Abstract: 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.).

Abstract: 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.

Abstract: Systems and methods for generating an application profile repository that comprises application profiles. These application profiles may indicate the likely processes executed by a given application as well as the usage metric signatures for those processes. Additionally, the application profiles may indicate the dependencies between applications. To overcome the technical issue of some application profiles being missing or incomplete, the application profile repository may comprise a first set of application profiles corresponding to applications with known dependencies and a second set of application profiles corresponding to applications with virtual dependencies. These virtual dependencies may be determined based on comparisons of the applications, their processes, device usage metrics while the processes are being executed, etc.

Abstract: 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.

Abstract: Systems and methods for uses and/or improvements to artificial intelligence applications. As one example, systems and methods for providing insights into how a given model processes and interprets data as well as the variables, parameters, and/or other determinations that are used to generate results using an observer model. The systems and methods may then compare the insights provided by the observer model to one or more criteria to validate the given model and/or its results against one or more rules, regulations, and/or other requirements, or to retrain the given model.

Abstract: 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.

Abstract: 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.

Abstract: 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.

Abstract: 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.

Abstract: The systems and methods provide continuous integration and continuous deployment pipeline management using pipeline-agnostic rule sets applied to pre-deployment code samples. The system standardizes the application of a rule set to a pre-deployment code sample to use data at a given location, but the system allows for the sample itself to determine which data stream of the data streams at that data location is used. As such, the systems and methods avoid the need for the plethora of rule sets that would overwhelm a user and defeat the purpose of the CaaS product, but the system allows for processing pre-deployment code samples that are specific to the sample.

Abstract: 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.

Abstract: Systems and methods are described herein for novel uses and/or improvements to data aggregation related to artificial intelligence applications, specifically applications related to aggregating time-series data. As one example, systems and methods are described herein for predicting effects when aggregating time-series data and modifying the one or more data streams used to populate a model profile and/or feed an artificial intelligence application with the time-series data.

Abstract: 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.

Abstract: 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.

Abstract: 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.

Abstract: A system or technique can analyze metadata from a first set of images using a neural network to identify classification information about individuals depicted in the images. The system generates data based on this classification information, including attributes of the individuals, and compares this data against predefined criteria for image presentations. If any attributes are found to be non-compliant with the criteria, the system queries a database of second image metadata linked to a set of images. This database includes data generated from prior presentations and classification information provided by individuals depicted in the images. The system locates compliant attributes in the second image metadata, retrieves an image linked to these compliant attributes, and modifies a presentation to include the retrieved image.

Abstract: 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.

Abstract: The systems and methods disclosed herein generate context-aware responses using semantically chunked information. The systems and methods disclosed herein partition a set of artifacts responsive to a query (e.g., a prompt for an artificial intelligence model such as a large language model) into a set of continuous chunks and associate each continuous chunk with a knowledge graph. The knowledge graph includes nodes representing chunks and edges indicating common attributes. The systems and methods disclosed herein modify node(s) in the graph by determining values of feature variables and adjusting edges in accordance with the values and generate contextualized chunks by associating or linking continuous chunks of node pairs using shared edges. The systems and methods disclosed herein use the contextualized chunks and query to generate a response using the artificial intelligence model.

Abstract: 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 combined into a consolidated token. The consolidated token may be input into a machine learning model to obtain a user interface token for an application. 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.

Abstract: 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.

Abstract: 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.

Abstract: 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, for each data record, determining, by the computer, 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, in response to detecting a new customer data requirement, updating, by the computer, 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, by the computer, the one or more computer-form files based upon each data category using each data record of each daily interval.

Abstract: Presented herein are systems and methods for binding web components to protect accessing of resources. A first server may receive, from a second server, a request to provide access to content for a first web component on a web application of a customer device. The first server may determine whether to issue an identifier to the customer device responsive to validating the request. The first server may generate, responsive to the determination, the identifier to bind the first web component with a second web component to permit access to the content for the first web component on the web application. The first server may transmit, to the second server, a response including the identifier and the content. The first server may communicate, via the second server, data associated with a user interaction with the content on the first web component bound with the second web component using the identifier.

Abstract: 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.

Abstract: A custom system data stream stitches together aspects of data streams from source systems. In one implementation, an analytics circuit extracts from a first data stream received from a system quality assurance (QA) management system, test case data. The analytics circuit parses metadata, such as a user story identifier, from the test case data. Based on the metadata, the analytics circuit parses, from a second data stream received from a system development lifecycle (SDLC) management computing system, SDLC item data, such as a project identifier. Based on the SDLC item data, the analytics circuit determines at least one impacted computer application. The custom system data stream is bound to a graphical user interface. In some implementations, the interface includes a plurality of side-by-side smart dials.

Abstract: 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.

Abstract: A system for dynamically revising a merchant page by one or more processors communicatively coupled to webservers hosting merchant pages may be configured to receive from a webserver a transaction amount and a unique identifier of a user interacting with the page without an input of a credit card number of the user and without the webserver accessing the credit card number in a data storage, identify, using the unique identifier and without receiving personally identifiable information from the webserver, a user profile including at least one dataset corresponding to a credit card of the user, and upon identifying an attribute of an alternative payment option for the user based on the user profile, generate an instruction to the webserver to dynamically render the page by displaying at least one interactive element including the alternative payment option based on an indicator of the credit card of the user.

Abstract: 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.

Abstract: Presented herein are techniques for establishing data provenance by generating one-time signatures. In examples, systems may include one or more hardware processors that receive, via an application programming interface (API), 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.

Abstract: Techniques are provided for performing automated operations to identify one or more computing devices associated with potential payees that are within a defined proximity of a user mobile device. Potential payees may include individuals and merchant point-of-sale computing systems. A processor-based computing system retrieves stored photographic and biographic information regarding the identified potential payees, and displays the retrieved photographic and biographic information for possible selection by a user of the mobile device. One or more payments from the user of the mobile device may be specified and initiated via the processor-based computing system to potential payees selected by the user, including for partial or full payment of a customer order of the user with a merchant associated with an identified point-of-sale computing system.

Abstract: A network system to provide mutable access tokens for access requests that eliminate a need for token replacement. The system allows an access token to be changed to update data in the token. When data stored with the token changes, such as when a user or partner has a change in status, a new token is not required to be requested, generated, dispersed, or stored. Conventional systems refuse the API call request and require the new token be provided. The described system instead completes the request while simultaneously notifying the user to subsequently retrieve an updated access token. Requesting, generating, communicating, and presenting a new token requires additional time, bandwidth, computing capacity, and system interactions. While performing new token acquisition in conventional systems, devices are forced to perform additional interactions, which may result in a time delay or in one or more devices exceeding capacity, becoming overloaded, and seizing.

Abstract: Methods and descriptions are described herein for using user-specific parameters to detect malicious activity in an interaction by a user. In particular, the system may receive user action data representing user actions for users relative to applications. The system may generate, using first user action data representing first user actions of a first user, parameters specific to the first user for determining whether interactions of the first user represent malicious activity. The system may receive, for the first user, a request for a pending interaction with a particular application. The system may then use a first user model trained on the first user action data to identify a set of parameters for identifying the malicious activity. The system may validate the request, in real time, by using the set of parameters to generate a likelihood that the request represents malicious activity.

Abstract: 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.

Abstract: Systems and methods are described for a three-tiered artificial intelligence architecture that synchronizes processing statement deployment across diverse computer networks. The system may receive, at a user interface, a first request for a first recommendation. The first recommendation includes a set of standardized processing statements for implementing on a function. The system may input the first request into a first network component. The first network component includes first, second, and third models. The first model is trained to generate a library of standardized processing statements. The second model is trained to generate a plurality of standardized processing characteristic values for each standardized processing statement in the library. The third model is trained to generate recommendations of sets of standardized processing statements to functions. The system receives an output from the first network component and generates for display the first recommendation based on the output.

Abstract: Systems and methods for performing data analytics on sensitive data, such as deduplication are disclosed herein. A first request to perform a first data analytics operation for a set of sensitive data instances may be received. A first set of image representations for the set of sensitive data instances may be retrieved. A first sensitive data instance and a second sensitive data instance may be clustered into a first cluster. The first data analytics operation may be performed on the first cluster.

Abstract: Systems and methods for a software development lifecycle traceability tool. In some aspects, the system may implement a traceability tool that generates a system data stream for an SDLC management platform that stitches together source data from multiple sources, trains a machine learning model to generate progress information and execution risks for a user story on the SDLC management platform, generates a graphical user interface with information for each user story and corresponding progress tracking indicia based on output from the machine learning model, and generates an event-based view of prior actions for each user story and one or more recommended actions to address execution risks.

Abstract: Systems and methods for providing an artificial intelligence-based solution in a dynamic environment that requires models with varying degrees of nuance and specialization. One such dynamic environment relates to generating dynamic human-like conversational responses based on complex data. In particular, systems and methods recite generating dynamic human-like conversational responses using a modular architecture featuring layered data models with gated neural networks. The modular architecture compartmentalizes the various components and functions of an application. That is, the architecture may support multiple layers, each featuring models performing specific functions and/or having been trained on using specific data and/or algorithms.

Abstract: 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.

Abstract: 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.).

Abstract: 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.).

Abstract: 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.

Abstract: Systems and methods are described herein for novel uses and/or improvements to artificial intelligence applications in an environment with limited or no available data. In particular, systems and methods are described herein for providing network arrangement recommendations based on power consumption predictions for selected applications within network arrangements featuring devices with non-homogeneous or unknown specifications.

Abstract: Systems and methods for data corroboration and forecasting in which a sever generates structured customer feedback data from unstructured customer verbatim data and uses unsupervised machine learning modeling techniques to identify similar word clusters within the structured customer feedback data, classifies the identified word clusters into multiple categories of customer issues, and detects one or more trending customer issues within the multiple categories. The server may also retrieve web/mobile application usage analytics data and system logs data and use unsupervised machine learning modeling techniques to corroborate customer web/mobile navigation and service issues associated with the structured customer feedback data.

Abstract: A document is obtained that include natural language text and a set of conditions are extracted from the document. In at least one embodiment, the set of conditions is extracted using a machine learning model trained on other natural language text. A validation process is performed to result in a validated set of conditions and a machine-readable data structure is generated to indicate one or more conditions of the validated set of conditions expressed within the document.

Abstract: 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.

Abstract: Presented herein are systems and methods for end-to-end encryption for session-less communications. A first server may receive, from a second server, a request to retrieve keys for a customer device to access a service. The request may include a device identifier and a first token encrypted using a first encryption key. The first server may determine, responsive to validating, that the customer device is to be issued a second token. The first server may identify least a portion of the first token decrypted using the first encryption key. The first server may generate a set of second encryption keys to be used by the customer device. The first server may package the second token to include (i) at least the portion of the first token and (ii) the set of second encryption keys. The first server may transmit, to the second server, a response including the second token.

Abstract: The systems and methods disclosed herein receive an output generation request from that includes input for generating an output using a language model. The input includes a set of alphanumeric characters associated with operative standards for a first set of actions. The system divides the set of alphanumeric characters into text subsets. For each text subset, a vector representation is determined. Prompts are created for each vector representation including the set of alphanumeric characters, query contexts, keywords, and/or the text subset. Each vector representation's prompt is input into the language model, which generates a second set of actions of related actions, where subsequently generated actions are based on prior generated actions. The system aggregates the second set of actions into a third set of actions and displays a graphical layout. The graphical layout displays a representation of the set of alphanumeric characters and the corresponding actions.

Abstract: 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.

Abstract: Systems and methods for facilitating updates to data pipelines using modularly-generated platform-agnostic data pipeline portions are disclosed. The system receives a user selection of a portion of a data pipeline comprising (i) a set of nodes each indicating a data pipeline component and (ii) a set of links linking the set of nodes. The system then generates a modular-portion of the data pipeline architecture, via a transformation component, based on the user selection and stores the modular-portion of the data pipeline architecture in a remote database. The system then receives an update to at least one node of the set of nodes of the modular-portion of the data pipeline architecture. The system then updates at least a subset of a set of pre-existing data pipelines that use the generated modular-portion of the data pipeline to incorporate the update to the at least one node of the set of nodes.

Abstract: Systems and methods that provide centralized communication across feature programming workflows using software development lifecycle tools that dynamically determine temporal alerts are disclosed herein. By dynamically determining events that are temporally relevant, the system accounts for changes in an application timeline that comprises a plurality of events, in which each event of the plurality of events indicates when a respective feature of the plurality of features is available for use by the application, and in which the location of each event constantly changes.