system

Abstract

Provide a system. 【Solution means】 Means for inputting and analyzing requirements from a user, Means for generating a list of optimal suppliers based on the requirements, Means for automatically creating a purchase order and sending it to the suppliers included in the list, Means for collecting and evaluating the proposed contents from the suppliers based on unified criteria, Means for setting interviews with the suppliers based on the evaluation, Means for obtaining and evaluating the trust information and safety information of the suppliers, Means for performing an ordering procedure for the finally selected supplier, Means for monitoring the operating status of manufacturing machines, selecting suppliers for supplying the next required components and repair services, and supporting orders, A system including the above.

Description

Technical Field

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

Background Art

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

Prior Art Documents

Patent Documents

[0003]

Patent Document 1

Summary of the Invention

Problems to be Solved by the Invention

[0004] In promoting digital transformation in modern enterprises, it is extremely important to select appropriate partner companies. However, it requires a great deal of time and effort to evaluate many factors including business expertise and reliability, and there is a problem that it is difficult to compare different candidates on a consistent basis. In addition, this process is inefficient, and there are risks such as delays in selection and selection of inappropriate partners.

Means for Solving the Problems

[0005] This invention is a system that utilizes an AI agent to centrally manage the partner company selection process, aiming to improve efficiency and reliability. The system includes means for collecting user requirements and generating an optimal list of partner companies based on them. Furthermore, it supports rapid and accurate selection by automating the creation of requests for proposals, evaluation of proposals from candidates, scheduling interviews, assessing creditworthiness and security information, and even the final ordering process. In this way, the entire process, from user input to final ordering, is systematized, achieving efficient and consistent selection.

[0006] A "user" is an individual or organization that operates the system and inputs requirements and conditions for selecting business partners.

[0007] "Requirements" refer to information such as the specific specifications, conditions, budget, and deadlines that the user requests from partner companies.

[0008] "Partner companies" are companies that users may potentially outsource part of their operations to, and are the recipients of proposals and services.

[0009] The "means for generating lists" refer to a function that extracts the most suitable partner companies based on the user's requirements and provides them in a list format.

[0010] A "Request for Proposal" is an official document that clearly outlines the user's requirements and requests specific proposals from partner companies.

[0011] "Means of evaluation" refers to the process of comparing collected proposals quantitatively or qualitatively and determining their merit or demerit according to certain criteria.

[0012] The "means of scheduling meetings" refer to a function that allows users to coordinate opportunities for direct communication with partner companies they have selected and to confirm specific dates.

[0013] "Credit information" refers to data that contributes to the assessment of reliability, including the history, transaction record, and financial status of partner companies.

[0014] "Security information" refers to data that shows the information protection policies, track record, or evaluations from third-party organizations of partner companies.

[0015] The "procurement process" is the process of formally requesting work from the final selected cooperating companies and concluding a contract. [Brief explanation of the drawing]

[0016] [Figure 1] This is a conceptual diagram showing an example of the configuration of a data processing system according to the first embodiment. [Figure 2] This is a conceptual diagram showing an example of the essential functions of a data processing device and a smart device according to the first embodiment. [Figure 3] This is a conceptual diagram showing an example of the configuration of a data processing system according to the second embodiment. [Figure 4] This is a conceptual diagram showing an example of the main functions of a data processing device and smart glasses according to the second embodiment. [Figure 5] This is a conceptual diagram showing an example of the configuration of a data processing system according to the third embodiment. [Figure 6] This is a conceptual diagram showing an example of the main functions of a data processing device and a headset-type terminal according to the third embodiment. [Figure 7] This is a conceptual diagram showing an example of the configuration of a data processing system according to the fourth embodiment. [Figure 8] This is a conceptual diagram showing an example of the main functions of a data processing device and a robot according to the fourth embodiment. [Figure 9] This shows an emotion map where multiple emotions are mapped. [Figure 10] This shows an emotion map where multiple emotions are mapped. [Figure 11] This is a sequence diagram showing the processing flow of the data processing system in Example 1. [Figure 12]It is a sequence diagram showing the processing flow of the data processing system in Application Example 1. [Figure 13] It is a sequence diagram showing the processing flow of the data processing system in Embodiment 2 when combined with an emotion engine. [Figure 14] It is a sequence diagram showing the processing flow of the data processing system in Application Example 2 when combined with an emotion engine.

Modes for Carrying Out the Invention

[0017] Hereinafter, an example of an embodiment of a system according to the technology of the present disclosure will be described with reference to the accompanying drawings.

[0018] First, the terms used in the following description will be explained.

[0019] In the following embodiments, a numbered processor (hereinafter simply referred to as "processor") may be a single arithmetic unit or a combination of multiple arithmetic units. Also, the processor may be a single type of arithmetic unit or a combination of multiple types of arithmetic units. Examples of arithmetic units include a CPU (Central Processing Unit), a GPU (Graphics Processing Unit), a GPGPU (General-Purpose computing on Graphics Processing Units), an APU (Accelerated Processing Unit), and the like.

[0020] In the following embodiments, a numbered RAM (Random Access Memory) is a memory in which information is temporarily stored and is used as a work memory by the processor.

[0021] In the following embodiments, the signed storage is one or more non-volatile storage devices that store various programs and various parameters. Examples of non-volatile storage devices include flash memory (SSD (Solid State Drive)), magnetic disks (e.g., hard disks), or magnetic tapes.

[0022] In the following embodiments, the signed communication interface (I / F) is an interface that includes a communication processor and an antenna, etc. The communication interface manages communication between multiple computers. Examples of communication standards applicable to the communication interface include wireless communication standards such as 5G (5th Generation Mobile Communication System), Wi-Fi (registered trademark), or Bluetooth (registered trademark).

[0023] In the following embodiments, "A and / or B" is synonymous with "at least one of A and B." That is, "A and / or B" means that it may be A alone, or B alone, or a combination of A and B. Furthermore, in this specification, the same concept as "A and / or B" applies when expressing three or more things linked by "and / or."

[0024] [First Embodiment]

[0025] Figure 1 shows an example of the configuration of the data processing system 10 according to the first embodiment.

[0026] As shown in Figure 1, the data processing system 10 includes a data processing device 12 and a smart device 14. An example of the data processing device 12 is a server.

[0027] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 is an example of a "computer" related to the technology of this disclosure. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[0028] The smart device 14 comprises a computer 36, a reception device 38, an output device 40, a camera 42, and a communication interface 44. The computer 36 comprises a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The reception device 38, output device 40, and camera 42 are also connected to the bus 52.

[0029] The reception device 38 is equipped with a touch panel 38A and a microphone 38B, etc., and receives user input. The touch panel 38A receives user input by detecting contact with an object (e.g., a pen or finger). The microphone 38B receives user input by detecting the user's voice. The control unit 46A transmits data indicating the user input received by the touch panel 38A and microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the data indicating the user input.

[0030] The output device 40 includes a display 40A and a speaker 40B, and presents data to the user 20 by outputting the data in a form perceptible to the user 20 (e.g., audio and / or text). The display 40A displays visible information such as text and images according to instructions from the processor 46. The speaker 40B outputs audio according to instructions from the processor 46. The camera 42 is a small digital camera equipped with an optical system such as a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor.

[0031] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various types of information between processor 46 and processor 28 via network 54.

[0032] Figure 2 shows an example of the main functions of the data processing device 12 and the smart device 14.

[0033] As shown in Figure 2, in the data processing device 12, a specific processing is performed by the processor 28. A specific processing program 56 is stored in the storage 32. The specific processing program 56 is an example of a "program" related to the technology of this disclosure. The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0034] The storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[0035] In the smart device 14, the processor 46 performs the reception output processing. The storage 50 stores the reception output program 60. The reception output program 60 is used in conjunction with a specific processing program 56 by the data processing system 10. The processor 46 reads the reception output program 60 from the storage 50 and executes the read reception output program 60 on the RAM 48. The reception output processing is realized by the processor 46 operating as a control unit 46A according to the reception output program 60 executed on the RAM 48.

[0036] Next, the specific processing performed by the specific processing unit 290 of the data processing device 12 will be described. In the following description, the data processing device 12 will be referred to as the "server" and the smart device 14 as the "terminal".

[0037] This invention is a system for streamlining the selection process of partner companies and for quickly selecting highly reliable business partners. The program of this system has a mechanism in which the server, terminals, and users work together in cooperation with each other.

[0038] System configuration:

[0039] The server, acting as the system's core, runs AI agents responsible for processing vast amounts of data. The terminal functions as the user interface, interacting with the user. The user, a company representative, inputs requirements and conditions for selecting partner companies through the terminal.

[0040] Program execution flow:

[0041] The user enters their requirements for selecting a business partner into a terminal. The terminal converts the input into the appropriate format and sends it to the server. The server uses an AI agent to list the most suitable partner companies based on the user's requirements. The generated list is presented to the user via the terminal.

[0042] The user selects companies to send a Request for Proposal (RFP) to from the displayed candidates. The server automatically creates an RFP and sends it to the selected partner companies. Once proposals from each partner company are returned to the server, an AI agent analyzes and evaluates the proposals. The evaluation results are provided to the user via the terminal, and if it is determined that a meeting with a particular partner company is necessary, a meeting is scheduled.

[0043] Furthermore, the server acquires credit and security information from selected partner companies and incorporates the results into the evaluation. Finally, the user drafts a proposal based on the evaluation results, and the ordering process is completed through the server's automated workflow.

[0044] Specific example:

[0045] For example, a user might request the construction of a new IT infrastructure. In this case, the user inputs the characteristics of the desired collaborating company (e.g., a company strong in cloud technology, a company capable of meeting short deadlines) into the system. The server's AI agent searches for and presents candidate companies that best match the requirements. A request for proposal (RPO) is automatically generated and sent to the selected candidate companies, after which the proposals obtained by the AI ​​are analyzed and evaluated. Based on these results, the user selects a company, drafts a proposal for approval while considering credit information and security information as needed, and finally concludes a contract.

[0046] This system allows companies to quickly select the best business partner for their needs, and the entire process is carried out transparently and efficiently.

[0047] The following describes the processing flow.

[0048] Step 1:

[0049] Users use a terminal to input specific requirements they seek from business partners (e.g., technical requirements, schedule, budget, etc.). This information forms the basis for future Requests for Proposals (RFPs).

[0050] Step 2:

[0051] The terminal converts the entered request requirements into a predetermined format and sends the data to the server. This step verifies the accuracy and completeness of the data.

[0052] Step 3:

[0053] The server uses an AI agent to generate a list of optimal partner companies based on the requirements. In doing so, it refers to market databases and historical company evaluation data to select candidates that meet the needs.

[0054] Step 4:

[0055] The terminal displays a list of partner companies sent from the server to the user. The user reviews this list and selects the companies they wish to request proposals from.

[0056] Step 5:

[0057] Based on the user's selection, the server automatically generates a Request for Proposal (RFP) using an AI agent and sends the RFP to the selected partner companies.

[0058] Step 6:

[0059] Proposals from partner companies are sent back to the server. The server's AI agent analyzes these proposals, evaluates them based on pre-set evaluation criteria, and assigns a score.

[0060] Step 7:

[0061] The evaluation results are sent to the terminal, and the user selects companies they wish to interview based on the evaluation. If necessary, the server schedules the interview and notifies both parties.

[0062] Step 8:

[0063] The server obtains credit and security information from an external database as part of a reliability check for selected partner companies and incorporates it into the evaluation.

[0064] Step 9:

[0065] The user makes the final decision on a collaborating company based on reliability assessments and then drafts a proposal for approval. The server automatically manages the approval process and completes the ordering procedure once approval is obtained.

[0066] (Example 1)

[0067] Next, we will describe Example 1. In the following description, the data processing device 12 will be referred to as the "server," and the smart device 14 will be referred to as the "terminal."

[0068] The traditional process for selecting partner companies was time-consuming and labor-intensive, and the evaluation criteria were inconsistent, making it difficult to quickly select reliable business partners.

[0069] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 1 is realized by the following means.

[0070] In this invention, the server includes processing means for receiving and analyzing requests, processing means for generating a list of optimal external organizations based on the requests, and processing means for automatically generating communication documents and sending them to the external organizations included in the list. This makes it possible to perform the selection process for partner companies efficiently and reliably.

[0071] A "request" is information that indicates the user's wishes or conditions for the system.

[0072] "Analysis" refers to the process of analyzing input information and converting it into a form that the system can understand.

[0073] "External organizations" refer to third-party groups or companies that are considered as collaborating companies or business partners.

[0074] A "list" is a list containing a set of external organizations that are candidates selected by the system.

[0075] "Communication documents" refer to automatically generated documents that include requests for proposals and other necessary information, and are sent to external organizations.

[0076] "Reply content" refers to information received by the system as a response to proposals or information from external organizations.

[0077] A "uniform standard" refers to a standardized and consistent scale or indicator used when conducting evaluations.

[0078] "Contact" refers to opportunities to communicate directly with external organizations during the selection process.

[0079] "Reliability information" refers to information that includes data related to the transaction history and creditworthiness of external organizations.

[0080] "Security information" refers to information that includes data related to the security measures and information protection of external organizations.

[0081] "Business procedures" refer to the processes related to ordering and contracting that are carried out with the ultimately selected external organization.

[0082] A "user interface" refers to the interface through which a user accesses the system and inputs and receives information.

[0083] "Human language processing" refers to the techniques and methods used to understand and analyze natural language.

[0084] This invention provides an information processing system for streamlining the process of selecting partner companies. The system operates with a combination of three components: a server, a terminal, and a user, each performing its respective role.

[0085] Server Role

[0086] The server, as the central component of the system, handles massive data processing. Generative AI models run on the server, generating lists of external organizations (partner companies) based on user requirements. This is done using AI agents, referencing past performance databases and market trends. The server also handles the automatic generation and transmission of Requests for Proposals (RFPs) to external organizations. This transmission is done via email or a dedicated business platform. Furthermore, the server receives responses from partner companies, analyzes them using natural language processing technology, and evaluates them. The evaluation analyzes candidate companies based on unified criteria and provides the results to the user.

[0087] Terminal role

[0088] The terminal functions as a user interface, providing a user-facing screen for inputting the requirements necessary for selecting partner companies. The information entered by the user is converted to an appropriate format and sent to the server. Furthermore, the server displays the information and evaluation results of the listed candidate companies in an easy-to-understand manner.

[0089] User roles

[0090] As a company representative, the user inputs requirements for selecting partner companies into a terminal and, based on a list provided by the server, requests proposals and schedules meetings with external organizations. The user then proceeds to select the final business partner based on the evaluation results provided. In addition, they can check the reliability and security information of external organizations as needed to make the optimal selection.

[0091] Specific example

[0092] For example, if a user is planning to build a new IT infrastructure and is looking for a partner company with strong cloud technology, the user enters their requirements into a terminal. The server then uses an AI agent to list suitable companies and present them to the user. Finally, the user enters into a contract with the most suitable partner company based on the evaluation results.

[0093] Example of a prompt

[0094] "To build a new IT infrastructure, select a partner company with strong cloud technology capabilities and the ability to meet short deadlines, and present the most suitable candidate company through this system."

[0095] Thus, the invented system simplifies the complex process of selecting collaborating companies, enabling the selection of business partners with greater accuracy and reliability.

[0096] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0097] Step 1:

[0098] The user inputs specific requirements and conditions for selecting partner companies into the terminal. This input includes business needs such as technical capabilities, deadlines, and budget. The terminal converts the input data into an appropriate format and sends the input content to the server. As part of data processing, the entered conditions are converted into a standardized format.

[0099] Step 2:

[0100] The server runs a generative AI model based on user requirements received from the terminal to generate a list of optimal external organizations. User requirements data and a database of past performance are used as input. The AI ​​agent analyzes this data and outputs a list of potential business partners. In this step, the AI ​​analysis model operates as a data computation tool, extracting appropriate candidates from a vast amount of data.

[0101] Step 3:

[0102] The server automatically generates a Request for Proposal (RFP) based on the generated list of potential collaborating companies. The inputs used are the list of potential companies and the user's requirements specifications. Once the RFP content is finalized, the server sends it to the designated external organization via email or business platform. Specifically, it generates the RFP based on a template and automates the sending process.

[0103] Step 4:

[0104] When proposals from selected external organizations are returned to the server, the server uses an AI agent to analyze the proposal content. The input information consists of the proposal document and related external data. The AI ​​analyzes the proposal content and outputs an evaluation result scored based on a unified evaluation criterion. In this step, natural language processing is used to analyze the text.

[0105] Step 5:

[0106] The terminal displays the evaluation results of partner companies provided by the server to the user in screen or report format. The output shows the user evaluation scores and the strengths and weaknesses of candidate companies in a visualized form. Based on this information, the user conducts specific negotiations and meetings with the companies that are necessary. The user makes a final selection and decides which companies will become business partners.

[0107] Step 6:

[0108] For companies selected by the user, the server automatically creates documents for business procedures and proceeds with the contract. These procedures include verification of credit and security information. The server's output consists of automatically generated contracts and finalized internal procedures.

[0109] (Application Example 1)

[0110] Next, we will explain Application Example 1. In the following explanation, the data processing device 12 will be referred to as the "server," and the smart device 14 will be referred to as the "terminal."

[0111] Not only is the selection of partner companies and suppliers time-consuming, but the evaluation criteria can become subjective and lack reliability. Furthermore, efficient management of the supply chain in accordance with the operating status of machinery on the manufacturing floor may not be possible, potentially leading to decreased production efficiency.

[0112] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 1 is realized by the following means.

[0113] In this invention, the server includes means for inputting and analyzing user requirements, means for generating a list of optimal suppliers, and means for automatically creating distribution requests and sending them to selected suppliers. This enables objective and efficient supplier selection and ordering procedures, as well as rapid supply chain management in accordance with the operating status of manufacturing machinery.

[0114] "Means for inputting and analyzing user requirements" refers to a device or method for users to input the conditions and preferences necessary for selecting a supplier, and for appropriately analyzing that information.

[0115] "Means for generating a list of optimal suppliers" refers to an apparatus or method for listing the most suitable suppliers based on the analyzed requirements.

[0116] "Means for automatically creating distribution request forms and sending them to selected suppliers" refers to a device or method that automatically generates a request form containing the necessary information for suppliers and sends it to the selected suppliers.

[0117] "Means for collecting proposals from suppliers and evaluating them according to unified standards" refers to an apparatus or method for collecting proposals submitted by suppliers and evaluating their contents based on consistent standards.

[0118] "Means for acquiring and evaluating supplier reliability and safety information" refers to an apparatus or method for collecting and evaluating credit information and safety data concerning suppliers.

[0119] "Means for monitoring the operational status of manufacturing machinery, selecting suppliers to provide the next necessary components and repair services, and supporting orders" refers to a device or method for monitoring manufacturing machinery in operation, selecting suppliers capable of providing the necessary parts and services according to its status, and supporting procurement.

[0120] This invention is a system that streamlines the selection of business partners and optimizes the management of parts and repair services in the manufacturing process. Servers, terminals, and users work together, using AI technology to analyze data and automate everything from supplier selection to ordering procedures.

[0121] The server receives and analyzes requirements submitted by users. This utilizes a cloud server and generative AI models (e.g., TENSORFLOW® or PyTorch). Requirements are entered via a terminal, which provides a user-friendly interface. This interface is designed to allow users to easily configure the conditions they require from suppliers.

[0122] The server also uses an AI model to generate a list of optimal suppliers based on the input requirements. The server automatically creates and sends distribution requests to the listed suppliers, allowing users to proceed with negotiations quickly without wasting time. After proposals are received, the server evaluates them using a unified standard. This evaluation utilizes natural language processing technology to perform a detailed analysis of the proposals.

[0123] Regarding information on the reliability and safety of suppliers, the server retrieves this information via a dedicated database and API, and incorporates it into the evaluation. Based on the evaluation results, a final contract is made with the supplier, enabling rapid supply chain management based on the operational status of manufacturing machinery.

[0124] For example, if a part of a manufacturing machine begins to malfunction, the user inputs information about the necessary parts or repairs via a terminal. The server can immediately select the most suitable supplier and initiate the process of quickly obtaining the required repair parts.

[0125] Examples of prompts for a generative AI model include the following:

[0126] "Create a model to optimize the process of selecting business partners. Recommend the best supplier using the following data format: { 'requirement': requirement, 'timeline': urgency}"

[0127] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0128] Step 1:

[0129] Users enter their requirements for supplier selection via a terminal. These requirements include detailed information about parts and repair services needed for the manufacturing machinery. The input data is converted to an appropriate format before being sent to the server.

[0130] Step 2:

[0131] The server receives input data and uses a generating AI model to list the most suitable suppliers. This AI model compares historical data with requirements to select qualified supplier candidates. The resulting list is then ranked based on indicators such as suitability and delivery time.

[0132] Step 3:

[0133] The server automatically creates a distribution request based on a list of ranked suppliers and sends it to the suppliers. The request includes details of the required parts and services, deadlines, and other conditions, and is transmitted to the relevant suppliers via electronic means.

[0134] Step 4:

[0135] When suppliers return proposals, the server collects the content of those proposals and analyzes them using natural language processing technology. The analysis results are scored based on evaluation criteria such as price, delivery time, and quality.

[0136] Step 5:

[0137] Based on the evaluation results, the server makes the final selection of suppliers, including reliability and security information. During this process, additional information about suppliers is collected using external databases and APIs and reflected in the overall evaluation.

[0138] Step 6:

[0139] Users enter into contracts with suppliers through the server and place orders for necessary parts and services. Once the contract is completed, the server automatically registers the order information in the system, optimizing the entire supply chain.

[0140] Furthermore, an emotion engine that estimates the user's emotions may be incorporated. That is, the identification processing unit 290 may use the emotion identification model 59 to estimate the user's emotions and perform identification processing using the user's emotions.

[0141] This invention is a system for recognizing user emotions during the selection process of partner companies and using that information to efficiently and reliably select business partners. The system consists of a server, terminals, and users, and uses an emotion engine to reflect emotional information in the relevant processes.

[0142] System configuration:

[0143] The server functions as the central hub for data processing, running the AI ​​agent and emotion engine. The terminal acts as the user interface, exchanging information with the user. The user inputs requirements and conditions for selecting business partners via the terminal. The emotion engine identifies emotions from the user's voice, text input, facial expressions, etc., and sends data to the server as needed.

[0144] Program execution flow:

[0145] When a user inputs requirements for selecting a partner company into the terminal, the terminal uses an emotion engine to analyze the user's emotional state. This emotional state information is incorporated into the analyzed requirements and sent to the server. On the server, an AI agent generates a list of optimal partner company candidates, taking into account both the requirements and the emotional information. The generated list is then presented to the user via the terminal.

[0146] When a user selects companies to send a Request for Proposal (RFP) to, the emotion engine re-analyzes the user's emotions and incorporates that information into the RFP's content. The server automatically generates the adjusted RFP and sends it to the selected partner companies.

[0147] When proposals are returned from partner companies, the server's AI agent evaluates them, incorporating emotional feedback from the user's review of the proposals. Based on this feedback, the priority of the proposals and the necessity of a meeting are determined.

[0148] Specific example:

[0149] For example, if a user is looking for a partner company for system development, the system evaluates the user's perceived stress level in addition to the technical requirements entered by the user, and presents a list accordingly. If a high stress level is detected, priority is given to selecting a company that provides more comprehensive support. Furthermore, if the user's emotions are positive when reviewing proposals, the system prioritizes scheduling meetings with those companies and applies emotional considerations when acquiring credit information.

[0150] This system allows us to incorporate user sentiment into the selection process, thereby supporting the selection of more accurate partner companies.

[0151] The following describes the processing flow.

[0152] Step 1:

[0153] The user uses a terminal to input requirements for selecting a business partner (e.g., technical requirements, deadlines, budget, etc.). At this time, an emotion engine is activated, and the user's emotional state is added to the input data in real time based on their facial expressions and voice.

[0154] Step 2:

[0155] The terminal formats the collected requests and emotional information and sends it to the server. This data, which includes not only user requests but also emotional information, is used for analysis on the server as multidimensional data.

[0156] Step 3:

[0157] The server uses an AI agent to analyze the received requests and emotional information. It accesses a database of partner companies and generates a list of partner companies that match the user's needs and take their emotional state into consideration.

[0158] Step 4:

[0159] The terminal visualizes and presents to the user a list of collaborating companies sent from the server. The user reviews the provided list and selects companies to which to send a Request for Proposal (RFP).

[0160] Step 5:

[0161] Once users are selected, the emotion engine analyzes their reactions again, and these reactions are reflected in the creation of the Request for Proposal (RFP). The server automatically generates the RFP based on this information and sends it to the selected companies.

[0162] Step 6:

[0163] After proposals from partner companies arrive at the server, the AI ​​agent evaluates the proposals. This evaluation takes into account not only pre-set criteria but also the emotional feedback received by users when they view the proposals.

[0164] Step 7:

[0165] The evaluation results are sent to the device, and the user reviews the suggestions. Prioritization is performed based on the evaluation, and emotional feedback recommends scheduling interviews with highly-rated companies.

[0166] Step 8:

[0167] The server obtains credit and security information from companies selected as interview candidates to verify their reliability. This information is also used in the final evaluation.

[0168] Step 9:

[0169] Ultimately, the user submits a proposal for approval based on the selection results via their terminal. The server then processes the proposal approval through an automated workflow and completes the ordering process after final approval.

[0170] (Example 2)

[0171] Next, we will describe Example 2. In the following description, the data processing device 12 will be referred to as the "server" and the smart device 14 as the "terminal".

[0172] The challenge in selecting business partners is that traditional methods fail to consider user emotions and psychological factors, making optimal selection difficult. Furthermore, quantitative criteria for evaluating proposals are insufficient, and a flexible approach tailored to individual user needs is required.

[0173] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 2 is realized by the following means.

[0174] In this invention, the server includes means for inputting and analyzing user requirements and emotional information, means for generating a list of optimal companies based on the requirements and emotional information, and means for automatically creating a request for proposal and sending it to the companies included in the list. This enables optimal company selection that takes user emotions into consideration and proposal evaluation that responds to emotional feedback.

[0175] "Requirements" are the conditions and expectations that users have of a company in order to achieve specific objectives or needs.

[0176] "Emotional information" refers to data on the user's psychological state and emotions, analyzed based on factors such as voice, text input, and facial expressions.

[0177] A "company" refers to an organization or legal entity that is being considered as a potential business partner.

[0178] A "Request for Proposal" is a document in which users request proposals from a company regarding services or products they wish to offer.

[0179] "Emotional feedback" refers to evaluation data of the psychological responses that users show to proposals and companies.

[0180] "Natural language processing" is a technology that enables computers to understand and process human language.

[0181] The embodiment of this invention is implemented as a system consisting of a server, a terminal, and a user. This system utilizes user sentiment information when selecting business partners to support a more accurate and effective selection process.

[0182] The server is responsible for central data processing and runs the AI ​​agent and emotion engine. Based on the user's requirements and emotional information received, the server generates a list of suitable candidate companies. Furthermore, it automatically creates a Request for Proposal (RPO) and sends it to the companies. When evaluating the submitted proposals, the server also takes the user's emotional feedback into consideration.

[0183] The terminal functions as an interface for exchanging information with the user. The user inputs their requirements through this terminal, which in turn activates the emotion engine. The emotion engine analyzes the user's emotions from voice, text input, and facial expressions, and collects this information. This allows the system to formulate suggestions that are more tailored to the user's needs.

[0184] Users select reliable business partners for business development and supply chain construction. During this process, users input their requirements into a terminal, and the system uses this information to select the most suitable companies. This approach allows users' emotions and psychological states to influence the selection process, resulting in highly accurate selections.

[0185] A concrete example is when a user is selecting a partner company for the development of a new product. The user inputs the necessary technical requirements along with their emotional state through their device. For example, for a user experiencing high stress levels, the server will list companies with enhanced support systems. This allows the user to select a company with greater confidence.

[0186] An example of a prompt is, "Please describe the process of listing partners that will reduce stress when selecting collaborating companies for system development." This prompt is used to support information processing tailored to the user's needs by leveraging a generative AI model.

[0187] This invention makes it possible to select business partners while taking user emotions into consideration, improving the accuracy and reliability of the selection process.

[0188] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0189] Step 1:

[0190] The user inputs the requirements for selecting partner companies using a terminal. The terminal receives this input and activates the emotion engine. It senses the user's voice, text input, and facial expressions to acquire emotional information. At this time, it analyzes the input text data and voice data and outputs the emotional state as numerical data.

[0191] Step 2:

[0192] The terminal integrates the request requirements and emotional information and sends it to the server. The server verifies the received data and starts processing it with an AI agent. Based on the request requirements and emotional data, the AI ​​agent generates a list of the most suitable companies from the company information in the database. In this process, it uses the emotional data to prioritize evaluating companies' responsiveness and company attributes that match user needs.

[0193] Step 3:

[0194] The server sends the generated list of candidate companies to the terminal. The terminal arranges the layout and presents the list to the user in a visually easy-to-understand format. The user reviews the presented list and selects a specific company. At this point, the terminal retrieves the user's selection data and restarts the emotion engine.

[0195] Step 4:

[0196] To create a Request for Proposal (R&D) for the company selected by the user, the terminal sends the user's selection information and re-analyzed sentiment information to the server. The server uses this information to automatically generate the R&D. The document includes wording that alleviates concerns and emphasizes special conditions, tailored to the user's emotional state.

[0197] Step 5:

[0198] The server sends automatically generated requests for proposals to selected companies. When companies respond with proposals, the server aggregates the content. An AI agent evaluates the submitted proposals and integrates them with user sentiment feedback to organize the evaluation criteria.

[0199] Step 6:

[0200] Users review proposals via their devices and provide feedback. The devices send this feedback data to a server, which then uses it to evaluate the company. This incorporates emotional information into the final proposal evaluation, allowing the server to determine the necessity and priority of interviews.

[0201] In this way, the system can select business partners that take user emotions into consideration.

[0202] (Application Example 2)

[0203] Next, we will explain application example 2. In the following explanation, the data processing device 12 will be referred to as a "server" and the smart device 14 as a "terminal".

[0204] Conventional partner selection systems fail to reflect the emotional state of users, resulting in the inability to select the most suitable partner for each user in terms of proposals and the establishment of collaborative relationships. Furthermore, the vehicle travel environment is not optimized with the user's psychological state in mind, limiting improvements to the travel experience and safety. This leads to issues such as insufficient user satisfaction and comfort during travel.

[0205] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means.

[0206] In this invention, the server includes means for analyzing user requirements and emotional states, means for detecting the user's emotions in real time using an emotion recognition device mounted on the vehicle, and means for generating suggestions to adjust the in-vehicle environment and range of movement based on the emotional state. This makes it possible to select the optimal collaborating business entity while taking the user's emotions into consideration, and to optimize the vehicle's movement environment.

[0207] "Requirements" refer to the preferences and requirements that users enter regarding the selection of a collaborating business entity, and include specific technical and operational criteria.

[0208] "Emotional state" refers to the state of the user's psychological and physiological responses, and is analyzed based on factors such as facial expressions and tone of voice.

[0209] A "cooperative entity" is a business partner that receives a request for proposal and whose business partnership is being considered.

[0210] An "emotion recognition device" refers to sensors and software that identify emotions from a user's facial expressions and voice.

[0211] "In-vehicle environment" refers to various adjustable elements related to user comfort, such as temperature, lighting, music, and seating position inside the vehicle.

[0212] "Travel range" refers to the planned route or path the vehicle will take, and is selected considering the user's destination and safety.

[0213] A "Request for Proposal" is an official document sent to potential business partners, clearly outlining the terms and requirements of the partnership.

[0214] "Evaluation criteria" refer to standardized scales and guidelines for evaluating the content of proposals and the creditworthiness of collaborating entities.

[0215] A "generative AI model" is an artificial intelligence learning system used to analyze user emotions and suggested content.

[0216] This system consists of three main components: users, terminals, and servers.

[0217] The terminal functions as an interface for users to input their requirements and for the system to directly sense their mood. The terminal is equipped with an emotion recognition device that analyzes facial expressions via voice input and a camera, and by analyzing this data in real time, it understands the user's emotional state.

[0218] The server is the central hub of this system's information processing, aggregating and analyzing all data. The server runs a generative AI model using Python and TensorFlow, simultaneously analyzing the user's input requirements and emotional state. Based on the emotional state, it generates suggestions regarding necessary adjustments to the in-vehicle environment and travel range, and determines a list of optimal collaborating entities. Based on the user's emotional feedback, the server evaluates the collaborating entity suggestions using natural language processing and automatically sets up meetings if necessary.

[0219] For example, if a user wants to travel in a relaxed environment, an emotion recognition device will detect that emotion, and the server will create and suggest a list of relaxing music. The vehicle can also suggest routes that allow the user to enjoy beautiful scenery, thus fulfilling the user's request.

[0220] An example of a prompt for a generative AI model is, "What suggestions can you offer to soothe the user?"

[0221] In this way, the system comprehensively considers the user's emotions and needs to provide a comfortable and efficient travel experience.

[0222] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0223] Step 1:

[0224] The terminal accepts user requests via voice or text input. The input data is preprocessed locally before being sent to the server. Specifically, noise reduction and text normalization are performed.

[0225] Step 2:

[0226] The device uses its camera and microphone to detect the user's facial expressions and voice, and performs emotion recognition in real time. The resulting emotion data is then processed using image analysis algorithms and speech recognition models. The analyzed emotion data is sent to a server.

[0227] Step 3:

[0228] The server aggregates received request conditions and emotional state data and analyzes their relationships using a generative AI model. TensorFlow is used to extract data features and understand the user's desires and psychological state. The output is a list of candidate collaborating entities best suited to the user's needs.

[0229] Step 4:

[0230] The server automatically generates a Request for Proposal (RPO) based on the generated list of potential collaborating entities. In this process, a template engine creates a document that reflects the user's requirements and emotional state. The generated RPO is then sent back to the terminal and displayed to the user.

[0231] Step 5:

[0232] Users review the displayed requests for proposals and potential collaborating entities and provide feedback. This feedback is returned to the server as user evaluation criteria and sentiment feedback.

[0233] Step 6:

[0234] The server integrates the received feedback and compares and evaluates the proposals from collaborating entities. It uses natural language processing technology to analyze the content of the proposals and evaluate their reliability and usability.

[0235] Step 7:

[0236] The server schedules meetings with partner companies as needed and generates suggestions for adjusting the in-vehicle environment and travel range. For example, if the user wants to relax, it suggests music playlists and routes, which are then presented to the user via the terminal.

[0237] The specific processing unit 290 transmits the result of the specific processing to the smart device 14. In the smart device 14, the control unit 46A causes the output device 40 to output the result of the specific processing. The microphone 38B acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0238] Data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of data generation model 58 is ChatGPT (registered trademark) (Internet search).<URL: https: / / openai.com / blog / chatgpt> ), Gemini (registered trademark) (Internet search) <url: https: gemini.google.com ?hl="ja">Examples of generative AI include the following. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and with inference data such as audio data representing speech, text data representing text, and image data representing images. The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference results in data formats such as audio data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[0239] In the above embodiment, an example was given in which specific processing is performed by the data processing device 12, but the technology of this disclosure is not limited thereto, and the specific processing may also be performed by the smart device 14.

[0240] [Second Embodiment]

[0241] Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment.

[0242] As shown in Figure 3, the data processing system 210 includes a data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0243] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 is an example of a "computer" related to the technology of this disclosure. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[0244] The smart glasses 214 include a computer 36, a microphone 238, a speaker 240, a camera 42, and a communication interface 44. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, and camera 42 are also connected to the bus 52.

[0245] The microphone 238 receives voice signals from the user 20 and receives instructions from the user 20. The microphone 238 captures the voice signals from the user 20, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0246] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the area around the user 20 (for example, an imaging range defined by a field of view equivalent to the width of a typical healthy person's field of vision).

[0247] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0248] Figure 4 shows an example of the main functions of the data processing device 12 and the smart glasses 214. As shown in Figure 4, the data processing device 12 performs specific processing using the processor 28. The storage 32 stores the specific processing program 56.

[0249] The specific processing program 56 is an example of a "program" relating to the technology of this disclosure. The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0250] The storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[0251] In the smart glasses 214, the processor 46 performs the reception output processing. The storage 50 stores the reception output program 60. The processor 46 reads the reception output program 60 from the storage 50 and executes the read reception output program 60 on the RAM 48. The reception output processing is realized by the processor 46 operating as a control unit 46A according to the reception output program 60 executed on the RAM 48.

[0252] Next, the identification processing performed by the identification processing unit 290 of the data processing device 12 will be described. In the following description, the data processing device 12 will be referred to as the "server" and the smart glasses 214 will be referred to as the "terminal".

[0253] This invention is a system for streamlining the selection process of partner companies and for quickly selecting highly reliable business partners. The program of this system has a mechanism in which the server, terminals, and users work together in cooperation with each other.

[0254] System configuration:

[0255] The server, acting as the system's core, runs AI agents responsible for processing vast amounts of data. The terminal functions as the user interface, interacting with the user. The user, a company representative, inputs requirements and conditions for selecting partner companies through the terminal.

[0256] Program execution flow:

[0257] The user enters their requirements for selecting a business partner into a terminal. The terminal converts the input into the appropriate format and sends it to the server. The server uses an AI agent to list the most suitable partner companies based on the user's requirements. The generated list is presented to the user via the terminal.

[0258] The user selects companies to send a Request for Proposal (RFP) to from the displayed candidates. The server automatically creates an RFP and sends it to the selected partner companies. Once proposals from each partner company are returned to the server, an AI agent analyzes and evaluates the proposals. The evaluation results are provided to the user via the terminal, and if it is determined that a meeting with a particular partner company is necessary, a meeting is scheduled.

[0259] Furthermore, the server acquires credit and security information from selected partner companies and incorporates the results into the evaluation. Finally, the user drafts a proposal based on the evaluation results, and the ordering process is completed through the server's automated workflow.

[0260] Specific example:

[0261] For example, a user might request the construction of a new IT infrastructure. In this case, the user inputs the characteristics of the desired collaborating company (e.g., a company strong in cloud technology, a company capable of meeting short deadlines) into the system. The server's AI agent searches for and presents candidate companies that best match the requirements. A request for proposal (RPO) is automatically generated and sent to the selected candidate companies, after which the proposals obtained by the AI ​​are analyzed and evaluated. Based on these results, the user selects a company, drafts a proposal for approval while considering credit information and security information as needed, and finally concludes a contract.

[0262] This system allows companies to quickly select the best business partner for their needs, and the entire process is carried out transparently and efficiently.

[0263] The following describes the processing flow.

[0264] Step 1:

[0265] Users use a terminal to input specific requirements they seek from business partners (e.g., technical requirements, schedule, budget, etc.). This information forms the basis for future Requests for Proposals (RFPs).

[0266] Step 2:

[0267] The terminal converts the entered request requirements into a predetermined format and sends the data to the server. This step verifies the accuracy and completeness of the data.

[0268] Step 3:

[0269] The server uses an AI agent to generate a list of optimal partner companies based on the requirements. In doing so, it refers to market databases and historical company evaluation data to select candidates that meet the needs.

[0270] Step 4:

[0271] The terminal displays a list of partner companies sent from the server to the user. The user reviews this list and selects the companies they wish to request proposals from.

[0272] Step 5:

[0273] Based on the user's selection, the server automatically generates a Request for Proposal (RFP) using an AI agent and sends the RFP to the selected partner companies.

[0274] Step 6:

[0275] Proposals from partner companies are sent back to the server. The server's AI agent analyzes these proposals, evaluates them based on pre-set evaluation criteria, and assigns a score.

[0276] Step 7:

[0277] The evaluation result is sent to the terminal, and the user selects the companies that hope for interviews based on the evaluation. If necessary, the server sets the interview schedule and notifies both parties.

[0278] Step 8:

[0279] As a reliability check for the selected partner company, the server obtains credit information and security information from an external database and incorporates them into the evaluation.

[0280] Step 9:

[0281] Based on the reliability evaluation, the user determines the final partner company and drafts a request for approval. The server automatically progresses the request for approval process and completes the order procedure once approval is obtained.

[0282] (Example 1)

[0283] Next, Example 1 will be described. In the following description, the data processing device 12 is referred to as the "server", and the smart glasses 214 are referred to as the "terminal".

[0284] The conventional process for selecting partner companies has the problem that it is difficult to quickly select reliable business partners because it requires time and effort and the evaluation criteria are not consistent.

[0285] The specific processing by the specific processing unit 290 of the data processing device 12 in Example 1 is realized by the following respective means.

[0286] In this invention, the server includes a processing means for receiving and analyzing a request, a processing means for generating a list of optimal external organizations based on the request, and a processing means for automatically generating a communication document and transmitting it to the external organizations included in the list. Thereby, it becomes possible to efficiently and reliably perform the process of selecting partner companies.

[0287] A "requirement" is information indicating the wishes and conditions input by a user to the system.

[0288] "Analysis" refers to the process of analyzing the input information and converting it into a form that can be understood by the system.

[0289] "External organization" refers to a third - party group or company considered as a cooperative enterprise or business partner.

[0290] A "list" is a list containing a set of candidate external organizations selected by the system.

[0291] A "communication document" refers to a document that is automatically generated and sent to an external organization, including a request for proposal and other necessary information.

[0292] "Reply content" refers to the information received by the system as a response to proposals or information from an external organization.

[0293] "Uniform standard" refers to a standardized and consistent scale or indicator used when conducting evaluations.

[0294] "Contact" refers to an opportunity to directly communicate with an external organization in the selection process.

[0295] "Reliable information" refers to information including data related to the transaction history and creditworthiness of an external organization.

[0296] "Security information" refers to information including data related to the security measures and information protection of an external organization.

[0297] "Business procedure" refers to the processing related to placing orders and signing contracts for the finally selected external organization.

[0298] "User operation screen" refers to an interface through which a user accesses the system to input and receive information.

[0299] "Human language processing" refers to the techniques and methods used to understand and analyze natural language.

[0300] This invention provides an information processing system for streamlining the process of selecting partner companies. The system operates with a combination of three components: a server, a terminal, and a user, each performing its respective role.

[0301] Server Role

[0302] The server, as the central component of the system, handles massive data processing. Generative AI models run on the server, generating lists of external organizations (partner companies) based on user requirements. This is done using AI agents, referencing past performance databases and market trends. The server also handles the automatic generation and transmission of Requests for Proposals (RFPs) to external organizations. This transmission is done via email or a dedicated business platform. Furthermore, the server receives responses from partner companies, analyzes them using natural language processing technology, and evaluates them. The evaluation analyzes candidate companies based on unified criteria and provides the results to the user.

[0303] Terminal role

[0304] The terminal functions as a user interface, providing a user-facing screen for inputting the requirements necessary for selecting partner companies. The information entered by the user is converted to an appropriate format and sent to the server. Furthermore, the server displays the information and evaluation results of the listed candidate companies in an easy-to-understand manner.

[0305] User roles

[0306] As an enterprise staff member, the user inputs the requirements for selecting a cooperative enterprise into the terminal, and based on the list presented by the server, makes a proposal request or interview arrangement with an external organization. The user proceeds with the selection of the final business partner based on the provided evaluation results. Additionally, the user can confirm the reliability information and security information of the external organization as needed to make an optimal selection.

[0307] Specific Example

[0308] For example, when the user is planning to build a new IT infrastructure and seeking a cooperative enterprise with strong cloud technology, the user inputs the requirements into the terminal. Subsequently, the server lists appropriate enterprises through the AI agent and presents them to the user. Finally, the user reaches a contract with the optimal cooperative enterprise based on the evaluation results.

[0309] Example of Prompt Sentence

[0310] "Select the optimal candidate enterprises through a system for selecting cooperative enterprises with strong cloud technology and the ability to respond to short delivery times for building a new IT infrastructure."

[0311] In this way, the invented system simplifies the complex process of selecting cooperative enterprises and enables the selection of business partners with high accuracy and reliability.

[0312] The flow of specific processing in Example 1 will be described using FIG. 11.

[0313] Step 1:

[0314] The user inputs specific requirements and conditions for selecting a cooperative enterprise into the terminal. This input includes business needs such as technical capabilities, delivery time response, and budget. The terminal converts the input data into an appropriate format and sends the input content to the server. As data processing, a process of converting the input conditions into a unified format is performed.

[0315] Step 2:

[0316] The server runs a generative AI model based on user requirements received from the terminal to generate a list of optimal external organizations. User requirements data and a database of past performance are used as input. The AI ​​agent analyzes this data and outputs a list of potential business partners. In this step, the AI ​​analysis model operates as a data computation tool, extracting appropriate candidates from a vast amount of data.

[0317] Step 3:

[0318] The server automatically generates a Request for Proposal (RFP) based on the generated list of potential collaborating companies. The inputs used are the list of potential companies and the user's requirements specifications. Once the RFP content is finalized, the server sends it to the designated external organization via email or business platform. Specifically, it generates the RFP based on a template and automates the sending process.

[0319] Step 4:

[0320] When proposals from selected external organizations are returned to the server, the server uses an AI agent to analyze the proposal content. The input information consists of the proposal document and related external data. The AI ​​analyzes the proposal content and outputs an evaluation result scored based on a unified evaluation criterion. In this step, natural language processing is used to analyze the text.

[0321] Step 5:

[0322] The terminal displays the evaluation results of partner companies provided by the server to the user in screen or report format. The output shows the user evaluation scores and the strengths and weaknesses of candidate companies in a visualized form. Based on this information, the user conducts specific negotiations and meetings with the companies that are necessary. The user makes a final selection and decides which companies will become business partners.

[0323] Step 6:

[0324] For companies selected by the user, the server automatically creates documents for business procedures and proceeds with the contract. These procedures include verification of credit and security information. The server's output consists of automatically generated contracts and finalized internal procedures.

[0325] (Application Example 1)

[0326] Next, we will explain Application Example 1. In the following explanation, the data processing device 12 will be referred to as the "server," and the smart glasses 214 will be referred to as the "terminal."

[0327] Not only is the selection of partner companies and suppliers time-consuming, but the evaluation criteria can become subjective and lack reliability. Furthermore, efficient management of the supply chain in accordance with the operating status of machinery on the manufacturing floor may not be possible, potentially leading to decreased production efficiency.

[0328] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 1 is realized by the following means.

[0329] In this invention, the server includes means for inputting and analyzing user requirements, means for generating a list of optimal suppliers, and means for automatically creating distribution requests and sending them to selected suppliers. This enables objective and efficient supplier selection and ordering procedures, as well as rapid supply chain management in accordance with the operating status of manufacturing machinery.

[0330] "Means for inputting and analyzing user requirements" refers to a device or method for users to input the conditions and preferences necessary for selecting a supplier, and for appropriately analyzing that information.

[0331] "Means for generating a list of optimal suppliers" refers to an apparatus or method for listing the most suitable suppliers based on the analyzed requirements.

[0332] "Means for automatically creating distribution request forms and sending them to selected suppliers" refers to a device or method that automatically generates a request form containing the necessary information for suppliers and sends it to the selected suppliers.

[0333] "Means for collecting proposals from suppliers and evaluating them according to unified standards" refers to an apparatus or method for collecting proposals submitted by suppliers and evaluating their contents based on consistent standards.

[0334] "Means for acquiring and evaluating supplier reliability and safety information" refers to an apparatus or method for collecting and evaluating credit information and safety data concerning suppliers.

[0335] "Means for monitoring the operational status of manufacturing machinery, selecting suppliers to provide the next necessary components and repair services, and supporting orders" refers to a device or method for monitoring manufacturing machinery in operation, selecting suppliers capable of providing the necessary parts and services according to its status, and supporting procurement.

[0336] This invention is a system that streamlines the selection of business partners and optimizes the management of parts and repair services in the manufacturing process. Servers, terminals, and users work together, using AI technology to analyze data and automate everything from supplier selection to ordering procedures.

[0337] The server receives and analyzes requirements submitted by the user. This process utilizes a cloud server and a generative AI model (e.g., TensorFlow or PyTorch). Requirements are entered via a terminal, which provides a user-friendly interface. This interface is designed to allow users to easily configure the conditions they require from the supplier.

[0338] The server also uses an AI model to generate a list of optimal suppliers based on the input requirements. The server automatically creates and sends distribution requests to the listed suppliers, allowing users to proceed with negotiations quickly without wasting time. After proposals are received, the server evaluates them using a unified standard. This evaluation utilizes natural language processing technology to perform a detailed analysis of the proposals.

[0339] Regarding information on the reliability and safety of suppliers, the server retrieves this information via a dedicated database and API, and incorporates it into the evaluation. Based on the evaluation results, a final contract is made with the supplier, enabling rapid supply chain management based on the operational status of manufacturing machinery.

[0340] For example, if a part of a manufacturing machine begins to malfunction, the user inputs information about the necessary parts or repairs via a terminal. The server can immediately select the most suitable supplier and initiate the process of quickly obtaining the required repair parts.

[0341] Examples of prompts for a generative AI model include the following:

[0342] "Create a model to optimize the process of selecting business partners. Recommend the best supplier using the following data format: { 'requirement': requirement, 'timeline': urgency}"

[0343] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0344] Step 1:

[0345] Users enter their requirements for supplier selection via a terminal. These requirements include detailed information about parts and repair services needed for the manufacturing machinery. The input data is converted to an appropriate format before being sent to the server.

[0346] Step 2:

[0347] The server receives input data and uses a generating AI model to list the most suitable suppliers. This AI model compares historical data with requirements to select qualified supplier candidates. The resulting list is then ranked based on indicators such as suitability and delivery time.

[0348] Step 3:

[0349] The server automatically creates a distribution request based on a list of ranked suppliers and sends it to the suppliers. The request includes details of the required parts and services, deadlines, and other conditions, and is transmitted to the relevant suppliers via electronic means.

[0350] Step 4:

[0351] When suppliers return proposals, the server collects the content of those proposals and analyzes them using natural language processing technology. The analysis results are scored based on evaluation criteria such as price, delivery time, and quality.

[0352] Step 5:

[0353] Based on the evaluation results, the server makes the final selection of suppliers, including reliability and security information. During this process, additional information about suppliers is collected using external databases and APIs and reflected in the overall evaluation.

[0354] Step 6:

[0355] Users enter into contracts with suppliers through the server and place orders for necessary parts and services. Once the contract is completed, the server automatically registers the order information in the system, optimizing the entire supply chain.

[0356] Furthermore, an emotion engine that estimates the user's emotions may be incorporated. That is, the identification processing unit 290 may use the emotion identification model 59 to estimate the user's emotions and perform identification processing using the user's emotions.

[0357] This invention is a system for recognizing user emotions during the selection process of partner companies and using that information to efficiently and reliably select business partners. The system consists of a server, terminals, and users, and uses an emotion engine to reflect emotional information in the relevant processes.

[0358] System configuration:

[0359] The server functions as the central hub for data processing, running the AI ​​agent and emotion engine. The terminal acts as the user interface, exchanging information with the user. The user inputs requirements and conditions for selecting business partners via the terminal. The emotion engine identifies emotions from the user's voice, text input, facial expressions, etc., and sends data to the server as needed.

[0360] Program execution flow:

[0361] When a user inputs requirements for selecting a partner company into the terminal, the terminal uses an emotion engine to analyze the user's emotional state. This emotional state information is incorporated into the analyzed requirements and sent to the server. On the server, an AI agent generates a list of optimal partner company candidates, taking into account both the requirements and the emotional information. The generated list is then presented to the user via the terminal.

[0362] When a user selects companies to send a Request for Proposal (RFP) to, the emotion engine re-analyzes the user's emotions and incorporates that information into the RFP's content. The server automatically generates the adjusted RFP and sends it to the selected partner companies.

[0363] When proposals are returned from partner companies, the server's AI agent evaluates them, incorporating emotional feedback from the user's review of the proposals. Based on this feedback, the priority of the proposals and the necessity of a meeting are determined.

[0364] Specific example:

[0365] For example, if a user is looking for a partner company for system development, the system evaluates the user's perceived stress level in addition to the technical requirements entered by the user, and presents a list accordingly. If a high stress level is detected, priority is given to selecting a company that provides more comprehensive support. Furthermore, if the user's emotions are positive when reviewing proposals, the system prioritizes scheduling meetings with those companies and applies emotional considerations when acquiring credit information.

[0366] This system allows us to incorporate user sentiment into the selection process, thereby supporting the selection of more accurate partner companies.

[0367] The following describes the processing flow.

[0368] Step 1:

[0369] The user uses a terminal to input requirements for selecting a business partner (e.g., technical requirements, deadlines, budget, etc.). At this time, an emotion engine is activated, and the user's emotional state is added to the input data in real time based on their facial expressions and voice.

[0370] Step 2:

[0371] The terminal formats the collected requests and emotional information and sends it to the server. This data, which includes not only user requests but also emotional information, is used for analysis on the server as multidimensional data.

[0372] Step 3:

[0373] The server uses an AI agent to analyze the received requests and emotional information. It accesses a database of partner companies and generates a list of partner companies that match the user's needs and take their emotional state into consideration.

[0374] Step 4:

[0375] The terminal visualizes and presents to the user a list of collaborating companies sent from the server. The user reviews the provided list and selects companies to which to send a Request for Proposal (RFP).

[0376] Step 5:

[0377] Once users are selected, the emotion engine analyzes their reactions again, and these reactions are reflected in the creation of the Request for Proposal (RFP). The server automatically generates the RFP based on this information and sends it to the selected companies.

[0378] Step 6:

[0379] After proposals from partner companies arrive at the server, the AI ​​agent evaluates the proposals. This evaluation takes into account not only pre-set criteria but also the emotional feedback received by users when they view the proposals.

[0380] Step 7:

[0381] The evaluation results are sent to the device, and the user reviews the suggestions. Prioritization is performed based on the evaluation, and emotional feedback recommends scheduling interviews with highly-rated companies.

[0382] Step 8:

[0383] The server obtains credit and security information from companies selected as interview candidates to verify their reliability. This information is also used in the final evaluation.

[0384] Step 9:

[0385] Ultimately, the user submits a proposal for approval based on the selection results via their terminal. The server then processes the proposal approval through an automated workflow and completes the ordering process after final approval.

[0386] (Example 2)

[0387] Next, we will describe Example 2. In the following description, the data processing device 12 will be referred to as the "server" and the smart glasses 214 will be referred to as the "terminal".

[0388] The challenge in selecting business partners is that traditional methods fail to consider user emotions and psychological factors, making optimal selection difficult. Furthermore, quantitative criteria for evaluating proposals are insufficient, and a flexible approach tailored to individual user needs is required.

[0389] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 2 is realized by the following means.

[0390] In this invention, the server includes means for inputting and analyzing user requirements and emotional information, means for generating a list of optimal companies based on the requirements and emotional information, and means for automatically creating a request for proposal and sending it to the companies included in the list. This enables optimal company selection that takes user emotions into consideration and proposal evaluation that responds to emotional feedback.

[0391] "Requirements" are the conditions and expectations that users have of a company in order to achieve specific objectives or needs.

[0392] "Emotional information" refers to data on the user's psychological state and emotions, analyzed based on factors such as voice, text input, and facial expressions.

[0393] A "company" refers to an organization or legal entity that is being considered as a potential business partner.

[0394] A "Request for Proposal" is a document in which users request proposals from a company regarding services or products they wish to offer.

[0395] "Emotional feedback" refers to evaluation data of the psychological responses that users show to proposals and companies.

[0396] "Natural language processing" is a technology that enables computers to understand and process human language.

[0397] The embodiment of this invention is implemented as a system consisting of a server, a terminal, and a user. This system utilizes user sentiment information when selecting business partners to support a more accurate and effective selection process.

[0398] The server is responsible for central data processing and runs the AI ​​agent and emotion engine. Based on the user's requirements and emotional information received, the server generates a list of suitable candidate companies. Furthermore, it automatically creates a Request for Proposal (RPO) and sends it to the companies. When evaluating the submitted proposals, the server also takes the user's emotional feedback into consideration.

[0399] The terminal functions as an interface for exchanging information with the user. The user inputs their requirements through this terminal, which in turn activates the emotion engine. The emotion engine analyzes the user's emotions from voice, text input, and facial expressions, and collects this information. This allows the system to formulate suggestions that are more tailored to the user's needs.

[0400] Users select reliable business partners for business development and supply chain construction. During this process, users input their requirements into a terminal, and the system uses this information to select the most suitable companies. This approach allows users' emotions and psychological states to influence the selection process, resulting in highly accurate selections.

[0401] A concrete example is when a user is selecting a partner company for the development of a new product. The user inputs the necessary technical requirements along with their emotional state through their device. For example, for a user experiencing high stress levels, the server will list companies with enhanced support systems. This allows the user to select a company with greater confidence.

[0402] An example of a prompt is, "Please describe the process of listing partners that will reduce stress when selecting collaborating companies for system development." This prompt is used to support information processing tailored to the user's needs by leveraging a generative AI model.

[0403] This invention makes it possible to select business partners while taking user emotions into consideration, improving the accuracy and reliability of the selection process.

[0404] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0405] Step 1:

[0406] The user inputs the requirements for selecting partner companies using a terminal. The terminal receives this input and activates the emotion engine. It senses the user's voice, text input, and facial expressions to acquire emotional information. At this time, it analyzes the input text data and voice data and outputs the emotional state as numerical data.

[0407] Step 2:

[0408] The terminal integrates the request requirements and emotional information and sends it to the server. The server verifies the received data and starts processing it with an AI agent. Based on the request requirements and emotional data, the AI ​​agent generates a list of the most suitable companies from the company information in the database. In this process, it uses the emotional data to prioritize evaluating companies' responsiveness and company attributes that match user needs.

[0409] Step 3:

[0410] The server sends the generated list of candidate companies to the terminal. The terminal arranges the layout and presents the list to the user in a visually easy-to-understand format. The user reviews the presented list and selects a specific company. At this point, the terminal retrieves the user's selection data and restarts the emotion engine.

[0411] Step 4:

[0412] To create a Request for Proposal (R&D) for the company selected by the user, the terminal sends the user's selection information and re-analyzed sentiment information to the server. The server uses this information to automatically generate the R&D. The document includes wording that alleviates concerns and emphasizes special conditions, tailored to the user's emotional state.

[0413] Step 5:

[0414] The server sends automatically generated requests for proposals to selected companies. When companies respond with proposals, the server aggregates the content. An AI agent evaluates the submitted proposals and integrates them with user sentiment feedback to organize the evaluation criteria.

[0415] Step 6:

[0416] Users review proposals via their devices and provide feedback. The devices send this feedback data to a server, which then uses it to evaluate the company. This incorporates emotional information into the final proposal evaluation, allowing the server to determine the necessity and priority of interviews.

[0417] In this way, the system can select business partners that take user emotions into consideration.

[0418] (Application Example 2)

[0419] Next, we will explain application example 2. In the following explanation, the data processing device 12 will be referred to as the "server," and the smart glasses 214 will be referred to as the "terminal."

[0420] Conventional partner selection systems fail to reflect the emotional state of users, resulting in the inability to select the most suitable partner for each user in terms of proposals and the establishment of collaborative relationships. Furthermore, the vehicle travel environment is not optimized with the user's psychological state in mind, limiting improvements to the travel experience and safety. This leads to issues such as insufficient user satisfaction and comfort during travel.

[0421] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means.

[0422] In this invention, the server includes means for analyzing user requirements and emotional states, means for detecting the user's emotions in real time using an emotion recognition device mounted on the vehicle, and means for generating suggestions to adjust the in-vehicle environment and range of movement based on the emotional state. This makes it possible to select the optimal collaborating business entity while taking the user's emotions into consideration, and to optimize the vehicle's movement environment.

[0423] "Requirements" refer to the preferences and requirements that users enter regarding the selection of a collaborating business entity, and include specific technical and operational criteria.

[0424] "Emotional state" refers to the state of the user's psychological and physiological responses, and is analyzed based on factors such as facial expressions and tone of voice.

[0425] A "cooperative entity" is a business partner that receives a request for proposal and whose business partnership is being considered.

[0426] An "emotion recognition device" refers to sensors and software that identify emotions from a user's facial expressions and voice.

[0427] "In-vehicle environment" refers to various adjustable elements related to user comfort, such as temperature, lighting, music, and seating position inside the vehicle.

[0428] "Travel range" refers to the planned route or path the vehicle will take, and is selected considering the user's destination and safety.

[0429] A "Request for Proposal" is an official document sent to potential business partners, clearly outlining the terms and requirements of the partnership.

[0430] "Evaluation criteria" refer to standardized scales and guidelines for evaluating the content of proposals and the creditworthiness of collaborating entities.

[0431] A "generative AI model" is an artificial intelligence learning system used to analyze user emotions and suggested content.

[0432] This system consists of three main components: users, terminals, and servers.

[0433] The terminal functions as an interface for users to input their requirements and for the system to directly sense their mood. The terminal is equipped with an emotion recognition device that analyzes facial expressions via voice input and a camera, and by analyzing this data in real time, it understands the user's emotional state.

[0434] The server is the central hub of this system's information processing, aggregating and analyzing all data. The server runs a generative AI model using Python and TensorFlow, simultaneously analyzing the user's input requirements and emotional state. Based on the emotional state, it generates suggestions regarding necessary adjustments to the in-vehicle environment and travel range, and determines a list of optimal collaborating entities. Based on the user's emotional feedback, the server evaluates the collaborating entity suggestions using natural language processing and automatically sets up meetings if necessary.

[0435] For example, if a user wants to travel in a relaxed environment, an emotion recognition device will detect that emotion, and the server will create and suggest a list of relaxing music. The vehicle can also suggest routes that allow the user to enjoy beautiful scenery, thus fulfilling the user's request.

[0436] An example of a prompt for a generative AI model is, "What suggestions can you offer to soothe the user?"

[0437] In this way, the system comprehensively considers the user's emotions and needs to provide a comfortable and efficient travel experience.

[0438] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0439] Step 1:

[0440] The terminal accepts user requests via voice or text input. The input data is preprocessed locally before being sent to the server. Specifically, noise reduction and text normalization are performed.

[0441] Step 2:

[0442] The device uses its camera and microphone to detect the user's facial expressions and voice, and performs emotion recognition in real time. The resulting emotion data is then processed using image analysis algorithms and speech recognition models. The analyzed emotion data is sent to a server.

[0443] Step 3:

[0444] The server aggregates received request conditions and emotional state data and analyzes their relationships using a generative AI model. TensorFlow is used to extract data features and understand the user's desires and psychological state. The output is a list of candidate collaborating entities best suited to the user's needs.

[0445] Step 4:

[0446] The server automatically generates a Request for Proposal (RPO) based on the generated list of potential collaborating entities. In this process, a template engine creates a document that reflects the user's requirements and emotional state. The generated RPO is then sent back to the terminal and displayed to the user.

[0447] Step 5:

[0448] Users review the displayed requests for proposals and potential collaborating entities and provide feedback. This feedback is returned to the server as user evaluation criteria and sentiment feedback.

[0449] Step 6:

[0450] The server integrates the received feedback and compares and evaluates the proposals from collaborating entities. It uses natural language processing technology to analyze the content of the proposals and evaluate their reliability and usability.

[0451] Step 7:

[0452] The server schedules meetings with partner companies as needed and generates suggestions for adjusting the in-vehicle environment and travel range. For example, if the user wants to relax, it suggests music playlists and routes, which are then presented to the user via the terminal.

[0453] The specific processing unit 290 transmits the result of the specific processing to the smart glasses 214. In the smart glasses 214, the control unit 46A causes the speaker 240 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0454] Data generation model 58 is a type of so-called generative AI (Artificial Intelligence). One example of data generation model 58 is ChatGPT (Internet search<URL: https: / / openai.com / blog / chatgpt> ), Gemini (Internet search) <url: https: gemini.google.com ?hl="ja">Examples of generative AI include the following. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and with inference data such as audio data representing speech, text data representing text, and image data representing images. The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference results in data formats such as audio data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[0455] In the above embodiment, an example was given in which specific processing is performed by the data processing device 12, but the technology of this disclosure is not limited thereto, and the specific processing may also be performed by the smart glasses 214.

[0456] [Third Embodiment]

[0457] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[0458] As shown in Figure 5, the data processing system 310 includes a data processing device 12 and a headset terminal 314. An example of the data processing device 12 is a server.

[0459] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 is an example of a "computer" related to the technology of this disclosure. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[0460] The headset terminal 314 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication interface 44, and a display 343. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, camera 42, and display 343 are also connected to the bus 52.

[0461] The microphone 238 receives voice signals from the user 20 and receives instructions from the user 20. The microphone 238 captures the voice signals from the user 20, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0462] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the area around the user 20 (for example, an imaging range defined by a field of view equivalent to the width of a typical healthy person's field of vision).

[0463] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0464] Figure 6 shows an example of the main functions of the data processing device 12 and the headset terminal 314. As shown in Figure 6, the data processing device 12 performs specific processing using the processor 28. The storage 32 stores the specific processing program 56.

[0465] The specific processing program 56 is an example of a "program" relating to the technology of this disclosure. The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0466] The storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[0467] In the headset terminal 314, the processor 46 performs the reception output processing. The storage 50 stores the reception output program 60. The processor 46 reads the reception output program 60 from the storage 50 and executes the read reception output program 60 on the RAM 48. The reception output processing is realized by the processor 46 operating as a control unit 46A according to the reception output program 60 executed on the RAM 48.

[0468] Next, the specific processing performed by the specific processing unit 290 of the data processing device 12 will be described. In the following description, the data processing device 12 will be referred to as the "server" and the headset terminal 314 will be referred to as the "terminal".

[0469] This invention is a system for streamlining the selection process of partner companies and for quickly selecting highly reliable business partners. The program of this system has a mechanism in which the server, terminals, and users work together in cooperation with each other.

[0470] System configuration:

[0471] The server, acting as the system's core, runs AI agents responsible for processing vast amounts of data. The terminal functions as the user interface, interacting with the user. The user, a company representative, inputs requirements and conditions for selecting partner companies through the terminal.

[0472] Program execution flow:

[0473] The user enters their requirements for selecting a business partner into a terminal. The terminal converts the input into the appropriate format and sends it to the server. The server uses an AI agent to list the most suitable partner companies based on the user's requirements. The generated list is presented to the user via the terminal.

[0474] The user selects companies to send a Request for Proposal (RFP) to from the displayed candidates. The server automatically creates an RFP and sends it to the selected partner companies. Once proposals from each partner company are returned to the server, an AI agent analyzes and evaluates the proposals. The evaluation results are provided to the user via the terminal, and if it is determined that a meeting with a particular partner company is necessary, a meeting is scheduled.

[0475] Furthermore, the server acquires credit and security information from selected partner companies and incorporates the results into the evaluation. Finally, the user drafts a proposal based on the evaluation results, and the ordering process is completed through the server's automated workflow.

[0476] Specific example:

[0477] For example, a user might request the construction of a new IT infrastructure. In this case, the user inputs the characteristics of the desired collaborating company (e.g., a company strong in cloud technology, a company capable of meeting short deadlines) into the system. The server's AI agent searches for and presents candidate companies that best match the requirements. A request for proposal (RPO) is automatically generated and sent to the selected candidate companies, after which the proposals obtained by the AI ​​are analyzed and evaluated. Based on these results, the user selects a company, drafts a proposal for approval while considering credit information and security information as needed, and finally concludes a contract.

[0478] This system allows companies to quickly select the best business partner for their needs, and the entire process is carried out transparently and efficiently.

[0479] The following describes the processing flow.

[0480] Step 1:

[0481] Users use a terminal to input specific requirements they seek from business partners (e.g., technical requirements, schedule, budget, etc.). This information forms the basis for future Requests for Proposals (RFPs).

[0482] Step 2:

[0483] The terminal converts the entered request requirements into a predetermined format and sends the data to the server. This step verifies the accuracy and completeness of the data.

[0484] Step 3:

[0485] The server uses an AI agent to generate a list of optimal partner companies based on the requirements. In doing so, it refers to market databases and historical company evaluation data to select candidates that meet the needs.

[0486] Step 4:

[0487] The terminal displays a list of partner companies sent from the server to the user. The user reviews this list and selects the companies they wish to request proposals from.

[0488] Step 5:

[0489] Based on the user's selection, the server automatically generates a Request for Proposal (RFP) using an AI agent and sends the RFP to the selected partner companies.

[0490] Step 6:

[0491] Proposals from partner companies are sent back to the server. The server's AI agent analyzes these proposals, evaluates them based on pre-set evaluation criteria, and assigns a score.

[0492] Step 7:

[0493] The evaluation results are sent to the terminal, and the user selects companies they wish to interview based on the evaluation. If necessary, the server schedules the interview and notifies both parties.

[0494] Step 8:

[0495] The server obtains credit and security information from an external database as part of a reliability check for selected partner companies and incorporates it into the evaluation.

[0496] Step 9:

[0497] The user makes the final decision on a collaborating company based on reliability assessments and then drafts a proposal for approval. The server automatically manages the approval process and completes the ordering procedure once approval is obtained.

[0498] (Example 1)

[0499] Next, we will describe Example 1. In the following description, the data processing device 12 will be referred to as the "server," and the headset-type terminal 314 will be referred to as the "terminal."

[0500] The traditional process for selecting partner companies was time-consuming and labor-intensive, and the evaluation criteria were inconsistent, making it difficult to quickly select reliable business partners.

[0501] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 1 is realized by the following means.

[0502] In this invention, the server includes processing means for receiving and analyzing requests, processing means for generating a list of optimal external organizations based on the requests, and processing means for automatically generating communication documents and sending them to the external organizations included in the list. This makes it possible to perform the selection process for partner companies efficiently and reliably.

[0503] A "request" is information that indicates the user's wishes or conditions for the system.

[0504] "Analysis" refers to the process of analyzing input information and converting it into a form that the system can understand.

[0505] "External organizations" refer to third-party groups or companies that are considered as collaborating companies or business partners.

[0506] A "list" is a list containing a set of external organizations that are candidates selected by the system.

[0507] "Communication documents" refer to automatically generated documents that include requests for proposals and other necessary information, and are sent to external organizations.

[0508] "Reply content" refers to information received by the system as a response to proposals or information from external organizations.

[0509] A "uniform standard" refers to a standardized and consistent scale or indicator used when conducting evaluations.

[0510] "Contact" refers to opportunities to communicate directly with external organizations during the selection process.

[0511] "Reliability information" refers to information that includes data related to the transaction history and creditworthiness of external organizations.

[0512] "Security information" refers to information that includes data related to the security measures and information protection of external organizations.

[0513] "Business procedures" refer to the processes related to ordering and contracting that are carried out with the ultimately selected external organization.

[0514] A "user interface" refers to the interface through which a user accesses the system and inputs and receives information.

[0515] "Human language processing" refers to the techniques and methods used to understand and analyze natural language.

[0516] This invention provides an information processing system for streamlining the process of selecting partner companies. The system operates with a combination of three components: a server, a terminal, and a user, each performing its respective role.

[0517] Server Role

[0518] The server, as the central component of the system, handles massive data processing. Generative AI models run on the server, generating lists of external organizations (partner companies) based on user requirements. This is done using AI agents, referencing past performance databases and market trends. The server also handles the automatic generation and transmission of Requests for Proposals (RFPs) to external organizations. This transmission is done via email or a dedicated business platform. Furthermore, the server receives responses from partner companies, analyzes them using natural language processing technology, and evaluates them. The evaluation analyzes candidate companies based on unified criteria and provides the results to the user.

[0519] Terminal role

[0520] The terminal functions as a user interface, providing a user-facing screen for inputting the requirements necessary for selecting partner companies. The information entered by the user is converted to an appropriate format and sent to the server. Furthermore, the server displays the information and evaluation results of the listed candidate companies in an easy-to-understand manner.

[0521] User roles

[0522] As a company representative, the user inputs requirements for selecting partner companies into a terminal and, based on a list provided by the server, requests proposals and schedules meetings with external organizations. The user then proceeds to select the final business partner based on the evaluation results provided. In addition, they can check the reliability and security information of external organizations as needed to make the optimal selection.

[0523] Specific example

[0524] For example, if a user is planning to build a new IT infrastructure and is looking for a partner company with strong cloud technology, the user enters their requirements into a terminal. The server then uses an AI agent to list suitable companies and present them to the user. Finally, the user enters into a contract with the most suitable partner company based on the evaluation results.

[0525] Example of a prompt

[0526] "To build a new IT infrastructure, select a partner company with strong cloud technology capabilities and the ability to meet short deadlines, and present the most suitable candidate company through this system."

[0527] Thus, the invented system simplifies the complex process of selecting collaborating companies, enabling the selection of business partners with greater accuracy and reliability.

[0528] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0529] Step 1:

[0530] The user inputs specific requirements and conditions for selecting partner companies into the terminal. This input includes business needs such as technical capabilities, deadlines, and budget. The terminal converts the input data into an appropriate format and sends the input content to the server. As part of data processing, the entered conditions are converted into a standardized format.

[0531] Step 2:

[0532] The server runs a generative AI model based on user requirements received from the terminal to generate a list of optimal external organizations. User requirements data and a database of past performance are used as input. The AI ​​agent analyzes this data and outputs a list of potential business partners. In this step, the AI ​​analysis model operates as a data computation tool, extracting appropriate candidates from a vast amount of data.

[0533] Step 3:

[0534] The server automatically generates a Request for Proposal (RFP) based on the generated list of potential collaborating companies. The inputs used are the list of potential companies and the user's requirements specifications. Once the RFP content is finalized, the server sends it to the designated external organization via email or business platform. Specifically, it generates the RFP based on a template and automates the sending process.

[0535] Step 4:

[0536] When proposals from selected external organizations are returned to the server, the server uses an AI agent to analyze the proposal content. The input information consists of the proposal document and related external data. The AI ​​analyzes the proposal content and outputs an evaluation result scored based on a unified evaluation criterion. In this step, natural language processing is used to analyze the text.

[0537] Step 5:

[0538] The terminal displays the evaluation results of partner companies provided by the server to the user in screen or report format. The output shows the user evaluation scores and the strengths and weaknesses of candidate companies in a visualized form. Based on this information, the user conducts specific negotiations and meetings with the companies that are necessary. The user makes a final selection and decides which companies will become business partners.

[0539] Step 6:

[0540] For companies selected by the user, the server automatically creates documents for business procedures and proceeds with the contract. These procedures include verification of credit and security information. The server's output consists of automatically generated contracts and finalized internal procedures.

[0541] (Application Example 1)

[0542] Next, we will explain Application Example 1. In the following explanation, the data processing device 12 will be referred to as the "server," and the headset-type terminal 314 will be referred to as the "terminal."

[0543] Not only is the selection of partner companies and suppliers time-consuming, but the evaluation criteria can become subjective and lack reliability. Furthermore, efficient management of the supply chain in accordance with the operating status of machinery on the manufacturing floor may not be possible, potentially leading to decreased production efficiency.

[0544] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 1 is realized by the following means.

[0545] In this invention, the server includes means for inputting and analyzing user requirements, means for generating a list of optimal suppliers, and means for automatically creating distribution requests and sending them to selected suppliers. This enables objective and efficient supplier selection and ordering procedures, as well as rapid supply chain management in accordance with the operating status of manufacturing machinery.

[0546] "Means for inputting and analyzing user requirements" refers to a device or method for users to input the conditions and preferences necessary for selecting a supplier, and for appropriately analyzing that information.

[0547] "Means for generating a list of optimal suppliers" refers to an apparatus or method for listing the most suitable suppliers based on the analyzed requirements.

[0548] "Means for automatically creating distribution request forms and sending them to selected suppliers" refers to a device or method that automatically generates a request form containing the necessary information for suppliers and sends it to the selected suppliers.

[0549] "Means for collecting proposals from suppliers and evaluating them according to unified standards" refers to an apparatus or method for collecting proposals submitted by suppliers and evaluating their contents based on consistent standards.

[0550] "Means for acquiring and evaluating supplier reliability and safety information" refers to an apparatus or method for collecting and evaluating credit information and safety data concerning suppliers.

[0551] "Means for monitoring the operational status of manufacturing machinery, selecting suppliers to provide the next necessary components and repair services, and supporting orders" refers to a device or method for monitoring manufacturing machinery in operation, selecting suppliers capable of providing the necessary parts and services according to its status, and supporting procurement.

[0552] This invention is a system that streamlines the selection of business partners and optimizes the management of parts and repair services in the manufacturing process. Servers, terminals, and users work together, using AI technology to analyze data and automate everything from supplier selection to ordering procedures.

[0553] The server receives and analyzes requirements submitted by the user. This process utilizes a cloud server and a generative AI model (e.g., TensorFlow or PyTorch). Requirements are entered via a terminal, which provides a user-friendly interface. This interface is designed to allow users to easily configure the conditions they require from the supplier.

[0554] The server also uses an AI model to generate a list of optimal suppliers based on the input requirements. The server automatically creates and sends distribution requests to the listed suppliers, allowing users to proceed with negotiations quickly without wasting time. After proposals are received, the server evaluates them using a unified standard. This evaluation utilizes natural language processing technology to perform a detailed analysis of the proposals.

[0555] Regarding information on the reliability and safety of suppliers, the server retrieves this information via a dedicated database and API, and incorporates it into the evaluation. Based on the evaluation results, a final contract is made with the supplier, enabling rapid supply chain management based on the operational status of manufacturing machinery.

[0556] For example, if a part of a manufacturing machine begins to malfunction, the user inputs information about the necessary parts or repairs via a terminal. The server can immediately select the most suitable supplier and initiate the process of quickly obtaining the required repair parts.

[0557] Examples of prompts for a generative AI model include the following:

[0558] "Create a model to optimize the process of selecting business partners. Recommend the best supplier using the following data format: { 'requirement': requirement, 'timeline': urgency}"

[0559] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0560] Step 1:

[0561] Users enter their requirements for supplier selection via a terminal. These requirements include detailed information about parts and repair services needed for the manufacturing machinery. The input data is converted to an appropriate format before being sent to the server.

[0562] Step 2:

[0563] The server receives input data and uses a generating AI model to list the most suitable suppliers. This AI model compares historical data with requirements to select qualified supplier candidates. The resulting list is then ranked based on indicators such as suitability and delivery time.

[0564] Step 3:

[0565] The server automatically creates a distribution request based on a list of ranked suppliers and sends it to the suppliers. The request includes details of the required parts and services, deadlines, and other conditions, and is transmitted to the relevant suppliers via electronic means.

[0566] Step 4:

[0567] When suppliers return proposals, the server collects the content of those proposals and analyzes them using natural language processing technology. The analysis results are scored based on evaluation criteria such as price, delivery time, and quality.

[0568] Step 5:

[0569] Based on the evaluation results, the server makes the final selection of suppliers, including reliability and security information. During this process, additional information about suppliers is collected using external databases and APIs and reflected in the overall evaluation.

[0570] Step 6:

[0571] Users enter into contracts with suppliers through the server and place orders for necessary parts and services. Once the contract is completed, the server automatically registers the order information in the system, optimizing the entire supply chain.

[0572] Furthermore, an emotion engine that estimates the user's emotions may be incorporated. That is, the identification processing unit 290 may use the emotion identification model 59 to estimate the user's emotions and perform identification processing using the user's emotions.

[0573] This invention is a system for recognizing user emotions during the selection process of partner companies and using that information to efficiently and reliably select business partners. The system consists of a server, terminals, and users, and uses an emotion engine to reflect emotional information in the relevant processes.

[0574] System configuration:

[0575] The server functions as the central hub for data processing, running the AI ​​agent and emotion engine. The terminal acts as the user interface, exchanging information with the user. The user inputs requirements and conditions for selecting business partners via the terminal. The emotion engine identifies emotions from the user's voice, text input, facial expressions, etc., and sends data to the server as needed.

[0576] Program execution flow:

[0577] When a user inputs requirements for selecting a partner company into the terminal, the terminal uses an emotion engine to analyze the user's emotional state. This emotional state information is incorporated into the analyzed requirements and sent to the server. On the server, an AI agent generates a list of optimal partner company candidates, taking into account both the requirements and the emotional information. The generated list is then presented to the user via the terminal.

[0578] When a user selects companies to send a Request for Proposal (RFP) to, the emotion engine re-analyzes the user's emotions and incorporates that information into the RFP's content. The server automatically generates the adjusted RFP and sends it to the selected partner companies.

[0579] When proposals are returned from partner companies, the server's AI agent evaluates them, incorporating emotional feedback from the user's review of the proposals. Based on this feedback, the priority of the proposals and the necessity of a meeting are determined.

[0580] Specific example:

[0581] For example, if a user is looking for a partner company for system development, the system evaluates the user's perceived stress level in addition to the technical requirements entered by the user, and presents a list accordingly. If a high stress level is detected, priority is given to selecting a company that provides more comprehensive support. Furthermore, if the user's emotions are positive when reviewing proposals, the system prioritizes scheduling meetings with those companies and applies emotional considerations when acquiring credit information.

[0582] This system allows us to incorporate user sentiment into the selection process, thereby supporting the selection of more accurate partner companies.

[0583] The following describes the processing flow.

[0584] Step 1:

[0585] The user uses a terminal to input requirements for selecting a business partner (e.g., technical requirements, deadlines, budget, etc.). At this time, an emotion engine is activated, and the user's emotional state is added to the input data in real time based on their facial expressions and voice.

[0586] Step 2:

[0587] The terminal formats the collected requests and emotional information and sends it to the server. This data, which includes not only user requests but also emotional information, is used for analysis on the server as multidimensional data.

[0588] Step 3:

[0589] The server uses an AI agent to analyze the received requests and emotional information. It accesses a database of partner companies and generates a list of partner companies that match the user's needs and take their emotional state into consideration.

[0590] Step 4:

[0591] The terminal visualizes and presents to the user a list of collaborating companies sent from the server. The user reviews the provided list and selects companies to which to send a Request for Proposal (RFP).

[0592] Step 5:

[0593] Once users are selected, the emotion engine analyzes their reactions again, and these reactions are reflected in the creation of the Request for Proposal (RFP). The server automatically generates the RFP based on this information and sends it to the selected companies.

[0594] Step 6:

[0595] After proposals from partner companies arrive at the server, the AI ​​agent evaluates the proposals. This evaluation takes into account not only pre-set criteria but also the emotional feedback received by users when they view the proposals.

[0596] Step 7:

[0597] The evaluation results are sent to the device, and the user reviews the suggestions. Prioritization is performed based on the evaluation, and emotional feedback recommends scheduling interviews with highly-rated companies.

[0598] Step 8:

[0599] The server obtains credit and security information from companies selected as interview candidates to verify their reliability. This information is also used in the final evaluation.

[0600] Step 9:

[0601] Ultimately, the user submits a proposal for approval based on the selection results via their terminal. The server then processes the proposal approval through an automated workflow and completes the ordering process after final approval.

[0602] (Example 2)

[0603] Next, we will describe Example 2. In the following description, the data processing device 12 will be referred to as the "server," and the headset-type terminal 314 will be referred to as the "terminal."

[0604] The challenge in selecting business partners is that traditional methods fail to consider user emotions and psychological factors, making optimal selection difficult. Furthermore, quantitative criteria for evaluating proposals are insufficient, and a flexible approach tailored to individual user needs is required.

[0605] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 2 is realized by the following means.

[0606] In this invention, the server includes means for inputting and analyzing user requirements and emotional information, means for generating a list of optimal companies based on the requirements and emotional information, and means for automatically creating a request for proposal and sending it to the companies included in the list. This enables optimal company selection that takes user emotions into consideration and proposal evaluation that responds to emotional feedback.

[0607] "Requirements" are the conditions and expectations that users have of a company in order to achieve specific objectives or needs.

[0608] "Emotional information" refers to data on the user's psychological state and emotions, analyzed based on factors such as voice, text input, and facial expressions.

[0609] A "company" refers to an organization or legal entity that is being considered as a potential business partner.

[0610] A "Request for Proposal" is a document in which users request proposals from a company regarding services or products they wish to offer.

[0611] "Emotional feedback" refers to evaluation data of the psychological responses that users show to proposals and companies.

[0612] "Natural language processing" is a technology that enables computers to understand and process human language.

[0613] The embodiment of this invention is implemented as a system consisting of a server, a terminal, and a user. This system utilizes user sentiment information when selecting business partners to support a more accurate and effective selection process.

[0614] The server is responsible for central data processing and runs the AI ​​agent and emotion engine. Based on the user's requirements and emotional information received, the server generates a list of suitable candidate companies. Furthermore, it automatically creates a Request for Proposal (RPO) and sends it to the companies. When evaluating the submitted proposals, the server also takes the user's emotional feedback into consideration.

[0615] The terminal functions as an interface for exchanging information with the user. The user inputs their requirements through this terminal, which in turn activates the emotion engine. The emotion engine analyzes the user's emotions from voice, text input, and facial expressions, and collects this information. This allows the system to formulate suggestions that are more tailored to the user's needs.

[0616] Users select reliable business partners for business development and supply chain construction. During this process, users input their requirements into a terminal, and the system uses this information to select the most suitable companies. This approach allows users' emotions and psychological states to influence the selection process, resulting in highly accurate selections.

[0617] A concrete example is when a user is selecting a partner company for the development of a new product. The user inputs the necessary technical requirements along with their emotional state through their device. For example, for a user experiencing high stress levels, the server will list companies with enhanced support systems. This allows the user to select a company with greater confidence.

[0618] An example of a prompt is, "Please describe the process of listing partners that will reduce stress when selecting collaborating companies for system development." This prompt is used to support information processing tailored to the user's needs by leveraging a generative AI model.

[0619] This invention makes it possible to select business partners while taking user emotions into consideration, improving the accuracy and reliability of the selection process.

[0620] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0621] Step 1:

[0622] The user inputs the requirements for selecting partner companies using a terminal. The terminal receives this input and activates the emotion engine. It senses the user's voice, text input, and facial expressions to acquire emotional information. At this time, it analyzes the input text data and voice data and outputs the emotional state as numerical data.

[0623] Step 2:

[0624] The terminal integrates the request requirements and emotional information and sends it to the server. The server verifies the received data and starts processing it with an AI agent. Based on the request requirements and emotional data, the AI ​​agent generates a list of the most suitable companies from the company information in the database. In this process, it uses the emotional data to prioritize evaluating companies' responsiveness and company attributes that match user needs.

[0625] Step 3:

[0626] The server sends the generated list of candidate companies to the terminal. The terminal arranges the layout and presents the list to the user in a visually easy-to-understand format. The user reviews the presented list and selects a specific company. At this point, the terminal retrieves the user's selection data and restarts the emotion engine.

[0627] Step 4:

[0628] To create a Request for Proposal (R&D) for the company selected by the user, the terminal sends the user's selection information and re-analyzed sentiment information to the server. The server uses this information to automatically generate the R&D. The document includes wording that alleviates concerns and emphasizes special conditions, tailored to the user's emotional state.

[0629] Step 5:

[0630] The server sends automatically generated requests for proposals to selected companies. When companies respond with proposals, the server aggregates the content. An AI agent evaluates the submitted proposals and integrates them with user sentiment feedback to organize the evaluation criteria.

[0631] Step 6:

[0632] Users review proposals via their devices and provide feedback. The devices send this feedback data to a server, which then uses it to evaluate the company. This incorporates emotional information into the final proposal evaluation, allowing the server to determine the necessity and priority of interviews.

[0633] In this way, the system can select business partners that take user emotions into consideration.

[0634] (Application Example 2)

[0635] Next, we will explain application example 2. In the following explanation, the data processing device 12 will be referred to as the "server," and the headset-type terminal 314 will be referred to as the "terminal."

[0636] Conventional partner selection systems fail to reflect the emotional state of users, resulting in the inability to select the most suitable partner for each user in terms of proposals and the establishment of collaborative relationships. Furthermore, the vehicle travel environment is not optimized with the user's psychological state in mind, limiting improvements to the travel experience and safety. This leads to issues such as insufficient user satisfaction and comfort during travel.

[0637] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means.

[0638] In this invention, the server includes means for analyzing user requirements and emotional states, means for detecting the user's emotions in real time using an emotion recognition device mounted on the vehicle, and means for generating suggestions to adjust the in-vehicle environment and range of movement based on the emotional state. This makes it possible to select the optimal collaborating business entity while taking the user's emotions into consideration, and to optimize the vehicle's movement environment.

[0639] "Requirements" refer to the preferences and requirements that users enter regarding the selection of a collaborating business entity, and include specific technical and operational criteria.

[0640] "Emotional state" refers to the state of the user's psychological and physiological responses, and is analyzed based on factors such as facial expressions and tone of voice.

[0641] A "cooperative entity" is a business partner that receives a request for proposal and whose business partnership is being considered.

[0642] An "emotion recognition device" refers to sensors and software that identify emotions from a user's facial expressions and voice.

[0643] "In-vehicle environment" refers to various adjustable elements related to user comfort, such as temperature, lighting, music, and seating position inside the vehicle.

[0644] "Travel range" refers to the planned route or path the vehicle will take, and is selected considering the user's destination and safety.

[0645] A "Request for Proposal" is an official document sent to potential business partners, clearly outlining the terms and requirements of the partnership.

[0646] "Evaluation criteria" refer to standardized scales and guidelines for evaluating the content of proposals and the creditworthiness of collaborating entities.

[0647] A "generative AI model" is an artificial intelligence learning system used to analyze user emotions and suggested content.

[0648] This system consists of three main components: users, terminals, and servers.

[0649] The terminal functions as an interface for users to input their requirements and for the system to directly sense their mood. The terminal is equipped with an emotion recognition device that analyzes facial expressions via voice input and a camera, and by analyzing this data in real time, it understands the user's emotional state.

[0650] The server is the central hub of this system's information processing, aggregating and analyzing all data. The server runs a generative AI model using Python and TensorFlow, simultaneously analyzing the user's input requirements and emotional state. Based on the emotional state, it generates suggestions regarding necessary adjustments to the in-vehicle environment and travel range, and determines a list of optimal collaborating entities. Based on the user's emotional feedback, the server evaluates the collaborating entity suggestions using natural language processing and automatically sets up meetings if necessary.

[0651] For example, if a user wants to travel in a relaxed environment, an emotion recognition device will detect that emotion, and the server will create and suggest a list of relaxing music. The vehicle can also suggest routes that allow the user to enjoy beautiful scenery, thus fulfilling the user's request.

[0652] An example of a prompt for a generative AI model is, "What suggestions can you offer to soothe the user?"

[0653] In this way, the system comprehensively considers the user's emotions and needs to provide a comfortable and efficient travel experience.

[0654] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0655] Step 1:

[0656] The terminal accepts user requests via voice or text input. The input data is preprocessed locally before being sent to the server. Specifically, noise reduction and text normalization are performed.

[0657] Step 2:

[0658] The device uses its camera and microphone to detect the user's facial expressions and voice, and performs emotion recognition in real time. The resulting emotion data is then processed using image analysis algorithms and speech recognition models. The analyzed emotion data is sent to a server.

[0659] Step 3:

[0660] The server aggregates received request conditions and emotional state data and analyzes their relationships using a generative AI model. TensorFlow is used to extract data features and understand the user's desires and psychological state. The output is a list of candidate collaborating entities best suited to the user's needs.

[0661] Step 4:

[0662] The server automatically generates a Request for Proposal (RPO) based on the generated list of potential collaborating entities. In this process, a template engine creates a document that reflects the user's requirements and emotional state. The generated RPO is then sent back to the terminal and displayed to the user.

[0663] Step 5:

[0664] Users review the displayed requests for proposals and potential collaborating entities and provide feedback. This feedback is returned to the server as user evaluation criteria and sentiment feedback.

[0665] Step 6:

[0666] The server integrates the received feedback and compares and evaluates the proposals from collaborating entities. It uses natural language processing technology to analyze the content of the proposals and evaluate their reliability and usability.

[0667] Step 7:

[0668] The server schedules meetings with partner companies as needed and generates suggestions for adjusting the in-vehicle environment and travel range. For example, if the user wants to relax, it suggests music playlists and routes, which are then presented to the user via the terminal.

[0669] The specific processing unit 290 transmits the result of the specific processing to the headset terminal 314. In the headset terminal 314, the control unit 46A causes the speaker 240 and display 343 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0670] Data generation model 58 is a type of so-called generative AI (Artificial Intelligence). One example of data generation model 58 is ChatGPT (Internet search<URL: https: / / openai.com / blog / chatgpt> ), Gemini (Internet search) <url: https: gemini.google.com ?hl="ja">Examples of generative AI include the following. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and with inference data such as audio data representing speech, text data representing text, and image data representing images. The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference results in data formats such as audio data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[0671] In the above embodiment, an example was given in which specific processing is performed by the data processing device 12, but the technology of this disclosure is not limited thereto, and specific processing may also be performed by the headset terminal 314.

[0672] [Fourth Embodiment]

[0673] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[0674] As shown in Figure 7, the data processing system 410 includes a data processing device 12 and a robot 414. An example of the data processing device 12 is a server.

[0675] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 is an example of a "computer" related to the technology of this disclosure. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[0676] The robot 414 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication interface 44, and a controlled object 443. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, camera 42, and controlled object 443 are also connected to the bus 52.

[0677] The microphone 238 receives voice signals from the user 20 and receives instructions from the user 20. The microphone 238 captures the voice signals from the user 20, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0678] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the area around the user 20 (for example, an imaging range defined by a field of view equivalent to the width of a typical healthy person's field of vision).

[0679] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0680] The controlled object 443 includes a display device, LEDs in the eyes, and motors that drive the arms, hands, and feet. The posture and gestures of the robot 414 are controlled by controlling the motors of the arms, hands, and feet. Some of the robot 414's emotions can be expressed by controlling these motors. Furthermore, the robot 414's facial expressions can also be expressed by controlling the illumination state of the LEDs in its eyes.

[0681] Figure 8 shows an example of the main functions of the data processing device 12 and the robot 414. As shown in Figure 8, the data processing device 12 performs specific processing using the processor 28. The storage 32 stores the specific processing program 56.

[0682] The specific processing program 56 is an example of a "program" relating to the technology of this disclosure. The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0683] The storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[0684] In robot 414, the processor 46 performs the reception output processing. The storage 50 stores the reception output program 60. The processor 46 reads the reception output program 60 from the storage 50 and executes the read reception output program 60 on the RAM 48. The reception output processing is realized by the processor 46 operating as a control unit 46A according to the reception output program 60 executed on the RAM 48.

[0685] Next, the specific processing performed by the specific processing unit 290 of the data processing device 12 will be described. In the following description, the data processing device 12 will be referred to as the "server" and the robot 414 as the "terminal".

[0686] This invention is a system for streamlining the selection process of partner companies and for quickly selecting highly reliable business partners. The program of this system has a mechanism in which the server, terminals, and users work together in cooperation with each other.

[0687] System configuration:

[0688] The server, acting as the system's core, runs AI agents responsible for processing vast amounts of data. The terminal functions as the user interface, interacting with the user. The user, a company representative, inputs requirements and conditions for selecting partner companies through the terminal.

[0689] Program execution flow:

[0690] The user enters their requirements for selecting a business partner into a terminal. The terminal converts the input into the appropriate format and sends it to the server. The server uses an AI agent to list the most suitable partner companies based on the user's requirements. The generated list is presented to the user via the terminal.

[0691] The user selects companies to send a Request for Proposal (RFP) to from the displayed candidates. The server automatically creates an RFP and sends it to the selected partner companies. Once proposals from each partner company are returned to the server, an AI agent analyzes and evaluates the proposals. The evaluation results are provided to the user via the terminal, and if it is determined that a meeting with a particular partner company is necessary, a meeting is scheduled.

[0692] Furthermore, the server acquires credit and security information from selected partner companies and incorporates the results into the evaluation. Finally, the user drafts a proposal based on the evaluation results, and the ordering process is completed through the server's automated workflow.

[0693] Specific example:

[0694] For example, a user might request the construction of a new IT infrastructure. In this case, the user inputs the characteristics of the desired collaborating company (e.g., a company strong in cloud technology, a company capable of meeting short deadlines) into the system. The server's AI agent searches for and presents candidate companies that best match the requirements. A request for proposal (RPO) is automatically generated and sent to the selected candidate companies, after which the proposals obtained by the AI ​​are analyzed and evaluated. Based on these results, the user selects a company, drafts a proposal for approval while considering credit information and security information as needed, and finally concludes a contract.

[0695] This system allows companies to quickly select the best business partner for their needs, and the entire process is carried out transparently and efficiently.

[0696] The following describes the processing flow.

[0697] Step 1:

[0698] Users use a terminal to input specific requirements they seek from business partners (e.g., technical requirements, schedule, budget, etc.). This information forms the basis for future Requests for Proposals (RFPs).

[0699] Step 2:

[0700] The terminal converts the entered request requirements into a predetermined format and sends the data to the server. This step verifies the accuracy and completeness of the data.

[0701] Step 3:

[0702] The server uses an AI agent to generate a list of optimal partner companies based on the requirements. In doing so, it refers to market databases and historical company evaluation data to select candidates that meet the needs.

[0703] Step 4:

[0704] The terminal displays a list of partner companies sent from the server to the user. The user reviews this list and selects the companies they wish to request proposals from.

[0705] Step 5:

[0706] Based on the user's selection, the server automatically generates a Request for Proposal (RFP) using an AI agent and sends the RFP to the selected partner companies.

[0707] Step 6:

[0708] Proposals from partner companies are sent back to the server. The server's AI agent analyzes these proposals, evaluates them based on pre-set evaluation criteria, and assigns a score.

[0709] Step 7:

[0710] The evaluation results are sent to the terminal, and the user selects companies they wish to interview based on the evaluation. If necessary, the server schedules the interview and notifies both parties.

[0711] Step 8:

[0712] The server obtains credit and security information from an external database as part of a reliability check for selected partner companies and incorporates it into the evaluation.

[0713] Step 9:

[0714] The user makes the final decision on a collaborating company based on reliability assessments and then drafts a proposal for approval. The server automatically manages the approval process and completes the ordering procedure once approval is obtained.

[0715] (Example 1)

[0716] Next, we will describe Example 1. In the following description, the data processing device 12 will be referred to as the "server" and the robot 414 as the "terminal".

[0717] The traditional process for selecting partner companies was time-consuming and labor-intensive, and the evaluation criteria were inconsistent, making it difficult to quickly select reliable business partners.

[0718] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 1 is realized by the following means.

[0719] In this invention, the server includes processing means for receiving and analyzing requests, processing means for generating a list of optimal external organizations based on the requests, and processing means for automatically generating communication documents and sending them to the external organizations included in the list. This makes it possible to perform the selection process for partner companies efficiently and reliably.

[0720] A "request" is information that indicates the user's wishes or conditions for the system.

[0721] "Analysis" refers to the process of analyzing input information and converting it into a form that the system can understand.

[0722] "External organizations" refer to third-party groups or companies that are considered as collaborating companies or business partners.

[0723] A "list" is a list containing a set of external organizations that are candidates selected by the system.

[0724] "Communication documents" refer to automatically generated documents that include requests for proposals and other necessary information, and are sent to external organizations.

[0725] "Reply content" refers to information received by the system as a response to proposals or information from external organizations.

[0726] A "uniform standard" refers to a standardized and consistent scale or indicator used when conducting evaluations.

[0727] "Contact" refers to opportunities to communicate directly with external organizations during the selection process.

[0728] "Reliability information" refers to information that includes data related to the transaction history and creditworthiness of external organizations.

[0729] "Security information" refers to information that includes data related to the security measures and information protection of external organizations.

[0730] "Business procedures" refer to the processes related to ordering and contracting that are carried out with the ultimately selected external organization.

[0731] A "user interface" refers to the interface through which a user accesses the system and inputs and receives information.

[0732] "Human language processing" refers to the techniques and methods used to understand and analyze natural language.

[0733] This invention provides an information processing system for streamlining the process of selecting partner companies. The system operates with a combination of three components: a server, a terminal, and a user, each performing its respective role.

[0734] Server Role

[0735] The server, as the central component of the system, handles massive data processing. Generative AI models run on the server, generating lists of external organizations (partner companies) based on user requirements. This is done using AI agents, referencing past performance databases and market trends. The server also handles the automatic generation and transmission of Requests for Proposals (RFPs) to external organizations. This transmission is done via email or a dedicated business platform. Furthermore, the server receives responses from partner companies, analyzes them using natural language processing technology, and evaluates them. The evaluation analyzes candidate companies based on unified criteria and provides the results to the user.

[0736] Terminal role

[0737] The terminal functions as a user interface, providing a user-facing screen for inputting the requirements necessary for selecting partner companies. The information entered by the user is converted to an appropriate format and sent to the server. Furthermore, the server displays the information and evaluation results of the listed candidate companies in an easy-to-understand manner.

[0738] User roles

[0739] As a company representative, the user inputs requirements for selecting partner companies into a terminal and, based on a list provided by the server, requests proposals and schedules meetings with external organizations. The user then proceeds to select the final business partner based on the evaluation results provided. In addition, they can check the reliability and security information of external organizations as needed to make the optimal selection.

[0740] Specific example

[0741] For example, if a user is planning to build a new IT infrastructure and is looking for a partner company with strong cloud technology, the user enters their requirements into a terminal. The server then uses an AI agent to list suitable companies and present them to the user. Finally, the user enters into a contract with the most suitable partner company based on the evaluation results.

[0742] Example of a prompt

[0743] "To build a new IT infrastructure, select a partner company with strong cloud technology capabilities and the ability to meet short deadlines, and present the most suitable candidate company through this system."

[0744] Thus, the invented system simplifies the complex process of selecting collaborating companies, enabling the selection of business partners with greater accuracy and reliability.

[0745] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0746] Step 1:

[0747] The user inputs specific requirements and conditions for selecting partner companies into the terminal. This input includes business needs such as technical capabilities, deadlines, and budget. The terminal converts the input data into an appropriate format and sends the input content to the server. As part of data processing, the entered conditions are converted into a standardized format.

[0748] Step 2:

[0749] The server runs a generative AI model based on user requirements received from the terminal to generate a list of optimal external organizations. User requirements data and a database of past performance are used as input. The AI ​​agent analyzes this data and outputs a list of potential business partners. In this step, the AI ​​analysis model operates as a data computation tool, extracting appropriate candidates from a vast amount of data.

[0750] Step 3:

[0751] The server automatically generates a Request for Proposal (RFP) based on the generated list of potential collaborating companies. The inputs used are the list of potential companies and the user's requirements specifications. Once the RFP content is finalized, the server sends it to the designated external organization via email or business platform. Specifically, it generates the RFP based on a template and automates the sending process.

[0752] Step 4:

[0753] When proposals from selected external organizations are returned to the server, the server uses an AI agent to analyze the proposal content. The input information consists of the proposal document and related external data. The AI ​​analyzes the proposal content and outputs an evaluation result scored based on a unified evaluation criterion. In this step, natural language processing is used to analyze the text.

[0754] Step 5:

[0755] The terminal displays the evaluation results of partner companies provided by the server to the user in screen or report format. The output shows the user evaluation scores and the strengths and weaknesses of candidate companies in a visualized form. Based on this information, the user conducts specific negotiations and meetings with the companies that are necessary. The user makes a final selection and decides which companies will become business partners.

[0756] Step 6:

[0757] For companies selected by the user, the server automatically creates documents for business procedures and proceeds with the contract. These procedures include verification of credit and security information. The server's output consists of automatically generated contracts and finalized internal procedures.

[0758] (Application Example 1)

[0759] Next, we will explain Application Example 1. In the following explanation, the data processing device 12 will be referred to as the "server" and the robot 414 as the "terminal".

[0760] Not only is the selection of partner companies and suppliers time-consuming, but the evaluation criteria can become subjective and lack reliability. Furthermore, efficient management of the supply chain in accordance with the operating status of machinery on the manufacturing floor may not be possible, potentially leading to decreased production efficiency.

[0761] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 1 is realized by the following means.

[0762] In this invention, the server includes means for inputting and analyzing user requirements, means for generating a list of optimal suppliers, and means for automatically creating distribution requests and sending them to selected suppliers. This enables objective and efficient supplier selection and ordering procedures, as well as rapid supply chain management in accordance with the operating status of manufacturing machinery.

[0763] "Means for inputting and analyzing user requirements" refers to a device or method for users to input the conditions and preferences necessary for selecting a supplier, and for appropriately analyzing that information.

[0764] "Means for generating a list of optimal suppliers" refers to an apparatus or method for listing the most suitable suppliers based on the analyzed requirements.

[0765] "Means for automatically creating distribution request forms and sending them to selected suppliers" refers to a device or method that automatically generates a request form containing the necessary information for suppliers and sends it to the selected suppliers.

[0766] "Means for collecting proposals from suppliers and evaluating them according to unified standards" refers to an apparatus or method for collecting proposals submitted by suppliers and evaluating their contents based on consistent standards.

[0767] "Means for acquiring and evaluating supplier reliability and safety information" refers to an apparatus or method for collecting and evaluating credit information and safety data concerning suppliers.

[0768] "Means for monitoring the operational status of manufacturing machinery, selecting suppliers to provide the next necessary components and repair services, and supporting orders" refers to a device or method for monitoring manufacturing machinery in operation, selecting suppliers capable of providing the necessary parts and services according to its status, and supporting procurement.

[0769] This invention is a system that streamlines the selection of business partners and optimizes the management of parts and repair services in the manufacturing process. Servers, terminals, and users work together, using AI technology to analyze data and automate everything from supplier selection to ordering procedures.

[0770] The server receives and analyzes requirements submitted by the user. This process utilizes a cloud server and a generative AI model (e.g., TensorFlow or PyTorch). Requirements are entered via a terminal, which provides a user-friendly interface. This interface is designed to allow users to easily configure the conditions they require from the supplier.

[0771] The server also uses an AI model to generate a list of optimal suppliers based on the input requirements. The server automatically creates and sends distribution requests to the listed suppliers, allowing users to proceed with negotiations quickly without wasting time. After proposals are received, the server evaluates them using a unified standard. This evaluation utilizes natural language processing technology to perform a detailed analysis of the proposals.

[0772] Regarding information on the reliability and safety of suppliers, the server retrieves this information via a dedicated database and API, and incorporates it into the evaluation. Based on the evaluation results, a final contract is made with the supplier, enabling rapid supply chain management based on the operational status of manufacturing machinery.

[0773] For example, if a part of a manufacturing machine begins to malfunction, the user inputs information about the necessary parts or repairs via a terminal. The server can immediately select the most suitable supplier and initiate the process of quickly obtaining the required repair parts.

[0774] Examples of prompts for a generative AI model include the following:

[0775] "Create a model to optimize the process of selecting business partners. Recommend the best supplier using the following data format: { 'requirement': requirement, 'timeline': urgency}"

[0776] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0777] Step 1:

[0778] Users enter their requirements for supplier selection via a terminal. These requirements include detailed information about parts and repair services needed for the manufacturing machinery. The input data is converted to an appropriate format before being sent to the server.

[0779] Step 2:

[0780] The server receives input data and uses a generating AI model to list the most suitable suppliers. This AI model compares historical data with requirements to select qualified supplier candidates. The resulting list is then ranked based on indicators such as suitability and delivery time.

[0781] Step 3:

[0782] The server automatically creates a distribution request based on a list of ranked suppliers and sends it to the suppliers. The request includes details of the required parts and services, deadlines, and other conditions, and is transmitted to the relevant suppliers via electronic means.

[0783] Step 4:

[0784] When suppliers return proposals, the server collects the content of those proposals and analyzes them using natural language processing technology. The analysis results are scored based on evaluation criteria such as price, delivery time, and quality.

[0785] Step 5:

[0786] Based on the evaluation results, the server makes the final selection of suppliers, including reliability and security information. During this process, additional information about suppliers is collected using external databases and APIs and reflected in the overall evaluation.

[0787] Step 6:

[0788] Users enter into contracts with suppliers through the server and place orders for necessary parts and services. Once the contract is completed, the server automatically registers the order information in the system, optimizing the entire supply chain.

[0789] Furthermore, an emotion engine that estimates the user's emotions may be incorporated. That is, the identification processing unit 290 may use the emotion identification model 59 to estimate the user's emotions and perform identification processing using the user's emotions.

[0790] This invention is a system for recognizing user emotions during the selection process of partner companies and using that information to efficiently and reliably select business partners. The system consists of a server, terminals, and users, and uses an emotion engine to reflect emotional information in the relevant processes.

[0791] System configuration:

[0792] The server functions as the central hub for data processing, running the AI ​​agent and emotion engine. The terminal acts as the user interface, exchanging information with the user. The user inputs requirements and conditions for selecting business partners via the terminal. The emotion engine identifies emotions from the user's voice, text input, facial expressions, etc., and sends data to the server as needed.

[0793] Program execution flow:

[0794] When a user inputs requirements for selecting a partner company into the terminal, the terminal uses an emotion engine to analyze the user's emotional state. This emotional state information is incorporated into the analyzed requirements and sent to the server. On the server, an AI agent generates a list of optimal partner company candidates, taking into account both the requirements and the emotional information. The generated list is then presented to the user via the terminal.

[0795] When a user selects companies to send a Request for Proposal (RFP) to, the emotion engine re-analyzes the user's emotions and incorporates that information into the RFP's content. The server automatically generates the adjusted RFP and sends it to the selected partner companies.

[0796] When proposals are returned from partner companies, the server's AI agent evaluates them, incorporating emotional feedback from the user's review of the proposals. Based on this feedback, the priority of the proposals and the necessity of a meeting are determined.

[0797] Specific example:

[0798] For example, if a user is looking for a partner company for system development, the system evaluates the user's perceived stress level in addition to the technical requirements entered by the user, and presents a list accordingly. If a high stress level is detected, priority is given to selecting a company that provides more comprehensive support. Furthermore, if the user's emotions are positive when reviewing proposals, the system prioritizes scheduling meetings with those companies and applies emotional considerations when acquiring credit information.

[0799] This system allows us to incorporate user sentiment into the selection process, thereby supporting the selection of more accurate partner companies.

[0800] The following describes the processing flow.

[0801] Step 1:

[0802] The user uses a terminal to input requirements for selecting a business partner (e.g., technical requirements, deadlines, budget, etc.). At this time, an emotion engine is activated, and the user's emotional state is added to the input data in real time based on their facial expressions and voice.

[0803] Step 2:

[0804] The terminal formats the collected requests and emotional information and sends it to the server. This data, which includes not only user requests but also emotional information, is used for analysis on the server as multidimensional data.

[0805] Step 3:

[0806] The server uses an AI agent to analyze the received requests and emotional information. It accesses a database of partner companies and generates a list of partner companies that match the user's needs and take their emotional state into consideration.

[0807] Step 4:

[0808] The terminal visualizes and presents to the user a list of collaborating companies sent from the server. The user reviews the provided list and selects companies to which to send a Request for Proposal (RFP).

[0809] Step 5:

[0810] Once users are selected, the emotion engine analyzes their reactions again, and these reactions are reflected in the creation of the Request for Proposal (RFP). The server automatically generates the RFP based on this information and sends it to the selected companies.

[0811] Step 6:

[0812] After proposals from partner companies arrive at the server, the AI ​​agent evaluates the proposals. This evaluation takes into account not only pre-set criteria but also the emotional feedback received by users when they view the proposals.

[0813] Step 7:

[0814] The evaluation results are sent to the device, and the user reviews the suggestions. Prioritization is performed based on the evaluation, and emotional feedback recommends scheduling interviews with highly-rated companies.

[0815] Step 8:

[0816] The server obtains credit and security information from companies selected as interview candidates to verify their reliability. This information is also used in the final evaluation.

[0817] Step 9:

[0818] Ultimately, the user submits a proposal for approval based on the selection results via their terminal. The server then processes the proposal approval through an automated workflow and completes the ordering process after final approval.

[0819] (Example 2)

[0820] Next, we will describe Example 2. In the following description, the data processing device 12 will be referred to as the "server" and the robot 414 as the "terminal".

[0821] The challenge in selecting business partners is that traditional methods fail to consider user emotions and psychological factors, making optimal selection difficult. Furthermore, quantitative criteria for evaluating proposals are insufficient, and a flexible approach tailored to individual user needs is required.

[0822] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 2 is realized by the following means.

[0823] In this invention, the server includes means for inputting and analyzing user requirements and emotional information, means for generating a list of optimal companies based on the requirements and emotional information, and means for automatically creating a request for proposal and sending it to the companies included in the list. This enables optimal company selection that takes user emotions into consideration and proposal evaluation that responds to emotional feedback.

[0824] "Requirements" are the conditions and expectations that users have of a company in order to achieve specific objectives or needs.

[0825] "Emotional information" refers to data on the user's psychological state and emotions, analyzed based on factors such as voice, text input, and facial expressions.

[0826] A "company" refers to an organization or legal entity that is being considered as a potential business partner.

[0827] A "Request for Proposal" is a document in which users request proposals from a company regarding services or products they wish to offer.

[0828] "Emotional feedback" refers to evaluation data of the psychological responses that users show to proposals and companies.

[0829] "Natural language processing" is a technology that enables computers to understand and process human language.

[0830] The embodiment of this invention is implemented as a system consisting of a server, a terminal, and a user. This system utilizes user sentiment information when selecting business partners to support a more accurate and effective selection process.

[0831] The server is responsible for central data processing and runs the AI ​​agent and emotion engine. Based on the user's requirements and emotional information received, the server generates a list of suitable candidate companies. Furthermore, it automatically creates a Request for Proposal (RPO) and sends it to the companies. When evaluating the submitted proposals, the server also takes the user's emotional feedback into consideration.

[0832] The terminal functions as an interface for exchanging information with the user. The user inputs their requirements through this terminal, which in turn activates the emotion engine. The emotion engine analyzes the user's emotions from voice, text input, and facial expressions, and collects this information. This allows the system to formulate suggestions that are more tailored to the user's needs.

[0833] Users select reliable business partners for business development and supply chain construction. During this process, users input their requirements into a terminal, and the system uses this information to select the most suitable companies. This approach allows users' emotions and psychological states to influence the selection process, resulting in highly accurate selections.

[0834] A concrete example is when a user is selecting a partner company for the development of a new product. The user inputs the necessary technical requirements along with their emotional state through their device. For example, for a user experiencing high stress levels, the server will list companies with enhanced support systems. This allows the user to select a company with greater confidence.

[0835] An example of a prompt is, "Please describe the process of listing partners that will reduce stress when selecting collaborating companies for system development." This prompt is used to support information processing tailored to the user's needs by leveraging a generative AI model.

[0836] This invention makes it possible to select business partners while taking user emotions into consideration, improving the accuracy and reliability of the selection process.

[0837] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0838] Step 1:

[0839] The user inputs the requirements for selecting partner companies using a terminal. The terminal receives this input and activates the emotion engine. It senses the user's voice, text input, and facial expressions to acquire emotional information. At this time, it analyzes the input text data and voice data and outputs the emotional state as numerical data.

[0840] Step 2:

[0841] The terminal integrates the request requirements and emotional information and sends it to the server. The server verifies the received data and starts processing it with an AI agent. Based on the request requirements and emotional data, the AI ​​agent generates a list of the most suitable companies from the company information in the database. In this process, it uses the emotional data to prioritize evaluating companies' responsiveness and company attributes that match user needs.

[0842] Step 3:

[0843] The server sends the generated list of candidate companies to the terminal. The terminal arranges the layout and presents the list to the user in a visually easy-to-understand format. The user reviews the presented list and selects a specific company. At this point, the terminal retrieves the user's selection data and restarts the emotion engine.

[0844] Step 4:

[0845] To create a Request for Proposal (R&D) for the company selected by the user, the terminal sends the user's selection information and re-analyzed sentiment information to the server. The server uses this information to automatically generate the R&D. The document includes wording that alleviates concerns and emphasizes special conditions, tailored to the user's emotional state.

[0846] Step 5:

[0847] The server sends automatically generated requests for proposals to selected companies. When companies respond with proposals, the server aggregates the content. An AI agent evaluates the submitted proposals and integrates them with user sentiment feedback to organize the evaluation criteria.

[0848] Step 6:

[0849] Users review proposals via their devices and provide feedback. The devices send this feedback data to a server, which then uses it to evaluate the company. This incorporates emotional information into the final proposal evaluation, allowing the server to determine the necessity and priority of interviews.

[0850] In this way, the system can select business partners that take user emotions into consideration.

[0851] (Application Example 2)

[0852] Next, we will explain application example 2. In the following explanation, the data processing device 12 will be referred to as the "server" and the robot 414 as the "terminal".

[0853] Conventional partner selection systems fail to reflect the emotional state of users, resulting in the inability to select the most suitable partner for each user in terms of proposals and the establishment of collaborative relationships. Furthermore, the vehicle travel environment is not optimized with the user's psychological state in mind, limiting improvements to the travel experience and safety. This leads to issues such as insufficient user satisfaction and comfort during travel.

[0854] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means.

[0855] In this invention, the server includes means for analyzing user requirements and emotional states, means for detecting the user's emotions in real time using an emotion recognition device mounted on the vehicle, and means for generating suggestions to adjust the in-vehicle environment and range of movement based on the emotional state. This makes it possible to select the optimal collaborating business entity while taking the user's emotions into consideration, and to optimize the vehicle's movement environment.

[0856] "Requirements" refer to the preferences and requirements that users enter regarding the selection of a collaborating business entity, and include specific technical and operational criteria.

[0857] "Emotional state" refers to the state of the user's psychological and physiological responses, and is analyzed based on factors such as facial expressions and tone of voice.

[0858] A "cooperative entity" is a business partner that receives a request for proposal and whose business partnership is being considered.

[0859] An "emotion recognition device" refers to sensors and software that identify emotions from a user's facial expressions and voice.

[0860] "In-vehicle environment" refers to various adjustable elements related to user comfort, such as temperature, lighting, music, and seating position inside the vehicle.

[0861] "Travel range" refers to the planned route or path the vehicle will take, and is selected considering the user's destination and safety.

[0862] A "Request for Proposal" is an official document sent to potential business partners, clearly outlining the terms and requirements of the partnership.

[0863] "Evaluation criteria" refer to standardized scales and guidelines for evaluating the content of proposals and the creditworthiness of collaborating entities.

[0864] A "generative AI model" is an artificial intelligence learning system used to analyze user emotions and suggested content.

[0865] This system consists of three main components: users, terminals, and servers.

[0866] The terminal functions as an interface for users to input their requirements and for the system to directly sense their mood. The terminal is equipped with an emotion recognition device that analyzes facial expressions via voice input and a camera, and by analyzing this data in real time, it understands the user's emotional state.

[0867] The server is the central hub of this system's information processing, aggregating and analyzing all data. The server runs a generative AI model using Python and TensorFlow, simultaneously analyzing the user's input requirements and emotional state. Based on the emotional state, it generates suggestions regarding necessary adjustments to the in-vehicle environment and travel range, and determines a list of optimal collaborating entities. Based on the user's emotional feedback, the server evaluates the collaborating entity suggestions using natural language processing and automatically sets up meetings if necessary.

[0868] For example, if a user wants to travel in a relaxed environment, an emotion recognition device will detect that emotion, and the server will create and suggest a list of relaxing music. The vehicle can also suggest routes that allow the user to enjoy beautiful scenery, thus fulfilling the user's request.

[0869] An example of a prompt for a generative AI model is, "What suggestions can you offer to soothe the user?"

[0870] In this way, the system comprehensively considers the user's emotions and needs to provide a comfortable and efficient travel experience.

[0871] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0872] Step 1:

[0873] The terminal accepts user requests via voice or text input. The input data is preprocessed locally before being sent to the server. Specifically, noise reduction and text normalization are performed.

[0874] Step 2:

[0875] The device uses its camera and microphone to detect the user's facial expressions and voice, and performs emotion recognition in real time. The resulting emotion data is then processed using image analysis algorithms and speech recognition models. The analyzed emotion data is sent to a server.

[0876] Step 3:

[0877] The server aggregates received request conditions and emotional state data and analyzes their relationships using a generative AI model. TensorFlow is used to extract data features and understand the user's desires and psychological state. The output is a list of candidate collaborating entities best suited to the user's needs.

[0878] Step 4:

[0879] The server automatically generates a Request for Proposal (RPO) based on the generated list of potential collaborating entities. In this process, a template engine creates a document that reflects the user's requirements and emotional state. The generated RPO is then sent back to the terminal and displayed to the user.

[0880] Step 5:

[0881] Users review the displayed requests for proposals and potential collaborating entities and provide feedback. This feedback is returned to the server as user evaluation criteria and sentiment feedback.

[0882] Step 6:

[0883] The server integrates the received feedback and compares and evaluates the proposals from collaborating entities. It uses natural language processing technology to analyze the content of the proposals and evaluate their reliability and usability.

[0884] Step 7:

[0885] The server schedules meetings with partner companies as needed and generates suggestions for adjusting the in-vehicle environment and travel range. For example, if the user wants to relax, it suggests music playlists and routes, which are then presented to the user via the terminal.

[0886] The specific processing unit 290 transmits the result of the specific processing to the robot 414. In the robot 414, the control unit 46A causes the speaker 240 and the controlled object 443 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0887] Data generation model 58 is a type of so-called generative AI (Artificial Intelligence). One example of data generation model 58 is ChatGPT (Internet search<URL: https: / / openai.com / blog / chatgpt> ), Gemini (Internet search) <url: https: gemini.google.com ?hl="ja">Examples of generative AI include the following. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and with inference data such as audio data representing speech, text data representing text, and image data representing images. The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference results in data formats such as audio data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[0888] In the above embodiment, an example was given in which the specific processing is performed by the data processing device 12, but the technology of this disclosure is not limited thereto, and the specific processing may also be performed by the robot 414.

[0889] Furthermore, the emotion identification model 59, acting as an emotion engine, may determine the user's emotion according to a specific mapping. Specifically, the emotion identification model 59 may determine the user's emotion according to a specific mapping, which is an emotion map (see Figure 9). Similarly, the emotion identification model 59 may also determine the robot's emotion, and the identification processing unit 290 may perform identification processing using the robot's emotion.

[0890] Figure 9 shows an emotion map 400 in which multiple emotions are mapped. In the emotion map 400, emotions are arranged in concentric circles radiating from the center. The closer to the center of the concentric circles, the more primitive the emotions are located. Further out of the concentric circles, emotions representing states and actions arising from mental states are located. Emotion is a concept that includes feelings and mental states. On the left side of the concentric circles, emotions that are generally generated from reactions occurring in the brain are located. On the right side of the concentric circles, emotions that are generally induced by situational judgment are located. Above and below the concentric circles, emotions that are generally generated from reactions occurring in the brain and induced by situational judgment are located. In addition, the emotion of "pleasure" is located on the upper side of the concentric circles, and the emotion of "displeasure" is located on the lower side. Thus, in the emotion map 400, multiple emotions are mapped based on the structure in which emotions arise, and emotions that are likely to occur simultaneously are mapped close together.

[0891] These emotions are distributed at the 3 o'clock position on the Emotion Map 400, and usually fluctuate between feelings of security and anxiety. In the right half of the Emotion Map 400, situational awareness takes precedence over internal feelings, resulting in a calm impression.

[0892] The inside of the Emotion Map 400 represents inner thoughts, while the outside represents actions. Therefore, the further you go from the outside of the Emotion Map 400, the more visible (expressed in actions) your emotions become.

[0893] Here, human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, it results in discomfort, and when they approach the ideal, it results in pleasure. Similarly, in robots, cars, motorcycles, etc., emotions can be created based on various balances, such as posture and battery level. When these balances deviate from the ideal, it results in discomfort, and when they approach the ideal, it results in pleasure. The emotion map can be generated, for example, based on Dr. Mitsuyoshi's emotion map (Research on a system for analyzing brain physiological signals of speech emotion recognition and emotion, Tokushima University, doctoral dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map contains emotions belonging to a region called "response," where sensation is dominant. The right half of the emotion map contains emotions belonging to a region called "situation," where situational awareness is dominant.

[0894] The emotion map defines two emotions that promote learning. One is the emotion around the middle of the negative "repentance" and "reflection" on the situation side. In other words, it is when the robot experiences negative emotions such as "I never want to feel this way again" or "I don't want to be scolded again." The other is the emotion around the positive "desire" on the reaction side. In other words, it is when the robot has positive feelings such as "I want more" or "I want to know more."

[0895] The emotion identification model 59 inputs user input into a pre-trained neural network, obtains emotion values ​​representing each emotion shown in the emotion map 400, and determines the user's emotion. This neural network is pre-trained based on multiple training data sets, which are combinations of user input and emotion values ​​representing each emotion shown in the emotion map 400. Furthermore, this neural network is trained so that emotions located close together have similar values, as shown in the emotion map 900 in Figure 10. Figure 10 shows an example where multiple emotions such as "reassured," "calm," and "confident" have similar emotion values.

[0896] The above description primarily focuses on the functions of the data processing device 12 in relation to this disclosure. However, the system related to this disclosure is not necessarily implemented on a server. The system related to this disclosure may be implemented as a general information processing system. This disclosure may be implemented, for example, as a software program that runs on a personal computer or as an application that runs on a smartphone. The method related to this disclosure may be provided to users in SaaS (Software as a Service) format.

[0897] In the above embodiment, an example was given in which a specific process is performed by a single computer 22. However, the technology of this disclosure is not limited thereto, and a distributed processing of the specific process may be performed by multiple computers, including computer 22. For example, a data generation model 58 may be provided in an external device of the data processing device 12, and the external device may generate data according to the input data.

[0898] In the above embodiment, an example was given in which the specific processing program 56 is stored in the storage 32, but the technology of this disclosure is not limited thereto. For example, the specific processing program 56 may be stored in a portable, computer-readable, non-temporary storage medium such as a USB (Universal Serial Bus) memory. The specific processing program 56 stored in the non-temporary storage medium is installed in the computer 22 of the data processing device 12. The processor 28 executes specific processing according to the specific processing program 56.

[0899] Alternatively, the specific processing program 56 may be stored in a storage device such as a server connected to the data processing device 12 via the network 54, and the specific processing program 56 may be downloaded and installed on the computer 22 in response to a request from the data processing device 12.

[0900] Furthermore, it is not necessary to store the entirety of the specific processing program 56 in a storage device such as a server connected to the data processing device 12 via the network 54, or to store the entirety of the specific processing program 56 in the storage 32; it is acceptable to store only a portion of the specific processing program 56.

[0901] The following types of processors can be used as hardware resources to perform specific processing. Examples of processors include a CPU, a general-purpose processor that functions as a hardware resource to perform specific processing by executing software, i.e., a program. Other examples of processors include dedicated electrical circuits, such as FPGAs (Field-Programmable Gate Arrays), PLDs (Programmable Logic Devices), or ASICs (Application Specific Integrated Circuits), which have circuit configurations specifically designed to perform specific processing. All of these processors have built-in or connected memory, and all of them perform specific processing by using memory.

[0902] The hardware resource that performs a specific process may consist of one of these various processors, or it may consist of a combination of two or more processors of the same or different types (for example, a combination of multiple FPGAs, or a combination of a CPU and an FPGA). Alternatively, the hardware resource that performs a specific process may consist of a single processor.

[0903] Examples of configurations using a single processor include, firstly, a configuration in which one or more CPUs and software are combined to form a single processor, and this processor functions as a hardware resource that performs a specific process. Secondly, there is a configuration using a processor that realizes the functions of the entire system, including multiple hardware resources that perform a specific process, on a single IC chip, as exemplified by SoCs (System-on-a-chip). In this way, a specific process is realized using one or more of the above types of processors as hardware resources.

[0904] Furthermore, the hardware structure of these various processors can more specifically utilize electrical circuits that combine circuit elements such as semiconductor devices. Also, the specific processing described above is merely an example. Therefore, it goes without saying that unnecessary steps can be deleted, new steps added, or the processing order rearranged, as long as it does not deviate from the main purpose.

[0905] The descriptions and illustrations presented above are detailed explanations of the technical aspects of this disclosure and are merely examples of the technical aspects. For example, the above descriptions of the structure, function, operation, and effect are examples of the structure, function, operation, and effect of the technical aspects of this disclosure. Therefore, it goes without saying that you may delete unnecessary parts, add new elements, or replace elements in the descriptions and illustrations presented above, as long as you do not deviate from the essence of the technical aspects of this disclosure. Furthermore, in order to avoid confusion and facilitate understanding of the technical aspects of this disclosure, explanations of common technical knowledge and the like that do not require special explanation to enable the implementation of the technical aspects of this disclosure have been omitted from the descriptions and illustrations presented above.

[0906] All documents, patent applications, and technical standards described herein are incorporated by reference to the same extent as if each individual document, patent application, and technical standard were specifically and individually noted as being incorporated by reference.

[0907] The following is further disclosed regarding the embodiments described above.

[0908] (Claim 1)

[0909] A means of inputting and analyzing user requirements,

[0910] A means for generating a list of optimal partner companies based on the aforementioned requirements,

[0911] A means of automatically creating a Request for Proposal and sending it to the cooperating companies included in the aforementioned list,

[0912] A means of collecting proposals from the aforementioned cooperating companies and evaluating them according to unified standards,

[0913] A means of setting up meetings with cooperating companies based on the aforementioned evaluation,

[0914] A means of acquiring and evaluating credit information and security information of partner companies,

[0915] The means of placing orders with the final selected cooperating companies,

[0916] A system that includes this.

[0917] (Claim 2)

[0918] The system according to claim 1, which provides a user interface for inputting the aforementioned requirements.

[0919] (Claim 3)

[0920] The system according to claim 1, wherein the evaluation criteria are based on proposed content analyzed using natural language processing.

[0921] "Example 1"

[0922] (Claim 1)

[0923] A processing means that receives and analyzes requests,

[0924] A processing means for generating a list of optimal external organizations based on the aforementioned requirements,

[0925] A processing means for automatically generating communication documents and sending them to external organizations included in the aforementioned list,

[0926] A processing means for collecting the content of replies from the aforementioned external organizations and evaluating them according to unified standards,

[0927] Processing means for setting up contact with an external organization based on the aforementioned evaluation,

[0928] A processing means for acquiring and evaluating reliability information and safety information from external organizations,

[0929] The means of carrying out business procedures for the external organization that was ultimately selected,

[0930] A system that includes this.

[0931] (Claim 2)

[0932] The system according to claim 1, which provides a user operation screen for inputting the aforementioned requests.

[0933] (Claim 3)

[0934] The system according to claim 1, wherein the evaluation criteria are based on the content of the response analyzed using human language processing.

[0935] "Application Example 1"

[0936] (Claim 1)

[0937] A means of inputting and analyzing user requirements,

[0938] A means for generating a list of optimal suppliers based on the above requirements,

[0939] A means of automatically creating a distribution request form and sending it to the suppliers included in the above list,

[0940] A means of collecting proposals from the aforementioned suppliers and evaluating them according to unified standards,

[0941] A means of setting up a meeting with the supplier based on the aforementioned evaluation,

[0942] Means for acquiring and evaluating supplier reliability and safety information,

[0943] A means of placing an order with the final selected supplier,

[0944] A means to monitor the operational status of manufacturing machinery, select suppliers for the next required components and repair services, and support orders.

[0945] A system that includes this.

[0946] (Claim 2)

[0947] The system according to claim 1, which provides a user interface for inputting the aforementioned requirements.

[0948] (Claim 3)

[0949] The system according to claim 1, wherein the evaluation criteria are based on proposed content analyzed using natural language processing, and the proposed content is evaluated by a generative AI model.

[0950] "Example 2 of combining an emotion engine"

[0951] (Claim 1)

[0952] A means for inputting and analyzing user requirements and emotional information,

[0953] A means for generating a list of optimal companies based on the aforementioned requirements and sentiment information,

[0954] A means of automatically creating a Request for Proposal and sending it to the companies included in the aforementioned list,

[0955] A means of collecting proposals from the aforementioned companies and user emotional feedback, and evaluating them according to a unified standard,

[0956] A means for setting up an interview with a company based on the aforementioned evaluation and emotional feedback,

[0957] A means of acquiring and evaluating corporate credit information and security information,

[0958] The means of placing an order with the final selected company,

[0959] A system that includes this.

[0960] (Claim 2)

[0961] The system according to claim 1, which provides a user interface for inputting the aforementioned requirements and emotional information.

[0962] (Claim 3)

[0963] The system according to claim 1, wherein the evaluation criteria are based on proposed content and sentiment feedback analyzed using natural language processing.

[0964] "Application example 2 when combining with an emotional engine"

[0965] (Claim 1)

[0966] A means of inputting and analyzing user requirements,

[0967] A means for generating a list of optimal collaborating entities based on the aforementioned requirements and the user's emotional state,

[0968] A means of detecting the user's emotions in real time using an emotion recognition device installed in the vehicle,

[0969] A means for generating suggestions to adjust the in-car environment and range of travel based on emotional state,

[0970] A means of automatically generating a Request for Proposal and sending it to the cooperating organizations included in the above list,

[0971] A means of collecting proposals from the aforementioned cooperating entities and evaluating them according to unified standards,

[0972] A means of setting up a meeting with the cooperating entity based on the aforementioned evaluation,

[0973] Means for acquiring and evaluating credit information and protection information of cooperating entities,

[0974] The means of placing orders with the final selected cooperating business entity,

[0975] A system that includes this.

[0976] (Claim 2)

[0977] The system according to claim 1, which provides a user interface for inputting the aforementioned requirements.

[0978] (Claim 3)

[0979] The system according to claim 1, wherein the evaluation criteria are based on the proposed content analyzed by a generative AI model and the user's emotional feedback. [Explanation of Symbols]

[0980] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Devices 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robots< / url:> < / url:> < / url:> < / url:>

Claims

1. A means of inputting and analyzing user requirements, A means for generating a list of optimal suppliers based on the above requirements, A means of automatically creating a distribution request form and sending it to the suppliers included in the above list, A means of collecting proposals from the aforementioned suppliers and evaluating them according to unified standards, A means of setting up a meeting with the supplier based on the aforementioned evaluation, Means for acquiring and evaluating supplier reliability and safety information, A means of placing an order with the final selected supplier, A means to monitor the operational status of manufacturing machinery, select suppliers for the next required components and repair services, and support orders. A system that includes this.

2. The system according to claim 1, which provides a user interface for inputting the aforementioned requirements.

3. The system according to claim 1, wherein the evaluation criteria are based on proposed content analyzed using natural language processing, and the proposed content is evaluated by a generative AI model.

Images