Systems and methods are described herein for novel uses and/or improvements for predicting, using machine learning models, a process for a user based on function execution. An indication of completion of a predetermined application function associated with a user may be detected and a plurality of stored parameters associated with the user may be identified. The predetermined application function and the parameters may be input into a machine learning model to determine/obtain a process prediction for the user. The process may include a number of functions that may be sent to a user device for execution.
Systems and methods are described herein for novel uses and/or improvements for using artificial intelligence to determine whether an image is valid and/or formatted appropriately for printing onto a physical object. An image validation system may receive an image, for example, from a user. The image may be received in order to print the image onto a physical object. When the image is received, the validation system may use a first machine learning model to format the image appropriately and then use another machine learning model to determine whether the image has an appropriate context (e.g., no violence). Based on that determination, the validation system may either send the image for printing or try to remove the offending content from the image.
A computer can connect with a middleware computing system. The middleware computing system may use application programming interfaces (APIs), webhooks, file-based integration, database replication, message queues, websockets, or direct integration to establish connections with different computing devices. The computer may request a data structure classification for an external data structure stored in a remote computing device. The middleware computing system can receive the request, identify the connection that the middleware computing device has with the remote computing device, and retrieve records for transactions performed by the external data structure from the remote computing device. The middleware computing system can use metadata in the records to automatically determine a data structure type of the data structure. The middleware computing system can generate instructions that cause the computer to link the external data structure with the profile.
Systems and methods are described herein for novel uses and/or improvements for predicting, using machine learning models, a process for a user based on function execution. An indication of completion of a predetermined application function associated with a user may be detected and a plurality of stored parameters associated with the user may be identified. The predetermined application function and the parameters may be input into a machine learning model to determine/obtain a process prediction for the user. The process may include a number of functions that may be sent to a user device for execution.
Systems and methods for real-time mapping and visualization generation of system components. The system may receive a first user request to generate a first visualization of a first configuration of a first subset of components in a first software system. The system may, in response to the first user request, retrieve a first software applications lineage log, wherein the first software applications lineage log comprises a log of event data of current processes being performed in the first software system. The system may generate a first feature input based on the first software applications lineage log. The system may input the first feature input into a first artificial intelligence model to generate a first output.
The systems and methods disclosed herein receive a dataset including an observed set of values for a set of variables. The system can use a first set of AI models to identify a set of anomalies in the observed set of values by comparing an observed set of patterns against multiple reference patterns. The system can use a second set of AI models to evaluate the identified anomalies by comparing an observed set of association rules with an expected set of association rules. The system can use a third set of AI models to generate reconfiguration commands to remove the identified anomalies. The reconfiguration commands can be automatically executed to modify the observed association rules to align with the expected association rules.
Systems and methods for assessing vulnerability of virtual resources are disclosed herein. A system may receive a request and parameterized rules from a database of parameterized rules for assessing potential vulnerability of user resources caused by a virtual resource transmission. The system may extract user vulnerability tolerance parameters indicative of user propensity for vulnerability and virtual resource vulnerability tolerance parameters indicative of vulnerability of the virtual resource. The system may input, into a parameterized rule, a user vulnerability tolerance parameter and a virtual resource vulnerability tolerance parameter to trigger execution of each parameterized rule. The system may then generate a match indicator indicating whether the virtual resource matches the user transmissions profile and, responsive to determining that the virtual resource does not match the user transmissions profile, trigger a temporary rejection on the transmission request requiring an override authorization.
G06F 21/57 - Certifying or maintaining trusted computer platforms, e.g. secure boots or power-downs, version controls, system software checks, secure updates or assessing vulnerabilities
G06F 21/55 - Detecting local intrusion or implementing counter-measures
Methods and descriptions are described herein for applying cascading machine learning models to command prompts. In particular, the system may receive a query indicating a computing process to be performed. The system may input a command prompt based on the query into a first instance of an LLM, which may output activities for performing the process. The system may input a first activity into a second instance of the LLM, which may output vulnerabilities associated with the first activity. The system may input a first vulnerability into a third instance of the LLM, which may output indications of available control tools for addressing the first vulnerability. The system may input a first control tool into a fourth instance of the LLM, which may output indications of monitoring tools for monitoring the first control tool. The system may then cause implementation of the first control tool and the first monitoring tool.
G06F 21/57 - Certifying or maintaining trusted computer platforms, e.g. secure boots or power-downs, version controls, system software checks, secure updates or assessing vulnerabilities
9.
SYSTEMS AND METHODS FOR ESTABLISHING DATA PROVENANCE BY GENERATING ONE-TIME SIGNATURES
Presented herein are system and methods for establishing data provenance by generating one-time signatures. A system may include one or more processors that receive, via an application programming interface (API) request, a request for a one-time signature and data associated with the request, provide a seed identifier and the data associated with the request to an HSM in a set of HSMs, and receive a response message from the HSM, the response message including a one-time signature. In examples, the response message and the one-time signature are provided to the device that transmitted the request for the one-time signature and the data associated with the request. Methods and non-transitory computer-readable mediums are also presented.
H04L 9/32 - Arrangements for secret or secure communicationsNetwork security protocols including means for verifying the identity or authority of a user of the system
Presented herein are system and methods for establishing data provenance by generating one- time signatures. A system may include one or more processors that receive, via an application programming interface (API) request, a request for a one-time signature and data associated with the request, provide a seed identifier and the data associated with the request to an HSM in a set of HSMs, and receive a response message from the HSM, the response message including a one-time signature. In examples, the response message and the one-time signature are provided to the device that transmitted the request for the one-time signature and the data associated with the request. Methods and non-transitory computer-readable mediums are also presented.
H04L 9/32 - Arrangements for secret or secure communicationsNetwork security protocols including means for verifying the identity or authority of a user of the system
Presented herein are systems and methods for aggregating data from disparate sources to output information. A computing system may transform a first plurality of datasets of a plurality of data sources by converting a first format of the corresponding data source for each of the first plurality of datasets to generate a second plurality of datasets in a second format of the computing system. The computing system may identify, from the second plurality of datasets, a subset of datasets using a feature selected for evaluation of a utility of the feature. The computing system may apply a machine learning model configured for the selected feature to the subset of datasets to generate an output that measures a likelihood of usefulness. The computing system may cause a visualization of the output for the feature to be displayed for presentation on a dashboard interface based on a template configured for the feature.
Systems and methods for managing resources across a global and/or cloud network. In particular, systems and methods for mitigating issues related to providing services while resources are off-line (or may potentially go off-line). For example, the systems and methods may mitigate issues related to providing services while resources are off-line (or may potentially go off-line) by monitoring network services at an aggregate level.
Systems and methods that providing status indications dependent on operations executed in accordance with a software programming workflow based on satisfaction of dependencies across feature programming workflows of the software programming workflow are disclosed herein. By selectively generating and causing indications to be displayed to relevant users at corresponding devices, the system can more accurately identify when the status of one event affects a different event, even when such events are associated with the development of different features of an application.
Methods and systems are described herein for monitoring fault events at a fiber optical network. In particular, a system may receive, from components of an optical network, corresponding component data structures comprising optical measurements. The system may extract, from a component data structure, a set of component metrics for light transmission signals being transmitted or received via fiber optic transmission lines at a corresponding component and input the component data structure into a first machine learning model to obtain an indication of an occurrence of an event at one or more components. The system may generate a prompt for input into a second machine learning model configured to identify corrective actions for addressing any events within optical networks to obtain one or more corrective actions for addressing the occurrence of the event.
G06F 11/07 - Responding to the occurrence of a fault, e.g. fault tolerance
H04B 10/079 - Arrangements for monitoring or testing transmission systemsArrangements for fault measurement of transmission systems using an in-service signal using measurements of the data signal
Presented herein are systems and methods for managing networked environments. A computer system may maintain a plurality of process flows to manage a plurality of services in a network. Each respective process flow of the plurality of process flows may identify: (i) a respective trigger to invoke the respective process flow to initiate a first process of a plurality of processes on at least one of the plurality of services. The computing system may select, responsive to detecting a trigger, a process flow from the plurality of process flows based on the trigger. The computing system may execute, in accordance with the process flow, at least one process of the plurality of processes on at least one of the plurality of services in the network.
Systems and methods for managing resources across a global and/or cloud network. In particular, systems and methods for mitigating issues related to providing services while resources are off-line (or may potentially go off-line). For example, the systems and methods may mitigate issues related to providing services while resources are off-line (or may potentially go off-line) by monitoring network services at an aggregate level.
Systems and methods for managing resources across a global and/or cloud network. In particular, systems and methods for mitigating issues related to providing services while resources are off-line (or may potentially go off-line). For example, the systems and methods may mitigate issues related to providing services while resources are off-line (or may potentially go off-line) by monitoring network services at an aggregate level.
Presented herein are systems and methods for processing tokens in identity assertions for access control to resources. A server may receive, via an interface from a gateway, a request to permit a customer device to access a resource associated with the server. The request may include an identifier for the customer device and a first token used to authenticate the customer device at the gateway. The server may generate, responsive to validating the first token, a second token to be used to authorize the customer device at the server for access to the resource. The server may store, on a database, an association identifying the identifier, the first token, and the second token. The server may perform the server, an action to permit the customer device access to the resource associated with the server based on the association maintained on the database.
The systems and methods disclosed herein generates responses generated by artificial intelligence (AI) models such as large language models (LLM) using intent-based rankings of retrieved information. The systems and methods disclosed herein receives an output generation request for the generation of an output using a set of AI models. Using a first AI model, a set of documents are retrieved using the received output generation request. The set of documents are partitioned into chunks. The chunks are ranked using a distance between the vector representation of the received output generation request and the vector representation of each chunk. A second AI model classifies the output generation request and chunks using an intent of the respective output generation request or chunk, and generates a second set of rankings using the intents. The set of AI models generate a response using the second set of rankings.
SYSTEMS AND METHODS FOR DETECTING REAL-TIME DELAYS IN ELECTRONIC COMMUNICATIONS OVER COMPUTER NETWORKS IN AN ONGOING MANNER AND IN ENVIRONMENTS IN WHICH USERS HAVE NO ACCESS TO INTERMEDIARY DEVICES USING A PARALLEL PROCESSING STREAM
Systems and methods for improvements for processing electronic communications over computer networks. As one example, systems and methods may ensure real-time (and/or near real-time) processing of electronic communications over computer networks by detecting real-time delays in electronic communications over computer networks in an ongoing manner and in environments in which users have no access to intermediary devices using a parallel processing stream.
Systems and methods for managing resources across a global and/or cloud network. In particular, systems and methods for mitigating issues related to providing services while resources are offline (or may potentially go offline). For example, the systems and methods may mitigate issues related to providing services while resources are offline (or may potentially go offline) by monitoring network services at an aggregate level.
G06Q 20/40 - Authorisation, e.g. identification of payer or payee, verification of customer or shop credentialsReview and approval of payers, e.g. check of credit lines or negative lists
G06F 16/27 - Replication, distribution or synchronisation of data between databases or within a distributed database systemDistributed database system architectures therefor
G06Q 20/36 - Payment architectures, schemes or protocols characterised by the use of specific devices using electronic wallets or electronic money safes
A component risk control generator includes a set of machine learning models, an automatic command set generator, and a model quality assessment engine. Using a set of received input items, the automatic command set generator generates a component activity generator command set. A trained upstream machine learning model generates a component activity output set, which is used to generate a set of cascading command sets. Using at least one command set in the set of cascading command sets, a set of trained downstream machine learning models generate a plurality of cascading command sets and downstream output sets based thereon. The output sets can be validated using the model quality assessment engine.
SYSTEMS AND METHODS FOR DETECTING REAL-TIME DELAYS IN ELECTRONIC COMMUNICATIONS OVER COMPUTER NETWORKS IN AN ONGOING MANNER AND IN ENVIRONMENTS IN WHICH USERS HAVE NO ACCESS TO INTERMEDIARY DEVICES USING A PARALLEL PROCESSING STREAM
Systems and methods for improvements for processing electronic communications over computer networks. As one example, systems and methods may ensure real-time (and/or near real-time) processing of electronic communications over computer networks by detecting real-time delays in electronic communications over computer networks in an ongoing manner and in environments in which users have no access to intermediary devices using a parallel processing stream.
Described herein are methods and systems to dynamically generate interactive graphical interfaces. Upon receiving a selection to display an execution page, a webserver may (1) transmit a request for a user identifier and (2) transmit a request for one or more options for a user. The webserver may receive an array including one or more options and instructions to display the one or more installment options on the execution page. Upon extracting the one or more options from the array, the webserver dynamically revises the webpage with at least one interactive graphical element having a visual characteristic of the webpage. Responsive to a second selection of the at least one interactive graphical element on the webpage, the webserver displays the one or more options as selectable graphical components having the visual characteristic of the webpage.
A component risk control generator includes a set of machine learning models, an automatic command set generator, and a model quality assessment engine. Using a set of received input items, the automatic command set generator generates a component activity generator command set. A trained upstream machine learning model generates a component activity output set, which is used to generate a set of cascading command sets. Using at least one command set in the set of cascading command sets, a set of trained downstream machine learning models generate a plurality of cascading command sets and downstream output sets based thereon. The output sets can be validated using the model quality assessment engine.
Systems and methods that provide status indications in response to detection of one or more duplicate operations between a first software programming workflow and a second software programming workflow are disclosed herein. By selectively generating status indications on display devices associated with designated users during separate software programming workflows as described, systems can be configured to accurately determine when redundant operations are to be executed across different software programming workflows and provide indications that reduce or eliminate the need for subsequent, duplicative operations to be executed when developing corresponding portions of different software programming workflows.
Systems and methods are described that allow for updating of software applications during testing of the application to detect errors as a result of execution of portions of the software application that prevent downstream portions of the software application from being evaluated. In an example, systems are described that are configured to detect errors during application execution. When an error is detected, the system obtains and executes specific script sets to debug the application. Based on the results of these debug operations, the system generates error reports that indicate issues within the software workflow. This ensures that errors in one part of the software do not hinder the evaluation of subsequent parts, allowing for a more efficient and thorough testing process.
A network system to allow global usage of data while allowing regional jurisdictions control over sensitive data. Different jurisdictions may declare different types of data as sensitive data that is not to be discoverable by another party. The system may receive data that includes encoded data at a first device from a second device (e.g., associated with a remote datacenter). The system may store the data at the first device. In response to receiving a request from a third entity, the system may request a cryptographic key for decoding one or more data fields of the encoded data. Based on decoding the associated field data, the system may transmit a response to the data request that includes the decoded data.
Systems and methods for managing resources across a global and/or cloud network. In particular, systems and methods for mitigating issues related to providing services while resources are offline (or may potentially go offline). For example, the systems and methods may mitigate issues related to providing services while resources are offline (or may potentially go offline) by monitoring network services at an aggregate level.
G06Q 20/40 - Authorisation, e.g. identification of payer or payee, verification of customer or shop credentialsReview and approval of payers, e.g. check of credit lines or negative lists
G06F 16/27 - Replication, distribution or synchronisation of data between databases or within a distributed database systemDistributed database system architectures therefor
G06Q 20/36 - Payment architectures, schemes or protocols characterised by the use of specific devices using electronic wallets or electronic money safes
Systems and methods for providing on-demand access to resources across global or cloud computer networks are described herein. In particular, the systems and methods can use transformer models to estimate proxy resource capacities. These proxy resource capacities can be leveraged to satisfy certain conditions for executing blockchain actions, enabling resources to be used even when a current capacity of those resources does not satisfy the conditions.
H04L 67/1097 - Protocols in which an application is distributed across nodes in the network for distributed storage of data in networks, e.g. transport arrangements for network file system [NFS], storage area networks [SAN] or network attached storage [NAS]
Presented herein are systems and methods for managing networked environments. A computer system may provide a user interface for configuring a plurality of groups of servers each hosting a resource for an application. The user interface may include: a first element configured to select, upon interaction, at least one group of servers from the plurality of groups of servers to which to install a patch for the application; a second element configured to identify, upon interaction, a first group of servers of the plurality of groups of servers to which to transfer network traffic associated with the application and communicated with a second group of servers of the plurality of groups of servers; and a third element configured to provide, upon interaction, one or more performance indicators for at least one of a plurality of functions of the application.
Systems and methods for detecting anomalies in generative outputs are disclosed herein. The system receives a user prompt indicating a request for data over a time period. The system inputs, into a model, the user prompt to cause the model to generate an output based on the user prompt. The system then generates the first tokens based on the output. To generate the second tokens, the system retrieves, based on the user prompt, sources relating to the data requested by the user prompt. The system then generates queries to request, from the sources, the data over the time period and generates the second tokens based on the retrieved data. The system then performs a comparison of the first tokens and the second tokens and accepts or rejects the output of the model based on the comparison.
A computer-implemented method for seamlessly processing transactions using distributed ledger technology. The method may comprise: linking one or more conventional accounts hosted in a conventional banking infrastructure to one or more DLT-based client accounts hosted on a distributed ledger, wherein the DLT application comprises a routing address configured to be used in conventional transaction infrastructure using conventional communication protocols; storing one or more wallet identifications for the one or more DLT-based client accounts and a mapping of the one or more wallet identifications to the one or more conventional accounts hosted in the conventional banking infrastructure; exchanging a sequence of messages to execute an asset transfer and complete a transaction lifecycle, the sequence of messages based on the first asset type; updating the distributed ledger based on the asset transfer; and sending appropriate messages to clients.
G06Q 20/40 - Authorisation, e.g. identification of payer or payee, verification of customer or shop credentialsReview and approval of payers, e.g. check of credit lines or negative lists
G06F 16/27 - Replication, distribution or synchronisation of data between databases or within a distributed database systemDistributed database system architectures therefor
G06Q 20/36 - Payment architectures, schemes or protocols characterised by the use of specific devices using electronic wallets or electronic money safes
Presented herein are systems and methods for the employment of machine learning models for image processing as may be performed by computing devices associated with an end user. A method may include obtaining video data comprising a plurality of frames including a document of a document type. The method may include executing an object recognition engine of a machine-learning architecture using image data of the plurality of frames, the object recognition engine trained to detect edges of documents. The method may include identifying, based on the edge detection, a plurality of boundaries for the document. The method may include validating, based on the plurality of boundaries, the document as the document type. The method may include transmitting via one or more networks, to a computer remote from the computing device, responsive to the validation of the type of document, the image data for the plurality of frames depicting the document.
G06V 10/24 - Aligning, centring, orientation detection or correction of the image
G06V 10/44 - Local feature extraction by analysis of parts of the pattern, e.g. by detecting edges, contours, loops, corners, strokes or intersectionsConnectivity analysis, e.g. of connected components
G06V 10/70 - Arrangements for image or video recognition or understanding using pattern recognition or machine learning
G06V 30/413 - Classification of content, e.g. text, photographs or tables
G06V 30/414 - Extracting the geometrical structure, e.g. layout treeBlock segmentation, e.g. bounding boxes for graphics or text
G06V 30/418 - Document matching, e.g. of document images
G06V 30/42 - Document-oriented image-based pattern recognition based on the type of document
35.
SYSTEMS AND METHODS FOR DETERMINING RESOURCE AVAILABILITY ACROSS GLOBAL OR CLOUD NETWORKS
Systems and methods are for managing resources across a global and/or cloud network. In particular, systems and methods are described for mitigating issues related to providing services using unstable resources (e.g., resources that may be off-line or potentially go off-line). For example, the systems and methods may mitigate issues related to providing services despite instability in resources by monitoring tokenized availability of a given resource using a decentralized blockchain network and performing actions based on that availability based on conditions specified within the code of action-specific self-executing programs themselves (as opposed to a centralized coordination device).
H04L 67/1097 - Protocols in which an application is distributed across nodes in the network for distributed storage of data in networks, e.g. transport arrangements for network file system [NFS], storage area networks [SAN] or network attached storage [NAS]
A network system to allow global usage of data while allowing regional jurisdictions control over sensitive data. Different jurisdictions may declare different types of data as sensitive data that is not to be discoverable by a third party. The system allows the data to be shared across jurisdiction boundaries with complete auditability, traceability, and compliance. The system allows a first jurisdiction computing device to control the usage of the data that is stored outside of the jurisdiction. The technology allows the first jurisdiction to propagate rules, tokenization protocols, and updates to the system to manage the sensitive data. The system detokenizes the data when the data is to be used for an approved purpose by an approved party. If the original jurisdiction has a change in permissions for sensitive data, the jurisdiction can propagate a tokenization to all data stored in the data management system outside of the jurisdiction.
Systems and methods for providing on-demand access to resources across global or cloud computer networks are described herein. In particular, the systems and methods can use transformer models to estimate proxy resource capacities. These proxy resource capacities can be leveraged to satisfy certain conditions for executing blockchain actions, enabling resources to be used even when a current capacity of those resources does not satisfy the conditions.
H04L 41/16 - Arrangements for maintenance, administration or management of data switching networks, e.g. of packet switching networks using machine learning or artificial intelligence
Systems and methods for securely accessing resources across global or cloud computer networks. In particular, systems and methods for using token-specific self-executing programs to securely access resources. For example, the use of token-specific self-executing programs, such as smart contracts designed to exchange a single NPT representing an off-chain product, enhances security in accessing resources by automating trustless transactions and enforcing predefined rules without intermediaries.
H04L 41/16 - Arrangements for maintenance, administration or management of data switching networks, e.g. of packet switching networks using machine learning or artificial intelligence
The systems and methods disclosed herein receives artifacts generated using a first set of models within a multi-model superstructure. The multi-model superstructure includes a second set of models to test the first set of models. The multi-model superstructure dynamically routes the artifacts of the first set of models to one or more models of the second set of models by (i) determining a set of dimensions of the artifacts against which to evaluate the artifacts and (ii) identifying the models in the second set used to test the particular dimension. The second set of models then assesses each artifact against a set of assessment metrics. If an artifact fails to meet one or more assessment metrics, the second set of models generates actions to align the artifact with the set of assessment metrics.
G06F 21/57 - Certifying or maintaining trusted computer platforms, e.g. secure boots or power-downs, version controls, system software checks, secure updates or assessing vulnerabilities
G06F 21/55 - Detecting local intrusion or implementing counter-measures
40.
Using a multi-model architecture for retrieval-augmented generation (RAG)
Systems and methods disclosed herein generate validated responses using artificial intelligence (AI)-based models. The system obtains/receives an output generation request (e.g., from a graphical user interface (GUI)) that can include a document set and a query set. The system classifies the query set by partitioning it into multiple query subsets and assigning a complexity score. Based on the classification, the system generates a computational workflow set using a first AI model set to retrieve a resource set responsive to the query set. The system executes the workflow using a second AI model set (the same as or different from the first AI model set) and validates the retrieved resources against predefined criteria (e.g., rules, guidelines). If the resources satisfy the criteria, the system generates a response using a third AI model set. The system can display a graphical layout on the GUI showing the request, retrieved resources, and/or generated response.
Systems and methods are described herein for providing seamless service route updates for service routing platforms. When a request for a new service route is received, the system may use the data within the request to transform the service route into a format supported by one or more platforms that will have the route setup. Once the transformation has been completed, the system may store the transformed service route or service routes in a route repository. In addition, the system may store the service route in a standardized format separately. The system may then determine that a new service route has been added and match the new service route with one or more service routing platforms. Once the match has been made, for each service route, the system may transmit the transformed service routes to the corresponding service routing platforms.
Presented herein are system and methods for controlling access to services for processing requests. A server maintains rule sets defined for risk levels to control access to second services. Each of the risk levels defines a respective group of rule sets from the rule sets to apply. The server receives a request including authentication information of a transaction type for an end user device to access a second service. The server determines risk parameters and a challenge threshold. The server identifies a risk level for the request based on the risk parameters. The server selects a group of rule sets to apply for the identified risk level and applies the group of rule sets to the authentication information to perform at least one of a denial, allowance, or challenge of the request of the transaction type using the challenge threshold, for the end user device to access the second service.
G06Q 20/40 - Authorisation, e.g. identification of payer or payee, verification of customer or shop credentialsReview and approval of payers, e.g. check of credit lines or negative lists
43.
MACHINE LEARNING MODELING TO IDENTIFY SENSITIVE DATA
Methods and systems herein identify and redact personally identifiable information. A PII sensitivity detection framework includes multiple layers where each layer corresponds to a computer model. The framework analyzes data stored within different data tables and predicts whether a data column includes PII. The first layer corresponds to an artificial intelligence model that analyzes each column metadata and predicts a first score indicative of a likelihood of PII. The second layer corresponds to a rule-based computer model that uses various rules to determine a second score indicative of a likelihood of PII for each column. The third layer corresponds to a column content model that analyzes content of each column using various natural language processing techniques to generate a third score indicative of a likelihood of PII. The framework masks data being presented to a user based on the scores generated via execution of one or more of the layers.
A model checking system configures a formal compliance document with remediation actions to correct security conflicts in an IAM system. The system applies a model checker on an abstract model of the IAM system to identify security conflicts and identifies remediation actions from the formal compliance document. The system applies the model checker after applying the first remediation action and determines whether the first remediation action creates another security conflict. If a remediation action is identified that does not create a new security conflict, then the system applies the identified remediation action. The formal compliance document is updated accordingly. When an operator revises code for a policy change, the system will apply the model checker on an abstract model of the IAM system with the code revision to identify security conflicts. If new security conflicts are not created in the simulation, then the system may deploy the code revision.
Systems and methods for enhanced software development lifecycle management using a pipeline of machine learning and adaptive models for anomaly detection and action generation. In some aspects, the system may generate a system data stream for an SDLC management platform that stitches together source data from multiple sources, generates a query to a first machine learning model for a user story identifier to generate trend information and anomalies, based on output from a second machine learning model including dynamic thresholds for the anomalies, determining at least one anomaly that satisfies a corresponding dynamic threshold, processing using an adaptive model the at least one anomaly to generate recommended actions to address the at least one anomaly, and generating for inclusion in the graphical user interface the one or more recommended actions.
G06Q 10/101 - Collaborative creation, e.g. joint development of products or services
G06Q 10/067 - Enterprise or organisation modelling
46.
SYSTEMS AND METHODS FOR REAL-TIME RECORDING OF TRANSFERS OF SELF-VALIDATING DIGITAL RECORDS ACROSS CRYPTOGRAPHICALLY SECURE NETWORKS USING CROSS-NETWORK REGISTRIES
Systems and methods for novel uses and/or improvements to blockchains and blockchain technology. As one example, systems and methods are described herein for self-validating digital records that may be transferred in real-time through a cross-network registry. For example, in a conventional system, minting a token (e.g., a digital record) involves writing a self-executing program that defines the transfer rules of the digital record. Once the self-executing program is written, it is deployed on a blockchain, and the digital record is minted by publishing it to a blockchain.
G06F 21/64 - Protecting data integrity, e.g. using checksums, certificates or signatures
G06Q 20/36 - Payment architectures, schemes or protocols characterised by the use of specific devices using electronic wallets or electronic money safes
G06Q 20/40 - Authorisation, e.g. identification of payer or payee, verification of customer or shop credentialsReview and approval of payers, e.g. check of credit lines or negative lists
G06Q 40/02 - Banking, e.g. interest calculation or account maintenance
H04L 9/00 - Arrangements for secret or secure communicationsNetwork security protocols
H04L 9/32 - Arrangements for secret or secure communicationsNetwork security protocols including means for verifying the identity or authority of a user of the system
47.
Machine learning-based anomaly detection and action generation system for software development lifecycle management
Systems and methods for enhanced software development lifecycle management using a pipeline of machine learning and adaptive models for anomaly detection and termination. In some aspects, the system may generate a system data stream for an SDLC management platform that stitches together source data from multiple sources, generates a query to a first machine learning model for a user story identifier to generate trend information and anomalies, based on output from a second machine learning model including dynamic thresholds for the anomalies, determining at least one anomaly that satisfies a corresponding dynamic threshold, based on output from an adaptive model not including recommended actions to address the at least one anomaly, executing a kill switch for the corresponding user story identifier and generating for inclusion in the graphical user interface an indication of the kill switch.
Presented herein are systems and methods for regularly updating computer-form files. A method may include obtaining, by a computer, raw data containing a plurality of data records associated with a customer from a plurality of databases, in response to detecting an error in an error data record, automatically correcting executing a machine learning architecture, the error. The method may include, for each data record, determining a data category indicating one or more computer-form files for a data entry of the data record based upon a preconfigured mapping between a type of data of the data entry mapped to the data category, and in response to detecting a new customer data requirement, updating the one or more computer-form files associated with each data category according to each data record of each daily interval, and at a preconfigured time, generating the one or more computer-form files.
The technology evaluates the compliance of an AI application with predefined vector constraints. The technology employs multiple specialized models trained to identify specific types of non-compliance with the vector constraints within AI-generated responses. One or more models evaluate the existence of certain patterns within responses generated by an AI model by analyzing the representation of the attributes within the responses. Additionally, one or more models can identify vector representations of alphanumeric characters in the AI model's response by assessing the alphanumeric character's proximate locations, frequency, and/or associations with other alphanumeric characters. Moreover, one or more models can determine indicators of vector alignment between the vector representations of the AI model's response and the vector representations of the predetermined characters by measuring differences in the direction or magnitude of the vector representations.
The technology evaluates the compliance of an AI application with predefined vector constraints. The technology employs multiple specialized models trained to identify specific types of non-compliance with the vector constraints within AI-generated responses. One or more models evaluate the existence of certain patterns within responses generated by an AI model by analyzing the representation of the attributes within the responses. Additionally, one or more models can identify vector representations of alphanumeric characters in the AI model's response by assessing the alphanumeric character's proximate locations, frequency, and/or associations with other alphanumeric characters. Moreover, one or more models can determine indicators of vector alignment between the vector representations of the AI model's response and the vector representations of the predetermined characters by measuring differences in the direction or magnitude of the vector representations.
Systems and methods for providing network validations for cloud-based network architectures are described herein. For example, the system may receive a network requirement for a first cloud-based network architecture. The system may receive a first network action that corresponds to the network requirement when facilitated by the first cloud-based network architecture. The system may process the first network action through the first cloud-based network architecture. The system may receive a first indicium of security components used to process the first network action through the first cloud-based network architecture. The system may compare the first indicium to a known indicium for processing the first network action through an approved cloud-based network architecture. The system may generate a first network validation based on comparing the first indicium to the known indicium.
Systems and methods for data retention while migrating objects and object metadata stored in object storage environments migrated across cloud ecosystems
Systems and methods for novel uses and/or improvements to data migration. In particular, systems and methods for data migration of metadata stored in object storage from one container to another, especially in instances when the metadata is destined for ingestion by an artificial intelligence application. The systems and methods ensure that all metadata (e.g., metadata stored in object storage) is preserved during data migration, including metadata such as content type, object lock mode, object lock retain until date, and/or custom metadata.
G06F 16/27 - Replication, distribution or synchronisation of data between databases or within a distributed database systemDistributed database system architectures therefor
G06F 16/2457 - Query processing with adaptation to user needs
A distributed private ledger function of a server of a first consortium member receives data representing an alias for one of its customers from the customer and also receives data that represents an alias for a customer of a second member replicated by a distributed private ledger function of a server of the second member to all members of the consortium. Thereafter, the distributed private ledger function of the first member's server identifies a recipient account of the second member's customer based on an account pointer associated with the alias of the second member's customer and initiates a transfer of funds from a source account of the first member's customer corresponding to an account pointer associated with the alias for the first member's customer to the identified recipient account of the second member's customer.
G06Q 20/10 - Payment architectures specially adapted for electronic funds transfer [EFT] systemsPayment architectures specially adapted for home banking systems
G06Q 20/40 - Authorisation, e.g. identification of payer or payee, verification of customer or shop credentialsReview and approval of payers, e.g. check of credit lines or negative lists
SYSTEMS AND METHODS FOR GENERATING ARTIFICIAL INTELLIGENCE MODELS AND/OR RULE ENGINES WITHOUT REQUIRING TRAINING DATA THAT IS SPECIFIC TO MODEL COMPONENTS AND OBJECTIVES
Systems and methods for generating code for artificial intelligence models without requiring training data that is specific to model components and objectives. For example, the system may receive an original version of a rule engine. The system may input the original version, using a first input condition, into a regeneration model to generate a first regenerated version of the rule engine. The system may determine whether the first regenerated version includes a first hallucination based on comparing the first regenerated version to alternative versions of the rule engine, wherein each of the alternative versions were generated using a respective alternative input condition. The system may, in response to determining that the first regenerated version includes the first hallucination, determining whether the first hallucination comprises a positive mutation.
The systems and methods disclosed herein receive alphanumeric characters defining operative boundaries for expected model use cases, along with operational data. The expected model use cases share common attributes, which are used by a first AI model to construct observed model use cases from the operational data. Each observed model use case includes features such as a text-based description, expected input and output, AI model(s) generating the expected output from the input, and/or data supporting the AI models. For each observed model use case, a second AI model maps the alphanumeric characters and features to a risk category, selecting from multiple risk categories based on the level of risk associated with the features. The system identifies criteria for the observed model use case within the alphanumeric characters and generates gaps by comparing the criteria with the features of the observed model use case.
Presented herein are systems and methods for the employment of machine learning models for image processing as may be performed by computing devices associated with an end user. A method may include obtaining video data comprising a plurality of frames including a document of a document type. The method may include executing an object recognition engine of a machine-learning architecture using image data of the plurality of frames, the object recognition engine trained to detect edges of documents. The method may include identifying, based on the edge detection, a plurality of boundaries for the document. The method may include validating, based on the plurality of boundaries, the document as the document type. The method may include transmitting via one or more networks, to a computer remote from the computing device, responsive to the validation of the type of document, the image data for the plurality of frames depicting the document.
A computer-implemented method comprising receiving a request to execute a transaction transferring transaction data from a first account to a second account associated with a second computing device in communication with a distributed ledger, the request comprising an identification of the first account and the second account; identifying a third account associated with a third computing device in communication with the distributed ledger; retrieving first account data for the first account and third account data for the third account from the distributed ledger; comparing a first value from the first account data and a third value from the third account data to a threshold; determining the transaction satisfies a transaction policy; and generating a record in the distributed ledger indicating the transaction transferring the transaction data from the first account to the second account in response to the determination that the transaction satisfies the transaction policy.
G06Q 20/40 - Authorisation, e.g. identification of payer or payee, verification of customer or shop credentialsReview and approval of payers, e.g. check of credit lines or negative lists
G06Q 40/04 - Trading Exchange, e.g. stocks, commodities, derivatives or currency exchange
58.
Accessing siloed data across disparate locations via a unified metadata graph systems and methods
Systems and methods for reducing usage of computational resources when accessing siloed data across disparate locations via a unified metadata graph are disclosed. The system receives a user-specified query indicating a request to access a set of data objects. The system then performs natural language processing on the user-specified query to determine a set of phrases corresponding to the user-specified query. The system then accesses a metadata graph to determine a node corresponding to the set of phrases. Using a location identifier corresponding to the determined node, the system determines a data silo storing at least one data object of the set of data objects. The system then generates for display, on a graphical user interface, a visual representation of the at least one data object.
A system facilitates a process for automatically deploying artificial intelligence (AI) models. The system receives, for a first artificial intelligence (AI) model used by an entity, a first request to deploy the first AI model to make the first AI model available for use in a production environment to process input data and generate corresponding outputs. A first model deployment location for the first model is selected based on a model deployment engine. The system generates scripts to deploy the first AI model to the selected location, then monitors operations parameters associated with the deployment of the first AI model. Based on the values of the operations parameters, the system updates the model deployment engine. In response to a second request to deploy a second AI model, the system uses the updated model deployment engine to select a second model deployment location for the second model.
A system facilitates a process for automatically generating artificial intelligence (AI) models. The system receives a first natural language input from a user that includes a set of phrases and an instruction to analyze data associated with the set of phrases using an AI model. The system accesses a metadata graph to determine a node corresponding to the set of phrases, where nodes in the metadata graph indicate internal data objects stored in data silos. The system processes the internal data objects indicated by the determined node to generate a first set of application data. The AI model is applied to the first set of application data to generate one or more outputs. Additional user inputs can be received to modify the first set of application data and apply the AI model to the modified data, until a desired data pipeline for the model has been constructed.
Systems and methods are for mitigating hindsight bias related to designing and using artificial intelligence models for outlier events. More specifically, systems and methods for the use of synthetic data in the training and/or validation of model predictions in order to prevent overfitting and generate predictions that attempt to predict the unpredictable.
H04L 41/16 - Arrangements for maintenance, administration or management of data switching networks, e.g. of packet switching networks using machine learning or artificial intelligence
62.
Querying data using specialized and generalized artificial intelligence models
The systems and methods disclosed herein relate to querying data using artificial intelligence models. A generalized model receives an output generation request and partitions it into segments mapped to specific domains, where each domain indicates associated databases and guidelines. The segments are routed to domain-specific models trained on domain-specific data, which generate query fragments by comparing performance metrics and system resource usage metrics. The query fragments are aggregated into an overall query that satisfies guidelines across domains. The systems and methods can include a feedback loop to adjust the domain-specific models using user interactions and performance metrics to dynamically adapt to a skill level or experience of the user.
G06F 16/383 - Retrieval characterised by using metadata, e.g. metadata not derived from the content or metadata generated manually using metadata automatically derived from the content
63.
Dynamic, control-sensitive data management platform
A network system to allow global usage of data while allowing regional jurisdictions control over sensitive data. Different jurisdictions may declare different types of data as sensitive data that is not to be discoverable by another party. The system may receive data that includes encoded data at a first device from a second device (e.g., associated with a remote datacenter). The system may store the data at the first device. In response to receiving a request from a third entity, the system may request a cryptographic key for decoding one or more data fields of the encoded data. Based on decoding the associated field data, the system may transmit a response to the data request that includes the decoded data.
Systems and methods for streamlining risk modeling in software development using natively sourced kernels are described. The system may receive a native kernel for the first model, wherein the native kernel comprises a native code sample and a native description of the native code sample. The system may input the native code sample into an artificial intelligence model to generate a first output. The system may filter the first output based on the native description to generate a first validation assessment for the first model. The system may generate for display, in the user interface, the first validation assessment.
The systems and methods provide a model deployment criterion. The model deployment criterion indicates a difference in a value against which the proxy model may be measured to determine when, if ever, the proxy model should be deployed to replace the existing rule engine. The model deployment criterion may be keyed to the proxy model (e.g., based on a difference in its size, throughput speed, number of changes, etc.), the existing rule engine (e.g., based on a difference in its age, update occurrences to its rule base, etc.), and/or comparisons between models (e.g., based on differences in results, throughput speed, efficiency, etc.).
Presented herein are system and methods for validating electronic documents. A first server having one or more processors coupled with memory may identify an electronic document of a customer device. The first server may validate a record of the electronic document in accordance with a consensus algorithm by communicating associated data to a plurality of second servers. The first server may generate a token using the electronic document in response to the plurality of second servers successfully validating the record of the electronic document. The first server may append the record of the electronic document corresponding to the token to the plurality of records on a distributed ledger. The first server may generate an instruction to store the token on a wallet of the customer device to authorize the use of the electronic document across the plurality of second servers.
H04L 9/32 - Arrangements for secret or secure communicationsNetwork security protocols including means for verifying the identity or authority of a user of the system
Presented herein are systems and methods for enabling a secure browsing session. Embodiments may include a computing device that executes software routines to receive a first indication to enter a secure browsing mode of a session; present data associated with a user for display on the display of the electronic device; cause the display to obscure one or more personally identifiable information of the data on the display of the electronic device; receive a second indication to reveal the one or more personally identifiable information; present the one or more personally identifiable information for display on the display of the electronic device.
Systems and methods for managing resources across a global and/or cloud network. In particular, systems and methods for mitigating issues related to providing services while resources are off-line (or may potentially go off-line). For example, the systems and methods may mitigate issues related to providing services while resources are off-line (or may potentially go off-line) by monitoring network services at an aggregate level.
Methods and systems are described herein for monitoring fault events at a fiber optical network. In particular, a system may receive, from components of an optical network, corresponding component data structures comprising optical measurements. The system may extract, from a component data structure, a set of component metrics for light transmission signals being transmitted or received via fiber optic transmission lines at a corresponding component and input the component data structure into a first machine learning model to obtain an indication of an occurrence of an event at one or more components. The system may generate a prompt for input into a second machine learning model configured to identify corrective actions for addressing any events within optical networks to obtain one or more corrective actions for addressing the occurrence of the event.
G06F 11/07 - Responding to the occurrence of a fault, e.g. fault tolerance
H04B 10/079 - Arrangements for monitoring or testing transmission systemsArrangements for fault measurement of transmission systems using an in-service signal using measurements of the data signal
70.
Systems and methods for account classification using a middleware system architecture
A computer can connect with a middleware computing system. The middleware computing system may use application programming interfaces (APIs), webhooks, file-based integration, database replication, message queues, websockets, or direct integration to establish connections with different computing devices. The computer may request a data structure classification for an external data structure stored in a remote computing device. The middleware computing system can receive the request, identify the connection that the middleware computing device has with the remote computing device, and retrieve records for transactions performed by the external data structure from the remote computing device. The middleware computing system can use metadata in the records to automatically determine a data structure type of the data structure. The middleware computing system can generate instructions that cause the computer to link the external data structure with the profile.
Presented herein are system and methods for validating electronic documents. A first server having one or more processors coupled with memory may identify an electronic document of a customer device. The first server may validate a record of the electronic document in accordance with a consensus algorithm by communicating associated data to a plurality of second servers. The first server may generate a token using the electronic document in response to the plurality of second servers successfully validating the record of the electronic document. The first server may append the record of the electronic document corresponding to the token to the plurality of records on a distributed ledger. The first server may generate an instruction to store the token on a wallet of the customer device to authorize the use of the electronic document across the plurality of second servers.
H04L 9/32 - Arrangements for secret or secure communicationsNetwork security protocols including means for verifying the identity or authority of a user of the system
72.
PEER-TO-PEER AUTHENTICATION WITH A SECURE CHANNEL COMMUNICATION
An identity verification system enables peer-to-peer authentication in a potentially insecure channel by leveraging a secure channel communication. The system authenticates a user via an identity verification application. The system provides a validation code to the user. The user communicates the validation code to a counterparty of the peer-to-peer communication. The system receives a request to authenticate the counterparty with the validation code and counterparty authentication data. The system authenticates the counterparty and sends the user the authentication of the counterparty. Alternatively, the user device communicates a request to generate a secure code for participants in a first insecure group application session. The user device selects an authenticated counterparty to receive the secure code from a list of authenticated counterparties. The user creates a second application session using the secure code as a password. Unauthenticated counterparties would not receive the secure code and are restricted from the new session.
Methods and systems are described herein for identifying matching parameters of groups of nodes in graphical representations. The system may generate, in a graphical user interface, a graphical representation of nodes representing users associated with an entity. The system may activate the graphical representation as links connecting pairs of nodes, with the links representing interactions between users. The system may identify a grouping of nodes having a level of local clustering indicative of undesired activity. The system may determine graphical parameters relating to the level of local clustering and may identify the same graphical parameters in other groupings of nodes in other graphical representations. The system may thus identify indications of undesired activity based on matching parameters of groups of nodes.
G06Q 20/40 - Authorisation, e.g. identification of payer or payee, verification of customer or shop credentialsReview and approval of payers, e.g. check of credit lines or negative lists
74.
Systems and methods for real-time data processing of unstructured data
Systems and methods for novel approaches and/or improvements to real-time data processing of unstructured data. In particular, the systems and methods describe real-time data processing of unstructured data without interstitial standardization. For example, the systems and methods describe real-time data processing of unstructured data in which both the input and the output to the data processing pipeline is unstructured data.
G06F 40/40 - Processing or translation of natural language
75.
SYSTEMS AND METHODS FOR IMPLEMENTING AN ARTIFICIAL INTELLIGENCE-BASED SOLUTION FOR PROMPT ORCHESTRATION BY SEGREGATING PROCESSING REQUESTS INTO NON-SERIALIZED TASKS
Systems and methods are described herein for a prompt engine microservice. The system may segregate a received request into a search function, a calculation function, and a schema selection function. The system may also create a new aggregation function that aggregates the results of the various processes into an input for an artificial intelligence model. By doing so, the system may process the new plurality of tasks in parallel and without the initial use of the artificial intelligence model.
The systems and methods provide a model deployment criterion. The model deployment criterion indicates a difference in a value against which the proxy model may be measured to determine when, if ever, the proxy model should be deployed to replace the existing rule engine. The model deployment criterion may be keyed to the proxy model (e.g., based on a difference in its size, throughput speed, number of changes, etc.), the existing rule engine (e.g., based on a difference in its age, update occurrences to its rule base, etc.), and/or comparisons between models (e.g., based on differences in results, throughput speed, efficiency, etc.).
Systems and methods are described herein for novel uses and/or improvements for customizing user interfaces for neurodiversity categories using machine learning models. In particular, one or more neurodiversity categories corresponding to a user are identified based on inputting user interaction data into a machine learning model. Based on the output of the machine learning model of one or more neurodiversity categories, user interface parameters are determined for those neurodiversity categories and a customized user interface is generated based on the user interface parameters. One or more applications with which the user interacts are then updated using the customized user interface.
G06F 3/04845 - Interaction techniques based on graphical user interfaces [GUI] for the control of specific functions or operations, e.g. selecting or manipulating an object, an image or a displayed text element, setting a parameter value or selecting a range for image manipulation, e.g. dragging, rotation, expansion or change of colour
A model checking system detects violations and conflicts in security and verification policies by running model checking processes. The system detects privilege escalation attacks in misconfigured identification and access management (“IAM”) policies by modeling security policy documents and IAM actions as logical formulas and then running model checking on the model. The system translates non-Boolean variables, such as string variables, into Boolean variables in order to apply an SAT model checker. The model checker also determines whether a policy violation can be achieved in a finite number of steps by elevating privileges of some compromised principal over multiple iterations of the model checking process, or proves absence thereof.
Systems and methods for measuring, grading, evaluating, and comparing AI models via a graphical user interface are disclosed. The technology obtains a set of application domains of the AI model in which an AI model will be used. The application domains are mapped to one or more guidelines to determine a set of guidelines that define operational boundaries of the AI model. The guidelines are used to generate assessment domains, each associated with specific benchmarks that include indicators of a degree of satisfaction with the guidelines. For each assessment domain, assessments are constructed to evaluate the AI model's degree of satisfaction with the corresponding guidelines. The AI model is then evaluated against the assessments. Based on these comparisons, grades are assigned to the AI model for each assessment domain. The application-domain-specific grades are generated and displayed at a GUI, reflecting the AI model's degree of satisfaction with the guidelines.
G06F 21/57 - Certifying or maintaining trusted computer platforms, e.g. secure boots or power-downs, version controls, system software checks, secure updates or assessing vulnerabilities
G06F 21/55 - Detecting local intrusion or implementing counter-measures
80.
SYSTEMS AND METHODS FOR CUSTOMIZING USER INTERFACES USING ARTIFICIAL INTELLIGENCE
Systems and methods are described herein for novel uses and/or improvements for designing user-specific interfaces using machine learning models. When a request to display certain data by an application is received, an application token and a user token may be retrieved and input into a machine learning model to obtain a prediction of a pre-defined user-application interface configuration. A user interface token for the application may then be generated. The user interface token may indicate user interface settings/configuration desired/preferred by a user. The user interface token may then be sent to the application to cause the application to display the data using user interface configurations within the user interface token.
G06F 3/0481 - Interaction techniques based on graphical user interfaces [GUI] based on specific properties of the displayed interaction object or a metaphor-based environment, e.g. interaction with desktop elements like windows or icons, or assisted by a cursor's changing behaviour or appearance
G06F 9/451 - Execution arrangements for user interfaces
81.
SYSTEM AND METHOD FOR GENERATING SUSPICIOUS ACTIVITY REPORTS USING MODELS
Presented herein are systems and methods for generating suspicious activity reports using large language models. A system may include one or more processors that obtain transaction data associated with a transaction from a client device and from one or more bank databases, apply a prompt generator on the transaction data to generate a large language model (LLM) prompt, and generate a machine-readable suspicious activity (SAR) report in accordance with an LLM prompt. The one or more processors may also apply the prompt generator on the transaction data based on determining that a fraud risk score associated with the transaction satisfies a reporting threshold score. Computer program products are also presented.
G06Q 20/40 - Authorisation, e.g. identification of payer or payee, verification of customer or shop credentialsReview and approval of payers, e.g. check of credit lines or negative lists
Presented herein are systems and methods for generating suspicious activity reports using large language models. A system may include one or more processors that obtain event data associated with an event from a client device and from one or more databases, apply a prompt generator on the event data to generate a large language model (LLM) prompt, and generate a machine-readable suspicious activity (SAR) report in accordance with an LLM prompt. The one or more processors may also apply the prompt generator on the event data based on determining that a fraud risk score associated with the event satisfies a reporting threshold score. Computer program products are also presented.
A computer can monitor network traffic on a blockchain computing network. The computer can determine a current level of network congestion on the blockchain computing network. The computer can execute a first machine learning model that predicts a timeseries of future transaction costs based on historical data and the current level network congestion level of the blockchain computing network. The computer can also execute a second machine learning model to predict a timeseries of future transaction sizes and UTXO types for the distributed ledger-based account based on historical transaction data. The computer can select one or more UTXOs to use to complete the transaction of the transaction request. The computer can append a block instance containing an identification of the selected one or more UTXOs to the blockchain to complete the transaction.
Presented herein are systems and methods for generating suspicious activity reports using large language models. A system may include one or more processors that obtain transaction data associated with a transaction from a client device and from one or more bank databases, apply a prompt generator on the transaction data to generate a large language model (LLM) prompt, and generate a machine-readable suspicious activity (SAR) report in accordance with an LLM prompt. The one or more processors may also apply the prompt generator on the transaction data based on determining that a fraud risk score associated with the transaction satisfies a reporting threshold score. Computer program products are also presented.
G06Q 20/40 - Authorisation, e.g. identification of payer or payee, verification of customer or shop credentialsReview and approval of payers, e.g. check of credit lines or negative lists
The disclosed data generation platform enables generation of an output in response to an output generation request based on tuning a routing model that enables model selection in a dynamic, system-sensitive manner. For example, the disclosed data generation platform receives an output generation request for a user device and generates a risk indicator associated with the output generation request. The platform can determine a current system state and generate a set of performance indicators and associated weighting values based on the risk indicator and the system state. The data generation platform can select a first routing model based on the weighting values. The data generation platform can provide the output generation request to the first routing model to generate an indication of a model with which to generate a model output responsive to the input. The data generation platform can enable access to the generated model output.
A computer can monitor network traffic on a blockchain computing network. The computer can determine a current level of network congestion on the blockchain computing network. The computer can execute a first machine learning model that predicts a timeseries of future transaction costs based on historical data and the current level network congestion level of the blockchain computing network. The computer can also execute a second machine learning model to predict a timeseries of future transaction sizes and UTXO types for the distributed ledger-based account based on historical transaction data. The computer can select one or more UTXOs to use to complete the transaction of the transaction request. The computer can append a block instance containing an identification of the selected one or more UTXOs to the blockchain to complete the transaction.
Systems and methods for a programming language-agnostic data modeling platform that is both less resource intensive and scalable. Additionally, the programming language-agnostic data modeling platform allows for advanced analytics to be run on descriptions of the known logical data models, to generate data offerings describing underlying data, and to easily format data for compatibility with artificial intelligence systems. The systems and methods use a supplemental data structure that comprises logical data modeling metadata, in which the logical data modeling metadata describes the logical data model in a common, standardized language. For example, the logical data modeling metadata may comprise a transformer lineage of the logical data model.
Systems and methods for tracking and verifying software capabilities may include obtaining, by a computer from a reporting database, a set of project requirements associated with a software under development, the set of project requirements replicated from a project database to the reporting database, obtaining, from the reporting database, a set of testing results generated by a test program applied to code of the software under development, the set of testing results replicated from a testing database to the reporting database, extracting, from the reporting database, each project requirement associated with the software under development and each testing result generated for the code of the software under development, wherein the computer extracts each project requirement and each testing result according to a set of mapping pre-configurations, and generating, by the computer, an email message combining the project requirement and the testing result to transmit to a set of recipients.
Systems and methods are described herein for novel uses and/or improvements for designing user-specific interfaces using machine learning models. When a request to display certain data by an application is received, an application token and a user token may be retrieved and input into a machine learning model to obtain a prediction of a pre-defined user-application interface configuration. A user interface token for the application may then be generated. The user interface token may indicate user interface settings/configuration desired/preferred by a user. The user interface token may then be sent to the application to cause the application to display the data using user interface configurations within the user interface token.
G06F 3/0481 - Interaction techniques based on graphical user interfaces [GUI] based on specific properties of the displayed interaction object or a metaphor-based environment, e.g. interaction with desktop elements like windows or icons, or assisted by a cursor's changing behaviour or appearance
G06F 9/451 - Execution arrangements for user interfaces
90.
Systems and methods for real-time mapping and visualization generation of system components in software systems
Systems and methods for real-time mapping and visualization generation of system components and inter system communications as well as for the generation of real-time recommendations for architectural recommendations. The systems and methods generate hierarchical workflow mappings of a computational network using event data from software applications lineage logs as well as component and artifact repositories. For example, while event data in software applications lineage logs is conventionally limited to identifying that a given process occurred, the systems and methods use the plurality of events detailed in the software applications lineage logs to create, using an artificial intelligence model, a network mapping of how system components are arranged and interact.
Presented herein are systems and methods for the employment of machine learning models for image processing. A method may include a capture of a video feed including image data of a document at a client device. The client device can provide the video feed to another computing device. The method can include, by the client device or the other computing device object recognition for recognizing a type of document and capturing an image exceeding a quality threshold of the document amongst the frames within the video feed. The method may further include the execution of other image processing operations on the image data to improve the quality of the image or features extracted therefrom. The method may further include anti-fraud detection or scoring operations to determine an amount of risk associated with the image data.
Presented herein are systems and methods for the employment of machine learning models for image processing as may be performed by computing devices associated with an end user. A method may include obtaining video data comprising a plurality of frames including a document of a document type. The method may include executing an object recognition engine of a machine-learning architecture using image data of the plurality of frames, the object recognition engine trained to detect edges of documents. The method may include identifying, based on the edge detection, a plurality of boundaries for the document. The method may include validating, based on the plurality of boundaries, the document as the document type. The method may include transmitting via one or more networks, to a computer remote from the computing device, responsive to the validation of the type of document, the image data for the plurality of frames depicting the document.
G06V 10/44 - Local feature extraction by analysis of parts of the pattern, e.g. by detecting edges, contours, loops, corners, strokes or intersectionsConnectivity analysis, e.g. of connected components
G06V 10/74 - Image or video pattern matchingProximity measures in feature spaces
The technology may be utilized in network environments that use fiber optic cables to connect components. Components of the network perform functions as transceivers that transmit and receive light signals via the fiber cable. When optic power data received from the transceivers is recognized as a fault, an event is identified. The system logs each received event and converts all measurements into a common unit of measurement. The system analyzes aggregated data from the transceivers to detect patterns and trends, monitor the received data and provide real time fault predictions. For example, a machine learning process may recognize subtle trends or patterns in the data, and use the recognition to predict potential failures. The system uses the received data to create a graphical user interface (“GUI”) that represents the health of the network.
H04B 10/079 - Arrangements for monitoring or testing transmission systemsArrangements for fault measurement of transmission systems using an in-service signal using measurements of the data signal
H04L 41/22 - Arrangements for maintenance, administration or management of data switching networks, e.g. of packet switching networks comprising specially adapted graphical user interfaces [GUI]
94.
GENERATIVE CYBERSECURITY EXPLOIT DISCOVERY AND EVALUATION
Described herein are systems and methods for discovering and proactively mitigating previously unknown security vulnerabilities. The systems and methods herein can utilize security vulnerability information to discover potential security threats and can utilize this information to generate an attack using a machine learning model, such as a large language model. Generated attacks can be carried out to assess impact of a security vulnerability. An output can be provided that represents the assessed impact. In some implementations, the systems and methods herein generate patches or other mitigations for security vulnerabilities, which can be tested and deployed to address security vulnerabilities.
G06F 21/57 - Certifying or maintaining trusted computer platforms, e.g. secure boots or power-downs, version controls, system software checks, secure updates or assessing vulnerabilities
G06F 21/55 - Detecting local intrusion or implementing counter-measures
Systems and methods for encrypting data are described herein. For example, the system may receive, from an application associated with a provider system, a request for a delivery token. The request may include an address and an identifier of a distribution system. The system may determine a distributor key for decrypting delivery tokens for the distribution system. The system may also identify an encryption key for encrypts delivery tokens, where the encryption key corresponds to the distributor key. The system may generate the delivery token, including the address, using the encryption key. The system may then transmit the delivery token to the application associated with the provider system.
H04L 9/32 - Arrangements for secret or secure communicationsNetwork security protocols including means for verifying the identity or authority of a user of the system
96.
GENERATING PREDICTED END-TO-END CYBER-SECURITY ATTACK CHARACTERISTICS VIA BIFURCATED MACHINE LEARNING-BASED PROCESSING OF MULTI-MODAL DATA SYSTEMS AND METHODS
Systems and methods for generating predicted end-to-end cyber-security attack characteristics via bifurcated machine learning-based processing of multi-modal data are disclosed. The system accesses multi-modal data indicating a set of security information related to a computing system. The system then generates a set of extracted characteristics indicating a cyber-security attack on the computing system, via a supervised machine learning model, using the multi-modal data. Using this information, the system generates a revised set of extracted characteristics indicating the cyber-security attack, via an unsupervised machine learning model, using the extracted set of characteristics indicating the cyber-security attack, where the revised set of characteristics includes at least one new characteristic that was not included in the extracted set of characteristics indicating the cyber-security attack on the computing system. The system then generates for display a graphical representation of the revised set of extracted characteristics.
G06F 21/57 - Certifying or maintaining trusted computer platforms, e.g. secure boots or power-downs, version controls, system software checks, secure updates or assessing vulnerabilities
G06F 21/55 - Detecting local intrusion or implementing counter-measures
97.
Generative cybersecurity exploit discovery and evaluation
Described herein are systems and methods for discovering and proactively mitigating previously unknown security vulnerabilities. The systems and methods herein can utilize security vulnerability information to discover potential security threats and can utilize this information to generate an attack using a machine learning model, such as a large language model. Generated attacks can be carried out to assess impact of a security vulnerability. An output can be provided that represents the assessed impact. In some implementations, the systems and methods herein generate patches or other mitigations for security vulnerabilities, which can be tested and deployed to address security vulnerabilities.
G06F 21/57 - Certifying or maintaining trusted computer platforms, e.g. secure boots or power-downs, version controls, system software checks, secure updates or assessing vulnerabilities
G06F 21/55 - Detecting local intrusion or implementing counter-measures
Methods and systems are described herein for generating and assigning resources based on timestamps. A plurality of permission messages associated with a plurality of authorization events may be received with each permission message including an authorization timestamp indicating a generation time of a corresponding permission message. In addition, a plurality of data records may be received with each data record including a corresponding plurality of parameters. Based on the permission messages and the data records, a resource multiplier is generated, and resources assigned to each data record are multiplied based on the resource multiplier.
Systems and methods are disclosed comprising instructions to access audio data of a captured interaction involving one or more participating users of a digital conference tool, receive a first narrative summary of the captured interaction comprising a set of first component narratives, generate a second narrative summary comprising a set of second component narratives, identify at least one component narrative among the set of second component narratives corresponding to the participating users, display the second component narratives of the second narrative summary at a user interface of the participating users, receive user feedback data comprising an adjustment to the displayed text content of the at least one second component narrative, generate a third narrative summary using the user feedback data received from each participating user of the captured interaction, and display the third narrative summary for the captured interaction at the user interface of each participating user.
A model checking system configures a formal compliance document with remediation actions to correct security conflicts in an IAM system. The system applies a model checker on an abstract model of the IAM system to identify security conflicts and identifies remediation actions from the formal compliance document. The system applies the model checker after applying the first remediation action and determines whether the first remediation action creates another security conflict. If a remediation action is identified that does not create a new security conflict, then the system applies the identified remediation action. The formal compliance document is updated accordingly. When an operator revises code for a policy change, the system will apply the model checker on an abstract model of the IAM system with the code revision to identify security conflicts. If new security conflicts are not created in the simulation, then the system may deploy the code revision.
