and between which departments and individuals information is distributed within a company.

[1497] "Metadata" is auxiliary information added to data, such as department, individual, timestamp, etc.

[1498] The present invention is a system for improving the efficiency of an in-house information distribution process. The system includes a terminal unit, a server unit, an AI unit, a gap detection unit, an optimization suggestion unit, and a report generation unit.

[1499] System configuration

[1500] Terminal means

[1501] The terminal means obtains permission from the user and automatically collects data such as internal communication logs, emails, document sharing, and meeting contents. This collection includes email servers (e.g., Microsoft Exchange), chat tools (e.g., Slack), document management systems (e.g., Google Drive), and meeting recording systems (e.g., Zoom). The terminal means transmits the collected data to the server means.

[1502] Server Means

[1503] The server means integrates the data collected from the terminal means and stores it centrally. The server standardizes the format of each piece of data and adds the necessary metadata (department, individual, timestamp, etc.). This integrated data is later analyzed by the AI ​​means. A cloud-based database system (e.g., Amazon RDS) is often used for the server means.

[1504] AI means

[1505] AI tools analyze collected and integrated data to generate and visualize information distribution channels. They use natural language processing (NLP) techniques to analyze text data and extract important keywords and themes. They also generate network diagrams to clarify how, when, and between which departments and individuals information is circulating. Examples of use include Google BERT and OpenAI's GPT model.

[1506] Gap Detection Means

[1507] The gap detection method analyzes the information distribution path generated by the AI ​​method and detects gaps or delays in the distribution of information. This method identifies areas where information is not being properly transmitted based on specific conditions and issues alerts or warnings.

[1508] Optimization proposal method

[1509] The optimization suggestion means generates optimal improvement suggestions to minimize the information gaps identified by the gap detection means. These suggestions include establishing an information sharing meeting, using a specific communication tool, raising the priority of specific information, etc. Specifically, it suggests prioritizing the notification of project information to the remote team and establishing regular information sharing meetings.

[1510] Report Generation Method

[1511] The report generation means creates a report to present information to the user based on the proposals generated by the optimization proposal means. The report is visually displayed through a dashboard means (e.g., Power BI) so that the user can intuitively grasp the flow of information and gaps. In addition, the report is notified to the user via email or chat tools as necessary.

[1512] Specific examples

[1513] Example 1: Critical project information isn't reaching the remote team

[1514] Based on the user's permission, the terminal means collects data from the project management tool and mail server. The server means integrates the data, and the AI ​​means analyzes the information distribution path. The gap detection means identifies when information has not reached the remote team, and the optimization proposal means generates proposals to increase the priority of information sharing. The report generation means displays improvement proposals on a dashboard, where the user can confirm and implement the improvement measures.

[1515] Example 2: Resolving communication errors between departments

[1516] The user uses the terminal means to collect data on the logs of the internal chat tool and meeting contents. The server means integrates this data, and the AI ​​means analyzes the flow of information. The gap detection means detects that information is not being communicated properly between specific departments, and the optimization proposal means proposes the establishment of regular information sharing meetings. The report generation means compiles the proposals into a report, which the user confirms and implements improvement measures.

[1517] As described above, this system efficiently manages the flow of information within a company, improving transparency and productivity.

[1518] Prompt Sentence Examples

[1519] "Proposing solutions when important project information isn't reaching a remote team"

[1520] "How to resolve communication errors between departments"

[1521] The present invention enables companies to optimize information distribution and improve business efficiency.

[1522] The flow of the identification process in the first embodiment will be described with reference to FIG.

[1523] Step 1:

[1524] The terminal means obtains permission from the user and collects various data from the company's email server, chat tool, document management system, and meeting recording system. Specifically, the terminal means connects to Microsoft Exchange to collect email data, collects chat logs using the Slack API, obtains file information using the Google Drive API, and obtains meeting recording data using the Zoom API. The collected data is temporarily stored in the terminal means in the form of raw data from each tool.

[1525] Input: User permission, API connection information for each tool

[1526] Output: Raw data collected from each tool

[1527] Step 2:

[1528] The server receives data collected from the terminal devices and converts it into a unified format. Specifically, it converts email content into text format, chat logs into JSON format, integrates document information into a common metadata format, and converts conference recording data into text. The converted data is then added with metadata such as sender, recipient, and timestamp, and is stored centrally in an integrated database.

[1529] Input: Raw data collected from each tool

[1530] Output: Data converted into a unified format, consolidated data with added metadata

[1531] Step 3:

[1532] The AI ​​tool analyzes the integrated data stored on the server. Specifically, it uses natural language processing technology to extract important keywords and themes from the text data. The AI ​​tool uses Google BERT and OpenAI's GPT model to analyze the content of emails and chat logs and identify relevant keywords and context. It also analyzes the content of documents and maps each piece of information as an information distribution channel.

[1533] Input: Data converted to a unified format, with added metadata

[1534] Output: Extracted keywords and themes, mapped information distribution channels

[1535] Step 4:

[1536] The gap detection method analyzes the information distribution channels generated by the AI ​​method and detects missing or delayed information. Specifically, it identifies which channels are not transmitting information based on specific pre-defined conditions (e.g., the time frame within which the required information should be released). An alert is generated about the detected gap, and the appropriate personnel are notified.

[1537] Input: Mapped information distribution channels, specific conditions

[1538] Output: Information gaps detected, alerts generated

[1539] Step 5:

[1540] The optimization suggestion unit generates suggestions to minimize information gaps detected by the gap detection unit, such as prioritizing communication of project information to remote teams, establishing regular information sharing meetings, recommending the use of specific communication tools, etc. The suggestions are generated based on the data and indicate specific areas for improvement.

[1541] Input: Detected information gaps

[1542] Output: Generated optimization proposals

[1543] Step 6:

[1544] The report generator compiles the optimization proposals into a report and notifies the user. Specifically, Power BI is used to create a dashboard that visually displays the proposals and the current status of information distribution. The report is then converted into PDF format and distributed to the user via email or chat tools.

[1545] Input: Generated optimization proposals

[1546] Output: Visual dashboards, PDF reports, and user notifications

[1547] Through the above steps, this system can efficiently manage the flow of information within a company, improving transparency and productivity.

[1548] (Application example 1)

[1549] Next, a description will be given of Application Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the robot 414 will be referred to as a "terminal."

[1550] In the distribution of information within a factory, important information is often not communicated efficiently, which can have a negative impact on production efficiency and quality control. Therefore, there is a need for a system that can quickly identify which department or worker is experiencing delays or deficiencies and make appropriate suggestions. In addition, there is a need for a method that visually shows the optimization of information communication and allows workers to intuitively understand it.

[1551] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 1 is realized by the following means.

[1552] In this invention, the server includes device means for collecting in-house information, storage means for integrating data collected from the device means, machine learning means for analyzing the integrated data by the storage means and mapping information distribution routes, gap detection means for automatically detecting information gaps from the information distribution routes generated by the machine learning means, proposal generation means for generating optimization proposals to minimize the information gaps detected by the gap detection means, a visualization device for visually presenting the proposals generated by the proposal generation means, means for using smart glasses as the visualization device, means for wirelessly transmitting data collected by the smart glasses to the storage means, and specific means for optimizing the information distribution routes. This makes it possible to quickly identify delays or gaps in information distribution within a factory and propose and implement optimal information sharing measures.

[1553] "Internal information" is a general term for information relating to business, communication, and operations carried out within a company or organization.

[1554] "Device means" refers to terminal equipment and its interface for collecting and transmitting information.

[1555] "Storage means" refers to a storage system for centrally storing collected data and managing it in an accessible form as needed.

[1556] "Machine learning tools" refers to algorithms and software used to analyze collected data, find patterns and trends, and map information distribution channels.

[1557] "Gap detection means" refers to a mechanism for automatically identifying gaps (lack of or delay in) information distribution.

[1558] "Proposal generator for generating optimization proposals" refers to algorithms and systems for generating specific improvement proposals to minimize detected deficiencies.

[1559] "Visualization device" refers to a device and software for visually presenting the generated proposals and the current state of information distribution.

[1560] "Smart glasses" refers to a wearable device that visually displays collected data in real time and has the ability to interactively share information with the user.

[1561] "Wireless communication" refers to technologies and protocols for transmitting and receiving data without a physical connection.

[1562] "Information distribution route" refers to a route diagram that shows how, when, and between which departments and individuals information is distributed.

[1563] "Specific means" refers to methods that specifically demonstrate the actions and technologies necessary to achieve optimized information distribution.

[1564] The present invention is a system for improving the efficiency of information distribution within a factory. A method for implementing a program for the system that realizes this application example will be specifically described below.

[1565] Data collection

[1566] The smart glasses are used as terminals. They collect data such as communication logs, emails, shared documents, and work instructions between workers in the factory in real time. This data is sent to a server via wireless communication.

[1567] Data Integration

[1568] The server consolidates and centrally stores the data sent from the smart glasses. A database is used as the storage medium. The collected data is converted into a unified format and stored with metadata such as department, individual, and timestamp.

[1569] Data analysis

[1570] The server uses machine learning techniques to analyze the integrated data. Specifically, it uses natural language processing (NLP) techniques to extract important keywords and themes from the collected text data. It also generates a network diagram of information distribution routes. This process uses libraries such as "spaCy" and "NetworkX."

[1571] Gap Detection

[1572] The server analyzes the information distribution routes generated by the machine learning method and automatically detects information gaps (lack of information or delays) using the gap detection method, thereby identifying areas where information transmission to specific departments or individuals is stalled.

[1573] Generate optimization suggestions

[1574] The server uses a proposal generator to generate optimization proposals based on the detected gaps, which suggest specific improvements, such as changing the information sharing method or introducing a new communication method.

[1575] Reporting and Visualization

[1576] The report generation means displays the generated proposals on the visualization device. The visualization device, smart glasses, visually presents the proposals and the current status of information distribution to workers in real time in a dashboard format, allowing workers to intuitively understand problems and improvement measures and implement them.

[1577] Specific examples

[1578] For example, consider a scenario where critical information about a particular manufacturing process isn't being communicated to a remote quality control team. The server analyzes the data collected by the smart glasses and detects the gaps. In this case, the server suggests setting up an information-sharing meeting or introducing new communication methods. These suggestions are presented in real time through the smart glasses, allowing workers to immediately implement improvements.

[1579] Prompt Sentence Examples

[1580] "It's possible that information isn't reaching certain departments through this information distribution network. What communication methods would be effective in making improvements?"

[1581] This makes it possible to quickly identify delays or lack of information flow within the factory and propose and implement optimal information sharing methods.

[1582] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[1583] Step 1:

[1584] The smart glasses collect data such as communication logs, emails, shared documents, and work instructions between workers in the factory in real time and transmit it to a server via wireless communication.

[1585] Input: Worker communication data

[1586] Output: Data collected by the smart glasses and transmitted to the server via wireless communication.

[1587] Step 2:

[1588] The server receives the data sent from the smart glasses and stores it in a centralized storage device. The collected data is converted into a unified format and metadata such as department, individual, and timestamp are added.

[1589] Input: Raw data sent from smart glasses

[1590] Output: Data converted into a unified format and with metadata added

[1591] Step 3:

[1592] The server uses machine learning techniques to analyze the stored data. Specifically, it uses natural language processing (NLP) techniques to extract important keywords and themes from the collected text data. It also generates a network diagram of information distribution routes.

[1593] Input: Data converted into a unified format and with metadata added

[1594] Output: A network diagram of important keywords and themes and information distribution channels

[1595] Step 4:

[1596] The server analyzes the information distribution path generated by the machine learning means and automatically detects information gaps (lack or delay) using the gap detection means.

[1597] Input: Network diagram of information distribution channels

[1598] Output: Detected information gaps

[1599] Step 5:

[1600] The server uses a proposal generator to generate optimization proposals based on the detected gaps, which suggest specific improvements, such as changing the information sharing method or introducing a new communication method.

[1601] Input: Detected information gaps

[1602] Output: Optimization suggestions

[1603] Step 6:

[1604] The server compiles the generated optimization proposals into a report using a report generation means and displays it on the visualization device. The smart glasses as a visualization device visually present the proposals and the current status of information distribution to workers in real time in dashboard format, allowing workers to intuitively understand problems and improvement measures and implement them.

[1605] Input: Optimization proposal

[1606] Output: Reports and dashboards displayed on smart glasses

[1607] This process flow makes it possible to quickly identify delays or lack of information distribution within the factory and propose and implement optimal information sharing methods.

[1608] Furthermore, an emotion engine that estimates the user's emotion may be further combined. That is, the identification processing unit 290 may estimate the user's emotion using the emotion identification model 59, and perform identification processing using the user's emotion.

[1609] The present invention is a system for improving the efficiency of an in-house information distribution process and optimizing information transmission by recognizing user emotions, and is realized by the following means.

[1610] System configuration

[1611] The system includes a terminal means, a server means, an AI means, a gap detection means, an optimization suggestion means, a report generation means, and an emotion engine.

[1612] Terminal means

[1613] The terminal means obtains permission from the user and automatically collects data such as in-house communication logs, emails, document sharing, meeting contents, etc. The collected data is sent to the server means.

[1614] Server Means

[1615] The server unit integrates the data collected from the terminal unit and stores it in a centralized manner. The server unit unifies the format of each data and adds necessary metadata, allowing for efficient data analysis.

[1616] AI means

[1617] AI tools analyze the collected data and generate and visualize information distribution channels. They primarily use natural language processing (NLP) technology to analyze text data and extract important keywords and themes. They also visualize how information is distributed as a network diagram.

[1618] Gap Detection Means

[1619] The gap detection method analyzes the information distribution channels generated by the AI ​​method and automatically detects missing or delayed information. It identifies gaps based on specific conditions and issues alerts or warnings.

[1620] Optimization proposal method

[1621] The optimization suggestion means generates specific suggestions for minimizing the information gaps identified by the gap detection means, such as establishing an information sharing meeting, using a specific communication tool, or increasing the priority of specific information.

[1622] Report Generation Method

[1623] The report generation means creates a report for presenting information to the user based on the proposals generated by the optimization proposal means. This report is visually displayed, allowing the user to intuitively grasp the flow of information and gaps.

[1624] Emotion Engine

[1625] The emotion engine recognizes user emotions and uses them as part of data analysis and optimization proposals. The emotion engine extracts user emotional data from the content of emails and chats and generates proposals to appropriately adjust the method of information transmission. The data obtained by the emotion engine is also reflected in the report generation means, providing appropriate feedback to the user.

[1626] Explanation of program processing

[1627] The program of the above system operates as follows.

[1628] Data collection

[1629] With the user's permission, the terminal means collects various data, including emails from the mail server, chat tool logs, file information from the document sharing system, and conference recordings, all of which are collected and sent to the server means.

[1630] Data Integration

[1631] The server receives the data collected from the terminals, integrates it, standardizes the data format, adds necessary metadata, and stores it in a database.

[1632] Data analysis

[1633] AI means analyzes the data stored on the server means, analyzes the text data using natural language processing technology, extracts important keywords and themes, and generates information distribution channels.

[1634] Information Visualization

[1635] The server means visualizes the information distribution route as a network diagram, allowing the user to intuitively understand the flow of information.

[1636] Gap Detection

[1637] The gap detection tool analyzes information distribution channels and automatically detects missing or delayed information. It identifies gaps based on specific conditions and issues alerts or warnings.

[1638] Optimization suggestions

[1639] An optimization suggestion unit generates specific suggestions for minimizing the gap. The suggestions are based on data and indicate specific measures for optimizing information distribution.

[1640] Report Generation

[1641] The report generation means compiles the proposals generated by the optimization proposal means into a report and provides the information visually to the user, thereby enabling the user to easily grasp the current state of information distribution and areas for improvement.

[1642] Emotion analysis

[1643] The emotion engine analyzes the content of users' emails and chats to extract emotional data. This allows the system to reflect the user's emotions and optimize the way information is communicated. Furthermore, the emotional data is reflected in the report generation tool, providing more appropriate feedback.

[1644] Specific examples

[1645] Example 1: Critical project information isn't reaching the remote team

[1646] The terminal means collects data from the user's email and project management tool. The server means integrates the data, and the AI ​​means analyzes important project information and identifies when information has not reached the remote team. The gap detection means detects problems, and the optimization suggestion means suggests establishing an information sharing meeting for the remote team. The report generation means displays improvement suggestions on a dashboard for the user to confirm. The emotion engine analyzes the emotion data of the remote team and suggests appropriate ways to communicate information. As a result, the remote team receives the latest information, allowing the project to progress smoothly.

[1647] Example 2: Resolving communication errors between departments

[1648] The terminal means collects logs from internal chat tools and meeting contents. The server means integrates this data, and the AI ​​means analyzes it to generate an information flow. The gap detection means detects problems with information transmission between departments, and the optimization proposal means proposes the establishment of regular information sharing meetings. The report generation means compiles this into a report and provides it visually to the user. The emotion engine analyzes the emotion data from each department and proposes the optimal communication method. This series of processes reduces miscommunication between departments and achieves efficient information transmission.

[1649] By combining an emotion engine, the system of the present invention realizes optimization of information distribution and improvement of transparency, taking into account the emotions of users.

[1650] The processing flow will be explained below.

[1651] Step 1:

[1652] The user performs initial setup to begin using the system. The user confirms the permission settings and grants access to individual data sources (email server, chat tool, document sharing system, conference system, etc.).

[1653] Step 2:

[1654] The device accesses data sources authorized by the user and collects various data. The device obtains email header information and text from the email server, collects chat tool logs, and obtains file information and metadata (author, update date, etc.) from the document sharing system. It also collects recording data and meeting minutes from the conference system.

[1655] Step 3:

[1656] The device sends the collected data to the server, which temporarily stores the data, converts it into an appropriate format, and then sends the data to the server through the interface.

[1657] Step 4:

[1658] The server receives the data sent from the devices and centrally integrates it. The server unifies the data format and adds the necessary metadata (department, individual, timestamp, etc.).

[1659] Step 5:

[1660] The server saves the data in a unified database. The server stores the data in a unified format in the database and generates an index to enable quick access.

[1661] Step 6:

[1662] The emotion engine analyzes the user's emotions from the collected data. The emotion engine uses natural language processing (NLP) technology to extract user emotional data from the content of emails and chats.

[1663] Step 7:

[1664] The server analyzes the data using AI means. The server uses natural language processing (NLP) techniques to analyze the text data and extract key keywords and themes.

[1665] Step 8:

[1666] The server generates information distribution routes and visualizes in the form of a network diagram how information is distributed between individual departments and individuals.

[1667] Step 9:

[1668] The server uses gap detection techniques to analyze the information flow and automatically detect missing or delayed information. The server identifies gaps based on specific conditions and sets relevant alerts.

[1669] Step 10:

[1670] The server uses an optimization proposal method to generate specific proposals to minimize the gap. The server compares past data and considers effective strategies and reflects them in the proposals.

[1671] Step 11:

[1672] The emotion engine generates additional suggestions based on the user's emotion data to optimize the effectiveness of the proposed improvement measures. The emotion engine proposes an optimal information transmission method that reflects the emotion data.

[1673] Step 12:

[1674] The server uses a report generator to generate a report containing the analysis results and optimization suggestions, which is formatted in a visually appealing format and prepared for display on a dashboard.

[1675] Step 13:

[1676] The server notifies the user of the report. The server displays the report on the dashboard and sends notifications via email or chat tools as needed.

[1677] Step 14:

[1678] Users can check the report through the dashboard and implement the suggested improvements. Users make decisions based on the report's contents and proceed with the process of optimizing the flow of information within the company in accordance with the system's suggestions.

[1679] Specific examples

[1680] Example 1: Critical project information isn't reaching the remote team

[1681] Step 1

[1682] The user configures the system and allows access to the remote team's mail server and chat tools.

[1683] Step 2

[1684] The device collects emails, chat logs, and documents related to the project from these data sources.

[1685] Step 3

[1686] The data collected by the terminal is sent to the server, where it is temporarily stored and formatted.

[1687] Step 4

[1688] The server receives the data and centrally integrates it, adding metadata to the data.

[1689] Step 5

[1690] The server stores the data converted into a unified format in a database and generates an index for easy access.

[1691] Step 6

[1692] The emotion engine analyzes the emotional data of the remote team from the collected data.

[1693] Step 7

[1694] The server uses AI tools to analyze project-related data and extract keywords and themes.

[1695] Step 8

[1696] The server creates an information distribution channel and makes it visible that information is not reaching the remote team.

[1697] Step 9

[1698] The server uses gap detection to identify missing information for the remote team and set alerts.

[1699] Step 10

[1700] The server proposes an information sharing meeting with the remote team.

[1701] Step 11

[1702] The emotion engine suggests ways to optimize information sharing based on emotional data from remote teams.

[1703] Step 12

[1704] The server compiles these into a report and displays it to the user on a dashboard.

[1705] Step 13

[1706] The server notifies the user of the report via email or chat tool.

[1707] Step 14

[1708] The user reviews the report and takes action to establish the proposed information sharing meeting.

[1709] Example 2: Resolving communication errors between departments

[1710] Step 1

[1711] Users configure the system to allow access to chat tools and conferencing systems used between specific departments.

[1712] Step 2

[1713] The device collects chat logs and meeting recordings from these data sources.

[1714] Step 3

[1715] The data collected by the terminal is sent to the server, where it is temporarily stored and formatted.

[1716] Step 4

[1717] The server receives the data and centrally integrates it, adding metadata to the data.

[1718] Step 5

[1719] The server stores the data converted into a unified format in a database and generates an index for quick access.

[1720] Step 6

[1721] The emotion engine analyzes the emotion data between departments from the collected data.

[1722] Step 7

[1723] The server uses AI tools to analyze communication-related data and extract keywords and themes.

[1724] Step 8

[1725] The server generates information distribution channels and visualizes problems in communication between departments.

[1726] Step 9

[1727] The server uses gap detection means to identify areas where information is not being properly communicated and sets alerts.

[1728] Step 10

[1729] The server proposes the establishment of regular information sharing meetings.

[1730] Step 11

[1731] The emotion engine suggests optimal communication methods based on the emotional data of each department.

[1732] Step 12

[1733] The server compiles these into a report and displays it to the user on a dashboard.

[1734] Step 13

[1735] The server notifies the user of the report via email or chat tool.

[1736] Step 14

[1737] The user reviews the report and takes action to establish the proposed information sharing meeting.

[1738] In this way, the system of the present invention efficiently manages information distribution while taking into account the feelings of users, thereby improving transparency and productivity.

[1739] Example 2

[1740] Next, a description will be given of Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the robot 414 will be referred to as a "terminal."

[1741] Conventional systems lack the means to streamline internal information distribution processes, resulting in inefficiencies in business operations due to lack of information and delays.In addition, information transmission is not optimized with consideration for user emotions, and specific measures to improve the quality of communication are needed.

[1742] The specific processing by the specific processing unit 290 of the data processing device 12 in the second embodiment is realized by the following means. In this invention, the server includes terminal means for collecting in-house information, server means for integrating the collected data, artificial intelligence means for analyzing the integrated data and mapping information distribution routes, gap detection means for automatically detecting information gaps from the generated information distribution routes, optimization proposal means for generating optimization proposals for minimizing the gaps, report generation means for reporting the generated proposals, and an emotion engine for analyzing user emotions. This makes it possible to improve the efficiency of the information distribution process and optimize information transmission taking user emotions into consideration.

[1743] "Terminal means" refers to a device that automatically collects company information with the user's permission.

[1744] "Server means" refers to a computer system for centrally storing and integrating data collected from terminal means.

[1745] "Artificial intelligence means" refers to algorithms and software for analyzing data integrated by the server means and mapping information distribution channels.

[1746] "Gap detection means" refers to a function that automatically detects a lack of or delay in information from the information distribution path generated by the artificial intelligence means.

[1747] The "optimization suggestion means" refers to a function that generates specific suggestions to minimize the information gaps detected by the gap detection means.

[1748] The "report generation means" refers to a function for creating documents and graphs for visually presenting information based on the proposals generated by the optimization proposal means.

[1749] An "emotion engine" refers to algorithms or software that analyzes users' emotions and optimizes the way information is communicated.

[1750] "Dashboard means" refers to a screen or interface that visually presents the reports generated by the report generation means and allows the user to intuitively grasp the information.

[1751] This invention is a system for optimizing information transmission by improving the efficiency of in-house information distribution processes and recognizing user emotions. The program for this system is configured using the following hardware and software.

[1752] System configuration

[1753] The system includes a terminal means, a server means, an artificial intelligence means, a gap detection means, an optimization suggestion means, a report generation means, and an emotion engine.

[1754] Terminal means

[1755] The terminal means obtains permission from the user and automatically collects data such as internal communication logs, emails, document sharing, and meeting contents. For example, this could be a computer or smartphone used by the user. The collected data is sent to the server means. A secure communication protocol (e.g., HTTPS) is used to send the data.

[1756] Server Means

[1757] The server means integrates the data collected from the terminal means and stores it in a centralized manner. For example, a high-performance server computer plays this role. The server standardizes the data format and converts it into, for example, JSON or XML format. It also adds necessary metadata (such as timestamp, sender, and destination) and stores it in a database.

[1758] Artificial Intelligence Tools

[1759] Artificial intelligence means placed within the server analyzes the stored data. Specifically, it uses natural language processing (NLP) technology to analyze text data and extract important keywords and themes. For example, machine learning models and deep learning models are used. It also analyzes email sending and receiving patterns to generate information distribution channels.

[1760] Gap Detection Means

[1761] The gap detection means analyzes the information distribution path generated by the artificial intelligence means and automatically detects missing or delayed information. It identifies gaps based on specific conditions (e.g., when information is not shared within a specific time) and issues an alert or warning to the user.

[1762] Optimization proposal method

[1763] The optimization suggestion means generates specific suggestions to address the problems identified by the gap detection means, such as establishing an information sharing meeting, using a specific communication tool, or raising the priority of information, thereby enabling users to optimize information distribution.

[1764] Report Generation Method

[1765] The report generation means creates a report to visually present information based on the proposals generated by the optimization proposal means. This report includes graphs and charts, allowing the user to intuitively understand the current state of information distribution and areas for improvement.

[1766] Emotion Engine

[1767] An emotion engine is an algorithm or software that analyzes a user's emotions and optimizes the method of communication. For example, it analyzes the content of a user's email or chat messages and extracts emotional data. This emotional data is reflected in the report generation tool and presented visually.

[1768] Specific examples

[1769] Example 1: Critical project information isn't reaching the remote team

[1770] The terminal means collects data from the user's email and project management tool. The server means integrates the data, and the artificial intelligence means analyzes the project information and identifies when important information has not reached the remote team. The gap detection means detects the problem, and the optimization suggestion means suggests establishing an information sharing meeting for the remote team. The report generation means displays improvement suggestions on a dashboard for the user to confirm. The emotion engine analyzes the emotion data of the remote team and suggests an appropriate method of communicating information. As a result, the remote team receives the latest information, and the project progresses smoothly.

[1771] Example 2: Resolving communication errors between departments

[1772] The terminal means collects logs from internal chat tools and the contents of meetings. The server means integrates this data, and the artificial intelligence means analyzes the flow of information. The gap detection means detects problems with information transmission between departments. The optimization proposal means proposes the establishment of regular information sharing meetings. The report generation means compiles this into a report and provides it visually to the user. The emotion engine analyzes the emotion data from each department and proposes the optimal communication method. This series of processes reduces miscommunication between departments and achieves efficient information transmission.

[1773] Prompt Sentence Examples

[1774] "Please extract emotional data from the content of this email."

[1775] "Collect and integrate data from your project management tools."

[1776] "Detect gaps in information flow between departments and generate optimization suggestions."

[1777] The system of the present invention uses a generative AI model to generate proposals in real time, significantly improving the user's work efficiency.

[1778] The flow of the identification process in the second embodiment will be described with reference to FIG.

[1779] Step 1: Data collection

[1780] With the user's permission, the device automatically collects data such as emails, chat logs, document sharing information, and meeting recordings. For example, the device connects to a mail server and obtains the latest email data. The collected data is sent from the device to the server via a secure communication protocol (e.g., HTTPS). The input is the permission information and various data obtained from the user, and the output is the integrated data transferred to the server.

[1781] Step 2: Data Integration

[1782] The server receives data sent from the terminal and stores it centrally. The server standardizes the format of the received data, converting it into, for example, JSON or XML format. It also adds necessary metadata (timestamp, sender, destination, etc.) and stores it in a database. The input is the raw data sent from the terminal, and the output is data stored in the database in a standardized format.

[1783] Step 3: Data analysis

[1784] The server passes the stored data to an artificial intelligence means, which uses natural language processing (NLP) techniques to analyze the text data and extract important keywords and themes. For example, it may extract specific keywords (e.g., "deadline" or "meeting") from the text of emails or chat logs. The input is the consolidated text data, and the output is the extracted keywords and themes.

[1785] Step 4: Visualize the information

[1786] The server visualizes the information distribution routes generated by the artificial intelligence means as a network diagram. When a user accesses the dashboard, the network diagram is displayed, allowing the user to intuitively understand important information flows and communication patterns. Specifically, nodes and edges are used to visually show who is sending information to whom. The input is the analysis results from the artificial intelligence means, and the output is visual information provided to the user.

[1787] Step 5: Gap detection

[1788] The gap detection means analyzes the generated information distribution channels and automatically detects missing or delayed information. It identifies gaps based on specific conditions (e.g., when information is not shared within a specific time frame) and issues alerts or warnings to users. The input is the generated information distribution channels, and the output is the identified gaps and alert information.

[1789] Step 6: Optimization suggestions

[1790] The optimization proposal means generates specific proposals to minimize the information gaps identified by the gap detection means. The proposals include measures such as establishing an information sharing meeting, using specific communication tools, and raising the priority of information. The input is the detected gap information, and the output is the generated optimization proposals.

[1791] Step 7: Generate Reports

[1792] The report generation means creates a report to visually present information based on the proposals generated by the optimization proposal means. The report includes graphs and charts, allowing the user to intuitively understand the current state of information distribution and areas for improvement. The input is the generated optimization proposals, and the output is the visually presented report.

[1793] Step 8: Sentiment Analysis

[1794] The emotion engine analyzes the content of the user's emails and chat messages and extracts emotional data. The emotional data is reflected in reports and suggests appropriate ways to communicate information. Specifically, if the user is under stress, the emotion engine suggests ways to alleviate the stress. The input is the content of the emails and chat messages, and the output is the extracted emotional data and suggestions.

[1795] (Application example 2)

[1796] Next, a description will be given of Application Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the robot 414 will be referred to as a "terminal."

[1797] Conventional factory management systems lacked the means to effectively collect and analyze work data and worker conversation data, preventing them from fully optimizing work efficiency and information distribution. Furthermore, it was difficult to improve information transmission while taking into account the emotional state of workers, which created a risk of lower worker satisfaction and work efficiency. This has led to a demand for improvements to production processes and worker satisfaction.

[1798] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 2 is realized by the following means.

[1799] In this invention, the server includes terminal means for collecting in-house information, server means for integrating data collected from the terminal means, AI means for analyzing the integrated data by the server means and mapping information distribution routes, gap detection means for automatically detecting information gaps from the information distribution routes generated by the AI ​​means, optimization proposal means for generating optimization proposals to minimize the information gaps detected by the gap detection means, report generation means for reporting the proposals generated by the optimization proposal means, robot means for collecting work data and worker conversations via environmental sensors, cameras, and microphones in the factory, an emotion engine for analyzing the work data and worker conversation data and extracting emotional information, emotion data optimization proposal means for generating proposals to optimize information distribution feedback based on the emotional information, and data visualization means for reporting and visually displaying the proposals generated by the emotion data optimization proposal means. This enables optimization of information distribution and work efficiency in the factory and improvement of information communication taking into account the emotions of workers.

[1800] "Internal information" refers to data, knowledge, and communication records generated and collected within a company or organization.

[1801] "Terminal means" is a device for collecting information and transmitting it to server means.

[1802] The "server means" is a system for integrating and centrally managing collected data.

[1803] "AI means" refers to technologies for analyzing collected and integrated data and generating and visualizing information distribution channels.

[1804] The "gap detection means" is a mechanism for automatically detecting data loss or delay based on the generated information distribution path.

[1805] "Optimization suggestions" are techniques for generating specific suggestions to minimize detected information gaps. ...

Claims

1. A terminal means for collecting internal company information; a server means for integrating data collected from the terminal means; AI means for analyzing the data integrated by the server means and mapping information distribution channels; a gap detection means for automatically detecting information gaps from the information distribution path generated by the AI ​​means; an optimization suggestion means for generating an optimization suggestion for minimizing the information gap detected by the gap detection means; The system includes a report generation means for reporting the suggestions generated by said optimization suggestion means.

2. 10. The system of claim 1, further comprising means for obtaining approval from the user.

3. 2. The system of claim 1, wherein said report generating means further comprises dashboard means for visually presenting the recommendations generated by said optimization recommendation means.

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