2017results about "Billing/invoicing" patented technology

The invention provides a rules engine that evaluates, modifies, and dynamically routes inputs to AI models based on provider restrictions and management rules. Provider restrictions are stored. Based on management rules and the detected provider restrictions, the input can be modified with contextual placeholders and prompts can be injected for input into the destination model. Reversal of the modifications can occur to the outputs. The system can notify the user, an administrator, and a supervisor regarding the security evaluation and remedial actions. The evaluations can be logged for auditing purposes.

The invention relates to a digital finance and taxation auditing method based on artificial intelligence, and the method comprises the following steps: S1, obtaining electric finance and taxation multi-source data, and carrying out the preprocessing of the data, and obtaining the preprocessed electric finance and taxation multi-source data; s2, constructing a finance and taxation knowledge graph according to the preprocessed power finance and taxation multi-source data; s3, constructing a multi-modal risk detection model, and obtaining a risk event list and a whole-course data traceability path according to the finance and taxation knowledge graph and the real-time electric finance and taxation multi-source data; s4, according to the risk event and traceability, based on a rule model, generating a structured decision instruction set including all key information and traceability links; and S5, converting the structured decision instruction set and the finance and tax knowledge graph into a standard relation graph for input, and dynamically displaying fund flow, risk events, abnormal aggregation and traceability paths among enterprises through D3. Js rendering. According to the invention, the efficiency and reliability of electric power digital finance and taxation auditing are significantly improved.

A system and method for facilitating collection of Road User Charges (RUC) using a digital currency within a Distributed Ledger Technology (DLT) network are disclosed. The system generates secure digital wallets for Road Users and Facility Owners, receives trip, position, distance, and cost data from vehicles, sensors, and facility systems, and encodes settlement logic into smart contracts. These contracts automatically execute jurisdiction-specific disbursements across blockchain, directed acyclic graph (DAG), or hashgraph frameworks, ensuring scalability and auditability without reliance on centralized tolling infrastructure. An AI Activity Module processes multi-source telemetry—including vehicle sensors, GNSS / PNT data, roadside infrastructure, and third-party traffic feeds—to detect congestion, forecast conditions, optimize routing, and dynamically adjust segment-level RUC pricing in real time. The integration of AI-driven adaptive pricing with distributed ledger settlement provides a decentralized, infrastructure-agnostic framework for secure, transparent, and verifiable road use fee collection across multiple jurisdictions, distinct from conventional tolling systems.

The present invention provides a system and method for data processing in procurement and supply chain application developed by codeless platform. The invention includes one or more large language model (LLM) agents configured for processing one or more input received on an electronic user interface. The invention includes selecting a tool selection agent from a tool repository and invoking the selected tool by a tool execution agent for processing at least one task to be executed.

Various embodiments are disclosed for a services policy communication system and method. In some embodiments, a communications device stores a set of device credentials for activating the communications device for a service on a network; and sends an access request to the network, the access request including the set of device credentials.

Computerized systems and methods are described for converting traditional technology products into an “As a Service” (AaS) model, facilitating the transition from capital expenses (CapEx) to operational expenses (OpEx). Methods include receiving user inputs for technology product selections and accessing a Real-Time Data Mesh (RTDM) to retrieve data. An Advanced Analytics and Machine Learning (AAML) Module analyzes user inputs and market data, optimizing the conversion into subscription-based services. Process results are displayed to the user through a Single Pane of Glass User Interface (SPOG UI). An AaS Conversion Module performs transition of products into customizable subscription packages. This method emphasizes dynamic pricing based on usage, flexibility, and / or scalability of services. Methods are provided for real-time reporting, subscription management, and vendor system integration, enabling a comprehensive AaS conversion process suitable for modern technology products and services.

A full-link reconciliation method based on a snowflake algorithm, an apparatus, a device, and a medium are provided. The method includes: responding to a reconciliation request of a cross-border service and acquiring bill source data from a service end; parsing the bill source data, and converting the bill source data into target bill data; calling a reconciliation rule engine to acquire a corresponding reconciliation rule, selecting a corresponding reconciliation algorithm, and performing a full-link reconciliation task on the target bill data to acquire a reconciliation result; transmitting unbalanced data to a corresponding exception processing system for a check processing of abnormal data; generating, based on an optimized snowflake algorithm, a unique target ID to perform a corresponding bill association with the reconciliation result, automatically penetrating, according to the unique target ID corresponding to the reconciliation result, full link data of an information flow and a fund flow, and tracing, according to the unique target ID, a whole process of the corresponding bill flowing between the fund flow and the information flow, to achieve a complete closed-loop of reconciliation.

Certain aspects of the disclosure pertain to workflow creation assistance with generative artificial intelligence. A problem statement can be received that is specified by a user in a natural language. At least one machine learning model can infer a workflow template that maps to the problem statement. Workflow template parameters can be determined, and parameter values generated based on the problem statement. Additional interaction with the user in the natural language can be performed to request and receive further data associated with the workflow template with the at least one machine-learning model. Subsequently, the workflow template can be populated with generated parameter values and provided to a workflow system that generates a workflow based on the workflow template.

An optical biomodule for detecting a disease specific biomarker(s) at an early stage, utilizing enhanced fluorescence emission (due to integration of three-dimensional (3-D) structures in a fluidic container) upon chemical binding / coupling of a disease specific biomarker(s) with its corresponding disease specific biomarker binder(s)-coupled with a fluorophore(s) positioned horizontally within a geometrical area (that includes a center region or a middle region) is disclosed. Furthermore, a corresponding disease specific biomarker binder(s) may include an amplifying chemical structure / sequence. The three-dimensional (3-D) structure(s) may be coupled with an optical resonator and / or a photonic crystal (PC) and / or a metamaterial and / or a metamaterial of Epsilon-Near-Zero (ENZ) of a suitable wavelength range.

Techniques are described for deploying software applications associated with document management. The techniques may include retrieving, by a computing system and from a software repository, configuration information for a software application for managing an electronic document, the configuration information specifying a plurality of widgets for deploying the software application. The techniques may further include receiving, by the computing system and from a client device, a request to access the software application. The techniques may further include generating, by the computing system, a response to the request comprising an indication of the configuration information and instructions configured to cause the client device to request, from the software repository, the plurality of widgets specified by the configuration information. The techniques may further include outputting, by the computing system, the response to the client device to provide the software application at the client device.

The present invention is related to data processing methods and systems thereof. According to an embodiment, the present invention provides a method of processing claim deduction documents using a machine learning model. The process begins by accessing data files and extracting information from them, which is subsequently stored. This document information, along with the machine learning model trained on various document formats, is used to classify the data files and generate tabular data. From this tabular data, data objects are created and included in an output data file. The information from the output file is then used to update the data of the machine learning model, optimizing it for improved future document processing. There are other embodiments as well.

Systems and methods are described for a maintaining consistent and reliable outputs from artificial intelligence (“AI”) based search systems that use pipelines with a dataset, AI model, and prompt. An application can send a query through a pipeline and set the result as a baseline for future results. The application can periodically resend the query through the pipeline and compare the new results to the baseline. If the new results vary from the baseline above a predetermined threshold, then corrective measures can be taken. This can include notifying an administrator or querying the pipeline for how to change the prompt so that results are more similar to the baseline.

An invoice processing system is proved. The system includes a database and a server in electronic communication with the database. The server is operative to: interpret an invoice; interpret historical data from the database, the historical data corresponding to at least one of one or more vendors of past invoices or one or more payors of past invoices; and in response to the interpretation of the invoice, determine a vendor of the invoice based at least in part on the historical data. The server is further operative to in response to the determination of the vendor, determine a value for each of one or more judgment fields via machine learning based at least in part on the historical data and the vendor; and transmit the invoice with the vendor and the value for each of the one or more judgment fields.

Methods and systems provide for determining cost values associated with artificial intelligence (AI) processing within a legal services platform. In one embodiment, the system receives a request for AI processing from a user, wherein the request comprises a quantity value associated with the AI processing; performs the AI processing to generate output based on the request; estimates a processing metric for the quantity value associated with the AI processing, wherein the processing metric comprises a value indicative of the amount of processing performed; determines a cost value based on the modified processing metric, wherein the cost value reflects the cost associated with the AI processing; and provides the cost value to the user for billing purposes in relation to the AI processing.

Computerized systems and methods are described for generating and optimizing vendor product roadmaps using predictive insights. Leveraging a Real-Time Data Mesh (RTDM) module, data from diverse sources including market trends, customer feedback, and technological advancements is aggregated and standardized. An Analytics and Machine-Learning (AAML) module analyzes this data to generate predictive insights, facilitating adjustments to existing product roadmaps. Dynamic adjustments are made using a roadmap optimization module, with communication facilitated through a Single Pane of Glass (SPoG) user interface (UI). Scenario analysis, decision-support systems, and continuous monitoring improve strategic decision-making.

Novel techniques are disclosed for providing vPLC-specific metadata service including customized vPLC-specific metadata. In certain embodiments, each vPLC may generate a customized metadata using its corresponding vPLC-specific customization instructions. In some embodiments, a vPLC-specific metadata service may be performed using pre-generated customized vPLC-specific metadata, on-the-fly customized metadata, pre-generated CSP-format metadata, or combinations thereof.

Techniques for facilitating connectivity to vPLCs created in a CSP-provided infrastructure in a region. Within the CSP-provided infrastructure in a region, when the destination of a packet is determined to be an endpoint associated with a particular vPLC, the packet is tagged with information related to the particular vPLC. The vPLC-related information for the particular vPLC can include, for example, a vPLC identifier identifying the particular vPLC, an identifier identifying a customer associated with the endpoint, a virtual cloud network identifier identifying a virtual cloud network (VCN) belonging to the particular vPLC and where the endpoint is part of the VCN, and other vPLC-related information. The packet is then routed or communicated within the CSP-provided infrastructure in a region along with the tagged vPLC-related information. The vPLC-related information is used as part of the connectivity and for routing of packets within the CSP-provided infrastructure in a region.

The present invention provides a digital twin-based charging station control system. A digital twin-based charging control method according to an embodiment of the present invention comprises the steps of: generating a virtual space, which is a three-dimensional virtual space obtained by combining information of the real world with a digital world, on the basis of information of a charging station in which a charger for charging a vehicle is installed; storing, in a database, information of a past event that has occurred at the charging station; and on the basis of the information of the past event that has occurred at the charging station, and on the basis of the event information in the virtual space, implementing the past event that has occurred at the charging station in the virtual space and reproducing same.

An apparatus for instantaneous generation of a pin placement quote in an injection molding process, the apparatus comprising at least a processor and a memory containing instructions configuring the at least a processor to receive part data related to at least a part from an entity, generate a pin placement schema as a function of the part data, provide a pin placement quote based on the pin placement schema, wherein providing the pin placement quote includes generating a projected part data by overlaying the pin placement schema onto the part data, determining a list of quote parameters, and formulating the pin placement quote by associating the list of quote parameters with the projected part data, and display the pin placement quote to the entity using a user interface at a display device.

The invention discloses a classified storage method and system for financial management electronic bills. The method comprises the following steps: acquiring text content and structured data of the electronic bills, extracting key information of the text content, combining the key information with the structured data, and constructing a multi-dimensional feature vector; constructing an electronic bill classification model, and training the electronic bill classification model based on the multi-dimensional feature vector; dynamically updating the trained electronic bill classification model based on the newly added bill type; classifying the electronic bills based on the dynamically updated electronic bill classification model, and adjusting a classification result; constructing a mapping relationship between the bill category and the business process, and automatically triggering the corresponding business process based on the classification result of the electronic bill; and analyzing a classification result of the electronic bills, and displaying an analysis result based on an interactive chart. According to the invention, refined intelligent classification of the electronic bills is realized, and the automation level and decision-making efficiency of financial management are improved.

The invention relates to an anomaly detection method and system based on multivariate data analysis and electronic equipment, and relates to the technical field of invoice detection, and the method comprises the steps: obtaining at least one piece of to-be-detected target invoice data in a target enterprise; preprocessing each piece of target invoice data to obtain corresponding invoice characteristic information of at least one dimension; inputting the invoice feature information of each piece of target invoice data into a preset anomaly detection model, and determining a target potential anomaly type of the corresponding target invoice data; on the basis of the target potential anomaly type, at least one target invoice feature dimension which is liable to be abnormal in the invoices of the target enterprise and at least one corresponding target invoice anomaly type, determining whether to carry out anomaly early warning or not; and if yes, verifying the target potential anomaly type to obtain a verified anomaly type, and giving an anomaly early warning for the verified anomaly type. The invoice abnormity early warning method has the effect of improving the accuracy of invoice abnormity early warning.

The invention includes a UI that allows addition of functional placeholders to an AI pipeline, and a rules engine that chooses pipeline objects during or before AI pipeline execution. The rules engine can select pipeline objects meeting the same function type and object rules of the functional placeholder. The rules engine also applies customizable security rules and artificial intelligence (AI) model routing to queries. Remedial actions are taken when the rule evaluation exceeds a threshold. The remedial actions include transforming the query by replacing sensitive information with a reversible placeholder. The actual result can be modified by reversing the placeholder and redacting additional sensitive information. The system can notify the user, an administrator, and a supervisor regarding the security evaluation and remedial actions. The evaluations can be logged for auditing purposes.

Systems and methods of the disclosure include: identifying, by a cloud resource management system, a cloud resource consumption model associated with one or more cloud resources; generating, by the cloud resource management system, a sequence of instructions implementing a smart contract based on the cloud resource consumption model; sending, to a distributed ledger network, the smart contract; receiving, by the cloud resource management system, cloud resource usage data associated with the one or more cloud resources; and causing, by transmitting a message reflecting the cloud resource usage data to the distributed ledger, the smart contract to be executed.

Systems for preparing, training, and deploying a machine learning algorithm for making medical condition state determinations include at least one processing unit that includes the machine learning algorithm. The at least one processing unit is programmed to receive image input from an imaging device, receive patient health data, encode the patient health data to convert the patient health data to encoded patient health data, and transmit the encoded patient health data into the machine learning algorithm. Systems are configured to make a medical condition state determination based on the image input and the encoded patient health data, via the machine learning algorithm, and provide visual output for the medical condition state determination via a display device such that the visual output may be augmented with the patient health data. Dynamic state information also may be input to the machine learning algorithm and used to make medical condition state determinations.

A blockchain-based system for managing music rights and royalties is disclosed. The system enables artists and contributors to upload music, assign revenue percentages, and track usage via smart contracts on a distributed ledger. Play counts are immutably recorded across distributor platforms. A contributor dashboard provides real-time access to royalty data. The platform incorporates Majority Reports to validate metadata matches and calculate royalties, and Minority Reports to flag discrepancies without payments, thereby ensuring data integrity. A Golden Box smart contract module replicates song rankings and applies predictive classification models to identify features associated with musical success. These mechanisms improve transparency, accuracy, and fraud prevention in royalty distribution.

A building system can include one or more memory devices having instructions stored thereon, that, when executed by one or more processors, cause the one or more processors to receive a first prediction generated using one or more first events enriched with contextual data, identify one or more second events or one or more second predictions associated with the first prediction, enrich the one or more second events or the one or more second predictions with the first prediction, and provide the one or more second events enriched with the first prediction or the one or more second predictions enriched with the first prediction.

The invention discloses an RPA-based full-automatic all-electricity invoice issuing method and system. The method comprises the following steps: constructing an RPA system: creating an RPA user, creating a business process, and defining a process rule; data acquisition and preprocessing: capturing basic transaction data, and performing data preprocessing on the captured data; invoicing and intelligent inspection: logging in a full-electricity invoice service platform, filling and submitting data information in an invoicing interface, and verifying various contents of an invoice; invoice circulation and signing determination: sending the issued full-electricity invoice to the user, and monitoring the invoice signing condition; and storage and filing management: uploading the issued all-electricity invoice to a management system. According to the invention, by introducing the RPA technology, the full-automatic processing of the full-electricity invoices from data acquisition and issuing to circulation and archiving is realized, the time consumption of manual operation is greatly reduced, and the invoice issuing efficiency is improved compared with the traditional manual or semi-automatic process.

Disclosed herein are methods and systems to generate code for a workflow. A non-limiting example of a method comprises executing, by a processor, a large language model, which receives an input of an intent associated with a workflow and provides a machine-readable description by: identifying, by searching a hierarchical data structure using a vector embedding associated with the intent, an object corresponding to the intent; identifying a set of data paths within the hierarchical data structure to retrieve the identified object; and generating the machine-readable description, the machine-readable description describing the object and at least one data path within the set of data paths; and generating, by the processor, code for the workflow using the machine-readable description.

Methods, systems, and computer-readable storage media for providing a set of column pairs, each column pair including a column of a bank statement table, and a column of a super invoice table, each column pair corresponding to a modality, the super invoice table including at least one row including data associated with multiple invoices, for each column pair, determining a feature descriptor based on an operator, a feature vector being provided based on feature descriptors of the set of column pairs, inputting the feature vector to a ML model that processes the feature vector to determine a probability of a match between the bank statement, and a super invoice represented by the super invoice table, and outputting a binary output representing one of a match and no match between the bank statement, and the super invoice based on the probability.

The invention discloses an invoice automatic auditing and intelligent settlement system for automobile road rescue, and relates to the technical field of intelligent settlement systems, a correction module integrates a machine learning model to analyze historical bills, automatically corrects unreasonable settlement amount and generates a correction suggestion report, a prompt module builds an intelligent priority matrix, and the intelligent priority matrix is sent to a server. The reminding frequency is dynamically adjusted according to parameters such as the historical settlement scale and the service life of a supplier, the settlement module recognizes an abnormal invoicing terminal by combining an invoicing equipment fingerprint recognition technology, a payment process is automatically triggered after verification is passed, a supplier credit portrait system is established, and a dynamic settlement scheme is supported. The settlement system establishes an intelligent priority matrix to realize important supplier high-frequency reminding and low-risk project low-frequency intervention, so that the whole process timeliness is guaranteed, the defects of a traditional rule engine in coping with complex scenes are effectively overcome, and the response capability and the autonomous decision-making capability of the system to abnormal behaviors are improved.