478 results about "E-commerce" patented technology

System, process, and method for gamifying physical assets, digital assets, and virtual assets through advertising and e-commerce, and system and method for personalized digital twin LLM chatbot gamification in e-commerce and emotional intelligence development. The first aspect of the invention is a system and process for creating and training a personalized digital twin LLM chatbot assistant. The digital twin is personalized for a given human user using training data regarding the human user. A second aspect of the invention is a system and process of creating and playing an e-commerce game, including the mechanics of the game. The e-commerce game may be created and / or played by employing personalized digital twin LLM chatbot assistant such as that described in the first aspect of this invention. State of the art technologies such as sensors, IoT devices, wearable devices, and VR / XR / AR can be integrated into the game.

The invention relates to the technical field of e-commerce, and discloses an e-commerce customer service processing system with intelligent quality inspection and high-concurrency simulation capabilities. According to the invention, millisecond index calculation and real-time alarm of full customer service dialogues are realized through a bypass monitoring technology, a traditional lagging mode of post sampling inspection is changed, and management response is converted from passive remedy to active intervention; a high-concurrency pressure simulation module reproduces a business scene in an e-commerce peak period by simulating a script of a real user behavior and a distributed cluster, and a real basis is provided for system capacity planning and stability guarantee; the integrated intelligent verbal skill evaluation module deeply analyzes the dialogue quality from multiple dimensions such as semantics, compliance and emotional change based on a natural language processing technology, and generates an evaluation report with guidance, so that the professional level of customer service is improved; and the service quality, the operation robustness and the management intelligence level of the e-commerce customer service system are improved.

The invention discloses an e-commerce advertisement accurate recommendation system based on deep learning, and relates to the technical field of e-commerce, and the system comprises the following modules: an entity index module used for constructing a node registry; the hypergraph construction module is used for continuously monitoring an event stream and constructing a dynamic hypergraph; the initial embedding module is used for extracting a node embedding tensor and a hyperedge embedding tensor; the graph representation learning module is used for extracting a final node embedding representation tensor and a final hyperedge embedding representation tensor by improving a GraphTransform model; the click rate estimation module is used for outputting the predicted click rate of each candidate advertisement; and the sorting decision module is used for screening a candidate advertisement list. According to the method, the limitation that a traditional recommendation method depends on simple characteristics and neglects high-order association and dynamic evolution information is overcome, and a powerful solution is provided for realizing efficient, accurate and personalized e-commerce advertisement recommendation.

The invention relates to the technical field of e-commerce logistics and supply chain management, and discloses a multi-channel e-commerce platform order processing optimization system and method, and the method comprises the steps: correcting physical inventory data through a Lagrange dual variable fed back in a previous period, calculating a virtual inventory, and receiving a discrete order flow; constructing a space-time hypergraph model of the to-be-processed order according to the commodity homogeneous attribute and the spatial neighborhood attribute; performing constraint optimization solution on the hypergraph model based on a Lagrangian relaxation algorithm, and generating an optimal order allocation matrix meeting productivity constraints and a Lagrangian dual variable of a current period; and analyzing the distribution matrix to generate an operation instruction, issuing the operation instruction to the physical warehouse, and feeding back the dual variables to the inventory calculation module. According to the method, collaborative operation benefits are mined through the space-time hypergraph, and a negative feedback closed loop with physical capacity pointing to front-end sales is constructed by using dual variables, so that performance cost optimization and flow adaptive control are realized.

Processing a payment transaction from a payer (operating a payer computing system) to a payee (operating a merchant computing system) by a secure computing system. The secure computing system outputs a financial account registration request form to the payer computing system (e.g., within a window or frame that is displayed within an ecommerce webpage provided by the merchant computing system) for the payer to provide sensitive financial account information, securely stores the sensitive financial account information, maintains compliance with an information security standard (e.g., a Payment Card Industry Data Security Standard), and provides a non-sensitive electronic data token representing the sensitive financial account information to the merchant computing system. The merchant computing system can then process the payment transaction using the sensitive financial account information represented by the non-sensitive electronic data token without actually receiving—and, therefore, having to secure—the underlying sensitive financial account information.

The invention relates to the technical field of e-commerce, and discloses a cross-border e-commerce abnormal order processing method and system based on reinforcement learning, and the method comprises the steps: collecting historical transaction records and real-time behavior records of a user, and obtaining an original data set; performing format unification according to the original data set to obtain a structured data set, and performing multi-dimensional feature extraction to obtain multi-dimensional behavior features; according to the multi-dimensional behavior characteristics, abnormal sequence analysis and risk level classification are carried out, and risk control adjustment parameters are matched; according to the risk control adjustment parameters and the structured data set, environment grouping, risk order identification and abnormal feature extraction are carried out to obtain abnormal distribution features; according to the abnormal distribution characteristics, comprehensive risk analysis is carried out through a pre-constructed neural network model, and a comprehensive risk score is obtained; and performing abnormal order judgment and risk control processing according to the comprehensive risk score and the historical transaction record, and optimizing a neural network model. The method improves the accuracy of abnormal order detection.

The invention discloses a regional consumption demand calculation method based on e-commerce platform data, and belongs to the technical field of regional consumption demand calculation. The problem that a regional consumption demand calculation method is inaccurate in calculation is solved. The method comprises the steps of collecting basic data, obtaining preprocessed regional transaction data through an abnormal value self-adaptive suppression and neighborhood spatial interpolation method, carrying out spatial association, and calculating a regional commodity consumption characteristic entropy value; decomposing a time trend and a space trend in the consumption data through a periodic function and a space overflow model, and calculating a decomposed consumption trend and a decomposed daily average consumption trend; considering the influence of price elasticity and logistics timeliness, and calculating a consumption demand coupling factor; performing spatial heterogeneity correction through a geographical weighted kernel function to obtain a corrected consumption demand coupling factor; the regional commodity demand index is calculated through nonlinear normalization and supply constraint. Compared with a traditional theoretical demand calculation method, the method has the advantage that the landing conversion rate is improved.

The invention relates to the technical field of e-commerce recommendation systems, and discloses a commodity recommendation system and method based on deep learning. The method comprises the steps of obtaining an interaction behavior sequence of a target user, performing framing and slicing to generate a behavior time period data frame set, and extracting multi-dimensional interaction features; entities and relationships are extracted from the commodity knowledge graph, the features are associated and matched with the entities, and a dynamic preference sub-graph is constructed; analyzing the sub-graph by using a multi-modal fusion neural network, and outputting an implicit intention vector; executing multi-hop reasoning in the knowledge graph based on the vector, and retrieving to generate a candidate commodity pool; and calculating potential association strength of the commodities and the users through cross-domain transfer learning, and performing sorting to generate a final recommendation list. According to the method, the short-term preference dynamic state of the user can be described in a fine-grained manner, and the implicit intention of the user is deeply mined through dynamic knowledge association and fusion analysis, so that the accuracy and individuation degree of a recommendation result are effectively improved.

The invention belongs to the technical field of computers, and provides a video virtual fitting method and device based on a diffusion model and a program product. The method comprises the following steps: firstly, acquiring a target clothing graph and a model video, and extracting depth features, masks and a model latent feature video; meanwhile, semantic codes of the clothes are extracted through a CLIP encoder. The first diffusion model processes the clothing graph and the CLIP code, and outputs a clothing space attention feature vector fusing fine-grained texture and semantics; and the second diffusion model serves as a video diffusion network, receives random noise, character features, masks and depth features, injects garment vectors and CLIP codes into the middle layer, and finally outputs a fitting video with naturally fitted and dynamically coherent garments. According to the method, the high-fidelity virtual fitting video can be efficiently generated according to the new clothing and model video, and the method has wide application value in the fields of electronic commerce and the like.

The invention belongs to the technical field of e-commerce logistics management, and particularly relates to an e-commerce product storage full-process monitoring management method, which comprises the steps of synchronously acquiring multi-source heterogeneous monitoring data in real time through a sensing equipment network; performing space-time alignment preprocessing on the original data, and converting heterogeneous data into a uniform data format; semantic fusion is carried out on the standardized data, and a job behavior knowledge graph with a complete spatio-temporal context is constructed; inputting the atlas into a pre-training hybrid model, and detecting an inventory abnormal event; and generating a strategy instruction in combination with the real-time order and resource data, carrying out joint analysis on an operator, and outputting a compliance score and a specific violation operation type. Through adoption of the technical scheme, complete reconstruction of the context of the operation behavior can be realized, the anomaly detection accuracy is remarkably improved, the abnormal event response delay is shortened, and the inventory loss rate caused by human errors is reduced.

Systems and methods for dynamically and automatically updating item prices on e-commerce platform are disclosed. In some embodiments, an item is offered for purchase with a current price on a website. When price elasticity data and predicted demand data for the item are both available, a first markdown price is generated for the item using a first model based on: the price elasticity data, the predicted demand data, and the current price. When the price elasticity data and the predicted demand data are not both available, a second markdown price is generated for the item using a second model based on: a decay rate, the current price, and availability of the predicted demand data. A bounded price is generated by applying an upper bound and a lower bound to either the first markdown price or the second markdown price; and transmitted to a computing device for updating the current price of the item on the website.

The invention relates to the technical field of e-commerce, in particular to an e-commerce intelligent prediction management method and system based on big data, and the method comprises the following steps: obtaining a real-time message in a buffer area, generating a throughput rate sequence based on a sliding window, carrying out the difference and mapping of throughput rates of adjacent periods, and constructing a gradient slope sequence; and extracting a transient abrupt change ratio by means of a moving average model to generate a pre-trigger level identifier, extracting a set boundary extreme value, carrying out quantitative evaluation to generate discrete parameters, correcting a transfer matrix, and executing steady-state iterative solution to obtain prediction confidence to construct an elastic control instruction. According to the method, the sudden change trend of the business time domain dimension is accurately captured and static reference deviation interference is eliminated, so that deep analysis and real-time correction of the dynamic transfer characteristics of the core transaction link are realized, and the dilemma of research and judgment distortion and scheduling lag existing in a traditional rule when facing burst traffic is thoroughly avoided; and the full-link prediction precision and the elastic resource management and control efficiency in a complex transaction scene are remarkably improved.

The invention discloses a logistics early warning management and automatic processing system and method based on multiple platforms and multiple shops, and relates to the technical field of e-commerce logistics management, and the system comprises a data collection and processing module which is used for carrying out the butt joint of order management systems of multiple e-commerce platforms through a standardized interface, obtaining the order logistics data of each shop in real time, and sending the order logistics data to a server; the original logistics data are cleaned, analyzed and standardized, and are aggregated and stored to the unified data storage module according to the dimensions of shops, platforms and logistics providers. According to the logistics early warning management and automatic processing system and method provided by the invention, comprehensive integration and unified display of logistics information are realized through integration of multi-platform order data, the tedious problem of cross-platform information viewing of merchants is solved, the operation requirements of different merchants are flexibly adapted through self-defined early warning rules and an intelligent grading mechanism, and the logistics early warning management and automatic processing system and method are high in practicability. The accuracy of abnormity identification is improved, logistics business work orders are automatically connected, and the abnormity processing flow is greatly simplified.

The invention relates to the technical field of computer vision, e-commerce and artificial intelligence, and discloses a computer vision-based online clothing size intelligent matching and fitting degree prediction method, which comprises the following steps of: firstly, reconstructing a high-precision three-dimensional human body model by processing an image uploaded by a user; meanwhile, three-dimensional sizes and physical attributes are deconstructed from a clothing commodity graph, then, a physical simulation engine is utilized to dynamically wear a clothing model on a user model for virtual fitting, the wearing effect is truly restored by simulating physical effects such as gravity and collision, quantitative fitting indexes such as tightness and oppression are calculated after simulation, and the fitting quality is improved. And based on multi-objective optimization, scoring different sizes, recommending an optimal size, and generating a visual report containing the 3D effect picture and the fitness thermodynamic diagram. According to the invention, automation and precision from the image to fitting degree prediction are realized, the problem of online clothes purchase size selection is effectively solved, the user experience is significantly improved, and the return rate is reduced.

The invention discloses an e-commerce customer behavior prediction method based on big data analysis. The method comprises the following steps: S1, acquiring original behavior data of e-commerce customers from a plurality of heterogeneous data sources in real time; s2, fusing and preprocessing the original behavior data, and constructing a unified customer behavior portrait data set which takes a customer ID as a main key and comprises time sequence features and static features; s3, based on a business target driven by new quality productivity, defining a customer behavior tag to be predicted; and S4, constructing a hybrid prediction model integrating multi-modal deep learning and a graph neural network. The method has the beneficial effects that multi-source heterogeneous data are fused, and the hybrid model integrating an attention mechanism, the graph neural network and multi-task learning is innovatively constructed; according to the method, the time sequence evolution rule and network association characteristics of customer behaviors can be captured at the same time, and accurate and collaborative prediction of multi-dimensional behaviors such as short-term conversion, long-term value, loss risks and potential demands is realized.

The invention relates to the field of e-commerce and augmented reality, and discloses a marketing dynamic display system and method based on data analysis and AR technology, and the system comprises a data collection module, a data processing module, an AR content generation display module and a feedback recommendation optimization module. The data acquisition module acquires behavior data and commodity data of a user, and the data processing module generates a user portrait and commodity characteristics and generates personalized recommendation; the AR content generation and display module dynamically generates augmented reality display content of the virtual commodity according to the recommendation result; and the feedback recommendation optimization module collects interactive behavior data of the user in real time, optimizes a recommendation strategy through Bayesian updating and reinforcement learning algorithms, and ensures that a recommendation result is continuously and dynamically adjusted. According to the system, the recommendation accuracy is improved through a closed-loop feedback mechanism, the shopping experience of the user is enhanced, the commodity conversion rate is effectively improved, and the system can be widely applied to the fields of personalized recommendation and augmented reality display of the e-commerce platform.

A system for privacy-preserving communication and compliance enforcement is disclosed. The invention introduces Virtual Identities (VIs) and Virtual Numbers (VNs) cryptographically bound to Compliance Jurisdiction Tokens (CJTs) generated in secure enclaves. Each CJT carries session identifiers, expiry, consent artifacts, and jurisdictional codes covering GDPR (EU), CCPA (US), HIPAA, PSD2, PCI-DSS, FATF AML, the Digital Personal Data Protection Act (India), and the Personal Information Protection Law (China). Forwarding of calls, payments, or data packets is blocked unless VI–CJT validation and ledger anchoring succeed. Unlike tokenization, the system enforces real-time consent revocation, immutable regulator-verifiable audit, cross-border adequacy validation, and dual cryptographic signatures (RSA / ECC with post-quantum upgrade). Industrial use spans telecom, finance, healthcare, e-commerce, SaaS, and IoT, ensuring privacy-by-design and data protection obligations are enforced at protocol level.

The application relates to the technical field of electronic commerce, in particular to an internet e-commerce live broadcast dynamic management system, which comprises the following steps: establishing a flow investment plan library, automatically supplementing flow according to the flow gap of live broadcast, collecting user data to establish a label, matching goods and benefits according to the label and outputting explanation guidance for the host, monitoring orders to remind the host to adjust the rhythm and force single, guiding to add purchase after ordering, synchronously realizing after-sales and pushing the repurchase benefit. In the application, the flow investment plan library is built in advance, the real-time flow data and the gap prediction are combined in the live broadcast, the automatic matching investment scheme is supplemented, the stable flow supply is guaranteed in the whole live broadcast process, the flow utilization efficiency is improved, the user shopping broadcast preference data is collected in real time, the user exclusive label is constructed, the goods, benefits and live broadcast explanation rhythm are accurately matched for the user, the user stay time and conversion willingness are improved, the conversion link dynamic management and control are synchronously realized, the related goods are intelligently matched after ordering to guide to add purchase, the whole-link after-sales system is built, and the unit price and the repurchase rate are improved.

Systems and methods are described for dynamic and predictive pricing for ecommerce systems and brick-and-mortar retail businesses for a selected geographic location or territory. In one example, a system comprises a computing device that is configured to receive a request to display a network page of an item on a client device. The computing device is further configured to determine a geographic location of the client device and determine a price for the item using a machine learning model based at least in part on the geographic location. The network page is displayed on the client device to include the price of the item.

The present disclosure relates to the technical field of electronic commerce, in particular to an e-commerce marketing method and system based on time sequence promotion, the method comprising: collecting user behavior data, commodity attribute data, marketing history data and time sequence time node data of an e-commerce platform to obtain a standardized time sequence data set; extracting user behavior time sequence features, commodity heat time sequence features and marketing effect time sequence features based on the standardized time sequence data set to construct a time sequence feature matrix; determining core promotion content, target population and promotion channels of each promotion period through a preset time sequence prediction model to generate an initial time sequence promotion strategy; performing a promotion operation at a corresponding time sequence node according to the initial time sequence promotion strategy, collecting user feedback data and promotion effect data in real time and inputting the data into the time sequence prediction model to dynamically adjust the promotion content, channel delivery ratio and time sequence node arrangement, etc., to form a time sequence promotion strategy; and performing effect evaluation on the time sequence promotion strategy based on evaluation indexes to generate an evaluation report.

The invention discloses an e-commerce accurate supply and demand matching method based on multiple dimensions, and relates to the technical field of e-commerce, and the method comprises the specific steps: collecting user, supplier, environment and supply chain side multi-dimensional structured and unstructured data, and carrying out the cleaning and integration to form a unified data set; constructing a demand side intelligent agent and a supply side intelligent agent and mapping states; establishing a multi-target reward function, adjusting the weight by using a market fluctuation coefficient, and training an intelligent agent to generate a matching matrix; fusing real-time data based on edge calculation, and calculating scores to output optimal matching; matching effect data is fed back to iterate an optimization strategy, and adaptive adjustment is triggered when a threshold value is exceeded; according to the method, the matching accuracy and stability are improved by collecting multi-dimensional data, constructing supply and demand double agents and dynamic mapping, a multi-target reward function and market fluctuation weight adjustment; and meanwhile, by utilizing edge calculation, cross-modal fusion and a self-adaptive strategy, real-time response and dynamic optimization are realized, the transaction efficiency is greatly improved, and the dispute rate is reduced.

Methods and apparatus are disclosed regarding an e-commerce system that places customers into a plurality of clusters and tailors services provided to a customer based on the cluster in which the customer is placed. In one embodiment, the e-commerce system defines the clusters based on purchase history data for customers having sufficient purchase history data. The e-commerce system then places customers without sufficient purchase history data into one of the defined clusters based on demographic data for the customer and demographic data for the customers in the cluster.

The invention belongs to the technical field of e-commerce, and particularly discloses an enterprise e-commerce process customization and substituted operation service system. The system comprises a user intention real-time analysis module used for outputting a user intention quantization vector through a deep behavior pattern recognition algorithm; the strategy knowledge graph construction module is used for constructing and dynamically updating a multi-dimensional association network containing commodities, user tags and marketing actions; the dynamic strategy coupling engine is used for inputting the intention vector into a knowledge graph and generating a matched composite marketing strategy instruction set through graph neural network reasoning; the strategy instruction atomization execution unit is used for decomposing the instruction set and scheduling the instruction set into atomic operation instructions; and the full-link effect tracking and feedback module is used for collecting effect data and feeding back the effect data to the knowledge graph to optimize the weight. According to the system, end-to-end intelligent automatic closed loop from user intention recognition to marketing strategy generation and execution is realized, and the timeliness, accuracy and self-adaptive capability of marketing response are improved.

The invention discloses a personalized product creation and display system, which relates to the technical field of e-commerce and consists of a creation module, a picture processing module, a product model module and a product preview module. Through the system, a customer needs to provide information or instructions through an AI drawing software plate arranged in a creation module so as to guide an AI to create an image required by the customer; in addition, pictures generated by the AI drawing system can be modified intelligently or manually, and finally 2D and 3D models of personalized products are previewed through the product previewing module, so that customers can visually know the style of final commodities. Therefore, the system not only allows customers to create personalized products through the system, but also can display 2D and 3D models of the products created by the customers in real time to meet the requirements of the customers; customers can easily create personalized commodities, and new innovation and convenience are brought to the field of electronic commerce.

The application discloses an e-commerce anti-user portrait system and method based on artificial intelligence and blockchains, comprising a data invasion containment module, a fuzzy user portrait module, an audit recommendation algorithm module and an intelligent comprehensive price comparison module; the data invasion containment module uses blockchain technology to clearly define the data ownership of each node, and decentralizes the data distribution to return the data ownership to the individual user, and desensitizes the authorized user data; the fuzzy user portrait module uses artificial intelligence to automatically search for irrelevant type goods or even opposite type goods; the audit recommendation algorithm module automatically audits the personalized recommendation algorithm, checks the fairness of the algorithm, and manually handles user complaints by accessing an expert system; the intelligent comprehensive price comparison module compares the prices of different e-commerce platforms and the historical prices of the same e-commerce platform, and integrates and displays the price comparison results. The application can effectively solve the problems of personal information leakage and big data killing in the e-commerce field, and improve user satisfaction.

A unified shopping and checkout system and method for a multiple merchant e-commerce platform which allows a buyer on a multiple merchant e-commerce platform to simultaneously complete a wide variety of types of transactions through a combined shopping interface, a cart building engine, and a unified checkout interface. The combined shopping interface includes both products and live bookings from many different merchants and allows them to be browsed, compared, and selected. The cart building engine allows for the aggregation of both products and live bookings into a single transaction by, in part, providing for the provision of the information needed to prior to allowing for the purchase of all selected products and / or bookings in a single transaction, and then provides for the use the information and funds provided during the transaction to complete corresponding transactions for the products and booking selected by the user, with the selected merchants.

The application relates to a secure storage method for e-commerce management system data, comprising the following steps: obtaining e-commerce data and classifying the e-commerce data; generating a data processing rule based on the classification result; deploying a data security agent for intercepting and analyzing an access request to a business database; when a data write request is intercepted, performing encryption processing or desensitization processing by the data security agent and storing the data write request into the business database; when a data query request is intercepted, performing risk assessment by the data security agent and triggering corresponding defense measures; providing a key for the encryption processing and the desensitization processing through a key management system and recording an execution process audit log of the data security agent; through data classification, security rule application and dynamic access control, the application can effectively solve the passive defense problem in the prior art, realize differentiated protection based on data business attributes and sensitivity, and reduce the global performance influence.

A system for a vehicle includes a wireless transceiver configured to communicate with a mobile device via a wireless connection encrypted using a digital key, wherein the digital key allows the mobile device to access a driving function of a vehicle; and one or more controllers programmed to responsive to a user input indicative of an intent to make a purchase by a user of the vehicle, transmit a request to the mobile device to launch a payment for the purchase via the wireless connection, perform an authentication by interacting with the user to authorize the payment, and responsive to the authentication, instruct the mobile device to proceed with the payment via the wireless connection.

The utility model relates to the technical field of mobile terminal equipment, and discloses a mobile electronic commerce transaction terminal which comprises a front cover, one side of the front cover is fixedly connected with a middle frame, one side of the middle frame is fixedly connected with a rear cover, two sides of the middle frame are fixedly connected with non-slip mats, the inner surface of the middle frame is fixedly connected with a partition plate, and the partition plate is fixedly connected with the rear cover. A baffle rod is fixedly connected to one side of the partition plate, one end of the baffle rod is fixedly connected with the middle frame, a battery is fixedly connected to one side of the baffle rod, one side of the battery is fixedly connected with the partition plate, a charging port is connected to the surface of the middle frame in a penetrating mode, and cables are fixedly connected to the two sides of the charging port. The arc surface of the cable is connected with the stop lever in a penetrating manner, one end of the cable is fixedly connected with the battery, and one side of the middle frame is fixedly connected with a concentration plate. In the prior art, the existing mobile equipment has the defects that the security is insufficient and the operation convenience needs to be improved in the electronic commerce transaction.