MACHINE LEARNING-DRIVEN DYNAMIC PRICING SYSTEM WITH BLOCKCHAIN SECURITY FOR E-COMMERCE

Abstract

This invention describes an approach of developing a smart and dynamic pricing structure that is based on a machine learning approach for e-commerce applications, however, it also uses Blockchain technology to ensure security and integrity of the relationship between prices and customers. Competitors’ prices, sales trends, customer class, market forces, and other dynamic data inputs are fed into the system and real time price setting is done by adjusting price ranges to market requirements. Pricing decisions are stored through distributed and cryptographic blocks that imply immutability, enabling compliance with legal requirements and building customer trust. Further, a verification interface enables a consumer to analyze the pricing decisions, enhancing the aspect of transparency. It is fully compatible with e-commerce platforms and offers a safe, flexible, and effective method for real-time pricing.

Specification

Description:Field of the Invention:
[001] The present invention belongs to the e-commerce technology category with a specific emphasis on a Machine Learning-Driven Dynamic Pricing System with Blockchain Security. This invention uses machine learning for dynamic pricing in real-time with the support of consumer and market intelligence and incorporating blockchain for pricing transactions authenticity. This model offers an effective and clear pricing model that improves the credibility and profitability for online stores.
Background of the Invention:
[002] A dynamic pricing model has become a popular tool for e-retailers in the current market environment for achieving the highest levels of revenues and satisfying market dynamics. Fluctuating pricing or dynamic pricing which is the act of changing prices of products in response to certain factors such as demand or competitor's prices and consumer behavior, proves beneficial in that it increases customer satisfaction with prices that are current and adjusted to suit certain conditions. However, the current dynamic pricing systems do not come equipped with such features, as they have been developed using fairly conventional static algorithms that are ill-suited for correctly analyzing large swaths of highly dynamic data in real time, leading to flawed pricing strategies. This challenge opens up a demand for a more dynamic pricing strategy that can be supported by data analytics and artificial intelligence.

[003] The evolution of ML has created potentiality on dynamic pricing. Some of the benefits of artificial intelligence in applications of generating dynamic prices include; Machine learning models have an ability to deal with large databases in which traditional algorithms deny them an ability to see the connection. This has made it possible for pricing systems to learn from experiences, anticipate future markets and set prices instantly making pricing systems advantageous to retailers. However, the issues of data protection and their availability continue to be a top concern up to this date. Pricing algorithms remain another significant concern among the consumers especially where the adjustments are done in real time and in disguise, which undermines consumer confidence.
[004] Blockchain technology appears to hold this as a solution to security and transparency problems. Blockchain could support all pricing changes and data transactions through an indelible record when transaction data are stored on an unalterable distributed ledger. Such high level of security is important in the process of consumer protection and trust since they can be assured of their freedom from any form of malpractice or fraud. However, combining blockchain with machine-learning-based pricing methods while providing a guarantee of optimal pricing as well as the integrity of user data remains a technological problem that has not been finally solved.

[005] The present invention, the Machine Learning-Driven Dynamic Pricing System with Blockchain Security for E-Commerce fills these gaps through the integration of machine learning and the blockchain. This invention reflects on an efficient and sophisticated way of pricing products and services in that prices can change depending on real-time data while guaranteeing that all pricing and transaction records are safe and can be trusted by the customers. This system can be considered as a new efficient and safe concept in solving the problem of dynamic pricing for being profitable but at the same time, transparent to the accounts of the e-commerce market. By this invention, the online retailers can execute the data-intensive pricing policies that can reflect varying and intricate market scenarios where all the changes done at any given time are trackable and auditable.

Summary of the Invention:
[006] The Machine Learning-Driven Dynamic Pricing System with Blockchain Security for E-Commerce is a sophisticated technological tool that can help e-retailers distinguish optimal pricing strategies through real-time, quantitative decision-making, with security and sharing connected through blockchain. The current invention combines the machine learning feature with a block chain to form one system of having automatic dynamic prices system that is able to respond to various inputs while having a block chain to record each transaction individually.

[007] In its essence, the system uses machine learning techniques to predict the numerous factors affecting price determination including current demand, competitor prices, previous buying behaviours, and customer categories. Through this analysis, the system provides updates prices based on constantly updated market conditions. These recommendations are dynamic which means that the system continuously unlearns and Learns from the previous transactions done with the customers. This approach means that the pricing strategy is dynamic and adjusts to the market and customer needs, thus placing the retailers in a strategic position especially when the market is shifting frequently.

[008] Besides effective dynamic pricing model, the system incorporates blockchain technology to work towards minimizing the problem of transparency, accurate record keeping and data security. Each price change, purchase or sale, and data exchange in the system is recorded on a blockchain. This makes all pricing data secure and open to an audit, which enables them to foster trust with consumers since no one can alter or manipulate prices. Transparency also enhances accountability in pricing practices, since authorized parties can easily check the validity of all the transactions recorded in the block chain. From the consumers' side, this results in the ability to have full trust in the price model, understanding that each deal goes through a sound and safe trail.

[009] The system features a dashboard view allowing e-commerce managers to fine-tune parameters and pricing strategies, examine trends in consumer purchasing patterns, competition, and own sales data. This operating environment facilitates the communication between users and the large and interdependent data set and model settings where users can apply the desired changes manually or automate the pricing process strictly according to its objectives, such as the maximum sales or profit per cent. Also, there is the analysis of the measures of different price strategies, which is rather useful for the retailers to monitor the results of their activities and make proper improvements basing on the collected data.

[010] The integrated system of Machine Learning-Driven Dynamic Pricing System with Blockchain Security is therefore comprehensive, flexible and secure to meet the demands of contemporary e-business. Combining the accuracy of machine learning with the reliability of blockchain, this invention provides online merchants with an effective way for competitive pricing based on their pricing data while being reliable, secure, and transparent for the consumers. This system does not only define how firms can increase profitability but it also ensures that there is equity in the market place making it a key advancement in the area of e-commerce.

Description of the invention:
[011] The presented invention Machine Learning-Driven Dynamic Pricing System with Blockchain Security for E-Commerce is an innovative combination of the adaptive machine learning algorithms and the decentralized blockchain technology designed to provide accurate and real-time data-driven pricing and advanced data protection. They provide real-time, multi-dimensional market condition adaptive pricing tools, while the underlying technology of encryption and transparency supports consumer and regulatory confidence. Below, the inventor identifies the key details of this invention's functional modules and architectural components.

[012] Machine Learning-Enhanced Dynamic Pricing Engine: At the core of this invention is a novel machine learning based pricing algorithm that can take in high dimensional inputs and generate contextually appropriate dynamic pricing responses. The system receives a flow of various signals, or rather inputs, encompassing but not limited to the microvariables such as current demand volatility, competitor's prices, past customer-buying behaviors, inventory levels, and even macrovariables such as trends in the economy. By employing reinforcement learning and other forms of supervised learning, such feedback forms a loop through which the system increases its correct forecast and response in terms of changing prices to the intended revenue optimization plan or market domination strategies. This mechanism provides the capability to the system to set pricing on its own where traditional preprogrammed pricing models totally lack, thus providing adaptivity to the system necessary in a dynamic e-commerce environment.

[013] The machine learning model implements both the batch and online learning strategy, which help in the continual updating of the model based on the newly accumulated data. For example, the model can set certain attributes, which may include customer loyalty information and seasons, thus enhancing the system's capacity to weigh its price recommendations. The end product here is thus a flexible and contextually cognisant pricing engine that is naturally outfitted with the capacity to learn new patterns and thusly enhance the accuracy of the values that it produces with every subsequent application.

[014] Blockchain-Secured Transaction and Price Adjustment Ledger: The framework has a blockchain system which creates a secure and unalterable record of every pricing change, every transaction, and interaction with data. This component ensures that every price change and a customer transaction are recorded based on the decentralized ledger, thus avoiding data manipulation and providing audit trails. Blockchain technology leverages distributed ledgers to bring in transparency in security architecture since every node on the network will have to validate each transaction before including it on the chain to reduce the potential of the underside manipulation.

[015] Centralized Command Dashboard with Analytical Reporting: The features of the invention are a central control console, a graphical application with various tools, which helps administrators to control the pricing policies, to view the current statistics and evaluate the accounts reflecting the past transactions. This type of pricing strategy brings the benefits of market analysis, customer behavior analysis, competitor's price analysis and the effectiveness of the pricing approach into one dashboard. Thus, a user can set up certain price parameters, enter specific profitable points, define discount limits, or register certain coefficients of elasticity to add more detailed logic into dynamic price strategy, pursuing certain strategic aims.

[016] In addition to the manual setup, the dashboard handles an automatic mode, in which the AI-based models continuously modify the price parameters on their own to meet predefined goals, including profit margin or stock sales. Such an approach is more flexible, so business users can manage the balance between automated machine learning adjustments and their override. In combination with performance reports, which can be displayed on the same dashboard, users get a set of recommendations on conversion rate impact, sales velocity, and customer segment profitability to adjust and fine-tune pricing rules.

[017] Integration with External Data Sources and Market Intelligence Feeds: To achieve complete information acquisitions for the price optimization, the system should enable integrated interface with external data inputs such as but not limited to real-time competitor price feed, industry trends collectors, stock management systems, and customer relations management (CRM) systems. This integration of data helps provide a dispersed and real-time view of the market environment needed for dynamic pricing; the system derives data from other streams to form a real-time, multi-view model of the market.

[018] This determines how much more reliable one data source is relative to another, and these weights change over time depending on changing context, such as seasonality or fluctuations in demand. This process ensures that the pricing engine has incorporated all the elements that are vital in arriving at a proper pricing decision while providing real time adjustment which has the ability to improve the system's flexibility in responding to changes in the market. For instance, the engine when coupled with historical price data together with current competitor prices during the high potent shopping seasons accurately determines pricing points.

[019] Consumer-Facing Transparency Features and Regulatory Compliance Mechanisms: This invention also encompasses techniques that facilitate transparency and enable compliance with regulatory requirements. Through the integration of the blockchain, consumers and the regulatory bodies receive evidence of the changes in the prices, and record of all transactions. Another transparency initiative gives consumers direct tools to be certain that the price they are quoted reflects fair market costs and is not inflated or fluctuating arbitrarily, integrated into the retailer's online portals. This functionality can be useful when working in industries with strict regulations or in regions where the issue of pricing discrimination is actively monitored.
[020] In the case of regulatory audits, the blockchain layer can generate compliance reports of the modified price, market information, and the particular consumer engagement that will become part of the blockchain layer. This transparency capability also helps in gaining consumer trust and compliance with the legal pricing regulations thus bringing the usefulness of the invention to multiple e-commerce verticals.

[021] Continuous Model Training and Evolutionary System Updates: The invention is architected to undergo continuous improvement through ongoing data acquisition and model retraining. As new transaction data and market behavior patterns are incorporated, the machine learning models autonomously retrain, enabling the system to adapt to evolving customer behaviors, competitive actions, and industry trends. The blockchain component maintains decentralized consistency, further fortifying the system against evolving cybersecurity threats. This adaptability extends the system's operational longevity, ensuring that it remains responsive and capable of optimizing dynamic pricing decisions in perpetually changing e-commerce environments.

BRIEF DESCRIPTION OF THE DRAWINGS
Fig. 1 Integrated Machine Learning and Blockchain-Powered Dynamic Pricing System Architecture

[022] Fig.1 illustrates, the "Machine Learning-Driven Dynamic Pricing System with Blockchain Security" has a complex and closely connected layout in a small space. At the center of the architecture is the Central Processing Hub that handles the integrated data processing, data pre-processing, model building and validation, predictive modeling, and dynamic pricing determination. This central hub connects several external Data Sources (such as competitor prices, customer preferences, and ERP systems) and provides the production of real-time data to the pipeline of this device learning. This way, decisions on pricing are safely stored in the Blockchain Security Layer with reference to a decentralized node system in a more centralized ledger that is open to authorized users only. The combined User Interfaces: Admin Dashboard & Customer interface will interface with the central system and blockchain layer to enable real-time dissemination and monitoring of dynamic prices, parameters settings and secure transaction confirmations. Some of the dependencies of one layer with the next and also the dependencies across layers emphasize the solid, cross-layer structure of the ecosystem, where real-time data trigger AI-based pricing, while the safety and the integrity of the process rely on the blockchain solution.

Fig. 2 Machine Learning-Enhanced Pricing Workflow

[023] Fig.2 illustrates Machine Learning-Enhanced Pricing Workflow shows the main activities and processes that are involved in a progressive pricing model leveraging machine learning and protected through blockchain. The first is the Input Data Layer, which consists of inputs such as competitor prices, historical sales data, customer segmentation, and market conditions all entering the Model Training and Adjustment Process. The process also includes supervised and reinforcement learning models for moderate and severe level that modifies a dynamic pricing model so that the model is updated each time and has enhanced precision. The final pricing output then feeding back on Admin Dashboard and every process data permanently stored in Blockchain Logging for the archiving and audit requirements. A Feedback Loop is created in order to send real-time market response data back to the model to be continuously trained and adapt to better suit the needs of the pricing strategy. This structure helps to facilitate price decisions that are timely appropriate and are anchored through Blockchain.
Fig. 3 Blockchain-Secured Transaction Flow
[024] This Fig. 3 illustrates how the Machine Learning-Driven Dynamic Pricing System integrates blockchain technology to create a secure, transparent process for recording pricing decisions. Starting from the Pricing Engine, each calculated price decision is encrypted and sent to a decentralized network of blockchain ledger nodes. The transaction is validated by these nodes, ensuring tamper-proof, immutable records. Each pricing decision and customer transaction is hashed and permanently stored on the blockchain, making it accessible for future verification and regulatory compliance. Additionally, a consumer verification feature allows customers to review the fairness of pricing through a simple interface, while an automated compliance module generates audit-ready reports based on the stored records, ensuring transparency and trust.
, Claims:1. Real-time, data feed-based price optimization software that quickly adjusts pricing strategies to reflect live data on competitor prices, sales trends, and other factors to ensure peak business performance and customer satisfaction.
2. A method of applying the calculated prices in real-time to different platforms of e-commerce appropriate connection to a dashboard to deploy them in applying real time results without any form of delays and to allow for dynamic response to the market forces.
3. Every pricing decision made is recorded in a blockchain database, and this record is sealed and checked for its integrity, thus increasing the level of trust and accountability for the transaction.
4. Anticipated and rewarding customers with a transparent record of the blockchain technology to check the fairness of the price determination process as a way of enhancing customer trust.
5. A compliance module that utilizes data from the blockchain to generate reports capable of passing through audits and which offer an audit trail of all pricing change processes.

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