System

Abstract

A system is provided.SOLUTION: A system comprising: means for collecting public information of enterprises; means for generating a matching list based on the collected information of the enterprises; means for notifying the generated matching list and proposal contents; means for setting a meeting between the proposed enterprises; means for managing a project progress based on a meeting result; and means for putting a product or service into a market according to the progress of the project.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] When companies work alone, the growth of their products and services often plateaus. Even when collaboration is considered, issues such as a lack of personnel and ideas, and an uncertain outlook for results can make it difficult to put the idea into practice. Furthermore, as the business matching market expands, there is a lack of means to quickly and effectively collaborate and bring new products and services to market. A system that solves these issues is needed. [Means for solving the problem]

[0005] The present invention solves the aforementioned problems with a system that includes a means for collecting public company information, a means for generating a matching list based on the collected company information, a means for notifying the generated matching list and proposal details, a means for arranging meetings between companies that have received proposals, a means for managing project progress based on the results of the meetings, and a means for bringing products or services to market as the project progresses. The system further includes a means for tracking project progress in real time and automatically updating it as needed, and also provides a means for conducting test marketing, analyzing the results, and collecting feedback. This enables companies to collaborate efficiently and quickly bring new products and services to market.

[0006] "Public corporate information" refers to information such as business operations, technology, patents, production locations, and financial information that companies disclose through their websites, government agency databases, social media, corporate reports, etc.

[0007] The "matching list" is a list of combinations of companies that is generated based on publicly available information collected from companies, and takes into account evaluations such as synergy effects, feasibility, social contribution, and interest.

[0008] The "proposal content" is detailed information about a collaboration proposed to a specific company based on the generated matching list.

[0009] "Means for setting up meetings" means scheduling and notification functions for proposed businesses to set up online or in-person meetings.

[0010] A "project progress management tool" is a tool for tracking the progress of a collaboration project and managing task completion status and deadlines.

[0011] "Means of bringing products or services to market" refers to the function of providing marketing, distribution, and sales support to release jointly developed products or services to the market.

[0012] "Real-time tracking" refers to the ability to monitor project progress in real time and update task lists and deadlines as needed.

[0013] "Means for conducting test marketing" is a function for conducting test sales of products or services, analyzing the results, and collecting feedback. [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] This invention is a system that collects publicly available information from companies, proposes collaboration between companies based on that information, executes it, and supports the launch of products and services on the market. Below, we will create a program for this system and explain its processing in natural language.

[0036] This system is broadly divided into six phases: data collection phase, matching list generation phase, proposal phase, collaborative meeting implementation phase, project management phase, and product / service creation phase.

[0037] Data Collection Phase

[0038] The server collects publicly available information about companies. It generates a list of companies to collect and gathers data from websites, government databases, social media, and company reports. The server periodically retrieves the latest information using web scraping technology and API requests. The collected data is analyzed and a profile of each company (including business activities, technology, patents, production locations, and financial information) is stored in a database.

[0039] Example: A server retrieves a product catalog from Company X's website, parses financial reports retrieved from a government database, and enters them into a database.

[0040] Matching list generation phase

[0041] The server runs an AI model based on the company information stored in the database and generates a matching list of companies based on the evaluation factors of social contribution, synergy effect, feasibility, and interest. The matching list is then sorted in order of evaluation, and appropriate proposals are made for each company.

[0042] Example: Based on the technical information of Company X, the server evaluates the synergy effect when combined with the sales channels of Company Y, and places it at the top of the matching list.

[0043] Proposal Phase

[0044] The server notifies each company's device of the generated matching list and the details of the proposal. The device receives the notification and displays the details of the proposal to the company representative. The user (company representative) can check the proposal and request more information if they are interested.

[0045] Example: The server notifies Company X and Company Y that "Your technology has the potential to create high synergy with Company Y's sales channels," and the terminal displays this information to the person in charge.

[0046] Collaboration MTG implementation phase

[0047] The user requests a meeting between the proposed companies. The server coordinates the schedules between the companies, determines the date and time of the online meeting, and shares relevant information. The device then sends a meeting reminder to the person in charge.

[0048] Example: Representatives from company X and company Y schedule an online meeting, and the server adds it to the schedule and sends reminders.

[0049] Project Management Phase

[0050] The server launches a project management tool based on the meeting results and sets tasks and deadlines. Users report the progress of each task and send updates to the server. The server tracks project progress in real time and automatically updates tasks and deadlines as needed.

[0051] Example: A server sets tasks for a collaborative project between companies X and Y and constantly updates the progress.

[0052] Product / service phase

[0053] The server creates a development plan for the product or service according to the project's progress. Users send feedback to the server during the development process. The server conducts test marketing, analyzes the results, and collects feedback. Finally, the server provides the optimal means for releasing the product or service to the market.

[0054] Example: The server conducts test sales of a new product jointly developed by Company X and Company Y, analyzes the sales data, proposes improvements based on the feedback, and prepares for another test marketing.

[0055] In this way, the system supports collaboration between companies at each phase, streamlining the launch of new products and services into the market.

[0056] The processing flow will be explained below.

[0057] Data Collection Phase

[0058] Step 1:

[0059] The server generates a list of companies from which data is collected, categorizing the list by company category and industry.

[0060] Step 2:

[0061] The server performs web scraping and API requests targeting corporate websites, government databases, social media, corporate reports, and more.

[0062] Step 3:

[0063] The server analyzes the collected information and extracts information such as each company's business activities, technology, patents, production locations, and financial information, and stores it in a database.

[0064] Example: The server scrapes product information from Company X's website, collects customer feedback from social media, performs text analysis on it, and registers it in a database.

[0065] Matching list generation phase

[0066] Step 4:

[0067] The server inputs company information stored in a database into the AI ​​model.

[0068] Step 5:

[0069] The server generates a matching list of companies based on the evaluation factors of social contribution, synergy effect, feasibility, and interest.

[0070] Step 6:

[0071] The server sorts the generated matching list in order of rating and creates appropriate proposals for each company.

[0072] Example: The server evaluates the synergy benefits of combining the technical information of company X with the sales network of company Y and places this at the top of the list.

[0073] Proposal Phase

[0074] Step 7:

[0075] The server notifies the target company's terminal of the generated matching list and the contents of the proposal.

[0076] Step 8:

[0077] The terminal receives the notification and displays the proposal to the company representative.

[0078] Step 9:

[0079] The user reviews the offer and, if interested, requests more information.

[0080] Example: The server notifies Company X and Company Y that "This collaboration will help expand your product offerings," and the terminal displays the notification to the person in charge.

[0081] Collaboration MTG implementation phase

[0082] Step 10:

[0083] The user sends a request to the server to set up a meeting between the proposed businesses.

[0084] Step 11:

[0085] The server coordinates schedules between companies and determines dates for online meetings.

[0086] Step 12:

[0087] The terminal notifies the user of meeting reminders.

[0088] Example: Company X and Company Y decide on a meeting date, the server adds it to the schedule, and the device sends a reminder.

[0089] Project Management Phase

[0090] Step 13:

[0091] The server starts a project management tool based on the meeting results and sets tasks and deadlines.

[0092] Step 14:

[0093] Users report their progress on tasks and send updates to the server.

[0094] Step 15:

[0095] The server tracks project progress in real time and automatically updates tasks and deadlines as needed.

[0096] Example: The server sets tasks for a collaborative project between company X and company Y and constantly updates the progress.

[0097] Product / service phase

[0098] Step 16:

[0099] The server formulates a product or service development plan according to the progress of the project.

[0100] Step 17:

[0101] Users provide feedback to the server during the development process.

[0102] Step 18:

[0103] The server conducts test marketing, analyzes the results, and collects feedback.

[0104] Step 19:

[0105] The server prepares the product or service incorporating the improvements for final release and brings it to market.

[0106] Example: The server conducts test sales of a new product jointly developed by Company X and Company Y, analyzes the sales data, and makes improvements and finalizes the specifications based on the feedback.

[0107] Example 1

[0108] 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."

[0109] There is a need to efficiently realize collaboration between companies and quickly bring new products and services to market. However, collecting public information about companies and finding the most suitable partner companies takes a great deal of time and effort. In addition, meeting arrangements and project management are often done manually, making efficient operations difficult. Furthermore, understanding the progress of projects in real time and receiving appropriate feedback is also a challenge.

[0110] 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.

[0111] In this invention, the server includes a means for collecting publicly available company information, a means for generating a matching list using a generative AI model based on the collected company information, and a means for notifying the company terminal of the generated matching list and proposal details. This allows for the rapid identification of optimal partnerships between companies and efficient meeting arrangements and project management. It also enables real-time tracking of project progress, analysis of test marketing results, and feedback collection, significantly improving overall efficiency.

[0112] "Public corporate information" refers to data published by companies, such as websites, government databases, social media, and company reports.

[0113] A "generative AI model" refers to an artificial intelligence algorithm that generates matching lists between companies based on collected data.

[0114] A "matching list" is a list generated to evaluate corporate information and propose optimal partnerships.

[0115] "Proposal content" refers to content that includes the benefits of collaboration between companies and specific proposals based on the generated matching list.

[0116] "Terminal" refers to a device (e.g., PC, tablet, smartphone) used by company personnel to receive and check information.

[0117] "Meeting request methods" refers to the methods by which company representatives can request and schedule meetings with other companies.

[0118] "Project Task" refers to the specific work or activities involved in a collaborative project between companies.

[0119] "Project Progression" refers to the process of how collaboration between companies is progressing.

[0120] "Development Plan" means a detailed plan for the development of a new product or service.

[0121] "Test marketing" refers to the activity of actually selling a new product or service on a trial basis in the market and analyzing the results.

[0122] This invention is a system that collects publicly available information from companies, proposes collaboration between companies based on that information, executes it, and supports the launch of products and services on the market. Specifically, this system is realized using the following hardware and software.

[0123] This system is broadly divided into six phases: data collection phase, matching list generation phase, proposal phase, collaboration meeting implementation phase, project management phase, and product / service creation phase. The processing in each phase is explained below.

[0124] Data Collection Phase

[0125] The server collects publicly available information about companies. It generates a list of target companies and uses web scraping technology and API requests to gather data from websites, government databases, social media, and company reports. It also periodically retrieves updated information, analyzes the collected data, and stores each company's profile (business operations, technology, patents, production locations, financial information, etc.) in a database.

[0126] Example: A server scrapes product catalogs from websites of companies in a specific region using the filtering criteria "technology companies" in the technology field, while simultaneously retrieving financial reports from a government financial database and registering them as profile information in the database.

[0127] Matching list generation phase

[0128] The server runs a generative AI model based on company information stored in a database, generating a matching list of companies based on evaluation factors such as social contribution, synergy effects, feasibility, and interest. The matching list is then sorted by evaluation, and appropriate proposals are made for each company.

[0129] Example: The server inputs a prompt such as "Please suggest the best partner company for Company X's technology" into the generative AI model, and the matching list that evaluates the synergy between Company X and Company Y is ranked at the top.

[0130] Proposal Phase

[0131] The server notifies each company's device of the generated matching list and the details of the proposal. The device receives the notification and displays the details of the proposal to the company representative. The user (company representative) can check the proposal and request more information if they are interested.

[0132] Example: The server sends a proposal such as "Your company's technology has the potential to create high synergy with Company Y's sales channels" to the terminals of Company X and Company Y, and displays it to the person in charge.

[0133] Collaboration Meeting Implementation Phase

[0134] The user requests a meeting between the proposed companies. The server coordinates the schedules between the companies and determines the date and time of the online meeting. The device then sends a meeting reminder to the person in charge.

[0135] Example: A person from company X makes a request to schedule an online meeting, the server coordinates the availability of a person from company Y, sets the appropriate date and time, and sends a reminder.

[0136] Project Management Phase

[0137] The server launches a project management tool based on the meeting results and sets each task and its deadline. Users report the progress of each task and send updates to the server. The server tracks the project progress in real time and automatically updates tasks and deadlines as needed.

[0138] Example: The server launches a task management tool and adds tasks such as "market research" and "technology development." The user reports progress and updates the record to say "market research completed."

[0139] Product / service phase

[0140] The server creates a product or service development plan according to the project progress. Users send feedback during the development process to the server. The server conducts test marketing, analyzes the results, and collects feedback. Finally, it provides the optimal means for releasing the product or service to the market.

[0141] Example: A server tests a new product in a specific region, analyzes the sales data, proposes improvements based on the results of the analysis, and prepares for another test marketing of the improved product.

[0142] As described above, this system performs detailed and specific processing at each phase, supporting collaboration between companies and streamlining the launch of new products and services into the market.

[0143] The flow of the identification process in the first embodiment will be described with reference to FIG.

[0144] Step 1:

[0145] The server collects publicly available information about companies. It receives filtering criteria (such as industry, size, and region) as input and generates a list of companies to collect as output. Specifically, it uses web scraping technology and API requests to collect data from company websites and government agency databases. This data is then stored in a database as a profile for each company, including production location, technology, patents, and financial information.

[0146] Step 2:

[0147] The server runs a generative AI model based on company information stored in a database. The input here is company profile data, and the output is a matching list based on evaluation factors such as social contribution, synergy effects, feasibility, and interest. Specifically, a prompt such as "Please suggest partner companies that are best suited to Company X's technology" is input into the generative AI model, and the AI ​​automatically generates a matching list.

[0148] Step 3:

[0149] The server notifies the company's device of the generated matching list and proposal details. It receives the matching list and proposal details as input and displays them on the device in a format that can be checked by the company representative as output. Specific operations include sending information using an email API or notification system, and displaying it on the device as a pop-up notification, etc.

[0150] Step 4:

[0151] The user (company representative) receives the notification and checks the proposal. If interested, the user requests more detailed information. The input is the notification of the proposal, and the output is a request for more information sent to the server. The specific operation is to click the "Request more information" button on the terminal's user interface.

[0152] Step 5:

[0153] A user requests a meeting between the proposed companies. As input, the user sends the meeting request information (date, time, participants, etc.) to the server, and as output, the user receives the adjusted schedule. Specifically, a company representative sends a request using an online scheduling system, and the server adjusts the schedule between the companies and sets an appropriate date and time.

[0154] Step 6:

[0155] The server launches a project management tool based on the meeting results and sets each task and its deadline. It receives the meeting deliverables and task information as input, and sets tasks and deadlines in the project management tool as output. Specifically, the server launches a task management tool (e.g., JIRA) and adds tasks such as "market research" and "technology development."

[0156] Step 7:

[0157] Users report the progress of each task and send updates to the server. The input is task progress information, and the output is a project management tool with updated progress. Specifically, the person in charge enters progress information such as "market research completed," and the server reflects it in the project management tool.

[0158] Step 8:

[0159] The server formulates a product or service development plan based on the project progress and conducts test marketing. It receives progress data and marketing plan information as input, and obtains test marketing results and feedback as output. Specifically, the server conducts test sales of a new product in a specific region, analyzes the data, and creates improvement proposals.

[0160] (Application example 1)

[0161] 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."

[0162] Conventional inter-company collaboration proposal and execution support systems collect and analyze publicly available information from companies to generate matching lists, but they are unable to take into account data on manufacturing processes and production equipment. This makes it difficult to make optimal proposals for actual factory operations. Furthermore, they lack the ability to easily monitor and automatically update the real-time progress of manufacturing processes, making efficient project management difficult. Furthermore, there is a lack of means to forecast demand and quickly and accurately gather feedback when test selling and launching newly developed products and services.

[0163] 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.

[0164] In this invention, the server includes means for collecting operation data from factory automation equipment or autonomous machines, means for analyzing the collected operation data and generating company profiles, means for proposing corporate collaborations based on the generated profiles, means for notifying the generated matching list and proposal details, means for setting up online meetings between proposed companies, means for managing project progress based on the meeting results and launching products or services as the project progresses, means for test selling new products using the collected data and providing feedback, and means for supporting product launches. This enables optimal collaboration proposals that take into account automation equipment data, enabling real-time monitoring of manufacturing processes and efficient project management. It also enables rapid and accurate feedback collection when launching new products or services.

[0165] "Corporate public information" refers to information that a company makes publicly available through its website, government agency databases, social media, corporate reports, etc.

[0166] The "matching list" is a list of potential collaborations between companies that is generated by running an AI model based on collected company information and evaluating factors such as social contribution, synergy effects, feasibility, and interest.

[0167] "Proposal content" refers to details and recommendations for collaboration between companies generated by the server based on the matching list.

[0168] The means for setting up a "meeting" is a method for the proposed companies to coordinate a date and time for a meeting online or offline and share information.

[0169] A "means for managing project progress" is a method for tracking project tasks, deadlines, and progress in real time based on meeting results, and automatically updating as needed.

[0170] "Factory automation equipment or autonomously operating machines" means machines or robots that automate manufacturing processes within a factory and are capable of operating independently.

[0171] "Operation data" refers to recorded information such as the operating status, performance indicators, and operating hours of automated equipment or autonomously operating machines in a factory.

[0172] A "company profile" is a detailed record of a company's business activities, technology, patents, production locations, financial information, etc., based on an analysis of collected operational data and publicly available information.

[0173] An "online meeting" is a meeting between companies held over the Internet, and refers to a means that allows for the coordination of dates and times and the sharing of information.

[0174] A "new product test launch" is the process of launching a newly developed product or service in a limited market and collecting response and sales data.

[0175] "Feedback" refers to information regarding evaluations and areas for improvement obtained during trial sales and project implementation.

[0176] "Go-to-market aids" are the methods and tools used to formally introduce new products and services to the market and promote their sales.

[0177] This invention is a system that collects publicly available information from companies, proposes collaboration between companies based on that information, executes it, and supports the launch of products and services on the market. Below, we will create a program for this system and explain its processing in natural language.

[0178] The system consists of three entities: a server, a terminal, and a user.

[0179] Data Collection Phase

[0180] The server collects publicly available information about companies. It generates a list of target companies and gathers data from websites, government databases, social media, and company reports. Specifically, it uses web scraping technology and API requests to periodically retrieve the latest information. The collected data is analyzed and a profile of each company (including business activities, technology, patents, production locations, and financial information) is stored in a database.

[0181] Example: A server retrieves a product catalog from Company A's website, parses financial reports retrieved from a government database, and enters them into a database.

[0182] Matching list generation phase

[0183] The server runs a generative AI model based on the company information stored in the database, and generates a matching list of companies based on the evaluation factors of social contribution, synergy effect, feasibility, and interest. The generated matching list is sorted in order of evaluation, and appropriate proposals are made for each company.

[0184] Example: The server evaluates the synergy effect when combining company A's technical information with company B's sales channels, and places it at the top of the matching list.

[0185] Proposal Phase

[0186] The server notifies each company's device of the generated matching list and the details of the proposal. The device receives the notification and displays the details of the proposal to the company representative. The user (company representative) can check the proposal and request more information if they are interested.

[0187] Example: The server notifies Company A and Company B that "Your technology has the potential to create high synergy with Company B's sales channels," and the terminal displays this information to the person in charge.

[0188] Collaboration MTG implementation phase

[0189] The user requests a meeting between the proposed companies. The server coordinates the schedules between the companies, determines the date and time of the online meeting, and shares relevant information. The device then sends a meeting reminder to the person in charge.

[0190] Example: Representatives from company A and company B schedule an online meeting, and the server adds it to the schedule and sends reminders.

[0191] Project Management Phase

[0192] The server launches a project management tool based on the meeting results and sets tasks and deadlines. Users report the progress of each task and send updates to the server. The server tracks project progress in real time and automatically updates tasks and deadlines as needed.

[0193] Example: The server sets tasks for a collaborative project between companies A and B and constantly updates the progress.

[0194] Product / service phase

[0195] The server creates a development plan for the product or service according to the project's progress. Users send feedback to the server during the development process. The server conducts test marketing, analyzes the results, and collects feedback. Finally, the server provides the optimal means for releasing the product or service to the market.

[0196] Example: The server conducts test sales of a new product jointly developed by Company A and Company B, analyzes the sales data, proposes improvements based on the feedback, and prepares for another test marketing.

[0197] Hardware and software used

[0198] The system uses the following hardware and software:

[0199] Hardware: Smartphone, head-mounted display (e.g., Oculus Quest 2)

[0200] Software: Python web scraping (BeautifulSoup), HTTP request library (requests), data analysis (pandas), generative AI models

[0201] Prompt Sentence Examples

[0202] An example of a prompt to input to a generative AI model is as follows:

[0203] "Develop a system that evaluates synergy between companies based on technical information and generates optimal collaboration proposals."

[0204] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[0205] Step 1: Data collection

[0206] The server collects publicly available information about companies. It generates a list of companies to collect and obtains data from websites, government databases, social media, and company reports. This is done using web scraping technology (BeautifulSoup) and API requests (requests library). The input is various publicly available information, and the output is a profile of each company (business details, technology, patents, production locations, financial information, etc.).

[0207] Step 2: Save to database

[0208] The server analyzes the collected data and stores it in a database. First, it analyzes the acquired HTML data and API responses to extract detailed information about each company. Next, it structures the extracted data and registers it in the database. The input is the analyzed information, and the output is the company profile registered in the database.

[0209] Step 3: Run the AI ​​model

[0210] The server runs a generative AI model based on the company information stored in the database. Specifically, it generates a matching list of companies based on the evaluation factors of social contribution, synergy effects, feasibility, and interest. The input is the company profile, and the output is a matching list of companies. At this stage, the model analyzes and evaluates the data.

[0211] Step 4: Proposal generation and notification

[0212] The server creates an optimal proposal for each company based on the generated matching list and notifies the device. The input is the matching list, and the output is a proposal notification sent to each company's device. The notification content includes details of the proposed collaboration, which the device displays to the person in charge.

[0213] Step 5: Meeting Schedule

[0214] The user reviews the proposal and, if interested, requests more information and a meeting from the server. The server coordinates schedules between the companies and determines the date and time of the online meeting. The input is the meeting request, and the output is the scheduled meeting date and time and related information. The terminal notifies the person in charge of the meeting with a reminder.

[0215] Step 6: Project Management

[0216] The server launches the project management tool based on the meeting results and sets tasks and deadlines. The input is the meeting results and the output is the set tasks and deadlines. Users report the progress of each task and send updates to the server. The server tracks the project progress in real time and automatically updates tasks and deadlines as needed.

[0217] Step 7: Product / Service

[0218] The server formulates a product or service development plan according to the project progress. The input is project progress information, and the output is a development plan. Users send feedback during the development process to the server, which then conducts test marketing. The test marketing results are analyzed, feedback is collected, and the server provides the optimal means for finally releasing the product or service to the market.

[0219] Through the above processing steps, the system of the present invention efficiently supports everything from collaboration between companies to the launch of products and services on the market.

[0220] 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.

[0221] This system collects publicly available information from companies and uses that information to propose and execute collaborations between companies, supporting the launch of products and services to market. Furthermore, by incorporating an emotion engine, it is possible to recognize user emotions and optimize proposal content and meeting progress based on those emotions. Below, we will create a program for this system and explain its processing in natural language.

[0222] This system is broadly divided into six phases: data collection phase, matching list generation phase, proposal phase, collaborative meeting implementation phase, project management phase, and product / service creation phase. In each phase, an emotion engine recognizes the user's emotions and responds appropriately.

[0223] Data Collection Phase

[0224] The server collects publicly available information about companies, generates a list of companies to collect, and performs web scraping and API requests targeting websites, government databases, social media, corporate reports, etc. The server analyzes the collected information and stores each company's business details, technology, patents, production locations, financial information, etc. in a database.

[0225] Example: The server scrapes product information from Company X's website, collects customer feedback from social media, performs text analysis on it, and registers it in a database.

[0226] Matching list generation phase

[0227] The server inputs the company information stored in the database into the AI ​​model and generates a matching list of companies based on the evaluation factors of social contribution, synergy effect, feasibility, and interest. The generated matching list is sorted in order of evaluation, and appropriate proposals are made for each company.

[0228] Example: The server evaluates the synergy benefits of combining the technical information of company X with the sales network of company Y and places this at the top of the list.

[0229] Proposal Phase

[0230] The server notifies each company's device of the generated matching list and the proposed content. The device receives the notification and displays the proposed content to the company representative. The user (company representative) checks the proposed content and, based on the emotions recognized by the emotion engine, requests more detailed information if they are interested.

[0231] Example: The server notifies Company X and Company Y that "This collaboration will help expand your product offerings," and the user's device displays the notification to the person in charge, and the emotion engine evaluates satisfaction.

[0232] Collaboration MTG implementation phase

[0233] The user requests a meeting between the proposed companies. The server coordinates the schedules between the companies and determines the date of the online meeting. The device notifies the user of meeting reminders. The emotion engine monitors the user's emotional state in real time during the meeting and provides necessary support.

[0234] Example: Representatives from Company X and Company Y decide on a date for a meeting, the server adds it to the schedule, the device sends a reminder, and the emotion engine detects tension and makes appropriate suggestions.

[0235] Project Management Phase

[0236] The server launches a project management tool based on the meeting results and sets tasks and deadlines. Users report their task progress and send updates to the server. The server tracks project progress in real time and automatically updates tasks and deadlines as needed. The emotion engine monitors users' emotions regarding project progress and provides appropriate support based on that.

[0237] Example: A server sets tasks for a collaborative project between companies X and Y, constantly updates the progress, and an emotion engine detects the user's stress and provides reminders and advice.

[0238] Product / service phase

[0239] The server formulates a product or service development plan according to the project's progress. Users send feedback during the development process to the server. The server conducts test marketing, analyzes the results, and collects feedback. Finally, the server provides the optimal means for releasing the product or service to the market. The emotion engine analyzes the emotional reactions of the feedback and uses them to improve the product or service.

[0240] Example: The server conducts test sales of a new product jointly developed by Company X and Company Y, analyzes the sales data, and makes improvements and finalizes the specifications based on the feedback. The emotion engine analyzes positive reactions and adjusts the final release.

[0241] In this way, this system supports collaboration between companies at each phase, and by utilizing an emotion engine, it optimizes proposal content and meeting progress, thereby streamlining the launch of new products and services into the market.

[0242] The processing flow will be explained below.

[0243] Data Collection Phase

[0244] Step 1:

[0245] The server generates a list of companies to collect data from. First, it selects target companies based on their industry and market needs and adds them to the list.

[0246] Step 2:

[0247] The servers perform web scraping and API requests targeting company websites, government databases, social media, company reports, etc. The information collection is carried out periodically.

[0248] Step 3:

[0249] The server analyzes the collected information and stores each company's business details, technology, patents, production locations, financial information, etc. in a database. The analysis uses technologies such as natural language processing.

[0250] Example: The server accesses Company X's website, scrapes the latest product information, analyzes customer reviews on social media, and registers this information in a database.

[0251] Matching list generation phase

[0252] Step 4:

[0253] The server inputs company information stored in a database into the AI ​​model, which learns from the existing data.

[0254] Step 5:

[0255] The server generates a matching list of companies based on the evaluation factors of social contribution, synergy, feasibility, and interest, with each factor being evaluated according to a pre-set weighting.

[0256] Step 6:

[0257] The server sorts the generated matching list in order of evaluation and creates appropriate proposals for each company, including specific collaboration benefits and prospects.

[0258] Example: The server calculates the synergy effect of combining the technological capabilities of company X with the sales network of company Y, and places this at the top of the list.

[0259] Proposal Phase

[0260] Step 7:

[0261] The server notifies each company's terminal of the generated matching list and the proposal details, including a summary of the proposal details.

[0262] Step 8:

[0263] The device receives the notification and displays the proposal to the company representative. The device uses an emotion engine to evaluate how the user feels about the proposal.

[0264] Step 9:

[0265] Users can review the suggestions and request more information if they are interested, and suggestions may be automatically customized based on the sentiment engine's evaluation.

[0266] Example: The server notifies company X and company Y that "this collaboration will help expand your product offering," the device displays the notification to the person in charge, and the emotion engine evaluates the level of interest.

[0267] Collaboration MTG implementation phase

[0268] Step 10:

[0269] The user sends a request to the server to set up a meeting between the proposed companies. The request is made online.

[0270] Step 11:

[0271] The server coordinates schedules between companies and determines the dates of online meetings. The server uses an emotion engine to evaluate whether the meeting dates are appropriate.

[0272] Step 12:

[0273] The device notifies users of meeting reminders, and during meetings, the emotion engine monitors users' emotions in real time and provides suggestions and support as needed.

[0274] Example: Representatives from Company X and Company Y decide on a meeting date, the server adds it to the schedule, the device sends a reminder, and the emotion engine detects the level of tension during the meeting and provides appropriate support.

[0275] Project Management Phase

[0276] Step 13:

[0277] The server launches a project management tool based on the meeting results, sets tasks and deadlines, and an emotion engine evaluates the suitability of the project progress.

[0278] Step 14:

[0279] Users report their progress on tasks and send updates to the server, and the emotion engine monitors the user's emotions regarding their progress.

[0280] Step 15:

[0281] The server tracks project progress in real time and automatically updates tasks and deadlines as needed, while the emotion engine optimizes projects based on stress and satisfaction.

[0282] Example: A server sets tasks for a collaborative project between companies X and Y, constantly updates the progress, and an emotion engine detects the user's stress and provides reminders and advice.

[0283] Product / service phase

[0284] Step 16:

[0285] The server formulates a product or service development plan according to the project progress, and the emotion engine analyzes user feedback on the development process.

[0286] Step 17:

[0287] Users submit feedback during the development process to the server, and an emotion engine evaluates the emotional response of the feedback.

[0288] Step 18:

[0289] The server conducts test marketing, analyzes the results, and collects feedback. The emotion engine then proposes improvements to the product or service based on the results of the test marketing.

[0290] Step 19:

[0291] The server prepares the product or service with the improvements for final release and launches it in the market, and the emotion engine analyzes the expectations and positive reactions to the final release.

[0292] Example: The server conducts a test sale of a new product jointly developed by Company X and Company Y, analyzes the sales data, makes improvements based on feedback, and an emotion engine analyzes positive reactions to adjust the final release.

[0293] Example 2

[0294] 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."

[0295] Collaboration between companies involves multiple processes, such as information gathering, proposals, meeting arrangements, project management, and product / service creation. However, efficiently managing these processes and increasing the success rate is difficult. Furthermore, there is a need for a method to optimize proposal content and meeting progress by appropriately reflecting user emotions. Currently, there is a lack of systems that automate these processes and optimize them by incorporating emotion analysis.

[0296] 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.

[0297] In this invention, the server includes: means for collecting public company information; means for generating a matching list using an artificial intelligence model based on the collected company information; means for notifying a user terminal of the generated matching list and proposal content; means for setting up online meetings between the proposed companies; means including an emotion engine for analyzing user emotions in real time during the meeting and providing support; means for managing project progress based on the meeting results; and means for launching products or services to the market according to the progress of the project. This makes it possible to automate the collaboration process between companies and optimize the effectiveness of proposals and meetings by analyzing user emotions.

[0298] "Public corporate information" is information about a company that is publicly available through websites, government databases, social media, company reports, etc.

[0299] "Artificial intelligence model" is a general term for machine learning and deep learning models used to generate matching lists of companies based on evaluation factors such as social contribution, synergy effects, feasibility, and interest.

[0300] The "matching list" is a list generated by an artificial intelligence model that evaluates and ranks the potential for collaboration between companies.

[0301] "User terminal" refers to a device used by a user to receive notifications from the system and check proposals and meeting information. Examples of such devices include PCs, smartphones, and tablets.

[0302] An "online meeting" is a type of remote conference that takes place in real time over the Internet, including, for example, video conferencing and web conferencing.

[0303] "Emotion engine" is a general term for emotion analysis software and hardware that analyzes a user's emotional state in real time and provides appropriate support based on the proposal content and meeting progress.

[0304] "Project progress" refers to a series of activities that involve managing collaborative projects discussed between companies to ensure they proceed as planned, and checking the progress of tasks and deliverables.

[0305] "Bringing a product or service to market" means releasing the product or service that is the result of a collaboration project into the marketplace in a form that is available for purchase by the general public.

[0306] "Real-time tracking" means constantly monitoring the progress of a project and updating the information as it progresses to keep it up to date.

[0307] "Test marketing" is the process of selling a product or service on a limited scale on a trial basis before launching it on the market, and then collecting and analyzing the results and feedback from consumers.

[0308] "Emotion analysis results" are data and evaluation information generated based on the user's emotional state analyzed by the emotion engine.

[0309] Overall structure

[0310] This invention is a system that collects publicly available information from companies and uses that information to propose and execute collaborations between companies, supporting the launch of products and services to the market. The system consists of six phases: data collection phase, matching list generation phase, proposal phase, collaboration meeting implementation phase, project management phase, and product / service creation phase. In each phase, an emotion engine is able to recognize the user's emotions and respond appropriately.

[0311] Data Collection Phase

[0312] The server collects publicly available information about companies. It generates a list of companies to collect and collects information from websites, government databases, social media, corporate reports, etc. using web scraping tools such as Scrapy and Beautiful Soup, as well as the Twitter API and Facebook Graph API. This collected information is analyzed using natural language processing tools (NLTK and spaCy), and each company's business details, technology, patents, production locations, financial information, etc. are stored in a cloud database (e.g., AWS RDS).

[0313] Examples:

[0314] The server scrapes product information from Company A's website, collects customer feedback from social media using the Twitter API, analyzes the text using NLTK, and registers it in a database.

[0315] Matching list generation phase

[0316] The server inputs the collected company information into a generative AI model trained with TensorFlow and PyTorch, and generates a matching list of companies based on four evaluation criteria: social contribution, synergy, feasibility, and interest. The matching list is then sorted in order of evaluation, and appropriate proposals are created for each company.

[0317] Examples:

[0318] The server evaluates the synergy effect that would be achieved by integrating Company A's technical information with Company B's sales network, and places it at the top of the matching list.

[0319] Proposal Phase

[0320] The server notifies each company's device of the generated matching list and the proposed content. The device receives the notification and displays the proposed content to the company representative. The user (company representative) checks the proposed content and, if interested, requests further information based on the emotions recognized by the emotion engine (e.g., Affectiva).

[0321] Examples:

[0322] The server notifies Company A and Company B of the proposal that "this collaboration will help expand your products," and the user's device displays the notification to the person in charge, and the emotion engine evaluates satisfaction.

[0323] Collaboration MTG implementation phase

[0324] Users request meetings between proposed companies online. The server coordinates schedules between the companies and determines the date using the Google Calendar API and Microsoft Exchange Web Services (EWS). The device notifies the user of meeting reminders. The emotion engine monitors the user's emotional state in real time during the meeting and provides necessary support.

[0325] Examples:

[0326] Representatives from companies A and B decide on a meeting date, the server adds it to the schedule, the device sends a reminder, and the emotion engine detects tension and makes appropriate suggestions.

[0327] Project Management Phase

[0328] The server launches project management tools such as Jira and Trello based on the meeting results and sets tasks and deadlines. Users report their task progress and send updates to the server. The server tracks project progress in real time and automatically updates tasks and deadlines as needed. The emotion engine monitors users' emotions regarding the project progress and provides appropriate support based on that.

[0329] Examples:

[0330] The server sets tasks for the collaborative project between Company A and Company B, constantly updates the progress, and the emotion engine detects the user's stress and provides reminders and advice.

[0331] Product / service phase

[0332] The server creates a development plan for the product or service as the project progresses. Users provide feedback to the server during the development process. The server conducts test marketing, analyzes the results, and collects feedback. Finally, the server determines the optimal method for releasing the product or service to the market. The emotion engine analyzes the emotional responses of the feedback and uses them to improve the product or service.

[0333] Examples:

[0334] The server conducts test sales of a new product jointly developed by Company A and Company B and analyzes the sales data. The final specifications are determined based on the feedback, and the emotion engine analyzes positive reactions to determine the optimal timing for market launch.

[0335] With this system in place, collaboration between companies can be managed efficiently, and proposals and meeting progress can be optimized using an emotion engine, resulting in more effective market launches of new products and services.

[0336] The flow of the identification process in the second embodiment will be described with reference to FIG.

[0337] Step 1:

[0338] Generate a list of companies

[0339] The server extracts basic information about the target companies (company name, website URL, social media accounts, etc.) from the cloud database and generates a list of companies.

[0340] Input: Cloud Database

[0341] Output: Company list

[0342] Specific operation: Executes an SQL query to extract company information and lists it as a company list.

[0343] Step 2:

[0344] Implementing information gathering

[0345] The server uses web scraping tools such as Scrapy and Beautiful Soup to gather product and technical information from company websites, and also uses the Twitter API and Facebook Graph API to retrieve social media data.

[0346] Input: Company List

[0347] Output: Collected company data

[0348] Specific operation: Based on the company list, web scraping and API calls are performed to collect text data.

[0349] Step 3:

[0350] Data analysis and storage

[0351] The server analyzes the collected data using Natural Language Toolkit (NLTK) and spaCy, and categorizes it into categories such as the company's business operations, technical information, financial status, etc. The analysis results are stored in a cloud database.

[0352] Input: Collected company data

[0353] Output: Analyzed company information database

[0354] Specific operation: Analyzes text data using NLTK and spaCy, extracts business details and technical information, and stores it in a database.

[0355] Step 4:

[0356] Generating a Matching List

[0357] The server inputs the collected company information into a generative AI model trained with TensorFlow and PyTorch, and generates a matching list of companies based on evaluation factors (social contribution, synergy effect, feasibility, and interest).

[0358] Input: Analyzed company information database

[0359] Output: Matching list

[0360] How it works: Company data is input into the AI ​​model to calculate a rating score, which is then sorted by score to create a matching list.

[0361] Step 5:

[0362] Sending proposal notifications

[0363] The server notifies each company's terminal of the generated matching list and the proposed content, and the terminal receives the notification and displays the proposed content to the company representative.

[0364] Input: Matching list, proposal details

[0365] Output: Proposal notification

[0366] Specific operation: The proposal content is sent to the company's device using an API and displayed to the person in charge.

[0367] Step 6:

[0368] Sentiment analysis and request more information

[0369] When the user reviews the suggestions, the emotion engine recognizes the user's emotions in real time and requests more detailed information if the user expresses interest.

[0370] Input: Proposal notification, user reaction

[0371] Output: More information request

[0372] What it does: The emotion engine analyzes the user's facial expressions and displays a request for more information button if they show interest.

[0373] Step 7:

[0374] Setting up an online meeting

[0375] The user requests a meeting between the proposed companies. The server coordinates the schedules between the companies using the Google Calendar API and Microsoft Exchange Web Services (EWS) and determines the date. The device then notifies the user of the meeting reminder.

[0376] Input: Meeting requests, calendar information for each company

[0377] Output: Meeting schedule and reminder notifications

[0378] What it does: Adjust schedules and send reminders using the Google Calendar API and more.

[0379] Step 8:

[0380] Emotion monitoring during meetings

[0381] The emotion engine monitors users' emotions in real time during meetings and provides necessary support.

[0382] Input: User emotion data during the meeting

[0383] Output: Support Suggestion

[0384] Specific behavior: Monitors the user's emotions during meetings and suggests taking a break or relaxing if tension is detected.

[0385] Step 9:

[0386] Use of project management tools

[0387] The server launches a project management tool like Jira or Trello based on the meeting results, sets tasks and deadlines, and users report their progress on the tasks and send updates to the server.

[0388] Inputs: Meeting results, project tasks

[0389] Output: Task management system updates

[0390] Specific actions: Create tasks in Jira or Trello based on the meeting results and enter progress.

[0391] Step 10:

[0392] Project progress tracking and automatic updates

[0393] The server tracks project progress in real time and automatically updates tasks and deadlines as needed. The emotion engine monitors users' emotions regarding the project progress and provides appropriate support.

[0394] Input: Project progress data, user emotion data

[0395] Output: Auto-updated tasks, support suggestions

[0396] Specific behavior: Analyzes project progress data and automatically updates task deadlines. If the emotion engine detects user stress, it suggests relief measures.

[0397] Step 11:

[0398] Bringing products and services to market

[0399] The server formulates a product or service development plan as the project progresses. Users provide feedback to the server during the development process, and the server conducts test marketing and analyzes the results. Ultimately, it determines the optimal method for releasing the product or service to the market. The emotion engine analyzes the emotional responses of the feedback and uses them to improve the product or service.

[0400] Input: Project progress data, feedback data

[0401] Output: Market launch plan, product improvement proposal

[0402] Specific operation: Formulate development plans based on project progress data, analyze test marketing results, and use the emotion engine to evaluate the emotional aspects of feedback and incorporate improvements.

[0403] (Application example 2)

[0404] 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."

[0405] While conventional inter-company collaboration support systems can collect public company information and generate matching lists, they struggle to effectively support the execution of proposed collaborations, ongoing project management, and the final product or service launch. Furthermore, little consideration has been given to monitoring the emotions and stress levels of company personnel in real time and providing appropriate support. The present invention aims to solve these issues and realize more effective and efficient inter-company collaboration.

[0406] 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.

[0407] In this invention, the server includes means for collecting public company information, means for generating a matching list based on the collected company information, means for notifying the generated matching list and proposal details, means for arranging meetings between proposed companies, means for monitoring the emotional state of users during meetings in real time, means for managing project progress based on the meeting results, and means for launching products or services on the market in accordance with the progress of the project. This allows for total support of the entire process of collaboration between companies, and makes it possible to provide appropriate support by monitoring the emotions and stress of company personnel in real time.

[0408] "Methods of collecting publicly available corporate information" refers to methods of collecting various information from corporate websites, government agency databases, social media, etc., using web scraping and API requests.

[0409] "Means for generating a matching list based on company information" refers to a means for using AI models and algorithms to create a list of companies that can cooperate based on collected company information.

[0410] "Means for notifying the generated matching list and proposal contents" refers to means for notifying the generated matching list and proposal contents to the person in charge at each company via email, a messaging app, etc.

[0411] "Means for setting up meetings between proposed companies" refers to means for scheduling online meetings between proposed companies.

[0412] The "means for monitoring the emotional state of a user during a meeting in real time" is a means for using an emotion engine to analyze the emotional state of participants during a meeting in real time and provide support as needed.

[0413] "Method of managing project progress based on meeting results" means using a project management tool to track task progress based on the results of the meeting and automatically update tasks and deadlines as needed.

[0414] "Means for bringing products or services to market as the project progresses" refers to means for formulating a development plan for a product or service as the project progresses, and managing the process up to its release to the market.

[0415] This system collects publicly available information from companies and uses that data to propose and execute collaborations between companies, supporting the launch of products and services to the market. It also uses an emotion engine to recognize user emotions and optimize proposal content and meeting progress. The system of this invention consists of the following phases:

[0416] Data Collection Phase

[0417] The server uses web scraping tools and API requests to collect publicly available information about companies from their websites, government databases, social media, etc. The collected information is stored in a database in the form of each company's business details, technology, patents, production location, financial information, etc. For example, the server uses a library called BeautifulSoup to scrape product information from company websites and uses API requests to collect customer feedback from social media.

[0418] Matching list generation phase

[0419] Based on the collected company information, the server uses an AI model to evaluate the synergy effects and feasibility of collaboration between companies and generate a matching list. This AI model uses a trained generative AI model to score companies based on specific evaluation factors (e.g., social contribution and interest). For example, the server evaluates the synergy effects of integrating the technologies and market networks of Company A and Company B and lists the optimal companies to apply to.

[0420] Proposal Phase

[0421] The server notifies each company's device of the generated matching list and the proposal details. The device receives the notification and displays the proposal details to the company representative. The emotion engine analyzes the representative's emotions in real time and provides feedback on whether the proposal details are appropriate. For example, the server notifies Company A and Company B that "this collaboration will improve logistics efficiency," and the user's device displays the notification. The emotion engine evaluates the representative's reaction and decides the next action.

[0422] Collaboration MTG implementation phase

[0423] The user requests a meeting between the proposed companies. The server coordinates the schedules between the companies and determines the date of the online meeting. In addition, the emotion engine monitors the user's emotional state in real time during the meeting and provides support as needed. For example, representatives from companies A and B decide on a meeting date, the server adds it to the schedule, the device sends a reminder, and the emotion engine detects tension and makes appropriate suggestions.

[0424] Project Management Phase

[0425] The server launches a project management tool based on the meeting results and sets tasks and deadlines. Users report their task progress and send updates to the server. The server tracks project progress in real time and automatically updates tasks and deadlines as needed. The emotion engine monitors the user's emotions regarding project progress and provides appropriate support based on that. For example, the server sets tasks for a collaborative project between companies A and B, and the emotion engine detects the user's stress and provides reminders and advice.

[0426] Product / service phase

[0427] The server formulates a product or service development plan according to the project's progress. Users send feedback during the development process to the server. The server conducts test marketing, analyzes the results, and collects feedback. Finally, the server provides the optimal means for releasing the product or service to the market. The emotion engine analyzes the emotional responses to the feedback and uses them to improve the product or service. For example, the server conducts test sales of a new product jointly developed by Company A and Company B, analyzes the sales data, and the emotion engine analyzes the positive responses and adjusts the final release.

[0428] Example prompt sentence:

[0429] "To improve manufacturing operations, we would like to develop a system that proposes collaboration in the logistics field based on publicly available data from manufacturing company A (https: / / example.com / companyA) and retail company B (https: / / example.com / companyB). How would you go about doing this?"

[0430] In this way, the present invention supports collaboration between companies at each phase, and by utilizing the emotion engine, it is possible to optimize proposal content and meeting progress, thereby making it possible to efficiently bring new products and services to market.

[0431] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[0432] Step 1:

[0433] The server collects publicly available information about companies. This process uses web scraping tools and API requests to gather the necessary data from company websites, government databases, social media, and other sources, and then stores the data in a database. For example, BeautifulSoup can be used to obtain product information from a company's website, and the Twitter API can be used to collect customer feedback. The input is a list of URLs for company websites and social media accounts, and the output is a database containing the collected company information.

[0434] Step 2:

[0435] The server uses an AI model based on the collected company information to generate a matching list. In this process, information about each company (technology, products, financial information, etc.) is entered into the AI ​​model as input data, and the system evaluates synergy effects and feasibility to list the optimal combination of companies. For example, an AI model trained with the Python library scikit-learn can be used to score the effectiveness of collaboration between Company A and Company B. The input is company information stored in the database, and the output is a scored company matching list.

[0436] Step 3:

[0437] The server notifies each company's device of the generated matching list and the proposal details. In this process, the optimal proposal is sent to each company via email or messaging app based on the generated matching list. The emotion engine also analyzes the emotional state of each person in charge and evaluates whether the proposal details are appropriate. For example, an email server can be used to notify each company's person in charge of the proposal details, and an emotion engine (e.g., emotion API) performs emotion analysis. The input is the matching list and email address, and the output is the result of sending the proposal notification and the person in charge's emotional evaluation.

[0438] Step 4:

[0439] The user requests a meeting between the proposed companies. In this process, a company representative requests to set up a meeting, and the server coordinates the schedules between the companies and determines the date of the online meeting. The emotion engine also monitors the user's emotional state during the meeting in real time and provides necessary support. For example, a meeting can be scheduled using the Google Calendar API, and the emotion engine can detect stress or tension during the meeting and provide appropriate advice. The input is the representative's meeting request, and the output is the scheduled meeting date and emotion evaluation data.

[0440] Step 5:

[0441] The server launches a project management tool based on the meeting results and sets tasks and deadlines. This process sets project tasks based on the meeting results and tracks project progress in real time. The emotion engine monitors the emotions of the person in charge as the project progresses and sends reminders and advice as necessary. For example, a project management tool such as JIRA is used to set tasks and track progress, and the emotion engine detects and responds to stress and a drop in motivation. The input is the meeting results and a project task setting request, and the output is the project progress tracking results and emotion evaluation data.

[0442] Step 6:

[0443] The server formulates a product or service development plan as the project progresses, and ultimately launches it on the market. In this process, a development plan is formulated based on the project's progress, test marketing is conducted to collect feedback, and the feedback is analyzed to make final adjustments and release the product to the market in an optimal form. For example, a prototype of a new product is released on a small scale to collect market reactions, and the sentiment engine analyzes positive feedback to formulate a final release plan. The input is the project's progress and the test marketing results, and the output is the final product or service release plan.

[0444] 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.

[0445] 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.

[0446] 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.

[0447] [Second embodiment]

[0448] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0449] 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.

[0450] 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).

[0451] 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.

[0452] 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.

[0453] 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).

[0454] 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.

[0455] 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.

[0456] 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.

[0457] 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.

[0458] 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.

[0459] 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."

[0460] This invention is a system that collects publicly available information from companies, proposes collaboration between companies based on that information, executes it, and supports the launch of products and services on the market. Below, we will create a program for this system and explain its processing in natural language.

[0461] This system is broadly divided into six phases: data collection phase, matching list generation phase, proposal phase, collaborative meeting implementation phase, project management phase, and product / service creation phase.

[0462] Data Collection Phase

[0463] The server collects publicly available information about companies. It generates a list of companies to collect and gathers data from websites, government databases, social media, and company reports. The server periodically retrieves the latest information using web scraping technology and API requests. The collected data is analyzed and a profile of each company (including business activities, technology, patents, production locations, and financial information) is stored in a database.

[0464] Example: A server retrieves a product catalog from Company X's website, parses financial reports retrieved from a government database, and enters them into a database.

[0465] Matching list generation phase

[0466] The server runs an AI model based on the company information stored in the database and generates a matching list of companies based on the evaluation factors of social contribution, synergy effect, feasibility, and interest. The matching list is then sorted in order of evaluation, and appropriate proposals are made for each company.

[0467] Example: Based on the technical information of Company X, the server evaluates the synergy effect when combined with the sales channels of Company Y, and places it at the top of the matching list.

[0468] Proposal Phase

[0469] The server notifies each company's device of the generated matching list and the details of the proposal. The device receives the notification and displays the details of the proposal to the company representative. The user (company representative) can check the proposal and request more information if they are interested.

[0470] Example: The server notifies Company X and Company Y that "Your technology has the potential to create high synergy with Company Y's sales channels," and the terminal displays this information to the person in charge.

[0471] Collaboration MTG implementation phase

[0472] The user requests a meeting between the proposed companies. The server coordinates the schedules between the companies, determines the date and time of the online meeting, and shares relevant information. The device then sends a meeting reminder to the person in charge.

[0473] Example: Representatives from company X and company Y schedule an online meeting, and the server adds it to the schedule and sends reminders.

[0474] Project Management Phase

[0475] The server launches a project management tool based on the meeting results and sets tasks and deadlines. Users report the progress of each task and send updates to the server. The server tracks project progress in real time and automatically updates tasks and deadlines as needed.

[0476] Example: A server sets tasks for a collaborative project between companies X and Y and constantly updates the progress.

[0477] Product / service phase

[0478] The server creates a development plan for the product or service according to the project's progress. Users send feedback to the server during the development process. The server conducts test marketing, analyzes the results, and collects feedback. Finally, the server provides the optimal means for releasing the product or service to the market.

[0479] Example: The server conducts test sales of a new product jointly developed by Company X and Company Y, analyzes the sales data, proposes improvements based on the feedback, and prepares for another test marketing.

[0480] In this way, the system supports collaboration between companies at each phase, streamlining the launch of new products and services into the market.

[0481] The processing flow will be explained below.

[0482] Data Collection Phase

[0483] Step 1:

[0484] The server generates a list of companies from which data is collected, categorizing the list by company category and industry.

[0485] Step 2:

[0486] The server performs web scraping and API requests targeting corporate websites, government databases, social media, corporate reports, and more.

[0487] Step 3:

[0488] The server analyzes the collected information and extracts information such as each company's business activities, technology, patents, production locations, and financial information, and stores it in a database.

[0489] Example: The server scrapes product information from Company X's website, collects customer feedback from social media, performs text analysis on it, and registers it in a database.

[0490] Matching list generation phase

[0491] Step 4:

[0492] The server inputs company information stored in a database into the AI ​​model.

[0493] Step 5:

[0494] The server generates a matching list of companies based on the evaluation factors of social contribution, synergy effect, feasibility, and interest.

[0495] Step 6:

[0496] The server sorts the generated matching list in order of rating and creates appropriate proposals for each company.

[0497] Example: The server evaluates the synergy benefits of combining the technical information of company X with the sales network of company Y and places this at the top of the list.

[0498] Proposal Phase

[0499] Step 7:

[0500] The server notifies the target company's terminal of the generated matching list and the contents of the proposal.

[0501] Step 8:

[0502] The terminal receives the notification and displays the proposal to the company representative.

[0503] Step 9:

[0504] The user reviews the offer and, if interested, requests more information.

[0505] Example: The server notifies Company X and Company Y that "This collaboration will help expand your product offerings," and the terminal displays the notification to the person in charge.

[0506] Collaboration MTG implementation phase

[0507] Step 10:

[0508] The user sends a request to the server to set up a meeting between the proposed businesses.

[0509] Step 11:

[0510] The server coordinates schedules between companies and determines dates for online meetings.

[0511] Step 12:

[0512] The terminal notifies the user of meeting reminders.

[0513] Example: Company X and Company Y decide on a meeting date, the server adds it to the schedule, and the device sends a reminder.

[0514] Project Management Phase

[0515] Step 13:

[0516] The server starts a project management tool based on the meeting results and sets tasks and deadlines.

[0517] Step 14:

[0518] Users report their progress on tasks and send updates to the server.

[0519] Step 15:

[0520] The server tracks project progress in real time and automatically updates tasks and deadlines as needed.

[0521] Example: The server sets tasks for a collaborative project between company X and company Y and constantly updates the progress.

[0522] Product / service phase

[0523] Step 16:

[0524] The server formulates a product or service development plan according to the progress of the project.

[0525] Step 17:

[0526] Users provide feedback to the server during the development process.

[0527] Step 18:

[0528] The server conducts test marketing, analyzes the results, and collects feedback.

[0529] Step 19:

[0530] The server prepares the product or service incorporating the improvements for final release and brings it to market.

[0531] Example: The server conducts test sales of a new product jointly developed by Company X and Company Y, analyzes the sales data, and makes improvements and finalizes the specifications based on the feedback.

[0532] Example 1

[0533] 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."

[0534] There is a need to efficiently realize collaboration between companies and quickly bring new products and services to market. However, collecting public information about companies and finding the most suitable partner companies takes a great deal of time and effort. In addition, meeting arrangements and project management are often done manually, making efficient operations difficult. Furthermore, understanding the progress of projects in real time and receiving appropriate feedback is also a challenge.

[0535] 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.

[0536] In this invention, the server includes a means for collecting publicly available company information, a means for generating a matching list using a generative AI model based on the collected company information, and a means for notifying the company terminal of the generated matching list and proposal details. This allows for the rapid identification of optimal partnerships between companies and efficient meeting arrangements and project management. It also enables real-time tracking of project progress, analysis of test marketing results, and feedback collection, significantly improving overall efficiency.

[0537] "Public corporate information" refers to data published by companies, such as websites, government databases, social media, and company reports.

[0538] A "generative AI model" refers to an artificial intelligence algorithm that generates matching lists between companies based on collected data.

[0539] A "matching list" is a list generated to evaluate corporate information and propose optimal partnerships.

[0540] "Proposal content" refers to content that includes the benefits of collaboration between companies and specific proposals based on the generated matching list.

[0541] "Terminal" refers to a device (e.g., PC, tablet, smartphone) used by company personnel to receive and check information.

[0542] "Meeting request methods" refers to the methods by which company representatives can request and schedule meetings with other companies.

[0543] "Project Task" refers to the specific work or activities involved in a collaborative project between companies.

[0544] "Project Progression" refers to the process of how collaboration between companies is progressing.

[0545] "Development Plan" means a detailed plan for the development of a new product or service.

[0546] "Test marketing" refers to the activity of actually selling a new product or service on a trial basis in the market and analyzing the results.

[0547] This invention is a system that collects publicly available information from companies, proposes collaboration between companies based on that information, executes it, and supports the launch of products and services on the market. Specifically, this system is realized using the following hardware and software.

[0548] This system is broadly divided into six phases: data collection phase, matching list generation phase, proposal phase, collaboration meeting implementation phase, project management phase, and product / service creation phase. The processing in each phase is explained below.

[0549] Data Collection Phase

[0550] The server collects publicly available information about companies. It generates a list of target companies and uses web scraping technology and API requests to gather data from websites, government databases, social media, and company reports. It also periodically retrieves updated information, analyzes the collected data, and stores each company's profile (business operations, technology, patents, production locations, financial information, etc.) in a database.

[0551] Example: A server scrapes product catalogs from websites of companies in a specific region using the filtering criteria "technology companies" in the technology field, while simultaneously retrieving financial reports from a government financial database and registering them as profile information in the database.

[0552] Matching list generation phase

[0553] The server runs a generative AI model based on company information stored in a database, generating a matching list of companies based on evaluation factors such as social contribution, synergy effects, feasibility, and interest. The matching list is then sorted by evaluation, and appropriate proposals are made for each company.

[0554] Example: The server inputs a prompt such as "Please suggest the best partner company for Company X's technology" into the generative AI model, and the matching list that evaluates the synergy between Company X and Company Y is ranked at the top.

[0555] Proposal Phase

[0556] The server notifies each company's device of the generated matching list and the details of the proposal. The device receives the notification and displays the details of the proposal to the company representative. The user (company representative) can check the proposal and request more information if they are interested.

[0557] Example: The server sends a proposal such as "Your company's technology has the potential to create high synergy with Company Y's sales channels" to the terminals of Company X and Company Y, and displays it to the person in charge.

[0558] Collaboration Meeting Implementation Phase

[0559] The user requests a meeting between the proposed companies. The server coordinates the schedules between the companies and determines the date and time of the online meeting. The device then sends a meeting reminder to the person in charge.

[0560] Example: A person from company X makes a request to schedule an online meeting, the server coordinates the availability of a person from company Y, sets the appropriate date and time, and sends a reminder.

[0561] Project Management Phase

[0562] The server launches a project management tool based on the meeting results and sets each task and its deadline. Users report the progress of each task and send updates to the server. The server tracks the project progress in real time and automatically updates tasks and deadlines as needed.

[0563] Example: The server launches a task management tool and adds tasks such as "market research" and "technology development." The user reports progress and updates the record to say "market research completed."

[0564] Product / service phase

[0565] The server creates a product or service development plan according to the project progress. Users send feedback during the development process to the server. The server conducts test marketing, analyzes the results, and collects feedback. Finally, it provides the optimal means for releasing the product or service to the market.

[0566] Example: A server tests a new product in a specific region, analyzes the sales data, proposes improvements based on the results of the analysis, and prepares for another test marketing of the improved product.

[0567] As described above, this system performs detailed and specific processing at each phase, supporting collaboration between companies and streamlining the launch of new products and services into the market.

[0568] The flow of the identification process in the first embodiment will be described with reference to FIG.

[0569] Step 1:

[0570] The server collects publicly available information about companies. It receives filtering criteria (such as industry, size, and region) as input and generates a list of companies to collect as output. Specifically, it uses web scraping technology and API requests to collect data from company websites and government agency databases. This data is then stored in a database as a profile for each company, including production location, technology, patents, and financial information.

[0571] Step 2:

[0572] The server runs a generative AI model based on company information stored in a database. The input here is company profile data, and the output is a matching list based on evaluation factors such as social contribution, synergy effects, feasibility, and interest. Specifically, a prompt such as "Please suggest partner companies that are best suited to Company X's technology" is input into the generative AI model, and the AI ​​automatically generates a matching list.

[0573] Step 3:

[0574] The server notifies the company's device of the generated matching list and proposal details. It receives the matching list and proposal details as input and displays them on the device in a format that can be checked by the company representative as output. Specific operations include sending information using an email API or notification system, and displaying it on the device as a pop-up notification, etc.

[0575] Step 4:

[0576] The user (company representative) receives the notification and checks the proposal. If interested, the user requests more detailed information. The input is the notification of the proposal, and the output is a request for more information sent to the server. The specific operation is to click the "Request more information" button on the terminal's user interface.

[0577] Step 5:

[0578] A user requests a meeting between the proposed companies. As input, the user sends the meeting request information (date, time, participants, etc.) to the server, and as output, the user receives the adjusted schedule. Specifically, a company representative sends a request using an online scheduling system, and the server adjusts the schedule between the companies and sets an appropriate date and time.

[0579] Step 6:

[0580] The server launches a project management tool based on the meeting results and sets each task and its deadline. It receives the meeting deliverables and task information as input, and sets tasks and deadlines in the project management tool as output. Specifically, the server launches a task management tool (e.g., JIRA) and adds tasks such as "market research" and "technology development."

[0581] Step 7:

[0582] Users report the progress of each task and send updates to the server. The input is task progress information, and the output is a project management tool with updated progress. Specifically, the person in charge enters progress information such as "market research completed," and the server reflects it in the project management tool.

[0583] Step 8:

[0584] The server formulates a product or service development plan based on the project progress and conducts test marketing. It receives progress data and marketing plan information as input, and obtains test marketing results and feedback as output. Specifically, the server conducts test sales of a new product in a specific region, analyzes the data, and creates improvement proposals.

[0585] (Application example 1)

[0586] 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."

[0587] Conventional inter-company collaboration proposal and execution support systems collect and analyze publicly available information from companies to generate matching lists, but they are unable to take into account data on manufacturing processes and production equipment. This makes it difficult to make optimal proposals for actual factory operations. Furthermore, they lack the ability to easily monitor and automatically update the real-time progress of manufacturing processes, making efficient project management difficult. Furthermore, there is a lack of means to forecast demand and quickly and accurately gather feedback when test selling and launching newly developed products and services.

[0588] 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.

[0589] In this invention, the server includes means for collecting operation data from factory automation equipment or autonomous machines, means for analyzing the collected operation data and generating company profiles, means for proposing corporate collaborations based on the generated profiles, means for notifying the generated matching list and proposal details, means for setting up online meetings between proposed companies, means for managing project progress based on the meeting results and launching products or services as the project progresses, means for test selling new products using the collected data and providing feedback, and means for supporting product launches. This enables optimal collaboration proposals that take into account automation equipment data, enabling real-time monitoring of manufacturing processes and efficient project management. It also enables rapid and accurate feedback collection when launching new products or services.

[0590] "Corporate public information" refers to information that a company makes publicly available through its website, government agency databases, social media, corporate reports, etc.

[0591] The "matching list" is a list of potential collaborations between companies that is generated by running an AI model based on collected company information and evaluating factors such as social contribution, synergy effects, feasibility, and interest.

[0592] "Proposal content" refers to details and recommendations for collaboration between companies generated by the server based on the matching list.

[0593] The means for setting up a "meeting" is a method for the proposed companies to coordinate a date and time for a meeting online or offline and share information.

[0594] A "means for managing project progress" is a method for tracking project tasks, deadlines, and progress in real time based on meeting results, and automatically updating as needed.

[0595] "Factory automation equipment or autonomously operating machines" means machines or robots that automate manufacturing processes within a factory and are capable of operating independently.

[0596] "Operation data" refers to recorded information such as the operating status, performance indicators, and operating hours of automated equipment or autonomously operating machines in a factory.

[0597] A "company profile" is a detailed record of a company's business activities, technology, patents, production locations, financial information, etc., based on an analysis of collected operational data and publicly available information.

[0598] An "online meeting" is a meeting between companies held over the Internet, and refers to a means that allows for the coordination of dates and times and the sharing of information.

[0599] A "new product test launch" is the process of launching a newly developed product or service in a limited market and collecting response and sales data.

[0600] "Feedback" refers to information regarding evaluations and areas for improvement obtained during trial sales and project implementation.

[0601] "Go-to-market aids" are the methods and tools used to formally introduce new products and services to the market and promote their sales.

[0602] This invention is a system that collects publicly available information from companies, proposes collaboration between companies based on that information, executes it, and supports the launch of products and services on the market. Below, we will create a program for this system and explain its processing in natural language.

[0603] The system consists of three entities: a server, a terminal, and a user.

[0604] Data Collection Phase

[0605] The server collects publicly available information about companies. It generates a list of target companies and gathers data from websites, government databases, social media, and company reports. Specifically, it uses web scraping technology and API requests to periodically retrieve the latest information. The collected data is analyzed and a profile of each company (including business activities, technology, patents, production locations, and financial information) is stored in a database.

[0606] Example: A server retrieves a product catalog from Company A's website, parses financial reports retrieved from a government database, and enters them into a database.

[0607] Matching list generation phase

[0608] The server runs a generative AI model based on the company information stored in the database, and generates a matching list of companies based on the evaluation factors of social contribution, synergy effect, feasibility, and interest. The generated matching list is sorted in order of evaluation, and appropriate proposals are made for each company.

[0609] Example: The server evaluates the synergy effect when combining company A's technical information with company B's sales channels, and places it at the top of the matching list.

[0610] Proposal Phase

[0611] The server notifies each company's device of the generated matching list and the details of the proposal. The device receives the notification and displays the details of the proposal to the company representative. The user (company representative) can check the proposal and request more information if they are interested.

[0612] Example: The server notifies Company A and Company B that "Your technology has the potential to create high synergy with Company B's sales channels," and the terminal displays this information to the person in charge.

[0613] Collaboration MTG implementation phase

[0614] The user requests a meeting between the proposed companies. The server coordinates the schedules between the companies, determines the date and time of the online meeting, and shares relevant information. The device then sends a meeting reminder to the person in charge.

[0615] Example: Representatives from company A and company B schedule an online meeting, and the server adds it to the schedule and sends reminders.

[0616] Project Management Phase

[0617] The server launches a project management tool based on the meeting results and sets tasks and deadlines. Users report the progress of each task and send updates to the server. The server tracks project progress in real time and automatically updates tasks and deadlines as needed.

[0618] Example: The server sets tasks for a collaborative project between companies A and B and constantly updates the progress.

[0619] Product / service phase

[0620] The server creates a development plan for the product or service according to the project's progress. Users send feedback to the server during the development process. The server conducts test marketing, analyzes the results, and collects feedback. Finally, the server provides the optimal means for releasing the product or service to the market.

[0621] Example: The server conducts test sales of a new product jointly developed by Company A and Company B, analyzes the sales data, proposes improvements based on the feedback, and prepares for another test marketing.

[0622] Hardware and software used

[0623] The system uses the following hardware and software:

[0624] Hardware: Smartphone, head-mounted display (e.g., Oculus Quest 2)

[0625] Software: Python web scraping (BeautifulSoup), HTTP request library (requests), data analysis (pandas), generative AI models

[0626] Prompt Sentence Examples

[0627] An example of a prompt to input to a generative AI model is as follows:

[0628] "Develop a system that evaluates synergy between companies based on technical information and generates optimal collaboration proposals."

[0629] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[0630] Step 1: Data collection

[0631] The server collects publicly available information about companies. It generates a list of companies to collect and obtains data from websites, government databases, social media, and company reports. This is done using web scraping technology (BeautifulSoup) and API requests (requests library). The input is various publicly available information, and the output is a profile of each company (business details, technology, patents, production locations, financial information, etc.).

[0632] Step 2: Save to database

[0633] The server analyzes the collected data and stores it in a database. First, it analyzes the acquired HTML data and API responses to extract detailed information about each company. Next, it structures the extracted data and registers it in the database. The input is the analyzed information, and the output is the company profile registered in the database.

[0634] Step 3: Run the AI ​​model

[0635] The server runs a generative AI model based on the company information stored in the database. Specifically, it generates a matching list of companies based on the evaluation factors of social contribution, synergy effects, feasibility, and interest. The input is the company profile, and the output is a matching list of companies. At this stage, the model analyzes and evaluates the data.

[0636] Step 4: Proposal generation and notification

[0637] The server creates an optimal proposal for each company based on the generated matching list and notifies the device. The input is the matching list, and the output is a proposal notification sent to each company's device. The notification content includes details of the proposed collaboration, which the device displays to the person in charge.

[0638] Step 5: Meeting Schedule

[0639] The user reviews the proposal and, if interested, requests more information and a meeting from the server. The server coordinates schedules between the companies and determines the date and time of the online meeting. The input is the meeting request, and the output is the scheduled meeting date and time and related information. The terminal notifies the person in charge of the meeting with a reminder.

[0640] Step 6: Project Management

[0641] The server launches the project management tool based on the meeting results and sets tasks and deadlines. The input is the meeting results and the output is the set tasks and deadlines. Users report the progress of each task and send updates to the server. The server tracks the project progress in real time and automatically updates tasks and deadlines as needed.

[0642] Step 7: Product / Service

[0643] The server formulates a product or service development plan according to the project progress. The input is project progress information, and the output is a development plan. Users send feedback during the development process to the server, which then conducts test marketing. The test marketing results are analyzed, feedback is collected, and the server provides the optimal means for finally releasing the product or service to the market.

[0644] Through the above processing steps, the system of the present invention efficiently supports everything from collaboration between companies to the launch of products and services on the market.

[0645] 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.

[0646] This system collects publicly available information from companies and uses that information to propose and execute collaborations between companies, supporting the launch of products and services to market. Furthermore, by incorporating an emotion engine, it is possible to recognize user emotions and optimize proposal content and meeting progress based on those emotions. Below, we will create a program for this system and explain its processing in natural language.

[0647] This system is broadly divided into six phases: data collection phase, matching list generation phase, proposal phase, collaborative meeting implementation phase, project management phase, and product / service creation phase. In each phase, an emotion engine recognizes the user's emotions and responds appropriately.

[0648] Data Collection Phase

[0649] The server collects publicly available information about companies, generates a list of companies to collect, and performs web scraping and API requests targeting websites, government databases, social media, corporate reports, etc. The server analyzes the collected information and stores each company's business details, technology, patents, production locations, financial information, etc. in a database.

[0650] Example: The server scrapes product information from Company X's website, collects customer feedback from social media, performs text analysis on it, and registers it in a database.

[0651] Matching list generation phase

[0652] The server inputs the company information stored in the database into the AI ​​model and generates a matching list of companies based on the evaluation factors of social contribution, synergy effect, feasibility, and interest. The generated matching list is sorted in order of evaluation, and appropriate proposals are made for each company.

[0653] Example: The server evaluates the synergy benefits of combining the technical information of company X with the sales network of company Y and places this at the top of the list.

[0654] Proposal Phase

[0655] The server notifies each company's device of the generated matching list and the proposed content. The device receives the notification and displays the proposed content to the company representative. The user (company representative) checks the proposed content and, based on the emotions recognized by the emotion engine, requests more detailed information if they are interested.

[0656] Example: The server notifies Company X and Company Y that "This collaboration will help expand your product offerings," and the user's device displays the notification to the person in charge, and the emotion engine evaluates satisfaction.

[0657] Collaboration MTG implementation phase

[0658] The user requests a meeting between the proposed companies. The server coordinates the schedules between the companies and determines the date of the online meeting. The device notifies the user of meeting reminders. The emotion engine monitors the user's emotional state in real time during the meeting and provides necessary support.

[0659] Example: Representatives from Company X and Company Y decide on a date for a meeting, the server adds it to the schedule, the device sends a reminder, and the emotion engine detects tension and makes appropriate suggestions.

[0660] Project Management Phase

[0661] The server launches a project management tool based on the meeting results and sets tasks and deadlines. Users report their task progress and send updates to the server. The server tracks project progress in real time and automatically updates tasks and deadlines as needed. The emotion engine monitors users' emotions regarding project progress and provides appropriate support based on that.

[0662] Example: A server sets tasks for a collaborative project between companies X and Y, constantly updates the progress, and an emotion engine detects the user's stress and provides reminders and advice.

[0663] Product / service phase

[0664] The server formulates a product or service development plan according to the project's progress. Users send feedback during the development process to the server. The server conducts test marketing, analyzes the results, and collects feedback. Finally, the server provides the optimal means for releasing the product or service to the market. The emotion engine analyzes the emotional reactions of the feedback and uses them to improve the product or service.

[0665] Example: The server conducts test sales of a new product jointly developed by Company X and Company Y, analyzes the sales data, and makes improvements and finalizes the specifications based on the feedback. The emotion engine analyzes positive reactions and adjusts the final release.

[0666] In this way, this system supports collaboration between companies at each phase, and by utilizing an emotion engine, it optimizes proposal content and meeting progress, thereby streamlining the launch of new products and services into the market.

[0667] The processing flow will be explained below.

[0668] Data Collection Phase

[0669] Step 1:

[0670] The server generates a list of companies to collect data from. First, it selects target companies based on their industry and market needs and adds them to the list.

[0671] Step 2:

[0672] The servers perform web scraping and API requests targeting company websites, government databases, social media, company reports, etc. The information collection is carried out periodically.

[0673] Step 3:

[0674] The server analyzes the collected information and stores each company's business details, technology, patents, production locations, financial information, etc. in a database. The analysis uses technologies such as natural language processing.

[0675] Example: The server accesses Company X's website, scrapes the latest product information, analyzes customer reviews on social media, and registers this information in a database.

[0676] Matching list generation phase

[0677] Step 4:

[0678] The server inputs company information stored in a database into the AI ​​model, which learns from the existing data.

[0679] Step 5:

[0680] The server generates a matching list of companies based on the evaluation factors of social contribution, synergy, feasibility, and interest, with each factor being evaluated according to a pre-set weighting.

[0681] Step 6:

[0682] The server sorts the generated matching list in order of evaluation and creates appropriate proposals for each company, including specific collaboration benefits and prospects.

[0683] Example: The server calculates the synergy effect of combining the technological capabilities of company X with the sales network of company Y, and places this at the top of the list.

[0684] Proposal Phase

[0685] Step 7:

[0686] The server notifies each company's terminal of the generated matching list and the proposal details, including a summary of the proposal details.

[0687] Step 8:

[0688] The device receives the notification and displays the proposal to the company representative. The device uses an emotion engine to evaluate how the user feels about the proposal.

[0689] Step 9:

[0690] Users can review the suggestions and request more information if they are interested, and suggestions may be automatically customized based on the sentiment engine's evaluation.

[0691] Example: The server notifies company X and company Y that "this collaboration will help expand your product offering," the device displays the notification to the person in charge, and the emotion engine evaluates the level of interest.

[0692] Collaboration MTG implementation phase

[0693] Step 10:

[0694] The user sends a request to the server to set up a meeting between the proposed companies. The request is made online.

[0695] Step 11:

[0696] The server coordinates schedules between companies and determines the dates of online meetings. The server uses an emotion engine to evaluate whether the meeting dates are appropriate.

[0697] Step 12:

[0698] The device notifies users of meeting reminders, and during meetings, the emotion engine monitors users' emotions in real time and provides suggestions and support as needed.

[0699] Example: Representatives from Company X and Company Y decide on a meeting date, the server adds it to the schedule, the device sends a reminder, and the emotion engine detects the level of tension during the meeting and provides appropriate support.

[0700] Project Management Phase

[0701] Step 13:

[0702] The server launches a project management tool based on the meeting results, sets tasks and deadlines, and an emotion engine evaluates the suitability of the project progress.

[0703] Step 14:

[0704] Users report their progress on tasks and send updates to the server, and the emotion engine monitors the user's emotions regarding their progress.

[0705] Step 15:

[0706] The server tracks project progress in real time and automatically updates tasks and deadlines as needed, while the emotion engine optimizes projects based on stress and satisfaction.

[0707] Example: A server sets tasks for a collaborative project between companies X and Y, constantly updates the progress, and an emotion engine detects the user's stress and provides reminders and advice.

[0708] Product / service phase

[0709] Step 16:

[0710] The server formulates a product or service development plan according to the project progress, and the emotion engine analyzes user feedback on the development process.

[0711] Step 17:

[0712] Users submit feedback during the development process to the server, and an emotion engine evaluates the emotional response of the feedback.

[0713] Step 18:

[0714] The server conducts test marketing, analyzes the results, and collects feedback. The emotion engine then proposes improvements to the product or service based on the results of the test marketing.

[0715] Step 19:

[0716] The server prepares the product or service with the improvements for final release and launches it in the market, and the emotion engine analyzes the expectations and positive reactions to the final release.

[0717] Example: The server conducts a test sale of a new product jointly developed by Company X and Company Y, analyzes the sales data, makes improvements based on feedback, and an emotion engine analyzes positive reactions to adjust the final release.

[0718] Example 2

[0719] 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."

[0720] Collaboration between companies involves multiple processes, such as information gathering, proposals, meeting arrangements, project management, and product / service creation. However, efficiently managing these processes and increasing the success rate is difficult. Furthermore, there is a need for a method to optimize proposal content and meeting progress by appropriately reflecting user emotions. Currently, there is a lack of systems that automate these processes and optimize them by incorporating emotion analysis.

[0721] 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.

[0722] In this invention, the server includes: means for collecting public company information; means for generating a matching list using an artificial intelligence model based on the collected company information; means for notifying a user terminal of the generated matching list and proposal content; means for setting up online meetings between the proposed companies; means including an emotion engine for analyzing user emotions in real time during the meeting and providing support; means for managing project progress based on the meeting results; and means for launching products or services to the market according to the progress of the project. This makes it possible to automate the collaboration process between companies and optimize the effectiveness of proposals and meetings by analyzing user emotions.

[0723] "Public corporate information" is information about a company that is publicly available through websites, government databases, social media, company reports, etc.

[0724] "Artificial intelligence model" is a general term for machine learning and deep learning models used to generate matching lists of companies based on evaluation factors such as social contribution, synergy effects, feasibility, and interest.

[0725] The "matching list" is a list generated by an artificial intelligence model that evaluates and ranks the potential for collaboration between companies.

[0726] "User terminal" refers to a device used by a user to receive notifications from the system and check proposals and meeting information. Examples of such devices include PCs, smartphones, and tablets.

[0727] An "online meeting" is a type of remote conference that takes place in real time over the Internet, including, for example, video conferencing and web conferencing.

[0728] "Emotion engine" is a general term for emotion analysis software and hardware that analyzes a user's emotional state in real time and provides appropriate support based on the proposal content and meeting progress.

[0729] "Project progress" refers to a series of activities that involve managing collaborative projects discussed between companies to ensure they proceed as planned, and checking the progress of tasks and deliverables.

[0730] "Bringing a product or service to market" means releasing the product or service that is the result of a collaboration project into the marketplace in a form that is available for purchase by the general public.

[0731] "Real-time tracking" means constantly monitoring the progress of a project and updating the information as it progresses to keep it up to date.

[0732] "Test marketing" is the process of selling a product or service on a limited scale on a trial basis before launching it on the market, and then collecting and analyzing the results and feedback from consumers.

[0733] "Emotion analysis results" are data and evaluation information generated based on the user's emotional state analyzed by the emotion engine.

[0734] Overall structure

[0735] This invention is a system that collects publicly available information from companies and uses that information to propose and execute collaborations between companies, supporting the launch of products and services to the market. The system consists of six phases: data collection phase, matching list generation phase, proposal phase, collaboration meeting implementation phase, project management phase, and product / service creation phase. In each phase, an emotion engine is able to recognize the user's emotions and respond appropriately.

[0736] Data Collection Phase

[0737] The server collects publicly available information about companies. It generates a list of companies to collect and collects information from websites, government databases, social media, corporate reports, etc. using web scraping tools such as Scrapy and Beautiful Soup, as well as the Twitter API and Facebook Graph API. This collected information is analyzed using natural language processing tools (NLTK and spaCy), and each company's business details, technology, patents, production locations, financial information, etc. are stored in a cloud database (e.g., AWS RDS).

[0738] Examples:

[0739] The server scrapes product information from Company A's website, collects customer feedback from social media using the Twitter API, analyzes the text using NLTK, and registers it in a database.

[0740] Matching list generation phase

[0741] The server inputs the collected company information into a generative AI model trained with TensorFlow and PyTorch, and generates a matching list of companies based on four evaluation criteria: social contribution, synergy, feasibility, and interest. The matching list is then sorted in order of evaluation, and appropriate proposals are created for each company.

[0742] Examples:

[0743] The server evaluates the synergy effect that would be achieved by integrating Company A's technical information with Company B's sales network, and places it at the top of the matching list.

[0744] Proposal Phase

[0745] The server notifies each company's device of the generated matching list and the proposed content. The device receives the notification and displays the proposed content to the company representative. The user (company representative) checks the proposed content and, if interested, requests further information based on the emotions recognized by the emotion engine (e.g., Affectiva).

[0746] Examples:

[0747] The server notifies Company A and Company B of the proposal that "this collaboration will help expand your products," and the user's device displays the notification to the person in charge, and the emotion engine evaluates satisfaction.

[0748] Collaboration MTG implementation phase

[0749] Users request meetings between proposed companies online. The server coordinates schedules between the companies and determines the date using the Google Calendar API and Microsoft Exchange Web Services (EWS). The device notifies the user of meeting reminders. The emotion engine monitors the user's emotional state in real time during the meeting and provides necessary support.

[0750] Examples:

[0751] Representatives from companies A and B decide on a meeting date, the server adds it to the schedule, the device sends a reminder, and the emotion engine detects tension and makes appropriate suggestions.

[0752] Project Management Phase

[0753] The server launches project management tools such as Jira and Trello based on the meeting results and sets tasks and deadlines. Users report their task progress and send updates to the server. The server tracks project progress in real time and automatically updates tasks and deadlines as needed. The emotion engine monitors users' emotions regarding the project progress and provides appropriate support based on that.

[0754] Examples:

[0755] The server sets tasks for the collaborative project between Company A and Company B, constantly updates the progress, and the emotion engine detects the user's stress and provides reminders and advice.

[0756] Product / service phase

[0757] The server creates a development plan for the product or service as the project progresses. Users provide feedback to the server during the development process. The server conducts test marketing, analyzes the results, and collects feedback. Finally, the server determines the optimal method for releasing the product or service to the market. The emotion engine analyzes the emotional responses of the feedback and uses them to improve the product or service.

[0758] Examples:

[0759] The server conducts test sales of a new product jointly developed by Company A and Company B and analyzes the sales data. The final specifications are determined based on the feedback, and the emotion engine analyzes positive reactions to determine the optimal timing for market launch.

[0760] With this system in place, collaboration between companies can be managed efficiently, and proposals and meeting progress can be optimized using an emotion engine, resulting in more effective market launches of new products and services.

[0761] The flow of the identification process in the second embodiment will be described with reference to FIG.

[0762] Step 1:

[0763] Generate a list of companies

[0764] The server extracts basic information about the target companies (company name, website URL, social media accounts, etc.) from the cloud database and generates a list of companies.

[0765] Input: Cloud Database

[0766] Output: Company list

[0767] Specific operation: Executes an SQL query to extract company information and lists it as a company list.

[0768] Step 2:

[0769] Implementing information gathering

[0770] The server uses web scraping tools such as Scrapy and Beautiful Soup to gather product and technical information from company websites, and also uses the Twitter API and Facebook Graph API to retrieve social media data.

[0771] Input: Company List

[0772] Output: Collected company data

[0773] Specific operation: Based on the company list, web scraping and API calls are performed to collect text data.

[0774] Step 3:

[0775] Data analysis and storage

[0776] The server analyzes the collected data using Natural Language Toolkit (NLTK) and spaCy, and categorizes it into categories such as the company's business operations, technical information, financial status, etc. The analysis results are stored in a cloud database.

[0777] Input: Collected company data

[0778] Output: Analyzed company information database

[0779] Specific operation: Analyzes text data using NLTK and spaCy, extracts business details and technical information, and stores it in a database.

[0780] Step 4:

[0781] Generating a Matching List

[0782] The server inputs the collected company information into a generative AI model trained with TensorFlow and PyTorch, and generates a matching list of companies based on evaluation factors (social contribution, synergy effect, feasibility, and interest).

[0783] Input: Analyzed company information database

[0784] Output: Matching list

[0785] How it works: Company data is input into the AI ​​model to calculate a rating score, which is then sorted by score to create a matching list.

[0786] Step 5:

[0787] Sending proposal notifications

[0788] The server notifies each company's terminal of the generated matching list and the proposed content, and the terminal receives the notification and displays the proposed content to the company representative.

[0789] Input: Matching list, proposal details

[0790] Output: Proposal notification

[0791] Specific operation: The proposal content is sent to the company's device using an API and displayed to the person in charge.

[0792] Step 6:

[0793] Sentiment analysis and request more information

[0794] When the user reviews the suggestions, the emotion engine recognizes the user's emotions in real time and requests more detailed information if the user expresses interest.

[0795] Input: Proposal notification, user reaction

[0796] Output: More information request

[0797] What it does: The emotion engine analyzes the user's facial expressions and displays a request for more information button if they show interest.

[0798] Step 7:

[0799] Setting up an online meeting

[0800] The user requests a meeting between the proposed companies. The server coordinates the schedules between the companies using the Google Calendar API and Microsoft Exchange Web Services (EWS) and determines the date. The device then notifies the user of the meeting reminder.

[0801] Input: Meeting requests, calendar information for each company

[0802] Output: Meeting schedule and reminder notifications

[0803] What it does: Adjust schedules and send reminders using the Google Calendar API and more.

[0804] Step 8:

[0805] Emotion monitoring during meetings

[0806] The emotion engine monitors users' emotions in real time during meetings and provides necessary support.

[0807] Input: User emotion data during the meeting

[0808] Output: Support Suggestion

[0809] Specific behavior: Monitors the user's emotions during meetings and suggests taking a break or relaxing if tension is detected.

[0810] Step 9:

[0811] Use of project management tools

[0812] The server launches a project management tool like Jira or Trello based on the meeting results, sets tasks and deadlines, and users report their progress on the tasks and send updates to the server.

[0813] Inputs: Meeting results, project tasks

[0814] Output: Task management system updates

[0815] Specific actions: Create tasks in Jira or Trello based on the meeting results and enter progress.

[0816] Step 10:

[0817] Project progress tracking and automatic updates

[0818] The server tracks project progress in real time and automatically updates tasks and deadlines as needed. The emotion engine monitors users' emotions regarding the project progress and provides appropriate support.

[0819] Input: Project progress data, user emotion data

[0820] Output: Auto-updated tasks, support suggestions

[0821] Specific behavior: Analyzes project progress data and automatically updates task deadlines. If the emotion engine detects user stress, it suggests relief measures.

[0822] Step 11:

[0823] Bringing products and services to market

[0824] The server formulates a product or service development plan as the project progresses. Users provide feedback to the server during the development process, and the server conducts test marketing and analyzes the results. Ultimately, it determines the optimal method for releasing the product or service to the market. The emotion engine analyzes the emotional responses of the feedback and uses them to improve the product or service.

[0825] Input: Project progress data, feedback data

[0826] Output: Market launch plan, product improvement proposal

[0827] Specific operation: Formulate development plans based on project progress data, analyze test marketing results, and use the emotion engine to evaluate the emotional aspects of feedback and incorporate improvements.

[0828] (Application example 2)

[0829] 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."

[0830] While conventional inter-company collaboration support systems can collect public company information and generate matching lists, they struggle to effectively support the execution of proposed collaborations, ongoing project management, and the final product or service launch. Furthermore, little consideration has been given to monitoring the emotions and stress levels of company personnel in real time and providing appropriate support. The present invention aims to solve these issues and realize more effective and efficient inter-company collaboration.

[0831] 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.

[0832] In this invention, the server includes means for collecting public company information, means for generating a matching list based on the collected company information, means for notifying the generated matching list and proposal details, means for arranging meetings between proposed companies, means for monitoring the emotional state of users during meetings in real time, means for managing project progress based on the meeting results, and means for launching products or services on the market in accordance with the progress of the project. This allows for total support of the entire process of collaboration between companies, and makes it possible to provide appropriate support by monitoring the emotions and stress of company personnel in real time.

[0833] "Methods of collecting publicly available corporate information" refers to methods of collecting various information from corporate websites, government agency databases, social media, etc., using web scraping and API requests.

[0834] "Means for generating a matching list based on company information" refers to a means for using AI models and algorithms to create a list of companies that can cooperate based on collected company information.

[0835] "Means for notifying the generated matching list and proposal contents" refers to means for notifying the generated matching list and proposal contents to the person in charge at each company via email, a messaging app, etc.

[0836] "Means for setting up meetings between proposed companies" refers to means for scheduling online meetings between proposed companies.

[0837] The "means for monitoring the emotional state of a user during a meeting in real time" is a means for using an emotion engine to analyze the emotional state of participants during a meeting in real time and provide support as needed.

[0838] "Method of managing project progress based on meeting results" means using a project management tool to track task progress based on the results of the meeting and automatically update tasks and deadlines as needed.

[0839] "Means for bringing products or services to market as the project progresses" refers to means for formulating a development plan for a product or service as the project progresses, and managing the process up to its release to the market.

[0840] This system collects publicly available information from companies and uses that data to propose and execute collaborations between companies, supporting the launch of products and services to the market. It also uses an emotion engine to recognize user emotions and optimize proposal content and meeting progress. The system of this invention consists of the following phases:

[0841] Data Collection Phase

[0842] The server uses web scraping tools and API requests to collect publicly available information about companies from their websites, government databases, social media, etc. The collected information is stored in a database in the form of each company's business details, technology, patents, production location, financial information, etc. For example, the server uses a library called BeautifulSoup to scrape product information from company websites and uses API requests to collect customer feedback from social media.

[0843] Matching list generation phase

[0844] Based on the collected company information, the server uses an AI model to evaluate the synergy effects and feasibility of collaboration between companies and generate a matching list. This AI model uses a trained generative AI model to score companies based on specific evaluation factors (e.g., social contribution and interest). For example, the server evaluates the synergy effects of integrating the technologies and market networks of Company A and Company B and lists the optimal companies to apply to.

[0845] Proposal Phase

[0846] The server notifies each company's device of the generated matching list and the proposal details. The device receives the notification and displays the proposal details to the company representative. The emotion engine analyzes the representative's emotions in real time and provides feedback on whether the proposal details are appropriate. For example, the server notifies Company A and Company B that "this collaboration will improve logistics efficiency," and the user's device displays the notification. The emotion engine evaluates the representative's reaction and decides the next action.

[0847] Collaboration MTG implementation phase

[0848] The user requests a meeting between the proposed companies. The server coordinates the schedules between the companies and determines the date of the online meeting. In addition, the emotion engine monitors the user's emotional state in real time during the meeting and provides support as needed. For example, representatives from companies A and B decide on a meeting date, the server adds it to the schedule, the device sends a reminder, and the emotion engine detects tension and makes appropriate suggestions.

[0849] Project Management Phase

[0850] The server launches a project management tool based on the meeting results and sets tasks and deadlines. Users report their task progress and send updates to the server. The server tracks project progress in real time and automatically updates tasks and deadlines as needed. The emotion engine monitors the user's emotions regarding project progress and provides appropriate support based on that. For example, the server sets tasks for a collaborative project between companies A and B, and the emotion engine detects the user's stress and provides reminders and advice.

[0851] Product / service phase

[0852] The server formulates a product or service development plan according to the project's progress. Users send feedback during the development process to the server. The server conducts test marketing, analyzes the results, and collects feedback. Finally, the server provides the optimal means for releasing the product or service to the market. The emotion engine analyzes the emotional responses to the feedback and uses them to improve the product or service. For example, the server conducts test sales of a new product jointly developed by Company A and Company B, analyzes the sales data, and the emotion engine analyzes the positive responses and adjusts the final release.

[0853] Example prompt sentence:

[0854] "To improve manufacturing operations, we would like to develop a system that proposes collaboration in the logistics field based on publicly available data from manufacturing company A (https: / / example.com / companyA) and retail company B (https: / / example.com / companyB). How would you go about doing this?"

[0855] In this way, the present invention supports collaboration between companies at each phase, and by utilizing the emotion engine, it is possible to optimize proposal content and meeting progress, thereby making it possible to efficiently bring new products and services to market.

[0856] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[0857] Step 1:

[0858] The server collects publicly available information about companies. This process uses web scraping tools and API requests to gather the necessary data from company websites, government databases, social media, and other sources, and then stores the data in a database. For example, BeautifulSoup can be used to obtain product information from a company's website, and the Twitter API can be used to collect customer feedback. The input is a list of URLs for company websites and social media accounts, and the output is a database containing the collected company information.

[0859] Step 2:

[0860] The server uses an AI model based on the collected company information to generate a matching list. In this process, information about each company (technology, products, financial information, etc.) is entered into the AI ​​model as input data, and the system evaluates synergy effects and feasibility to list the optimal combination of companies. For example, an AI model trained with the Python library scikit-learn can be used to score the effectiveness of collaboration between Company A and Company B. The input is company information stored in the database, and the output is a scored company matching list.

[0861] Step 3:

[0862] The server notifies each company's device of the generated matching list and the proposal details. In this process, the optimal proposal is sent to each company via email or messaging app based on the generated matching list. The emotion engine also analyzes the emotional state of each person in charge and evaluates whether the proposal details are appropriate. For example, an email server can be used to notify each company's person in charge of the proposal details, and an emotion engine (e.g., emotion API) performs emotion analysis. The input is the matching list and email address, and the output is the result of sending the proposal notification and the person in charge's emotional evaluation.

[0863] Step 4:

[0864] The user requests a meeting between the proposed companies. In this process, a company representative requests to set up a meeting, and the server coordinates the schedules between the companies and determines the date of the online meeting. The emotion engine also monitors the user's emotional state during the meeting in real time and provides necessary support. For example, a meeting can be scheduled using the Google Calendar API, and the emotion engine can detect stress or tension during the meeting and provide appropriate advice. The input is the representative's meeting request, and the output is the scheduled meeting date and emotion evaluation data.

[0865] Step 5:

[0866] The server launches a project management tool based on the meeting results and sets tasks and deadlines. This process sets project tasks based on the meeting results and tracks project progress in real time. The emotion engine monitors the emotions of the person in charge as the project progresses and sends reminders and advice as necessary. For example, a project management tool such as JIRA is used to set tasks and track progress, and the emotion engine detects and responds to stress and a drop in motivation. The input is the meeting results and a project task setting request, and the output is the project progress tracking results and emotion evaluation data.

[0867] Step 6:

[0868] The server formulates a product or service development plan as the project progresses, and ultimately launches it on the market. In this process, a development plan is formulated based on the project's progress, test marketing is conducted to collect feedback, and the feedback is analyzed to make final adjustments and release the product to the market in an optimal form. For example, a prototype of a new product is released on a small scale to collect market reactions, and the sentiment engine analyzes positive feedback to formulate a final release plan. The input is the project's progress and the test marketing results, and the output is the final product or service release plan.

[0869] 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.

[0870] 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.

[0871] 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.

[0872] [Third embodiment]

[0873] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0874] 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.

[0875] 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).

[0876] 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.

[0877] 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.

[0878] 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).

[0879] 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.

[0880] 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.

[0881] 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.

[0882] 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.

[0883] 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.

[0884] 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."

[0885] This invention is a system that collects publicly available information from companies, proposes collaboration between companies based on that information, executes it, and supports the launch of products and services on the market. Below, we will create a program for this system and explain its processing in natural language.

[0886] This system is broadly divided into six phases: data collection phase, matching list generation phase, proposal phase, collaborative meeting implementation phase, project management phase, and product / service creation phase.

[0887] Data Collection Phase

[0888] The server collects publicly available information about companies. It generates a list of companies to collect and gathers data from websites, government databases, social media, and company reports. The server periodically retrieves the latest information using web scraping technology and API requests. The collected data is analyzed and a profile of each company (including business activities, technology, patents, production locations, and financial information) is stored in a database.

[0889] Example: A server retrieves a product catalog from Company X's website, parses financial reports retrieved from a government database, and enters them into a database.

[0890] Matching list generation phase

[0891] The server runs an AI model based on the company information stored in the database and generates a matching list of companies based on the evaluation factors of social contribution, synergy effect, feasibility, and interest. The matching list is then sorted in order of evaluation, and appropriate proposals are made for each company.

[0892] Example: Based on the technical information of Company X, the server evaluates the synergy effect when combined with the sales channels of Company Y, and places it at the top of the matching list.

[0893] Proposal Phase

[0894] The server notifies each company's device of the generated matching list and the details of the proposal. The device receives the notification and displays the details of the proposal to the company representative. The user (company representative) can check the proposal and request more information if they are interested.

[0895] Example: The server notifies Company X and Company Y that "Your technology has the potential to create high synergy with Company Y's sales channels," and the terminal displays this information to the person in charge.

[0896] Collaboration MTG implementation phase

[0897] The user requests a meeting between the proposed companies. The server coordinates the schedules between the companies, determines the date and time of the online meeting, and shares relevant information. The device then sends a meeting reminder to the person in charge.

[0898] Example: Representatives from company X and company Y schedule an online meeting, and the server adds it to the schedule and sends reminders.

[0899] Project Management Phase

[0900] The server launches a project management tool based on the meeting results and sets tasks and deadlines. Users report the progress of each task and send updates to the server. The server tracks project progress in real time and automatically updates tasks and deadlines as needed.

[0901] Example: A server sets tasks for a collaborative project between companies X and Y and constantly updates the progress.

[0902] Product / service phase

[0903] The server creates a development plan for the product or service according to the project's progress. Users send feedback to the server during the development process. The server conducts test marketing, analyzes the results, and collects feedback. Finally, the server provides the optimal means for releasing the product or service to the market.

[0904] Example: The server conducts test sales of a new product jointly developed by Company X and Company Y, analyzes the sales data, proposes improvements based on the feedback, and prepares for another test marketing.

[0905] In this way, the system supports collaboration between companies at each phase, streamlining the launch of new products and services into the market.

[0906] The processing flow will be explained below.

[0907] Data Collection Phase

[0908] Step 1:

[0909] The server generates a list of companies from which data is collected, categorizing the list by company category and industry.

[0910] Step 2:

[0911] The server performs web scraping and API requests targeting corporate websites, government databases, social media, corporate reports, and more.

[0912] Step 3:

[0913] The server analyzes the collected information and extracts information such as each company's business activities, technology, patents, production locations, and financial information, and stores it in a database.

[0914] Example: The server scrapes product information from Company X's website, collects customer feedback from social media, performs text analysis on it, and registers it in a database.

[0915] Matching list generation phase

[0916] Step 4:

[0917] The server inputs company information stored in a database into the AI ​​model.

[0918] Step 5:

[0919] The server generates a matching list of companies based on the evaluation factors of social contribution, synergy effect, feasibility, and interest.

[0920] Step 6:

[0921] The server sorts the generated matching list in order of rating and creates appropriate proposals for each company.

[0922] Example: The server evaluates the synergy benefits of combining the technical information of company X with the sales network of company Y and places this at the top of the list.

[0923] Proposal Phase

[0924] Step 7:

[0925] The server notifies the target company's terminal of the generated matching list and the contents of the proposal.

[0926] Step 8:

[0927] The terminal receives the notification and displays the proposal to the company representative.

[0928] Step 9:

[0929] The user reviews the offer and, if interested, requests more information.

[0930] Example: The server notifies Company X and Company Y that "This collaboration will help expand your product offerings," and the terminal displays the notification to the person in charge.

[0931] Collaboration MTG implementation phase

[0932] Step 10:

[0933] The user sends a request to the server to set up a meeting between the proposed businesses.

[0934] Step 11:

[0935] The server coordinates schedules between companies and determines dates for online meetings.

[0936] Step 12:

[0937] The terminal notifies the user of meeting reminders.

[0938] Example: Company X and Company Y decide on a meeting date, the server adds it to the schedule, and the device sends a reminder.

[0939] Project Management Phase

[0940] Step 13:

[0941] The server starts a project management tool based on the meeting results and sets tasks and deadlines.

[0942] Step 14:

[0943] Users report their progress on tasks and send updates to the server.

[0944] Step 15:

[0945] The server tracks project progress in real time and automatically updates tasks and deadlines as needed.

[0946] Example: The server sets tasks for a collaborative project between company X and company Y and constantly updates the progress.

[0947] Product / service phase

[0948] Step 16:

[0949] The server formulates a product or service development plan according to the progress of the project.

[0950] Step 17:

[0951] Users provide feedback to the server during the development process.

[0952] Step 18:

[0953] The server conducts test marketing, analyzes the results, and collects feedback.

[0954] Step 19:

[0955] The server prepares the product or service incorporating the improvements for final release and brings it to market.

[0956] Example: The server conducts test sales of a new product jointly developed by Company X and Company Y, analyzes the sales data, and makes improvements and finalizes the specifications based on the feedback.

[0957] Example 1

[0958] 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."

[0959] There is a need to efficiently realize collaboration between companies and quickly bring new products and services to market. However, collecting public information about companies and finding the most suitable partner companies takes a great deal of time and effort. In addition, meeting arrangements and project management are often done manually, making efficient operations difficult. Furthermore, understanding the progress of projects in real time and receiving appropriate feedback is also a challenge.

[0960] 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.

[0961] In this invention, the server includes a means for collecting publicly available company information, a means for generating a matching list using a generative AI model based on the collected company information, and a means for notifying the company terminal of the generated matching list and proposal details. This allows for the rapid identification of optimal partnerships between companies and efficient meeting arrangements and project management. It also enables real-time tracking of project progress, analysis of test marketing results, and feedback collection, significantly improving overall efficiency.

[0962] "Public corporate information" refers to data published by companies, such as websites, government databases, social media, and company reports.

[0963] A "generative AI model" refers to an artificial intelligence algorithm that generates matching lists between companies based on collected data.

[0964] A "matching list" is a list generated to evaluate corporate information and propose optimal partnerships.

[0965] "Proposal content" refers to content that includes the benefits of collaboration between companies and specific proposals based on the generated matching list.

[0966] "Terminal" refers to a device (e.g., PC, tablet, smartphone) used by company personnel to receive and check information.

[0967] "Meeting request methods" refers to the methods by which company representatives can request and schedule meetings with other companies.

[0968] "Project Task" refers to the specific work or activities involved in a collaborative project between companies.

[0969] "Project Progression" refers to the process of how collaboration between companies is progressing.

[0970] "Development Plan" means a detailed plan for the development of a new product or service.

[0971] "Test marketing" refers to the activity of actually selling a new product or service on a trial basis in the market and analyzing the results.

[0972] This invention is a system that collects publicly available information from companies, proposes collaboration between companies based on that information, executes it, and supports the launch of products and services on the market. Specifically, this system is realized using the following hardware and software.

[0973] This system is broadly divided into six phases: data collection phase, matching list generation phase, proposal phase, collaboration meeting implementation phase, project management phase, and product / service creation phase. The processing in each phase is explained below.

[0974] Data Collection Phase

[0975] The server collects publicly available information about companies. It generates a list of target companies and uses web scraping technology and API requests to gather data from websites, government databases, social media, and company reports. It also periodically retrieves updated information, analyzes the collected data, and stores each company's profile (business operations, technology, patents, production locations, financial information, etc.) in a database.

[0976] Example: A server scrapes product catalogs from websites of companies in a specific region using the filtering criteria "technology companies" in the technology field, while simultaneously retrieving financial reports from a government financial database and registering them as profile information in the database.

[0977] Matching list generation phase

[0978] The server runs a generative AI model based on company information stored in a database, generating a matching list of companies based on evaluation factors such as social contribution, synergy effects, feasibility, and interest. The matching list is then sorted by evaluation, and appropriate proposals are made for each company.

[0979] Example: The server inputs a prompt such as "Please suggest the best partner company for Company X's technology" into the generative AI model, and the matching list that evaluates the synergy between Company X and Company Y is ranked at the top.

[0980] Proposal Phase

[0981] The server notifies each company's device of the generated matching list and the details of the proposal. The device receives the notification and displays the details of the proposal to the company representative. The user (company representative) can check the proposal and request more information if they are interested.

[0982] Example: The server sends a proposal such as "Your company's technology has the potential to create high synergy with Company Y's sales channels" to the terminals of Company X and Company Y, and displays it to the person in charge.

[0983] Collaboration Meeting Implementation Phase

[0984] The user requests a meeting between the proposed companies. The server coordinates the schedules between the companies and determines the date and time of the online meeting. The device then sends a meeting reminder to the person in charge.

[0985] Example: A person from company X makes a request to schedule an online meeting, the server coordinates the availability of a person from company Y, sets the appropriate date and time, and sends a reminder.

[0986] Project Management Phase

[0987] The server launches a project management tool based on the meeting results and sets each task and its deadline. Users report the progress of each task and send updates to the server. The server tracks the project progress in real time and automatically updates tasks and deadlines as needed.

[0988] Example: The server launches a task management tool and adds tasks such as "market research" and "technology development." The user reports progress and updates the record to say "market research completed."

[0989] Product / service phase

[0990] The server creates a product or service development plan according to the project progress. Users send feedback during the development process to the server. The server conducts test marketing, analyzes the results, and collects feedback. Finally, it provides the optimal means for releasing the product or service to the market.

[0991] Example: A server tests a new product in a specific region, analyzes the sales data, proposes improvements based on the results of the analysis, and prepares for another test marketing of the improved product.

[0992] As described above, this system performs detailed and specific processing at each phase, supporting collaboration between companies and streamlining the launch of new products and services into the market.

[0993] The flow of the identification process in the first embodiment will be described with reference to FIG.

[0994] Step 1:

[0995] The server collects publicly available information about companies. It receives filtering criteria (such as industry, size, and region) as input and generates a list of companies to collect as output. Specifically, it uses web scraping technology and API requests to collect data from company websites and government agency databases. This data is then stored in a database as a profile for each company, including production location, technology, patents, and financial information.

[0996] Step 2:

[0997] The server runs a generative AI model based on company information stored in a database. The input here is company profile data, and the output is a matching list based on evaluation factors such as social contribution, synergy effects, feasibility, and interest. Specifically, a prompt such as "Please suggest partner companies that are best suited to Company X's technology" is input into the generative AI model, and the AI ​​automatically generates a matching list.

[0998] Step 3:

[0999] The server notifies the company's device of the generated matching list and proposal details. It receives the matching list and proposal details as input and displays them on the device in a format that can be checked by the company representative as output. Specific operations include sending information using an email API or notification system, and displaying it on the device as a pop-up notification, etc.

[1000] Step 4:

[1001] The user (company representative) receives the notification and checks the proposal. If interested, the user requests more detailed information. The input is the notification of the proposal, and the output is a request for more information sent to the server. The specific operation is to click the "Request more information" button on the terminal's user interface.

[1002] Step 5:

[1003] A user requests a meeting between the proposed companies. As input, the user sends the meeting request information (date, time, participants, etc.) to the server, and as output, the user receives the adjusted schedule. Specifically, a company representative sends a request using an online scheduling system, and the server adjusts the schedule between the companies and sets an appropriate date and time.

[1004] Step 6:

[1005] The server launches a project management tool based on the meeting results and sets each task and its deadline. It receives the meeting deliverables and task information as input, and sets tasks and deadlines in the project management tool as output. Specifically, the server launches a task management tool (e.g., JIRA) and adds tasks such as "market research" and "technology development."

[1006] Step 7:

[1007] Users report the progress of each task and send updates to the server. The input is task progress information, and the output is a project management tool with updated progress. Specifically, the person in charge enters progress information such as "market research completed," and the server reflects it in the project management tool.

[1008] Step 8:

[1009] The server formulates a product or service development plan based on the project progress and conducts test marketing. It receives progress data and marketing plan information as input, and obtains test marketing results and feedback as output. Specifically, the server conducts test sales of a new product in a specific region, analyzes the data, and creates improvement proposals.

[1010] (Application example 1)

[1011] 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."

[1012] Conventional inter-company collaboration proposal and execution support systems collect and analyze publicly available information from companies to generate matching lists, but they are unable to take into account data on manufacturing processes and production equipment. This makes it difficult to make optimal proposals for actual factory operations. Furthermore, they lack the ability to easily monitor and automatically update the real-time progress of manufacturing processes, making efficient project management difficult. Furthermore, there is a lack of means to forecast demand and quickly and accurately gather feedback when test selling and launching newly developed products and services.

[1013] 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.

[1014] In this invention, the server includes means for collecting operation data from factory automation equipment or autonomous machines, means for analyzing the collected operation data and generating company profiles, means for proposing corporate collaborations based on the generated profiles, means for notifying the generated matching list and proposal details, means for setting up online meetings between proposed companies, means for managing project progress based on the meeting results and launching products or services as the project progresses, means for test selling new products using the collected data and providing feedback, and means for supporting product launches. This enables optimal collaboration proposals that take into account automation equipment data, enabling real-time monitoring of manufacturing processes and efficient project management. It also enables rapid and accurate feedback collection when launching new products or services.

[1015] "Corporate public information" refers to information that a company makes publicly available through its website, government agency databases, social media, corporate reports, etc.

[1016] The "matching list" is a list of potential collaborations between companies that is generated by running an AI model based on collected company information and evaluating factors such as social contribution, synergy effects, feasibility, and interest.

[1017] "Proposal content" refers to details and recommendations for collaboration between companies generated by the server based on the matching list.

[1018] The means for setting up a "meeting" is a method for the proposed companies to coordinate a date and time for a meeting online or offline and share information.

[1019] A "means for managing project progress" is a method for tracking project tasks, deadlines, and progress in real time based on meeting results, and automatically updating as needed.

[1020] "Factory automation equipment or autonomously operating machines" means machines or robots that automate manufacturing processes within a factory and are capable of operating independently.

[1021] "Operation data" refers to recorded information such as the operating status, performance indicators, and operating hours of automated equipment or autonomously operating machines in a factory.

[1022] A "company profile" is a detailed record of a company's business activities, technology, patents, production locations, financial information, etc., based on an analysis of collected operational data and publicly available information.

[1023] An "online meeting" is a meeting between companies held over the Internet, and refers to a means that allows for the coordination of dates and times and the sharing of information.

[1024] A "new product test launch" is the process of launching a newly developed product or service in a limited market and collecting response and sales data.

[1025] "Feedback" refers to information regarding evaluations and areas for improvement obtained during trial sales and project implementation.

[1026] "Go-to-market aids" are the methods and tools used to formally introduce new products and services to the market and promote their sales.

[1027] This invention is a system that collects publicly available information from companies, proposes collaboration between companies based on that information, executes it, and supports the launch of products and services on the market. Below, we will create a program for this system and explain its processing in natural language.

[1028] The system consists of three entities: a server, a terminal, and a user.

[1029] Data Collection Phase

[1030] The server collects publicly available information about companies. It generates a list of target companies and gathers data from websites, government databases, social media, and company reports. Specifically, it uses web scraping technology and API requests to periodically retrieve the latest information. The collected data is analyzed and a profile of each company (including business activities, technology, patents, production locations, and financial information) is stored in a database.

[1031] Example: A server retrieves a product catalog from Company A's website, parses financial reports retrieved from a government database, and enters them into a database.

[1032] Matching list generation phase

[1033] The server runs a generative AI model based on the company information stored in the database, and generates a matching list of companies based on the evaluation factors of social contribution, synergy effect, feasibility, and interest. The generated matching list is sorted in order of evaluation, and appropriate proposals are made for each company.

[1034] Example: The server evaluates the synergy effect when combining company A's technical information with company B's sales channels, and places it at the top of the matching list.

[1035] Proposal Phase

[1036] The server notifies each company's device of the generated matching list and the details of the proposal. The device receives the notification and displays the details of the proposal to the company representative. The user (company representative) can check the proposal and request more information if they are interested.

[1037] Example: The server notifies Company A and Company B that "Your technology has the potential to create high synergy with Company B's sales channels," and the terminal displays this information to the person in charge.

[1038] Collaboration MTG implementation phase

[1039] The user requests a meeting between the proposed companies. The server coordinates the schedules between the companies, determines the date and time of the online meeting, and shares relevant information. The device then sends a meeting reminder to the person in charge.

[1040] Example: Representatives from company A and company B schedule an online meeting, and the server adds it to the schedule and sends reminders.

[1041] Project Management Phase

[1042] The server launches a project management tool based on the meeting results and sets tasks and deadlines. Users report the progress of each task and send updates to the server. The server tracks project progress in real time and automatically updates tasks and deadlines as needed.

[1043] Example: The server sets tasks for a collaborative project between companies A and B and constantly updates the progress.

[1044] Product / service phase

[1045] The server creates a development plan for the product or service according to the project's progress. Users send feedback to the server during the development process. The server conducts test marketing, analyzes the results, and collects feedback. Finally, the server provides the optimal means for releasing the product or service to the market.

[1046] Example: The server conducts test sales of a new product jointly developed by Company A and Company B, analyzes the sales data, proposes improvements based on the feedback, and prepares for another test marketing.

[1047] Hardware and software used

[1048] The system uses the following hardware and software:

[1049] Hardware: Smartphone, head-mounted display (e.g., Oculus Quest 2)

[1050] Software: Python web scraping (BeautifulSoup), HTTP request library (requests), data analysis (pandas), generative AI models

[1051] Prompt Sentence Examples

[1052] An example of a prompt to input to a generative AI model is as follows:

[1053] "Develop a system that evaluates synergy between companies based on technical information and generates optimal collaboration proposals."

[1054] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[1055] Step 1: Data collection

[1056] The server collects publicly available information about companies. It generates a list of companies to collect and obtains data from websites, government databases, social media, and company reports. This is done using web scraping technology (BeautifulSoup) and API requests (requests library). The input is various publicly available information, and the output is a profile of each company (business details, technology, patents, production locations, financial information, etc.).

[1057] Step 2: Save to database

[1058] The server analyzes the collected data and stores it in a database. First, it analyzes the acquired HTML data and API responses to extract detailed information about each company. Next, it structures the extracted data and registers it in the database. The input is the analyzed information, and the output is the company profile registered in the database.

[1059] Step 3: Run the AI ​​model

[1060] The server runs a generative AI model based on the company information stored in the database. Specifically, it generates a matching list of companies based on the evaluation factors of social contribution, synergy effects, feasibility, and interest. The input is the company profile, and the output is a matching list of companies. At this stage, the model analyzes and evaluates the data.

[1061] Step 4: Proposal generation and notification

[1062] The server creates an optimal proposal for each company based on the generated matching list and notifies the device. The input is the matching list, and the output is a proposal notification sent to each company's device. The notification content includes details of the proposed collaboration, which the device displays to the person in charge.

[1063] Step 5: Meeting Schedule

[1064] The user reviews the proposal and, if interested, requests more information and a meeting from the server. The server coordinates schedules between the companies and determines the date and time of the online meeting. The input is the meeting request, and the output is the scheduled meeting date and time and related information. The terminal notifies the person in charge of the meeting with a reminder.

[1065] Step 6: Project Management

[1066] The server launches the project management tool based on the meeting results and sets tasks and deadlines. The input is the meeting results and the output is the set tasks and deadlines. Users report the progress of each task and send updates to the server. The server tracks the project progress in real time and automatically updates tasks and deadlines as needed.

[1067] Step 7: Product / Service

[1068] The server formulates a product or service development plan according to the project progress. The input is project progress information, and the output is a development plan. Users send feedback during the development process to the server, which then conducts test marketing. The test marketing results are analyzed, feedback is collected, and the server provides the optimal means for finally releasing the product or service to the market.

[1069] Through the above processing steps, the system of the present invention efficiently supports everything from collaboration between companies to the launch of products and services on the market.

[1070] 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.

[1071] This system collects publicly available information from companies and uses that information to propose and execute collaborations between companies, supporting the launch of products and services to market. Furthermore, by incorporating an emotion engine, it is possible to recognize user emotions and optimize proposal content and meeting progress based on those emotions. Below, we will create a program for this system and explain its processing in natural language.

[1072] This system is broadly divided into six phases: data collection phase, matching list generation phase, proposal phase, collaborative meeting implementation phase, project management phase, and product / service creation phase. In each phase, an emotion engine recognizes the user's emotions and responds appropriately.

[1073] Data Collection Phase

[1074] The server collects publicly available information about companies, generates a list of companies to collect, and performs web scraping and API requests targeting websites, government databases, social media, corporate reports, etc. The server analyzes the collected information and stores each company's business details, technology, patents, production locations, financial information, etc. in a database.

[1075] Example: The server scrapes product information from Company X's website, collects customer feedback from social media, performs text analysis on it, and registers it in a database.

[1076] Matching list generation phase

[1077] The server inputs the company information stored in the database into the AI ​​model and generates a matching list of companies based on the evaluation factors of social contribution, synergy effect, feasibility, and interest. The generated matching list is sorted in order of evaluation, and appropriate proposals are made for each company.

[1078] Example: The server evaluates the synergy benefits of combining the technical information of company X with the sales network of company Y and places this at the top of the list.

[1079] Proposal Phase

[1080] The server notifies each company's device of the generated matching list and the proposed content. The device receives the notification and displays the proposed content to the company representative. The user (company representative) checks the proposed content and, based on the emotions recognized by the emotion engine, requests more detailed information if they are interested.

[1081] Example: The server notifies Company X and Company Y that "This collaboration will help expand your product offerings," and the user's device displays the notification to the person in charge, and the emotion engine evaluates satisfaction.

[1082] Collaboration MTG implementation phase

[1083] The user requests a meeting between the proposed companies. The server coordinates the schedules between the companies and determines the date of the online meeting. The device notifies the user of meeting reminders. The emotion engine monitors the user's emotional state in real time during the meeting and provides necessary support.

[1084] Example: Representatives from Company X and Company Y decide on a date for a meeting, the server adds it to the schedule, the device sends a reminder, and the emotion engine detects tension and makes appropriate suggestions.

[1085] Project Management Phase

[1086] The server launches a project management tool based on the meeting results and sets tasks and deadlines. Users report their task progress and send updates to the server. The server tracks project progress in real time and automatically updates tasks and deadlines as needed. The emotion engine monitors users' emotions regarding project progress and provides appropriate support based on that.

[1087] Example: A server sets tasks for a collaborative project between companies X and Y, constantly updates the progress, and an emotion engine detects the user's stress and provides reminders and advice.

[1088] Product / service phase

[1089] The server formulates a product or service development plan according to the project's progress. Users send feedback during the development process to the server. The server conducts test marketing, analyzes the results, and collects feedback. Finally, the server provides the optimal means for releasing the product or service to the market. The emotion engine analyzes the emotional reactions of the feedback and uses them to improve the product or service.

[1090] Example: The server conducts test sales of a new product jointly developed by Company X and Company Y, analyzes the sales data, and makes improvements and finalizes the specifications based on the feedback. The emotion engine analyzes positive reactions and adjusts the final release.

[1091] In this way, this system supports collaboration between companies at each phase, and by utilizing an emotion engine, it optimizes proposal content and meeting progress, thereby streamlining the launch of new products and services into the market.

[1092] The processing flow will be explained below.

[1093] Data Collection Phase

[1094] Step 1:

[1095] The server generates a list of companies to collect data from. First, it selects target companies based on their industry and market needs and adds them to the list.

[1096] Step 2:

[1097] The servers perform web scraping and API requests targeting company websites, government databases, social media, company reports, etc. The information collection is carried out periodically.

[1098] Step 3:

[1099] The server analyzes the collected information and stores each company's business details, technology, patents, production locations, financial information, etc. in a database. The analysis uses technologies such as natural language processing.

[1100] Example: The server accesses Company X's website, scrapes the latest product information, analyzes customer reviews on social media, and registers this information in a database.

[1101] Matching list generation phase

[1102] Step 4:

[1103] The server inputs company information stored in a database into the AI ​​model, which learns from the existing data.

[1104] Step 5:

[1105] The server generates a matching list of companies based on the evaluation factors of social contribution, synergy, feasibility, and interest, with each factor being evaluated according to a pre-set weighting.

[1106] Step 6:

[1107] The server sorts the generated matching list in order of evaluation and creates appropriate proposals for each company, including specific collaboration benefits and prospects.

[1108] Example: The server calculates the synergy effect of combining the technological capabilities of company X with the sales network of company Y, and places this at the top of the list.

[1109] Proposal Phase

[1110] Step 7:

[1111] The server notifies each company's terminal of the generated matching list and the proposal details, including a summary of the proposal details.

[1112] Step 8:

[1113] The device receives the notification and displays the proposal to the company representative. The device uses an emotion engine to evaluate how the user feels about the proposal.

[1114] Step 9:

[1115] Users can review the suggestions and request more information if they are interested, and suggestions may be automatically customized based on the sentiment engine's evaluation.

[1116] Example: The server notifies company X and company Y that "this collaboration will help expand your product offering," the device displays the notification to the person in charge, and the emotion engine evaluates the level of interest.

[1117] Collaboration MTG implementation phase

[1118] Step 10:

[1119] The user sends a request to the server to set up a meeting between the proposed companies. The request is made online.

[1120] Step 11:

[1121] The server coordinates schedules between companies and determines the dates of online meetings. The server uses an emotion engine to evaluate whether the meeting dates are appropriate.

[1122] Step 12:

[1123] The device notifies users of meeting reminders, and during meetings, the emotion engine monitors users' emotions in real time and provides suggestions and support as needed.

[1124] Example: Representatives from Company X and Company Y decide on a meeting date, the server adds it to the schedule, the device sends a reminder, and the emotion engine detects the level of tension during the meeting and provides appropriate support.

[1125] Project Management Phase

[1126] Step 13:

[1127] The server launches a project management tool based on the meeting results, sets tasks and deadlines, and an emotion engine evaluates the suitability of the project progress.

[1128] Step 14:

[1129] Users report their progress on tasks and send updates to the server, and the emotion engine monitors the user's emotions regarding their progress.

[1130] Step 15:

[1131] The server tracks project progress in real time and automatically updates tasks and deadlines as needed, while the emotion engine optimizes projects based on stress and satisfaction.

[1132] Example: A server sets tasks for a collaborative project between companies X and Y, constantly updates the progress, and an emotion engine detects the user's stress and provides reminders and advice.

[1133] Product / service phase

[1134] Step 16:

[1135] The server formulates a product or service development plan according to the project progress, and the emotion engine analyzes user feedback on the development process.

[1136] Step 17:

[1137] Users submit feedback during the development process to the server, and an emotion engine evaluates the emotional response of the feedback.

[1138] Step 18:

[1139] The server conducts test marketing, analyzes the results, and collects feedback. The emotion engine then proposes improvements to the product or service based on the results of the test marketing.

[1140] Step 19:

[1141] The server prepares the product or service with the improvements for final release and launches it in the market, and the emotion engine analyzes the expectations and positive reactions to the final release.

[1142] Example: The server conducts a test sale of a new product jointly developed by Company X and Company Y, analyzes the sales data, makes improvements based on feedback, and an emotion engine analyzes positive reactions to adjust the final release.

[1143] Example 2

[1144] 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."

[1145] Collaboration between companies involves multiple processes, such as information gathering, proposals, meeting arrangements, project management, and product / service creation. However, efficiently managing these processes and increasing the success rate is difficult. Furthermore, there is a need for a method to optimize proposal content and meeting progress by appropriately reflecting user emotions. Currently, there is a lack of systems that automate these processes and optimize them by incorporating emotion analysis.

[1146] 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.

[1147] In this invention, the server includes: means for collecting public company information; means for generating a matching list using an artificial intelligence model based on the collected company information; means for notifying a user terminal of the generated matching list and proposal content; means for setting up online meetings between the proposed companies; means including an emotion engine for analyzing user emotions in real time during the meeting and providing support; means for managing project progress based on the meeting results; and means for launching products or services to the market according to the progress of the project. This makes it possible to automate the collaboration process between companies and optimize the effectiveness of proposals and meetings by analyzing user emotions.

[1148] "Public corporate information" is information about a company that is publicly available through websites, government databases, social media, company reports, etc.

[1149] "Artificial intelligence model" is a general term for machine learning and deep learning models used to generate matching lists of companies based on evaluation factors such as social contribution, synergy effects, feasibility, and interest.

[1150] The "matching list" is a list generated by an artificial intelligence model that evaluates and ranks the potential for collaboration between companies.

[1151] "User terminal" refers to a device used by a user to receive notifications from the system and check proposals and meeting information. Examples of such devices include PCs, smartphones, and tablets.

[1152] An "online meeting" is a type of remote conference that takes place in real time over the Internet, including, for example, video conferencing and web conferencing.

[1153] "Emotion engine" is a general term for emotion analysis software and hardware that analyzes a user's emotional state in real time and provides appropriate support based on the proposal content and meeting progress.

[1154] "Project progress" refers to a series of activities that involve managing collaborative projects discussed between companies to ensure they proceed as planned, and checking the progress of tasks and deliverables.

[1155] "Bringing a product or service to market" means releasing the product or service that is the result of a collaboration project into the marketplace in a form that is available for purchase by the general public.

[1156] "Real-time tracking" means constantly monitoring the progress of a project and updating the information as it progresses to keep it up to date.

[1157] "Test marketing" is the process of selling a product or service on a limited scale on a trial basis before launching it on the market, and then collecting and analyzing the results and feedback from consumers.

[1158] "Emotion analysis results" are data and evaluation information generated based on the user's emotional state analyzed by the emotion engine.

[1159] Overall structure

[1160] This invention is a system that collects publicly available information from companies and uses that information to propose and execute collaborations between companies, supporting the launch of products and services to the market. The system consists of six phases: data collection phase, matching list generation phase, proposal phase, collaboration meeting implementation phase, project management phase, and product / service creation phase. In each phase, an emotion engine is able to recognize the user's emotions and respond appropriately.

[1161] Data Collection Phase

[1162] The server collects publicly available information about companies. It generates a list of companies to collect and collects information from websites, government databases, social media, corporate reports, etc. using web scraping tools such as Scrapy and Beautiful Soup, as well as the Twitter API and Facebook Graph API. This collected information is analyzed using natural language processing tools (NLTK and spaCy), and each company's business details, technology, patents, production locations, financial information, etc. are stored in a cloud database (e.g., AWS RDS).

[1163] Examples:

[1164] The server scrapes product information from Company A's website, collects customer feedback from social media using the Twitter API, analyzes the text using NLTK, and registers it in a database.

[1165] Matching list generation phase

[1166] The server inputs the collected company information into a generative AI model trained with TensorFlow and PyTorch, and generates a matching list of companies based on four evaluation criteria: social contribution, synergy, feasibility, and interest. The matching list is then sorted in order of evaluation, and appropriate proposals are created for each company.

[1167] Examples:

[1168] The server evaluates the synergy effect that would be achieved by integrating Company A's technical information with Company B's sales network, and places it at the top of the matching list.

[1169] Proposal Phase

[1170] The server notifies each company's device of the generated matching list and the proposed content. The device receives the notification and displays the proposed content to the company representative. The user (company representative) checks the proposed content and, if interested, requests further information based on the emotions recognized by the emotion engine (e.g., Affectiva).

[1171] Examples:

[1172] The server notifies Company A and Company B of the proposal that "this collaboration will help expand your products," and the user's device displays the notification to the person in charge, and the emotion engine evaluates satisfaction.

[1173] Collaboration MTG implementation phase

[1174] Users request meetings between proposed companies online. The server coordinates schedules between the companies and determines the date using the Google Calendar API and Microsoft Exchange Web Services (EWS). The device notifies the user of meeting reminders. The emotion engine monitors the user's emotional state in real time during the meeting and provides necessary support.

[1175] Examples:

[1176] Representatives from companies A and B decide on a meeting date, the server adds it to the schedule, the device sends a reminder, and the emotion engine detects tension and makes appropriate suggestions.

[1177] Project Management Phase

[1178] The server launches project management tools such as Jira and Trello based on the meeting results and sets tasks and deadlines. Users report their task progress and send updates to the server. The server tracks project progress in real time and automatically updates tasks and deadlines as needed. The emotion engine monitors users' emotions regarding the project progress and provides appropriate support based on that.

[1179] Examples:

[1180] The server sets tasks for the collaborative project between Company A and Company B, constantly updates the progress, and the emotion engine detects the user's stress and provides reminders and advice.

[1181] Product / service phase

[1182] The server creates a development plan for the product or service as the project progresses. Users provide feedback to the server during the development process. The server conducts test marketing, analyzes the results, and collects feedback. Finally, the server determines the optimal method for releasing the product or service to the market. The emotion engine analyzes the emotional responses of the feedback and uses them to improve the product or service.

[1183] Examples:

[1184] The server conducts test sales of a new product jointly developed by Company A and Company B and analyzes the sales data. The final specifications are determined based on the feedback, and the emotion engine analyzes positive reactions to determine the optimal timing for market launch.

[1185] With this system in place, collaboration between companies can be managed efficiently, and proposals and meeting progress can be optimized using an emotion engine, resulting in more effective market launches of new products and services.

[1186] The flow of the identification process in the second embodiment will be described with reference to FIG.

[1187] Step 1:

[1188] Generate a list of companies

[1189] The server extracts basic information about the target companies (company name, website URL, social media accounts, etc.) from the cloud database and generates a list of companies.

[1190] Input: Cloud Database

[1191] Output: Company list

[1192] Specific operation: Executes an SQL query to extract company information and lists it as a company list.

[1193] Step 2:

[1194] Implementing information gathering

[1195] The server uses web scraping tools such as Scrapy and Beautiful Soup to gather product and technical information from company websites, and also uses the Twitter API and Facebook Graph API to retrieve social media data.

[1196] Input: Company List

[1197] Output: Collected company data

[1198] Specific operation: Based on the company list, web scraping and API calls are performed to collect text data.

[1199] Step 3:

[1200] Data analysis and storage

[1201] The server analyzes the collected data using Natural Language Toolkit (NLTK) and spaCy, and categorizes it into categories such as the company's business operations, technical information, financial status, etc. The analysis results are stored in a cloud database.

[1202] Input: Collected company data

[1203] Output: Analyzed company information database

[1204] Specific operation: Analyzes text data using NLTK and spaCy, extracts business details and technical information, and stores it in a database.

[1205] Step 4:

[1206] Generating a Matching List

[1207] The server inputs the collected company information into a generative AI model trained with TensorFlow and PyTorch, and generates a matching list of companies based on evaluation factors (social contribution, synergy effect, feasibility, and interest).

[1208] Input: Analyzed company information database

[1209] Output: Matching list

[1210] How it works: Company data is input into the AI ​​model to calculate a rating score, which is then sorted by score to create a matching list.

[1211] Step 5:

[1212] Sending proposal notifications

[1213] The server notifies each company's terminal of the generated matching list and the proposed content, and the terminal receives the notification and displays the proposed content to the company representative.

[1214] Input: Matching list, proposal details

[1215] Output: Proposal notification

[1216] Specific operation: The proposal content is sent to the company's device using an API and displayed to the person in charge.

[1217] Step 6:

[1218] Sentiment analysis and request more information

[1219] When the user reviews the suggestions, the emotion engine recognizes the user's emotions in real time and requests more detailed information if the user expresses interest.

[1220] Input: Proposal notification, user reaction

[1221] Output: More information request

[1222] What it does: The emotion engine analyzes the user's facial expressions and displays a request for more information button if they show interest.

[1223] Step 7:

[1224] Setting up an online meeting

[1225] The user requests a meeting between the proposed companies. The server coordinates the schedules between the companies using the Google Calendar API and Microsoft Exchange Web Services (EWS) and determines the date. The device then notifies the user of the meeting reminder.

[1226] Input: Meeting requests, calendar information for each company

[1227] Output: Meeting schedule and reminder notifications

[1228] What it does: Adjust schedules and send reminders using the Google Calendar API and more.

[1229] Step 8:

[1230] Emotion monitoring during meetings

[1231] The emotion engine monitors users' emotions in real time during meetings and provides necessary support.

[1232] Input: User emotion data during the meeting

[1233] Output: Support Suggestion

[1234] Specific behavior: Monitors the user's emotions during meetings and suggests taking a break or relaxing if tension is detected.

[1235] Step 9:

[1236] Use of project management tools

[1237] The server launches a project management tool like Jira or Trello based on the meeting results, sets tasks and deadlines, and users report their progress on the tasks and send updates to the server.

[1238] Inputs: Meeting results, project tasks

[1239] Output: Task management system updates

[1240] Specific actions: Create tasks in Jira or Trello based on the meeting results and enter progress.

[1241] Step 10:

[1242] Project progress tracking and automatic updates

[1243] The server tracks project progress in real time and automatically updates tasks and deadlines as needed. The emotion engine monitors users' emotions regarding the project progress and provides appropriate support.

[1244] Input: Project progress data, user emotion data

[1245] Output: Auto-updated tasks, support suggestions

[1246] Specific behavior: Analyzes project progress data and automatically updates task deadlines. If the emotion engine detects user stress, it suggests relief measures.

[1247] Step 11:

[1248] Bringing products and services to market

[1249] The server formulates a product or service development plan as the project progresses. Users provide feedback to the server during the development process, and the server conducts test marketing and analyzes the results. Ultimately, it determines the optimal method for releasing the product or service to the market. The emotion engine analyzes the emotional responses of the feedback and uses them to improve the product or service.

[1250] Input: Project progress data, feedback data

[1251] Output: Market launch plan, product improvement proposal

[1252] Specific operation: Formulate development plans based on project progress data, analyze test marketing results, and use the emotion engine to evaluate the emotional aspects of feedback and incorporate improvements.

[1253] (Application example 2)

[1254] 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."

[1255] While conventional inter-company collaboration support systems can collect public company information and generate matching lists, they struggle to effectively support the execution of proposed collaborations, ongoing project management, and the final product or service launch. Furthermore, little consideration has been given to monitoring the emotions and stress levels of company personnel in real time and providing appropriate support. The present invention aims to solve these issues and realize more effective and efficient inter-company collaboration.

[1256] 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.

[1257] In this invention, the server includes means for collecting public company information, means for generating a matching list based on the collected company information, means for notifying the generated matching list and proposal details, means for arranging meetings between proposed companies, means for monitoring the emotional state of users during meetings in real time, means for managing project progress based on the meeting results, and means for launching products or services on the market in accordance with the progress of the project. This allows for total support of the entire process of collaboration between companies, and makes it possible to provide appropriate support by monitoring the emotions and stress of company personnel in real time.

[1258] "Methods of collecting publicly available corporate information" refers to methods of collecting various information from corporate websites, government agency databases, social media, etc., using web scraping and API requests.

[1259] "Means for generating a matching list based on company information" refers to a means for using AI models and algorithms to create a list of companies that can cooperate based on collected company information.

[1260] "Means for notifying the generated matching list and proposal contents" refers to means for notifying the generated matching list and proposal contents to the person in charge at each company via email, a messaging app, etc.

[1261] "Means for setting up meetings between proposed companies" refers to means for scheduling online meetings between proposed companies.

[1262] The "means for monitoring the emotional state of a user during a meeting in real time" is a means for using an emotion engine to analyze the emotional state of participants during a meeting in real time and provide support as needed.

[1263] "Method of managing project progress based on meeting results" means using a project management tool to track task progress based on the results of the meeting and automatically update tasks and deadlines as needed.

[1264] "Means for bringing products or services to market as the project progresses" refers to means for formulating a development plan for a product or service as the project progresses, and managing the process up to its release to the market.

[1265] This system collects publicly available information from companies and uses that data to propose and execute collaborations between companies, supporting the launch of products and services to the market. It also uses an emotion engine to recognize user emotions and optimize proposal content and meeting progress. The system of this invention consists of the following phases:

[1266] Data Collection Phase

[1267] The server uses web scraping tools and API requests to collect publicly available information about companies from their websites, government databases, social media, etc. The collected information is stored in a database in the form of each company's business details, technology, patents, production location, financial information, etc. For example, the server uses a library called BeautifulSoup to scrape product information from company websites and uses API requests to collect customer feedback from social media.

[1268] Matching list generation phase

[1269] Based on the collected company information, the server uses an AI model to evaluate the synergy effects and feasibility of collaboration between companies and generate a matching list. This AI model uses a trained generative AI model to score companies based on specific evaluation factors (e.g., social contribution and interest). For example, the server evaluates the synergy effects of integrating the technologies and market networks of Company A and Company B and lists the optimal companies to apply to.

[1270] Proposal Phase

[1271] The server notifies each company's device of the generated matching list and the proposal details. The device receives the notification and displays the proposal details to the company representative. The emotion engine analyzes the representative's emotions in real time and provides feedback on whether the proposal details are appropriate. For example, the server notifies Company A and Company B that "this collaboration will improve logistics efficiency," and the user's device displays the notification. The emotion engine evaluates the representative's reaction and decides the next action.

[1272] Collaboration MTG implementation phase

[1273] The user requests a meeting between the proposed companies. The server coordinates the schedules between the companies and determines the date of the online meeting. In addition, the emotion engine monitors the user's emotional state in real time during the meeting and provides support as needed. For example, representatives from companies A and B decide on a meeting date, the server adds it to the schedule, the device sends a reminder, and the emotion engine detects tension and makes appropriate suggestions.

[1274] Project Management Phase

[1275] The server launches a project management tool based on the meeting results and sets tasks and deadlines. Users report their task progress and send updates to the server. The server tracks project progress in real time and automatically updates tasks and deadlines as needed. The emotion engine monitors the user's emotions regarding project progress and provides appropriate support based on that. For example, the server sets tasks for a collaborative project between companies A and B, and the emotion engine detects the user's stress and provides reminders and advice.

[1276] Product / service phase

[1277] The server formulates a product or service development plan according to the project's progress. Users send feedback during the development process to the server. The server conducts test marketing, analyzes the results, and collects feedback. Finally, the server provides the optimal means for releasing the product or service to the market. The emotion engine analyzes the emotional responses to the feedback and uses them to improve the product or service. For example, the server conducts test sales of a new product jointly developed by Company A and Company B, analyzes the sales data, and the emotion engine analyzes the positive responses and adjusts the final release.

[1278] Example prompt sentence:

[1279] "To improve manufacturing operations, we would like to develop a system that proposes collaboration in the logistics field based on publicly available data from manufacturing company A (https: / / example.com / companyA) and retail company B (https: / / example.com / companyB). How would you go about doing this?"

[1280] In this way, the present invention supports collaboration between companies at each phase, and by utilizing the emotion engine, it is possible to optimize proposal content and meeting progress, thereby making it possible to efficiently bring new products and services to market.

[1281] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[1282] Step 1:

[1283] The server collects publicly available information about companies. This process uses web scraping tools and API requests to gather the necessary data from company websites, government databases, social media, and other sources, and then stores the data in a database. For example, BeautifulSoup can be used to obtain product information from a company's website, and the Twitter API can be used to collect customer feedback. The input is a list of URLs for company websites and social media accounts, and the output is a database containing the collected company information.

[1284] Step 2:

[1285] The server uses an AI model based on the collected company information to generate a matching list. In this process, information about each company (technology, products, financial information, etc.) is entered into the AI ​​model as input data, and the system evaluates synergy effects and feasibility to list the optimal combination of companies. For example, an AI model trained with the Python library scikit-learn can be used to score the effectiveness of collaboration between Company A and Company B. The input is company information stored in the database, and the output is a scored company matching list.

[1286] Step 3:

[1287] The server notifies each company's device of the generated matching list and the proposal details. In this process, the optimal proposal is sent to each company via email or messaging app based on the generated matching list. The emotion engine also analyzes the emotional state of each person in charge and evaluates whether the proposal details are appropriate. For example, an email server can be used to notify each company's person in charge of the proposal details, and an emotion engine (e.g., emotion API) performs emotion analysis. The input is the matching list and email address, and the output is the result of sending the proposal notification and the person in charge's emotional evaluation.

[1288] Step 4:

[1289] The user requests a meeting between the proposed companies. In this process, a company representative requests to set up a meeting, and the server coordinates the schedules between the companies and determines the date of the online meeting. The emotion engine also monitors the user's emotional state during the meeting in real time and provides necessary support. For example, a meeting can be scheduled using the Google Calendar API, and the emotion engine can detect stress or tension during the meeting and provide appropriate advice. The input is the representative's meeting request, and the output is the scheduled meeting date and emotion evaluation data.

[1290] Step 5:

[1291] The server launches a project management tool based on the meeting results and sets tasks and deadlines. This process sets project tasks based on the meeting results and tracks project progress in real time. The emotion engine monitors the emotions of the person in charge as the project progresses and sends reminders and advice as necessary. For example, a project management tool such as JIRA is used to set tasks and track progress, and the emotion engine detects and responds to stress and a drop in motivation. The input is the meeting results and a project task setting request, and the output is the project progress tracking results and emotion evaluation data.

[1292] Step 6:

[1293] The server formulates a product or service development plan as the project progresses, and ultimately launches it on the market. In this process, a development plan is formulated based on the project's progress, test marketing is conducted to collect feedback, and the feedback is analyzed to make final adjustments and release the product to the market in an optimal form. For example, a prototype of a new product is released on a small scale to collect market reactions, and the sentiment engine analyzes positive feedback to formulate a final release plan. The input is the project's progress and the test marketing results, and the output is the final product or service release plan.

[1294] 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.

[1295] 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.

[1296] 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.

[1297] [Fourth embodiment]

[1298] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[1299] 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.

[1300] 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).

[1301] 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.

[1302] 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.

[1303] 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).

[1304] 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.

[1305] 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.

[1306] 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.

[1307] 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.

[1308] 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.

[1309] 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.

[1310] 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."

[1311] This invention is a system that collects publicly available information from companies, proposes collaboration between companies based on that information, executes it, and supports the launch of products and services on the market. Below, we will create a program for this system and explain its processing in natural language.

[1312] This system is broadly divided into six phases: data collection phase, matching list generation phase, proposal phase, collaborative meeting implementation phase, project management phase, and product / service creation phase.

[1313] Data Collection Phase

[1314] The server collects publicly available information about companies. It generates a list of companies to collect and gathers data from websites, government databases, social media, and company reports. The server periodically retrieves the latest information using web scraping technology and API requests. The collected data is analyzed and a profile of each company (including business activities, technology, patents, production locations, and financial information) is stored in a database.

[1315] Example: A server retrieves a product catalog from Company X's website, parses financial reports retrieved from a government database, and enters them into a database.

[1316] Matching list generation phase

[1317] The server runs an AI model based on the company information stored in the database and generates a matching list of companies based on the evaluation factors of social contribution, synergy effect, feasibility, and interest. The matching list is then sorted in order of evaluation, and appropriate proposals are made for each company.

[1318] Example: Based on the technical information of Company X, the server evaluates the synergy effect when combined with the sales channels of Company Y, and places it at the top of the matching list.

[1319] Proposal Phase

[1320] The server notifies each company's device of the generated matching list and the details of the proposal. The device receives the notification and displays the details of the proposal to the company representative. The user (company representative) can check the proposal and request more information if they are interested.

[1321] Example: The server notifies Company X and Company Y that "Your technology has the potential to create high synergy with Company Y's sales channels," and the terminal displays this information to the person in charge.

[1322] Collaboration MTG implementation phase

[1323] The user requests a meeting between the proposed companies. The server coordinates the schedules between the companies, determines the date and time of the online meeting, and shares relevant information. The device then sends a meeting reminder to the person in charge.

[1324] Example: Representatives from company X and company Y schedule an online meeting, and the server adds it to the schedule and sends reminders.

[1325] Project Management Phase

[1326] The server launches a project management tool based on the meeting results and sets tasks and deadlines. Users report the progress of each task and send updates to the server. The server tracks project progress in real time and automatically updates tasks and deadlines as needed.

[1327] Example: A server sets tasks for a collaborative project between companies X and Y and constantly updates the progress.

[1328] Product / service phase

[1329] The server creates a development plan for the product or service according to the project's progress. Users send feedback to the server during the development process. The server conducts test marketing, analyzes the results, and collects feedback. Finally, the server provides the optimal means for releasing the product or service to the market.

[1330] Example: The server conducts test sales of a new product jointly developed by Company X and Company Y, analyzes the sales data, proposes improvements based on the feedback, and prepares for another test marketing.

[1331] In this way, the system supports collaboration between companies at each phase, streamlining the launch of new products and services into the market.

[1332] The processing flow will be explained below.

[1333] Data Collection Phase

[1334] Step 1:

[1335] The server generates a list of companies from which data is collected, categorizing the list by company category and industry.

[1336] Step 2:

[1337] The server performs web scraping and API requests targeting corporate websites, government databases, social media, corporate reports, and more.

[1338] Step 3:

[1339] The server analyzes the collected information and extracts information such as each company's business activities, technology, patents, production locations, and financial information, and stores it in a database.

[1340] Example: The server scrapes product information from Company X's website, collects customer feedback from social media, performs text analysis on it, and registers it in a database.

[1341] Matching list generation phase

[1342] Step 4:

[1343] The server inputs company information stored in a database into the AI ​​model.

[1344] Step 5:

[1345] The server generates a matching list of companies based on the evaluation factors of social contribution, synergy effect, feasibility, and interest.

[1346] Step 6:

[1347] The server sorts the generated matching list in order of rating and creates appropriate proposals for each company.

[1348] Example: The server evaluates the synergy benefits of combining the technical information of company X with the sales network of company Y and places this at the top of the list.

[1349] Proposal Phase

[1350] Step 7:

[1351] The server notifies the target company's terminal of the generated matching list and the contents of the proposal.

[1352] Step 8:

[1353] The terminal receives the notification and displays the proposal to the company representative.

[1354] Step 9:

[1355] The user reviews the offer and, if interested, requests more information.

[1356] Example: The server notifies Company X and Company Y that "This collaboration will help expand your product offerings," and the terminal displays the notification to the person in charge.

[1357] Collaboration MTG implementation phase

[1358] Step 10:

[1359] The user sends a request to the server to set up a meeting between the proposed businesses.

[1360] Step 11:

[1361] The server coordinates schedules between companies and determines dates for online meetings.

[1362] Step 12:

[1363] The terminal notifies the user of meeting reminders.

[1364] Example: Company X and Company Y decide on a meeting date, the server adds it to the schedule, and the device sends a reminder.

[1365] Project Management Phase

[1366] Step 13:

[1367] The server starts a project management tool based on the meeting results and sets tasks and deadlines.

[1368] Step 14:

[1369] Users report their progress on tasks and send updates to the server.

[1370] Step 15:

[1371] The server tracks project progress in real time and automatically updates tasks and deadlines as needed.

[1372] Example: The server sets tasks for a collaborative project between company X and company Y and constantly updates the progress.

[1373] Product / service phase

[1374] Step 16:

[1375] The server formulates a product or service development plan according to the progress of the project.

[1376] Step 17:

[1377] Users provide feedback to the server during the development process.

[1378] Step 18:

[1379] The server conducts test marketing, analyzes the results, and collects feedback.

[1380] Step 19:

[1381] The server prepares the product or service incorporating the improvements for final release and brings it to market.

[1382] Example: The server conducts test sales of a new product jointly developed by Company X and Company Y, analyzes the sales data, and makes improvements and finalizes the specifications based on the feedback.

[1383] Example 1

[1384] 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."

[1385] There is a need to efficiently realize collaboration between companies and quickly bring new products and services to market. However, collecting public information about companies and finding the most suitable partner companies takes a great deal of time and effort. In addition, meeting arrangements and project management are often done manually, making efficient operations difficult. Furthermore, understanding the progress of projects in real time and receiving appropriate feedback is also a challenge.

[1386] 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.

[1387] In this invention, the server includes a means for collecting publicly available company information, a means for generating a matching list using a generative AI model based on the collected company information, and a means for notifying the company terminal of the generated matching list and proposal details. This allows for the rapid identification of optimal partnerships between companies and efficient meeting arrangements and project management. It also enables real-time tracking of project progress, analysis of test marketing results, and feedback collection, significantly improving overall efficiency.

[1388] "Public corporate information" refers to data published by companies, such as websites, government databases, social media, and company reports.

[1389] A "generative AI model" refers to an artificial intelligence algorithm that generates matching lists between companies based on collected data.

[1390] A "matching list" is a list generated to evaluate corporate information and propose optimal partnerships.

[1391] "Proposal content" refers to content that includes the benefits of collaboration between companies and specific proposals based on the generated matching list.

[1392] "Terminal" refers to a device (e.g., PC, tablet, smartphone) used by company personnel to receive and check information.

[1393] "Meeting request methods" refers to the methods by which company representatives can request and schedule meetings with other companies.

[1394] "Project Task" refers to the specific work or activities involved in a collaborative project between companies.

[1395] "Project Progression" refers to the process of how collaboration between companies is progressing.

[1396] "Development Plan" means a detailed plan for the development of a new product or service.

[1397] "Test marketing" refers to the activity of actually selling a new product or service on a trial basis in the market and analyzing the results.

[1398] This invention is a system that collects publicly available information from companies, proposes collaboration between companies based on that information, executes it, and supports the launch of products and services on the market. Specifically, this system is realized using the following hardware and software.

[1399] This system is broadly divided into six phases: data collection phase, matching list generation phase, proposal phase, collaboration meeting implementation phase, project management phase, and product / service creation phase. The processing in each phase is explained below.

[1400] Data Collection Phase

[1401] The server collects publicly available information about companies. It generates a list of target companies and uses web scraping technology and API requests to gather data from websites, government databases, social media, and company reports. It also periodically retrieves updated information, analyzes the collected data, and stores each company's profile (business operations, technology, patents, production locations, financial information, etc.) in a database.

[1402] Example: A server scrapes product catalogs from websites of companies in a specific region using the filtering criteria "technology companies" in the technology field, while simultaneously retrieving financial reports from a government financial database and registering them as profile information in the database.

[1403] Matching list generation phase

[1404] The server runs a generative AI model based on company information stored in a database, generating a matching list of companies based on evaluation factors such as social contribution, synergy effects, feasibility, and interest. The matching list is then sorted by evaluation, and appropriate proposals are made for each company.

[1405] Example: The server inputs a prompt such as "Please suggest the best partner company for Company X's technology" into the generative AI model, and the matching list that evaluates the synergy between Company X and Company Y is ranked at the top.

[1406] Proposal Phase

[1407] The server notifies each company's device of the generated matching list and the details of the proposal. The device receives the notification and displays the details of the proposal to the company representative. The user (company representative) can check the proposal and request more information if they are interested.

[1408] Example: The server sends a proposal such as "Your company's technology has the potential to create high synergy with Company Y's sales channels" to the terminals of Company X and Company Y, and displays it to the person in charge.

[1409] Collaboration Meeting Implementation Phase

[1410] The user requests a meeting between the proposed companies. The server coordinates the schedules between the companies and determines the date and time of the online meeting. The device then sends a meeting reminder to the person in charge.

[1411] Example: A person from company X makes a request to schedule an online meeting, the server coordinates the availability of a person from company Y, sets the appropriate date and time, and sends a reminder.

[1412] Project Management Phase

[1413] The server launches a project management tool based on the meeting results and sets each task and its deadline. Users report the progress of each task and send updates to the server. The server tracks the project progress in real time and automatically updates tasks and deadlines as needed.

[1414] Example: The server launches a task management tool and adds tasks such as "market research" and "technology development." The user reports progress and updates the record to say "market research completed."

[1415] Product / service phase

[1416] The server creates a product or service development plan according to the project progress. Users send feedback during the development process to the server. The server conducts test marketing, analyzes the results, and collects feedback. Finally, it provides the optimal means for releasing the product or service to the market.

[1417] Example: A server tests a new product in a specific region, analyzes the sales data, proposes improvements based on the results of the analysis, and prepares for another test marketing of the improved product.

[1418] As described above, this system performs detailed and specific processing at each phase, supporting collaboration between companies and streamlining the launch of new products and services into the market.

[1419] The flow of the identification process in the first embodiment will be described with reference to FIG.

[1420] Step 1:

[1421] The server collects publicly available information about companies. It receives filtering criteria (such as industry, size, and region) as input and generates a list of companies to collect as output. Specifically, it uses web scraping technology and API requests to collect data from company websites and government agency databases. This data is then stored in a database as a profile for each company, including production location, technology, patents, and financial information.

[1422] Step 2:

[1423] The server runs a generative AI model based on company information stored in a database. The input here is company profile data, and the output is a matching list based on evaluation factors such as social contribution, synergy effects, feasibility, and interest. Specifically, a prompt such as "Please suggest partner companies that are best suited to Company X's technology" is input into the generative AI model, and the AI ​​automatically generates a matching list.

[1424] Step 3:

[1425] The server notifies the company's device of the generated matching list and proposal details. It receives the matching list and proposal details as input and displays them on the device in a format that can be checked by the company representative as output. Specific operations include sending information using an email API or notification system, and displaying it on the device as a pop-up notification, etc.

[1426] Step 4:

[1427] The user (company representative) receives the notification and checks the proposal. If interested, the user requests more detailed information. The input is the notification of the proposal, and the output is a request for more information sent to the server. The specific operation is to click the "Request more information" button on the terminal's user interface.

[1428] Step 5:

[1429] A user requests a meeting between the proposed companies. As input, the user sends the meeting request information (date, time, participants, etc.) to the server, and as output, the user receives the adjusted schedule. Specifically, a company representative sends a request using an online scheduling system, and the server adjusts the schedule between the companies and sets an appropriate date and time.

[1430] Step 6:

[1431] The server launches a project management tool based on the meeting results and sets each task and its deadline. It receives the meeting deliverables and task information as input, and sets tasks and deadlines in the project management tool as output. Specifically, the server launches a task management tool (e.g., JIRA) and adds tasks such as "market research" and "technology development."

[1432] Step 7:

[1433] Users report the progress of each task and send updates to the server. The input is task progress information, and the output is a project management tool with updated progress. Specifically, the person in charge enters progress information such as "market research completed," and the server reflects it in the project management tool.

[1434] Step 8:

[1435] The server formulates a product or service development plan based on the project progress and conducts test marketing. It receives progress data and marketing plan information as input, and obtains test marketing results and feedback as output. Specifically, the server conducts test sales of a new product in a specific region, analyzes the data, and creates improvement proposals.

[1436] (Application example 1)

[1437] 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."

[1438] Conventional inter-company collaboration proposal and execution support systems collect and analyze publicly available information from companies to generate matching lists, but they are unable to take into account data on manufacturing processes and production equipment. This makes it difficult to make optimal proposals for actual factory operations. Furthermore, they lack the ability to easily monitor and automatically update the real-time progress of manufacturing processes, making efficient project management difficult. Furthermore, there is a lack of means to forecast demand and quickly and accurately gather feedback when test selling and launching newly developed products and services.

[1439] 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.

[1440] In this invention, the server includes means for collecting operation data from factory automation equipment or autonomous machines, means for analyzing the collected operation data and generating company profiles, means for proposing corporate collaborations based on the generated profiles, means for notifying the generated matching list and proposal details, means for setting up online meetings between proposed companies, means for managing project progress based on the meeting results and launching products or services as the project progresses, means for test selling new products using the collected data and providing feedback, and means for supporting product launches. This enables optimal collaboration proposals that take into account automation equipment data, enabling real-time monitoring of manufacturing processes and efficient project management. It also enables rapid and accurate feedback collection when launching new products or services.

[1441] "Corporate public information" refers to information that a company makes publicly available through its website, government agency databases, social media, corporate reports, etc.

[1442] The "matching list" is a list of potential collaborations between companies that is generated by running an AI model based on collected company information and evaluating factors such as social contribution, synergy effects, feasibility, and interest.

[1443] "Proposal content" refers to details and recommendations for collaboration between companies generated by the server based on the matching list.

[1444] The means for setting up a "meeting" is a method for the proposed companies to coordinate a date and time for a meeting online or offline and share information.

[1445] A "means for managing project progress" is a method for tracking project tasks, deadlines, and progress in real time based on meeting results, and automatically updating as needed.

[1446] "Factory automation equipment or autonomously operating machines" means machines or robots that automate manufacturing processes within a factory and are capable of operating independently.

[1447] "Operation data" refers to recorded information such as the operating status, performance indicators, and operating hours of automated equipment or autonomously operating machines in a factory.

[1448] A "company profile" is a detailed record of a company's business activities, technology, patents, production locations, financial information, etc., based on an analysis of collected operational data and publicly available information.

[1449] An "online meeting" is a meeting between companies held over the Internet, and refers to a means that allows for the coordination of dates and times and the sharing of information.

[1450] A "new product test launch" is the process of launching a newly developed product or service in a limited market and collecting response and sales data.

[1451] "Feedback" refers to information regarding evaluations and areas for improvement obtained during trial sales and project implementation.

[1452] "Go-to-market aids" are the methods and tools used to formally introduce new products and services to the market and promote their sales.

[1453] This invention is a system that collects publicly available information from companies, proposes collaboration between companies based on that information, executes it, and supports the launch of products and services on the market. Below, we will create a program for this system and explain its processing in natural language.

[1454] The system consists of three entities: a server, a terminal, and a user.

[1455] Data Collection Phase

[1456] The server collects publicly available information about companies. It generates a list of target companies and gathers data from websites, government databases, social media, and company reports. Specifically, it uses web scraping technology and API requests to periodically retrieve the latest information. The collected data is analyzed and a profile of each company (including business activities, technology, patents, production locations, and financial information) is stored in a database.

[1457] Example: A server retrieves a product catalog from Company A's website, parses financial reports retrieved from a government database, and enters them into a database.

[1458] Matching list generation phase

[1459] The server runs a generative AI model based on the company information stored in the database, and generates a matching list of companies based on the evaluation factors of social contribution, synergy effect, feasibility, and interest. The generated matching list is sorted in order of evaluation, and appropriate proposals are made for each company.

[1460] Example: The server evaluates the synergy effect when combining company A's technical information with company B's sales channels, and places it at the top of the matching list.

[1461] Proposal Phase

[1462] The server notifies each company's device of the generated matching list and the details of the proposal. The device receives the notification and displays the details of the proposal to the company representative. The user (company representative) can check the proposal and request more information if they are interested.

[1463] Example: The server notifies Company A and Company B that "Your technology has the potential to create high synergy with Company B's sales channels," and the terminal displays this information to the person in charge.

[1464] Collaboration MTG implementation phase

[1465] The user requests a meeting between the proposed companies. The server coordinates the schedules between the companies, determines the date and time of the online meeting, and shares relevant information. The device then sends a meeting reminder to the person in charge.

[1466] Example: Representatives from company A and company B schedule an online meeting, and the server adds it to the schedule and sends reminders.

[1467] Project Management Phase

[1468] The server launches a project management tool based on the meeting results and sets tasks and deadlines. Users report the progress of each task and send updates to the server. The server tracks project progress in real time and automatically updates tasks and deadlines as needed.

[1469] Example: The server sets tasks for a collaborative project between companies A and B and constantly updates the progress.

[1470] Product / service phase

[1471] The server creates a development plan for the product or service according to the project's progress. Users send feedback to the server during the development process. The server conducts test marketing, analyzes the results, and collects feedback. Finally, the server provides the optimal means for releasing the product or service to the market.

[1472] Example: The server conducts test sales of a new product jointly developed by Company A and Company B, analyzes the sales data, proposes improvements based on the feedback, and prepares for another test marketing.

[1473] Hardware and software used

[1474] The system uses the following hardware and software:

[1475] Hardware: Smartphone, head-mounted display (e.g., Oculus Quest 2)

[1476] Software: Python web scraping (BeautifulSoup), HTTP request library (requests), data analysis (pandas), generative AI models

[1477] Prompt Sentence Examples

[1478] An example of a prompt to input to a generative AI model is as follows:

[1479] "Develop a system that evaluates synergy between companies based on technical information and generates optimal collaboration proposals."

[1480] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[1481] Step 1: Data collection

[1482] The server collects publicly available information about companies. It generates a list of companies to collect and obtains data from websites, government databases, social media, and company reports. This is done using web scraping technology (BeautifulSoup) and API requests (requests library). The input is various publicly available information, and the output is a profile of each company (business details, technology, patents, production locations, financial information, etc.).

[1483] Step 2: Save to database

[1484] The server analyzes the collected data and stores it in a database. First, it analyzes the acquired HTML data and API responses to extract detailed information about each company. Next, it structures the extracted data and registers it in the database. The input is the analyzed information, and the output is the company profile registered in the database.

[1485] Step 3: Run the AI ​​model

[1486] The server runs a generative AI model based on the company information stored in the database. Specifically, it generates a matching list of companies based on the evaluation factors of social contribution, synergy effects, feasibility, and interest. The input is the company profile, and the output is a matching list of companies. At this stage, the model analyzes and evaluates the data.

[1487] Step 4: Proposal generation and notification

[1488] The server creates an optimal proposal for each company based on the generated matching list and notifies the device. The input is the matching list, and the output is a proposal notification sent to each company's device. The notification content includes details of the proposed collaboration, which the device displays to the person in charge.

[1489] Step 5: Meeting Schedule

[1490] The user reviews the proposal and, if interested, requests more information and a meeting from the server. The server coordinates schedules between the companies and determines the date and time of the online meeting. The input is the meeting request, and the output is the scheduled meeting date and time and related information. The terminal notifies the person in charge of the meeting with a reminder.

[1491] Step 6: Project Management

[1492] The server launches the project management tool based on the meeting results and sets tasks and deadlines. The input is the meeting results and the output is the set tasks and deadlines. Users report the progress of each task and send updates to the server. The server tracks the project progress in real time and automatically updates tasks and deadlines as needed.

[1493] Step 7: Product / Service

[1494] The server formulates a product or service development plan according to the project progress. The input is project progress information, and the output is a development plan. Users send feedback during the development process to the server, which then conducts test marketing. The test marketing results are analyzed, feedback is collected, and the server provides the optimal means for finally releasing the product or service to the market.

[1495] Through the above processing steps, the system of the present invention efficiently supports everything from collaboration between companies to the launch of products and services on the market.

[1496] 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.

[1497] This system collects publicly available information from companies and uses that information to propose and execute collaborations between companies, supporting the launch of products and services to market. Furthermore, by incorporating an emotion engine, it is possible to recognize user emotions and optimize proposal content and meeting progress based on those emotions. Below, we will create a program for this system and explain its processing in natural language.

[1498] This system is broadly divided into six phases: data collection phase, matching list generation phase, proposal phase, collaborative meeting implementation phase, project management phase, and product / service creation phase. In each phase, an emotion engine recognizes the user's emotions and responds appropriately.

[1499] Data Collection Phase

[1500] The server collects publicly available information about companies, generates a list of companies to collect, and performs web scraping and API requests targeting websites, government databases, social media, corporate reports, etc. The server analyzes the collected information and stores each company's business details, technology, patents, production locations, financial information, etc. in a database.

[1501] Example: The server scrapes product information from Company X's website, collects customer feedback from social media, performs text analysis on it, and registers it in a database.

[1502] Matching list generation phase

[1503] The server inputs the company information stored in the database into the AI ​​model and generates a matching list of companies based on the evaluation factors of social contribution, synergy effect, feasibility, and interest. The generated matching list is sorted in order of evaluation, and appropriate proposals are made for each company.

[1504] Example: The server evaluates the synergy benefits of combining the technical information of company X with the sales network of company Y and places this at the top of the list.

[1505] Proposal Phase

[1506] The server notifies each company's device of the generated matching list and the proposed content. The device receives the notification and displays the proposed content to the company representative. The user (company representative) checks the proposed content and, based on the emotions recognized by the emotion engine, requests more detailed information if they are interested.

[1507] Example: The server notifies Company X and Company Y that "This collaboration will help expand your product offerings," and the user's device displays the notification to the person in charge, and the emotion engine evaluates satisfaction.

[1508] Collaboration MTG implementation phase

[1509] The user requests a meeting between the proposed companies. The server coordinates the schedules between the companies and determines the date of the online meeting. The device notifies the user of meeting reminders. The emotion engine monitors the user's emotional state in real time during the meeting and provides necessary support.

[1510] Example: Representatives from Company X and Company Y decide on a date for a meeting, the server adds it to the schedule, the device sends a reminder, and the emotion engine detects tension and makes appropriate suggestions.

[1511] Project Management Phase

[1512] The server launches a project management tool based on the meeting results and sets tasks and deadlines. Users report their task progress and send updates to the server. The server tracks project progress in real time and automatically updates tasks and deadlines as needed. The emotion engine monitors users' emotions regarding project progress and provides appropriate support based on that.

[1513] Example: A server sets tasks for a collaborative project between companies X and Y, constantly updates the progress, and an emotion engine detects the user's stress and provides reminders and advice.

[1514] Product / service phase

[1515] The server formulates a product or service development plan according to the project's progress. Users send feedback during the development process to the server. The server conducts test marketing, analyzes the results, and collects feedback. Finally, the server provides the optimal means for releasing the product or service to the market. The emotion engine analyzes the emotional reactions of the feedback and uses them to improve the product or service.

[1516] Example: The server conducts test sales of a new product jointly developed by Company X and Company Y, analyzes the sales data, and makes improvements and finalizes the specifications based on the feedback. The emotion engine analyzes positive reactions and adjusts the final release.

[1517] In this way, this system supports collaboration between companies at each phase, and by utilizing an emotion engine, it optimizes proposal content and meeting progress, thereby streamlining the launch of new products and services into the market.

[1518] The processing flow will be explained below.

[1519] Data Collection Phase

[1520] Step 1:

[1521] The server generates a list of companies to collect data from. First, it selects target companies based on their industry and market needs and adds them to the list.

[1522] Step 2:

[1523] The servers perform web scraping and API requests targeting company websites, government databases, social media, company reports, etc. The information collection is carried out periodically.

[1524] Step 3:

[1525] The server analyzes the collected information and stores each company's business details, technology, patents, production locations, financial information, etc. in a database. The analysis uses technologies such as natural language processing.

[1526] Example: The server accesses Company X's website, scrapes the latest product information, analyzes customer reviews on social media, and registers this information in a database.

[1527] Matching list generation phase

[1528] Step 4:

[1529] The server inputs company information stored in a database into the AI ​​model, which learns from the existing data.

[1530] Step 5:

[1531] The server generates a matching list of companies based on the evaluation factors of social contribution, synergy, feasibility, and interest, with each factor being evaluated according to a pre-set weighting.

[1532] Step 6:

[1533] The server sorts the generated matching list in order of evaluation and creates appropriate proposals for each company, including specific collaboration benefits and prospects.

[1534] Example: The server calculates the synergy effect of combining the technological capabilities of company X with the sales network of company Y, and places this at the top of the list.

[1535] Proposal Phase

[1536] Step 7:

[1537] The server notifies each company's terminal of the generated matching list and the proposal details, including a summary of the proposal details.

[1538] Step 8:

[1539] The device receives the notification and displays the proposal to the company representative. The device uses an emotion engine to evaluate how the user feels about the proposal.

[1540] Step 9:

[1541] Users can review the suggestions and request more information if they are interested, and suggestions may be automatically customized based on the sentiment engine's evaluation.

[1542] Example: The server notifies company X and company Y that "this collaboration will help expand your product offering," the device displays the notification to the person in charge, and the emotion engine evaluates the level of interest.

[1543] Collaboration MTG implementation phase

[1544] Step 10:

[1545] The user sends a request to the server to set up a meeting between the proposed companies. The request is made online.

[1546] Step 11:

[1547] The server coordinates schedules between companies and determines the dates of online meetings. The server uses an emotion engine to evaluate whether the meeting dates are appropriate.

[1548] Step 12:

[1549] The device notifies users of meeting reminders, and during meetings, the emotion engine monitors users' emotions in real time and provides suggestions and support as needed.

[1550] Example: Representatives from Company X and Company Y decide on a meeting date, the server adds it to the schedule, the device sends a reminder, and the emotion engine detects the level of tension during the meeting and provides appropriate support.

[1551] Project Management Phase

[1552] Step 13:

[1553] The server launches a project management tool based on the meeting results, sets tasks and deadlines, and an emotion engine evaluates the suitability of the project progress.

[1554] Step 14:

[1555] Users report their progress on tasks and send updates to the server, and the emotion engine monitors the user's emotions regarding their progress.

[1556] Step 15:

[1557] The server tracks project progress in real time and automatically updates tasks and deadlines as needed, while the emotion engine optimizes projects based on stress and satisfaction.

[1558] Example: A server sets tasks for a collaborative project between companies X and Y, constantly updates the progress, and an emotion engine detects the user's stress and provides reminders and advice.

[1559] Product / service phase

[1560] Step 16:

[1561] The server formulates a product or service development plan according to the project progress, and the emotion engine analyzes user feedback on the development process.

[1562] Step 17:

[1563] Users submit feedback during the development process to the server, and an emotion engine evaluates the emotional response of the feedback.

[1564] Step 18:

[1565] The server conducts test marketing, analyzes the results, and collects feedback. The emotion engine then proposes improvements to the product or service based on the results of the test marketing.

[1566] Step 19:

[1567] The server prepares the product or service with the improvements for final release and launches it in the market, and the emotion engine analyzes the expectations and positive reactions to the final release.

[1568] Example: The server conducts a test sale of a new product jointly developed by Company X and Company Y, analyzes the sales data, makes improvements based on feedback, and an emotion engine analyzes positive reactions to adjust the final release.

[1569] Example 2

[1570] 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."

[1571] Collaboration between companies involves multiple processes, such as information gathering, proposals, meeting arrangements, project management, and product / service creation. However, efficiently managing these processes and increasing the success rate is difficult. Furthermore, there is a need for a method to optimize proposal content and meeting progress by appropriately reflecting user emotions. Currently, there is a lack of systems that automate these processes and optimize them by incorporating emotion analysis.

[1572] 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.

[1573] In this invention, the server includes: means for collecting public company information; means for generating a matching list using an artificial intelligence model based on the collected company information; means for notifying a user terminal of the generated matching list and proposal content; means for setting up online meetings between the proposed companies; means including an emotion engine for analyzing user emotions in real time during the meeting and providing support; means for managing project progress based on the meeting results; and means for launching products or services to the market according to the progress of the project. This makes it possible to automate the collaboration process between companies and optimize the effectiveness of proposals and meetings by analyzing user emotions.

[1574] "Public corporate information" is information about a company that is publicly available through websites, government databases, social media, company reports, etc.

[1575] "Artificial intelligence model" is a general term for machine learning and deep learning models used to generate matching lists of companies based on evaluation factors such as social contribution, synergy effects, feasibility, and interest.

[1576] The "matching list" is a list generated by an artificial intelligence model that evaluates and ranks the potential for collaboration between companies.

[1577] "User terminal" refers to a device used by a user to receive notifications from the system and check proposals and meeting information. Examples of such devices include PCs, smartphones, and tablets.

[1578] An "online meeting" is a type of remote conference that takes place in real time over the Internet, including, for example, video conferencing and web conferencing.

[1579] "Emotion engine" is a general term for emotion analysis software and hardware that analyzes a user's emotional state in real time and provides appropriate support based on the proposal content and meeting progress.

[1580] "Project progress" refers to a series of activities that involve managing collaborative projects discussed between companies to ensure they proceed as planned, and checking the progress of tasks and deliverables.

[1581] "Bringing a product or service to market" means releasing the product or service that is the result of a collaboration project into the marketplace in a form that is available for purchase by the general public.

[1582] "Real-time tracking" means constantly monitoring the progress of a project and updating the information as it progresses to keep it up to date.

[1583] "Test marketing" is the process of selling a product or service on a limited scale on a trial basis before launching it on the market, and then collecting and analyzing the results and feedback from consumers.

[1584] "Emotion analysis results" are data and evaluation information generated based on the user's emotional state analyzed by the emotion engine.

[1585] Overall structure

[1586] This invention is a system that collects publicly available information from companies and uses that information to propose and execute collaborations between companies, supporting the launch of products and services to the market. The system consists of six phases: data collection phase, matching list generation phase, proposal phase, collaboration meeting implementation phase, project management phase, and product / service creation phase. In each phase, an emotion engine is able to recognize the user's emotions and respond appropriately.

[1587] Data Collection Phase

[1588] The server collects publicly available information about companies. It generates a list of companies to collect and collects information from websites, government databases, social media, corporate reports, etc. using web scraping tools such as Scrapy and Beautiful Soup, as well as the Twitter API and Facebook Graph API. This collected information is analyzed using natural language processing tools (NLTK and spaCy), and each company's business details, technology, patents, production locations, financial information, etc. are stored in a cloud database (e.g., AWS RDS).

[1589] Examples:

[1590] The server scrapes product information from Company A's website, collects customer feedback from social media using the Twitter API, analyzes the text using NLTK, and registers it in a database.

[1591] Matching list generation phase

[1592] The server inputs the collected company information into a generative AI model trained with TensorFlow and PyTorch, and generates a matching list of companies based on four evaluation criteria: social contribution, synergy, feasibility, and interest. The matching list is then sorted in order of evaluation, and appropriate proposals are created for each company.

[1593] Examples:

[1594] The server evaluates the synergy effect that would be achieved by integrating Company A's technical information with Company B's sales network, and places it at the top of the matching list.

[1595] Proposal Phase

[1596] The server notifies each company's device of the generated matching list and the proposed content. The device receives the notification and displays the proposed content to the company representative. The user (company representative) checks the proposed content and, if interested, requests further information based on the emotions recognized by the emotion engine (e.g., Affectiva).

[1597] Examples:

[1598] The server notifies Company A and Company B of the proposal that "this collaboration will help expand your products," and the user's device displays the notification to the person in charge, and the emotion engine evaluates satisfaction.

[1599] Collaboration MTG implementation phase

[1600] Users request meetings between proposed companies online. The server coordinates schedules between the companies and determines the date using the Google Calendar API and Microsoft Exchange Web Services (EWS). The device notifies the user of meeting reminders. The emotion engine monitors the user's emotional state in real time during the meeting and provides necessary support.

[1601] Examples:

[1602] Representatives from companies A and B decide on a meeting date, the server adds it to the schedule, the device sends a reminder, and the emotion engine detects tension and makes appropriate suggestions.

[1603] Project Management Phase

[1604] The server launches project management tools such as Jira and Trello based on the meeting results and sets tasks and deadlines. Users report their task progress and send updates to the server. The server tracks project progress in real time and automatically updates tasks and deadlines as needed. The emotion engine monitors users' emotions regarding the project progress and provides appropriate support based on that.

[1605] Examples:

[1606] The server sets tasks for the collaborative project between Company A and Company B, constantly updates the progress, and the emotion engine detects the user's stress and provides reminders and advice.

[1607] Product / service phase

[1608] The server creates a development plan for the product or service as the project progresses. Users provide feedback to the server during the development process. The server conducts test marketing, analyzes the results, and collects feedback. Finally, the server determines the optimal method for releasing the product or service to the market. The emotion engine analyzes the emotional responses of the feedback and uses them to improve the product or service.

[1609] Examples:

[1610] The server conducts test sales of a new product jointly developed by Company A and Company B and analyzes the sales data. The final specifications are determined based on the feedback, and the emotion engine analyzes positive reactions to determine the optimal timing for market launch.

[1611] With this system in place, collaboration between companies can be managed efficiently, and proposals and meeting progress can be optimized using an emotion engine, resulting in more effective market launches of new products and services.

[1612] The flow of the identification process in the second embodiment will be described with reference to FIG.

[1613] Step 1:

[1614] Generate a list of companies

[1615] The server extracts basic information about the target companies (company name, website URL, social media accounts, etc.) from the cloud database and generates a list of companies.

[1616] Input: Cloud Database

[1617] Output: Company list

[1618] Specific operation: Executes an SQL query to extract company information and lists it as a company list.

[1619] Step 2:

[1620] Implementing information gathering

[1621] The server uses web scraping tools such as Scrapy and Beautiful Soup to gather product and technical information from company websites, and also uses the Twitter API and Facebook Graph API to retrieve social media data.

[1622] Input: Company List

[1623] Output: Collected company data

[1624] Specific operation: Based on the company list, web scraping and API calls are performed to collect text data.

[1625] Step 3:

[1626] Data analysis and storage

[1627] The server analyzes the collected data using Natural Language Toolkit (NLTK) and spaCy, and categorizes it into categories such as the company's business operations, technical information, financial status, etc. The analysis results are stored in a cloud database.

[1628] Input: Collected company data

[1629] Output: Analyzed company information database

[1630] Specific operation: Analyzes text data using NLTK and spaCy, extracts business details and technical information, and stores it in a database.

[1631] Step 4:

[1632] Generating a Matching List

[1633] The server inputs the collected company information into a generative AI model trained with TensorFlow and PyTorch, and generates a matching list of companies based on evaluation factors (social contribution, synergy effect, feasibility, and interest).

[1634] Input: Analyzed company information database

[1635] Output: Matching list

[1636] How it works: Company data is input into the AI ​​model to calculate a rating score, which is then sorted by score to create a matching list.

[1637] Step 5:

[1638] Sending proposal notifications

[1639] The server notifies each company's terminal of the generated ...

Claims

1. means of collecting public information about companies; A means for generating a matching list based on the collected company information; a means for notifying the generated matching list and proposal; A means for setting up meetings between the proposed companies; A means of managing project progress based on meeting results; A system that includes the means to bring a product or service to market as the project progresses.

2. 10. The system of claim 1, further comprising means for tracking project progress in real time and automatically updating as needed.

3. 10. The system according to claim 1, further comprising means for conducting test marketing, analyzing the results, and collecting feedback.

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