787 results about "E-commerce" patented technology

The invention relates to the technical field of e-commerce data processing, in particular to an e-commerce sales platform background data management method and system. The method comprises the following steps: S1, collecting a commodity dynamic data stream, a user behavior event stream and a promotion strategy stream in real time, and generating a standardized data stream through time sequence alignment; s2, constructing a dynamic coupling data cube; s3, executing a real-time decision: in response to the payment request, selecting an inventory distribution or replacement commodity pushing strategy based on a commodity-user coupling matrix value; in response to the resource overload state, triggering a resource scheduling strategy; and S4, dynamically adjusting calculation parameters of the coupling matrix according to decision execution feedback. By solving the problems of real-time standardization processing of multi-source heterogeneous data and real-time coupling of dynamic inventory and user behaviors, the data processing capacity, inventory distribution efficiency and recommendation accuracy of the platform are remarkably improved.

Implementations of the present disclosure provide receiving, by a conversational commerce optimizer system, a user input from a user, the user input being provided to a chatbot of an e-commerce platform, providing a refined query based on the user input, selecting, by the conversational commerce optimizer system, a foundation model that is to be queried using the refined query, determining a set of search results from a database that stores data representative of products available through the e-commerce platform, prompting the foundation model using a prompt that is generated based on the refined query and the set of search results, the foundation model providing an output responsive to the prompt, and providing a response to the user through the user interface of the chatbot, the response being generated based on the output.

The invention discloses a live broadcast e-commerce compliance auditing method and system based on penetration type supervision, and relates to the technical field of e-commerce compliance supervision, and the method comprises the steps: carrying out the Hash abstract calculation of a judgment result data set and a multi-source data set, carrying out the partitioning storage of the data set in a block chain, verifying the storage process through a timestamp and a multi-node consensus protocol, and carrying out the verification of the verification result; forming an evidence storage data chain; automatically matching the evidence storage data chain with a compliance rule, identifying potential illegal behaviors and risk levels, and forming a compliance result set; performing fusion calculation on the compliance result set, historical violation records and user feedback data to generate a comprehensive risk score, and automatically triggering real-time compliance intervention measures according to the comprehensive risk score to form an intervention result set; and re-storing the intervention result set, the comprehensive risk score and the violation behavior in the block chain to form a closed-loop evidence storage data chain. According to the invention, supervision transparency is enhanced, data integrity is ensured, and reliable support is provided for real-time intervention and closed-loop auditing.

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.

An ecommerce messaging system described herein comprises one or more ecommerce extension apps that interact with a messaging app. These embodiments leverage the extension core to manage session apps and smart services for using tap-to-message and tap-to-update processes to generate, update, and manage order workflows and in-app stores through branded messages communicated between customer and seller session apps to share message transcripts on devices of the same user groups via the messaging host, empower ecommerce with action-key-based communication, serialize-deserialize processes, and dynamic update propagation, manage return-refund workflows with temporary tables until one-time entity data model updates can be performed for session apps, and configure multi-store shopping systems on sellers' multiple devices using a store-as-a-service model to enable distributors to distribute production copies of assigned stores to authorized publishers to add custom promotions, which will be published together as production copies to their subscribed user groups for multi-store shopping and tracking.

The invention relates to the technical field of electronic commerce, in particular to a personalized analysis method for cultural and creative products based on collaborative filtering, which comprises the following steps: acquiring browsing, collection and purchase behavior data of a user, calculating interval change of adjacent behaviors, and adjusting behavior influence based on browsing frequency, collection density and purchase conversion. And calculating a behavior contribution proportion in combination with the collection frequency and the stay duration, and extracting a short-term behavior trend to obtain a user behavior change trend parameter. According to the method, the behavior change rate and the time interval are combined for screening, the recommended content can quickly adapt to user interest changes, the recommendation matching degree is improved through utilization of social interaction data, social recommendation is more targeted, the scene applicability of the recommended content is enhanced through consideration of environmental factors such as geographic positions and weather, and the user experience is improved. The dynamic calculation of the multi-dimensional weight optimizes the recommendation sequence, and improves the long-term effectiveness and user experience of the recommendation system.

The invention discloses an e-commerce intelligent customer service AI dynamic duty and automatic recovery system, and relates to the technical field of e-commerce service optimization, and the system comprises the steps: calculating a problem emergency degree value based on a multi-dimensional evaluation index through a problem emergency degree judgment module, and achieving the dynamic evaluation of the priority of a problem; through a manual customer service switching module, a dynamic weight distribution mechanism is adopted to distribute problems to manual service seats corresponding to professional skills; analyzing the dialogue content of the customer service and the user in real time through a dialogue context analysis module, and generating a dialogue satisfaction curve; through an AI customer service switching module, when the satisfaction curve tends to be stable, the dialogue is handed over to the AI customer service for continuous service; and service strategy optimization is carried out based on the dialogue historical record and the knowledge base through an optimization module. According to the invention, an efficient and intelligent service mechanism is constructed from the aspects of emergency degree evaluation, manual and AI customer service collaborative service, dynamic optimization and the like, and the response efficiency and user experience of a customer service system are improved.

The invention discloses a supplier material market price evaluation method and system based on AI networking search, belongs to the technical field of electronic commerce and data processing, and provides a double-weight confidence model based on time sequence fluctuation and statistical deviation, data are grouped according to platform types and suppliers, a price fluctuation oscillogram is generated for each group of data, and the price fluctuation oscillogram is calculated. Calculating average price and standard deviation in the group; the global average price is introduced, the confidence coefficient of a single group of data is dynamically quantified through a normalization formula, low weight is given to high-fluctuation and high-deviation data, noise interference is effectively suppressed, and the reference of the finally output optimized market price is improved; in addition, structured data with timestamps are generated and the grouping average price and confidence factor of each platform are synchronously output, so that purchasing personnel are assisted to transversely compare the quotation rationality of suppliers; and finally, the output optimized market price is fused with multi-source high-confidence data, so that enterprises are helped to accurately grasp the market price interval, and the bargaining capability and the purchasing efficiency are improved.

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 a dynamic hierarchical encryption system for multi-tenant e-commerce data, and relates to the technical field of multi-tenant data security, the system comprises an encryption management center, the encryption management center is in communication connection with the following modules: a data sensing and grading module, which is used for collecting multi-source context data of an e-commerce platform, and sending the multi-source context data to the encryption management center; performing basic sensitivity level division on the context data; and the multi-tenant knowledge graph construction module is used for constructing a multi-tenant knowledge graph based on the tenant-data-authority association network in combination with the multi-source context data. According to the method, dynamic self-adaptive adjustment of encryption strength is realized through cooperation of the fruit fly algorithm and the knowledge graph, the system dynamically optimizes combination of key length and the encryption algorithm based on data grading labels and real-time context data, and the fruit fly algorithm searches an optimal solution through iteration, so that the system has high encryption efficiency while ensuring compliance. And encryption delay is controlled within a real-time requirement range, so that the user experience in a multi-tenant scene is remarkably improved.

The present disclosure relates to systems and methods of facilitating a virtual reality (VR) ecommerce transaction, such as an immersive VR ecommerce transaction. The system can include a head-mounted display designed for rendering a virtual environment. The display can receive inputs through a variety of user interface mechanisms. The system can include an input device, either integrated with the head-mounted display or operable within the virtual environment, for capturing user interactions related to the ecommerce checkout process. The system can include a processor communicatively coupled to the head-mounted display and the at least one input device. The processor can dynamically generate a virtual environment representative of a checkout interface that is adaptable to multiple ecommerce platforms. Optionally, the system can process user interactions within the virtual checkout interface, for example, including product selection, payment information entry, and shipping details confirmation, to complete real-world transactions.

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 invention discloses a transaction data processing method, relates to the technical field of data processing, and is suitable for transaction data real-time processing and risk control monitoring in a high-concurrency scene. The method comprises the steps that transaction data are collected and preprocessed, and a transaction data structure of a unified format is constructed; performing data fragmentation based on a distributed consistency hash rule; extracting multi-dimensional feature vectors to construct a transaction behavior feature set; calculating an abnormal score of the transaction sample based on the isolated forest model, and judging a transaction abnormal state according to a score result; verifying the multi-fragment data processing consistency through a consistency verification function; a detection result and abnormal feedback are reported through a RESTfulAPI interface; and the number of system fragments is dynamically adjusted according to the real-time throughput, so that load balancing is realized. The method has the advantages of efficient data processing, intelligent anomaly detection, flexible system expansion and the like, and is suitable for various electronic commerce, financial payment and real-time transaction monitoring systems.

The invention belongs to the field of electronic commerce, and provides a multi-channel advertisement putting effect analysis method and system based on real-time calculation, and the method comprises the steps: generating a unique click identifier CLICKID for each advertisement putting channel, and writing the CLICKID and corresponding click information into a real-time data storage when a user clicks an advertisement; obtaining order data through an order return mechanism or a timed task, and inputting the order data into an attribution program; retrieving a corresponding click record in a preset multi-dimensional index data structure on the basis of the CLICKID; and associating the order data with the corresponding advertisement putting plan information to form an attribution result, and writing the attribution result and the order information into an attribution result database. According to the invention, the attribution accuracy and efficiency can be 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.

The invention discloses a cross-platform user consumption preference real-time prediction and recommendation system, and relates to the technical field of e-commerce, and the system comprises a comprehensive recommendation management platform which is in communication connection with the following modules: a user portrait construction module which is used for collecting basic information of a new user, the entity relationship and the attribute information in the knowledge graph are utilized to construct a user portrait; and the commodity knowledge graph construction module is used for collecting and sorting cross-platform commodity information and constructing a commodity knowledge graph. According to the method, hidden demands of users are effectively mined and long-tail commodities are found and recommended by utilizing meta-path reasoning and cross-domain knowledge migration technologies of the knowledge graph, so that the exposure ratio of hot commodities to the long-tail commodities is balanced, excessive recommendation of the hot commodities is avoided, the long-tail commodities are ensured to obtain proper display opportunities, and the user experience is improved. The commodity diversity of the platform can be improved, sales of commodities of small people can be promoted, and long-term development of the platform is facilitated.

The invention relates to the technical field of e-commerce safety supervision, and discloses a multi-tenant shopping mall safety supervision method and system based on big data analysis, and the method comprises the steps: carrying out the hierarchical heterogeneous data adaption, and constructing unified data representation; constructing an identity association graph based on probabilistic reasoning; carrying out distributed feature learning by adopting a federal map neural network; hierarchical threat detection and risk assessment are realized; constructing a safety response mechanism of differential privacy protection; deploying a cloud native architecture and a self-healing mechanism; according to the method, the problems of heterogeneous data processing, cross-platform identity association, privacy protection and safety supervision balance, system reliability and the like in a multi-tenant shopping mall environment are effectively solved, the accuracy, comprehensiveness and reliability of safety supervision are improved, meanwhile, user data privacy is protected, and comprehensive technical support is provided for safety operation of a shopping mall.

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.

Disclosed herein is a service provider system and method to generate a persona token for a user, store the persona token at a restricted access database, and return a persona token identifier to the user. The persona token is used to generate persona insights, that can be provided to an artificial intelligence / machine learning algorithm along with an inquiry. The algorithm may identify goods or services based on the persona insights. Also disclosed herein are system and methods to attribute a user clicking on a product link, retaining and using the product information to link item level details to purchases, and making a purchase on the linked e-commerce / merchant website such that the originating website knows that a purchase ultimately resulted from the clicked ad.

The invention belongs to the technical field of electronic commerce, and particularly relates to an intelligent commodity inventory distribution method and system for multiple virtual warehouses, and the method comprises the following steps: S1, detecting a commodity inventory change event, and verifying whether a commodity enables a virtual warehouse function or not; s2, if the virtual warehouse is started, querying all virtual warehouse inventory information associated with the commodity, calculating a merged inventory amount through an inventory merging algorithm, and synchronizing the merged commodity inventory information; s3, distributing the merged inventory data to a target sales platform according to a channel selection decision model; s4, receiving an order request, and verifying whether the commodity enables a virtual warehouse function or not; s5, if the virtual warehouse is started, obtaining inventory information and priority scores of all associated virtual warehouses, and generating a warehouse priority list; and S6, carrying out optimal matching on the order demand and the commodity inventory information merged in the S2, and generating an order distribution result.

The invention relates to the technical field of e-commerce, in particular to a multi-merchant user behavior deep learning analysis method, which comprises the steps of collecting interaction data of each merchant end, calculating a gradient based on a local learning model, packaging slices and uploading the slices to a cloud end; aggregating gradients, constructing a behavior relation graph and generating a causal tensor, and inputting the causal tensor and the graph domain multi-scale features into a time sequence embedding model to obtain time embedding; generating commercial tenant high-dimensional pulse vectors through random projection and binary mapping, and aggregating the commercial tenant high-dimensional pulse vectors into cross-commercial tenant vectors; executing a generative reverse process on the cross-merchant vector according to a noise strategy to obtain a prediction vector; the prediction vector and time are embedded and mapped into an energy matrix, an action vector is sampled through quantum optimization, and the action vector and a cross-merchant vector are input into a reinforcement learning network to output a recommendation decision; and generating an update gradient according to user clicking and payment feedback, and returning the update gradient to the merchant end to form a self-calibration closed loop. According to the method, cross-merchant collaborative recommendation is realized on the premise of not exposing original data, and cold start recall and real-time conversion rate are improved.

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 discloses a commodity recommendation method and device, electronic equipment, a storage medium and a computer program, and belongs to the technical field of electronic commerce. The method comprises the following steps: acquiring historical behavior data of a user; based on the historical behavior data, determining long-term preference characteristics of the user; based on the long-term preference characteristics and commodity attribute data of the to-be-recommended commodity, outputting simulation comment information of the to-be-recommended commodity through a large language model; based on the simulation comment information and the commodity attribute data, determining prediction evaluation information of the to-be-recommended commodity; and based on the long-term preference features, the prediction evaluation information and the commodity attribute data, determining recommendation evaluation information of the to-be-recommended commodity. According to the method, the view angle of the user is simulated through the large language model, the subjective viewpoint of the user on the commodity can be reflected, the individuation degree of the recommendation result is improved, changes of user interests and consumption habits can be tracked, and the accuracy of the recommendation result is improved.

An apparatus for performing product matching may include: a processor configured to receive a search as product item for searching for a catalog product that matches a target product; classify the target product in a product taxonomy tree including a plurality of taxonomy nodes, by identifying a taxonomy node to which the target product belongs, among the taxonomy nodes; obtain product data associated with the target product from an internal source and an external source and measure data quality; extract attributes from the product data based on a machine learning model; validate the attribute in response to the attribute corresponding to defined mandatory attribute of the taxonomy node to which the target product belongs, and provide a search result based on the validated attributes; re-rank searched results governed by relevancy score and display matched set of products to the customer above defined re-rank confidence score.

The invention belongs to the technical field of e-commerce, and discloses a customized furniture marketing and ordering transaction system fused with an AR scene. The method comprises the following steps of: accurately constructing a microscopic interest attenuation curve by collecting an interaction track and visual staying data of a user in an AR environment, and realizing dynamic quantification of purchase intention; the transaction path fracture node is innovatively identified, the conversion resistance index is calculated, the seamless transaction trigger point map is automatically generated, and smooth conversion from experience to transaction is realized. According to the invention, the physical attributes of the furniture are mapped into digital right parameters, and a mixed transaction mode containing two channels of real object purchase and digital asset subscription is constructed, so that the furniture value is transferred and added among different users. Especially in a hotel scene, a digital asset bidding transaction channel between an original guest and a new guest is established, temporary use experience is converted into continuous commercial value, and industrial ecology of efficient utilization of resources and co-creation of multi-party value is realized.

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.