Automatic cost calculation and safety matching platform based on order drawings and specifications based on AI analysis

Abstract

We provide a system, device, method, program, and recording medium that utilizes artificial intelligence (AI) technology to automatically analyze order drawings and specifications, estimate material costs, processing costs, labor hours, etc., calculate costs, and provide a secure matching function between purchasers and contractors. [Solution] When ordering various manufactured products, including but not limited to metal processing parts, construction parts, medical equipment parts, electronic equipment parts, and automobile parts, the method accepts design information in various formats, including CAD data, PDF drawings, text specifications, 3D model data, point cloud data, and hologram data, as input, and automatically estimates the part shape, materials, processing process, and required labor hours using artificial intelligence technology, and automatically calculates the cost based on these estimation results.

Description

[Technical Field]

[0001] The present invention belongs to the technical field of BtoB (Business to Business) trading platforms in the manufacturing industry, and in particular relates to a system, device, method, program, and recording medium that utilizes artificial intelligence (AI) technology to automatically analyze order drawings and specifications, estimate material costs, processing costs, labor hours, etc., calculate costs, and provide a secure matching function between purchasers and contractors.

[0002] More specifically, when ordering various manufactured products, including but not limited to metal processing parts, construction parts, medical equipment parts, electronic equipment parts, and automobile parts, the technology accepts design information in various formats, including CAD data, PDF drawings, text specifications, 3D model data, point cloud data, and hologram data, as input, and automatically estimates the part shape, materials, processing steps, and required labor hours using artificial intelligence technology, and automatically calculates the cost based on these estimation results. Here, artificial intelligence technology includes, but is not limited to, machine learning, deep learning, reinforcement learning, genetic algorithms, fuzzy logic, expert systems, neural networks, convolutional neural networks, recurrent neural networks, transformers, generative AI, large-scale language models, image recognition, pattern recognition, natural language processing, speech recognition, knowledge graphs, ontological inference, Bayesian networks, decision trees, support vector machines, clustering, dimensionality reduction, anomaly detection, time series analysis, optimization algorithms, simulated annealing, particle swarm optimization, quantum machine learning, neuromorphic computing, swarm intelligence, evolutionary computing, multi-agent systems, cognitive computing, artificial general intelligence (AGI), artificial specific intelligence (ANI), and artificial super intelligence (ASI). This encompasses all currently known artificial intelligence technologies and those that may be developed in the future.

[0003] Furthermore, the present invention relates to a platform technology that integrates various security and quality assurance functions, such as secure management of confidential design information, NDA (Non-Disclosure Agreement) linkage, access control, price validity assessment, and user evaluation system. These functions are realized via any current or future communication means, including, but not limited to, wired communication, wireless communication, internet communication, intranet communication, cloud communication, edge communication, satellite communication, optical communication, quantum communication, 5G communication, 6G communication, Wi-Fi communication, Bluetooth communication, NFC communication, LoRaWAN communication, millimeter wave communication, terahertz communication, molecular communication, DNA communication, electroencephalogram communication, neural interface communication, holographic communication, and space-time communication. [Background technology]

[0004] Traditionally, the process for ordering parts in the manufacturing industry has typically involved the purchaser creating manufacturing drawings and specifications, sending them to multiple factories and suppliers to request quotes, and then each factory manually checking and analyzing the drawing contents to select materials, determine processing processes, calculate labor hours, and finally calculate an estimated price.

[0005] This traditional quotation process had the following issues: First, drawing analysis and process consideration required a large amount of time and effort from specialized engineers, and it often took days to weeks to provide a quote, hindering rapid ordering decisions. Also, the accuracy of the quotation and pricing depended heavily on the experience and subjectivity of the person in charge, which could result in large price differences between factories even for the same drawing.

[0006] Furthermore, even though blueprints and specifications often contain important design know-how and confidential information, security measures for sending and receiving such information via email or file sharing services were insufficient, raising concerns about the risk of information leaks. In particular, problems such as unauthorized copying, secondary use, and leaking of blueprint data to competitors were becoming more serious.

[0007] In recent years, online B2B matching platforms have become popular, and services that connect clients and contractors over the Internet are being offered. However, these existing platforms mainly only provide a means of communication between companies and basic transaction management functions, and have not fully realized advanced functions such as automatic cost calculation from drawings or automatic determination of whether processing is possible.

[0008] Furthermore, advances in artificial intelligence technology have improved the accuracy of current image recognition and natural language processing, and attempts to apply these technologies to manufacturing are increasing. In the future, more advanced artificial intelligence technologies are expected to be realized, such as artificial general intelligence (AGI), artificial superintelligence (ASI), quantum artificial intelligence, biological artificial intelligence, hybrid artificial intelligence, swarm intelligence, collective intelligence, and distributed intelligence. Furthermore, the fusion of AI with human intelligence augmentation technologies, such as brain-computer interfaces, neural implants, mind-reading technology, emotion recognition technology, creativity enhancement technology, intuition enhancement technology, and cognition enhancement technology, is expected to create new possibilities that go beyond the limits of conventional artificial intelligence.

[0009] However, at present, it is not yet possible to integrate and put into practical use these advanced technologies into a comprehensive analysis system that can handle the diversity of manufacturing drawings (2D drawings, 3D drawings, 4D drawings, nD drawings, hand-drawn drawings, CAD data, point cloud data, hologram data, virtual reality data, augmented reality data, mixed reality data, brainwave pattern data, emotional pattern data, etc.), the terminology specific to the manufacturing industry, multilingual support, cultural differences, regional regulations, international standards, industry standards, company-specific standards, changes over time, technological advances, material innovations, etc. Furthermore, in the area of ​​communications technology, progress is expected from the current 5G communications to future 6G and 7G, as well as to revolutionary communications technologies such as quantum communications, optical communications, terahertz communications, holographic communications, brainwave communications, DNA communications, molecular communications, and space-time communications. However, the technological foundations for manufacturing platforms that can accommodate these diverse communications methods are not yet fully developed. [Prior art documents] [Patent documents]

[0010] Patent Publication No. 2019-102065 Summary of the Invention [Problem to be solved by the invention]

[0011] In light of the problems with the prior art described above in the background art, the problems that the present invention aims to solve are as follows.

[0012] The first challenge is to provide technology that can automatically and accurately estimate material types, processing processes, required labor hours, etc. from order drawings and specifications, and quickly calculate costs based on these estimation results. Because traditional manual analysis takes too much time and costs, automation using AI technology is needed. However, this is merely simple automation, and there is room for further technological improvement, such as improving estimation accuracy and supporting a variety of drawing formats.

[0013] The second challenge is to create a database of information on the facilities capacity, processing technology, and available materials of factories and manufacturers, and to realize a function that automatically determines whether a part can be manufactured by comparing it with the required specifications of the ordered part. This will enable the purchaser to efficiently select a factory that can handle the part, and the contractor to avoid unnecessary work on projects that are outside their own range of support. However, improvements are needed, including more advanced judgment algorithms and more flexible judgment criteria.

[0014] The third challenge is to provide comprehensive security functions for safely sharing and managing highly confidential design drawings and specifications. Specifically, multi-layered security measures are required, including encryption technology, access control, watermarking, viewing log management, and NDA coordination. However, these technologies are merely combinations of known security technologies, and security enhancements tailored to the unique needs of the manufacturing industry are required.

[0015] The fourth challenge is to realize a function that objectively evaluates the validity of the calculated estimated price and presents a range of fair prices by comparing it with past trading results and market prices. This will prevent unfairly high prices and low prices from being set, and make it possible to create a fair trading environment. However, there is room for improvement, including but not limited to, refinement of the price evaluation algorithm and methods for reflecting market trends.

[0016] The fifth challenge is to provide a rating system based on transaction records, a review function, and a function to detect and remove malicious users in order to build trust among platform users. However, some aspects of the system are merely the implementation of simple rating functions, such as ensuring the fairness of the rating system and preventing rating manipulation, and more advanced trust rating technology needs to be developed. Means to solve the problem

[0017] In order to solve the above problems, the present invention provides the following means. However, these means are merely examples of the present invention, and the present invention is not limited to these, and various modifications and improvements are possible within the scope of the technical concept of the present invention.

[0018] The first aspect of the present invention provides a drawing and specification analysis unit using an artificial intelligence analysis engine. This analysis unit extracts geometric and physical features, such as the outer shape, internal structure, dimensions, hole locations, surface roughness, and material density distribution, from 2D drawings, 3D drawings, 4D drawings (including time axes), nD drawings, virtual reality drawings, augmented reality drawings, mixed reality drawings, hologram drawings, etc., using currently known image recognition technologies and visual recognition technologies that may be developed in the future. It also obtains surface information, volume, material properties, stress distribution, temperature distribution, magnetic field distribution, and electric field distribution from 3D CAD data, point cloud data, mesh data, voxel data, NURBS data, parametric data, feature-based data, etc., and extracts material specifications, surface treatment requirements, tolerance information, functional requirements, performance requirements, aesthetic requirements, and sensory requirements from text specifications, audio specifications, video specifications, electroencephalogram patterns, emotion patterns, etc., using a variety of analysis technologies, including but not limited to natural language processing, voice recognition, image analysis, electroencephalogram analysis, and emotion analysis. The artificial intelligence technologies used here include, but are not limited to, current machine learning and deep learning, as well as artificial general intelligence (AGI), artificial super intelligence (ASI), artificial specific intelligence (ANI), swarm intelligence, collective intelligence, distributed intelligence, quantum intelligence, biological intelligence, hybrid intelligence, artificial life, digital twin intelligence, conscious AI, creative AI, intuitive AI, emotional AI, empathetic AI, moral AI, philosophical AI, etc. This term encompasses all intelligent processing technologies developed in the process of elucidating the essence of intelligence. However, analysis methods are not limited to these, and analytical accuracy, processing speed, energy efficiency, and scope can be expanded by improving machine learning models, introducing new artificial intelligence technologies, imitating biological processes, utilizing physical phenomena, utilizing chemical reactions, applying quantum effects, etc.

[0019] The second aspect of the present invention is to provide a manufacturing feasibility determination unit linked to a factory equipment database. This determination unit determines the specifications of the equipment (conventional machine tools, CNC machine tools, 3D printers, 4D printers, molecular assemblers, nanomanipulators, robot arms, collaborative robots, autonomous robots, swarm robots, soft robots, biorobots, quantum processing machines, laser processing machines, electron beam processing machines, ion beam processing machines, plasma processing machines, ultrasonic processing machines, chemical processing equipment, bioprocessing equipment, etc.) owned by each factory, manufacturing base, virtual factory, automated factory, unmanned factory, space factory, undersea factory, mobile factory, etc., the specifications of the materials that can be processed (metals, resins, ceramics, composite materials, nanomaterials, biomaterials, smart materials, metamaterials, artificial muscles, self-repairing materials, shape memory materials, phase change materials, electronic materials, optical materials, magnetic materials, superconducting materials, etc.), the specifications of the equipment ... Information such as material characteristics (e.g., conductive materials, quantum materials, biodegradable materials, recycled materials), compatibility tolerances, production capacity, quality control capabilities, environmental compatibility, sustainability, and carbon neutrality compatibility is compiled into a database using current and future data management technologies, including but not limited to relational databases, NoSQL databases, graph databases, time series databases, distributed databases, blockchain databases, quantum databases, DNA databases, and holographic storage. The analysis results are then combined with multidimensional matching, probabilistic matching, fuzzy matching, semantic matching, ontology-based matching, etc. to determine whether the product is "compatible," "conditionally compatible," "difficult to compatible," "requires technological development," or "requires capital investment." The assessment algorithm is continuously improved using machine learning, deep learning, reinforcement learning, evolutionary learning, self-learning, transfer learning, lifelong learning, meta-learning, curriculum learning, etc., to improve the accuracy, speed, and range of assessment. However, this is only an example, and other assessment methods, such as rule-based assessment, statistical methods, optimization methods, simulation-based assessment, digital twin assessment, virtual reality assessment, and augmented reality assessment, can also be applied alone or in combination.

[0020] The third aspect of the present invention is to provide a multifaceted cost calculation unit that calculates direct material costs, indirect material costs, material loss costs, material management costs, material quality costs, etc. from estimated material type, quantity, quality, supplier, procurement time, inventory status, price fluctuations, supply and demand balance, etc., and calculates machining costs, manual labor costs, assembly costs, inspection costs, testing costs, certification costs, quality assurance costs, etc. from estimated processing steps, man-hours, equipment utilization rate, worker skills, quality requirements, accuracy requirements, surface treatment requirements, heat treatment requirements, inspection requirements, etc., and calculates total costs by adding design costs, development costs, tool costs, jig costs, mold costs, setup costs, transportation costs, insurance costs, customs duties, environmental costs, carbon offset costs, waste disposal costs, recycling costs, life cycle costs, etc. as needed. Cost calculations refer to standard cost databases, market price information, real-time price information, forecast price information, past trading performance, data on competitors, international market information, exchange rate information, interest rate information, inflation information, political risk information, geopolitical risk information, climate change risk information, etc. However, these data sources are not limited to these and external API integration, IoT sensor integration, satellite data integration, social media analysis, news analysis, patent analysis, academic paper analysis, government statistical analysis, international organization data analysis, proprietary price models, AI price prediction models, quantum computing optimization models, blockchain-based price information, distributed price information networks, etc. Furthermore, cost calculations are designed to support not only a single currency but also future means of value exchange such as multiple currencies, virtual currencies, central bank digital currencies (CBDCs), commodity currencies, time currencies, energy currencies, and carbon credit currencies, making them flexibly adaptable to changes in the economic system.

[0021] A fourth aspect of the present invention is to provide a comprehensive security management unit, which can provide a variety of authentication methods, including data encryption (symmetric encryption, asymmetric encryption, hybrid encryption, stream encryption, block encryption, elliptic curve encryption, lattice encryption, multivariate encryption, code-based encryption, post-quantum encryption, quantum key distribution, quantum cryptography, DNA encryption, chaos encryption, biometric encryption, neuro-encryption, etc.), multi-factor authentication (password, PIN, biometric authentication, behavioral authentication, voice authentication, face authentication, iris authentication, fingerprint authentication, vein authentication, DNA authentication, brainwave authentication, heart rate authentication, gait authentication, keystroke authentication, mouse operation authentication, location information authentication, time information authentication, device authentication, certificate authentication, token authentication, one-time password, push authentication, risk-based authentication, adaptive authentication, context authentication, zero-trust authentication, etc.), role-based access control (RBAC), attribute-based access control (ABAC), mandatory access control (MAC), discretionary access control (DAC), rule-based access control, time-based access control, location-based access control, device-based access control, etc. It provides integrated functions such as: access control, risk-based access control, AI-based access control, quantum access control, etc.), drawing watermarking (visible watermarking, invisible watermarking, robust watermarking, fragile watermarking, zero watermarking, blind watermarking, semi-blind watermarking, multiple watermarking, holographic watermarking, quantum watermarking, blockchain watermarking, DNA watermarking, steganography, fingerprinting, etc.), operation log recording (access log, operation log, change log, deletion log, download log, print log, screenshot log, chronological log, geographic log, device log, network log, application log, system log, security log, audit log, forensic log, blockchain log, quantum log, etc.), NDA electronic signature integration (digital signature, electronic certificate, PKI, timestamp, hash chain, blockchain signature, quantum signature, biometric signature, multiple signature, threshold signature, blind signature, ring signature, group signature, delegated signature, aggregate signature, etc.).However, while security technology is rapidly advancing, so too are attack technologies, and these technologies are not the only ones being adopted. More advanced encryption methods, authentication technologies, privacy protection technologies, anonymization technologies, homomorphic encryption, zero-knowledge proofs, secure multi-party computation, trusted execution environments, hardware security modules, quantum-safe cryptography, homomorphic encryption, differential privacy, etc. may also be adopted.

[0022] A fifth aspect of the present invention is to provide a price validity evaluation unit and a user evaluation management unit. The price validity evaluation unit performs multidimensional comparison of the calculated estimated price with similar past cases, industry average prices, international market prices, forecast prices, AI estimated prices, crowdsourcing prices, blockchain recorded prices, distributed price information, real-time market prices, term prices, option prices, etc., and calculates a validity score, confidence interval, risk level, forecast accuracy, market position, etc., using statistical analysis, machine learning analysis, deep learning analysis, time series analysis, regression analysis, Bayesian analysis, Monte Carlo analysis, sensitivity analysis, scenario analysis, stress testing, risk analysis, uncertainty analysis, fuzzy analysis, neural network analysis, genetic algorithm analysis, particle swarm optimization analysis, quantum computing analysis, etc. The user evaluation management department collects post-transaction mutual evaluations, multi-level evaluations, multi-dimensional evaluations, time-series evaluations, contextual evaluations, emotional evaluations, intention evaluations, satisfaction evaluations, recommendation evaluations, and repeat business evaluations, and calculates scores from various angles, such as trust scores, expertise scores, response quality scores, delivery deadlines, price reasonableness scores, communication scores, innovativeness scores, sustainability scores, and social responsibility scores. It provides detection, analysis, and prevention functions for malicious users, fraudulent activities, fraudulent transactions, price manipulation, rating manipulation, review manipulation, impersonation, multiple accounts, bot activity, money laundering, and terrorist financing using natural language processing, sentiment analysis, behavioral analysis, network analysis, graph analysis, anomaly detection, machine learning, deep learning, reinforcement learning, federated learning, and privacy-preserving machine learning. However, these evaluation methods are merely examples, and more sophisticated evaluation algorithms, new reliability indicators, social science evaluation methods, psychological evaluation methods, behavioral economics evaluation methods, game theoretic evaluation methods, complex systems theory evaluation methods, network theory evaluation methods, etc. may also be introduced. Effect of the invention

[0023] The present invention provides the following effects: However, these effects are merely examples of the present invention, and different effects may be obtained depending on the embodiment and operating conditions.

[0024] The first effect is a significant reduction in the time from placing an order to responding to a quote. The quotation process, which previously took days or weeks, can now be completed in minutes to hours with AI automatic analysis. However, the reduction in time varies depending on the complexity of the drawings and the factory's response status, and a uniform time reduction is not necessarily guaranteed.

[0025] The second effect is improved estimation accuracy and standardization. Compared to conventional methods that rely on subjective manual judgment, objective analysis by AI ensures consistent estimation quality. However, since the accuracy of AI analysis depends on the quality and quantity of learning data, continuous data accumulation and learning are important, and other methods of improving accuracy must also be considered.

[0026] The third benefit is improved security of confidential information. Multi-layered security features significantly reduce the risk of leaking drawings, allowing online transactions to be conducted with peace of mind. However, security is not absolute, and measures must be continually strengthened to respond to new threats.

[0027] The fourth effect is improved transparency and fairness in transactions. The price validity evaluation function prevents unfair pricing, and the user rating system makes it easier to select trustworthy trading partners. However, the effectiveness of the evaluation system varies depending on how it is operated and the standards set, so these functions are not perfect. MODE FOR CARRYING OUT THE INVENTION

[0028] Hereinafter, embodiments for carrying out the present invention will be described in detail. However, the embodiments shown below are merely examples of the present invention, and various modifications and improvements are possible within the scope of the technical concept of the present invention, and the present invention is not limited to these embodiments. First embodiment

[0029] In at least one embodiment, a B2B quotation platform system with basic AI analysis functions is provided. This system has a configuration in which an orderer terminal, a contractor terminal, and a server device are connected via a network. The server device is equipped with a drawing and specification analysis unit, a manufacturing feasibility determination unit, and a cost calculation unit.

[0030] The Drawing and Specification Analysis Unit has the ability to use artificial intelligence technology to extract part shapes from design information in various formats, including, but not limited to, 2D drawings, 3D drawings, CAD data, hand-drawn drawings, photographic images, video data, audio data, text data, binary data, etc. Specifically, it analyzes drawing images using current and future artificial intelligence technologies, including, but not limited to, deep learning-based image recognition models, convolutional neural networks (CNNs), recurrent neural networks (RNNs), transformer models, generative adversarial networks (GANs), variational autoencoders (VAEs), graph neural networks (GNNs), attention mechanisms, vision transformers (ViTs), BERT, GPT, LLaMA, etc., to identify geometric elements, including lines, arcs, dimension lines, notes, symbols, patterns, etc., and then infer the part's 3D shape, material properties, processing requirements, etc., from the combination of these elements. However, artificial intelligence technology is advancing every day, and the methods listed here are only a few examples. Any information processing technology, including new artificial intelligence technology, quantum computing technology, biocomputing technology, neuromorphic technology, etc., that will be developed in the future, can also be applied.

[0031] The manufacturing feasibility determination unit compares the equipment information (machining machine type, processable sizes, compatible materials, etc.) registered in the factory database with the estimation results from the analysis unit, and automatically determines whether each factory can handle the request. The determination results are output along with a certainty factor, allowing the purchaser to prioritize the factory that is most likely to be able to handle the request. However, the determination criteria and the method for calculating the certainty factor differ depending on the implementation, and in some cases these methods are the only ones used. Second embodiment

[0032] At least one embodiment provides a system with enhanced 3D CAD data analysis capabilities. This embodiment supports standard 3D file formats such as STEP, IGES, and STL, and has the ability to directly extract shape information, volume, surface area, and other data from CAD data.

[0033] The 3D model analysis unit extracts surface information from the 3D model using mesh analysis technology, estimates the processing direction from the normal vector of each surface, and automatically detects undercut shapes and difficult-to-process areas. It also calculates the amount of material used with high precision using volume calculations and estimates the surface treatment cost using surface area calculations. However, the 3D analysis method is not limited to this, and other methods such as point cloud processing and voxel analysis can also be applied. Third embodiment

[0034] In at least one embodiment, a system is provided that has a specification analysis function that utilizes natural language processing. In this embodiment, the system is equipped with a function that automatically extracts information such as material specifications, surface treatment requirements, and quality standards from text-based specifications.

[0035] The natural language processor uses current and future natural language processing technologies, including but not limited to large-scale language models, Transformer architectures, BERT, GPT, T5, RoBERTa, DeBERTa, ELECTRA, ALBERT, DistilBERT, ChatGPT, Claude, Gemini, LLaMA, PaLM, Chinchilla, Gopher, Jurassic, OPT, BLOOM, GLM, ERNIE, context embedding, semantic embedding, knowledge graph embedding, multimodal learning, cross-modal learning, zero-shot learning, few-shot learning, in-context learning, chain of sorts, thought trees, iterative refinement, self-correcting learning, reinforcement learning with human feedback (RLHF), constitutional AI, red teaming, etc. to decompose specification text at the word, sentence, paragraph, and document levels, and to identify material names, standard numbers, numerical information, quality requirements, functional requirements, etc. using functions such as named entity extraction, relationship extraction, sentiment analysis, intent inference, summary generation, question answering, and dialogue generation. Furthermore, implicit requirements, design intentions, constraints, optimization goals, etc. are inferred from contexts such as "stainless steel is used to improve corrosion resistance" using intent analysis, context understanding, inference, common sense inference, causal inference, analogical inference, etc. However, natural language processing technology is rapidly developing, and these methods are just a few examples, and it is also possible to introduce any language processing technology developed in the future, including language understanding technology, multilingual processing technology, multimodal language technology, neural language interface technology, etc. Fourth embodiment

[0036] At least one embodiment provides a system with enhanced security features. This embodiment employs advanced encryption technologies such as end-to-end encryption, zero-knowledge proofs, and homomorphic encryption, and implements a function for performing analysis processing on drawing data while keeping it encrypted.

[0037] The encryption processor encrypts the drawing data upon upload using current and future encryption technologies, including, but not limited to, AES-128, AES-192, AES-256, RSA, Elliptic Curve Cryptography (ECC), Diffie-Hellman key exchange, hash functions (e.g., SHA-256, SHA-3, Blake2), digital signatures (e.g., DSA, ECDSA), homomorphic encryption, multi-party computation (MPC), secret sharing, zero-knowledge proofs, fully homomorphic encryption (FHE), lattice cryptography, multivariate cryptography, code-based cryptography, homogeneous map cryptography, quantum key distribution (QKD), quantum cryptography, post-quantum cryptography, lightweight cryptography, stream ciphers, block ciphers, symmetric cryptography, asymmetric cryptography, hybrid cryptography, authenticated encryption, message authentication codes (MAC), digital watermarking, steganography, chaos cryptography, DNA cryptography, biometric cryptography, etc., and manages the encryption keys in a secure environment, including a key management system, hardware security module (HSM), trusted execution environment (TEE), secure enclave, quantum secure communications, etc. Using homomorphic encryption processing with new-generation computing technologies, including but not limited to quantum computing, DNA computing, optical computing, neuromorphic computing, membrane computing, and molecular computing, artificial intelligence processing, machine learning processing, statistical processing, optimization processing, etc. are performed without decrypting the data, and only the analysis results are decrypted and provided. This creates a system in which even server administrators, cloud providers, and third parties cannot view the original drawing contents. However, as encryption and computing technologies are constantly evolving and there are constraints such as computational costs, communication costs, and storage costs, it is necessary to select the optimal technology by taking into account the balance between practicality, efficiency, security level, etc., and other safety methods, privacy protection technologies, anonymization technologies, etc., in addition to these technologies, can also be considered. Fifth embodiment

[0038] At least one embodiment provides a transaction history management system that utilizes blockchain technology. In this embodiment, all transaction processes, from quotation requests to contract signing, manufacturing completion, and acceptance inspection, are recorded on the blockchain, creating a transaction history that cannot be tampered with.

[0039] The blockchain management unit records each transaction step on the blockchain as a transaction and automatically executes contract terms using smart contracts. For example, it implements a mechanism whereby payment is automatically made once production completion is confirmed. However, blockchain technology has issues with processing speed and scalability, and implementations must take these constraints into account, so it is not necessarily limited to this technology. Sixth embodiment

[0040] At least one embodiment provides a system with a function for responding to real-time price fluctuations. In this embodiment, a function is implemented to monitor market fluctuations in material prices in real time and automatically reflect these fluctuations in estimated prices.

[0041] The price monitoring unit uses current and future computing technologies, including but not limited to real-time data processing, streaming data processing, batch processing, edge computing, fog computing, cloud computing, distributed computing, parallel computing, GPU computing, TPU computing, FPGA computing, ASIC computing, quantum computing, analog computing, optical computing, etc., to periodically, continuously, and in real time obtain various market factors such as price information from commodity exchanges, price lists from material manufacturers, exchange rates, interest rates, inflation rates, supply and demand balances, geopolitical risks, climate change, natural disasters, pandemics, technological innovations, and policy changes, and then analyzes them using time series analysis, regression analysis, principal component analysis, factor analysis, cluster analysis, discriminant analysis, decision tree analysis, random forest, gradient boosting, support vector machine, Bayesian network, hidden Markov model, state space model, ARIMA model, GARCH, etc. Current and future data analysis and artificial intelligence technologies, including but not limited to models, machine learning, deep learning, reinforcement learning, transfer learning, meta-learning, federated learning, automated machine learning (AutoML), and neural architecture search (NAS), are used to analyze price fluctuation patterns, periodicities, trends, seasonality, anomalies, outliers, structural changes, etc. When sudden price fluctuations, market manipulation, bubbles, crashes, etc. are detected, existing quotes are automatically updated using anomaly detection by machine learning, statistical testing, threshold monitoring, pattern matching, etc., and notifications are sent to relevant parties via various communication methods, including email, SMS, push notifications, voice calls, chatbots, AR / VR notifications, and IoT device notifications. However, price fluctuation predictions, market analysis, economic forecasts, etc. are inherently uncertain, complex, and nonlinear, making perfect predictions and absolute accuracy difficult. Therefore, these functions only provide reference information and decision-making support, and actual trading decisions, investment decisions, risk management, etc. require final human judgment, expert advice, and the collection of diverse information. Seventh embodiment

[0042] In at least one embodiment, an international transaction support system with multilingual capabilities is provided. In this embodiment, functions are implemented to automatically translate drawing notes, specifications, user interfaces, etc., facilitating transactions with overseas factories.

[0043] The multilingual processing unit uses neural machine translation technology to translate technical documents with high accuracy, accurately converting specialized terminology and abbreviations specific to the manufacturing industry. It also automatically converts units (inches to millimeters) and conforms to standards (JIS to ASTM) while taking cultural differences into account. However, translation accuracy varies depending on the language pair and field of expertise, and manual confirmation is recommended for important information. Multilingual support methods other than these translation functions are also being considered. Eighth embodiment

[0044] In at least one embodiment, a 3D drawing confirmation system utilizing AR (Augmented Reality) technology is provided. In this embodiment, a function is implemented to superimpose a 3D model on the actual work site via the camera of a smartphone or tablet device, helping users understand the contents of the drawing.

[0045] The AR display unit acquires the device's position and orientation information, constructs a 3D spatial coordinate system, and places a virtual 3D model in real space. Users can rotate and enlarge the 3D model using their fingers or touch operations to check the part shape from different angles. It also provides a function to overlay dimensional information and material information on the 3D model. However, AR technology depends on device performance and environmental conditions, and stable operation is not guaranteed in all situations, and in some cases, it is just a display technology. Ninth embodiment

[0046] At least one embodiment provides a system with a quality control function for AI learning data. In this embodiment, the system automatically evaluates the quality of drawing data and transaction performance data used for learning, and implements a function to eliminate low-quality data to improve the performance of the AI ​​model.

[0047] The data quality control unit quantitatively evaluates the resolution, noise level, missing information, etc. of drawings and calculates a quality score. It also checks the consistency of transaction data (validity of price and specifications, etc.) and detects and removes outliers. It also analyzes data bias and automatically adjusts the balance of the learning dataset. However, the definition of data quality is somewhat subjective, making complete automation difficult, and other approaches besides these quality control methods can also be applied. Tenth embodiment

[0048] At least one embodiment provides a manufacturing planning support system that integrates a predictive maintenance function. In this embodiment, the system analyzes the operating status and maintenance history of factory equipment, predicts equipment failures, and reflects the results in the manufacturing schedule.

[0049] The predictive analysis unit performs time-series analysis of equipment sensor data (vibration, temperature, current values, etc.) to detect abnormal patterns. It uses machine learning to learn signs of failure and calculates the probability of failure and the expected time of failure. These prediction results are reflected in the manufacturing plan, and a production schedule is automatically generated that avoids equipment with a high risk of failure. However, prediction accuracy varies depending on the type of equipment and usage conditions, making 100% prediction difficult, and these prediction functions may only play a supporting role. Eleventh embodiment

[0050] At least one embodiment provides an environmentally friendly quotation system with a carbon footprint calculation function. In this embodiment, the system automatically calculates the amount of CO2 emissions generated in the manufacturing process and implements a function to support factory selection that takes environmental impact into consideration.

[0051] The environmental impact calculation section estimates CO2 emissions at each stage, including material procurement, processing, and transportation, and calculates an overall environmental impact score. It also takes into account the factory's energy source (renewable energy ratio, etc.) and location (transport distance). Clients can select factories by comparing both price and environmental impact. However, since calculating environmental impact involves many assumptions, it is difficult to calculate accurate figures, and these functions only provide approximate values. Twelfth embodiment

[0052] At least one embodiment provides a system for automatically generating drawing descriptions that utilizes voice recognition technology. In this embodiment, the system provides a function for explaining the contents of drawings aloud to assist visually impaired people and users who have difficulty reading drawings.

[0053] The voice generation unit automatically generates explanatory text based on the results of drawing analysis, such as "This part is cylindrical with a diameter of 50 mm and has a 10 mm through hole in the center," and outputs it aloud using text-to-speech technology. The system also provides a dialogue function that responds to user voice questions such as "What is this part made of?" However, the accuracy of voice recognition and voice synthesis is affected by environmental noise and dialects, and complete understanding cannot be guaranteed. Therefore, other support methods beyond these voice technologies are also considered. Thirteenth embodiment

[0054] At least one embodiment provides a defect rate reduction support system with a quality prediction function. In this embodiment, the system analyzes past manufacturing results and quality data, predicts the manufacturing quality of new parts in advance, and recommends factories with low defect rates.

[0055] The quality prediction department comprehensively analyzes factors such as the complexity of the part shape, material properties, processing tolerances, and the factory's technical level to calculate a manufacturing quality risk score. Machine learning is used to learn from factories that have had a high number of defects with similar parts in the past, and patterns that are likely to cause problems under specific processing conditions, and a prediction model is then constructed. However, quality is affected by many factors, making perfect predictions impossible, and these prediction results are provided only as reference information; final quality assurance is the responsibility of each factory. Fourteenth embodiment

[0056] At least one embodiment provides an automated contract system with smart contract functionality. This embodiment automates the entire process from quotation approval to contract signing and payment, reducing manual administrative work.

[0057] The contract automation department automatically converts quotation details into digital contracts and concludes them with electronic signatures. When certain conditions, such as the completion of manufacturing or inspection, are met, payment processing is automatically carried out according to pre-set conditions. In the event of a dispute, the system works with arbitration institutions to quickly resolve the issue. However, human judgment is sometimes required to interpret the contract contents or respond to exceptional circumstances, making complete automation difficult, and flexible responses beyond these automated functions are also necessary. Fifteenth embodiment

[0058] At least one embodiment provides a supply chain management system with a risk analysis function, which analyzes external risks such as natural disasters, political situations, and economic conditions, and evaluates the stability of the supply chain.

[0059] The Risk Analysis Department collects and analyzes external information such as weather data, earthquake information, political news, and currency fluctuations to quantify the location risk of each factory. It predicts the probability and duration of supply interruptions based on past disaster history and recovery time data. Clients can reduce risk by distributing orders to multiple factories. However, changes in the external environment are difficult to predict, and risk analysis is merely a probabilistic estimate and does not guarantee that an event will actually occur. Therefore, other risk assessment methods, not limited to these analytical methods, can also be applied. Sixteenth embodiment

[0060] In at least one embodiment, a system having an intellectual property right protection function is provided. In this embodiment, a function is implemented to automatically detect patented technologies and designs included in drawings and to provide advance warning of the risk of intellectual property right infringement.

[0061] The intellectual property search unit compares uploaded drawings with existing patent and design databases to calculate the degree of similarity. If a high degree of similarity is detected, a warning is displayed along with the relevant intellectual property information. The system also detects novel design elements and recommends patent applications. However, determining intellectual property rights requires specialized legal judgment, and automated search results are only for reference. The final decision must be left to a patent attorney or other expert. Therefore, other intellectual property protection methods beyond these search functions are also possible. Seventeenth embodiment

[0062] At least one embodiment provides a system with a manufacturing process optimization function, which analyzes part shapes and factory equipment and automatically proposes optimal machining sequences and tool selections.

[0063] The Process Optimization Department considers the geometric constraints of the part, material properties, precision requirements, etc., and lists possible machining routes. It evaluates the machining time, tool wear, quality risks, etc. for each route, and selects the optimal process overall. It also proposes efficiency improvements such as simultaneous machining of multiple parts and shortening setup time. However, optimization is the result of calculations under given conditions, and unexpected problems may arise in the actual manufacturing site. Therefore, these optimization proposals are for reference only, and the final decision must be made by a manufacturing site expert. Eighteenth embodiment

[0064] At least one embodiment provides a system for shortening delivery times with an inventory linkage function. In this embodiment, a function is implemented to grasp the material inventory status of a factory in real time and propose alternative designs using in-stock materials.

[0065] The inventory management collaboration unit connects with each factory's inventory management system via API, obtaining information on material type, quantity, expected arrival dates, etc. in real time. If a material required for an ordered part is out of stock, it proposes an alternative material that meets the performance requirements and automatically generates a corresponding design change proposal. This shortens the material procurement period and achieves an overall reduction in delivery time. However, material changes may affect performance and quality, and alternative proposals are only for reference; the final material selection is the responsibility of the purchaser. Therefore, other delivery time reduction methods besides these proposal functions are also possible. Nineteenth embodiment

[0066] At least one embodiment provides a system with a collaborative design function, which allows a client and a contractor to jointly consider design changes and realize an optimal design that takes manufacturability into consideration.

[0067] The collaborative design support section provides a function that allows contractors to add manufacturing improvement proposals in the form of comments to the initial design uploaded by the client. It automatically calculates the impact of design changes on cost and quality and generates a comparison report of the changes before and after. The version management function makes it possible to track the history of design changes and revert to previous versions as needed. However, design changes require complex engineering judgment, and the results of automatic calculations are only approximations, so detailed verification is the designer's responsibility. Therefore, other collaborative methods, not limited to these support functions, can also be applied. Twentieth embodiment

[0068] In at least one embodiment, a system with an energy efficiency optimization function is provided, which predicts the energy consumption of a manufacturing process and proposes energy-saving processing methods.

[0069] The Energy Analysis Department analyzes the power consumption, processing time, equipment utilization rate, etc. of each processing step to calculate total energy consumption. It proposes energy reduction measures such as using highly efficient tools, optimizing processing conditions, and integrating equipment operations. It also takes into account the factory's renewable energy usage rate and recommends manufacturing methods with a low environmental impact. However, since many variables are involved in predicting energy consumption, there is a possibility that the actual consumption will differ. Therefore, these analysis results are only approximate values, and detailed energy-saving measures must be considered by experts at each factory. Twenty-first embodiment

[0070] In at least one embodiment, a system having an education and training support function is provided. In this embodiment, learning content is provided for manufacturing engineers and designers, and a function for supporting the improvement of technical skills is implemented.

[0071] The Learning Support Department provides anonymized examples of past transactions as learning materials, creating an environment where participants can learn about concepts such as estimating calculations and selecting manufacturing processes. It also offers interactive learning functions, such as virtual factory tours using VR (virtual reality) technology and processing experiences using simulation functions. It also evaluates the skill level of engineers and automatically generates learning plans optimized for each individual. However, learning effectiveness varies from person to person, and not all users will necessarily achieve the same results. Therefore, these educational systems only play a supplementary role, and practical experience is essential for actual technical acquisition. Twenty-second embodiment

[0072] At least one embodiment provides a system with an anomaly detection function that automatically detects quote prices and trading patterns that are significantly different from normal, enabling early detection of fraudulent transactions and abnormal market trends.

[0073] The anomaly detection unit uses statistical methods and machine learning to learn normal trading patterns and detects abnormal transactions that deviate from these. For example, it automatically detects estimated prices that deviate significantly from market prices, large volumes of trading by specific users, and unnatural evaluation patterns. Detected anomalies are notified to administrators, who, if necessary, suspend trading or conduct further investigations. However, anomaly detection carries the risk of false positives (determining normal transactions as abnormal) and oversights (determining abnormal transactions as normal), and detection accuracy is not 100%; these are merely detection functions, and the final decision requires human confirmation. Twenty-third embodiment

[0074] At least one embodiment provides a system having a function for supporting compliance with legal regulations. In this embodiment, a function for automatically checking compliance with various legal regulations (safety standards, environmental regulations, quality standards, etc.) related to the manufacturing industry is implemented.

[0075] The Regulatory Check Department identifies applicable laws and regulations based on the part's use, materials, manufacturing method, etc., and evaluates compliance with design specifications. For example, for medical device parts, it checks standard requirements such as ISO13485 and FDA guidelines, and for automotive parts, it checks IATF16949. If non-conformities are detected, it proposes corrections or presents alternative specifications. However, because laws and regulations are complex and frequently revised, it is difficult to fully comply with them. Therefore, these check functions only provide reference information, and the final determination of compliance with laws and regulations must be made by experts or certification bodies. Twenty-fourth embodiment

[0076] In at least one embodiment, a system having a supplier development support function is provided. In this embodiment, a function is implemented to support manufacturers wishing to enter the market to improve their technical capabilities and establish a quality control system.

[0077] The Supplier Development Department evaluates the current capabilities of companies hoping to enter the market and identifies areas of technical deficiency and quality control items that need improvement. It supports gradual improvement of capabilities by assisting in the formulation of improvement plans, introducing technical instructors, and recommending capital investment. Certificates are issued to companies that reach a certain level, creating an environment in which purchasers can do business with peace of mind. However, improving technical capabilities and quality control capabilities takes time and continuous effort, and dramatic improvements cannot be expected in a short period of time. Therefore, these support functions are merely auxiliary, and the final responsibility lies with each company. Twenty-fifth embodiment

[0078] At least one embodiment provides a system with a digital twin function, which reproduces the entire manufacturing process in a digital space and implements a function for executing a virtual manufacturing simulation.

[0079] The Digital Twin Construction Department creates 3D models of factory equipment layout, worker movement patterns, material flow, etc., and reproduces the entire manufacturing process on a computer. When manufacturing new parts, simulations are performed in digital space before actual production begins to identify problems in advance and optimize the process. By linking with real-time data, the actual manufacturing situation can be reflected in the digital twin, making it possible to manage budgets and actual results and identify areas for improvement. However, the accuracy of digital twins is limited compared to the complexity of the real world, making it difficult to accurately reproduce all phenomena, and these simulation results are only approximations, which means unexpected problems may occur in actual manufacturing. Twenty-sixth embodiment

[0080] At least one embodiment of the present invention provides a user satisfaction improvement system with a sentiment analysis function. In this embodiment, the system analyzes user comments and feedback during the transaction process using natural language processing, and implements a function to quantify satisfaction and stress factors.

[0081] The sentiment analysis unit extracts emotional expressions from text data such as chat history, review comments, and inquiry details, and classifies them as positive, negative, or neutral. It identifies factors causing dissatisfaction (price, delivery time, quality, communication, etc.) and generates improvement suggestions. It also analyzes the behavioral patterns of satisfied users to extract best practices for building good business relationships. However, because the interpretation of emotions is subjective and varies greatly depending on the context and cultural background, the results of the automated analysis are only for reference; important decisions require human confirmation. Other methods for improving satisfaction beyond these analysis functions are also possible. Twenty-seventh embodiment

[0082] At least one embodiment provides a system having an automatic scheduling function, which efficiently schedules multiple manufacturing requests and implements a function to maximize factory utilization rates while meeting delivery deadlines.

[0083] The scheduling optimization unit generates an optimal manufacturing schedule using optimization methods such as genetic algorithms and simulated annealing, taking into account the manufacturing time, priority, delivery date constraints, and equipment constraints for each part. It dynamically responds to plan changes due to emergency requests or equipment failures, automatically rescheduling. It also has a function that continuously improves the accuracy of manufacturing time predictions based on past performance data. However, since many unforeseen factors occur on the manufacturing floor, perfect scheduling is impossible, and these optimization results are merely theoretical values. Flexible adjustments are required in actual operations, so other scheduling methods, not just automated functions, can also be used in conjunction with it. Twenty-eighth embodiment

[0084] In at least one embodiment, a system with a crowdsourcing function is provided, which exposes complex design challenges or technical problems to a wide range of experts and invites solutions through crowdsourcing.

[0085] The Crowdsourcing Management Department anonymizes difficult technical problems and makes them public, soliciting solutions from engineers around the world. It automatically evaluates the proposed solutions and predicts their feasibility and effectiveness. It pays rewards to outstanding proposers to encourage continued participation. It also analyzes each engineer's area of ​​expertise and capabilities based on their past proposals, matching them with appropriate problems. However, there is a large variation in quality in crowdsourcing, and expert judgment is required to verify the content of proposals. Therefore, utilizing external parties is merely a supplementary measure, and the client must bear final responsibility. Twenty-ninth embodiment

[0086] In at least one embodiment, a system with a sustainability assessment function is provided, which assesses the sustainability of the entire manufacturing process and calculates an overall sustainability score from environmental, social, and economic perspectives.

[0087] The Sustainability Assessment Department analyzes the entire life cycle of a product, from raw material procurement to manufacturing, use, and disposal, quantifying the environmental impact (CO2 emissions, resource consumption, waste generation, etc.), social impact (working conditions, contribution to the local community, human rights issues, etc.), and economic effect (job creation, technological innovation, improved competitiveness, etc.). It also evaluates the impact on corporate value from the perspective of ESG investment, providing reference information for investment decisions. However, sustainability assessment criteria are diverse, there are no unified indicators, and these assessment results are only one perspective; multifaceted consideration is required, and other approaches, not limited to assessment methods, can also be applied. Thirty Embodiment

[0088] At least one embodiment provides a system with a virtual factory tour function. In this embodiment, a function is implemented that allows users to virtually tour the inside of a factory using VR technology and visually confirm the manufacturing process and quality control system.

[0089] The VR Factory Tour Department turns footage of the inside of a factory taken with a 360-degree camera into VR content that can be viewed on a head-mounted display or web browser. It allows users to virtually check the operating status of manufacturing equipment, the skill level of workers, the implementation status of quality control, and more. It also provides a function that provides detailed explanations of equipment specifications and technical features using audio guides and explanatory text. However, the VR content is limited to the situation at the time of shooting and does not reflect real-time factory conditions, so it is not a complete substitute for an actual factory tour; these virtual tour functions merely provide supplementary information. Thirty-first embodiment

[0090] At least one embodiment provides a system having a technology trend analysis function, which analyzes the latest trends in manufacturing technology and material technology and implements a function to predict future technological innovations.

[0091] The Technology Trends Analysis Department extracts technological trends from sources such as academic papers, patent information, industry news, and technology exhibition reports, and predicts the maturity and timing of practical application of emerging technologies. It continuously monitors progress in technology fields such as 3D printing, nanomaterials, IoT, and robotics, and analyzes their impact on the manufacturing industry. It identifies components and processes likely to be affected by technological innovations and promotes early consideration of countermeasures. However, because technological developments are difficult to predict and are greatly influenced by social and economic conditions, these predictions are for reference only, and important decisions such as investment decisions require careful consideration. Other information gathering methods, not limited to forecasting methods, should also be used in conjunction with these methods. Thirty-second embodiment

[0092] In at least one embodiment, a system with an automatic quotation generation function is provided. In this embodiment, a function is implemented to automatically generate a standard quotation format based on the results of AI analysis, reducing the administrative burden on the factory.

[0093] The quotation generation unit integrates cost calculation results, manufacturing specifications, delivery date information, etc., and automatically arranges them in an industry-standard quotation layout. It also provides a function to adjust the level of detail of the quotation (approximate, detailed, ultra-detailed, etc.) according to customer requests and display only the necessary items. It also has the ability to learn from past quotation templates and generate customized quotation that reflects the characteristics of each factory. However, because the contents of the quotation carry legal responsibility, the automatically generated results are only drafts, and final content confirmation and approval require human judgment, so other efficiency techniques besides these automation functions can also be applied. Thirty-third embodiment

[0094] At least one embodiment provides a system having a competitive analysis function, which analyzes the trends of competitors in the market and evaluates the competitive advantage of one's own company.

[0095] The Competitive Analysis Department analyzes competitors' technological capabilities, pricing levels, service details, etc. from publicly available information (websites, press releases, financial reports, etc.) and generates comparative reports with the company. It visualizes the company's competitive position using indicators such as market share, customer satisfaction, and technological innovation, and identifies areas for improvement. It also monitors competitors' new technology adoption and capital investment trends, enabling early detection of signs of market change. However, competitive analysis is merely speculation based on publicly available information and may differ from the actual competitive situation. Therefore, these analysis results are provided only as reference information, and strategic decisions require careful consideration. Thirty-fourth embodiment

[0096] In at least one embodiment, a system having a disaster response function is provided. In this embodiment, the system is equipped with a function for predicting the impact of natural disasters and man-made disasters on the manufacturing industry and supporting the formulation of a business continuity plan (BCP).

[0097] The Disaster Impact Analysis Department evaluates the risk of natural disasters such as earthquakes, typhoons, and floods based on geographical conditions, and quantifies the disaster vulnerability of each factory. It predicts the probability and duration of supply chain interruptions based on past disaster cases and recovery time data. It automatically generates countermeasures such as selecting alternative factories, dispersing inventory, and establishing an emergency communication system, supporting the formulation of BCPs. However, because the scale and impact of disasters are difficult to predict and unexpected situations can occur, these prediction results are only for reference; the formulation of an actual BCP requires the expertise of experts, and continuous review of disaster response is important. Thirty-fifth embodiment

[0098] At least one embodiment provides a system having a cost structure visualization function. In this embodiment, a function is implemented to perform a detailed analysis of the calculated cost breakdown and identify potential cost reduction opportunities.

[0099] The Cost Analysis Department breaks down cost elements such as material costs, processing costs, and administrative costs hierarchically, and visualizes the percentage of each element in the total cost using pie charts and bar graphs. It identifies abnormally high cost elements by comparing with past similar parts and the industry average. It quantitatively presents cost reduction proposals through material changes, process improvements, volume effects, etc., and predicts the reduction effects. However, because cost structures are determined by complex factors, elements can be overlooked in a simple comparative analysis, and the results of these analyses are only estimates, so detailed cost reduction studies require specialized knowledge from the manufacturing site. Thirty-sixth embodiment

[0100] At least one embodiment provides a system with a design automation function, which automatically generates an optimal design that satisfies required specifications while taking into account manufacturing and cost constraints.

[0101] The design optimization unit evaluates the performance of design proposals using simulation technologies such as strength calculations, thermal analysis, and fluid analysis, and optimizes the shape using genetic algorithms and topology optimization. It optimizes objective functions such as minimizing weight and maximizing strength while satisfying manufacturing constraints (minimum machining diameter, draft angle, etc.) and economic constraints (target cost, delivery date, etc.). It visualizes the relationship between performance and cost resulting from design changes in a Pareto chart to support designer decision-making. However, design optimization is a theoretical value based on a calculation model, and there may be discrepancies with actual performance. These automated design results are only for reference, and the final design decision must be made by the designer. Thirty-seventh embodiment

[0102] At least one embodiment provides a system having a customer behavior analysis function. In this embodiment, the system analyzes the behavioral patterns of platform users and implements a function to provide individually customized services.

[0103] The behavioral analysis unit collects and analyzes user behavior data, such as login times, viewed pages, search keywords, and quotation request patterns, to identify individual preferences and trends. Similar users are clustered using machine learning, and persona-based service optimization is performed. A personalized user experience is provided, including suggested factories, price alert settings, and priority notification of new features. However, behavioral analysis is subject to limitations from the perspective of privacy protection, and there are limits to the accuracy of the analysis. These personalization features are provided only as reference information, and the final decision must be made by the user. Thirty-eighth embodiment

[0104] At least one embodiment provides a system having a standardization promotion function. In this embodiment, the system is equipped with a function for promoting the standardization of part design and manufacturing processes, thereby realizing cost reduction and quality improvement.

[0105] The Standardization Analysis Department extracts frequently used part shapes, materials, tolerances, etc. from past design data and builds a standard parts library. It recommends the use of standard parts when creating new designs, and when custom parts are required, it proposes design proposals that comply with standard specifications. It also promotes the standardization of manufacturing processes between factories, helping to reduce quality variations and establish a mutual backup system between factories. However, standardization also has an aspect of limiting design freedom, and it is difficult to meet all requirements with standard products. Therefore, promoting standardization is just one approach, and it is important to strike an appropriate balance with individual optimization. Thirty-ninth embodiment

[0106] At least one embodiment provides a system having a contract management function. In this embodiment, all contract processes from order placement to delivery are managed in an integrated manner, and a function for monitoring the status of contract fulfillment is implemented.

[0107] The Contract Management Department automatically tracks each stage, including quotation approval, purchase order issuance, start of production, progress report, inspection, and payment, and visualizes the contract fulfillment status on a dashboard. It also detects risks of contract non-fulfillment, such as delivery delays and quality issues, and sends warning notifications to relevant parties. It also automates exception handling, such as managing changes to contract terms, processing additional orders, and handling returns and exchanges. However, interpreting contract content and resolving disputes requires legal expertise, and there are limits to how much automated processing a system can perform. These management functions merely streamline administrative processes, and important decisions still require human confirmation. Fortieth Embodiment

[0108] At least one embodiment provides a system having an engineer skill management function. In this embodiment, a function is implemented to quantify the skill levels of engineers working in the manufacturing industry and support appropriate task allocation.

[0109] The Skills Evaluation Department comprehensively analyzes an engineer's years of experience, qualification status, past manufacturing performance, quality evaluation, etc., and quantifies their skill level by technical field. It estimates the skill level required for new projects, recommends suitable candidates, and identifies skills that are lacking. It also recommends training programs for engineers and provides functions to support planned skill improvement. However, an engineer's skills also depend on experience and intuitive judgment, which are difficult to quantify, and cannot be fully expressed by numerical evaluation alone. Therefore, these evaluation results are only for reference, and actual work assignments are largely determined by the judgment of on-site managers. Forty-first embodiment

[0110] At least one embodiment provides a system with a raw material traceability function. In this embodiment, the system traces the entire flow of raw materials used in a product, from the source of procurement to the final product, and implements a function to support rapid investigation of the cause when a quality problem occurs.

[0111] The Traceability Management Department uses blockchain technology to record raw material lot numbers, manufacturer information, quality inspection results, and other information, creating unalterable historical information. Processing conditions, workers, inspection results, and other information for each manufacturing process are also recorded, enabling traceability from the final product back to the raw materials. When a quality issue occurs, the extent of the impact and cause analysis are quickly performed, and recall targets are also automatically identified as necessary. However, traceability systems rely on the accuracy of records, and input errors or omissions can undermine their reliability. These tracking functions are not perfect, and detailed analysis by experts is required to investigate serious quality issues. Forty-second embodiment

[0112] At least one embodiment provides a system having a manufacturing cost prediction function. In this embodiment, a function for predicting long-term manufacturing cost trends is implemented, taking into account future fluctuations in material prices, rising labor costs, equipment depreciation, etc.

[0113] The Cost Forecasting Department analyzes past price fluctuation data, economic indicators, industry trends, etc., and calculates future cost trends using a time-series forecasting model. It provides forecast results under multiple scenarios (optimistic, realistic, pessimistic), taking into account the effects of seasonal fluctuations, business cycles, technological innovation, etc. It provides information that can be used to set price adjustment clauses in long-term contracts and determine the timing of capital investment. However, future forecasts are inherently uncertain and can fluctuate significantly due to unexpected external factors (disasters, changes in political situations, etc.). Therefore, these forecast results are for reference only, and important decision-making requires a comprehensive review of multiple information sources. Forty-third embodiment

[0114] At least one embodiment provides a system with a manufacturing quality prediction function. In this embodiment, the system implements a function to predict the quality level of the final product in advance based on design specifications, material properties, manufacturing conditions, etc., and to support the prevention of quality problems.

[0115] The Quality Prediction Department uses machine learning to learn the correlation between past manufacturing data and quality results, predicting the quality risks of new products. It comprehensively evaluates the mechanical properties of materials, the stability of processing conditions, the skill level of workers, and other factors to quantify the probability of defects and quality variations. Based on the prediction results, it proposes optimization of manufacturing conditions and additional inspection items, achieving preventative quality control. However, because product quality is affected by many factors, perfect prediction is difficult, and these prediction functions only provide reference information. An appropriate inspection system and continuous improvement activities are essential for actual quality assurance. Forty-fourth embodiment

[0116] At least one embodiment provides a system with a supply chain optimization function. In this embodiment, a function is implemented to support supply chain design that manages multiple factories, logistics companies, and inventory bases in an integrated manner and achieves both minimization of total costs and shortened delivery times.

[0117] The supply chain optimization unit calculates the optimal supply chain configuration using mathematical optimization techniques, taking into account factors such as demand forecasts, production capacity, transportation costs, and inventory costs. It also takes into account constraints such as geographical constraints, risk diversification, and sustainability. It evaluates robustness against demand fluctuations and supply constraints and proposes a flexible supply chain design that can accommodate multiple scenarios. However, supply chain optimization is a complex combinatorial problem, and there are trade-offs in calculation time and solution quality. These optimization results are merely theoretical values, and many adjustments are required for actual operation. Forty-fifth embodiment

[0118] In at least one embodiment, a system having a technology transfer support function is provided. In this embodiment, a function is implemented to facilitate technology transfer from a factory with advanced manufacturing technology to a factory that wishes to acquire that technology.

[0119] The Technology Transfer Support Department matches technology providers with technology acquirers and manages the entire process, from concluding technology transfer agreements to providing actual technical training. It achieves efficient technology transfer through functions such as translating technical documents, providing remote training systems, progress management, and effectiveness measurement. It also creates a continuous support system after technology transfer to help the technology take root and develop. However, technology transfer is subject to intellectual property rights and competitive constraints, and not all technologies are transferable. Therefore, these support functions merely act as an intermediary, and the success of the actual technology transfer depends on the efforts and compatibility between the parties involved. Forty-sixth embodiment

[0120] At least one embodiment provides a system with an energy management function, which monitors and analyzes the energy consumption of manufacturing processes and quantifies the effects of energy-saving measures.

[0121] The Energy Management Department monitors the power consumption, operating hours, load factor, etc. of each manufacturing facility in real time and calculates energy usage efficiency. By comparing with past data, it detects abnormal consumption patterns and identifies equipment deterioration and room for optimizing operating conditions. It also calculates the effect of introducing renewable energy and the payback period of energy-saving equipment, and supports the formulation of energy strategies. However, energy management involves trade-offs with manufacturing quality and productivity, and simply pursuing energy conservation may result in a decrease in overall efficiency. Therefore, these analysis results are only one perspective, and a comprehensive judgment is required. Forty-seventh embodiment

[0122] In at least one embodiment, a system with virtual prototyping capabilities is provided that implements functionality for evaluating product performance and design verification through computer simulation before physical prototypes are built.

[0123] The Virtual Prototyping Department integrates various simulation technologies, including structural analysis using the finite element method, fluid analysis, thermal analysis, and electromagnetic field analysis, to evaluate product performance from multiple angles. The robustness of the design is evaluated through probabilistic analysis that takes into account uncertainties in material properties, manufacturing tolerances, and the operating environment. It also provides a function to visualize simulation results in 3D and support intuitive understanding. However, simulation accuracy depends on the assumptions made in the analysis model, and it is difficult to completely reproduce actual phenomena. Therefore, these virtual test results are only for reference, and for important products, verification through physical testing is essential. Forty-eighth embodiment

[0124] At least one embodiment provides a system having a function for automatically generating work instructions. In this embodiment, a function is implemented to automatically generate detailed work instructions for workers from manufacturing drawings and process plans, thereby supporting efficiency improvement at the manufacturing site.

[0125] The work instruction generation unit combines drawing information with standard operating procedures to automatically create part-specific work instructions. It automates the optimization of work procedures, tool selection, and quality checkpoint settings, standardizing the knowledge of experienced workers. It also has a function to adjust the level of detail in the instructions according to the worker's skill level, generating instructions optimized for each individual. However, in manufacturing, tacit knowledge not recorded on drawings is important, and automatically generated instructions alone may not be sufficient. Therefore, these standardization functions are merely basic support; on-site ingenuity and improvement activities are essential to improving quality and efficiency. Forty-ninth embodiment

[0126] At least one embodiment of the present invention provides a system having a failure prediction function. In this embodiment, the system is equipped with a function for predicting equipment failures in advance based on status monitoring data of manufacturing equipment and supporting the optimization of preventive maintenance.

[0127] The failure prediction unit uses machine learning to analyze equipment status data acquired from vibration sensors, temperature sensors, current sensors, etc., to detect precursor patterns of failure. Past failure history and correlation analysis are used to identify precursor signals for each type of failure, and calculate the probability of failure and the expected timing of failure. An optimal maintenance plan is created based on the prediction results, minimizing the impact of planned shutdowns on production. However, equipment failures occur due to complex factors, and it is impossible to predict all failure patterns. Therefore, these prediction functions only provide supplementary information, and it is important to combine them with the judgment of experienced maintenance engineers. 50th embodiment

[0128] At least one embodiment provides a system having a continuous improvement support function, which analyzes various data of the manufacturing process, automatically detects improvement opportunities, and supports continuous improvement activities.

[0129] In addition to traditional statistical methods, the Improvement Opportunity Analysis Department uses artificial intelligence, machine learning, deep learning, reinforcement learning, meta-learning, transfer learning, lifelong learning, curriculum learning, self-supervised learning, contrastive learning, multi-task learning, multimodal learning, cross-modal learning, federated learning, continuous learning, online learning, active learning, semi-supervised learning, weakly supervised learning, zero-shot learning, few-shot learning, in-context learning, prompted learning, chain of sorts, thought trees, iterative reasoning, self-correction, critical thinking, creative thinking, analogical thinking, direct learning, and more. Utilizing current and future artificial intelligence, cognitive science, and neuroscience technologies, including but not limited to sensory thinking, insightful thinking, integrative thinking, systems thinking, design thinking, computational thinking, and quantum thinking, we continuously monitor and analyze multidimensional indicators such as production efficiency, quality levels, cost structure, customer satisfaction, employee satisfaction, environmental impact, social impact, innovation level, sustainability, resilience, agility, diversity, and inclusion, to identify areas for improvement, hidden problems, potential risks, new opportunities, creative solutions, and disruptive innovations. The analysis utilizes a combination of a variety of analytical methods, including benchmarking with other companies, industry benchmarking, international benchmarking, historical benchmarking, theoretical benchmarking, internal process comparisons, time series change analysis, causal analysis, correlation analysis, abnormal value analysis, outlier analysis, trend analysis, seasonal analysis, periodicity analysis, structural change analysis, scenario analysis, sensitivity analysis, stress testing, Monte Carlo simulation, agent-based modeling, system dynamics, complex network analysis, game theory analysis, behavioral economics analysis, cognitive psychology analysis, social psychology analysis, and organizational psychology analysis, to generate comprehensive and in-depth improvement proposals that go beyond the limits of human cognition.In addition, past improvement cases, success cases, failure cases, cancellation cases, postponement cases, etc. are structured and databased using graph databases, knowledge graphs, ontologies, semantic networks, Bayesian networks, causal graphs, decision trees, random forests, etc., and solutions, alternative measures, preventive measures, symptomatic treatments, fundamental treatments, etc. for similar problems, related problems, derived problems, inverse problems, meta-problems, etc. are provided through functions such as automatic recommendation, dialogue recommendation, explainable recommendation, transparent recommendation, and fairness-ensuring recommendation. However, the essence of improvement activities depends heavily on human and social factors such as on-site ingenuity, employee initiative, organizational culture, leadership, teamwork, communication, trust, shared values, a shared vision, and a shared sense of purpose, and system proposals merely provide reference information, decision-making support, learning support, and communication support.In addition to these support functions, what is most important is to improve employee engagement, strengthen organizational learning capabilities, foster a culture of innovation, build a culture of continuous improvement, promote a data-driven culture, improve digital literacy, develop critical thinking, encourage creative thinking, spread systems thinking, and utilize design thinking.The harmonious integration of technical solutions with human and organizational solutions is the key to successful continuous improvement. Fifty-first embodiment

[0130] In at least one embodiment, a post-design integrated quotation platform system is provided. In this embodiment, when a user completes design work and posts design information such as final design drawings, 3D CAD data, specifications, and BOM (bill of materials) to the platform, the system automatically executes a comprehensive quotation service based on various manufacturing conditions and vendor characteristics.

[0131] The design information reception unit automatically analyzes the specified manufacturing conditions from the uploaded design data. If a material is specified in advance (e.g., explicit specifications such as "use SUS304 stainless steel" or "aluminum alloy A6061"), a quote is generated assuming manufacturing using the specified material. If no material is specified, the AI ​​material recommendation engine comprehensively evaluates the part's use, strength requirements, environmental conditions, cost constraints, etc., and proposes multiple optimal material candidates (e.g., first recommendation, second recommendation, cost-oriented, performance-oriented, etc.), and performs parallel quotes for each material. If weight (in grams) is included in the design data, that value is used; if not, it is automatically calculated based on the volume calculation of the 3D CAD data and material density. If quantity is specified (e.g., "1 prototype" or "1,000 mass production"), a quote is generated based on that condition. If not specified, automatic quotes are generated in parallel for multiple options (e.g., 1 prototype, small lot of 10 units, medium lot of 100 units, large lot of 1,000 units, extra-large lot of 10,000 units, etc.).

[0132] The vendor profiling engine analyzes and evaluates the characteristics of each registered manufacturer across multiple dimensions. Using past order data, it calculates scores for factors such as "large-lot production capability" (monthly production capacity, facility size, personnel structure, etc.), "small-lot flexibility" (minimum lot size, setup efficiency, individualized response capabilities, etc.), "prototype development capability" (engineer skills, facility precision, development speed, etc.), "high-precision processing capability" (facility precision, quality control system, technical certification, etc.), "short-term delivery capability" (priority production system, inventory management, logistics efficiency, etc.), "cost competitiveness" (price level, efficiency, economies of scale, etc.), "technological innovation" (introduction of new technologies, R&D investment, patent ownership, etc.), "quality stability" (defect rate, complaint rate, certifications, etc.), "environmental awareness" (ISO14001, carbon neutrality, waste reduction, etc.), and "international response capability" (export performance, multilingual support, compliance with international standards, etc.).

[0133] The smart matching algorithm performs a multidimensional match between the required specifications of new parts (materials, dimensions, tolerances, quantity, delivery time, quality, etc.) and each supplier's characteristic score, automatically selecting the most suitable candidate supplier. For prototypes, it weights "prototype development capabilities," "technological innovation capabilities," and "ability to respond to short delivery times," for large-lot production it weights "large-lot production capabilities," "cost competitiveness," and "quality stability," and for high-precision parts it weights "high-precision processing capabilities," "quality stability," and "technical certification," etc. The matching results are presented along with a compatibility score, allowing users to select a supplier based on objective data.

[0134] The dynamic pricing proposal system automatically generates individually optimized pricing proposals for each of the extracted candidate vendors. It analyzes each vendor's areas of expertise, current order status, equipment utilization rate, past price trends, market competition, seasonal factors, etc. in real time to calculate multiple pricing options such as "standard price," "competitive bid price," "value-added price," and "urgent price." It also automatically creates detailed estimates that include tiered pricing according to quantity (economies of scale), price adjustments according to delivery date (rush fee, regular rate, marginal delivery discount), quality options (standard quality, high quality, ultra-high quality), and additional services (surface treatment, heat treatment, inspection, packaging, delivery, etc.), and presents them to designers in an easy-to-understand comparison table format.

[0135] Through a transparent price adjustment mechanism, some of the quote information presented to users (statistical information such as market price ranges, competitive conditions, and supply-demand balance) is shared with each manufacturer in an anonymized form. However, specific quotes and company names from other companies are kept confidential, and information is provided only to the extent that it does not impede fair competition. This provides an environment in which manufacturers can objectively understand their own price positioning and independently consider price revisions, changes to terms, value-added proposals, and technical improvement proposals as necessary. This transparent price formation process creates a healthy market environment that balances fair prices for purchasers and fair profits for recipients. However, price determination is entirely left to the independent business judgment of each vendor, and the system is designed to not encourage price control or collusion, and strictly adheres to relevant laws and regulations, including the Antimonopoly Act. Fifty-second embodiment

[0136] At least one embodiment provides an integrated manufacturing platform for the printing and publishing industry. This embodiment significantly expands on existing systems focused on metal processing and machine part manufacturing, implementing a set of functions specialized for quotation, ordering, and manufacturing management for graphic-related products such as printed matter, publications, packaging, and promotional materials.

[0137] The print data analysis engine automatically analyzes native files created with major design software such as Adobe Creative Suite (Illustrator, Photoshop, InDesign, Acrobat, etc.), CorelDRAW, and QuarkXPress, as well as standard print formats (PDF / X, EPS, AI, PSD, TIFF, JPEG, etc.). It extracts detailed technical specifications directly related to print production, such as color information (RGB, CMYK, spot colors (Pantone, DIC, etc.), gold and silver), resolution (72 dpi, 150 dpi, 300 dpi, 600 dpi, etc.), image quality, vector / raster mix, font embedding, transparency effect usage, overprint / knockout settings, trapping settings, bleed / margin settings, etc.) and other design complexity (number of colors, gradients, special effects, number of layers, etc.) and calculates a printability score.

[0138] The automated printability diagnostic system comprehensively checks extracted design data from the perspective of print quality. It automatically detects issues such as insufficient image resolution (e.g., using 72 dpi web data for commercial printing), over-gamut issues (e.g., color loss when converting from the RGB gamut to the CMYK gamut), font problems (e.g., failure to outline, use of fonts incompatible for printing), insufficient bleed, inadequate crop marks and guides, problems with mixing spot and process colors, printability of thin lines and small letters, and compatibility of paper quality and printing method. It also provides advance warning of potential quality issues and suggested solutions. For detected issues, it also offers suggestions for automatic corrections, alternative designs, and changes to printing methods.

[0139] The printing materials and specifications selection advisor automatically selects and proposes the optimal specifications from among paper type (high-quality paper, coated paper, matte paper, art paper, Japanese paper, recycled paper, synthetic paper, waterproof paper, light-resistant paper, scented paper, antibacterial paper, etc.), paper thickness and weight (various standards such as ream weight, grammage, and thickness in μm), paper size (standard and special sizes such as A, B, 46, 56, 69, and Hatron), surface treatment (gloss PP, matte PP, gloss varnish, matte varnish, UV printing, foil stamping, embossing, debossing, etc.), binding specifications (saddle stitching, perfect binding, hardback binding, spiral binding, ring binding, etc.), special processing (die cutting, window cutting, perforation, folding, lamination, etc.), taking into comprehensive consideration the end use of the printed material (commercial printing, publication printing, package printing, outdoor advertising, in-store POP tools, etc.), required quality level, durability requirements, environmental requirements, budget constraints, etc.

[0140] The printing company characteristics database provides detailed profiling of each printing company's areas of expertise (offset printing, digital printing, gravure printing, flexographic printing, silkscreen printing, large-format inkjet printing, 3D printing, etc.), details of equipment owned (printer manufacturer and model, number of colors, maximum print size, print speed, post-processing equipment, etc.), quality control system (ISO9001, ISO12647, G7 certification, JAPAN COLOR certification, etc.), special technology capabilities (UV printing, scent printing, 3D printing, holographic printing, phosphorescent printing, thermo-indicating printing, etc.), lot size range (on-demand single copy support, small lot specialty, medium lot standard, large lot support, extra-large lot support, etc.), delivery date support capabilities (same-day finishing, next-day finishing, short delivery time support, standard delivery time, long-term planning support, etc.), and ancillary services (planning and design, photography, design revisions, proofreading, delivery, inventory management, etc.), enabling precise matching with the required specifications of printed materials.

[0141] The printing cost calculation system accurately reflects the complex cost structure unique to the printing industry. It calculates detailed cost information by item, including basic fees (initial costs such as plate costs, plate making costs, CTP costs, and printing plate costs), paper costs (unit price of paper x amount used + loss rate), ink costs (unit prices by type such as CMYK process ink, spot color ink, UV curable ink, etc.), printing labor costs (press operation costs, labor costs, electricity costs, etc.), post-processing costs (process costs for cutting, folding, collating, binding, surface treatment, special processing, etc.), inspection and quality control costs, packaging and delivery costs, and administrative overhead. It also provides realistic pricing information that reflects dramatic price fluctuations based on lot size (e.g., effects of plate cost dispersion, improved paper handling efficiency, improved work efficiency), fee fluctuations based on delivery date (e.g., express rates, standard rates, flexible delivery discounts), seasonal factors (e.g., charges for busy periods such as the year-end and New Year holidays and fiscal year-end), and quality options (e.g., color proofing, machine proofing, and on-site inspection fees). However, please be aware that prices in the printing industry are subject to large fluctuations due to external factors such as fluctuations in paper prices, ink prices, exchange rates, and supply-demand balance, and that the calculated prices are for reference only, with the final prices being determined through direct negotiations with each printing company.

Claims

1. An information processing device in a BtoB integrated trading platform system for the manufacturing industry, in which a purchaser uploads design drawings and specifications and automatically obtains quotations from multiple manufacturers, comprising: (a) a design information analysis unit that extracts geometric features and material requirements from the uploaded design drawings; (b) a material selection unit that estimates suitable materials for manufacturing based on the extracted requirements; and (c) a cost calculation unit that works in conjunction with a manufacturer database to refer to the factory profile information of each manufacturer and calculates material costs, processing costs, and labor hours, wherein the processing of each of the above units is performed using a machine learning model, and the information processing device outputs comparable quotations from multiple manufacturers, including manufacturer suitability evaluation information, to the purchaser.

2. 2. The information processing device according to claim 1, wherein the design information analysis unit has a multidimensional analysis function for comprehensively analyzing 2D drawings, 3D CAD data, and text specifications.

3. 3. The information processing device according to claim 1, wherein the factory profile information includes equipment capacity, supported material types, and quality certification information of each manufacturer, and the material selection unit has a function of selecting an optimum material from among metal materials, resin materials, and ceramic materials.

4. 2. The information processing device according to claim 1, wherein the cost calculation unit has a function of calculating material costs, processing costs, and management costs by item, and evaluating the manufacturability and delivery date suitability of each manufacturer as the manufacturer suitability evaluation information.

5. 2. The information processing device according to claim 1, further comprising a security management unit that integrates and manages the information extracted by the design information analysis unit and the estimate information through encryption processing and that has an access control function.

6. 2. The information processing device according to claim 1, wherein the BtoB integrated trading platform system is connected to a purchaser terminal and a plurality of manufacturer terminals via a network, and has a real-time quotation information exchange function.