Data processing system

Abstract

The present application relates to a kind of comprehensive implementation in the client information management of legal business activities, business activity support, client relationship strengthening legal business support system, uniformly manages the various data such as customer attribute information, transaction history, feedback, contract information, project progress, external clue information, competitive product information, market trend on server, and using generative AI will customer demand extraction, potential customer specific, business task automatic generation, sales forecast, competitive product analysis, proposal material production, feedback analysis, project and contract management, training offer, communication optimization etc. are automated and optimized, thereby realizing the business efficiency of both business undertakers and customers, the high degree of decision, customer satisfaction improvement, competitive strength strengthening, continuous business relationship construction.

Description

Technical Field

[0001] The technology disclosed herein relates to a system. Background Technology

[0002] Patent Document 1 discloses a personalized chatbot control method executed by at least one processor, the method comprising: receiving user speech; adding the user speech to a prompt containing instructions related to a chatbot role; encoding the prompt; and inputting the encoded prompt into a language model to generate chatbot speech in response to the user speech.

[0003] Patent document 1: Japanese Patent Application Publication No. 2022-180282.

[0004] The present invention addresses the challenges faced by business operators in corporate operations, including the complexity of customer information management, difficulties in identifying new potential customers, inefficiency in business activities, insufficient accuracy in sales forecasting, difficulties in competitor analysis, tedious preparation of proposal materials, and inadequate utilization of customer feedback. It also addresses the dual challenges faced by customers, such as difficulties in clarifying their needs, tedious feedback provision and analysis, insufficient data sharing, inefficient project management, difficulty in grasping market trends, and the necessity for smooth communication. By providing comprehensive and real-time functions such as unified management of information utilizing generative AI, data analysis, automation of business activities, feedback collection and analysis, project management, communication optimization, training, and market insights, the invention aims to improve business efficiency, enhance decision-making, increase customer satisfaction, strengthen competitiveness, and build sustainable business relationships for both business operators and customers. This comprehensively solves the fundamental problems of information fragmentation, inconsistent responses, inefficient business processes, and difficulties in building customer relationships in traditional corporate operations. Summary of the Invention

[0005] The means to solve the problem involved in this invention is to construct a corporate sales support system comprising a customer information management department, a sales activity support department, and a customer relationship enhancement department. In the customer information management department, customer attribute information, transaction history, interests, contract information, feedback information, project progress information, shared data, training history, and market insight information obtained from corporate sales personnel and customers are uniformly accumulated and managed. Generative AI is then used to analyze this information to estimate the potential needs and future sales potential of each customer. In the sales activity support department, based on the information analyzed in the customer information management department and externally obtained lead information, competitor information, and market trend information, generative AI is used to analyze the information. AI is used for identifying potential customers, automatically generating tasks for business activities, sales forecasting, competitor analysis, automatic generation of proposal materials, optimization of business strategies, and provision of training content. In the Customer Relationship Enhancement Department, information from the Customer Information Management Department and the Business Activity Support Department is utilized to use generative AI for collecting and analyzing customer feedback, providing support for clarifying needs, data sharing, joint project management, contract management support, problem-solving support, communication optimization, and proposing measures to improve customer satisfaction. As a result, unified information management is achieved for both the legal representative and the customer, business processes are automated, decision-making is enhanced through data utilization, customer satisfaction is improved, competitiveness is strengthened, and continuous business relationship building is achieved. Attached Figure Description

[0006] Figure 1 This is a conceptual diagram illustrating an example of the configuration of a data processing system according to the first embodiment.

[0007] Figure 2 This is a conceptual diagram illustrating an example of the main functions of the data processing apparatus and smart device according to the first embodiment.

[0008] Figure 3 This is a conceptual diagram illustrating an example of the data processing system configuration in the second embodiment.

[0009] Figure 4 This is a conceptual diagram illustrating an example of the main functions of the data processing device and smart glasses according to the second embodiment.

[0010] Figure 5 This is a conceptual diagram illustrating an example of the data processing system configuration in the third embodiment.

[0011] Figure 6 This is a conceptual diagram illustrating an example of the main functions of the data processing device and head-mounted terminal according to the third embodiment.

[0012] Figure 7 This is a conceptual diagram illustrating an example of the data processing system configuration in the fourth embodiment.

[0013] Figure 8 This is a conceptual diagram illustrating an example of the functions of the main parts of the data processing device and robot according to the fourth embodiment.

[0014] Figure 9 It represents an emotion graph that maps multiple emotions.

[0015] Figure 10 It represents an emotion graph that maps multiple emotions.

[0016] Explanation of reference numerals in the attached figures Data processing systems 10, 210, 310, and 410 12 Data processing device 14 Smart devices 214 Smart Glasses 314 Head-mounted terminal 414 Robot. Detailed Implementation

[0017] Hereinafter, an example of an implementation of the system involved in this disclosure will be described with reference to the accompanying drawings.

[0018] First, let's explain the terms used in the following description.

[0019] In the following embodiments, the processor (hereinafter referred to as "processor") can be a single computing device or a combination of multiple computing devices. Furthermore, a processor can be a single computing device or a combination of multiple computing devices. Examples of computing devices include CPU (Central Processing Unit), GPU (Graphics Processing Unit), GPGPU (General-Purpose computing on Graphics Processing Units), APU (Accelerated Processing Unit), etc.

[0020] In the following implementation, the labeled RAM (Random Access Memory) is a memory that temporarily stores information and is used by the processor as working memory.

[0021] In the following embodiments, the labeled memory is one or more non-volatile storage devices used to store various programs and various parameters, etc. Examples of non-volatile storage devices include flash memory (SSD (Solid State Drive)), disk (e.g., hard disk) or magnetic tape, etc.

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

[0023] In the following implementation, "A and / or B" is synonymous with "at least one of A and B". That is, "A and / or B" means that it can be only A, only B, or a combination of A and B. Furthermore, in this specification, when "and / or" connects more than three items, the same approach as "A and / or B" applies.

[0024] [First Implementation] Figure 1 An example of the configuration of the data processing system 10 according to the first embodiment is shown.

[0025] like Figure 1 As shown, the data processing system 10 includes a data processing device 12 and an intelligent device 14. An example of the data processing device 12 is a server.

[0026] The data processing apparatus 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 is an example of a "computer" as understood in this disclosure. The computer 22 includes a processor 28, RAM 30, and memory 32. The processor 28, RAM 30, and memory 32 are connected to a bus 34. Furthermore, the database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. An example of the network 54 includes a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[0027] The smart device 14 includes a computer 36, a receiver 38, an output device 40, a camera 42, and a communication I / F 44. The computer 36 includes a processor 46, RAM 48, and memory 50. The processor 46, RAM 48, and memory 50 are connected to a bus 52. Furthermore, the receiver 38, output device 40, camera 42, and communication I / F 44 are also connected to the bus 52.

[0028] The receiving device 38 includes a touchscreen 38A and a microphone 38B, etc., for receiving user input. The touchscreen 38A receives user input generated by contact with an indicator (e.g., a pen or finger). The microphone 38B receives user input generated by sound by detecting the user's voice. The control unit 46A in the processor 46 sends data representing user input received through the touchscreen 38A and microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the data representing the user input.

[0029] The output device 40 includes a display 40A and a speaker 40B, and presents data to the user 20 by outputting data in a form perceptible to the user 20 (e.g., sound and / or text). The display 40A displays visual information such as text and images according to instructions from the processor 46. The speaker 40B outputs sound 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 imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor.

[0030] Communication I / F 44 is connected to network 54. Communication I / F 44 and 26 are responsible for sending and receiving various information between processor 46 and processor 28 via network 54.

[0031] Figure 2 An example of the main functions of the data processing device 12 and the smart device 14 is shown.

[0032] like Figure 2 As shown, in the data processing apparatus 12, specific processing is performed by the processor 28. A specific processing program 56 is stored in the memory 32. The specific processing program 56 is an example of a "program" as understood in this disclosure. The processor 28 reads the specific processing program 56 from the memory 32 and executes the read specific processing program 56 on the RAM 30. Specific processing is implemented by the processor 28 operating as a specific processing unit 290 based on the specific processing program 56 executed on the RAM 30.

[0033] The memory 32 stores a data generation model 58 and an emotion-specific model 59. The data generation model 58 and the emotion-specific model 59 are used by the specific processing unit 290. The specific processing unit 290 can use the emotion-specific model 59 to infer the user's emotions and perform specific processing based on the user's emotions. The emotion inference function using the emotion-specific model 59 (emotion-specific function) includes various inferences and predictions related to the user's emotions, but is not limited to this example. Furthermore, emotion inference and prediction also include, for example, emotion analysis (analysis).

[0034] In the smart device 14, the processor 46 performs the receive / output processing. The memory 50 stores the receive / output program 60. The receive / output program 60 is used in conjunction with the data processing system 10 and the specific processing program 56. The processor 46 reads the receive / output program 60 from the memory 50 and executes the read receive / output program 60 on the RAM 48. The receive / output processing is implemented by the processor 46 acting as a control unit 46A based on the receive / output program 60 executed on the RAM 48. Furthermore, the smart device 14 may also have the same data generation model and emotion-specific model as the data generation model 58 and the emotion-specific model 59, and use these models to perform the same processing as the specific processing unit 290. The receive / output processing is implemented by the processor 46 acting as a control unit 46A based on the receive / output program 60 executed on the RAM 48.

[0035] In addition, other devices besides the data processing device 12 may also have a data generation model 58.

[0036] For example, a server device (e.g., a generation server) may have a data generation model 58.

[0037] In this case, the data processing device 12 obtains the processing results (prediction results, etc.) using the data generation model 58 by communicating with a server device that has the data generation model 58. Furthermore, the data processing device 12 can be a server device or a user-owned terminal device (e.g., a mobile phone, robot, home appliance, etc.). Next, an example of the processing performed by the data processing system 10 of the first embodiment will be described.

[0038] (Example 1) The specific processing flow in Example 1 will be described below. Each part of the system described below is implemented by the data processing device 12 and the smart device 14. Furthermore, the data processing device 12 is referred to as the "server" and the smart device 14 is referred to as the "terminal".

[0039] (Implementation methods for carrying out the present invention) This invention relates to a corporate sales support system comprised of a server and terminals. The Customer Information Management Department, Sales Activity Support Department, and Customer Relationship Enhancement Department are primarily implemented on the server, while the terminals function as interfaces for sales representatives and customers. The following provides a comprehensive and detailed explanation of the structure of each department, the specific application methods of generative AI, the role division between the server and terminals, and specific application examples. Specifically, this system employs a distributed computing architecture. The server has a parallel processing cluster consisting of multiple computing nodes. The terminal connects to the server via a secure communication protocol. The system incorporates multiple neural networks, including a large-scale language model. This neural network is based on a Transformer architecture with a self-attention mechanism. The system converts the input natural language text into multi-dimensional word embedding vectors. The system performs operations on these vectors in a high-dimensional space. This allows the system to perform non-traditional information processing, distinct from human mental activity. The system models the tacit knowledge of sales representatives into quantitative tensor data. This transforms the individual-dependent sales process into objective and reproducible computer processing. The system reduces response latency to milliseconds through parallel data processing. This enables real-time sales support. This system is applied to customer management use cases in B2B businesses such as manufacturing and IT services.

[0040] First, the Customer Information Management Department implements a unified database on the server, accumulating and managing customer attribute information (such as company name, salesperson's name, department, position, contact information, industry, number of employees, capital, location, sales volume, financial statements, transaction start date, transaction history, purchase history, interests, contract information, contract start / end date, contract amount, contract terms, feedback information, questionnaire results, project progress information, shared data, training history, market insights, past complaint history, consultation history, customer satisfaction score, salesperson's history, visit history, email or chat communication history, etc.) entered by sales representatives or customers on the server. For example, when a sales representative enters new customer information from the terminal, the Customer Information Management Department on the server receives this information and stores it in the database. Similarly, when a customer enters their company's needs or feedback from the terminal, this information is also managed on the server. Furthermore, when a sales representative enters the content of a discussion or the customer's reaction from the terminal after a negotiation, this information is also managed on the server. Generative AI plays a crucial role in analyzing this vast and diverse data, for example, inferring each customer's potential needs or future sales potential from past transaction history or feedback. Specifically, generative AI uses natural language processing technology to analyze customers' freely written feedback, extracting keywords or sentiments such as "hope for shorter delivery times," "interest in new products," and "dissatisfaction with the support system," automatically categorizing and tagging each customer's areas of interest or topics. Furthermore, for numerical data such as sales data or transaction frequency, generative AI also performs time-series analysis or clustering, automatically extracting customers with a high probability of reordering within the next six months, customers with a high risk of cancellation, and customers with a high probability of additional sales. For example, based on a customer's purchase cycle or the trend of past order amounts, generative AI can automatically calculate the "predicted date of the next order" or "proposed order amount" and notify the sales representative. Specifically, this customer information management department possesses a distributed relational database and a NoSQL database for storing unstructured data. This system normalizes the input customer attribute information. This system tokenizes text data and converts it into a 512-dimensional numerical vector. This system inputs the numerical vector into a large-scale language model. This large-scale language model has a pre-learned Transformer encoder. As input data, the system receives, for example, a text tensor representing "desire to shorten delivery time," or a time-series array representing purchase amounts over the past 12 months. The system combines these input data to generate a multimodal feature tensor. This feature tensor is then input into a multilayer perceptron. The system outputs either a probability score from 0 to 1 representing customer interest, or a timestamp indicating the predicted date of the next order.This system outputs structured data such as "Attention level to new products: 0.85" and "Predicted next order date: October 1, 2023". The system compares the output probability score with a predetermined threshold. If the score exceeds the threshold, the system triggers a subsequent alert generation module. This allows the system to achieve high-precision customer classification without relying on human intuition. The system uses a cross-entropy error function and optimizes the model weights through gradient descent. This continuously improves prediction accuracy. This system is applied to customer churn prediction use cases in the corporate business departments of financial institutions.

[0041] The Sales Support Department is also implemented on the server. Based on data accumulated and analyzed in the Customer Information Management Department, as well as lead information obtained from external sources (such as business card data from trade shows, inquiries from websites, responses on social media, leads from advertising, company information from external databases, industry news, and publicly available financial information), competitor information (such as competitor product information, pricing information, news reports, social media posts, reasons for past lost sales, and competitor sales strategy analysis), and market trend information (such as industry reports, economic indicators, trend analysis, and changes in laws and regulations), generative AI optimizes sales activities. For example, by automatically reading business card data obtained at trade shows using OCR, the generative AI automatically determines the priority of potential customers based on similarity to past sales patterns or customer attributes. Furthermore, when sales personnel input the progress of sales activities or negotiation content from the terminal, the generative AI analyzes it and automatically generates specific sales tasks or proposals such as "The best time to contact them is in 3 days," "Proposing service B to this customer is more effective than product A," and "A new proposal needs to be made based on competitor activities," which are then displayed as a ToDo list or notification on the terminal to the sales personnel. For example, if the negotiation record contains entries such as "price negotiations have reached a stalemate" or "awaiting decision-maker approval," the generative AI will propose actions such as "suggesting price adjustment plans" and "creating additional information for decision-makers." Regarding sales forecasting, the generative AI comprehensively analyzes past negotiation data, current case progress, customer purchasing tendencies, industry trends, and seasonal factors to automatically calculate values ​​or trends such as "this month's sales forecast is 12 million yen," "the probability of achieving the next period's target is 85%," and "sales to specific customer groups are showing a decreasing trend." Regarding competitor analysis, the generative AI also analyzes information about competitors obtained from external sources, providing strategic insights to sales managers such as "because competitor X has released a new product, we should change the content of our proposal," "competitor Y's advantage lies in price, but our company can differentiate itself through its support system," and "because competitor Z has started a price reduction campaign, our company is also exploring temporary discounts." In the automatic generation of proposal materials, generative AI automatically creates "Proposal Templates for Client A," "Customized Proposal Materials for Client B," "Competitive Comparison Tables," and "ROI Simulations" based on each client's attributes, past proposal content, competitive landscape, areas of interest, and industry trends. These templates can then be edited and output on the terminal. For example, when a sales representative presses the "Create Proposal" button on the terminal, the generative AI automatically generates a draft, which the sales representative can then supplement and revise as needed.Regarding the provision of training content, generative AI automatically selects the best teaching materials, videos, FAQs, case studies, role-playing scenarios, etc., based on the skills, areas of interest, past course attendance history, sales performance, and feedback of sales personnel or customers, so that they can be viewed on the terminal. For example, basic sales skills materials are automatically distributed to new sales personnel, while advanced negotiation skills or industry trend analysis materials are automatically distributed to experienced sales personnel. Specifically, this sales activity support department has a stream processing engine that processes lead information or market trend information obtained through external APIs in real time. This system inputs the pixel data of the acquired business card image into a convolutional neural network. This system extracts feature maps of text regions from the image tensor. This system inputs the extracted feature maps into a recurrent neural network to decode the text data. This system combines the decoded text data with past transaction data and calculates customer similarity as cosine similarity. This system uses a large-scale language model to generate sales tasks. This system receives text vectors of negotiation records and embedding vectors of competitor news reports as input. This system outputs labels and reliability scores for recommended sales actions. Examples of outputs include "Prompt for price adjustment (reliability 0.92)" and "Create additional data (reliability 0.88)". The system writes the output action labels into the terminal's task management database. This system uses a Long Short-Term Memory (LSTM) network for sales forecasting. It takes a time-series array of past sales performance as input and outputs future sales forecasts as continuous values. The system uses mean squared error as a loss function for learning. This allows for high-precision sales forecasting that captures seasonal variations or subtle market changes. In the automatic generation of proposal data, the system uses conditional generative adversarial networks or autoregressive language models. This significantly reduces the data creation time for sales personnel and promotes efficient use of computer resources. This system is applied to proposal activities in software sales.

[0042] The Customer Relationship Enhancement Department is also implemented on the server. While leveraging information from the Customer Information Management Department and the Sales and Operations Support Department, generative AI supports customer relationship building and customer satisfaction improvement. For example, when a customer inputs feedback through a terminal, the generative AI analyzes the content, automatically categorizing it into "positive opinions," "improvement requests," "complaints," "new feature requests," "dissatisfaction with prices," and "evaluation of the support system," and proposes specific actions to the sales representative, such as "a prompt apology is needed," "forwarding feedback on new features to the development department," "exploring a re-evaluation of prices," and "proposals to strengthen the support system." For instance, if a customer inputs "delivery date is delayed," the generative AI proposes "analysis of the reasons for the delivery delay," "suggested alternative solutions," and "automatic generation of an apology email." As a support for clarifying needs, the generative AI automatically extracts information based on past customer statements or input, purchase history, consultation content, survey results, and industry trends, such as "your company's issues are likely cost reduction and shortening delivery time," "interest in new business development," and "exploring IT infrastructure upgrades," and provides these suggestions to the sales representative or the customer themselves. Regarding data sharing, for materials managed on the server, such as progress reports, contracts, quotations, delivery notes, invoices, project plans, meeting minutes, FAQs, and training materials, generative AI determines access permissions or timing, distributing them to terminals in the form of "automatically sending weekly project progress reports," "automatically reminding users during contract update periods," and "automatically distributing relevant materials when new products are released." In collaborative project management, generative AI analyzes task progress or the workload of responsible parties, delivery dates, resource allocation, risk factors, and changes in customer requirements, automatically notifying them of "reminding responsible party B due to task A's delay," "the overall project progress rate is 80%," "proposing early response due to risk factors," and "impact analysis of customer specification change requests," etc. As contract management support, generative AI analyzes contract content or update periods, past negotiation history, contract condition change history, payment status, and contract breach risks, enabling "automatic notification 30 days before contract expiration," "timing of price modification negotiations," "risk assessment when contract conditions are changed," and "automatic detection of non-payment risks," etc. In problem-solving support, for issues or questions sent by customers or sales personnel through the terminal, generative AI retrieves and summarizes past cases or knowledge bases, FAQs, company manuals, external public information, expert insights, etc., and provides real-time answers in the form of "suggesting solutions for similar cases", "extracting relevant items from FAQs", "proposals to be upgraded to experts", and "step-by-step guidance until the problem is solved".As a communication optimization tool, generative AI analyzes each customer's communication history or reaction tendencies, past response history, personality traits, industry characteristics, and cultural background, proposing personalized communication methods or timings such as "Customer C responds better to chat than email," "Customer D prefers to contact at the beginning of the week," "Regular phone follow-ups with Customer E are effective," and "Customer F takes time to make decisions, so information needs to be provided as early as possible." Regarding customer satisfaction improvement proposals, generative AI also comprehensively analyzes customer evaluations or feedback, transaction history, competitive landscape, industry trends, and customer development stages, suggesting specific measures to sales personnel such as "Regular follow-ups are effective," "Prioritize the introduction of new services," "Customized proposals for each customer," "Implement loyalty programs," and "Invite customers to participate in customer events." Specifically, the Customer Relationship Enhancement Department uses a natural language processing model to perform sentiment analysis on customer feedback. The system inputs the feedback text into an encoder based on the BERT architecture. The system receives a labeled embedding tensor of strings such as "dissatisfied with the support system" as input data. The system outputs the probability distribution of positive, negative, and neutral categories. This system outputs numerical data such as "negative probability 0.95" and "complaint classification indicator 1". Based on this output, the system automatically triggers the complaint response module. The system inputs past customer statements or purchase history into a graph neural network. It calculates the weights of edges between customer nodes and product nodes, quantifying potential needs as distances in a vector space. This allows the system to automatically extract hidden issues such as "cost reduction" or "IT infrastructure upgrades". In collaborative project management, the system inputs task progress data or resource allocation data into a reinforcement learning agent. Based on Markov decision processes, the system outputs the optimal resource reallocation strategy that minimizes project delay risk. Following the output strategy, the system automatically generates reminders to be sent to the responsible parties. For communication optimization, the system analyzes customer response history using a collaborative filtering algorithm. This determines the best means or time period for contacting each customer. Through these unconventional computational methods, the system maximizes customer satisfaction as an objective indicator. This system is applied to long-term relationship building use cases in the consulting industry.

[0043] The terminal provides a user interface for sales representatives or customers to access various parts of the server, such as through a web browser, smartphone application, tablet application, or dedicated business terminal. Within the terminal, users can view, edit, and input sales tasks, proposal materials, progress reports, feedback results, training content, contract information, communication proposals, project management screens, dashboards, alert notifications, FAQs, and knowledge bases sent from the server. For example, applications include: sales representatives confirming customer information from their location via smartphone and inputting feedback immediately after discussions; customers inputting company requirements or project progress via tablet terminals; sales managers monitoring overall sales progress or risk cases on dashboards; customers viewing FAQs or training videos; and sales representatives receiving alert notifications and responding immediately. Specifically, this terminal has a communication control unit that sends and receives data packets with the server using an asynchronous communication protocol. It also has a rendering processing unit that parses received JSON-formatted data and dynamically renders it on a graphical user interface. The terminal detects user touch operations or keyboard input through an event listener. The terminal temporarily stores detected input events along with timestamps as log data in local storage. This terminal sends the log data to the server in batches at predetermined times. The system minimizes the impact of network latency through a data caching mechanism on the terminal side. This improves the system's response speed to user operations. The terminal receives alarm notifications from the server as push notifications in the background process. The terminal evaluates the priority score of the received notifications. If the priority score exceeds a predetermined threshold, the terminal immediately displays a pop-up dialog box on the screen. This prevents the omission of important business tasks. To efficiently manage the terminal's hardware resources, the system implements a power-saving algorithm that limits unnecessary background communication. This system is applied to field sales use cases that support business activities in mobile environments.

[0044] Furthermore, the application of generative AI extends beyond simple data analysis or automatic generation. Examples include automatically detecting input errors or omissions by sales personnel and providing input assistance or corrective proposals; offering chatbot functionality for 24 / 7 automated response to customer inquiries; providing personalized sales suggestions in real-time based on past sales personnel behavior patterns or performance; forecasting future demand or risks based on customer purchasing tendencies or market trends; automatically generating performance analysis or improvement proposals for the entire team for sales managers; and automatically designing optimal pricing strategies or promotional proposals for each customer. These applications cover multiple aspects. For instance, if the sales personnel's input contains ambiguities or omissions, generative AI will provide input assistance such as "Please enter the specific negotiated amount" or "Please record the decision-maker's name." Even if a customer inquiry is received late at night, generative AI will automatically provide an answer and hand over the response to the sales personnel the following morning. Furthermore, based on the sales personnel's past tendency to lose deals, generative AI will suggest, "For cases where the same reason for loss is likely, additional proposals should be made early." For example, based on customer purchase history or industry trends, the system can make demand forecasts such as "Propose to secure inventory in advance because demand for a specific product may increase next month." For example, when a sales manager checks the overall team activity on a dashboard, generative AI automatically generates improvement proposals such as "Recommend strengthening lead generation measures because the number of new cases acquired this month is trending downwards." For example, based on each customer's past price negotiation history or competitive situation, the system automatically designs pricing strategies such as "A 10% discount is effective for this customer" or "Propose special offers to counter competitors' activities." Specifically, to achieve input assistance functions, this system installs an autoregressive model on a character-by-character basis. The system acquires the text stream being entered by the sales representative in real time. The system converts this text stream into a context vector and calculates the probability distribution of the next word. The system displays the sequence of words with the highest probability as prediction candidates on the screen. In the chatbot function, the system employs a retrieval-enhanced generative architecture. The system converts customer inquiry text into query vectors. The system retrieves highly similar internal company documents from a vector database. The system combines the text of the retrieved documents with the original inquiry text as input prompts for a large-scale language model. This system outputs natural language response text. For example, for a question about the specifications of product A, the system outputs a specific response text: "Product A has dimensions X and a weight Y." In demand forecasting, this system uses a gradient boosting decision tree algorithm. The system takes as input a multi-dimensional feature vector, including past purchase history, seasonal dummy variables, and macroeconomic indicators. As output, the system outputs the predicted sales quantity for each product in the following month as a continuous value. If the output predicted sales quantity is lower than the inventory threshold, the system automatically generates an order recommendation alert.This system reduces the risk of inventory shortages and contributes to supply chain efficiency. This system is applied to wholesale business use cases in the retail industry.

[0045] Thus, in this embodiment of the invention, by employing generative AI on the server to handle the accumulation, analysis, automatic generation, and proposal of various information, sales personnel or customers on the terminal side can intuitively and in real-time utilize and input information, thereby highly optimizing the entire process of corporate business activities, achieving unified information management, business automation, enhanced decision-making through data utilization, improved customer satisfaction, strengthened competitiveness, and the construction of long-term business relationships. For example, when sales personnel are exploring new potential customers, generative AI automatically extracts the best targets based on past transaction patterns or market trends, and even automatically generates contact methods or proposal content. For example, when a customer inputs the company's topics or requirements, generative AI analyzes the content and immediately proposes the best solutions or related services. For example, generative AI automatically manages project progress or contract update timing, notifying and reminding relevant personnel at appropriate times. For example, when sales personnel or customers need training or market insights, generative AI automatically selects and distributes the best content based on individual needs or skill levels. For example, based on the sales personnel's past tendency to lose orders, generative AI makes suggestions such as "additional proposals should be made early for cases that may have the same reason for losing orders." For example, based on customer purchase history or industry trends, the system can generate demand forecasts such as "Propose securing inventory in advance because demand for a specific product may increase next month." For example, when a sales manager checks the team's activity status on a dashboard, generative AI automatically generates improvement proposals such as "Recommend strengthening lead generation measures because the number of new cases acquired this month is trending downwards." For example, based on each customer's past price negotiation history or competitive situation, the system can automatically design pricing strategies such as "A 10% discount is effective for this customer" or "Propose special offers to counter competitors' activities." Including these specific application examples, through the coordinated actions of the server and terminals and the high utilization of generative AI, the system fundamentally eliminates the inefficiencies, information fragmentation, and reliance on individual responses in traditional corporate sales activities, achieving continuous corporate growth and improved customer satisfaction. Specifically, this system has an orchestration engine that comprehensively manages multiple artificial intelligence models. The system integrates the inference results of various models through ensemble learning. This offsets the prediction errors of a single model, improving the overall inference accuracy of the system. The system applies data augmentation techniques to customer purchase history data. The system uses synthetic minority class oversampling to artificially augment sample data of lost cases. This system improves the generalization performance of machine learning models on imbalanced datasets. It applies reinforcement learning algorithms in the automatic design of pricing strategies. The system defines the current customer state and market environment as a state vector. It defines the suggested discount rate as the action space. It sets future expected revenue as the reward function. The system uses the Q-learning algorithm to calculate the optimal discount rate that maximizes cumulative reward.This system takes as input either "the customer's past price elasticity" or "the competitor's current price." As output, it outputs a "recommended discount rate of 10%." This output is then passed to the quotation generation module, automatically updating the quotation. This system thus achieves a data-driven pricing process that does not rely on human rules of thumb. This system is applied to the subscription service update business use case.

[0046] (System Composition) The system described in this implementation comprises a Customer Information Management Department, a Sales Support Department, a Customer Relationship Enhancement Department, a Data Analysis Department, a Proposal Generation Department, a Project Management Department, a Contract Management Department, a Training Provision Department, a Communication Optimization Department, and an Interface Department. Specifically, this system containerizes and installs these departments as microservices. The system controls communication between containers via a service mesh. This improves the system's scalability and fault tolerance. The system employs a configuration where each department can scale independently horizontally. Under high load, the system automatically adds container instances of the Data Analysis Department or the Proposal Generation Department. This enables dynamic allocation of computing resources and optimizes hardware resource utilization. To share data among departments, the system utilizes a distributed message queue system. This allows for asynchronous processing, improving the overall system throughput. This system is applied to the construction of a sales support platform for large-scale enterprises.

[0047] The Customer Information Management Department is responsible for uniformly accumulating and managing customer attribute information input by legal entity sales representatives or customers, such as company name, sales representative name, department, position, contact information, industry, number of employees, capital, location, sales volume, financial statements, transaction start date, transaction history, purchase history, interests, contract information, contract start / end date, contract amount, contract terms, feedback information, questionnaire results, project progress information, shared data, training history, market insights, past complaint history, consultation history, customer satisfaction score, sales representative history, visit history, and email or chat communication history, all stored in a database. For example, when a sales representative inputs new customer information from a terminal, this information is received and stored in the database. Similarly, information input by customers regarding their needs or feedback is also managed. Furthermore, information input by sales representatives after a negotiation, such as the content of the negotiation or the customer's reaction, becomes part of the management scope. Finally, based on the customer's purchase cycle or past order amounts, the department can automatically calculate the next order forecast date or projected order amount and notify the sales representative. Specifically, this Customer Information Management Department possesses data pipelines for integrating diverse input data. This system automates the extraction, transformation, and loading processes. It removes noise from input text data and uses statistical methods to fill in missing values. To analyze customer purchase cycles, the system applies Hidden Markov Models. Past purchase history is input as an observation sequence. Changes in customer purchase intent are used to infer potential states. As output, the system calculates the probability distribution of the transition to the next purchase state. The system outputs numerical data such as "probability of purchase within 30 days: 0.75". If this probability exceeds a predetermined threshold, the system calculates a specific date as the predicted date for the next order. The system automatically links the calculated date data to the sales representative's calendar application. This optimizes the sales representative's contact timing and prevents missed opportunities. By optimizing the database index structure, the system reduces the response time for retrieval queries on millions of customer records to sub-millisecond levels. This system is applied to customer management use cases involving diverse and frequent transactions within the trading company.

[0048] The Sales Activities Support Department optimizes sales activities using generative AI, drawing upon data accumulated and analyzed in the Customer Information Management Department, as well as externally sourced leads (e.g., business card data from trade shows, website inquiries, SNS responses, advertising leads, company information from external databases, industry news, publicly available financial information, etc.), competitor information (e.g., competitor product information, pricing information, news reports, SNS posts, past reasons for lost sales, competitor sales strategy analysis, etc.), and market trend information (e.g., industry reports, economic indicators, trend analysis, legal and regulatory changes, etc.). For example, it automatically reads business card data acquired at trade shows using OCR, and based on similarities to past sales patterns or customer attributes, automatically determines the priority of potential customers. Similarly, when a sales representative inputs the progress of a sales activity or the content of a discussion, generative AI analyzes it, automatically generating the best time for the next contact or proposal content, and presenting this as a ToDo list or notification on the sales representative's device. For example, if the negotiation record contains entries such as "price negotiations are deadlocked" or "awaiting decision-maker approval," the generative AI proposal will suggest price adjustment options or create additional information for decision-makers. For example, regarding sales forecasting, by comprehensively analyzing past negotiation data, current case progress, customer purchasing tendencies, industry trends, and seasonal factors, the system automatically calculates this month's sales expectations, the probability of achieving next period's targets, and the sales tendencies of specific customer groups. Specifically, this Sales Activities Support Department has a web crawler module that uses web scraping technology to regularly collect information from external data sources. This system inputs the collected unstructured text into a named entity extraction model. This system extracts entities such as company names, product names, and prices, and constructs a knowledge graph. In the OCR processing of business card data, this system uses a model combining convolutional neural networks and connection-time classification loss functions. This allows the system to achieve high recognition accuracy even for blurry text or special fonts. In generating sales tasks, this system uses a Transformer-based sequence transformation model. This system receives the embedding vector of the text "price negotiations are deadlocked" as input. This system outputs a probability score for the action category "Prompt for Price Adjustment Plan". In sales forecasting, this system uses a hybrid model combining an autoregressive integral moving average model and a neural network. The system receives past sales time-series data and external factor data as input tensors. As output, the system outputs a confidence interval for the future sales forecast. For example, the system outputs data such as "This month's sales forecast is 12 million yen, with an error margin of ±500,000 yen". This system thus achieves refined sales forecasting that accounts for uncertainty. This system is applicable to use cases such as real estate corporations dealing with high-value goods.

[0049] The Customer Relationship Enhancement Department utilizes information from the Customer Information Management Department and the Sales Support Department to provide generative AI support for building customer relationships and improving customer satisfaction. For example, when customers provide feedback, the generative AI analyzes the content, automatically categorizing it into positive opinions, improvement requests, complaints, new feature requests, price dissatisfaction, and evaluations of the support system. It then proposes specific actions to sales representatives, such as prompt apologies, forwarding feedback on new features to the development department, exploring price reassessments, and strengthening the support system. For example, if a customer reports a "delivery delay," the generative AI proposes an analysis of the reasons for the delay, suggests alternative solutions, and automatically generates an apology email. Furthermore, based on past customer statements or input, purchase history, inquiries, survey results, and industry trends, the system automatically extracts topics or concerns and provides them to sales representatives or the customer themselves. For example, for data or progress reports, contracts, quotations, delivery notes, invoices, project plans, meeting minutes, FAQs, training materials, etc., managed on the server, generative AI determines access permissions or timing, and distributes them to terminals in the form of automatic weekly sending of project progress reports, automatic reminders for contract updates, and automatic distribution of relevant materials when new products are released. Specifically, this Customer Relationship Enhancement Department maps feedback text from customers to a multi-dimensional vector space. This system uses a support vector machine to calculate the hyperplane that classifies this vector into multiple categories. This system receives the TF-IDF feature vector of the text "Delivery date delayed" as input. This system outputs the probability score of belonging to the "complaint" category. This system uses the category with the highest score as the judgment result. When the judgment result is "complaint", this system activates the natural language generation model. This system uses the history of responding to similar complaints in the past as prompt input and outputs a draft text of an apology email. This system automatically inserts the output draft text into the email client of the sales representative. In customer topic extraction, this system uses the Latent Dirichlet Allocation Model as the topic model. This system takes a collection of past customer messages as input and outputs the probability distribution of potential topics. Based on this, the system detects keyword clusters with a high probability of occurrence related to "cost reduction." In the automatic distribution of materials, the system combines a rule-based inference engine and a machine learning model. The system determines the current stage of the customer's project and generates and sends the URLs of the required documents at the appropriate time. This system is applied to customer support use cases in IT system import projects.

[0050] The Data Analysis Department utilizes generative AI to perform high-level analysis of diverse data collected from the Customer Information Management Department, Sales Activity Support Department, and Customer Relationship Enhancement Department. For example, it comprehensively analyzes customer purchase or negotiation history, feedback content, industry trend data, competitor information, and sales representative activity history to extract factors such as sales forecasts or cancellation risks, upsell / cross-sell potential, and factors influencing customer satisfaction scores for each customer. For example, it combines time series analysis, clustering, and natural language processing to monitor changes in customer behavior patterns or needs in real time. For example, it correlates sales activity results with sales representative behavior patterns to extract success factors or areas for improvement. For example, it analyzes competitor activities or market trends to provide insights that can be reflected in the company's sales strategy or product development. Specifically, this Data Analysis Department leverages the Hadoop ecosystem or Spark clusters for distributed processing of large-scale datasets. To analyze customer behavior patterns, the system executes clustering algorithms such as k-means or DBSCAN. The system normalizes customer purchase frequency, average purchase amount, and most recent purchase date (RFM) metrics as input to a multi-dimensional feature vector. This system outputs the cluster ID to which each customer belongs. For example, it assigns labels such as "High-Quality Customer Cluster" and "Choking Risk Cluster." In predicting cancellation risk, the system uses a random forest algorithm. Input features include decreased service usage frequency or the number of complaints. Output a cancellation probability score from 0 to 1. For example, it outputs a numerical value of "Cancellation Probability 0.85." If this score exceeds a threshold, the system automatically triggers retention measures. In analyzing the actions of sales representatives, the system applies process mining techniques. Using event logs of sales activities as input, the system outputs a Petri net of a standard process model for successful negotiations. This visualizes bottleneck sales processes, improving business efficiency. The system is applied to customer success use cases in SaaS businesses.

[0051] The Proposal Generation Department utilizes generative AI to automatically generate proposal templates, customized proposal materials, competitor comparison tables, ROI simulations, imported case studies, FAQ sets, and price simulation data based on each client's attributes, past proposal content, competitive landscape, client areas of interest, and industry trends. For example, when a sales representative presses the "Create Proposal" button on the terminal, the generative AI automatically generates a draft, which the sales representative can then supplement and revise as needed. For example, it automatically inserts optimal case studies or import effects based on the client's industry or size and past purchasing history. For example, it automatically generates materials emphasizing the company's advantages based on competitors' proposal content or pricing information. For example, it automatically adds FAQs or explanations of supporting systems based on the client's areas of interest or topics. Specifically, this Proposal Generation Department possesses a multimodal generation model integrating natural language generation technology and document layout parsing technology. The system receives structured data in JSON format, containing client attribute data, past proposal text, and product specification data. This data is embedded into prompt templates and input into a large-scale language model. This system outputs markup language data containing the titles, body text, and metadata of the images to be inserted for each slide of the proposal. For example, it outputs text data such as "Slide 1: Proposal for Solving Your Company's Problem" and "Slide 2: ROI Simulation Results," along with corresponding numerical data for charts and graphs. The system then passes the output markup data to a PDF generation engine to render the final proposal file. In the ROI simulation generation, the system performs Monte Carlo simulations. Using the client's current cost structure and the price of the planned imported products as input parameters, the system calculates the probability distribution of return on investment based on multiple scenarios. The system outputs the calculation results as visual charts and graphs, which are automatically embedded into the proposal materials. This system thereby reduces the human resources required for material preparation and ensures consistent proposal quality. This system is applied to the planning and proposal use case in advertising agencies.

[0052] The Project Management Department is responsible for the unified management of progress or tasks, responsible parties, delivery dates, resource allocation, risk factors, and changes in requirements from clients for joint projects with clients. It utilizes generative AI for tasks such as detecting or alerting on progress delays, early identification of risk factors, prioritizing tasks, optimizing resource allocation, and automatically generating progress reports for clients. For example, if task A is delayed, an automatic reminder is sent to responsible party B. The progress rate or risk factors of the entire project are visualized on a dashboard. In the event of a specification change request from a client, an impact analysis is automatically performed, and relevant personnel are notified. Progress reports are automatically generated based on delivery dates or milestones and distributed to clients or relevant personnel within the company. Specifically, the Project Management Department manages Gantt charts or Kanban data structures on a graph database. The system models the dependencies between tasks as directed acyclic graphs. The system uses the critical path method to calculate the shortest completion time for the project. The system collects progress data for each task in real time and inputs it into a graph neural network. The system outputs a score, providing the probability of delay for the entire project or the bottleneck risk of a specific task. This system, for example, outputs data such as "the probability of a delay in the entire project due to the delay of task A is 0.7". Based on this score, the system executes a resource reallocation algorithm. Using a constraint satisfaction problem solver, the system outputs an optimal task allocation plan that balances delivery deadlines with workload leveling for responsible personnel. The system uses a natural language processing model to parse specification change request texts from clients. The system automatically calculates the incremental increase in additional work hours or costs based on the impact of specific changes on the existing task map. The system outputs the calculation results as an impact analysis report and notifies relevant personnel. This system thereby reduces project management uncertainty and achieves computer-based dynamic resource optimization. This system is applicable to project management use cases in system development or the construction industry.

[0053] The Contract Management Department manages contract content or update periods, past negotiation history, contract term change history, payment status, default risks, etc., and uses generative AI for automatic notifications before contract expiration, timing proposals for price revision negotiations, risk assessments when contract terms change, automatic detection of non-payment risks, and proposals for optimal terms when updating contracts. For example, it automatically sends notifications to sales representatives and customers 30 days before contract expiration. It automatically generates optimal price revision proposals based on past negotiation history. It automatically extracts risk factors when contract terms change and alerts relevant personnel. It automatically detects non-payment risks in the event of payment delays and proposes early responses. Specifically, this Contract Management Department has a contract parsing pipeline that combines optical character recognition and natural language processing. The system takes scanned contract images as input and extracts text data. The extracted text is then input into a BERT-based named entity extraction model. The system outputs key-value pairs including contract parties, contract amount, validity period, and whether there are automatic update clauses. This system outputs structured data such as "Contract End Date: March 31, 2024" and "Automatic Update: Yes," and stores it in a database. The system compares the current date with the contract end date, and performs automatic batch notifications if the difference is within 30 days. In price revision negotiations, the system uses historical negotiation data and current market price data as input to a reinforcement learning model. The system outputs the optimal suggested price that minimizes customer churn risk while maximizing benefits. For non-payment risk detection, the system uses Isolation Forest as an anomaly detection algorithm. The system takes the customer's past payment delay days or financial indicators as input features. The system outputs an anomaly score and generates an alert if the score exceeds a threshold. This system thereby strengthens contract management compliance and reduces financial risk. This system is applicable to contract management use cases in the leasing or talent dispatch industries.

[0054] The Training Provision Department is a department that uses generative AI to automatically select the best teaching materials, videos, FAQs, case studies, role-playing scenarios, etc., based on the skills or areas of interest of sales personnel or clients, their past learning history, sales performance, and feedback, making them accessible on the terminal. For example, it automatically distributes basic sales skills materials to new sales personnel. For example, it automatically distributes advanced negotiation techniques or industry trend analysis materials to experienced sales personnel. For example, it automatically provides product introduction manuals or case study videos to clients. For example, it automatically proposes individualized training plans to overcome weaknesses for sales personnel whose sales performance has stagnated. Specifically, this Training Provision Department has a hybrid recommendation system that combines content-based filtering and collaborative filtering. This system receives personal data of sales personnel, past learning history vectors, and current sales performance indicators as input. This input data is fed into a multi-layer neural network to calculate the expected evaluation score for each training content. As output, this system outputs a list of the top N content IDs with the highest expected evaluation scores. This system outputs data such as "Content ID101 (Basic Negotiation Skills): Score 0.9" and "Content ID205 (Industry Trends): Score 0.85". Based on the output list, the system dynamically displays recommended content on the user interface. In generating role-playing scenarios, the system utilizes a large-scale language model. The system uses the sales representative's weaknesses (e.g., "closing skills") as input prompts. As output, the system provides dialogue scripts with virtual customers or interactive scenario data containing branching conditions. The system transcribes the sales representative's speech input using a speech recognition engine and evaluates the text using a natural language understanding model. As output, the system calculates scores for the appropriateness or emotional expression of the speech, providing real-time feedback. This system thus provides a personalized learning experience, significantly improving the efficiency of talent development. This system is applied to educational use cases for MR in pharmaceutical companies, etc.

[0055] The Communication Optimization Department analyzes each customer's communication history or reaction tendencies, past response history, customer personality traits, industry characteristics, cultural background, etc., and uses generative AI to propose the best communication methods, timing, content, and frequency. For example, if it determines that customer C responds better to chat than email, it recommends contacting them via chat. For example, since customer D prefers to contact at the beginning of the week, a follow-up email is automatically sent at the beginning of the week. For example, since regular phone follow-ups with customer E are effective, a reminder to call is sent to the sales representative. For example, since customer F spends time on decision-making, it proposes providing information as early as possible or multiple follow-ups. Specifically, this Communication Optimization Department constructs customer action log data into a time-series tensor. This system inputs this tensor into a recurrent neural network with an attention mechanism. The system receives a sequence of features such as past email opening times, link clicks, and chat reply times as input data. The system outputs the distribution of reaction probabilities for each communication channel (email, chat, phone), as well as heatmap data of reaction probabilities for each week / time period. This system outputs numerical data such as "chat response probability 0.8" and "response probability at 10 AM Monday 0.9". Based on these outputs, the system automatically optimizes message sending scheduling. To infer a customer's personality traits, the system extracts linguistic features from past text communications. These extracted features are input into a personality inference model, outputting scores for the Big Five personality traits. Based on the output personality traits, the system dynamically adjusts the tone and style of generated messages (e.g., "logical and concise expression" or "empathetic and polite expression"). This maximizes customer interaction and prevents lost opportunities due to communication mismatches. The system can also be applied to B2C sales of high-end goods or private banking use cases targeting affluent individuals.

[0056] The Interface Department provides user interfaces for sales representatives or customers to access various departments on the server, implemented in the form of web browsers, smartphone applications, tablet applications, dedicated business terminals, etc. For example, sales representatives can confirm customer information via smartphone from their location and input feedback on the spot after a meeting. For example, customers can input company requirements or project progress via tablet terminals. For example, sales managers can monitor overall sales progress or risk cases on dashboards. For example, customers can view FAQs or training videos. Specifically, this Interface Department adopts a single-page application architecture to achieve high-speed screen migration on the client side. This system uses RESTful APIs or GraphQL to asynchronously exchange data with the server. In the dashboard rendering, this system utilizes hardware acceleration technologies such as WebGL to smoothly render charts containing a large number of data points. This system collects heatmap data of user eye movement or mouse clicks and uses it to improve user experience. This system receives location information data obtained from the GPS sensor of the mobile terminal as input. This system compares this location information with the location information in the customer database, sorts the list of accessible customers near the current location in distance order, and outputs it. This system outputs data such as "Customer A: Distance 1.2 km" and "Customer B: Distance 3.5 km," displaying them as pins on the map interface. To operate offline, the system utilizes a local caching mechanism with Service Workers. When network connectivity is restored, the system automatically synchronizes locally saved input data with the server. This enables seamless business execution independent of location or communication environment. This system is applicable to use cases such as route-based sales or on-site repair services.

[0057] Specific examples of prompts read by generative AI when implementing this invention include: "Please propose the next product and contact method based on the customer's past feedback and purchase history," "Please create a list of potential customers from business card data obtained at trade shows and assign them priority," "Please summarize customer complaints and propose three response strategies," "Please calculate this month's sales forecast based on data from the past three years and current case progress," "Please automatically generate proposal materials for new product launches to counter competitors," "Please analyze the progress and risk factors of the project and create alerts to relevant personnel," "Please extract a list of customers with upcoming contract renewals and create the best renewal proposal content," and "Please analyze the activity history of each sales representative and generate individual suggestions to improve performance," etc. Specifically, this system has a prompt engineering module for dynamically constructing these prompts. The system embeds appropriate variables into the prompt template based on the user's intent or context. As input data, the system receives an array of transaction records from the past three years extracted from the customer's purchase history database, or text data of complaint response history. The system serializes this data into JSON format and incorporates it into the context of the prompt. This system dynamically adjusts the temperature parameter or Top-P value, which serves as an inference parameter for a large-scale language model, based on the nature of the task. For example, in a task requiring accurate sales forecasting, the system sets the temperature parameter to 0.1 to promote deterministic output. In a task requiring the generation of proposal ideas, the system sets the temperature parameter to 0.8 to generate diverse outputs. The system receives generated textual or structured data as output. It applies post-processing filters to the output data to detect and eliminate inappropriate expressions or illusions (unfounded output). For example, in cases where the generated sales forecast value is an outlier, exceeding 10 times the past maximum value, the system executes a feedback loop instructing recalculation. This ensures the reliability and security of the generative AI output, building a practical system capable of withstanding enterprise environments. This system is applicable to use cases such as business strategy proposal support in management planning departments.

[0058] (Implementation steps) Step 1: Customer Information Input and Accumulation. Sales representatives or customers input detailed customer information from the terminal, including company name, representative name, department, position, contact information, industry, number of employees, capital, location, sales volume, financial statements, transaction start date, transaction history, purchase history, interests, contract information, contract start / end date, contract amount, contract terms, feedback information, questionnaire results, project progress information, shared data, training history, market insights, past complaint history, consultation history, customer satisfaction score, sales representative history, visit history, and email or chat communication history. The customer information management department on the server accumulates and manages this information in a unified database. For example, when a sales representative inputs new customer information, or when a customer inputs their company's needs or feedback, or when they input the content of a discussion or the customer's reaction after a negotiation, all of these become managed objects. Specifically, this system performs real-time data validation processing on the input customer information. This system uses regular expressions to validate the appropriateness of the format of phone numbers or email addresses from the input text data. This system compares the input company name with an external legal entity information database API, automatically supplementing any missing information such as the official name, legal entity number, and location. The system uses encryption algorithms to convert the input data into a secure form before writing it to a distributed database. Transaction management is implemented during data writing to ensure the ACID properties of the data. The system inputs the text data of the negotiation content into a natural language processing model. Keywords are extracted from the text, and a vector space model is used to calculate the similarity with existing customer data. As output, the system outputs a probability score for duplicate customer records. If, for example, a score of "duplicate probability 0.95" is output, the system initiates a name and address merging module and proposes data integration. This maintains the accuracy and consistency of information within the database, providing a foundation for improving the accuracy of subsequent AI analysis. By automating data input, the system eliminates errors caused by manual operations, achieving computer-based improvements in the information management process. This system is applied to input use cases related to customer response history in call centers.

[0059] Step 2: Analyze Customer Data Using Generative AI. The data analysis department on the server uses generative AI to analyze accumulated customer information, extracting each customer's potential needs, future sales potential, cancellation risks, and upsell / cross-sell possibilities. For example, generative AI uses natural language processing to extract keywords and sentiment from customers' free-flowing feedback, and uses time series analysis and clustering to analyze the progression of purchase cycles and order amounts. Specific examples of prompts for the generative AI to read include, "Based on this customer's past feedback and purchase history, propose the next product and contact method," and "Assess cancellation risks based on the customer's purchase history and questionnaire results." Specifically, this data analysis department generates a multidimensional tensor that integrates the customer's purchase history and feedback text. This data analysis department utilizes a hybrid neural network that combines a large-scale Transformer-based language model for natural language processing with a Long Short-Term Memory network for processing time series data. As input data, this data analysis department obtains a one-dimensional array normalized with purchase amounts from the past twelve months, and a 512-dimensional dense vector converted from feedback text into word embedding vectors. As a specific example of input data, this data parsing unit receives an array of twelve floating-point numbers representing monthly purchase amounts, and a vector representing the text "Slow Response to Support". This data parsing unit combines this input data to generate a 124-dimensional integrated feature vector. This hybrid neural network receives this integrated feature vector as input. This hybrid neural network uses a self-attention mechanism to calculate the hidden correlation between specific periods of the purchase history and specific words within the feedback. As output, this hybrid neural network generates a continuous score from zero to one representing the cancellation risk, and a probability distribution vector representing the product category to be proposed next. As a specific example of output, this hybrid neural network outputs 0.85 as the cancellation risk score and a vector representing the probability distribution of the product category, indicating a 70% chance of cloud storage expansion options and a 20% chance of security audit services. This data parsing unit sends these outputs to the subsequent task generation module. If the cancellation risk score exceeds a pre-set threshold of 0.8, the task generation module automatically generates an immediate follow-up task for the terminal handling the sales. Through this configuration, the system replaces the cancellation risk assessment, which relies on human intuition, with vector operations in a high-dimensional space. Therefore, this system achieves improved processing speed through parallelization of computation while maintaining the objectivity of guarantee assessment. This technology is particularly effective in the specific use case of improving customer retention rates in subscription software sales. This hybrid neural network pre-learns by using backpropagation to minimize the cross-entropy error function in a binary classification task where past cancellations are treated as positive examples. The system uses an adaptive moment estimation algorithm, a type of gradient descent, to optimize the weight parameters.This system, as a data augmentation technique, employs artificially synthesized minority class data to eliminate imbalances in the learning data. As a result, this system significantly improves the accuracy of inferences based on unknown customer data.

[0060] Step 3: Acquisition of Lead Information and External Data, and Specific Targeting of Potential Customers. The Sales and Events Support Department acquires external data such as business card data obtained at trade shows, inquiry information from websites, social media response data, lead data from advertising, company information from external databases, industry news, and publicly available financial information. It then uses generative AI to automatically prioritize potential customers based on similarity to past sales patterns or customer attributes. Specific examples of prompts for the generative AI include, "Please create a list of potential customers from the business card data obtained at trade shows and assign them a priority order," and "Please extract new lead candidates based on company information obtained from external databases." Specifically, the Sales and Events Support Department integrates the diverse external data to construct a large-scale knowledge graph representing the relationships between companies. The department uses a graph neural network to parse this knowledge graph. As input data, the department acquires feature vectors representing the attribute information of each company, and adjacency matrices representing transactional or competitive relationships between companies. As a specific example of input data, this Sales Support Department receives a 128-dimensional numerical vector normalized to the number of employees or capital, and a binary adjacency matrix representing the history of business card exchanges at trade shows. This graph neural network executes a message-passing algorithm. The graph neural network aggregates feature vectors between adjacent nodes, updating the hidden state of each node. As output, the graph neural network outputs a score from zero to one representing the probability of each company becoming a new potential customer, and classification data representing recommended contact channels. As a specific example of output, this graph neural network outputs 0.92 as a potential customer score for company A and outputs email as a recommended contact channel. This Sales Support Department sends this output to the subsequent list generation module. This list generation module extracts companies with a potential customer score of 0.7 or higher, generating a priority list sorted in descending order of score. Through this structure, the system eliminates existing rule-based extraction methods that rely solely on the attributes of a single company. This system precisely identifies potential customers by computing the complex network structure between companies in a high-dimensional space. Therefore, this system significantly reduces the exploration costs for sales personnel and improves the overall computational efficiency of the system. This technology is highly effective in specific use cases such as expanding into new markets or following up on trade shows. This graph neural network uses past sales performance as teacher data and learns using an interval ranking loss function. The system suppresses overfitting by applying node dropout. The system improves the convergence speed of learning by assembling batch normalization layers. Therefore, this system maintains high generalization performance even in dynamically changing market environments.

[0061] Step 4: Automated Task Generation and Optimization for Sales Activities. The Sales Activity Support Department uses generative AI to analyze the progress or negotiation content of sales activities, customer reactions, competitor information, market trends, etc., inputted by sales personnel. It automatically generates the optimal timing or proposal for the next contact, and sales tasks, which are then presented to sales personnel as a ToDo list or notification on the terminal. For example, if the negotiation record contains entries such as "price negotiations are deadlocked" or "awaiting decision-maker approval," the generative AI proposes actions such as price adjustment plans or creating additional materials for decision-makers. Specific examples of prompts for the generative AI to read include "Please analyze the negotiation record to generate the optimal timing and proposal for the next contact" or "Please list the tasks needed for the future from the sales activity history." Specifically, this Sales Activity Support Department has a decision engine that integrates reinforcement learning algorithms and Transformer models to analyze the sales personnel's action history and negotiation text. As input data, this Sales Activity Support Department obtains categorical variables representing the progress stage of the negotiation, and 256-dimensional textual features vectorized from the negotiation memo through natural language processing. As a specific example of input data, this Sales Support Department receives a one-hot vector representing the "Proposal Stage" as a progress stage, and a dense vector transformed from the text "Price Negotiations Reached an Impasse." This decision engine defines these input data as a state space. As an action space, this decision engine defines multiple sales actions, such as prompting for a price adjustment plan, sending additional information, and requesting a direct meeting with the decision-maker. This decision engine uses a deep Q-network to calculate the expected reward value of each action in the current state. As output, this decision engine outputs an identifier representing the optimal sales action and a timestamp indicating the optimal date to execute that action. As a specific example of output, this decision engine outputs "Create ROI data for the decision-maker" as the action identifier and "10:00 AM on the next business day" as the execution date. This Sales Support Department sends this output to the subsequent task management module. Based on the received action identifier and timestamp, this task management module automatically registers the task in the calendar application on the terminal. Through this structure, the system eliminates subjective task setting based on human experience rules. The system formulates task generation as a mathematical optimization problem of maximizing rewards based on past successes. Therefore, this system improves the conversion rate of sales activities while reducing the cognitive load required for task management. This technology is particularly effective in the specific use case of B2B solutions involving complex decision-making. This deep Q-network uses past negotiation data for pre-learning through offline reinforcement learning. The system is designed with a reward function that provides positive rewards for successful negotiations and negative rewards for lost negotiations. The system ensures learning stability by using a target network and an experience replay buffer.Therefore, this system can also accurately predict the best next step even in unknown negotiation situations.

[0062] Step 5: Sales Forecasting, Competitive Analysis, and Business Strategy Proposal. The Sales Activities Support Department uses generative AI to comprehensively analyze past negotiation data or current case progress, customer purchasing tendencies, industry trends, seasonal factors, competitor product or pricing information, news reports, SNS posts, past reasons for lost orders, and competitor business strategy analysis. It automatically calculates and proposes this month's sales forecast or the probability of achieving next period's target, sales tendencies of specific customer groups, and amendments to business strategies based on competitor trends. Specific examples of prompts for the generative AI include "Please calculate this month's sales forecast based on sales data from the past 3 years and current case progress," and "Please propose a business strategy to counter competitors' new product launches." Specifically, to simultaneously process multivariate time-series data and unstructured text data, the Sales Activities Support Department has a predictive model that combines a one-dimensional convolutional neural network and a self-attention mechanism. As input data, the Sales Activities Support Department obtains a multi-dimensional time-series tensor containing monthly sales figures from the past thirty-six months, case progress rates, and sentiment scores extracted from competitor news reports. As a specific example of input data, the Sales Support Department receives an array of continuous values ​​representing monthly sales, an array of integer values ​​from zero to one hundred representing case progress rates, and an array of sentiment scores from negative one to positive one calculated from news reports. This forecasting model uses a one-dimensional convolutional layer to extract local trend features from the time-series data. This forecasting model uses a self-attention layer to calculate the long-term impact of specific past events on current sales. As output, this forecasting model outputs continuous values ​​representing the predicted sales for the next month, and percentage values ​​representing the probability of achieving the target. As a specific example of output, this forecasting model outputs 50 million yen as the predicted sales and 85% as the probability of achieving the target. The Sales Support Department sends this output to the subsequent strategic proposal module. If the probability of achieving the target is less than 50%, this strategic proposal module automatically generates a discount activity implementation plan based on competitor pricing information. Through this structure, the system eliminates existing sales forecasting methods based on simple linear regression. This system models non-linear market changes or competitor movements in a high-dimensional feature space. Therefore, this system significantly reduces forecasting errors and supports management decision-making. This technology is highly effective in the specific use case of quarterly sales target management or business strategy formulation. The prediction model uses mean squared error as the loss function and is optimized through supervised learning on past datasets. The system prevents overfitting to noisy market data by applying Dropout layers and L2 regularization. The system employs cosine annealing as the learning rate scheduling technique to avoid getting trapped in local optima. Therefore, this system achieves high-accuracy and stable sales forecasting.

[0063] Step 6: Automatic Generation of Proposal Materials and Documents. The proposal material generation department uses generative AI to automatically generate proposal templates, customized proposal materials, competitor comparison tables, ROI simulations, imported case studies, FAQ sets, price simulation data, etc., based on each client's attributes, past proposal content, competitor status, client's areas of interest, industry trends, etc., and enables them to be edited and output on the terminal. Specific examples of prompts for the generative AI to read include "Please generate a draft proposal for client A" or "Please create proposal materials including a competitor comparison table and ROI simulation." Specifically, this proposal material generation department has a multimodal generation engine that integrates a large-scale language model and generative adversarial networks to dynamically construct documents that meet the client's inherent requirements. As input data, this proposal material generation department receives text prompts representing the client's industry or topic, and a structured JSON object containing the specifications of the proposed product. As a specific example of input data, this proposal material generation department receives the text prompt "Cost reduction proposal for the manufacturing industry," and a set of key-value pairs representing the product's price or specifications. This multimodal generation engine uses a large-scale language model to generate the structure and explanatory text of each slide in a proposal from input text prompts. It also uses generative adversarial networks to automatically generate charts or graphs that match the explanatory text content. As output, the engine provides a binary file in presentation format with configured text and images, as well as a spreadsheet containing ROI simulation results. For example, it outputs a ten-page slideshow and a numerical matrix representing five years of ROI. The proposal generation unit then sends these outputs to the interface unit. The interface unit previews the generated file in a browser on the terminal and accepts final adjustments by the sales representative. This architecture eliminates the existing document creation process of manually editing templates. By combining natural language processing and image generation technologies, the system instantly generates highly personalized proposal materials. This drastically reduces the time required for document creation, providing sales representatives with an environment where they can focus on dialogue with clients. This technology is particularly effective in specific use cases such as proposals for complex system integration or the sale of customized products. This large-scale language model is fine-tuned using a corpus of past high-quality proposals. The system employs reinforcement learning techniques based on human feedback to improve the persuasiveness and accuracy of the generated text. To optimize the layout of the generated materials, a heuristic constraint-satisfying algorithm is used. As a result, the system consistently outputs visually superior and logically sound proposal materials.

[0064] Step 7: Collection and Analysis of Customer Feedback and Needs. The Customer Relationship Enhancement Department uses generative AI to analyze customer feedback or requests input through terminals, questionnaire results, and consultation content, automatically classifying them into positive opinions, improvement requests, complaints, new feature requests, price dissatisfaction, and evaluations of the support system, and proposing rapid responses or specific actions to sales personnel. Specific examples of prompts for the generative AI to read include "Please summarize the customer feedback and propose three response strategies" and "Please extract the customer's main needs from the questionnaire results." Specifically, to extract deeper customer psychology from unstructured text data, this Customer Relationship Enhancement Department has a pre-trained language model based on bidirectional encoder representation. As input data, this department acquires a text array containing feedback text sent by customers and the customer's past consultation history. Specific examples of input data include the fifty-character text "The interface for the new feature is difficult to use," and log data from the past three support chats. This language model tokenizes the input text, converting each token into a 768-dimensional embedding vector. This language model uses a multi-layered Transformer encoder to compute a context-aware self-attention vector, generating a semantic representation vector of the entire text. The language model outputs a probability distribution vector representing the category of feedback and a continuous score representing sentiment polarity from -1 to +1. As a concrete example, the language model outputs a vector representing a 90% probability of the category "improvement request," with a sentiment polarity score of -0.6. The Customer Relationship Enhancement Department sends these outputs to the subsequent action proposal module. If the sentiment polarity score is below -0.5, the action proposal module determines that the customer's dissatisfaction is high and automatically sends an alert to the support department manager instructing for urgent action. Through this structure, the system eliminates existing text classification methods that rely on keyword matching. By parsing the context in a high-dimensional space, the system accurately detects sarcasm or implied dissatisfaction. Therefore, the system reduces the risk of contract cancellations due to delayed customer responses, improving customer satisfaction. This technology is extremely effective in specific use cases such as large-scale customer support centers or contract cancellation prevention for subscription services. This language model is pre-learned using millions of general text samples and then fine-tuned using a domain-specific corpus containing industry-specific jargon or product names. The system uses a cross-entropy error function to optimize the weights of the classification layer. By applying gradient pruning, the system prevents gradient explosion during learning, ensuring model stability. Therefore, this system exhibits robust parsing performance for diverse customer expressions.

[0065] Step 8: Project, Contract, and Data Sharing Management. The Project Management Department and Contract Management Department centrally manage the progress or tasks, responsible parties, delivery dates, resource allocation, risk factors, changes in client requirements, contract content or update dates, past negotiation history, contract condition change history, payment status, and contract breach risks of joint projects with clients. They utilize generative AI for detecting or alerting on progress delays, early detection of risk factors, prioritization of tasks, optimization of resource allocation, automatic notification before contract expiration, timing of price modification negotiations, risk assessment during contract condition changes, and automatic detection of non-payment risks. Specific examples of prompts that the generative AI can read include, "Please analyze the project progress and risk factors, and create an alert to be sent to relevant personnel," and "Please extract a list of clients with upcoming contract update dates and create the best update proposal." Specifically, to comprehensively monitor the progress of complex projects and contract risks, the Project Management Department and Contract Management Department possess an anomaly detection engine that combines an autoencoder and a long short-term memory network. The project management department receives a numerical vector representing the difference between the scheduled and actual working hours for each task, and a time series array representing resource utilization rates, as input data. Specifically, the input data includes floating-point numbers normalized to the number of days of task delay and an array representing the percentage of engineers' weekly utilization rates. The anomaly detection engine uses a Long Short-Term Memory (LSTM) network to learn dynamic change patterns in the time series data and an autoencoder to construct a low-dimensional latent representation of normal project states. The engine calculates the reconstruction error when reconstructing the input data. As output, the engine outputs an anomaly score ranging from zero to one hundred, representing the project's delay risk, and a contribution vector representing the feature quantities of the main risk factors. For example, an anomaly score of eighty-five is output, and the contribution vector shows an index indicating that "overload of a specific engineer" is the largest factor. The project management department sends these outputs to the subsequent resource optimization module. If the anomaly score exceeds a threshold of seventy, the resource optimization module generates an automatic allocation plan for alternative resources based on the contribution vector and displays it to the project manager's terminal. This system eliminates the existing progress management method of visually verifying Gantt charts by humans. It employs a mathematical anomaly detection method based on reconstruction errors in multivariate time series data. This allows the system to identify potential delay risks early, significantly increasing the probability of project success. This technique is particularly effective in the specific use case of managing large-scale system development projects or long-term consulting contracts. The autoencoder uses data from past successful projects as normal data and is trained through unsupervised learning to minimize the mean squared error. The system enhances the expressive power of the latent representation by adding a sparse regularization term to the loss function.This system also applies the same time series analysis to predict contract renewal periods, detecting a decrease in customer usage frequency and automatically triggering retention measures. Thus, this system achieves a high degree of automation in both project management and contract management.

[0066] Step 9: Training, Knowledge Provision, and Communication Optimization. The Training Provision Department, based on the skills or areas of interest of sales representatives or clients, their past course attendance history, sales performance, and feedback, uses generative AI to automatically select the best teaching materials or videos, FAQs, case studies, role-playing scenarios, etc., and makes them available for viewing on the terminal. Furthermore, the Communication Optimization Department analyzes each client's communication history or reaction tendencies, past coping history, client personality traits, industry characteristics, and cultural background, using generative AI to propose the best communication methods, timing, content, and frequency. Specific examples of prompts for the generative AI to read include, "Please analyze the activity history of each sales representative and generate individual suggestions to improve performance," and "Please propose the best communication methods and timing for client C." Specifically, the Training Provision Department and the Communication Optimization Department possess a personalized engine integrating deep collaborative filtering and reinforcement learning to provide content and dialogue strategies tailored to each user's characteristics. The Training Provision Department uses this as input data to obtain a sparse binary vector representing the sales representative's past course attendance history and a continuous vector representing recent sales performance. As a specific example of input data, this training provider receives a 100-dimensional vector representing the number of lessons attended out of 100 teaching materials, along with a numerical value representing the monthly achievement rate. This personalization engine uses a multilayer perceptron to learn the nonlinear interaction between user feature vectors and project feature vectors. As output, this personalization engine outputs an array of predicted evaluation scores from zero to one, representing the recommendation level for each teaching material. For example, this personalization engine outputs a score of 0.95 for the teaching material "Advanced Negotiation Skills" and 0.2 for "Basic Product Knowledge." This output is then sent to the subsequent display control module. This display control module extracts the top three teaching materials by predicted evaluation score and displays them preferentially on the terminal's dashboard. Through this configuration, the system eliminates the need for existing educational methods that provide uniform training to all employees. This system maps individual skill gaps and learning tendencies in a high-dimensional space, dynamically generating optimal learning paths. Therefore, this system maximizes learning efficiency and improves the overall operational capabilities of the organization. This technology is extremely effective in specific use cases such as talent development in sales organizations with thousands of employees or improving customer engagement. This personalized engine uses past class attendance history and performance improvement data as teacher data, employing a binary cross-entropy loss function for learning. To address the cold start problem, the system adopts a hybrid approach for new users, combining attribute-based and content-based recommendation. In communication optimization, the system also constructs a Markov decision process model that uses customer response rate as a reward, continuously optimizing the optimal email sending time or tone through reinforcement learning. Thus, this system achieves a self-evolving platform that continuously improves user experience.

[0067] Step 10: The interface department provides user interfaces for sales personnel or customers to access various parts of the server, in the form of web browsers, smartphone applications, tablet applications, dedicated business terminals, etc., enabling them to view, edit, and input sales tasks, proposal materials, progress reports, feedback results, training content, contract information, communication proposals, project management screens, dashboards, alarm notifications, FAQs, knowledge bases, etc. For example, it can be used in various ways such as: sales managers can grasp the overall sales progress or risk cases on the dashboard; sales personnel can receive alarm notifications and respond immediately; customers can view FAQs or training videos; sales personnel can confirm customer information from their mobile phones while on the go and input feedback on the spot after the discussion, etc. Specifically, in order to process massive data streams without delay and provide intuitive visual information, this interface department has a data distribution architecture that integrates real-time stream processing engines and edge computing technologies. As input data, this interface department obtains JSON format event message groups asynchronously sent from various parts of the server, as well as metadata indicating location information or device status obtained from the sensors of the terminal. As specific examples of input data, this interface unit receives event messages indicating sales forecast updates and GPS coordinate data sent from the sales representative's smartphone. This stream processing engine utilizes an in-memory database to aggregate and filter this input data in milliseconds. As output, this stream processing engine outputs an optimized structured dataset for dashboard visualization and a payload for push notifications corresponding to urgency levels. As a specific example of output, this stream processing engine outputs an array of time-series data points for chart visualization and a payload indicating an emergency alert from a major customer complaint. This interface unit pushes and distributes these outputs to various terminals via secure WebSocket communication. Terminal-side applications use the received dataset to render smooth 3D charts or heatmaps using WebGL technology. Through this configuration, the system eliminates existing data update methods that rely on periodic batch processing. By adopting an event-driven architecture, the system minimizes the latency from data occurrence to screen display. Therefore, this system enables sales managers or representatives to make rapid decisions based on the latest information. This technology is highly effective in specific use cases such as mobile sales activities in constantly changing market environments or real-time monitoring in large-scale call centers. To conserve communication bandwidth, this system employs virtual DOM technology, which updates the screen by receiving only differential data on the terminal side. To ensure continued operation even in environments with unstable network connections, the system features an offline-priority design with local caching and synchronization queues on the terminal side. Therefore, this system provides a seamless and comfortable user experience even from a smartphone used while traveling.

[0068] (specific use case) For example, when a corporate sales representative is identifying new potential clients and initiating contact strategies, the sales representative inputs data from business cards obtained at trade shows, inquiries from websites, social media responses, and company information from external databases into the Customer Information Management Department. The Sales Activity Support Department aggregates this data and provides a prompt to the generative AI: "Based on the business card data obtained at trade shows and past sales patterns, create a list of potential clients and assign them priority." The generative AI comprehensively analyzes past sales attributes or negotiation history, industry, company size, sales representative's position, and past purchasing tendencies, scoring the accuracy of each potential client's sale and automatically generating a priority list. The sales representative can then view this list on the terminal and initiate contact plans sequentially from the highest-priority potential clients. Specifically, this Sales Activity Support Department utilizes an ensemble learning model combining graph neural networks and random forests to accurately extract promising potential clients from heterogeneous data sources. As input data, the Sales Support Department receives textual feature vectors containing job or department names extracted from business card data, and numerical vectors containing financial indicators of the companies obtained from external databases. Specifically, the Sales Support Department receives a 300-dimensional array vectorized from the job title "Information Systems Manager," and a 5-dimensional numerical array representing sales growth rate or operating profit margin. The ensemble learning model uses a graph neural network to calculate the centrality score in the inter-firm transaction network and a random forest to calculate the probability of closing a deal based on attribute data. As output, the ensemble learning model outputs a comprehensive closing accuracy score from zero to one hundred for each potential customer, and a vector representing the importance of the feature on which the score is based. Specifically, the ensemble learning model outputs 88 as the closing accuracy score and a vector representing the largest contribution as the feature importance "past performance in other companies in the same industry." The Sales Support Department then sends this output to the subsequent list generation module. This list generation module extracts potential customers whose closing accuracy scores exceed a pre-set threshold of seventy and generates a contact list arranged in score order. This system eliminates the reliance on the intuition or experience of sales personnel in existing lead selection processes. By integrating multiple machine learning algorithms, it offsets the biases of a single model, dramatically improving prediction accuracy. This allows sales resources to be focused on leads with the highest return on investment. This technology is extremely effective in specific use cases such as handling large volumes of leads after large-scale trade shows or creating target lists for new business. The ensemble learning model uses both past sales and lost sales data to learn in a way that minimizes cross-entropy error. The system uses Bayesian optimization as a hyperparameter optimization technique to automatically adjust the depth or number of decision trees in the random forest.For missing feature values, this system employs the K-nearest neighbor interpolation algorithm to ensure data integrity. Therefore, this system performs robust inferences even with incomplete external data.

[0069] Furthermore, when sales representatives discuss upsell proposals for existing customers, the system uses a prompt such as "Please propose products or services that are effective for upsell based on this customer's past purchase history and feedback" to allow generative AI to analyze past purchase history, feedback information, consultation history, questionnaire results, contract information, etc., accumulated in the customer information management department. The generative AI extracts information such as the utilization of products purchased by the customer in the past, requirements or issues contained in the feedback, areas of interest contained in the consultation content, and future investment intentions shown in the questionnaire, automatically generating the best upsell proposal content for each customer. Sales representatives can then create proposal documents or email texts for customers based on this proposal content and send them from their terminals. Specifically, this customer information management department has a self-attention recommendation model based on the Transformer architecture to generate the best upsell proposals that arouse customers' potential purchase desire. As input data, this customer information management department obtains a sequence of item IDs arranged chronologically from the customer's past purchase history, as well as embedding vectors of keywords extracted from the feedback text. As a specific example of input data, this Customer Information Management Department receives an integer array representing the IDs of three previously purchased products, and a 256-dimensional vector transformed from the keyword "enhanced security." This recommendation model uses a self-attention mechanism, simultaneously calculating the dependencies between past purchases and the semantic relevance between feedback keywords. As output, this recommendation model outputs a probability distribution vector representing the upsell recommendation score for all products in the catalog, and a natural language text fragment explaining the proposal's rationale. As a specific example of output, this recommendation model outputs a vector representing an 80% probability of "Advanced Support Program" as the superior product, and outputs the text "Based on past consultation history regarding security incidents" as the proposal's rationale. This Customer Information Management Department sends these outputs to the subsequent proposal generation module. This proposal generation module inserts the information of the product with the highest probability into a template, automatically generating a draft proposal for sales representatives. Through this structure, the system eliminates simple rule-based cross-selling proposal methods. This system achieves contextual understanding in a high-dimensional space that integrates customer behavioral history and unstructured text. Therefore, this system enables persuasive upsell proposals directly related to the client's current issues, increasing the average order value. This technology is extremely effective in specific use cases such as planned upgrades to cloud services or extended proposals for maintaining contracts. The recommendation model uses past successful upsell cases as positive examples, along with negative examples generated using negative sampling, and pre-trains using a contrastive learning framework. The system uses the Adam optimizer in model weight updates, eliminating instability in the early learning stages through pre-warming scheduling of the learning rate. During inference, the system uses a beam search algorithm to generate the most natural and logical proposal reasoning text. Thus, this system provides sales representatives with high-quality proposal content that can be presented to clients as is.

[0070] Furthermore, based on the sales representative's activity history or negotiation progress, competitor information, and market trends, the Sales Activity Support Department provides the generative AI with the prompt, "Calculate this month's sales forecast based on data from the past three years and current case progress." The generative AI then comprehensively analyzes past sales trends, the progress of current cases, customer purchase cycles, seasonal factors in the industry, and competitor activities, quantifying and outputting the probability of achieving this month's sales expectations or targets, as well as risk factors. Sales managers can confirm this sales forecast on the dashboard and, as needed, revise their sales strategies or reassess resource allocations. Specifically, to achieve high-precision future forecasts that consider complex market factors, the Sales Activity Support Department possesses a hybrid forecasting engine that integrates time-series forecasting algorithms and Long Short-Term Memory (LSTM) networks. As input data, the Sales Activity Support Department obtains a one-dimensional time-series array containing daily sales data from the past thirty-six months, and a multi-dimensional matrix containing the order accuracy and expected amount for each current case. As a specific example of input data, the Sales Support Department receives an array of 1,080 floating-point numbers representing daily sales amounts, and a 50-row, 3-column matrix containing data on 50 ongoing cases. This hybrid forecasting engine uses a time-series forecasting algorithm to decompose the seasonal or trend components of the sales data and uses a Long Short-Term Memory (LSTM) network to learn the non-linear variation patterns of case progress. As output, the hybrid forecasting engine outputs a time-series array representing daily sales forecasts for the next three months, and upper and lower limits representing a 95% confidence interval for forecast uncertainty. As a specific example of output, the hybrid forecasting engine outputs 100 million yen as the predicted sales amount at the end of the month, and outputs a confidence interval representing a range of 90 million to 110 million yen. The Sales Support Department sends these outputs to the subsequent dashboard control module. If the probability of the predicted sales amount falling below the target value exceeds 30%, the dashboard control module displays a red alert icon on the screen to alert the manager. Through this structure, the system eliminates existing sales forecasting methods that rely on the subjective estimates of sales personnel. The system mathematically integrates past statistical trends and current pipeline conditions to provide objective and quantitative forecasts. Therefore, this system enables management to make rapid, data-driven course corrections or resource reallocations. This technology is highly effective in specific use cases such as improving the accuracy of quarterly financial statements and optimizing production plans based on demand forecasts in manufacturing. This hybrid forecasting engine learns using a backpropagation algorithm over time to minimize past forecast errors. To mitigate the impact of outliers, the system applies robust scaling preprocessing to the input data. The system uses the mean absolute error rate (MAR) as the evaluation metric for the forecasting model and automatically relearns the model periodically. Thus, the system maintains forecast accuracy even in the face of drastic changes in the market environment.

[0071] In addition, the Customer Relationship Enhancement Department uses the prompt "Please summarize the feedback from the customer and propose three response strategies" to enable generative AI to analyze feedback or requests input by customers through terminals and survey results. The generative AI automatically categorizes the feedback into positive opinions, improvement requests, complaints, new feature requests, price dissatisfaction, and evaluations of the support system, and proposes rapid response strategies or specific actions (such as apologizing, forwarding feedback to the new feature development department, discussing re-evaluation of prices, and proposals to strengthen the support system) to the sales representatives. Sales representatives can then quickly and accurately implement customer responses based on these proposals. Specifically, to extract useful insights from large amounts of unstructured text, this Customer Relationship Enhancement Department has a text parsing engine that combines text topic modeling based on latent Dirichlet assignment and a large-scale language model. As input data, this Customer Relationship Enhancement Department receives feedback text of several hundred words sent by customers, as well as categorical variables representing customer attribute information. For example, the Customer Relationship Enhancement Department receives the text "The system's response speed is slow, causing obstacles to business" and an identifier indicating that the customer's contract plan is "Enterprise Edition". This text parsing engine uses text topic modeling to extract latent topic distributions from text groups and employs large-scale language models to analyze the context and sentiment polarity of the text. As output, the engine provides a category label representing the feedback's category and an array of natural language text representing specific response strategy candidates. For example, the engine outputs "Major Complaint" as the category label and three response strategies: "Immediate escalation to the technical department," "Temporary enhancement of server resources," and "Send an apology email to the customer." The Customer Relationship Enhancement Department then forwards these outputs to the subsequent workflow management module. When the category label is "Major Complaint," the workflow management module bypasses the usual approval process and immediately adds the response instruction to the technical department's task queue. This architecture eliminates the existing process of manually categorizing questionnaires and discussing responses. The system uses natural language processing technology to accurately understand the intent of the text and instantly deduce the optimal action. This dramatically shortens the lead time for customer responses and prevents secondary complaints. This technology is highly effective in specific use cases such as the operation of large-scale customer support centers or the acceleration of user feedback loops for SaaS products. This large-scale language model uses a corpus of past successful customer support responses and optimizes it through instruction fine-tuning. In text tokenization, the system employs byte-pair encoding as a sub-word segmentation algorithm to improve robustness against unknown words. To evaluate the appropriateness of the generated response strategies, the system incorporates an autoregressive scoring mechanism. Therefore, the system consistently proposes safe and appropriate customer response actions.

[0072] Furthermore, the project management department centrally manages the progress or tasks, responsible parties, delivery dates, resource allocation, risk factors, and changes in client requirements for joint projects. It then provides a prompt to the generative AI, stating, "Please analyze the project progress and risk factors, and create alerts for relevant personnel." The generative AI automatically detects delays or risk factors and generates appropriate alerts or reminders for relevant personnel. This proactively prevents project delays or disputes, improving client satisfaction. Specifically, to quantify the complex uncertainties of projects and identify potential bankruptcy risks early on, the project management department possesses a risk inference engine that integrates Bayesian networks and recurrent neural networks. As input data, the project management department receives time-series data of binary vectors representing the completion status of each task, and a continuous value matrix representing the working time of project members. For example, the project management department receives an array of zeros and ones representing the task completion status over the past two weeks, and a floating-point matrix representing the daily overtime hours of each member. This risk inference engine uses a recurrent neural network to extract hidden state vectors from time-series data and a Bayesian network to perform probabilistic inference based on inter-task dependencies. The engine outputs a continuous score from zero to one representing the probability of project delivery delays, along with the task IDs on the critical path that are the root cause of the delay. For example, the engine outputs 0.82 as the delay probability score and the ID of the "database design phase" as the root cause task. The project management department sends these outputs to the subsequent alert generation module. When the delay probability score exceeds a threshold of 0.7, the alert generation module automatically sends a warning message containing the root cause task ID and a recommended resource allocation plan to the relevant personnel's terminals. This structure eliminates existing risk management methods that rely on the project manager's rule of thumb. The system uses a probabilistic model to mathematically simulate the ripple effects of task delays on subsequent tasks. Therefore, the system enables proactive countermeasures before problems surface, improving project success rates. This technology is highly effective in specific use cases such as large-scale system integration projects or project schedule management involving multiple suppliers. This recurrent neural network uses historical project data as teacher data and optimizes weights using backpropagation over time. In the structure learning of the Bayesian network, the system uses the Markov chain Monte Carlo method to estimate the posterior probability distribution. To improve the system's tolerance to noise in the input data, a Gaussian-based variational autoencoder is incorporated as a preprocessing step. Therefore, this system can achieve high-accuracy risk prediction even from incomplete progress report data.

[0073] In addition, the Contract Management Department manages contract content or update periods, past negotiation history, contract term change history, payment status, and default risks. It provides a prompt to the generative AI, "Please extract a list of customers whose contract update dates are approaching and create the best update proposal." The generative AI then analyzes the contract expiration date, past negotiation history, customer purchasing tendencies, and payment status to automatically generate the timing of contract updates, the best proposal content, price revision options, and term change options. Sales personnel can then use this information to smoothly advance contract update negotiations. Specifically, to predict customer churn risk and derive optimal retention measures, the Contract Management Department has a contract optimization engine that integrates a survival time analysis model and a Transformer-based generative model. As input data, the Contract Management Department obtains time-series event data representing customers' past contract update history and a numerical vector representing the service usage frequency over the past six months. For example, the Contract Management Department receives arrays of the past five contract update dates and contract amounts, and a ten-dimensional numerical vector representing the number of logins or feature usages per month. This contract optimization engine uses the Cox proportional hazards model as a lifespan analysis model to calculate the risk rate of each customer rejecting the next update. The engine also uses a Transformer model to generate optimal proposal conditions to reduce this risk. As output, the engine provides a percentage value representing the probability of a successful contract update, along with structured data containing proposal content including price discounts or additional options. For example, the engine outputs a 60% probability of success and JSON data representing a 5% discount on the basic fee and three months of free premium support as proposal content. The contract management department then sends these outputs to the subsequent document generation module. This module automatically inserts the received proposal content into the contract update notification template and places it in the sales representative's confirmation queue. This structure eliminates the need for the existing uniform approach of sending one-size-fits-all update notifications to all customers. The system analyzes each customer's usage and churn risk in a high-dimensional space, dynamically generating individually optimized offers. Thus, the system maximizes contract update rates and customer lifetime value while suppressing unnecessary price reductions. This technology is highly effective in specific use cases such as improving churn rates or increasing service retention rates in SaaS businesses. This survival time analysis model processes past churn and retention customer data as right-hand truncated data to learn in a way that maximizes the partial likelihood function. In the Transformer model learning process, the system uses proximal policy optimization as a reinforcement learning algorithm, setting a reward function that balances profit margin and renewal rate. To infer customer price sensitivity, the system incorporates past price change response data as covariates into the model.Therefore, this system calculates the optimal proposal conditions without compromising profitability.

[0074] Furthermore, the training department, based on the skills or areas of interest of sales representatives or clients, their past course attendance history, sales performance, and feedback, provides the generative AI with the prompt, "Please analyze the activity history of each sales representative and generate individual suggestions for performance improvement." The generative AI then analyzes each sales representative's strengths / weaknesses or activity tendencies, past successes or failures, and automatically generates individually optimized training plans, materials, or suggestions. Sales representatives can then use these suggestions for self-study to improve their sales capabilities. Specifically, this training department, to address the skill gaps of individual sales representatives and dynamically construct optimal learning paths, possesses an adaptive learning engine that integrates knowledge graphs and deep reinforcement learning. As input data, the training department obtains a performance vector representing the sales representative's win rate or average order value in past negotiations, and an array of training module IDs from past courses. For example, the training department receives a five-dimensional numerical vector representing a 40% win rate or an average order value of 3 million yen, and an integer array representing the IDs of five course materials already attended. This adaptive learning engine uses a knowledge graph to map the semantic relationships between business skills and various training materials. It employs a deep reinforcement learning agent to calculate the optimal prompt order for transferring performance from the current state to the target state. As output, the engine provides the ID of the recommended training material to be listened to next, along with a predicted increase in win rate expected by listening to that material. For example, the engine outputs "Advanced Refutation Handling Techniques" as the recommended training material ID and a positive 5% expected increase in win rate. The training department then sends these outputs to the subsequent user interface control module. This module highlights the recommended training material at the top of the sales representative's learning dashboard and sends a notification prompting them to attend the training. This structure eliminates the limitations of existing static training programs that force all employees to take fixed courses. Based on individual weaknesses and learning history, the system provides a personalized learning experience in real time. Thus, the system achieves the direct business result of improved sales performance while minimizing the time required for learning. This technology is highly effective in specific use cases such as shortening onboarding training periods or providing ongoing skills enhancement support in large-scale sales organizations. This deep reinforcement learning agent, within a learning environment formalized as a Markov decision process, receives improved sales performance as a reward and updates its Q-value. To address the exploration-exploitation dilemma, the system employs an ε-greedy algorithm to continuously test the effectiveness of unknown learning materials. In constructing the knowledge graph, the system uses natural language processing to automatically extract entities and relationships from the company's sales manuals or success case studies. Therefore, the system consistently provides advanced training reflecting the latest proprietary technologies within the company.

[0075] In this way, the system of the present invention utilizes generative AI in all business processes at the sales site for data analysis, automatic proposal generation, task generation, document creation, risk detection, communication optimization, and training provision, comprehensively supporting sales personnel or customers in improving business efficiency, decision-making sophistication, customer satisfaction, competitiveness, and building sustainable business relationships. Specifically, to integrate the aforementioned multiple artificial intelligence modules and ensure overall system scalability and fault tolerance, the system employs a distributed processing platform based on a microservice architecture. As input data, the system acquires massive transaction data asynchronously generated from various business processes, as well as telemetry data representing the resource usage of each module. Specific examples of input data include log data containing thousands of API requests per second, and time-series metrics representing the CPU utilization or memory consumption of each container. This distributed processing platform uses service mesh technology to control communication between microservices and uses load balancing algorithms to dynamically route traffic. As output, the distributed processing platform outputs control commands instructing each container to scale horizontally or scale back horizontally, and health check scores representing the overall system robustness. As a specific example of output, this distributed processing base outputs a control command to increase the number of replicas of the data parsing module from three to five, and outputs 99.9% as a health check score. This system sends these outputs to the subsequent container orchestration engine. Based on the control command, this container orchestration engine launches new container instances in milliseconds, enhancing processing power. Through this architecture, the system eliminates the single point of failure inherent in existing monolithic system architectures. This system enables the independent deployment and scaling of individual AI modules, maintaining stable responsiveness even under high loads. Thus, the system maximizes the utilization efficiency of computing resources and reduces infrastructure operating costs. This technology is extremely effective in specific use cases such as global-scale enterprise platforms accessed simultaneously by tens of thousands of users. To ensure data consistency between modules, the system employs an event sourcing model and a command query responsibility separation model. To achieve continuous integration and continuous delivery of machine learning models, the system builds an automated MLOps pipeline. When a decrease in the model's inference accuracy is detected, the system automatically triggers a relearning process using the latest dataset. Therefore, the system consistently delivers the highest-performance AI models to the production environment.

[0076] (Application Example 1) The specific processing flow corresponding to Use Case 1 will be described below. The various parts of the system described below are implemented by the data processing device 12 and the intelligent device 14. Furthermore, the data processing device 12 is referred to as the "server" and the intelligent device 14 is referred to as the "terminal".

[0077] (Implementation methods for carrying out the present invention) The embodiment for implementing this invention is a logistics center operation support system consisting of a server and multiple terminals, comprising a cargo information management department, an operator management department, an equipment management department, a scheduling optimization department, a delivery route optimization department, an education program proposal department, a customer response proposal department, and a fault precursor detection department. Each department is primarily implemented on the server, while the terminals function as interfaces for field operators, managers, equipment operators, and customer response personnel. Specifically, this logistics center operation support system, in order to comprehensively process massive amounts of logistics data and dynamic field conditions in real time, possesses a hybrid distributed processing architecture that integrates edge computing and cloud computing. As input data, this system acquires high-frequency streaming data sent from various terminals or on-site IoT sensors, as well as structured data including external traffic or meteorological information. Specific examples of input data include location coordinate data received ten times per second from sensors mounted on forklifts, and numerical data on precipitation probability or wind speed obtained from the meteorological agency's API. The system performs data filtering and compression at the edge nodes and forwards it to the main server in the cloud. This server uses a parallel computing cluster equipped with multiple graphics processing units to process the received large-scale data. This server outputs synchronization control signals for each subsystem, as well as a JSON dataset representing the overall system operation status for a comprehensive dashboard. Specifically, it outputs control signals for path changes of automated guided vehicles (AGVs) and numerical data representing the overall cargo throughput of the center. This system transmits these outputs to terminals and control devices via a low-latency network. This architecture eliminates the need for existing centralized architectures that concentrate all processing load on a central server. It optimally distributes real-time processing at the edge and advanced machine learning inference in the cloud. This allows for millisecond-level response times to physical changes on-site while minimizing network bandwidth consumption. This technology is particularly effective in the specific use case of fully automating a large-scale, mega-logistics center with tens of thousands of continuously moving goods. The system uses containerization to install each management unit as an independent microservice and dynamically scales horizontally based on load. The system uses the gRPC protocol for inter-service communication, minimizing data serialization overhead. The system features redundancy for automatic failover in case of failure. Therefore, this system guarantees stable operation 24 hours a day, 365 days a year without interruption.

[0078] The Cargo Information Management Department meticulously registers, accumulates, and manages all goods entering the logistics center, including cargo ID, category, weight, dimensions, storage temperature conditions, scheduled arrival date, scheduled departure date, delivery destination, priority, special handling information (e.g., refrigerated, frozen, dangerous goods, high-value goods, fragile items), inbound / outbound history, past malfunction history, barcode or RFID tag information, packaging status, owner information, delivery provider information, cargo photographs, storage area information, handling history, inspection results, damage / contamination records, insurance coverage, and relevant legal and regulatory information. For example, storage temperature or shelf life for refrigerated food, batch number or expiration date for pharmaceuticals, impact sensor data or anti-static history for precision machinery, and legal compliance status or storage area restrictions for dangerous goods are also managed. For instance, by automatically registering goods upon arrival using barcodes or RFID and automatically recording them upon departure, real-time inventory status and cargo tracking are achieved. For example, based on the priority of goods or delivery deadlines, inbound and outbound operations are prioritized, or storage areas are automatically allocated. For example, AI is used to analyze image data captured during goods inspection, automatically determining for damage or contamination, and immediately notifying managers if anomalies are found. For example, the handling history of goods is recorded in time series, allowing detailed tracking of which operator used which equipment and along which path. Specifically, this Goods Information Management Department possesses a multimodal recognition system that integrates a computer vision model based on convolutional neural networks and an RFID stream processing engine to accurately track and evaluate the physical state of goods in the digital space. As input data, this Goods Information Management Department acquires three-channel RGB image tensors from a high-resolution camera installed at the inspection gate, and a time-series array of tag IDs continuously read from an RFID reader. As a specific example of input data, this Goods Information Management Department receives image data of a 1024x1024 pixel cardboard box, and a hexadecimal array of tag IDs read 50 times per second. This computer vision model uses multiple convolutional and pooling layers to extract spatial feature maps of edges or textures from the images. This computer vision model outputs a continuous score from zero to one representing the probability of cargo damage, as well as the coordinate data of the bounding box representing the damaged area. As a specific example, the computer vision model outputs a damage probability of 0.95 and an array representing the XY coordinates of the indentation in the upper right corner of the image. The cargo information management department sends these outputs to the subsequent anomaly handling module. If the damage probability exceeds a threshold of 0.8, the anomaly handling module immediately sends a control command to the conveyor belt control device to discharge the cargo into the rejection channel. Through this configuration, the system eliminates the existing inspection process that relies on operator visual inspection.This system achieves high-precision and instantaneous determination of the status of high-speed moving goods by fusing deep learning image analysis with radio wave-based individual recognition. This eliminates bottlenecks in inspection operations, dramatically increasing the overall throughput of the logistics center. This technology is particularly effective in specific use cases such as the inbound / outbound lines of pharmaceuticals or precision machinery, where strict quality control is required. The computer vision model is pre-learned using image datasets of past normal and damaged goods, minimizing cross-entropy error. As a data augmentation technique, the system applies random image rotation, cropping, and brightness adjustment to improve the model's robustness to changes in lighting conditions. For RFID read data, the system uses a Kalman filter to remove noise and estimate the accurate three-dimensional position of the goods. Thus, this system achieves complete synchronization between the physical and digital worlds.

[0079] The operator management department manages operator IDs, names, qualifications (e.g., forklift driver qualifications, hazardous materials handling qualifications, sanitation manager qualifications, etc.), job categories, past work history, error history, skills evaluations, work schedules, health status, each operator's work efficiency or safety indicators, vital sign data (heart rate, body temperature, steps, fatigue level, etc.) obtained from wearable devices, attendance records, training and instruction history, commendation / disciplinary history, each operator's accident / attention report history, communication history between operators, and operators' desired work schedules or leave applications. For example, it automatically assigns tasks so that only forklift license holders can handle goods handling in specific areas. For example, it avoids assigning high-risk tasks to operators with a history of frequent errors. For example, it obtains operators' health status or fatigue levels from wearable devices and recommends rest when there is a high risk of overwork. For example, it quantitatively evaluates each operator's work efficiency or safety indicators and utilizes this information for optimizing training programs or staffing. For example, it analyzes operators' communication history to evaluate teamwork issues or leadership performance. Specifically, to balance operator safety and productivity, this operator management department employs a human resource management engine that integrates a time-series analytical model based on a Long Short-Term Memory (LSTM) network and mathematical optimization algorithms. As input data, the department acquires a time-series tensor of vital signs obtained from wearable devices worn by operators, and a multi-dimensional feature vector representing each operator's skill level. For example, the input data includes a 60x2 matrix of values ​​from heart rate and accelerometers recorded at one-minute intervals over the past hour, and integer values ​​evaluating forklift operation skills across five stages. The time-series analytical model uses an LTM network to learn the temporal variation patterns of vital sign data, quantifying the degree of fatigue accumulation. As output, the model provides a continuous score from zero to one hundred representing each operator's fatigue level, and a percentage value representing the probability of a hazard occurring within the next hour. For example, the model outputs 85 as the fatigue score and 70% as the probability of a hazard occurring. The operator management department then sends these outputs to the subsequent task allocation module. This task allocation module interrupts the currently active tasks of operators whose fatigue scores exceed a threshold of 80, and sends a mandatory rest notification to their smartwatches. This configuration eliminates existing safety management methods that rely on managerial patrols or self-reporting. The system mathematically detects early signs of accidents through continuous monitoring of biometric data and deep learning-based predictions. Thus, the system significantly reduces the incidence of workplace accidents while achieving optimal staffing based on operators' health status. This technology is particularly effective in specific use cases such as operations in cold storage warehouses with harsh temperature environments or sorting operations during late-night hours.This time series analysis model uses the correlation between past vital sign data and event reports as teacher data and employs a mean squared error function for learning. In task allocation optimization, the system uses mixed-integer linear programming to solve for the objective function that minimizes overall task latency while leveling the load on each operator. To protect individual privacy, the system uses a federated learning approach, anonymizing vital sign data before performing analysis on edge devices. Thus, this system constructs an advanced predictive model while meeting security requirements.

[0080] The Equipment Management Department manages detailed information such as equipment ID, equipment category (e.g., handling robots, automated sorting machines, forklifts, refrigerators, surveillance cameras, conveyor belts, pallet racks, safety doors, generators, air conditioning equipment, etc.), installation location, operating time, maintenance history, sensor data (temperature, humidity, vibration, current value, power consumption, etc.), fault history, replacement history of consumable parts, operating efficiency or failure rate of each piece of equipment, equipment operating schedule, equipment utilization reservation status, equipment statutory inspection history, equipment manufacturer, model, manufacturing date, equipment warranty period, and equipment insurance status. For example, it monitors the operating time or remaining battery level of handling robots in real time, temperature fluctuations in refrigerators, and abnormal vibrations in sorting machines, issuing immediate alarms if any abnormal signs are detected. For example, based on equipment operating status or maintenance history, it automatically proposes preventative maintenance or parts replacement opportunities. For example, it analyzes equipment operating efficiency or failure rate as data for determining equipment investment or layout changes. For example, based on equipment utilization reservation status, it optimizes work schedules or personnel allocation. Specifically, this Equipment Management Department uses a time-series database to accumulate sensor data as a multidimensional tensor. This system uses a Long Short-Term Memory (LSTM) network or a Transformer model with self-attention to analyze time-series data. The input data is a multi-dimensional time-series array normalized from past periods, including temperature, vibration, and current values. For example, the input data might include a 24-hour battery voltage variation vector for a handling robot, a minute-by-minute temperature change array for a refrigerator, and a high-frequency vibration spectrum tensor for a sorting machine. The model extracts latent features from the input data. The output is an anomaly score representing the deviation from the normal data distribution. Examples of output data include an anomaly probability value ranging from 0.0 to 1.0, a numerical vector representing the remaining time until fault prediction, and a classification label for the cause of the anomaly. If the anomaly score exceeds a pre-set threshold, the equipment management department sends an immediate alert to the administrator terminal as a follow-up action. The equipment management department also outputs a trigger signal for component replacement to the preventative maintenance module. Thus, this system proactively prevents sudden equipment shutdowns. This system significantly improves equipment uptime. This system transcends the limitations of human visual inspection, capturing minute anomalies in high-dimensional spaces. In use cases such as logistics centers or large-scale manufacturing plants, this system delivers the technological benefits of reduced maintenance costs and maximized operational efficiency.

[0081] The scheduling optimization department uses generative AI to comprehensively analyze cargo, operator, and equipment information accumulated in the cargo information management, operator management, and equipment management departments, as well as traffic and weather information acquired from external sources (such as rainfall, snowfall, temperature, approaching typhoons, road congestion, accidents, road closures, and disaster information). It considers cargo type and priority, operator skills and qualifications, equipment operating and idle status, traffic congestion, and weather risks to automatically determine the optimal operator allocation, operation timing, and equipment utilization plan for each cargo. For example, it prioritizes outbound operations for cargo with approaching delivery deadlines or high-priority cargo. It also moves outbound times earlier when traffic congestion is predicted and prioritizes indoor storage areas when a typhoon is approaching. Furthermore, it can automatically plan batch processing to efficiently move multiple cargoes. It assigns appropriate operators to handle heavy or hazardous materials based on operator skills or qualifications and past work history. Finally, it automatically adjusts work plans based on equipment utilization and maintenance schedules. Specifically, this scheduling optimization department employs a hybrid AI architecture combining graph neural networks and deep reinforcement learning models. The data input to this model is heterogeneous hybrid data containing cargo attribute vectors, operator skill matrices, equipment operating status tensors, and time-series arrays of the external environment. For example, the input data includes numerical vectors representing the delivery deadlines and weights of each cargo, probability tensors representing the distribution of past job completion times for operators, and scalar values ​​representing predicted delay times due to traffic congestion. This system maps these input data into a common latent space. The model explores the optimal combination of tasks and resources. The output data from this model is a binary matrix representing the allocation of operators and equipment for each cargo, and a continuous value vector representing the start time of each job. For example, the output data includes a list of the best operator IDs for specific high-priority cargoes, cluster labels for the batched cargo groups, and an array of timestamps representing the start and end times of each job. This scheduling optimization department sends the output allocation matrix to the execution control module for further processing. The execution control module pushes specific job instructions to the portable terminals of each operator. This system, acting as a reward function in deep reinforcement learning, aims to minimize overall task completion time and maximize resource utilization. It eliminates existing scheduling methods that rely on human intuition. This system solves high-dimensional constraints in real time. In use cases such as large-scale logistics hubs or port container terminals, this system effectively prevents task delays and dramatically increases throughput.

[0082] The delivery route optimization unit uses generative AI to automatically propose the shortest and safest delivery routes based on factors such as the delivery destination, the loading status of the delivery vehicle, traffic information, past delay history, weather information, the delivery vehicle's fuel consumption / distance / maintenance status, the delivery driver's skill or work status, the available time slots at the delivery destination, the geographical conditions of the delivery destination (e.g., slopes, narrow roads, snow-covered areas), and the availability of loading restrictions or special equipment on the delivery vehicle. For example, it avoids roads prone to congestion or accidents and recalculates routes to efficiently traverse multiple delivery destinations in real time. For example, it prioritizes assigning the shortest routes for refrigerated or urgent goods. For example, it automatically optimizes the assembly or delivery sequence of goods to maximize the loading efficiency of the delivery vehicle. For example, it selects routes with lower risk based on weather or road closure information. For example, it automatically initiates plans for scheduled delivery or nighttime delivery based on the available time slots at the delivery destination. Specifically, this delivery route optimization unit possesses a path exploration engine that integrates a convolutional neural network for extracting spatial features and a regression model for predicting traffic changes over time series. The data input to this model consists of graph structure data representing the road network, traffic volume tensors for each node, and geographic coordinate vectors of the delivery destinations. Specifically, the input data includes a numerical array representing real-time congestion length, a probability distribution of movement time between nodes based on past delay records, and scalar values ​​representing the current load weight and remaining fuel of the delivery vehicle. This system performs normalization and missing value imputation preprocessing on this input data. The model uses a reinforcement learning algorithm to calculate the optimal route. The output data from this model consists of a permutation vector representing the visit order, a numerical array representing the predicted movement time for each interval, and coordinate point cloud data of the recommended driving route. Specific examples of the output data include a list of optimized node IDs for visiting 15 delivery destinations, timestamps of arrival times for each destination, and polygonal data representing congestion areas to be avoided. The delivery route optimization unit forwards the output route data to the navigation module for further processing. This navigation module displays a directional route guide on the delivery driver's in-vehicle terminal. The system recalculates the route in real time in case of sudden weather changes or unexpected accidents. This system derives optimal solutions under dynamic and complex constraints that are difficult for human rules of thumb to handle. In use cases such as last-mile delivery or long-haul transportation, this system delivers clear technical benefits, including reduced fuel consumption, minimized delivery delays, and increased vehicle uptime.

[0083] The education program proposal department analyzes each operator's skill evaluation, past error history, customer feedback, changes in work content, training history, operator's expectations or career plans, accident / hazard reports on-site, operator's health status or vital signs data, and newly introduced equipment or business processes. Using generative AI, it automatically generates individually optimized education / training plans. For example, for operators with frequent forklift operation errors, it proposes "safe driving training" or "hands-on retraining." For example, for operators with low customer evaluations, it recommends "customer etiquette training." For example, for new operators, it focuses on assigning basic work procedures or safety training. For example, when work content changes significantly, it automatically proposes training to master the new work procedures. For example, when new equipment is introduced on-site, it automatically assigns training on operating that equipment to the relevant operators. For example, based on the operator's career plan or desired job type, it also automatically proposes leadership training or training geared towards management positions. Specifically, the Education Program Proposal Department employs a composite AI architecture that combines a large-scale language model for natural language processing with a collaborative filtering algorithm that forms the recommendation system. The data input to this model includes time-series vectors representing operators' action history, text embedding tensors of customer feedback, and natural language vectors from on-site accident reports. For example, the input data includes a numerical array representing the number of sorting errors made by a specific operator over the past year, a 768-dimensional embedding representation of customer complaint texts, and feature vectors extracted from the operation manuals of newly introduced equipment. This system integrates this input data to quantify the skill gaps of each operator. The output data from this model includes a list of recommended educational content IDs, a score array representing the priority of each training session, and structured data of individualized learning schedules. For example, the output data includes a recommendation score of 0.95 for encouraging safe driving training, a timestamp indicating the specific date and time of a customer's etiquette training session, and a URL linking to a simulation program for operating new equipment. The Education Program Proposal Department then sends the output educational plan to the learning management system for further processing. This learning management system automatically distributes training and lecture instructions to the terminals of target operators. During model learning, the system uses the reduction rate of operational errors after training and lectures as a reward signal to update weights. This system eliminates monotonous group training, providing adaptive learning tailored to individual characteristics. In use cases such as large-scale manufacturing or logistics centers, this system achieves the technical effects of increasing operator skill retention, drastically reducing human error, and optimizing training costs.

[0084] The Customer Response Proposal Department uses generative AI to analyze customer feedback (such as delivery delays, damaged goods, quality issues, delivery errors, incomplete packaging, errors in delivery notes, delays in responding to inquiries, complaint content, survey results, and social media reviews). In cases of low ratings or complaints, specific response strategies are proposed to the sales representative, including "automatic generation of apologies," "compensation proposals," "explanations of measures to prevent recurrence," "retraining proposals for the representative," and "proposals for regular customer follow-ups." For example, it automatically generates proposals such as "apologies for delivery delays and next-time discount coupons" or "compensation guides for damaged goods and explanations of measures to prevent recurrence." Similarly, in cases of high customer feedback, it automatically generates "thank-you emails" or "service proposals for outstanding customers." Furthermore, it immediately notifies the representative of any negative posts on social media, prompting a swift response. Finally, it analyzes customer inquiries, automatically extracting the best possible response from FAQs or manuals and providing suggestions to the representative. Specifically, the Customer Response Proposal Department possesses a large-scale language model based on Transformer and a classification neural network specialized for sentiment analysis. The data input to this model includes customer inquiry texts, strings from call logs converted from a speech recognition system, and structured data representing past transaction history. For example, input data includes a 500-word complaint text about delivery delays, a multidimensional tensor vectorizing negative posts on social media, and numerical vectors representing the customer's past purchase amounts and frequencies. The system performs morphological parsing and stop word removal preprocessing on the input text. The model determines the customer's emotional polarity and the urgency of their request from the input data. The output data from this model includes sentiment scores, category labels, and automatically generated response text. For example, output data includes a sentiment score of 0.88 indicating anger, cluster labels indicating compensation requests, and a 300-word apology text implying a discount coupon for the next transaction. The Customer Response Proposal Department sends the output response strategies to the Sales Support dashboard for further processing. This dashboard displays recommended actions on the responsible person's screen. This system automatically sends an email to the customer upon the operator pressing the confirmation button. It eliminates the subjective and time-consuming task of creating documents based on human operators. The system uses natural language processing technology to accurately extract potential customer complaints. In use cases such as call centers or e-commerce customer support, this system delivers significantly reduced response times, increased customer satisfaction, and prevention of secondary complaints.

[0085] The fault precursor detection unit uses generative AI to analyze real-time equipment sensor data and operator movement data obtained from the equipment management department and operator management department, as well as fault history and past accident patterns, environmental data such as temperature, humidity, noise, and illumination at the site, abnormal behavior patterns extracted from on-site surveillance camera images, abnormal operator vital sign data, on-site hazard reports, and lifespan prediction data of equipment consumable parts. When it detects abnormal temperature rises and vibrations in the equipment, operator movement patterns deviating from normal, situations similar to past fault occurrences, rapid changes in the on-site environment, abnormal operator physical condition, or signs of impending depletion of equipment consumable parts, it immediately sends an alarm notification to the management terminal, automatically generating and prompting necessary response strategies and attention alerts. For example, if the temperature of the handling robot rises sharply, or if the operator frequently moves along a path different from normal, it notifies the manager as "equipment abnormality risk" or "work procedure deviation risk." For example, it compares past fault patterns with current data and issues alarms prompting early response when the risk of recurrence is high. For example, it automatically generates a "parts replacement recommendation" notification when the lifespan of equipment consumable parts is nearing its end. For example, if the illumination or noise level at the work site exceeds the baseline value, an alarm is issued as an "abnormal working environment." Similarly, if an operator's heart rate or body temperature shows abnormal values, a notification is sent to management as a "risk of abnormal physical condition." Specifically, this fault prediction detection unit employs a multimodal AI architecture that integrates a recurrent neural network for processing time-series data and a three-dimensional convolutional neural network for parsing image data. The data input to this model includes high-frequency sensor data arrays from the equipment, continuous image tensors from monitoring cameras, and time-series vectors representing the operator's vital signs. For example, the input data includes a 100 Hz vibration spectrum obtained from the motors of the handling robot, a three-dimensional tensor representing the shift in the operator's skeletal coordinates, and an array of heart rate fluctuations obtained from a smartwatch. This system synchronizes these heterogeneous data and projects them into a common feature space. The model uses an autoencoder learned from the data distribution under normal conditions to calculate the reconstruction error. The output data from this model includes anomaly probability scores for each monitored object, spatial coordinate data representing the location of the anomaly, and classification labels representing the type of the predicted fault. Specific examples of output data include a probability value of 0.92 representing the risk of equipment malfunction, coordinate groups representing the bounding boxes of abnormal activity areas within surveillance camera images, and text labels indicating signs of component lifespan depletion. If the output malfunction detection unit's score exceeds a predetermined threshold, it sends a control signal to the emergency stop module as a follow-up process. This emergency stop module safely cuts off the power to the device. This system can detect subtle environmental changes or complex malfunctions that are imperceptible to a human monitor in an instant.In use cases such as chemical plants or large-scale automated warehouses, this system plays a crucial role in preventing major accidents, minimizing downtime, and ensuring on-site safety.

[0086] In this implementation, the various departments work together to optimize the overall operation of the logistics center, maximizing operational efficiency, minimizing risks, improving customer satisfaction, ensuring on-site safety, reducing costs, and alleviating the burden on on-site managers and operators. For example, from goods receiving to outbound, delivery, customer response, equipment maintenance, training, and fault prevention, generative AI performs advanced analysis, optimization, and automatic proposals throughout all business processes, creating a system that can flexibly and in real-time respond to changes in on-site conditions or external environments. For instance, when goods are received, AI automatically determines damage or contamination, and immediately notifies managers if any abnormalities are found. For instance, AI constantly monitors the health status or fatigue level of operators, automatically recommending rest if the risk of overwork increases. For instance, AI analyzes sensor data from equipment to detect early signs of abnormalities or the depletion of the lifespan of consumable parts, automatically generating maintenance plans. For instance, AI analyzes customer complaints or negative feedback, automatically generating optimal apologies or compensation plans, preventing recurrence, and providing feedback to the responsible party. For example, AI analyzes environmental data or surveillance camera footage to instantly detect anomalies in the work environment or abnormal operator behavior and issue alerts. AI also optimizes delivery routes or work schedules in real time, flexibly responding to traffic congestion or weather changes. By comprehensively implementing these functions, it becomes a system that comprehensively supports the operational efficiency, safety, customer satisfaction, and continuous growth of the logistics center. Specifically, this system does not operate independently; instead, it employs a distributed microservice architecture that synchronizes data in real time via a central integrated control server. This integrated control server possesses a meta-learning model for integrating the inference results output from each department. The data input to this model includes multi-dimensional combined tensors representing the overall state of the system, such as inventory vectors from the Goods Information Management Department, task allocation matrices from the Scheduling Optimization Department, and anomaly score arrays from the Fault Prediction Detection Department. For specific examples, input data includes scalar values ​​representing the current total stored weight within the center, average vital sign scores representing the fatigue level of operating operators, and percentage arrays representing the operating rates of various equipment. This system processes this input data in real time and calculates the overall optimization indicators for the center. The output data from this model consists of control vectors instructing the overall resource reallocation of the system, as well as parameter adjustment signals for each component. Specific examples of the output data include enhanced control values ​​instructing air conditioning output to specific storage areas, schedule change flags for advancing rest times, and Boolean values ​​triggering the recalculation of delivery routes. This system feeds the output control signals back to each subsystem as subsequent processing. Using reinforcement learning, the system continuously updates weights with the objective function of maximizing overall processing throughput and minimizing energy consumption. This system is not limited to local optimization but autonomously explores the global optimal solution.In use cases such as hub locations in large supply chains, this system delivers the technological benefits of drastically reduced operating costs and a high degree of self-healing capability in response to changes in the external environment.

[0087] (System Composition) The system described in this embodiment includes a cargo information management department, an operator management department, an equipment management department, a scheduling optimization department, a delivery route optimization department, an education plan proposal department, a customer response proposal department, and a fault prediction detection department. The cargo information management department meticulously registers, accumulates, and manages all cargo entering the logistics center, including cargo ID, cargo category, weight, dimensions, storage temperature conditions, scheduled arrival date, scheduled departure date, delivery destination, priority, special handling information, inbound / outbound history, past fault history, barcode or RFID tag information, packaging status, owner information, delivery provider information, cargo photographs, storage area information, handling history, inspection results, damage / contamination records, insurance coverage, and relevant legal and regulatory information. For example, in the case of refrigerated food, it records storage temperature and shelf life; in the case of pharmaceuticals, it records batch number and expiration date; and in the case of precision instruments, it records impact transmission data. Sensor data, anti-static countermeasures history, and, in the case of hazardous materials, compliance status and storage area restrictions are also managed. Automatic registration is performed using barcodes or RFID upon goods entering the warehouse, and automatic recording is also performed upon goods leaving the warehouse, enabling real-time inventory status and goods tracking. Inbound and outbound operations are prioritized based on goods priority and delivery deadlines, and storage areas are automatically allocated. Generative AI is used to analyze image data captured during goods inspection to automatically determine for damage or contamination; any anomalies are immediately notified to managers. The handling history of goods is recorded chronologically, allowing detailed tracking of which operator used which equipment and along which path. Specifically, this goods information management department constructs a hybrid processing architecture that links edge computing devices with cloud servers. To analyze image data captured during goods inspection, this system installs an image recognition model based on a deep residual network. The data input to this model consists of RGB image tensors acquired from a high-resolution camera and point cloud data from a depth sensor. For example, the input data includes a 1024×1024 pixel image matrix representing the appearance of the cardboard box, a 128-bit identification code obtained from an RFID reader, and acceleration vectors recorded by an impact sensor over the past 24 hours. The system performs denoising and contrast enhancement preprocessing on the input image. The model extracts feature maps of edges or textures from the image using convolutional layers. The output data from this model includes a score representing the probability of cargo damage, a segmentation mask representing the damaged area, and a cargo classification label. Specific examples of the output data include a damage probability value of 0.85 representing a dent in the outer box, a group of pixel coordinates of abnormal areas within a specific image, and a binary symbol indicating the need for refrigeration. If the output damage probability exceeds a threshold, the cargo information management department sends a rejection signal to the automated sorting machine as a follow-up process. The automated sorting machine physically removes the cargo to the inspection error area. This system completely eliminates omissions caused by human visual inspection.This system uses pattern recognition in a high-dimensional image feature space to instantly detect minute stains or packaging anomalies. In use cases such as pharmaceutical logistics or the transportation of precision instruments, this system achieves the technical effect of rigorous quality management and overwhelmingly high-speed inspection operations.

[0088] The operator management department manages operator IDs, names, qualifications, job categories, past work history, error history, skill evaluations, work schedules, health status, each operator's work efficiency and safety indicators, vital sign data obtained from wearable devices, attendance records, training and training history, commendation / disciplinary history, each operator's accident / fright report history, communication history between operators, and operators' desired schedules and leave application information in detail. For example, it automatically assigns tasks to ensure only forklift license holders can handle goods handling in specific areas, avoids assigning high-risk tasks to operators with a history of errors, obtains operators' health status and fatigue levels from wearable devices and recommends rest when there is a high risk of overwork, quantitatively evaluates each operator's work efficiency and safety indicators and utilizes this evaluation in training programs and staffing optimization, and analyzes operators' communication history to evaluate teamwork issues and leadership performance. Specifically, to process the continuously streaming biometric data from wearable devices, this operator management department possesses a time-series analysis model that combines a one-dimensional convolutional neural network and a long short-term memory network. The data input to this model is a multi-dimensional time-series vector obtained from sensors worn by operators. Specifically, the input data includes heart rate arrays sampled at 1-second intervals, motion waveform tensors from a triaxial accelerometer, and continuous data representing scalar values ​​indicating skin temperature variations. The system preprocesses this input data by applying a moving average filter to remove high-frequency noise. The model extracts latent features of operator fatigue or stress levels from the time-series data. The output data from this model represents real-time fatigue scores, the probability of decreased attention, and recommended rest periods. Specific examples of the output data include a value representing a fatigue score of 85 (from 0 to 100), a probability value of 0.75 indicating an increasing risk of fall, and a timestamp indicating an immediate 15-minute rest. If the output fatigue score exceeds a safety threshold, the operator management department sends an alarm signal to the scheduling optimization department as a follow-up action. The scheduling optimization department then immediately reassigns the operator's task to other standby personnel. This system eliminates the reliance on subjective observation by managers in existing labor management practices. This system scientifically detects early signs of overwork or accidents through objective and continuous analysis of biological data. In use cases such as warehouse operations in harsh environments or night shift scheduling, this system delivers the crucial technical benefits of preventing workplace accidents and maintaining labor productivity.

[0089] The Equipment Management Department manages detailed information such as equipment ID, equipment category, installation location, operating time, maintenance history, sensor data (temperature / humidity / vibration / current / power consumption, etc.), fault history, replacement history of consumable parts, operating efficiency and failure rate of each piece of equipment, equipment operating schedule, equipment utilization reservation status, equipment statutory inspection history, equipment manufacturer / model / manufacturing date, equipment warranty period, and equipment insurance status. For example, it monitors in real time the operating time or battery level of handling robots, temperature fluctuations in refrigerators, and abnormal vibrations of sorting machines. If any abnormal signs are detected, an alarm is immediately issued. Based on the equipment's operating status or maintenance history, it automatically proposes preventative maintenance or parts replacement opportunities. It analyzes the equipment's operating efficiency or failure rate and uses this as a basis for judging equipment investment or layout changes. Based on the equipment utilization reservation status, it optimizes work schedules or personnel allocation. Specifically, to process the massive sensor data collected from the industrial IoT device cluster in real time, the Equipment Management Department employs a federated learning architecture that combines edge-side inference and cloud-side relearning. For anomaly detection, the system uses a variational autoencoder and a generative adversarial network unsupervised learning model. The data input to this model consists of multivariate time-series tensors from a group of sensors installed on various devices. Specifically, the input data includes three-phase current waveform data from a conveyor belt motor, a spectrum representing the cooling fan speed, and stress vectors from strain sensors on pallet racks. The system applies a Fast Fourier Transform (FFT) to the input data, converting it from the time domain to the frequency domain. The model learns the data distribution during normal operation and calculates how much the input data deviates from this distribution. The output data from this model includes anomaly scores based on reconstruction errors, contribution vectors of sensors presumed to be the cause of the anomaly, and continuous values ​​representing the predicted remaining life until failure. Specific examples of the output data include a score of 0.9 indicating the occurrence of an anomaly, a weighted array indicating that vibration in a specific bearing is the primary cause of the anomaly, and numerical data indicating 48 hours remaining until failure. If the predicted remaining life is lower than a specified value, the equipment management department sends an API request to the automated ordering system for maintenance parts as a follow-up action. This system eliminates scheduled calendar-based maintenance, achieving complete condition-based maintenance. In use cases such as large-scale manufacturing lines or automated logistics centers, this system effectively reduces equipment downtime to the extreme and optimizes maintenance costs.

[0090] The scheduling optimization department uses generative AI to comprehensively analyze cargo, operator, and equipment information accumulated in the cargo information management department, operator management department, and equipment management department, as well as traffic and weather information obtained from external sources. It considers cargo type and priority, operator skills and qualifications, equipment operating and idle status, traffic congestion, and weather risks, automatically determining the optimal operator allocation, operation timing, and equipment utilization plan for each cargo. For example, it prioritizes outbound operations for cargo with near delivery deadlines or high-priority cargo; advances outbound times when traffic congestion is anticipated; prioritizes indoor storage areas when typhoons are approaching; and automatically plans batch processing to efficiently move multiple cargoes. Based on operator skills and qualifications and past work history, it assigns appropriate operators to handle heavy or hazardous materials, and automatically adjusts work plans based on equipment utilization and maintenance schedules. Specifically, to solve complex constraint satisfaction problems, this scheduling optimization department possesses an advanced computing engine that integrates a tensor network model with simulated quantum annealing algorithms and deep reinforcement learning agents. The data input to this model consists of a massive state matrix representing the current state of all resources within the center, and context vectors representing changes in the external environment. Specifically, the input data includes a multidimensional array representing the priority and processing requirements of 5000 unprocessed goods, a matrix representing the current location and skill level of 100 operators, and graph data representing real-time traffic congestion information for surrounding roads. This system formulates this input data into the state space of a Markov decision process. The model uses the policy gradient method to learn the optimal action value function that maximizes future cumulative rewards. The output data from this model is an action matrix representing the allocation of all resources at each time step, and a probability distribution of predicted task completion times. Specific examples of the output data include binary flags indicating that operator A is assigned to picking operations in area B, a node sequence representing the movement path of a specific handling robot, and a score of 0.95 representing the probability that the overall task will be completed before the scheduled time. The scheduling optimization unit sends the output action matrix as subsequent processing to the programmable logic controllers of each device and the operators' terminals. This system dynamically generates and updates schedules or work plans created by human planners over several hours, in milliseconds. In use cases involving rapid changes in demand or sudden absences, this system delivers an overwhelming technical advantage by minimizing resource idle time and maximizing throughput.

[0091] The delivery route optimization unit uses generative AI to automatically propose the shortest and safest delivery routes based on factors such as the delivery destination, vehicle loading status, traffic information, past delay history, weather information, vehicle fuel consumption / distance / maintenance status, driver skills and work status, available time slots at the delivery destination, geographical conditions of the delivery destination, vehicle loading restrictions, and the presence of special equipment. For example, it recalculates routes in real time to avoid congested or accident-prone roads and efficiently navigates multiple delivery destinations. It prioritizes shortest routes for refrigerated or urgent goods, automatically optimizes cargo assembly or delivery order to maximize vehicle loading efficiency, considers weather or road control information to select low-risk routes, and automatically formulates plans for scheduled or nighttime delivery based on available time slots at the delivery destination. Specifically, to address the dynamic traveling salesman problem and vehicle routing problems in real time, this delivery route optimization unit employs a reinforcement learning model combining graph attention networks and deep Q-networks. The data input to this model consists of directed graph data composed of delivery destination nodes and road edges, dynamic state vectors of each vehicle, and time-series tensors of weather / traffic. For example, the input data includes a matrix representing the latitude and longitude coordinates and specified time ranges of 50 delivery destinations, scalar values ​​representing the current load weight and remaining fuel of each truck, and a time series array representing the travel time of major road segments. This system calculates the distance matrix between nodes in the input data and performs preprocessing to initialize the graph structure. The model uses a reinforcement learning agent to explore the optimal node access order that minimizes total travel time and fuel consumption. The output data from this model consists of an optimized route permutation vector for each vehicle, predicted arrival times for each interval, and polygonal data for navigation. Specific examples of the output data include an index list representing the access order for vehicle A, a timestamp representing the predetermined arrival time at the next delivery destination, and a group of coordinate points for detour routes to avoid congestion. The system sends the output route data to the vehicle's onboard terminal in real time as subsequent processing, updating the driver's navigation screen. This system eliminates fixed delivery routes or path selections relying on driver intuition, providing mathematically optimal solutions based on high-dimensional dynamic data. In use cases such as last-mile delivery or wide-area supply chain transportation, this system achieves the clear technical effects of minimizing delivery delays, increasing vehicle uptime, and reducing carbon dioxide emissions.

[0092] The Education Program Proposal Department analyzes each operator's skill evaluation, past error history, customer feedback, changes in work content, training history, expectations and career plans, accident / fright reports, health status and vital signs data, and newly introduced equipment and business processes. Using generative AI, it automatically generates individually optimized education and training plans. For example, it proposes safe driving training or skills retraining for operators with frequent forklift operation errors; recommends customer etiquette training for operators with low customer ratings; focuses on basic work procedures or safety training for new operators; automatically proposes training on new work procedures when work content changes significantly; automatically assigns training on operating new equipment to relevant operators; and automatically proposes leadership training or management-oriented training based on the operator's career plan or desired position. Specifically, to achieve individualized learning optimization, the Education Program Proposal Department employs a deep learning architecture that integrates a knowledge tracking model and a content-based recommendation algorithm. The data input to this model consists of a time-series vector representing the operator's past task execution logs, a multidimensional tensor representing the context of errors, and a feature matrix of available educational content. Specifically, the input data includes a numerical array representing the shift in picking speed and error rate for a particular operator over the past three months, a scalar value representing the number of times the forklift was braked, and metadata representing the difficulty and time required for each training program. This system infers the operator's current potential skill level from the input data. The model uses a recurrent neural network to predict the skill improvement after attending specific educational sessions. The output data from this model includes a ranking list of optimized educational content for each operator, predicted skill improvement scores, and a timeline of learning schedules. Specific examples of the output data include a recommendation list prioritizing VR simulation training for safety confirmation steps, a predicted 20% reduction in error rate after attending the training, and a one-hour online learning schedule set for the next rest day. The educational plan proposal department then sends the output educational plans to the human resources management system for further processing, automatically securing training appointment slots. This system eliminates the need for standardized manual training, providing data-driven adaptive learning. In use cases such as logistics environments with high employee turnover or manufacturing industries requiring multi-skilled workers, this system delivers clear technical benefits, including shorter training periods, standardized work quality, and prevention of major accidents.

[0093] The Customer Response Proposal Department uses generative AI to analyze customer feedback. Based on issues such as delivery delays, damaged goods, quality issues, delivery errors, inadequate packaging, errors in delivery notes, delays in responding to inquiries, complaint content, survey results, and social media reviews, it proposes specific response strategies to sales personnel in the case of low ratings or complaints. These strategies include automatically generating apology proposals, compensation proposals, explanations of measures to prevent recurrence, retraining proposals for personnel, and proposals for regular customer follow-ups. For example, it automatically generates proposals for apologies for delivery delays and next-time discount coupons, or compensation guidelines and explanations of measures to prevent recurrence for damaged goods. In the case of high-rated customer feedback, it automatically generates thank-you emails or service proposals for outstanding customers. It also immediately notifies personnel when negative posts are detected on social media to facilitate rapid response. Furthermore, it analyzes customer inquiries and automatically extracts the best response from FAQs or manuals to provide to personnel. Specifically, this Customer Response Proposal Department possesses a dedicated large-scale language model, which is a pre-trained Transformer model for natural language understanding and generation fine-tuned using data from specific business domains. The data input to this model consists of text messages from customers, transcribed text of voice calls, and structured vectors representing customers' past purchasing actions. Specifically, input data includes a 400-word complaint expressing anger about damaged goods, scalar values ​​representing the number of past inquiries, and numerical data representing customer lifetime value. The system performs tokenization and positional encoding preprocessing on the input text. The model uses a self-attention mechanism to extract important keywords or contextual dependencies within the text. Output data from this model includes the customer's sentiment polarity score, inquiry intent classification labels, and a text tag column for the automatically generated response. Specific examples of output data include a sentiment score of 0.95 representing negativity, cluster labels representing product replacement requests, and a 500-word draft response containing a polite apology and specific return procedures. The customer service proposal department forwards the draft response to the customer support system's user interface for further processing and adds it to the operator's pending confirmation queue. This system eliminates existing automated responses based on simple combinations of fixed text, performing high-level natural language generation fully adapted to the context. This system demonstrates its effectiveness in addressing drastic reductions in lead times, alleviating operator workload, and decreasing customer churn in use cases such as large-scale e-commerce platforms or global logistics services.

[0094] The fault precursor detection unit uses generative AI to analyze real-time equipment sensor data and operator movement data obtained from the equipment management department and operator management department, as well as fault history and past accident patterns, environmental data such as temperature, humidity, noise, and illuminance at the site, abnormal behavior patterns extracted from on-site surveillance camera images, abnormal operator vital sign data, on-site fright reports, and lifespan prediction data of equipment consumable parts. It detects abnormal temperature increases and vibrations in equipment, unusual operator movement patterns, situations similar to past fault occurrences, rapid changes in the on-site environment, abnormal operator physical condition, and signs of impending depletion of equipment consumable parts. The system immediately sends alarm notifications to the management terminal, automatically generating and prompting necessary response strategies and attention alerts. For example, it notifies the manager of potential equipment malfunctions or deviations from operational procedures when the temperature of the handling robot rises sharply or when operators frequently move along unusual paths. It compares past failure patterns with current data, issuing alarms to prompt early intervention when the risk of recurrence is high. It automatically generates replacement recommendations when consumable parts are nearing the end of their lifespan. It issues alarms when ambient light or noise levels exceed baseline values, indicating an abnormal working environment. It notifies the manager of potential health risks when an operator's heart rate or body temperature shows abnormal values. Specifically, to simultaneously extract spatial and temporal anomaly patterns, this fault precursor detection unit uses a deep learning model that cascades a 3D convolutional neural network and a long short-term memory network. The data input to this model consists of synchronized video stream tensors from multiple surveillance cameras located on-site, and multivariate time-series arrays from environmental sensors. For example, the input data includes a sequence of RGB images with a resolution of 1920×1080, acquired at 30 frames per second, a heatmap matrix representing the temperature distribution within the space, and an acoustic feature vector representing noise level variations. This system calculates optical flow from the input video stream and performs preprocessing to extract the trajectories of dynamic objects. The model uses a latent spatial model learned from normal work scenes or equipment operating patterns to calculate the reconstruction error between the input data and the normal model. The output data from this model includes an anomaly probability score for each frame, bounding boxes of the spatial coordinates of the anomaly, and category labels for the predicted fault. Specific examples of the output data include an anomaly score of 0.98 representing early signs of a fire, a group of pixel coordinates representing a specific area in the upper right corner of the screen, and text labels indicating a worker has fallen. If the anomaly score exceeds a critical threshold, the fault precursor detection unit automatically activates the entire building's alarm system as a follow-up process, and simultaneously pushes a clipped video of the scene to the administrator's smartphone. This system surpasses the limits of human visual surveillance, integrating multiple sensor modalities to detect minute anomalies.In use cases such as hazardous materials handling facilities or unmanned warehouses operating 24 hours a day, this system delivers the crucial technical benefit of preventing catastrophic accidents and minimizing damage.

[0095] Specific examples of prompts read by generative AI when implementing this invention include: "Please propose the best work allocation based on today's scheduled incoming goods and operator skill sheets," "Please extract equipment requiring maintenance from equipment operating time and sensor data," "Please compare past failure history with current data, and generate an alarm if any abnormal signs are found," "Please automatically generate an apology and response strategy for low customer feedback," "Please optimize today's delivery route by considering traffic congestion information," "Please create individualized training plans based on each operator's error history and skill evaluation," and "Please extract abnormal behavior patterns from on-site surveillance camera footage, and notify if there is a risk." Specifically, this system has a dynamic prompt generation module using prompting engineering techniques to provide structured instructions to a large-scale language model. The data input to this module includes real-time system state vectors collected from various subsystems, natural language queries from users, and metadata representing task constraints. For example, the input data includes scalar values ​​representing the number of currently unprocessed tasks, JSON-formatted text representing the operating history of a specific equipment ID, and strings of system prompts specifying the output format. This system dynamically constructs context-aware prompt strings that are easy for the model to understand, based on these input data. The model then performs inference based on these prompts, generating the optimal solution strategy or text. Output data from this model can be executable code snippets, structured JSON data, or natural language responses. Specific examples of output data include SQL queries for updating operator schedules, parameter sets for adjusting anomaly detection thresholds, and natural language text forming apologies to customers. The system then passes the output data to a syntax parsing module for further processing, converting it into commands that the system can directly execute. This system eliminates static, rule-based command input, enabling advanced system control through a natural language interface. In use cases where even field managers without specialized programming knowledge can operate complex AI systems, this system significantly enhances ease of use and dramatically accelerates the decision-making process.

[0096] (Implementation steps) Step 1: Registration and Management of Cargo Information. For all cargo moving into the logistics center, the system inputs cargo ID, cargo category, weight, dimensions, storage temperature conditions, scheduled arrival date, scheduled departure date, delivery destination, priority, special handling information, inbound / outbound history, barcode or RFID tag information, packaging status, owner information, delivery provider information, cargo photos, storage area information, handling history, inspection results, damage and contamination records, insurance coverage, and relevant legal and regulatory information from the terminal. The cargo information management department centrally accumulates and manages this information on the server. For example, in the case of refrigerated food, storage temperature and shelf life are also managed; in the case of pharmaceuticals, batch number and expiration date are also managed; and in the case of precision instruments, impact sensor data is also managed. Specifically, in this step, the system performs distributed transaction processing to optimize data synchronization between edge devices and the cloud database. The data input to this system consists of image tensors from multispectral cameras located at the loading entrance and radio wave received intensity vectors from the RFID gateway. As a specific example, the input data includes a high-resolution image matrix simultaneously capturing multiple goods on a pallet, the inherent identifiers of the RFID tags attached to each item, and numerical arrays of received signal strength. This system applies object detection algorithms to the input images to preprocess and extract the bounding boxes of each item. The system then compares the extracted image features with the RFID data, associating the physical state of the goods with logical information. The output data from this system consists of structured records of standardized goods attributes and commit logs indicating writes to the database. Specific examples of the output data include JSON objects storing item IDs, weights, storage temperature conditions, etc., and insert commands for specific tables in the database. The system asynchronously replicates the output records to multiple nodes in a distributed database for subsequent processing. This system completely eliminates manual data input or individual barcode scanning. In environments with large volumes of goods moving at high speeds, this system prevents data loss or inconsistencies. In use cases such as large cross-docking delivery points, this system improves the throughput of inbound processing and ensures the real-time accuracy of inventory data.

[0097] Step 2: Registration and Management of Operator and Equipment Information. The Operator Management Department manages detailed operator information including ID, name, qualifications, applicable job categories, past work history, error history, skill evaluations, work schedules, health status, vital signs data, training / attendance history, commendation and disciplinary history, accident and shock report history, desired work schedules, and leave application information. The Equipment Management Department manages detailed equipment information including ID, equipment category, location, operating time, maintenance history, sensor data, fault history, replacement history of consumable parts, operating efficiency and failure rate, usage reservation status, statutory inspection history, warranty period, and insurance coverage. Specifically, in this step, to track the dynamic status changes of operators and equipment, the system constructs a real-time data pipeline combining an in-memory database and a stream processing engine. The data input to the system is a time-series stream of GPS coordinates and vital signs data sent from the operator's smart device, and an operating status vector output from the equipment's programmable logic controller. For example, the input data includes the operator's latitude and longitude information updated every second, numerical arrays representing heart rate and body surface temperature, and scalar values ​​representing the current position and remaining battery level of the handling robot. This system performs preprocessing on these high-frequency input data, including outlier filtering and timestamp synchronization. The system uses a stream processing engine to calculate moving averages or state transition events in real time. The output data from this system consists of updated state vectors and event messages notifying of state changes. Specific examples of output data include updated scores representing the operator's current fatigue level, state flags indicating equipment transition from standby to operation, and alarm messages indicating entry into a specific area. The system publishes the output event messages to a message queue system for further processing, allowing other subsystems to subscribe instantly. This system eliminates latency caused by batch processing, maintaining the up-to-date state of all resources on-site with millisecond precision. In use cases requiring dynamic task allocation or collision avoidance control in automated warehouses, this system effectively improves overall system responsiveness and provides complete transparency in resource management.

[0098] Step 3: Scheduling Optimization. The scheduling optimization department uses generative AI to comprehensively analyze cargo information, operator information, equipment information, traffic information, and weather information. It considers cargo type and priority, operator skills and qualifications, equipment operating and idle status, traffic congestion, and weather risks, automatically determining the optimal operator allocation, work timing, and equipment utilization plan for each cargo. Specific examples of prompts for the generative AI include "Please propose the best work allocation based on today's scheduled inbound cargo and operator skill list," and "Please optimize today's work schedule considering traffic congestion information." Specifically, in this step, the system drives an inference engine that hybridizes evolutionary algorithms and deep neural networks to solve multi-objective optimization problems. The data input to this model consists of multi-dimensional tensors representing the latest states of cargo, operators, and equipment constructed in Steps 1 and 2, and environmental context vectors obtained from external APIs. For example, the input data includes a matrix representing a list of cargo waiting to be dispatched and their expiration dates, an array representing the current fatigue level and location coordinates of each operator, and graph data representing real-time traffic congestion information of surrounding roads. This system integrates these input data to generate a potential vector representing the current system state. While satisfying multiple constraints, the model evaluates candidate schedules that minimize overall processing time in parallel. The output data from this model consists of an optimized task allocation matrix and timeline data representing the predetermined start / end times of each task. Specific examples of the output data include binary matrices representing pairs of operator IDs and cargo IDs, node sequences representing the movement paths of each handling robot, and timestamps representing the predicted completion time of the overall task. The system then sends the output schedule data to the execution management module for further processing, distributing specific work instructions to the equipment on-site. This system eliminates static planning based on human experience rules, dynamically and continuously recalculating schedules according to changes in conditions. In use cases such as peak demand or sudden failures, this system delivers the crucial technical benefit of eliminating bottlenecks and maximizing overall throughput.

[0099] Step 4: Delivery Route Optimization. The delivery route optimization department uses generative AI to automatically propose the shortest and safest delivery routes based on factors such as delivery destination, vehicle loading status, traffic information, past delay history, weather information, driver skills and work status, available time slots, and geographical conditions. Specific examples of prompts for the generative AI include "Please propose the best delivery route for today based on traffic congestion information and the list of delivery destinations," and "Please plan a priority delivery route for refrigerated goods." Specifically, in this step, the system executes a routing engine that integrates graph convolutional networks and reinforcement learning to handle dynamic traffic conditions and complex delivery constraints simultaneously. The data input to this model includes the geographical coordinate vectors of the delivery destination node group, tensors representing the loading status of each vehicle, and dynamic graph data containing real-time traffic congestion information. For example, the input data includes matrices representing the latitude and longitude of 50 delivery destinations and specified time periods, scalar values ​​representing the current load weight and remaining fuel of each truck, and time-series arrays representing link travel times on major roads. The system calculates the distance matrix between nodes from the input data and performs preprocessing to initialize the graph structure. This model uses a reinforcement learning agent to explore the optimal node access sequence that minimizes total travel time and fuel consumption. The output data from this model consists of an optimized route permutation vector for each vehicle, predicted arrival times for each interval, and polygonal data for navigation. Specific examples of the output data include an index list representing the access sequence for vehicle A, a timestamp indicating the scheduled arrival time at the next delivery destination, and a set of coordinate points for detour routes to avoid congestion. The system sends the output route data to the vehicle's onboard terminal in real time for further processing, updating the driver's navigation screen. This system eliminates fixed delivery routes or path selections relying on driver intuition, providing mathematically optimal solutions based on high-dimensional dynamic data. In use cases such as last-mile delivery or wide-area supply chain transportation, this system achieves the definite technical effects of minimizing delivery delays, increasing vehicle uptime, and reducing CO2 emissions.

[0100] Step 5: Automatic Proposal of Educational Plans. The Educational Plan Proposal Department analyzes each operator's skill evaluation, past error history, customer feedback, changes in job content, training history, career path, accident and shock reports, health status, and information on new equipment on-site, and uses generative AI to automatically generate individually optimized education and training plans. Specific examples of prompts for the generative AI to read include "Please create an individualized educational plan based on each operator's error history and skill evaluation" and "Please extract the training subjects accompanying the introduction of new equipment." Specifically, this Educational Plan Proposal Department converts operator attribute data and historical data into multi-dimensional numerical vectors. This Educational Plan Proposal Department possesses a large-scale Transformer-based language model specialized for natural language processing tasks. As preprocessing of the input data, this Educational Plan Proposal Department tokenizes the text data. This Educational Plan Proposal Department maps the tokenized data to an embedding vector space. This Educational Plan Proposal Department, for example, generates a 512-dimensional input tensor. The data input to this large-scale language model contains a time-series array representing the frequency of past errors by the operators. The input data to this large-scale language model includes sentiment scores from customer feedback. The input data also includes text vectors from the user manuals for new equipment. This large-scale language model uses a self-attention mechanism to calculate the hidden correlations between input elements. This large-scale language model performs matrix operations in high-dimensional space. The output of this large-scale language model is structured data containing identifiers of recommended educational modules. This large-scale language model outputs a probability distribution representing the suitability of each educational module. For example, this large-scale language model outputs 0.95 as a score for re-education on recommended safety confirmation steps. This large-scale language model outputs 0.88 as a recommended score for new equipment operation training. The educational program proposal department performs a threshold determination on the output probability distribution. The educational program proposal department extracts educational modules that exceed the threshold. The educational program proposal department sends the extracted educational modules to the schedule management module. The schedule management module checks against the operators' work schedules and automatically schedules training sessions. Through this process, the system develops objective educational plans that do not rely on the subjective opinions of managers. This system reduces the accident rate caused by insufficient operator skills. This system achieves optimal distribution of educational content while suppressing unnecessary consumption of computer resources. The large-scale language model is continuously fine-tuned using feedback data representing past educational outcomes. The system uses backpropagation to update the weights of the neural network. The system performs learning in a manner that minimizes the cross-entropy error function. Specific applications of this technology include operator training on production lines in manufacturing plants and safety and health education on construction sites.

[0101] Step 6: Automated Proposal of Customer Response Strategies. The Customer Response Proposal Department uses generative AI to analyze customer feedback and complaints, survey results, SNS reviews, and consultation content. In cases of low ratings or complaints, it automatically generates specific response strategies, including apology proposals, compensation proposals, explanations of measures to prevent recurrence, retraining proposals for responsible personnel, and follow-up proposals for the customer. Examples of prompts for the generative AI to read include "Please automatically generate an apology and response strategy for low customer feedback" and "Please propose a response plan for negative posts on SNS." Specifically, the Customer Response Proposal Department continuously collects unstructured text data via external interfaces. This department inputs the collected text data into a natural language processing engine. This engine features a hybrid model combining recurrent neural networks and Transformer architectures. The department performs preprocessing to remove noise from the input text. The preprocessed text is then converted into word embedding vectors. The data input to this hybrid model includes text tensors representing the content of the customer's complaint. The data input to this hybrid model includes numerical vectors representing the customer's past purchase history. The data input to this hybrid model also includes time-series data representing the dates and times of posts on social media. This hybrid model applies multi-layered self-attention computation to the input vectors. This hybrid model determines the sentiment polarity within the text. This hybrid model outputs classification labels representing the categories of response strategies. This hybrid model outputs a sequence of generated apology text. This hybrid model outputs numerical data representing the recommended compensation amount. This hybrid model, for example, outputs 0.92 as a probability score for proposing a product replacement. This hybrid model outputs 0.85 as a probability score for issuing the next discount coupon. The customer response proposal department inputs the output text sequence into the syntax parsing module. This syntax parsing module verifies the grammatical accuracy of the generated text. The customer response proposal department forwards the verified response strategy data to the automated email sending module. This automated email sending module instantly sends messages to the customer's contact information. Through this series of processes, this system completes initial response in milliseconds without human intervention. This system reduces the risk of customer dissatisfaction spreading on social media. This system prevents damage to brand value caused by delayed response. This hybrid model is constructed using supervised learning that employs past successful responses as data for correct answers. The system uses gradient descent to optimize the model's parameters. Specific applications of this technology include customer support in e-commerce platforms and the automation of call center operations in telecommunications operators.

[0102] Step 7: Fault Prediction Detection and Alarm Sending. The fault prediction detection unit uses generative AI to analyze equipment sensor data, operator movement data, fault history, accident modes, on-site environmental data, surveillance camera images, operator vital sign data, and consumable component lifespan prediction data. Upon detecting abnormal temperature increases and vibrations in the equipment, unusual operator movement patterns, situations similar to past fault occurrences, rapid changes in the on-site environment, abnormal operator health, or signs of impending depletion of consumable components, it immediately sends an alarm notification to the management terminal, automatically generating and prompting necessary response strategies and alerts. Specific examples of prompts for the generative AI to read include: "Please compare past fault history with current data; generate an alarm if any abnormal signs are found," and "Please detect abnormal signs from the equipment's sensor data." Specifically, this fault prediction detection unit receives real-time streaming data from multiple IoT devices installed on-site. This fault prediction detection unit possesses a multimodal neural network for integrating and processing the received heterogeneous mixed data. This fault prediction detection unit inputs time-series sensor data into a one-dimensional convolutional neural network. This fault precursor detection unit inputs image frames from the monitoring camera into a 3D convolutional neural network. The data input to this multimodal neural network includes a time-series array representing the temperature changes of the equipment. The data input to this multimodal neural network includes a numerical vector representing the operator's vital signs, such as heart rate. The data input to this multimodal neural network includes a spatial coordinate tensor representing the operator's movement trajectory. This multimodal neural network integrates the feature vectors extracted from each modality in a combination layer. This multimodal neural network executes an anomaly detection algorithm in the integrated high-dimensional feature space. The multimodal neural network outputs a continuous score representing the probability of faults in each piece of equipment. This multimodal neural network outputs a classification label representing the type of anomaly. For example, this multimodal neural network outputs 0.89 as the probability of vibration anomalies caused by bearing wear in a motor. This multimodal neural network outputs 0.95 as the operator's heatstroke risk score. This fault precursor detection unit determines whether the output probability score exceeds a preset safety threshold. If the fault precursor detection unit detects that the threshold has been exceeded, it immediately initiates an interruption process. This fault precursor detection unit activates a generative AI module to generate specific avoidance step text. The unit then pushes the generated text and warning signs to the management terminal. Through this process, the system achieves preventative maintenance rather than reactive response. This system minimizes production line downtime caused by sudden equipment stoppages. It eliminates data processing latency, enabling high-speed inference in edge computing environments. This multimodal neural network utilizes unsupervised learning using an autoencoder based on normal data to calculate anomaly levels. A specific application of this technology is equipment monitoring systems in large chemical plants.As a specific application area of ​​this technology, a safety management system for workers on construction sites can be cited as an example.

[0103] (specific use case) For example, imagine a scenario where a large quantity of diverse goods, including refrigerated food, precision instruments, hazardous materials, and general merchandise, are simultaneously received into a logistics center. The Cargo Information Management Department uses barcodes or RFID tags to register detailed information for each item, including its ID, category, weight, dimensions, storage temperature, scheduled arrival date, scheduled departure date, delivery destination, priority, special handling information, inspection images, damage and contamination records, and insurance coverage. For refrigerated food, storage temperature and shelf life are also managed; for precision instruments, impact sensor data and anti-static measures history are also managed; and for hazardous materials, compliance with regulations and storage area restrictions are also managed. The Operations Management Department manages each operator's forklift driving qualifications, hazardous materials handling qualifications, past work history, error history, skills evaluations, work schedules, health status, vital signs data, and training / attendance history. The Equipment Management Department manages detailed information on handling robots, automated sorting machines, refrigerators, surveillance cameras, conveyors, etc., including equipment IDs, operating times, maintenance history, sensor data, fault history, and replacement history of consumable parts. Specifically, this system is built on a digital twin platform that recreates the complex dynamic environment of the logistics center in digital space. The Cargo Information Management Department analyzes the intensity data of radio waves transmitted at high frequencies from RFID readers. It uses a Kalman filter to estimate the three-dimensional position coordinates of the cargo in real time. The Cargo Information Management Department stores the acquired cargo attribute data in a normalized group of tables in a relational database. The Cargo Information Management Department inputs high-resolution image data acquired from inspection cameras into a convolutional neural network. The data input to this convolutional neural network includes a 124-pixel square RGB image tensor. The data input to this convolutional neural network includes a two-dimensional array containing depth information of the cargo's outer packaging. This convolutional neural network detects minute scratches or dents within an image. As output, it provides a binary label indicating the presence or absence of damage. It also outputs the bounding box coordinates of the damaged area. For example, it outputs 0.98 as a probability score representing the corner crushing of a cardboard box. The operator management department collects time-series data on heart rate and body surface temperature from wearable devices. This data is then input into a long short-term memory network to calculate a fatigue score. The equipment management department converts the motor current and vibration frequency data of the handling robot to the frequency domain. It uses a fast Fourier transform to extract features. This system accumulates this massive amount of heterogeneous data in a unified data lake. It generates a unified format of multidimensional vectors for subsequent optimization algorithms. This data integration prevents information silos. It reduces data communication overhead between subsystems. It visualizes the operational status of physical logistics resources with millisecond precision. A specific application of this technology is the automated sorting system for air cargo in international hub airports.As a specific application area of ​​this technology, the integrated management of fulfillment centers for large-scale e-commerce can be cited as an example.

[0104] The scheduling optimization department uses generative AI to comprehensively analyze cargo priority, delivery deadlines, operator skills and qualifications, equipment operating status, traffic information, and weather information. For example, by providing a prompt such as "Please propose the best work allocation based on today's scheduled inbound goods and operator skill tables," it automatically assigns refrigerated food to qualified operators responsible for refrigerated areas, hazardous materials to those with hazardous materials qualifications, and heavy objects to operators with high physical fitness ratings. Simultaneously, it optimizes the utilization plans of handling robots and sorting machines. In the event of anticipated traffic congestion or severe weather, it automatically adjusts outbound times and work sequences to minimize delivery delay risks. Specifically, this scheduling optimization department possesses a deep reinforcement learning model for solving constrained optimization problems. The department defines the state space within the logistics center based on Markov decision processes. The data input to this deep reinforcement learning model includes numerical vectors representing the remaining time until the delivery deadline of each cargo. The data input to this deep reinforcement learning model also includes matrix data representing the current location and skill level of operators. The data input to this deep reinforcement learning model includes time-series arrays of precipitation probability and congestion length obtained from an external API. This deep reinforcement learning model employs an Actor-Critic architecture. It calculates the optimal action value from the state space performance. As output, it outputs a task assignment matrix for each operator. It also outputs a sequence of nodes representing the routing paths of the handling robots. For example, it outputs a command instructing operator A to pick goods in the hazardous materials area. It outputs a control signal instructing handling robot B to move to the refrigerated area. The scheduling optimization unit converts the output assignment matrix into control protocols for each device. This unit then transmits the converted control signals to the terminals of each operator and the handling robots via a wireless communication network. The system recalculates the schedule in real time based on dynamic environmental changes. This system completely eliminates manual assignment work performed by human schedulers. It minimizes operator downtime and dramatically increases the overall throughput of the facility. This deep reinforcement learning model is learned through millions of trials in a simulated environment to maximize the reward function. This system updates weights in a way that minimizes delay penalties and energy consumption. Specific applications of this technology include the automated scheduling of container handling operations in port terminals and the just-in-time delivery management of parts supply in large-scale manufacturing plants.

[0105] The delivery route optimization department uses generative AI to analyze data based on a list of delivery destinations, vehicle loading status, traffic information, past delay history, weather information, driver skills and attendance, and available delivery times for each destination. It provides suggestions such as "Please propose the best delivery route for today based on traffic congestion information and the delivery destination list," prioritizing the delivery of refrigerated goods via the shortest route. It recalculates routes for efficient travel to multiple delivery destinations in real time and optimizes loading efficiency and delivery order. Specifically, this delivery route optimization department employs a graph neural network to solve the traveling salesman problem and vehicle routing problems. The department models the road network as a directed graph consisting of nodes and edges. The data input to this graph neural network includes a two-dimensional tensor representing the geographic coordinates of each delivery destination. It also includes a numerical vector representing the real-time average vehicle speed of each road segment. Finally, it includes an array of scalar values ​​representing the ratio of each vehicle's current loaded weight to its maximum capacity. The graph neural network uses a message-passing algorithm to aggregate information between adjacent nodes. Finally, it extracts the topological features of the entire graph. This graph neural network outputs a vector representing the optimal order in which each vehicle should visit the nodes. The output also represents time-series data indicating the estimated arrival times at each delivery destination. For example, it outputs path data indicating a detour from node one to node five to vehicle C. It outputs 0.99 as a probability score for completing delivery within the temperature maintenance limit of refrigerated goods. The delivery route optimization unit converts the output path data into a GPS coordinate sequence for the navigation system. This unit then publishes the converted data to the onboard terminals of each delivery vehicle in real time. When a sudden traffic congestion is detected, the system recalculates alternative routes in milliseconds. The system reduces vehicle fuel consumption and minimizes carbon dioxide emissions. Mathematically, the system minimizes the risk of penalties for delivery delays. This graph neural network optimizes edge weights using supervised learning with vast amounts of past driving history data. Specific applications of this technology include dynamic routing for last-mile delivery in urban areas and vehicle allocation planning systems for long-haul trucking covering wide areas.

[0106] The Education Program Proposal Department uses generative AI to analyze each operator's error history, skill evaluations, customer feedback, accident and near-miss incident reports, health status, and information on new equipment on-site. By providing prompts such as "Please develop an individualized education plan based on each operator's error history and skill evaluation," the department automatically assigns safe driving training to operators with frequent forklift operation errors, customer etiquette training to operators with low customer evaluations, and basic work procedures and safety training to new operators. Specifically, the department possesses a deep learning-based recommendation system that integrates multi-faceted operator evaluation data to quantify potential skill gaps. The department employs a hybrid recommendation algorithm that combines collaborative filtering and content-based filtering. The data input to the recommendation system includes one-hot vectors representing the locations of past operator errors, multi-dimensional vectors embedding near-miss incident reports in natural language text, and normalized numerical arrays representing the operator's age and years of experience. This recommendation system uses a multilayer perceptron to learn the nonlinear interactions between input vectors. It calculates the inner product of the operator's feature space and the feature space of the educational content. As output, it provides a ranking list of suitability scores for each educational content. It also outputs numerical data representing the estimated learning time required until the course is completed. For example, it outputs 0.94 as a recommendation score for a VR simulation training on forklift slewing operations and 0.87 as a recommendation score for a complaint handling role-playing training. The educational program proposal department extracts the higher-order content from the output ranking list. This department automatically sends the URI of the extracted content to the operator's personal terminal. This system eliminates monotonous group training, providing adaptive learning tailored to individual weaknesses. It reduces the time cost of education while improving compliance with on-site safety standards. It eliminates cognitive biases inherent in human managers manually developing educational plans. The system continuously improves recommendation accuracy through reinforcement learning, using post-course test scores and error reduction rates as reward signals. Specific applications of this technology include skills enhancement support systems for nurses in healthcare institutions and automated training systems for part-time employees in multi-store retail businesses.

[0107] The Customer Response Proposal Department uses generative AI to analyze customer feedback and complaints, survey results, social media reviews, and inquiries. It provides prompts such as "Automatically generate apologies and response strategies for low customer feedback," automatically generating apologies and next-time discount coupons for delivery delays, compensation guidelines and preventative measures for damaged goods, and rapid response plans for negative social media posts, all of which are then sent to the responsible personnel. Specifically, this department possesses a processing pipeline that integrates a large-scale language model optimized for natural language generation tasks and a rule-based expert system. The department uses named entity recognition algorithms to identify customer names and order numbers from the input text data. The data input to this large-scale language model includes text sequences describing customer complaints, time-series data representing the historical delivery status of the corresponding orders, and contextual data vectorized from the company's official response guidelines. The large-scale language model uses Transformer decoder blocks to autoregressively generate natural, context-appropriate response text. This large-scale language model outputs string data for draft articles sent to customers. It also outputs integer scores from one to ten representing the urgency of the response. For example, it outputs text containing apologies for delivery delays and codes for 20% discount coupons. It also outputs text containing a link to a return label for damaged goods. The customer response proposal department inputs the draft articles into an expert system. This expert system uses regular expression matching to verify the absence of legally risky terms. The customer response proposal department records the verified response strategies in the customer management database. A pop-up notification is displayed on the representative's dashboard. This system reduces the lead time from complaint occurrence to initial response prompts to mere seconds. It reduces the workload of representatives and ensures consistent response quality. It statistically significantly reduces customer churn. The large-scale language model is fine-tuned using the response history of past high-performing customer support representatives. A specific application of this technology is a real-time request response system from hotel guests. As a specific application area of ​​this technology, the automation of technical support windows for software services can be cited as an example.

[0108] The fault precursor detection unit uses generative AI to analyze equipment sensor data, operator movement data, fault history, accident modes, on-site environmental data, surveillance camera images, operator vital sign data, and consumable component lifespan prediction data. By providing prompts such as "Compare with past fault history and current data; generate an alarm if any abnormal signs are found," it instantly detects abnormal temperature rises and vibrations in the handling robot, abnormal operator movement, rapid changes in the on-site environment, signs of consumable component lifespan depletion, and abnormal operator physical conditions. It then sends alarm notifications to the management terminal, automatically generating and prompting necessary response strategies and attention alerts. Specifically, this fault precursor detection unit possesses a distributed machine learning architecture that combines feature quantities processed once at the edge device side with high-level inference at the cloud side. This fault precursor detection unit executes an autoencoder network specializing in anomaly detection for time-series data. The data input to this autoencoder network includes numerical tensors from the triaxial accelerometers of the handling robot's joint motors. The data input to this autoencoder network also includes time-series arrays of skin temperature and pulse data obtained from the operator's smartwatch. The data input to this autoencoder network contains vectors of environmental data acquired from temperature and humidity sensors within the factory. This autoencoder network compresses the input data into a low-dimensional latent space. This autoencoder network reconstructs the original data from the latent space. As output, this autoencoder network outputs anomaly scores representing continuous values ​​of the reconstruction error between the input data and the reconstructed data. This autoencoder network outputs identifiers of sensors presumed to be the cause of the anomaly. For example, this autoencoder network outputs an anomaly score exceeding a threshold of 0.91 for a conveyor belt drive motor. This autoencoder network outputs 0.85 as a score representing abnormal operator dwell time. When the output anomaly score exceeds a specified value, this fault prediction detection unit invokes a large-scale language model. This large-scale language model receives the anomaly score and past maintenance records as input. This large-scale language model generates text data representing specific inspection steps. This fault prediction detection unit sends the generated text data and an emergency stop command to the control system. This system mathematically captures the signs of minute changes imperceptible to humans. This system prevents major workplace accidents and large-scale equipment damage before they occur. This system reduces unnecessary periodic maintenance, enabling the transition to condition-based maintenance. This autoencoder network is constructed through unsupervised learning using only data from normal operation. Specific applications of this technology include anomaly monitoring systems for reactor cooling systems in nuclear power plants, and predictive maintenance systems for vehicle status in fleet management of autonomous vehicles.

[0109] In this way, the system of the present invention utilizes advanced analysis, optimization, and automatic proposals of generative AI in all business processes of cargo management, operator allocation, equipment operation, work scheduling, delivery planning, education and training, customer response, and fault prevention at the logistics center site. This comprehensively supports on-site efficiency, risk reduction, improved customer satisfaction, enhanced safety, cost reduction, and reduced workload for on-site managers and operators. Specifically, the system possesses an orchestration engine that centrally controls multiple independent neural network modules. This orchestration engine asynchronously processes the massive data streams generated from each business process via a central message broker. The data input to this orchestration engine includes tensor groups of inference results output from each subsystem. The data input to this orchestration engine includes vectors of system indicators representing the overall resource utilization rate of the system. The data input to this orchestration engine includes time-series arrays of macroeconomic indicators and meteorological data representing changes in the external environment. This orchestration engine uses reinforcement learning algorithms to explore the global optimal solution for the entire system. As output, this orchestration engine outputs a control matrix representing the dynamic allocation ratio of computing resources for each subsystem. This orchestration engine outputs predicted values ​​of the overall system performance indicators. This orchestration engine, for example, outputs a control signal that increases GPU resource allocation to the fault precursor detection module by 30%. This orchestration engine outputs the numerical data of 100,000 items as a predicted value for the next day's logistics throughput. Based on the output control matrix, this system automatically scales the number of replicas of containerized applications. This system achieves comprehensive optimization of the entire facility, rather than local optimization, through this autonomous feedback loop. This system solves complex multivariate optimization problems in real time, exceeding the cognitive limits of human managers. This system maximizes the efficiency of hardware resource utilization, dramatically reducing the cost of cloud computing. This system employs a federated learning approach that links the learning processes of various modules. This system improves the generalization performance of the model while protecting data privacy. Specific applications of this technology include integrated management platforms for urban infrastructure in smart cities. Specific applications of this technology include enterprise resource planning systems that optimize the overall global supply chain.

[0110] The specific processing unit 290 sends the result of the specific processing obtained in Embodiment 1 or Application Example 1 described above 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 voice representing the user's input on the result of the specific processing. The control unit 46A sends the voice data representing the user's 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 voice data.

[0111] Data generation model 58 is what is known as generative AI (Artificial Intelligence).

[0112] One example of data generation model 58 includes ChatGPT (registered trademark) (Internet search).<URL: https: / / openai.com / blog / chatgpt> Generative AI, etc.

[0113] The data generation model 58 is obtained by performing deep learning on a neural network. A prompt containing instructions is input to the data generation model 58, along with inference data such as speech data representing speech, text data representing text, and image data representing images (e.g., data from still images or moving images). The data generation model 58 infers the input inference data based on the instructions shown in the prompt and outputs the inference result in one or more data forms, such as speech data, text data, and image data. The data generation model 58 may include, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the aforementioned specific processing while using the data generation model 58. The data generation model 58 may also be a fine-tuned model to output inference results from prompts without instructions; in this case, the data generation model 58 can output inference results from prompts without instructions. The data processing device 12, etc., includes multiple data generation models 58, including AI other than generative AI. AI other than generative AI includes, but is not limited to, linear regression, logistic regression, decision trees, random forests, support vector machines (SVM), k-means clustering, convolutional neural networks (CNN), recurrent neural networks (RNN), generative adversarial networks (GAN), or Naive Bayes. Furthermore, AI can also act as an AI agent. Additionally, when the processing described above is performed by AI, this processing may be partially or entirely performed by AI, but is not limited to this example. Moreover, processing performed by AI, including generative AI, can be replaced by rule-based processing, and vice versa.

[0114] Furthermore, the processing performed by the aforementioned data processing system 10 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart device 14, but it can also be executed jointly by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart device 14. Additionally, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the smart device 14 or external devices, and the smart device 14 acquires or collects information required for processing from the data processing device 12 or external devices.

[0115] In the above embodiments, examples of specific processing performed by the data processing device 12 are given, but the technology disclosed herein is not limited to this, and specific processing may also be performed by the smart device 14.

[0116] [Second Implementation] Figure 3 An example of the configuration of the data processing system 210 according to the second embodiment is shown.

[0117] like Figure 3 As shown, the data processing system 210 includes a data processing device 12 and smart glasses 214. One example of the data processing device 12 is a server.

[0118] The data processing apparatus 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 is an example of a "computer" as understood in this disclosure. The computer 22 includes a processor 28, RAM 30, and memory 32. The processor 28, RAM 30, and memory 32 are connected to a bus 34. Furthermore, the database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. An example of the network 54 includes a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[0119] The smart glasses 214 include a computer 36, a microphone 238, a speaker 240, a camera 42, and a communication I / F 44. The computer 36 includes a processor 46, RAM 48, and memory 50. The processor 46, RAM 48, and memory 50 are connected to a bus 52. Furthermore, the microphone 238, speaker 240, camera 42, and communication I / F 44 are also connected to the bus 52.

[0120] Microphone 238 receives instructions from user 20 by receiving the sounds emitted by user 20. Microphone 238 captures the sounds emitted by user 20, converts the captured sounds into speech data, and outputs it to processor 46. Speaker 240 outputs sound according to the instructions from processor 46.

[0121] Camera 42 is a small digital camera equipped with an optical system such as a lens, aperture and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, used to capture the area around user 20 (e.g., the shooting range defined by an angle of view equivalent to the field of vision of an average healthy person).

[0122] Communication I / F 44 is connected to network 54. Communication I / F 44 and 26 are responsible for the transmission and reception of various information between processor 46 and processor 28 via network 54. The transmission and reception of various information between processor 46 and processor 28 using communication I / F 44 and 26 is performed in a secure state.

[0123] Figure 4 An example of the main functions of the data processing device 12 and the smart glasses 214 is shown. Figure 4 As shown, in the data processing device 12, specific processing is performed by the processor 28. The memory 32 stores a specific processing program 56.

[0124] The specific processing program 56 is an example of a "program" as disclosed herein. The processor 28 reads the specific processing program 56 from the memory 32 and executes the read specific processing program 56 on the RAM 30. Specific processing is implemented by the processor 28 operating as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0125] The memory 32 stores a data generation model 58 and an emotion-specific model 59. The data generation model 58 and the emotion-specific model 59 are used by the specific processing unit 290. The specific processing unit 290 can use the emotion-specific model 59 to infer the user's emotions and perform specific processing based on the user's emotions. The emotion inference function using the emotion-specific model 59 (emotion-specific function) includes various inferences and predictions related to the user's emotions, but is not limited to this example. Furthermore, emotion inference and prediction also include, for example, emotion analysis (analysis).

[0126] In the smart glasses 214, the processor 46 performs the receive / output processing. The memory 50 stores the receive / output program 60. The processor 46 reads the receive / output program 60 from the memory 50 and executes the read receive / output program 60 on the RAM 48. The receive / output processing is implemented by the processor 46 operating as a control unit 46A based on the receive / output program 60 executed on the RAM 48. Furthermore, the smart glasses 214 may also have the same data generation model and emotion-specific model as the data generation model 58 and the emotion-specific model 59, and use these models to perform the same processing as the specific processing unit 290.

[0127] Next, the specific processing performed by the specific processing unit 290 of the data processing device 12 will be described. Each part of the system described below is implemented by the data processing device 12 and the smart glasses 214. 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".

[0128] (Example 1) Since the specific processing flow is the same as that in Embodiment 1 described in the first embodiment above, the description is omitted.

[0129] (Application Example 1) Since the specific processing flow is the same as that in Application Example 1 described in the first embodiment above, the description is omitted.

[0130] The specific processing unit 290 sends the result of the specific processing obtained in Embodiment 1 or Application Example 1 described above 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 voice representing the user's input on the specific processing result. The control unit 46A sends the voice data representing the user's input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the voice data.

[0131] Furthermore, the processing performed by the aforementioned data processing system 10 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart glasses 214, but it can also be executed jointly by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart glasses 214. Additionally, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the smart glasses 214 or external devices, and the smart glasses 214 acquires or collects information required for processing from the data processing device 12 or external devices.

[0132] In the above embodiments, examples of specific processing performed by the data processing device 12 are given, but the technology disclosed herein is not limited to this, and specific processing may also be performed by the smart glasses 214.

[0133] [Third Implementation] Figure 5 An example of the configuration of the data processing system 310 according to the third embodiment is shown.

[0134] like Figure 5 As shown, the data processing system 310 includes a data processing device 12 and a head-mounted terminal 314. An example of the data processing device 12 is a server.

[0135] The data processing apparatus 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 is an example of a "computer" as understood in this disclosure. The computer 22 includes a processor 28, RAM 30, and memory 32. The processor 28, RAM 30, and memory 32 are connected to a bus 34. Furthermore, the database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. An example of the network 54 includes a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[0136] The head-mounted terminal 314 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication I / F 44, and a display 343. The computer 36 includes a processor 46, RAM 48, and memory 50. The processor 46, RAM 48, and memory 50 are connected to a bus 52. Furthermore, the microphone 238, speaker 240, camera 42, display 343, and communication I / F 44 are also connected to the bus 52.

[0137] Microphone 238 receives instructions from user 20 by receiving the sounds emitted by user 20. Microphone 238 captures the sounds emitted by user 20, converts the captured sounds into speech data, and outputs it to processor 46. Speaker 240 outputs sound according to the instructions from processor 46.

[0138] Camera 42 is a small digital camera equipped with an optical system such as a lens, aperture and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, used to capture the area around user 20 (e.g., the shooting range defined by an angle of view equivalent to the field of vision of an average healthy person).

[0139] Communication I / F 44 is connected to network 54. Communication I / F 44 and 26 are responsible for the transmission and reception of various information between processor 46 and processor 28 via network 54. The transmission and reception of various information between processor 46 and processor 28 using communication I / F 44 and 26 is performed in a secure state.

[0140] Figure 6 An example of the main functions of the data processing device 12 and the head-mounted terminal 314 is shown. Figure 6 As shown, in the data processing device 12, specific processing is performed by the processor 28. The memory 32 stores a specific processing program 56.

[0141] The specific processing program 56 is an example of a "program" as disclosed herein. The processor 28 reads the specific processing program 56 from the memory 32 and executes the read specific processing program 56 on the RAM 30. Specific processing is implemented by the processor 28 operating as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0142] The memory 32 stores a data generation model 58 and an emotion-specific model 59. The data generation model 58 and the emotion-specific model 59 are used by the specific processing unit 290.

[0143] In the head-mounted terminal 314, the processor 46 performs the receiving and output processing.

[0144] The memory 50 stores a receive / output program 60. The processor 46 reads the receive / output program 60 from the memory 50 and executes the read receive / output program 60 on the RAM 48. The receive / output processing is implemented by the processor 46 operating the control unit 46A based on the receive / output program 60 executed on the RAM 48.

[0145] Next, the specific processing performed by the specific processing unit 290 of the data processing device 12 will be described. Each part of the system described below is implemented by the data processing device 12 and the head-mounted terminal 314. In the following description, the data processing device 12 will be referred to as the "server" and the head-mounted terminal 314 will be referred to as the "terminal".

[0146] (Example 1) Since the specific processing flow is the same as that in Example 1 described in the first embodiment above, the description is omitted.

[0147] (Application Example 1) Since the specific processing flow is the same as that in Application Example 1 described in the first embodiment above, the description is omitted.

[0148] The specific processing unit 290 sends the result of the specific processing obtained in Embodiment 1 or Application Example 1 described above to the head-mounted terminal 314. In the head-mounted terminal 314, the control unit 46A causes the speaker 240 and the display 343 to output the result of the specific processing. The microphone 238 acquires voice representing the user's input on the specific processing result. The control unit 46A sends the voice data representing the user's input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the voice data.

[0149] Furthermore, the processing performed by the aforementioned data processing system 10 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the head-mounted terminal 314, but it can also be executed jointly by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the head-mounted terminal 314. Additionally, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the head-mounted terminal 314 or external devices, and the head-mounted terminal 314 acquires or collects information required for processing from the data processing device 12 or external devices.

[0150] In the above embodiments, examples of specific processing performed by the data processing device 12 are given, but the technology disclosed herein is not limited to this, and specific processing may also be performed by the head-mounted terminal 314.

[0151] [Fourth Implementation] Figure 7 An example of the configuration of the data processing system 410 according to the fourth embodiment is shown.

[0152] like Figure 7 As shown, 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.

[0153] The data processing apparatus 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 is an example of a "computer" as understood in this disclosure. The computer 22 includes a processor 28, RAM 30, and memory 32. The processor 28, RAM 30, and memory 32 are connected to a bus 34. Furthermore, the database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. An example of the network 54 includes a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[0154] Robot 414 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication I / F 44, and a control object 443. Computer 36 includes a processor 46, RAM 48, and memory 50. The processor 46, RAM 48, and memory 50 are connected to a bus 52. Furthermore, the microphone 238, speaker 240, camera 42, control object 443, and communication I / F 44 are also connected to the bus 52.

[0155] Microphone 238 receives instructions from user 20 by receiving the sounds emitted by user 20. Microphone 238 captures the sounds emitted by user 20, converts the captured sounds into speech data, and outputs it to processor 46. Speaker 240 outputs sound according to the instructions from processor 46.

[0156] Camera 42 is a small digital camera equipped with an optical system such as a lens, aperture and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, used to capture the area around user 20 (e.g., the shooting range defined by an angle of view equivalent to the field of vision of an average healthy person).

[0157] Communication I / F 44 is connected to network 54. Communication I / F 44 and 26 are responsible for the transmission and reception of various information between processor 46 and processor 28 via network 54. The transmission and reception of various information between processor 46 and processor 28 using communication I / F 44 and 26 is performed in a secure state.

[0158] The controlled object 443 includes a display device, LEDs for the eyes, and motors for driving the arms, hands, and feet. The posture and movements of the robot 414 are controlled by controlling the motors for the arms, hands, and feet. Some of the robot 414's emotions can be expressed by controlling these motors. In addition, facial expressions of the robot 414 can also be expressed by controlling the illumination state of the LEDs for the robot 414's eyes.

[0159] Figure 8 An example of the main functions of the data processing device 12 and the robot 414 is shown. For example... Figure 8 As shown, in the data processing device 12, specific processing is performed by the processor 28. The memory 32 stores a specific processing program 56.

[0160] The specific processing program 56 is an example of a "program" as disclosed herein. The processor 28 reads the specific processing program 56 from the memory 32 and executes the read specific processing program 56 on the RAM 30. Specific processing is implemented by the processor 28 operating as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0161] The memory 32 stores a data generation model 58 and an emotion-specific model 59. The data generation model 58 and the emotion-specific model 59 are used by the specific processing unit 290.

[0162] In robot 414, the processor 46 performs the receive / output processing. The memory 50 stores the receive / output program 60. The processor 46 reads the receive / output program 60 from the memory 50 and executes the read receive / output program 60 on RAM 48. The receive / output processing is implemented by the processor 46 operating the control unit 46A based on the receive / output program 60 executed on RAM 48.

[0163] Next, the specific processing performed by the specific processing unit 290 of the data processing device 12 will be described. Each part of the system described below is implemented by the data processing device 12 and the robot 414. In the following description, the data processing device 12 will be referred to as the "server" and the robot 414 will be referred to as the "terminal".

[0164] (Example 1) Since the specific processing flow is the same as that in Example 1 described in the first embodiment above, the description is omitted.

[0165] (Application Example 1) Since the specific processing flow is the same as that in Application Example 1 described in the first embodiment above, the description is omitted.

[0166] The specific processing unit 290 sends the result of the specific processing obtained in Embodiment 1 or Application Example 1 described above 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 voice representing the user's input on the result of the specific processing. The control unit 46A sends the voice data representing the user's input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the voice data.

[0167] Furthermore, the processing performed by the data processing system 10 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the robot 414, but it can also be executed jointly by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the robot 414. Additionally, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the robot 414 or external devices, and the robot 414 acquires or collects information required for processing from the data processing device 12 or external devices.

[0168] In the above embodiments, examples of specific processing performed by the data processing device 12 are given, but the technology disclosed herein is not limited to this, and specific processing may also be performed by the robot 414.

[0169] Furthermore, the emotion-specific model 59, serving as an emotion engine, can determine the user's emotion based on a specific mapping. Specifically, the emotion-specific model 59 can determine the user's emotion based on an emotion graph that serves as a specific mapping (see...). Figure 9 The robot's emotions can be determined by the emotion-specific model 59. In addition, the emotion-specific model 59 can also determine the robot's emotions in the same way, and the specific processing unit 290 can also perform specific processing using the robot's emotions.

[0170] Figure 9This is a diagram representing an emotion map 400 that maps various emotions. In the emotion map 400, emotions are arranged radially from the center in concentric circles. The closer to the center of the concentric circles, the more primitive the emotion is. Further out on the concentric circles, emotions are arranged representing states or actions arising from mood. Emotion is a concept that includes both feelings and mental states. To the left of the concentric circles, emotions generated by reactions occurring in the brain are arranged roughly. To the right of the concentric circles, emotions guided by situational judgments are arranged roughly. Above and below the concentric circles, emotions generated by reactions occurring in the brain and guided by situational judgments are arranged roughly. Furthermore, the emotion of "pleasure" is arranged above the concentric circles, and the emotion of "unpleasantness" is arranged below. Thus, in the emotion map 400, various emotions are mapped according to the structure of emotion generation, while easily generated emotions are mapped nearby.

[0171] These emotions are distributed at the 3 o'clock position on the Emotion Chart 400, and usually fluctuate between peace and unease. In the right half of the Emotion Chart 400, because situational awareness is more dominant than internal feelings, it gives a sense of calm.

[0172] The inner side of the emotion diagram 400 represents the mind, and the outer side of the emotion diagram 400 represents actions. Therefore, the further you go to the outer side of the emotion diagram 400, the more the emotion can be seen (manifested in actions).

[0173] Here, human emotions are based on a balance of various factors such as posture and blood sugar levels. When these balances deviate from the ideal, it indicates unhappiness; when they approach the ideal, it indicates pleasure. In robots, cars, and motorcycles, emotions can also be created based on a balance of factors such as posture and remaining battery power. When these balances deviate from the ideal, it indicates unhappiness; when they approach the ideal, it indicates pleasure. Emotion maps can be generated, for example, based on Dr. Mitsuyoshi's emotion map (Research on Speech Emotion Recognition and Brain Physiological Signal Analysis Systems for Emotion, Tokushima University, Doctoral Dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). In the left half of the emotion map, emotions belonging to the "Reaction" domain, where sensation is dominant, are arranged. Furthermore, in the right half of the emotion map, emotions belonging to the "Situation" domain, where situational cognition is dominant, are arranged.

[0174] The emotion map defines two types of emotions that promote learning. One is a negative emotion located near the middle of "repentance" or "reflection" on the situation side. That is, when the robot experiences negative emotions such as "I never want to feel this way again" or "I never want to be scolded again." The other is a positive emotion located near "desire" on the response side. That is, when the robot experiences positive feelings such as "wanting more" or "wanting to know more."

[0175] The emotion-specific model 59 feeds user input into a pre-learned neural network to obtain emotion values ​​representing each emotion shown in the emotion graph 400, and determines the user's emotion. This neural network is pre-learned based on multiple learning data sets that combine user input with emotion values ​​representing each emotion shown in the emotion graph 400. Furthermore, this neural network is learned to... Figure 10 As shown in sentiment graph 900, nearby sentiment values ​​are similar to each other. Figure 10 Examples show how emotions such as "peace of mind," "stability," and "reassurance" can result in similar emotional values.

[0176] The above description primarily focuses on the functions of the data processing device 12 to illustrate the system disclosed herein. However, the system disclosed herein may not necessarily be implemented on a server. The system disclosed herein can be implemented as a general information processing system. For example, it can be implemented as a software program running on a personal computer, an application running on a smartphone, etc. The methods disclosed herein can be provided to users in the form of SaaS (Software as a Service).

[0177] In the above embodiments, an example of specific processing performed by a single computer 22 is given. However, the technology disclosed herein is not limited to this, and distributed processing of specific processes by multiple computers, including computer 22, is also possible. For example, the data generation model 58 may also be provided in an external device of the data processing apparatus 12, where data is generated based on input data.

[0178] In the above embodiments, an example of storing a specific processing program 56 in memory 32 is illustrated, but the technology disclosed herein is not limited thereto. For example, the specific processing program 56 may also be stored in a portable computer-readable non-transitory storage medium such as a USB (Universal Serial Bus) memory. The specific processing program 56 stored in the non-transitory storage medium is installed in the computer 22 of the data processing device 12. The processor 28 performs specific processing according to the specific processing program 56.

[0179] Alternatively, the specific processing program 56 can be stored in a storage device such as a server connected to the data processing device 12 via a network 54, and the specific processing program 56 can be downloaded and installed into the computer 22 upon request from the data processing device 12.

[0180] Furthermore, it is not necessary to store the entire specific process 56 in a storage device such as a server connected to the data processing device 12 via the network 54, nor is it necessary to store the entire specific process 56 in the memory 32; a portion of the specific process 56 may also be stored.

[0181] As a hardware resource for performing specific processing, various processors can be used. For example, a CPU can be listed as a processor; it is a general-purpose processor that functions as a hardware resource for performing specific processing by executing software, i.e., a program. Furthermore, as a processor, a dedicated circuit can be listed as a processor; it is a processor with a circuit structure specifically designed for performing specific processing, such as an FPGA (Field-Programmable Gate Array), a PLD (Programmable Logic Device), or an ASIC (Application Specific Integrated Circuit).

[0182] Every processor has built-in or connected memory, and every processor performs specific processes by using memory.

[0183] The hardware resources for performing a specific process can consist of one of these various processors, or a combination of two or more processors of the same or different types (e.g., a combination of multiple FPGAs, or a combination of a CPU and an FPGA). Furthermore, the hardware resources for performing a specific process can also be a single processor.

[0184] As an example of a single processor, the first type consists of a combination of one or more CPUs and software, which functions as a hardware resource to perform specific processing. The second type uses a processor, such as a System-on-a-chip (SoC), which implements the entire system functionality, including multiple hardware resources performing specific processing, using a single IC chip. In this case, the specific processing is implemented using one or more of the aforementioned processors that serve as hardware resources.

[0185] Furthermore, as the hardware architecture of these various processors, more specifically, circuits combining semiconductor elements and other circuit components can be used. Moreover, the specific process described above is merely an example. Therefore, it goes without saying that, without departing from the main point, unnecessary steps can be removed, new steps can be added, or the processing order can be changed.

[0186] The foregoing descriptions and illustrations are detailed explanations of the parts covered by this disclosure and are merely one example of this disclosure. For instance, the descriptions of the above-described structure, function, role, and effect are just one example of the structure, function, role, and effect of the parts covered by this disclosure. Therefore, it goes without saying that, without departing from the spirit of this disclosure, unnecessary parts can be deleted, new elements can be added, or replacements can be made to the foregoing descriptions and illustrations. Furthermore, to avoid confusion and facilitate understanding of the parts covered by this disclosure, explanations of technical common sense that does not require special explanation for implementing this disclosure have been omitted from the foregoing descriptions and illustrations.

[0187] All documents, patent applications and technical standards described in this specification are incorporated herein by reference as if they were specifically and individually described as incorporated by reference.

[0188] In addition, the following notes further explain the above explanation.

[0189] (Note 1) A logistics center operation support system comprises a cargo information management department, an operator management department, an equipment management department, a scheduling optimization department, a delivery route optimization department, an education plan proposal department, a customer response proposal department, and a fault prediction detection department. The cargo information management department uniformly accumulates and manages cargo information such as cargo ID, cargo category, weight, dimensions, storage temperature conditions, scheduled inbound date, scheduled outbound date, delivery destination, and priority. The operator management department manages operator information such as operator ID, name, qualifications, applicable job categories, past work history, error history, skill evaluation, and work schedule. The equipment management department manages equipment information such as equipment ID, equipment category, installation location, operating time, maintenance history, sensor data such as temperature and humidity, and fault history. The scheduling optimization department uses generative AI based on cargo priority, delivery schedules, equipment availability, external traffic information, and weather information. The system automatically determines the optimal timing and allocation of inbound and outbound operations; the delivery route optimization department uses generative AI to automatically propose the shortest and safest delivery routes based on delivery destinations, vehicle loading status, traffic information, past delay history, and weather information; the education plan proposal department analyzes each operator's skill evaluation, past error history, customer feedback, and changes in work content, and uses generative AI to automatically generate individually optimized education and training plans; the customer response proposal department uses generative AI to analyze customer feedback, and in the event of low ratings or complaints, it proposes specific response strategies such as automatically generating apology letters, compensation proposals, and explanations of countermeasures to prevent recurrence; and the fault precursor detection department uses generative AI to check real-time data such as equipment temperature and vibration, abnormal operator movement, and past fault patterns, and immediately issues an alarm when abnormal signs are detected.

[0190] (Note 2) As described in Appendix 1, the logistics center operation support system is characterized in that the scheduling optimization department uses generative AI to comprehensively analyze the cargo information, operator information, and equipment information accumulated in the cargo information management department, the operator management department, and the equipment management department, as well as traffic information and weather information obtained from external sources. Taking into account the type and priority of cargo, the skills and qualifications of operators, the operating status and idle status of equipment, traffic congestion and weather risks, etc., the system automatically determines the best operator allocation, operation timing, and equipment utilization plan for each cargo, thereby maximizing operational efficiency and reducing risks.

[0191] (Note 3) As described in Appendix 1, the logistics center operation support system is characterized in that the fault precursor detection unit uses generative AI to analyze real-time equipment sensor data and operator movement data obtained from the equipment management unit and the operator management unit, as well as fault history and past accident patterns. When abnormal temperature rise and vibration of equipment, operator movement patterns that are different from usual, or situations similar to those that occurred in the past are detected, an alarm notification is immediately sent to the management terminal, and necessary response strategies and attention reminders are automatically generated and prompted.

Claims

1. A corporate business support system, comprising a customer information management department, a business activity support department, and a customer relationship enhancement department, characterized in that, The Customer Information Management Department will uniformly accumulate and manage customer attribute information, transaction history, interests, contract information, feedback information, project progress information, shared data, training history, and market insight information obtained from legal entity sales representatives and customers. Generative AI will then be used to analyze this information to infer each customer's potential needs and future sales potential. The Sales Activity Support Department, based on the information analyzed by the Customer Information Management Department and externally obtained leads, competitor information, and market trend information, will use generative AI to perform tasks such as identifying potential customers, automatically generating sales activities, sales forecasting, competitor analysis, automatically generating proposal materials, optimizing sales strategies, and providing training content. The Customer Relationship Enhancement Department will utilize the information from the Customer Information Management Department and the Sales Activity Support Department, using generative AI to collect and analyze customer feedback, provide support for clarifying needs, share data, manage joint projects, support contract management, provide problem-solving support, optimize communication, and propose measures to improve customer satisfaction.

2. The corporate business support system as described in claim 1, characterized in that, The Sales Support Department uses generative AI to comprehensively analyze the attribute information, past transaction history, interests, feedback information, and externally acquired leads, competitor information, and market trend information accumulated in the Customer Information Management Department for each customer. It automatically generates the best contact methods, sales timing, proposal content, and sales tasks for each potential customer and provides prompts as a ToDo list for the sales representative. At the same time, it automatically calculates sales forecasts and sales targets, and then automatically proposes adjustments to the sales strategy based on the competitor analysis results.

3. The corporate business support system as described in claim 1, characterized in that, The Customer Relationship Enhancement Department uses generative AI to analyze customer feedback, demand input, progress information of joint projects, shared data, contract information, training history, market insights, and other information. For each customer, it automatically proposes the best feedback response strategies, project management tasks, data sharing methods, contract update timing, training content, market insight content, and communication methods to support improved customer satisfaction and the building of long-term relationships.

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