52269results about "Commerce" patented technology

An emergency response and crisis management system comprises a computing system which performs steps comprising: aggregating real-time and historical data from multiple data sources; processing the data using artificial intelligence techniques; analyzing the processed data to detect and classify active crises to predict the likely development and trajectory of the ongoing crises, and assess potential short-term and long-term impacts across one or more sectors comprising one or more of public health, economy, infrastructure, environment, and social stability; generating multi-dimensional, interactive visualizations of the analysis results comprising one or more of geospatial maps, time series charts, and network diagrams; providing recommendations for crisis management and response tailored to different stakeholder roles; facilitating integration with external systems and applications through a comprehensive application programming interface (API) layer; and continuously adapting the system's models and algorithms based on one or more of real-world outcomes, expert input, and user feedback.

The invention relates to the technical field of data processing, and relates to a blockchain-based supply chain carbon data credible management method, which is characterized in that energy consumption of supplier production equipment is acquired in real time through deploying an edge computing gateway, and supplier production data is acquired in real time through the edge computing gateway deployed on the supplier production equipment; the real-time working condition parameters of the logistics transportation carrier are obtained in combination with the vehicle-mounted terminal, the material disassembly data of the recovery processing link are integrated, and a multi-source data acquisition system of the whole link of the supply chain is constructed. And the block chain network carries out credible processing on the data, a regional carbon emission factor in the industry reference database is called to carry out deviation verification, the intelligent contract engine executes an audit decision based on a preset compliance rule, and when it is detected that the energy consumption deviates from a reference threshold value, a data correction process of multi-party signature confirmation is triggered. According to the method, full-link credible evidence storage and dynamic optimization of the carbon data are realized, and the supply chain carbon emission accounting precision and the cross-border auditing efficiency are improved.

The present invention provides a supply chain multidimensional data mining and intelligent recommendation decision method based on a knowledge graph, which relates to the field of supply chain management technology, including constructing a dynamic supply chain knowledge graph containing the temporal evolution relationship of nodes, and calculating the multidimensional feature representation of nodes based on the graph, and training a supply chain recommendation model. Then, according to the target product parameters and constraints, the graph reasoning mechanism is used to calculate the correlation between the target product and the supplier, and the candidate suppliers are screened. Finally, combined with supplier evaluation and historical transaction data, a dynamic constraint optimization model is used to perform multi-objective optimization, and a recommended decision plan containing supplier ranking and matching degree is generated. The present invention can effectively integrate multidimensional data, realize accurate supplier recommendation, and improve supply chain efficiency and decision-making intelligence.

The technology described herein relates to systems, methods, and computer storage media, among other things, for providing search query intent specificity. Embodiments may include identifying a search query performed using a search engine and generating a query vector for the search query by aggregating search result embeddings (e.g., item listing vectors) of search results from the search query. Further, in some embodiments, similarities (e.g., cosine similarities) between the query vector and the item listing vectors can be determined. As such, an intent specificity of the search query can be determined. Further, in some embodiments, the intent specificity can be used to train an intent specificity machine learning model for generating intent specificity scores for other search queries. Based on the intent specificity scores determined using the one or more trained intent specificity machine learning models, determinations can be made with respect to precision and recall, etc.

The present disclosure relates to systems and methods for facilitating interoperability between blockchain networks to facilitate bridging of non-fungible tokens (NFTs) across chains. In embodiments, a method includes receiving a bridging request at a first bridge smart contract to transfer a source NFT from a first blockchain to a second blockchain. Upon verification, the first bridge smart contract locks the source NFT. An oracle server determines whether a clone NFT associated with the source NFT exists on the second blockchain. If so, the oracle instructs a second bridge smart contract to unlock the clone NFT. If no clone NFT exists, the oracle instructs minting of the clone NFT and assigning ownership of the clone NFT to the user's account on the second blockchain. The clone NFT remains controllable by the user while the source NFT stays locked on the first blockchain, maintaining a one-to-one relationship between the tokens across chains.

A system for multi-stage planning of construction processes and resource allocation, consisting of: a central planning engine configured to receive input data, including architectural design models, structural constraints, procurement schedules, and historical performance indicators; a task decomposition processor that is operationally connected to the central planning engine and configured to generate a hierarchical construction task graph by decomposing macro-level construction milestones into mid-level and micro-level subtasks, with each subtask having time estimates, location identifiers, resource requirements, and mutual dependencies; a hybrid planning processing unit configured to resolve time and resource constraints across the entire task diagram; a resource coordination controller that is operationally connected to the central planning engine, wherein the resource coordination controller includes a real-time database of work units, machines and material stocks, each resource being tagged with attributes such as availability, usage history, operating status and spatial location; a multitude of distributed execution units distributed across the construction zones, each distributed execution unit comprising an embedded controller, sensor interfaces, task status processing logic, and communication circuitry, each distributed execution unit being configured to receive planning instructions from the central planning machine, execute localized control logic for task confirmation and resource activation, and transmit task execution data back to the central planning machine; an adaptive conflict resolution processing unit that is operationally connected to the central planning engine and configured to detect conflicts in task execution or resource conflicts, simulate alternative task-resource allocation scenarios using a real-time multi-agent model, and autonomously update the task graph with revised task sequences and resource allocations; and A dashboard for the construction process, configured to visualize task progress, deviations from the planned schedule, and resource efficiency metrics, with the dashboard also being able to receive manual override inputs or approve automated conflict resolution proposals generated by the adaptive conflict resolution module.

The invention discloses a multi-element sales planning agent system and method, and aims to improve the intelligence and precision of sales planning. The system comprises a collection module, an analysis module, an optimization module, a creation module and a generation module. The collection module is used for receiving multi-modal data such as marketing targets and extracting key marketing elements. The analysis module is used for generating a target user portrait and extracting marketing strategy analysis data. And the optimization module is used for calculating a medium putting weight by utilizing reinforcement learning and generating a medium strategy scheme. And the creation module generates a propagation theme and marketing content by adopting a generative artificial intelligence technology. And the generation module predicts a delivery effect by using a machine learning model and dynamically optimizes a medium strategy and a content scheme. Through multi-modal data fusion, intelligent analysis and optimization, closed-loop processing from data acquisition to marketing execution is realized, the marketing decision-making efficiency is improved, and brand promotion accuracy and market adaptability are enhanced.

A device may use a device discovery process to automatically determine a configuration of a home network system of the user. A device may store data representative of the determined configuration of a home network system of the user in a data repository. A device may use the data representative of the determined configuration of the home network system of the user to automatically verify a compliance by the user with a condition for receiving the targeted advertisement. A device may provide the targeted advertisement to the user when the determined configuration of the home network system of the user is determined to be in compliance with the condition for receiving the targeted advertisement.

An example operation may include one or more of receiving content from a conversation between a user on a user device and a second user on a second user device that is connected to the user device via a network, identifying a topic of the conversation based on execution of a generative artificial intelligence (GenAI) model on the received content from the conversation, identifying a call list that is previously stored in memory that is associated with the topic of the conversation based on keywords included in the identified topic of the conversation, and adding an identifier of the user to the call list stored in memory.

An example operation may include one or more of receiving an identifier of a product from a digital wallet on a user device, identifying one or more payment cards stored within the digital wallet on the user device, determining benefits that will be obtained by using each of the one or more payment cards to purchase the product via execution of an LLM on the identifier of the product and a corpus of credit card documents, and displaying a chat message within a chat window on the user device with a description of the determined benefits that will be obtained.

The invention relates to a dynamic advertisement content intelligent delivery method based on user emotion recognition. According to the method, face image data when a user watches an advertisement are collected in real time through a camera device, the data are input into a computer vision sentiment analysis model, sight focus data and a user experience score are generated, and the user experience score comprises the interestingness, the concentration degree and the sentiment tendency; based on the score, selecting the advertisement content with the highest matching degree with the current user state from a preset advertisement content library, and dynamically adjusting the visual layout and the key information element position of the advertisement content according to the sight focus data identified by the sentiment analysis model; in addition, according to the method, the presentation mode of the advertisement is adjusted according to the real-time change of the user experience score, so that the individuation and interaction experience of the advertisement are enhanced; the method can improve the precise delivery of the advertisement content and the user attention, and is suitable for application scenes such as digital advertisement screens, e-commerce platforms, intelligent retail terminals and the like.

An example operation includes one or more of connecting a local user profile hosted within a software application hosted by a first platform with a profile hosted by a data source on a second platform, extracting a first set of profile features from the profile hosted by the data source and a second set of profile features from the local user profile, determining a new profile feature to add to the local user profile based on the execution of an artificial intelligence (AI) model on a combination of the first and second sets of profile features, and displaying information about the new profile feature on a user interface of the first or second platform.

An example operation may include one or more of storing a database of payment card data, receiving an identifier of a product from a digital wallet on a user device, identifying one or more payment cards stored within the digital wallet on the user device, determining the benefits that will be obtained by using each of the one or more payment cards to purchase the product via execution of an LLM on the identifier of the product and the database of payment card data; and displaying a chat message within a chat window on the user device with a description of the determined benefits that will be obtained.

The invention relates to the field of bidding risk assessment, and discloses an AI technology-based intelligent bidding data analysis method and system and a storage medium, and the method comprises the following steps: obtaining bidding data, constructing a multi-dimensional space-time tensor, and extracting low-dimensional features through dynamic tensor decomposition; establishing a dynamic enterprise relationship graph, modeling an enterprise cooperation relationship by using a graph neural network, and capturing a bidding behavior mode between enterprises; a multi-target joint optimization model is constructed, and market trend prediction, enterprise credit assessment and risk early warning are synchronously realized; performing adversarial training by adopting a generative adversarial network, and dynamically adjusting task weights in combination with Pareto optimization to balance multi-task conflicts; and outputting a market prediction report, an enterprise credit portrait and a risk early warning list, and providing policy and regulation change influence analysis and a risk evidence chain. According to the method, bidding and tendering data are deeply mined through the AI technology, the market insight and the abnormal bidding behavior recognition capability are improved, and the method has wide application value.

Embodiments described herein relate to systems and methods for automatically generating content for a generative answer interface of a collaboration platform. The system receives a natural language user input identifies corresponding blocks of text using an content extraction service. A prompt is generated using the blocks of text and used to obtain a generative response. The generative response and links to corresponding content are displayed in the generative answer interface and can be inserted into content of the collaboration platform. The systems and methods described use a network architecture that includes a prompt generation service and a set of one or more purpose-configured large language model instances (LLMs) and / or other trained classifiers or natural language processors used to provide generative responses for content collaboration platforms.

A block chain double-chain cold chain traceability management system for meat quality fluctuation belongs to the technical field of traceability management systems and comprises a data acquisition and preprocessing module, a composite index module, a security verification module, a federated learning and AI resource management module, a fragmentation and verification architecture module and an automatic error correction module. According to the method, data chain storage environment data hash and transaction chain record operation logs are innovatively separated through a block chain double-chain architecture, hash fingerprints are synchronized in combination with an anchoring technology, cross-chain consistency is ensured, long-term non-tampering of data is realized through an anti-quantum hash algorithm, resource allocation is optimized through hierarchical storage design, and the method is suitable for large-scale popularization and application. The composite index technology is fused with hypergraph dynamic index to trace production batches, vehicles, warehouses and space-time quadtree dynamic subdivision areas, complex query response time is remarkably compressed, path deviation real-time alarm is achieved, the edge node cache hit rate is increased, and full-link data safety and query efficiency are remarkably improved.

A recommendation system generates recommendations for user-item pairs based on embeddings in hyperbolic space. Each user and item may be associated with a local hyperbolic embedding representing the user or item in hyperbolic space. The hyperbolic embedding may be modified by neighborhood information. Because the hyperbolic space may have no closed form for combining neighbor information, the local embedding may be converted to a tangent space for neighborhood aggregation information and converted back to hyperbolic space for a neighborhood-aware embedding to be used in the recommendation score.

A computing device for predicting an expected loss for a set of claim transactions is provided. The computing device predicts, at a first machine learning model, a claim frequency of the set of claim transactions over a given time period and trained using historical frequency data and based on a segment type defining a type of claim, each type of segment having peril types. The computing device also predicts, at a second machine learning model, claim severity of the set of claim transactions during the given time period, the second machine learning model trained using historical severity data and based on the segment type and the corresponding peril types. The computing device then determines the expected loss for the set of claim transactions over the given time period by applying a product of prediction of the first machine learning model and the second machine learning model.

The present invention provides a large language model-based system and method for data processing in application developed by codeless platform. The invention includes identification of intent of a user to process procurement, supply chain, application integration, application restructuring or development scenarios.

The invention relates to the technical field of image processing, and discloses a dried persimmon quality detection method and system based on machine vision. The method comprises the following steps: performing multi-angle multi-spectral imaging through an industrial camera array to generate a standard image set; extracting quantitative dried persimmon appearance characteristics; the internal quality is subjected to nondestructive testing by using a spectrum; identifying defects and grading; analyzing a quality trend and establishing an early warning mechanism; and correlating process parameters to optimize the production process, and constructing a quality tracing system. According to the invention, comprehensive nondestructive testing of the appearance and internal quality of the dried persimmons is realized, a closed-loop system of quality early warning and process optimization is formed through data analysis and mining, and the quality stability and traceability of dried persimmon production are improved.

The invention provides a knowledge graph construction and product recommendation method and system based on user data, and relates to the technical field of artificial intelligence, and the method comprises the steps: constructing an initial knowledge graph through user historical behavior data, generating an optimization graph through a multi-layer neural network comprising a multi-granularity layer, a graph attention layer and a graph convolution layer, and performing bidirectional random walk sampling based on real-time behavior data of a target user to obtain a related sub-graph, calculating a product node importance score, and performing sorting pushing by adopting a multi-target optimization algorithm. According to the method, multi-dimensional accurate description of user interests can be realized, the recommendation accuracy and diversity are improved, and meanwhile, the commercial value is considered.

The invention discloses a customer service data quality inspection method and device based on dynamic reasoning, equipment and a medium, and the method comprises the steps: analyzing customer service dialogue data, and generating structured dialogue data containing an intention label and a key problem node; according to an intention label in the structured dialogue data, a framework is generated through retrieval enhancement, and matched domain knowledge is recalled in real time from a standard knowledge base so as to construct a dynamic context knowledge graph; taking the dynamic context knowledge graph as input, utilizing a thinking chain prompt template to guide a large model to carry out step-by-step logical reasoning, and outputting a preliminary quality inspection conclusion chain; performing retrieval verification on assertion nodes in the initial quality inspection conclusion chain to generate a traceable final quality inspection conclusion; and based on the final quality inspection conclusion, calculating a quantitative score of the customer service dialogue data and generating a visual thinking chain report. And through dynamic retrieval of the knowledge base and logical reasoning, the accuracy, interpretability and adaptability of customer service data quality inspection are improved.

The invention discloses a pharmaceutical quality traceability decision-making method and system based on a dynamic knowledge graph, and relates to the technical field of pharmaceutical quality control. The method comprises the following steps: defining a dynamic knowledge graph containing quality-related entities, wherein the dynamic knowledge graph at least comprises a quality event entity, a production element entity and a time sequence causal relationship between the quality event entity and the production element entity; capturing production data related to quality in real time, and generating event data with a quality rule identifier based on a preset triggering condition; and carrying out dynamic state modeling on the production element entity to generate a state vector containing a time sequence attenuation characteristic and a quality influence weight. By constructing the dynamic knowledge graph and designing the time sequence causal reasoning algorithm, real-time capture, dynamic modeling and causal relationship analysis of production data are achieved, the defects of the traditional technology in real-time performance and relevance are overcome, and an efficient and accurate intelligent solution is provided for pharmaceutical quality traceability.

A computing system for adaptive electronic message classification employs a multi-agent architecture comprising a media feature analysis system, a user context refinement system, and a response synthesis system. The media feature analysis system generates pillar scores including message type, intent, and link risk scores with associated confidence values using trained classification models. When pillar scores and confidence values do not satisfy predetermined threshold conditions, the user context refinement system dynamically constructs contextual prompts using the pillar scores and confidence values as input parameters. User responses generate score modification data that refines the pillar scores and contextual response data for recommendation generation. The response synthesis system generates refined classifications and personalized recommendations using the refined pillar scores and contextual response data. An orchestration system coordinates agent interactions using learned uncertainty points and implements asymmetric influence algorithms with variable weighting based on content and URL analysis concordance.

The invention provides a supply chain multistage warehousing intelligent scheduling and collaboration method and system, and relates to the technical field of intelligent warehousing, and the method comprises the steps: carrying out the hierarchical modeling prediction of the short-term and long-term demands of each stage of warehouse through employing a long-short-term memory network based on historical order data, and obtaining the inventory demand; setting each warehouse node as an independent intelligent agent, and generating an inventory allocation strategy through strategy iteration optimization among the intelligent agents; converting the inventory allocation strategy into a scheduling instruction containing an allocation object, an allocation quantity and allocation time based on a rule knowledge base; and in the edge computing unit of each warehouse node, receiving a scheduling instruction and performing sorting execution, when inventory abnormity or resource conflict is detected, initiating an emergency cooperation request to an adjacent warehouse node, returning response information by the warehouse node receiving the emergency cooperation request according to the own resource state, and performing scheduling according to the response information. And determining an emergency processing scheme through local negotiation between the nodes.

The invention discloses a supply chain full-process traceability system based on digital twinning and block chains, and belongs to the technical field of digital twinning and block chains, and the system comprises a data collection layer which obtains original data from each link in real time, and carries out the preprocessing of the original data through an edge computing device; the twinborn model building layer is used for building digital twinborn models corresponding to all links of the supply chain respectively and generating link-level optimization parameters through simulation analysis; the block chain network layer is used for verifying and storing shared data and key traceability information among the digital twin models; the collaborative optimization module is used for dynamically adjusting a cross-link collaborative strategy based on a multi-agent reinforcement learning algorithm; and the data traceability layer responds to an external traceability query request and generates a full-link visual traceability map. By means of the collaborative algorithm based on multi-agent reinforcement learning, automation and intellectualization of information interaction among all link models are guaranteed, and personalized and multi-terminal traceability service can be provided for users.

The invention relates to the technical field of block chains, and discloses a supply chain tracing method and system based on a block chain, and the method comprises the steps: carrying out the collection and feature learning of the product identification information and transaction records of a supply chain node, and obtaining a supply chain transaction feature vector; based on the supply chain transaction feature vector, executing intelligent contract verification strategy confirmation and weighted voting verification on a transaction group to obtain a consensus confirmation block; extracting transaction data from the consensus confirmation block, constructing a double-Hash Merkle tree structure and generating supply chain traceability features; and executing a commodity tracing contract, a quality verification contract and a responsibility judgment contract according to the supply chain tracing characteristics, and generating an execution log and a visual tracing report, thereby reducing the number of verification nodes and network communication overhead while ensuring the block verification reliability, improving the consensus efficiency, and improving the objectivity and efficiency of the tracing process.

A computerized method for comprehensive ESG performance management quantifies multi-dimensional business value through integrated processes. A universal sustainability intelligence module receives ESG data from multiple sources, encompassing environmental, social, and governance information. An AI-driven performance intelligence engine generates sustainability insights using a universal framework that includes topic-agnostic insight generation, cross-topic opportunity optimization, universal project evaluation, and integrated pathway development. A multi-dimensional value quantification engine calculates business value metrics across cost reduction, revenue enhancement, risk mitigation, capital structure optimization, workforce value creation, supply chain sustainability, and intangible value creation. A causal linkage analysis engine establishes relationships between ESG improvements and business outcomes using attribution algorithms. A blockchain trust foundation stores immutable records using cryptographic verification. The system generates comprehensive ESG performance reports including sustainability insights, business value metrics, and verified attribution of business value to specific ESG improvements.

The invention discloses a cross-border e-commerce integrated management platform, and relates to the technical field of cross-border e-commerce management, and the platform comprises a multi-modal semantic reconstruction module which is used for collecting multi-modal policy data of a trade country related to cross-border e-commerce business, and generating cross-modal policy features through cross-modal alignment and fusion; the compliance knowledge graph construction module is used for constructing a policy knowledge graph based on the cross-modal policy characteristics, calculating compliance risk values for fuzzy terms in the cross-modal policy characteristics, marking high-risk commodities and adding risk attributes to the policy knowledge graph; the compliance decision driving module is used for constructing a multi-dimensional state vector, generating a sub-node action set through random sampling, constraining a strategy update amplitude and selecting an action corresponding to an optimal sub-node; and the strategy execution and feedback module is used for executing the selected action, synchronously adjusting declaration parameters, optimizing transportation distribution of the split parcels, monitoring a customs clearance result in real time and feeding back a punishment value, so that the accuracy of fuzzy policy understanding is realized.