42 - Scientific, technological and industrial services, research and design
Goods & Services
(1) Referrals in the field of wealth management, investment management and financial planning; providing on-line referrals in the field of wealth management, investment management and financial planning; referrals in field of wealth management, investment management, and financial planning, namely, evaluating, qualifying, matching, and connecting qualified investors with registered investment advisors; lead generation services
(2) Providing temporary use of online non-downloadable software for lead generation in the field of wealth management; providing temporary use of online non-downloadable software for enabling qualified investors to search for registered investment advisors based on user-specified criteria and access curated registered investment advisor recommendations; providing online non-downloadable software using artificial intelligence (AI) for enabling registered investment advisors to access financial models and evaluate the financial backgrounds, profiles, needs and goals of prospective wealth management clients for purposes of matching them with wealth management, investment management, and financial planning services, institutions or individuals; providing temporary use of online non-downloadable software using artificial intelligence (AI) for enabling registered investment advisors to access and evaluate qualified sales leads
2.
SYSTEM AND METHOD FOR TRANSLATING A FIRST CODING LANGUAGE INTO A SECOND CODING LANGUAGE
Systems and methods for translating a first coding language into a second coding language train a machine learning (ML) model on a first coding language specific data set relating to the first coding language, in which the ML model is trained to translate one or more code sets of the first coding language to respective one or more code sets of the second coding language; using the ML model, generate various unit test cases, in which the unit test cases run the one or more code sets of the second coding language in parallel with the one or more code sets of the first coding language; iteratively test and refine the ML model until a maturity threshold is reached; and upon reaching the maturity threshold, containerize the one or more code sets of the second coding language into one or more applications.
Electronic document generation systems and methods include a processor, a memory component communicatively coupled to the processor, and machine-readable instructions. The machine-readable instructions cause the system to access an electronic document generation template including placeholders with nesting values indicative of a number populating conditions to generate an electronic document, populate one or more placeholders associated with corresponding nesting values of zero with data from a master data store or data that is based on a user input to a prompt, prompt, via a graphical user interface, the user to respond to one or more conditional prompts, populate one or more placeholders associated with the next lowest corresponding nesting value based on a user input to the one or more conditional prompts, and generate the electronic document based on the one or more placeholders as populated via the electronic document generation template for display on the graphical user interface.
Systems and methods for translating a first coding language into a second coding language train a machine learning (ML) model on a first coding language specific data set relating to the first coding language, in which the ML model is trained to translate one or more code sets of the first coding language to respective one or more code sets of the second coding language; using the ML model, generate various unit test cases, in which the unit test cases run the one or more code sets of the second coding language in parallel with the one or more code sets of the first coding language; iteratively test and refine the ML model until a maturity threshold is reached; and upon reaching the maturity threshold, containerize the one or more code sets of the second coding language into one or more applications.
42 - Scientific, technological and industrial services, research and design
Goods & Services
Providing online non-downloadable software for client onboarding and client relationship management; providing online non-downloadable software that enables users to manage and automate workflows and processes related to client onboarding and client relationship management; providing online non-downloadable software the enables users to share, collect, import, transmit, upload, integrate, aggregate, manage, organize, and analyze data as part of client onboarding; providing online non-downloadable software that enables users to monitor progress and report on the status of client onboarding; providing online non-downloadable software to facilitate the signing, submission and management of documents as part of client onboarding.
42 - Scientific, technological and industrial services, research and design
Goods & Services
Software as a service (SAAS) services in the nature of an online, non-downloadable information delivery, transaction management and reporting software platform that enables individual investors, financial advisors and wealth intermediaries to manage alternative investments through the investment lifecycle; software as a service (SAAS) services in the nature of an online, non-downloadable information delivery, transaction management and reporting software platform that enables individual investors, financial advisors and wealth intermediaries to discover, research, analyze, execute, trade, and settle alternative investments; software as a service (SAAS) services in the nature of an online, non-downloadable information delivery, transaction management and reporting software platform that provides individual investors, financial advisors and wealth intermediaries with access to interactive tools for analyzing and modeling alternative investment portfolios, forecasting and projecting risks, income and liquidity associated with alternative investments, automating financial advisor permissions and alerts, preparing trade proposals, presentations, and marketing materials for alternative investments, generating, reviewing and signing documents associated with the trading of alternative investments, and post-trade cash processing and tax, performance, risk, regulatory and self reporting associated with alternative investments; software as a service (SAAS) services in the nature of an online, non-downloadable information delivery, transaction management and reporting software platform that enables individual investors, financial advisors and wealth intermediaries to access financial information and educational resources in the field of alternative investments; software as a service (SAAS) services in the nature of an online, non-downloadable information delivery, transaction management, and reporting software platform that provides individual investors, financial advisors and wealth intermediaries with access to personalized financial news and curated investment options and strategies in the field of alternative investments; software as a service (SAAS) services in the nature of an online non-downloadable information delivery, transaction management and reporting software platform that enables individual investors, financial advisors and wealth intermediaries to monitor the progress of client and investment onboarding in the field of alternative investments.
Provided are processes, systems, and methods for automation of investigation and resolution of transaction breaks such as, for example, a trade break, a reconciliation break, a dividend break, or a security position break. Breaks (e.g., discrepancies in records of transactions) often occur when there exist different systems or applications that operate in concert. A break refers to a situation where there is a discrepancy between details, attributes, or characteristics of a transaction across different computing systems or applications. In many cases, there exists a need to ensure consistency between the records of these different systems. Thus, for example, an entity may desire to resolve those breaks, such as to reduce risk, maintain compliance, or to provide accurate accounting of records.
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
09 - Scientific and electric apparatus and instruments
42 - Scientific, technological and industrial services, research and design
Goods & Services
Downloadable computer software using artificial intelligence (AI) for use in connection with banking services, financial research services, investment management and advisory services, wealth management, financial planning, asset management, integration, aggregation and analysis of financial data, financial portfolio analysis, evaluation, tracking, analysis, forecasting, consultancy, advisory and research services related to securities and other financial instruments, trust services, namely, investment and trust company services, trust services, namely, estate trust management, securities trading and investment services for others, money management services, financial custody services, namely, maintaining possession of financial assets for others for financial management purposes, securities lending services, clearing services, namely, clearing and reconciling financial transactions, financial services, namely, financial management and advisory services related to negotiable financial instruments traded on local stock exchanges that represent shares of foreign companies, namely, depositary receipts, database management and investment fund and portfolio accounting; downloadable computer software featuring artificial intelligence (AI) that enables users to build features and functionalities into web and mobile-based applications, platforms and programs; downloadable computer software used to implement and scale artificial intelligence for integration into web and mobile-based applications, platforms and programs; downloadable computer software featuring artificial intelligence (AI) for the development and deployment of AI-enabled applications, platforms, and programs. Providing temporary use of non-downloadable software using artificial intelligence (AI) for use in connection with downloadable computer software using artificial intelligence (AI) for use in connection with banking services, financial research services, investment management and advisory services, wealth management, financial planning, asset management, integration, aggregation and analysis of financial data, financial portfolio analysis, evaluation, tracking, analysis, forecasting, consultancy, advisory and research services related to securities and other financial instruments, trust services, namely, investment and trust company services, trust services, namely, estate trust management, securities trading and investment services for others, money management services, financial custody services, namely, maintaining possession of financial assets for others for financial management purposes, securities lending services, clearing services, namely, clearing and reconciling financial transactions, financial services, namely, financial management and advisory services related to negotiable financial instruments traded on local stock exchanges that represent shares of foreign companies, namely, depositary receipts, database management and investment fund and portfolio accounting; providing temporary use of non-downloadable software featuring artificial intelligence (AI) that enables users to build features and functionalities into web and mobile-based applications, platforms and programs; providing temporary use of non-downloadable computer software used to implement and scale artificial intelligence for integration into third party web and mobile-based applications, platforms and programs; providing temporary use of non-downloadable computer software featuring artificial intelligence (AI) for the development and deployment of AI-enabled applications, platforms and programs; providing temporary use of non-downloadable computer software consisting of foundational models, namely, large artificial intelligence models trained on a large quantity of data for processing, understanding, analyzing, and generating text, images, speech, sounds, and video; providing temporary use of non-downloadable computer software featuring artificial intelligence (AI) for machine learning and data mining to enable predictive analytics and predictive modeling.
9.
ELECTRONIC DOCUMENT COLLABORATION SYSTEMS AND METHODS
Electronic document collaboration systems and methods includes one or more processors, one or more memory components communicatively coupled to the one or more processors, and machine readable instructions. The machine readable instructions cause the system to access an electronic document on a graphical user interface by a participant user of a collaborator group of one or more collaborator groups assigned to an entity of a plurality of entities, add a comment to the displayed electronic document via an entry by the participant user into the electronic document, assign the comment a unique identifier, prompt the participant user to select a security designation for the comment in response to the comment being added to the electronic document, and associate the security designation with the unique identifier of the comment.
G06F 3/0484 - 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
Provided are processes, systems, and methods for the investigation and resolution of transaction breaks such as, for example, reconciliation breaks. Breaks (e.g., discrepancies in records of transactions) often occur when there exists different systems or applications that operate in concert. A break refers to a situation where there is a discrepancy between details, attributes, or characteristics of a transaction across different computing systems or applications. In many cases, there exists a need to ensure consistency between the records of these different systems. Thus, for example, an entity may desire to resolve those breaks, such as to reduce risk, maintain compliance, or accurate accounting of records.
Provided are processes, systems, and methods for the investigation and resolution of transaction breaks such as, for example, reconciliation breaks. Breaks (e.g., discrepancies in records of transactions) often occur when there exist different systems or applications that operate in concert. A break refers to a situation where there is a discrepancy between details, attributes, or characteristics of a transaction across different computing systems or applications. In many cases, there exists a need to ensure consistency between the records of these different systems. Thus, for example, an entity must resolve those breaks, such as to reduce risk, maintain compliance, or accurate accounting of records.
The disclosure relates to systems and methods of improved attention for neural networks. A system may access a first array of entries and a second array of entries, wherein the first array represents a first word embedding generated from one or more first characters of an input and the second array represents a second word embedding from the one or more second characters of the input. A system may generate at least a first difference matrix based on the first array and at least a second difference matrix based on the second array. A system may determine a difference value based on vector based at least in part on the difference value. A system may provide the context vector to subsequent layers of a neural network.
G06F 40/284 - Lexical analysis, e.g. tokenisation or collocates
13.
METHODS AND SYSTEMS FOR GENERATING ELECTRONIC COMMUNICATIONS FEATURING CONSISTENT DATA STRUCTURING AND DYNAMICALLY-DETERMINED DATA CONTENT FOR END-USER SPECIFIC DATA IN ENVIRONMENTS WITH DATA STORAGE CONSTRAINTS
The methods and systems for improving communication distribution. In particular, the methods and systems for improving communication distribution in environments where there is both the need for end-user specific data (e.g., customized content) and/or data storage constraints. For example, in order to address the security/privacy concerns during communication distribution, the methods and systems use a novel architecture that limits the amount of data that must be stored. Specifically, the system does not require permanent storage of communications featuring end-user specific data prior to the distribution of these communications. Accordingly, the storage requirements are greatly diminished, and privacy/security concerns are avoided.
Disclosed is an improved computer architecture for generating an electronic form by having a user input a portion of form data and obtaining the remaining portion of the format data from a data storage that stores reusable data for various forms. A master data object is configured to store “request-agnostic data,” which is typically that portion of form data that does not differ, or is common, between various forms. The data that differs between various forms, such as the data that is specific to a form, may be considered as “request-specific data.” When a form generation request is received, the user may be prompted to input request-specific data, but not the request-agnostic data. The system automatically obtains the request-agnostic data from the master data object, and integrates the request-agnostic data with the request-specific data to generate the form.
36 - Financial, insurance and real estate services
Goods & Services
Securities lending; financing services; asset-based lending services, namely, securities lending; securities lending services involving segregated custody accounts; financial services, namely, collateral and liquidity management in the nature of financial securities; financial services, namely, management and operation of a securities lending program in which participants can access, borrow and transfer select financial securities into custody and clearing accounts to satisfy short sale delivery obligations; financial services, namely, management and operation of a securities lending program in which participants can obtain financing in the nature of loaned securities and collateralize borrowing activities with cash and non-cash collateral.
16.
SYSTEMS AND METHODS FOR FACILITATING DECOUPLED DISTRIBUTION FROM A MAINFRAME TO DISTRIBUTED PLATFORMS
Systems and methods for facilitating decoupled distribution from a mainframe to one or more distributed platforms curate a data feed from the mainframe; read the data feed; generate an input file for distribution to an event platform topic representing the data feed; in which the event platform topic is hosted on an event platform; write the input file to the event platform topic; and broadcast the data feed to one or more distributed consumers upon request from a given distributed consumer.
36 - Financial, insurance and real estate services
Goods & Services
(1) Securities lending
(2) Financing services; asset-based lending services, namely, securities lending; securities lending services involving segregated custody accounts; financial services, namely, collateral and liquidity management in the nature of financial securities; financial services, namely, management and operation of a securities lending program in which participants can access, borrow and transfer select financial securities into custody and clearing accounts to satisfy short sale delivery obligations; financial services, namely, management and operation of a securities lending program in which participants can obtain financing in the nature of loaned securities and collateralize borrowing activities with cash and non-cash collateral
18.
System and methods for controlled access to computer resources
Provided is a system and method for enabling of access to a computer resource by a computer system comprising: providing to a user an interface configured to receive a request for access to a computer resource; determining if the user is permitted to access the computer resource based on a user profile; providing a user verification interface configured to receive user identity verification information; determining if the user identity verification information is valid in response to a reply to the request for user identify verification information; and in response to determining that the user is permitted access to the computer resource and that the user verification information is valid: updating a security policy to reflect that the user is permitted to access the computer resource, and providing access to the computer resource for a limited time duration.
Provided is a system and method for enabling of access to a computer resource by a computer system comprising: providing to a user an interface configured to receive a request for access to a computer resource; determining if the user is permitted to access the computer resource based on a user profile; providing a user verification interface configured to receive user identity verification information; determining if the user identity verification information is valid in response to a reply to the request for user identify verification information; and in response to determining that the user is permitted access to the computer resource and that the user verification information is valid: updating a security policy to reflect that the user is permitted to access the computer resource, and providing access to the computer resource for a limited time duration.
The disclosure relates to user interfaces that reconcile non-linear data and a table format to display the non-linear data across various screen sizes without loss of information. A system may access non-linear data such as a hierarchical data structure. The system may access a plurality of data elements that represent the non-linear data for rendering in a display container, which has a display height that varies depending on the screen size of a given display device. The system may obtain the available display height and determine a number of columns to render based on the display height and the plurality of data elements. The system may fill the columns with the plurality of data elements using a columnar format that maintains contiguity of groups of data elements that span more than one column. The system may configure a patch to visually indicate the contiguity.
The disclosure relates to systems and methods of determining gradient-based directional drivers of deep learning models. A system may access a plurality of features and a group definition that specifies one or more groups of features. The system may provide the plurality of features as input to a deep learning model trained to generate a model output based on a model function and the plurality of features. The system may obtain, for each feature, a gradient that represents a rate of change of the model function based on the feature and then aggregate, based on the group definition, the gradients obtained from the deep learning model; and for each group of features from among the one or more groups of features: determine a directional driver based on the aggregated gradients, the directional driver indicating an impact of the group of features on the model output.
A central service provider manages a blockchain network that writes the cryptographic hash of each executed transaction in a block to the blockchain network. For each executed transaction, the central service provider generates and transmits a transaction receipt such that a party can verify that the transaction was appropriately executed. Additionally, a party can check that the party's records are correct by providing transaction data describing details of transactions recorded in the party's records to the central service provider. The central service provider verifies the party's records by comparing the transaction data in the party's records to the blocks of transaction records in the blockchain network. In some scenarios, the central service provider may identify or receive an identification of a discrepancy arising from one or more transactions. The central service provider can reconcile the identified discrepancy.
G06F 16/00 - Information retrievalDatabase structures thereforFile system structures therefor
G06F 11/14 - Error detection or correction of the data by redundancy in operation, e.g. by using different operation sequences leading to the same result
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
H04L 9/00 - Arrangements for secret or secure communicationsNetwork security protocols
23.
SEGMENTED MACHINE LEARNING-BASED MODELING WITH PERIOD-OVER-PERIOD ANALYSIS
The disclosure relates to systems and methods of generating behavior classifications that predict a behavior of an entity by training and executing a base machine learning (ML) model, a plurality of segmented ML models, and a merged ML model. Training data may be historical entity data, which may be grouped into different segments that describe the entity. The base ML model may be trained to predict entity behavior across a plurality of segments. Each segmented ML model may be trained to the generate a segmented behavior class that predicts entity behavior based on a respective segment. A system may provide the base class and the plurality of segmented classes as input to a merged model that was trained based on weights for each of the base ML model and the plurality of segmented ML models to generate a behavior classification representing a prediction of the entity behavior.
The disclosure relates to systems and methods of modeling recovery of a Computer Network System (CNS) based on real-world recovery operations that are non-linear. The system may use a bin packing model that simulates recovery that eliminates idle times in linear estimates for data recovery. For example, the bin packing model may model recovery by actively simulating real-world conditions during recovery in a non-linear fashion. In particular, the bin packing model may fill the active bin with data objects that can be processed and the queue will include data objects waiting to be recovered. As a data object in the active bin is recovered, recovery of another data object may be made available. As such, the bin packing model may move a data object in the queue to the active bin as recovery progresses. Such progression is non-linear and modeled as such.
36 - Financial, insurance and real estate services
Goods & Services
Fund portfolio accounting, reconciliation and reporting, and business management and administration services. Banking services; financial analysis and research services; financial information and advisory services; financial affairs and monetary affairs, namely, financial information, management and analysis services; financial data analysis; financial services, namely, wealth management; financial services, namely, asset management; financial data analysis services, namely, integration, aggregation and analysis of financial data, data concerning financial assets, and financial research data, the foregoing being performed by electronic means; financial services, namely, financial portfolio analysis services; financial services, namely, evaluation, tracking, analysis, forecasting, consultancy, advisory and research services relating to securities and other financial instruments; financial services, namely, financial planning; investment management and advisory services; trust services, namely, investment and trust company services; trust services, namely, estate trust management; securities services, namely, securities trade execution services, financial securities exchange services, and securities deposit services; securities trading and investment services for others via the internet; providing financial services with respect to securities and other financial instruments and products, namely, money management services; financial custody services, namely, maintaining possession of financial assets for others for financial management purposes; securities lending services; clearing services, namely, clearing and reconciling financial transactions via a global computer network; financial investment services, namely, administering the issuance of negotiable financial instruments traded on local stock exchanges that represent shares of foreign companies, namely, depositary receipts; depositary receipts services, namely, financial management and advisory services related to negotiable financial instruments traded on local stock exchanges that represent shares of foreign companies.
The present application relates to embodiments for detecting firewall drift. In some embodiments, a first set of firewall rules of a first firewall for a first instance of a distributed application, a second set of firewall rules of a second firewall for a second instance of the distributed application, and a mapping of IP addresses to identifiers of services from amongst a first set of services of the first instance and a second set of services of the second instance may be obtained. First connectivity data and second connectivity data may be generated indicating, for each of IP address associated with the first and second set of firewall rules, a respective port number over which communications between a respective IP address are transmitted, and generating comparison data indicating whether firewall drift is detected based on a comparison of the first connectivity data and the second connectivity data.
The disclosure relates to systems and methods of generating a grid pattern display from hierarchical data structures. A system may access a hierarchical data structure comprising a root node, a parent node, and a leaf node. The system may obtain a leaf node size and determine a margin for each cell and each group in the grid pattern to ensure uniform spacing for the leaf nodes in the grid pattern. The system may set presentation layer data based on the determined spacing to generate a set of data elements of the presentation layer. The system may split the set of data elements of the presentation layer based on a size of a display container, the display container being used to display the grid pattern. The system may consume the presentation layer into a display layer to render a grid transformation of the hierarchical data.
The disclosure relates to systems and methods of generating a grid pattern display from hierarchical data structures. A system may access a hierarchical data structure comprising a root node, a parent node, and a leaf node. The system may obtain a leaf node size and determine a margin for each cell and each group in the grid pattern to ensure uniform spacing for the leaf nodes in the grid pattern. The system may set presentation layer data based on the determined spacing to generate a set of data elements of the presentation layer. The system may split the set of data elements of the presentation layer based on a size of a display container, the display container being used to display the grid pattern. The system may consume the presentation layer into a display layer to render a grid transformation of the hierarchical data.
Provided is a method including identifying one or more injection points in a flow of an application and determining that a first injection point of the one or more injection points permits scenario injection. The method further includes injecting first scenario source code for a first scenario function at the first injection point in source code of the application and storing the application including the first scenario source code for the first scenario function. The method further includes receiving instruction to activate the first scenario function, activating the first scenario function. Furthermore, the method includes running the application when the first scenario function is activated, such that the running the application when the first scenario function is activated causes the application to operate concurrently with the first scenario function and providing a first application output.
Provided is a method including identifying one or more injection points in a flow of an application and determining that a first injection point of the one or more injection points permits scenario injection. The method further includes injecting first scenario source code for a first scenario function at the first injection point in source code of the application and storing the application including the first scenario source code for the first scenario function. The method further includes receiving instruction to activate the first scenario function, activating the first scenario function. Furthermore, the method includes running the application when the first scenario function is activated, such that the running the application when the first scenario function is activated causes the application to operate concurrently with the first scenario function and providing a first application output.
The system uses the non-repudiatory persistence of blockchain technology to store all task statuses and results across the distributed computer network in an immutable blockchain database. Coupled with the resiliency of the stored data, the system may determine a sequence of processing tasks for a given processing request and use the sequence to detect and/or predict failures. Accordingly, in the event of a detected system failure, the system may recover the results prior to the failure, minimizing disruptions to processing the request and improving hardware resiliency.
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
H04L 67/10 - Protocols in which an application is distributed across nodes in the network
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]
35.
Multi-modal-based generation of data synchronization instructions
In certain embodiments, multi-modal-based generation of settlement instructions may be facilitated. In some embodiments, a portfolio of a live environment may be emulated in a projected environment. A target portfolio may be generated in the projected environment based on the emulated portfolio. Partial synchronization between the target portfolio of the projected environment and the portfolio of the live environment may be performed such that a first subset of changes to the portfolio of the live environment are reflected in the target portfolio of the projected environment. Subsequent to the partial synchronization, the target portfolio of the projected environment may be updated such that the update of the target portfolio accounts for the first subset of changes. Subsequent to the update of the target portfolio, settlement instructions may be generated based on differences between the target portfolio of the projected environment and the portfolio of the live environment.
G06F 16/27 - Replication, distribution or synchronisation of data between databases or within a distributed database systemDistributed database system architectures therefor
The disclosure relates to systems and methods of deep learning with data discontinuities. A discontinuity may refer to a concatenation point in a series of data, such as a time series data, at which two sequences of data have been joined. A system may address data discontinuities by tuning a model parameter that minimizes error around concatenation points. For example, the model parameter may include a sample weight that is applied to data values adjacent to the concatenation points. A sample weight may be specifically tuned based on one or more characteristics of the input data. The characteristics may include a frequency of concatenation points relative to the length of a concatenated time series, a magnitude of the discontinuity, and/or other characteristics of the input data having discontinuities. In this manner, optimizers for deep learning penalize error resulting from the discontinuities, which may reduce overall modeling error.
The disclosure relates to systems and methods of mapping deep learning activation functions to input data. For example, a system may select one or more activation functions for one or more layers of a neural network based on properties that cause modeling errors or otherwise should be accounted for. The properties that may cause modeling error or otherwise should be accounted for in deep learning may include skewness, kurtosis, range boundedness, and/or other properties. The selected activation functions may be placed at one or more layers of a neural network. In this manner, the neural network may be tuned with specific activation functions that align with the properties of the input data.
Systems, software, and methods are disclosed for generating a prediction of time-series data from data sets. A system is configured to: retrieve, for each product of a set of products, the time-series data including time-value pairs; select a first product; compute a correlation value between the first product and other products and for one or more degrees of lag to obtain a set of correlation values representing the correlations between the first product to the other products assessed at prior times; select a subset of products based at least in part on the correlation values; provide the time-series data associated with each product from the subset of products and the first product to a machine learning model trained to predict a future value of the first product based on values of the subset of products; and obtain prediction data representing a set of predicted values for the first product.
G06Q 10/04 - Forecasting or optimisation specially adapted for administrative or management purposes, e.g. linear programming or "cutting stock problem"
The systems and methods relate to electronic signature verification based on topological stochastic models (TSM). The TSM may be trained on samples of known authentic signatures of a signee. Training the TSM may include TSM features extraction on the training samples to extract feature vectors, TSM features aggregation to aggregate the feature vectors, and optimal threshold estimation to determine an optimal threshold value. The optimal threshold value and overall aggregate of feature vectors may be used to evaluate feature vectors extracted from a signature to be verified. For example, a distance between the resulting feature vector extracted from the input sequence and the aggregated feature vector is determined. The distance is compared to the optimal threshold value to determine whether the signature in the input image is verified. The signature in the input image is verified if the distance is less than or equal to the optimal threshold value.
The systems and methods relate to electronic signature verification based on topological stochastic models (TSM). The TSM may be trained on samples of known authentic signatures of a signee. Training the TSM may include TSM features extraction on the training samples to extract feature vectors, TSM features aggregation to aggregate the feature vectors, and optimal threshold estimation to determine an optimal threshold value. The optimal threshold value and overall aggregate of feature vectors may be used to evaluate feature vectors extracted from a signature to be verified. For example, a distance between the resulting feature vector extracted from the input sequence and the aggregated feature vector is determined. The distance is compared to the optimal threshold value to determine whether the signature in the input image is verified. The signature in the input image is verified if the distance is less than or equal to the optimal threshold value.
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 10/84 - Arrangements for image or video recognition or understanding using pattern recognition or machine learning using probabilistic graphical models from image or video features, e.g. Markov models or Bayesian networks
G06V 40/30 - Writer recognitionReading and verifying signatures
The disclosure relates to systems and methods of detecting anomalies using a plurality of machine learning models. Each of the machine learning models may be trained to detect a respective behavior of historical data values for a given metric. Thus, a system may perform anomaly detection based on different behaviors of the same metric of data, reducing instances of false positive anomaly detection while also reducing instances of false negative reporting. The plurality of machine learning models may be trained to detect anomalies across multiple different types of metrics as well, providing robust multi-metric anomaly detection across a range of behaviors of historical data values. The system may implement a pluggable architecture for the plurality of machine learning models in which models may be added or removed from pluggable architecture. In this way, the system may detect anomalies using a configurable set of machine learning models.
Provided is a system and method for enabling of access to a computer resource by a computer system comprising: providing to a user an interface configured to receive a request for access to a computer resource; determining if the user is permitted to access the computer resource based on a user profile; providing a user verification interface configured to receive user identity verification information; determining if the user identity verification information is valid in response to a reply to the request for user identify verification information; and in response to determining that the user is permitted access to the computer resource and that the user verification information is valid: updating a security policy to reflect that the user is permitted to access the computer resource, and providing access to the computer resource for a limited time duration.
The disclosure relates to systems and methods of generating a mixture model for approximating non-normal distributions of time series data. The mixture model may include clusters of normal distributions that together approximate a non-normal distribution. The mixture model may be used to normalize input data for machine learning models. For example, a machine learning model such as an autoencoder may be trained to make predictions on the normalized input data. The predictions may relate to the time series of data. In one example, the time series of data may be market data for a security. The market data my include one or more features that are normalized using the mixture model. The predictions may include a predicted rate at which a lender will charge to borrow a security for short selling, where such rate may depend on the market data for the security.
The disclosure relates to systems and methods of generating a mixture model for approximating non-normal distributions of time series data. The mixture model may include clusters of normal distributions that together approximate a non-normal distribution. The mixture model may be used to normalize input data for machine learning models. For example, a machine learning model such as an autoencoder may be trained to make predictions on the normalized input data. The predictions may relate to the time series of data. In one example, the time series of data may be market data for a security. The market data my include one or more features that are normalized using the mixture model. The predictions may include a predicted rate at which a lender will charge to borrow a security for short selling, where such rate may depend on the market data for the security.
G06N 3/0442 - Recurrent networks, e.g. Hopfield networks characterised by memory or gating, e.g. long short-term memory [LSTM] or gated recurrent units [GRU]
The disclosure relates to systems and methods of generating a mixture model for approximating non-normal distributions of time series data. The mixture model may include clusters of normal distributions that together approximate a non-normal distribution. The mixture model may be used to normalize input data for machine learning models. For example, a machine learning model such as an autoencoder may be trained to make predictions on the normalized input data. The predictions may relate to the time series of data. In one example, the time series of data may be market data for a security. The market data my include one or more features that are normalized using the mixture model. The predictions may include a predicted rate at which a lender will charge to borrow a security for short selling, where such rate may depend on the market data for the security.
The disclosure relates to systems and methods of generating a mixture model for approximating non-normal distributions of time series data. The mixture model may include clusters of normal distributions that together approximate a non-normal distribution. The mixture model may be used to normalize input data for machine learning models. For example, a machine learning model such as an autoencoder may be trained to make predictions on the normalized input data. The predictions may relate to the time series of data. In one example, the time series of data may be market data for a security. The market data my include one or more features that are normalized using the mixture model. The predictions may include a predicted rate at which a lender will charge to borrow a security for short selling, where such rate may depend on the market data for the security.
Provided are systems, methods, and programming for facilitating real-time determination of a process completion likelihood. In some embodiments, data including an update to a system, the update occurring at a first time, wherein updates to the system are permitted until an expiration time may be obtained, a set of fixed descriptors of the system may be retrieved and/or received, and a set of status updates describing the system at prior times may be obtained. Each status update of the set of one or more status updates includes at least (i) an update to the first system and (ii) a time that the respective status update occurred. Based on the data, the fixed descriptors, and the status updates, using a trained machine learning model, a failure/success score indicating a likelihood that, at the expiration time, the system satisfies a threshold condition may be computed and stored in memory.
Provided are systems, methods, and programming for facilitating real-time determination of a process completion likelihood. In some embodiments, data including an update to a system, the update occurring at a first time, wherein updates to the system are permitted until an expiration time may be obtained, a set of fixed descriptors of the system may be retrieved and/or received, and a set of status updates describing the system at prior times may be obtained. Each status update of the set of one or more status updates includes at least (i) an update to the first system and (ii) a time that the respective status update occurred. Based on the data, the fixed descriptors, and the status updates, using a trained machine learning model, a failure/success score indicating a likelihood that, at the expiration time, the system satisfies a threshold condition may be computed and stored in memory.
Provided is a method including obtaining a plurality of time series data sets and a synthetic time series data set that is associated with a first machine learning label such that the first machine learning label is associated with a first time series data set of the plurality of time series data sets. The method includes inputting the synthetic time series data set, the first machine learning label, a second machine learning label that is associated with a second time series data set of the plurality of time series data sets, and a series of generated values in a time series simulation machine learning model that includes a trained generative adversarial network. The method includes running a generator neural network included in the trained generative adversarial network with the inputs and then generating a synthetic second time series data set for the second time series data set.
Methods and systems for scheduling execution of an automated controls assessment include receiving a user input to generate an automated controls assessment audit; receiving an area of audit for the audit; receiving a category of the audit; receiving scheduling data for executing the audit; determining whether the scheduling data is met; responsive to determining that the scheduling data is met, transmitting, to an API-based agent, an instruction to execute the audit; receiving, from the API-based agent, a response to the audit; processing, using a library of reusable features for controls assessment audits for a plurality of computer domains, the response to generate a result of the audit; and generating, for display, on a display device, an instance of a first user interface, wherein the instance of the first user interface comprises the result of the automated controls assessment audit.
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
51.
METHODS AND SYSTEMS FOR BALANCING LOADS IN DISTRIBUTED COMPUTER NETWORKS FOR COMPUTER PROCESSING REQUESTS WITH VARIABLE RULE SETS AND DYNAMIC PROCESSING LOADS
Methods and systems are described for balancing loads in distributed computer networks for computer processing requests with variable rule sets and dynamic processing loads. The methods and systems may include determining an initial allocation of the plurality of processing requests to the plurality of available domains that has a lowest initial sum excess processing load. The methods and systems may then retrieve an updated estimated processing load for at least one of the plurality of processing requests and determine a secondary allocation of the plurality of processing requests to the plurality of available domains.
Provided is a failover automation system and method comprising: obtaining, by a processor, a process inventory for a failover of an application from a first datacenter to a second data center; generating, by the processor, a data model for the failover based on the process inventor; generating, by the processor, a workflow for the failover based on the data model; assembling, by the processor, a set of one or more virtual engineers to perform the failover for the application based on the workflow; and performing, by the processor, the failover for the application with the set of one or more virtual engineers based on the workflow.
G06F 11/20 - Error detection or correction of the data by redundancy in hardware using active fault-masking, e.g. by switching out faulty elements or by switching in spare elements
53.
System and methods for application failover automation
Provided is a failover automation system and method comprising: obtaining, by a processor, a process inventory for a failover of an application from a first datacenter to a second data center; generating, by the processor, a data model for the failover based on the process inventor; generating, by the processor, a workflow for the failover based on the data model; assembling, by the processor, a set of one or more virtual engineers to perform the failover for the application based on the workflow; and performing, by the processor, the failover for the application with the set of one or more virtual engineers based on the workflow.
Provided is an architecture drift detection system and method including: obtaining a first set of architecture design metrics associated with a first application; obtaining first set of data metrics associated with a first instance of the first application that is installed at a first server computing system; obtaining a second set of data metrics associated with a second instance of the first application that is installed at a second server computing system; determining, using the first set of data metrics and the second set of data metrics, that at least one of the first instance, the first server computing system, the second instance, or the second server computing system deviates from one or more architecture design metrics from the first set of architecture design metrics associated with the first application; and providing a deviation notification indicating a deviation from the one or more architecture design metrics.
H04L 47/762 - Admission controlResource allocation using dynamic resource allocation, e.g. in-call renegotiation requested by the user or requested by the network in response to changing network conditions triggered by the network
Provided is an architecture drift detection system and method including: obtaining a first set of architecture design metrics associated with a first application; obtaining first set of data metrics associated with a first instance of the first application that is installed at a first server computing system; obtaining a second set of data metrics associated with a second instance of the first application that is installed at a second server computing system; determining, using the first set of data metrics and the second set of data metrics, that at least one of the first instance, the first server computing system, the second instance, or the second server computing system deviates from one or more architecture design metrics from the first set of architecture design metrics associated with the first application; and providing a deviation notification indicating a deviation from the one or more architecture design metrics.
Systems for predicting communication settlement times across disparate networks store a first tier of a machine learning architecture comprising multiple machine learning models and an aggregation layer; store a second tier comprising rule sets for predicting settlement times; receive multiple data feeds corresponding to multiple communication data types; generate feature inputs based on the data feeds; input the feature inputs into the respective models to generate respective outputs; generate, using the aggregation layer, a third feature input based on the outputs; determine, based on the third feature input, a first rule set for predicting settlement times; receive a communication; predict a settlement time based on the first rule set; determine an aggregated communication load at a first time based on the settlement time; determine a performance availability requirement based on the load; determine a recommendation based on the performance availability requirement; and generate the recommendation based on the settlement time.
The present application relates to embodiments for detecting firewall drift. In some embodiments, a first set of firewall rules of a first firewall for a first instance of a distributed application, a second set of firewall rules of a second firewall for a second instance of the distributed application, and a mapping of IP addresses to identifiers of services from amongst a first set of services of the first instance and a second set of services of the second instance may be obtained. First connectivity data and second connectivity data may be generated indicating, for each of IP address associated with the first and second set of firewall rules, a respective port number over which communications between a respective IP address are transmitted, and generating comparison data indicating whether firewall drift is detected based on a comparison of the first connectivity data and the second connectivity data.
The present application relates to embodiments for detecting firewall drift. In some embodiments, a first set of firewall rules of a first firewall for a first instance of a distributed application, a second set of firewall rules of a second firewall for a second instance of the distributed application, and a mapping of IP addresses to identifiers of services from amongst a first set of services of the first instance and a second set of services of the second instance may be obtained. First connectivity data and second connectivity data may be generated indicating, for each of IP address associated with the first and second set of firewall rules, a respective port number over which communications between a respective IP address are transmitted, and generating comparison data indicating whether firewall drift is detected based on a comparison of the first connectivity data and the second connectivity data.
In certain embodiments, multi-modal-based generation of settlement instructions may be facilitated. In some embodiments, a portfolio of a live environment may be emulated in a projected environment. A target portfolio may be generated in the projected environment based on the emulated portfolio. Partial synchronization between the target portfolio of the projected environment and the portfolio of the live environment may be performed such that a first subset of changes to the portfolio of the live environment are reflected in the target portfolio of the projected environment. Subsequent to the partial synchronization, the target portfolio of the projected environment may be updated such that the update of the target portfolio accounts for the first subset of changes. Subsequent to the update of the target portfolio, settlement instructions may be generated based on differences between the target portfolio of the projected environment and the portfolio of the live environment.
G06F 16/00 - Information retrievalDatabase structures thereforFile system structures therefor
G06F 16/27 - Replication, distribution or synchronisation of data between databases or within a distributed database systemDistributed database system architectures therefor
Disclosed is an improved computer architecture for generating an electronic data layout by having a user input a portion of dataset and obtaining the remaining portion of the format data from a data storage that stores reusable data for various data layouts. A master data object is configured to store “request-agnostic data,” which is typically that portion of dataset that does not differ, or is common, between various data layouts. The data that differs between various data layouts, such as the data that is specific to a form, may be considered as “request-specific data.” When a data layout generation request is received, the user may be prompted to input request-specific data, but not the request-agnostic data. The system automatically obtains the request-agnostic data from the master data object, and integrates the request-agnostic data with the request-specific data to generate the form.
36 - Financial, insurance and real estate services
42 - Scientific, technological and industrial services, research and design
Goods & Services
Financial collateral management; financial management services, namely, financial collateral optimization services; financial evaluation, management and administration of financial guarantees in the nature of collateral management services; financial asset management services, namely, managing and optimizing assets used to secure loans and securities; financial services, namely, electronic financial trading of collateralized financial products and commodities; financial services, namely, mitigating credit risk associated with financial transactions; collateral financial management services, namely, mitigating credit risk associated with derivatives transactions; financial analysis featuring the calculation and optimization of financial product portfolios; financial information, advisory and consultancy services with regard to collateral assessment, coverage and valuation, and cross-product margin requirements. Providing an online, non-downloadable information delivery, transaction management and reporting software platform in the field of collateral management; platform as a service (PAAS) featuring a software platform for managing, analyzing, evaluating, trading, valuing and optimizing collateralized assets and financial product portfolios, mitigating credit risk associated with financial transactions, and liquidity and inventory management; platform as a service (PAAS) featuring a software platform for automating and optimizing collateral management in connection with the trading and handling of futures contracts and managing collateral communication workflows; platform as a service (PAAS) featuring a software platform for the purpose of providing historical and real-time data relating to margins and collateralized assets.
62.
NON-LINEAR SUBJECT BEHAVIOR PREDICTION SYSTEMS AND METHODS
The present disclosure relates to predicting non-linear subject behavior that occurs in response to stimulus content. Determining a behavior model for the subject is described. The behavior model describes a density of an observed behavior of a subject. A prior probability subject behavior distribution associated with the observed behavior is determined. The prior probability subject behavior distribution comprises an assumption describing the observed behavior. The prior probability subject behavior distribution comprises a Gamma prior probability distribution. The non-linear subject behavior is predicted based on the stimulus content, the Gamma prior probability distribution, and the behavior model.
Methods and systems for adaptive, template-independent handwriting extraction from images using machine learning models and without manual localization or review. For example, the system may receive an input image, wherein the input image comprises native printed content and handwritten content. The system may process the input image with a model to generate an output image, wherein the output image comprises extracted handwritten content based on the native handwritten content. The system may process the output image to digitally recognize the extracted handwritten content. The system may generate a digital representation of the input image, wherein the digital representation comprises the native printed content and the digitally recognized extracted handwritten content.
Methods and systems are disclosed for generating optimal data predictions in time series data signals based on empirically-optimized model selection, noise filtering, and window size selection using machine learning models. For example, the system may receive a first subset of time series data. The system may receive a prediction horizon. The system may generate a feature input based on the first subset of time series data and the prediction horizon. The system may input the feature input into a machine learning model, wherein the machine learning model includes multiple components. The system may receive an output from the machine learning model. The system may generate for display, on a user interface, a prediction for the first subset of time series data at the prediction horizon based on the output.
Methods and systems are disclosed for generating optimal data predictions in time series data signals based on empirically-optimized model selection, noise filtering, and window size selection using machine learning models. For example, the system may receive a first subset of time series data. The system may receive a prediction horizon. The system may generate a feature input based on the first subset of time series data and the prediction horizon. The system may input the feature input into a machine learning model, wherein the machine learning model includes multiple components. The system may receive an output from the machine learning model. The system may generate for display, on a user interface, a prediction for the first subset of time series data at the prediction horizon based on the output.
A method for real-time data processing is described. The method being implemented on a computer system having one or more physical processors programmed with computer program instructions which, when executed, perform the method. The method comprising allocating a real-time dataset associated with a real-time data interaction to a node in a chain of nodes, wherein each node is representative of a user in the real-time data interaction; setting a node status of the node for the real-time dataset to pending; and independently of (i) a node status of the one or more upstream nodes and (ii) a node status of the one or more downstream nodes: periodically determining, by the computer system, an availability status of the node; and in response to the availability status satisfying the criterion, setting the node status for the real-time dataset as settled.
A central service provider manages a blockchain network that writes the cryptographic hash of each executed transaction in a block to the blockchain network. For each executed transaction, the central service provider generates and transmits a transaction receipt such that a party can verify that the transaction was appropriately executed. Additionally, a party can check that the party's records are correct by providing transaction data describing details of transactions recorded in the party's records to the central service provider. The central service provider verifies the party's records by comparing the transaction data in the party's records to the blocks of transaction records in the blockchain network. In some scenarios, the central service provider may identify or receive an identification of a discrepancy arising from one or more transactions. The central service provider can reconcile the identified discrepancy.
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
G06F 11/14 - Error detection or correction of the data by redundancy in operation, e.g. by using different operation sequences leading to the same result
H04L 9/00 - Arrangements for secret or secure communicationsNetwork security protocols
36 - Financial, insurance and real estate services
42 - Scientific, technological and industrial services, research and design
Goods & Services
Providing mutual, hedge, exchange-traded, closed end fund, and SMA fund performance services; providing financial attribution and risk factor attribution in the nature of financial risk assessment services; providing financial services in the nature of financial market scenario analytics, financial risk measurement and fund exposure reporting, namely, financial reporting on topics of risk reward projections, market cap, region exposure, credit quality, maturity evaluations, and results from market scenario analysis, fund comparisons and correlations. Providing an online non-downloadable software platform for use as a financial modeling tool that enables users to perform financial market scenario analytics and financial and investment portfolio analysis; providing an online non-downloadable software platform that enables users to assess and measure investment performance, risk, returns, and expenses; providing an online non-downloadable software platform that enables uses to compare investment portfolios against benchmark investment portfolios.
69.
Methods and systems for generating predictions based on time series data using an ensemble modeling
The methods and systems provide an ensemble approach that combines multiple single-model-solutions to produce optimal forward-looking forecasts. Moreover, the methods and systems provide an architecture for this ensemble approach that ensures that the limitations for individual ensemble model components are compensated by other ensemble model components as inputs and outputs from ensemble model components are fed from one ensemble model component to another in a specific order to generate a final output upon which a conservative prediction is based.
G06F 17/18 - Complex mathematical operations for evaluating statistical data
70.
Methods and systems for generating electronic communications featuring consistent data structuring and dynamically-determined data content for end-user specific data in environments with data storage constraints
The methods and systems for improving communication distribution. In particular, the methods and systems for improving communication distribution in environments where there is both the need for end-user specific data (e.g., customized content) and/or data storage constraints. For example, in order to address the security/privacy concerns during communication distribution, the methods and systems use a novel architecture that limits the amount of data that must be stored. Specifically, the system does not require permanent storage of communications featuring end-user specific data prior to the distribution of these communications. Accordingly, the storage requirements are greatly diminished, and privacy/security concerns are avoided.
G06F 16/2457 - Query processing with adaptation to user needs
71.
METHODS AND SYSTEMS FOR GENERATING ELECTRONIC COMMUNICATIONS FEATURING CONSISTENT DATA STRUCTURING AND DYNAMICALLY-DETERMINED DATA CONTENT FOR END-USER SPECIFIC DATA IN ENVIRONMENTS WITH DATA STORAGE CONSTRAINTS
The methods and systems for improving communication distribution. In particular, the methods and systems for improving communication distribution in environments where there is both the need for end-user specific data (e.g., customized content) and/or data storage constraints. For example, in order to address the security/privacy concerns during communication distribution, the methods and systems use a novel architecture that limits the amount of data that must be stored. Specifically, the system does not require permanent storage of communications featuring end-user specific data prior to the distribution of these communications. Accordingly, the storage requirements are greatly diminished, and privacy/security concerns are avoided.
The systems and methods provide a machine learning model that can exploit long time dependency for time-series sequences, perform end-to-end learning of dimension reduction and clustering, or train on long time-series sequences with low computation complexity. For example, the methods and systems use a novel, unsupervised temporal representation learning model. The model may generate cluster-specific temporal representations for long-history time series sequences and may integrate temporal reconstruction and a clustering objective into a joint end-to-end model.
G06V 40/30 - Writer recognitionReading and verifying signatures
G06F 18/21 - Design or setup of recognition systems or techniquesExtraction of features in feature spaceBlind source separation
G06F 18/214 - Generating training patternsBootstrap methods, e.g. bagging or boosting
G06F 18/22 - Matching criteria, e.g. proximity measures
G06F 18/23213 - Non-hierarchical techniques using statistics or function optimisation, e.g. modelling of probability density functions with fixed number of clusters, e.g. K-means clustering
G06V 10/40 - Extraction of image or video features
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
G06V 10/762 - Arrangements for image or video recognition or understanding using pattern recognition or machine learning using clustering, e.g. of similar faces in social networks
G06V 10/764 - Arrangements for image or video recognition or understanding using pattern recognition or machine learning using classification, e.g. of video objects
G06V 10/774 - Generating sets of training patternsBootstrap methods, e.g. bagging or boosting
G06V 10/82 - Arrangements for image or video recognition or understanding using pattern recognition or machine learning using neural networks
73.
SYSTEM AND METHOD OF CODE EXECUTION AT A VIRTUAL MACHINE ALLOWING FOR EXTENDIBILITY AND MONITORING OF CUSTOMIZED APPLICATIONS AND SERVICES
A processing system allows external systems to customize and extend services without increasing system intricacy. The processing platform maintains cloud containers that support virtual machines for external systems. An external system provides code for execution on a virtual machine that is supported by a cloud container. Cloud containers provide a boundary for executing code such that the processing platform may limit types of code an external system can run at a cloud container. The external system code can provide new services or may build upon existing public services, and external systems may designate their services as being available to other external systems by publishing the access information in a global application programming interface (API) maintained by the processing platform. Since the external systems submit instructions for execution within their assigned cloud containers, the services and applications are developed without affecting the underlying functionality of the processing platform.
A processing system allows external systems to customize and extend services without increasing system intricacy. The processing platform maintains cloud containers that support virtual machines for external systems. An external system provides code for execution on a virtual machine that is supported by a cloud container. Cloud containers provide a boundary for executing code such that the processing platform may limit types of code an external system can run at a cloud container. The external system code can provide new services or may build upon existing public services, and external systems may designate their services as being available to other external systems by publishing the access information in a global application programming interface (API) maintained by the processing platform. Since the external systems submit instructions for execution within their assigned cloud containers, the services and applications are developed without affecting the underlying functionality of the processing platform.
H04L 9/00 - Arrangements for secret or secure communicationsNetwork security protocols
H04L 9/06 - Arrangements for secret or secure communicationsNetwork security protocols the encryption apparatus using shift registers or memories for blockwise coding, e.g. D.E.S. systems
75.
METHODS AND SYSTEMS FOR USING MACHINE LEARNING MODELS THAT GENERATE CLUSTER-SPECIFIC TEMPORAL REPRESENTATIONS FOR TIME SERIES DATA IN COMPUTER NETWORKS
The systems and methods provide a machine learning model that can exploit long time dependency for time-series sequences, perform end-to-end learning of dimension reduction and clustering, or train on long time-series sequences with low computation complexity. For example, the methods and systems use a novel, unsupervised temporal representation learning model. The model may generate cluster-specific temporal representations for long-history time series sequences and may integrate temporal reconstruction and a clustering objective into a joint end-to-end model.
G06F 16/28 - Databases characterised by their database models, e.g. relational or object models
76.
METHODS AND SYSTEMS FOR USING MACHINE LEARNING MODELS THAT GENERATE CLUSTER-SPECIFIC TEMPORAL REPRESENTATIONS FOR TIME SERIES DATA IN COMPUTER NETWORKS
The systems and methods provide a machine learning model that can exploit long time dependency for time-series sequences, perform end-to-end learning of dimension reduction and clustering, or train on long time-series sequences with low computation complexity. For example, the methods and systems use a novel, unsupervised temporal representation learning model. The model may generate cluster-specific temporal representations for long-history time series sequences and may integrate temporal reconstruction and a clustering objective into a joint end-to-end model.
Methods and systems are described for improving hardware resiliency during serial processing tasks in distributed computer networks. In particular, the system uses may use the non-repudiatory persistence of blockchain technology to store all task statuses and results across the distributed computer network in an immutable blockchain database. Coupled with the resiliency of the stored data, the system may determine a sequence of processing tasks for a given processing request and use the sequence to detect and/or predict failures. Accordingly, in the event of a detected system failure, the system may recover the results prior to the failure, minimizing disruptions to processing the request and improving hardware resiliency.
H04L 9/06 - Arrangements for secret or secure communicationsNetwork security protocols the encryption apparatus using shift registers or memories for blockwise coding, e.g. D.E.S. systems
A system stores transaction data in a ring chain architecture. A ring chain comprises blocks of data stored as a length-limited block chain in a ring buffer configuration. A block of transactions is stored on a ring chain until enough new blocks are added to overwrite the ring buffer with new blocks. The system stores multiple ring chains that update at varying frequencies. A new block on a lower frequency ring chain stores an aggregation of data from the blocks that were added to a higher frequency ring chain in the time since the previous addition of a block to the lower frequency ring chain. Thus, a system of ring chains stores progressively summarized state transition data over progressively longer time intervals while maintaining immutability of the record and reducing storage requirements.
H04L 9/06 - Arrangements for secret or secure communicationsNetwork security protocols the encryption apparatus using shift registers or memories for blockwise coding, e.g. D.E.S. systems
H04L 43/08 - Monitoring or testing based on specific metrics, e.g. QoS, energy consumption or environmental parameters
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
36 - Financial, insurance and real estate services
42 - Scientific, technological and industrial services, research and design
Goods & Services
Financial services, namely, real-time execution, settlement and reporting services for asset managers related to currency trading; financial advisory and information services in the field of currency trading; multiple currency and foreign exchange transaction services offered to asset managers via an information delivery, transaction management, and reporting platform. Software as a service (SAAS) featuring an online, non-downloadable information delivery, transaction management and reporting software platform via a global computer network that enables asset managers to customize, execute, view, manage, and evaluate foreign exchange transactions, namely, currency trades; software as a service (SAAS) featuring an online, non-downloadable information delivery, transaction management and reporting software platform via a global computer network that enables asset managers to evaluate foreign exchange prices, maximize price netting opportunities across custodian accounts, and manage risk and liquidity related to foreign exchange transactions; software as a service (SAAS) featuring an online, non-downloadable information delivery, transaction management and reporting software platform via a global computer network that enables asset managers to manage and view investment accounts and portfolio information in real-time.
36 - Financial, insurance and real estate services
42 - Scientific, technological and industrial services, research and design
Goods & Services
(1) Financial services, namely, real-time execution, settlement and reporting services for asset managers related to currency trading; financial advisory and information services in the field of currency trading; multiple currency and foreign exchange transaction services offered to asset managers via an information delivery, transaction management, and reporting platform
(2) Software as a service (SAAS) featuring an online, non-downloadable information delivery, transaction management and reporting software platform via a global computer network that enables asset managers to customize, execute, view, manage, and evaluate foreign exchange transactions, namely, currency trades; software as a service (SAAS) featuring an online, non-downloadable information delivery, transaction management and reporting software platform via a global computer network that enables asset managers to evaluate foreign exchange prices, maximize price netting opportunities across custodian accounts, and manage risk and liquidity related to foreign exchange transactions; software as a service (SAAS) featuring an online, non-downloadable information delivery, transaction management and reporting software platform via a global computer network that enables asset managers to manage and view investment accounts and portfolio information in real-time
81.
Multi-modal-based generation of data synchronization instructions
In certain embodiments, multi-modal-based generation of settlement instructions may be facilitated. In some embodiments, a portfolio of a live environment may be emulated in a projected environment. A target portfolio may be generated in the projected environment based on the emulated portfolio. Partial synchronization between the target portfolio of the projected environment and the portfolio of the live environment may be performed such that a first subset of changes to the portfolio of the live environment are reflected in the target portfolio of the projected environment. Subsequent to the partial synchronization, the target portfolio of the projected environment may be updated such that the update of the target portfolio accounts for the first subset of changes. Subsequent to the update of the target portfolio, settlement instructions may be generated based on differences between the target portfolio of the projected environment and the portfolio of the live environment.
G06F 16/27 - Replication, distribution or synchronisation of data between databases or within a distributed database systemDistributed database system architectures therefor
The system uses the non-repudiatory persistence of blockchain technology to store all task statuses and results across the distributed computer network in an immutable blockchain database. Coupled with the resiliency of the stored data, the system may determine a sequence of processing tasks for a given processing request and use the sequence to detect and/or predict failures. Accordingly, in the event of a detected system failure, the system may recover the results prior to the failure, minimizing disruptions to processing the request and improving hardware resiliency.
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
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]
H04L 9/00 - Arrangements for secret or secure communicationsNetwork security protocols
G06N 3/084 - Backpropagation, e.g. using gradient descent
83.
Methods and systems for balancing loads in distributed computer networks for computer processing requests with variable rule sets and dynamic processing loads
Methods and systems are described for balancing loads in distributed computer networks for computer processing requests with variable rule sets and dynamic processing loads. The methods and systems may include determining an initial allocation of the plurality of processing requests to the plurality of available domains that has a lowest initial sum excess processing load. The methods and systems may then retrieve an updated estimated processing load for at least one of the plurality of processing requests and determine a secondary allocation of the plurality of processing requests to the plurality of available domains.
36 - Financial, insurance and real estate services
Goods & Services
Securities lending; securities financing; management of portfolios comprising securities; providing financial services with respect to securities and other financial instruments and products, namely, trading of and investments in securities and financial instruments and products for others; securities trading services for others via the Internet and the global information network; financial and investment services, namely, management and brokerage in the fields of stocks, bonds, options, commodities, futures and other securities, and the investment of funds of others; financial evaluation, tracking, analysis, forecasting, consultancy, advisory and research services relating to securities and other financial instruments; financial data analysis; financial information services provided online from a computer database or a global computer network, namely, providing information in the field securities trading.
36 - Financial, insurance and real estate services
Goods & Services
(1) Securities lending; securities financing; management of portfolios comprising securities; providing financial services with respect to securities and other financial instruments and products, namely, trading of and investments in securities and financial instruments and products for others; securities trading services for others via the Internet and the global information network; financial and investment services, namely, management and brokerage in the fields of stocks, bonds, options, commodities, futures and other securities, and the investment of funds of others; financial evaluation, tracking, analysis, forecasting, consultancy, advisory and research services relating to securities and other financial instruments; financial data analysis; financial information services provided online from a computer database or a global computer network, namely, providing information in the field securities trading.
42 - Scientific, technological and industrial services, research and design
Goods & Services
Providing online non-downloadable software for electronically trading securities; providing online non-downloadable software for securities lending; providing online non-downloadable software for analyzing financial data to predict securities lending rates and fee income on securities loans.
36 - Financial, insurance and real estate services
42 - Scientific, technological and industrial services, research and design
Goods & Services
Electronic financial trading services; securities trading and investing services for others via the internet; securities lending services; management of portfolios comprising securities; providing financial services with respect to securities and other financial instruments and products, namely, trading of and investments in securities and financial instruments and products for others; investment advisory services; financial administration of investment accounts; providing digitized financial and financial investment related information for use by others. Providing online non-downloadable software using artificial intelligence for use in securities trading and investing services and securities lending services; providing a website featuring non-downloadable software using artificial intelligence for use in securities lending, securities trading, and investing services; providing online non-downloadable software for use in securities trading, investing services, and securities lending services.
88.
Methods and systems for predicting successful data transmission during mass communications across computer networks featuring disparate entities and imbalanced data sets using machine learning models
Methods and systems for predicting successful data transmission during mass communications across computer networks featuring disparate entities and imbalanced data sets using machine learning models. For example, the methods and systems provide a prediction as to whether or not a communication will be successful prior to the transmission being sent. Moreover, in some embodiments, the methods and systems described herein provide probability of a successful transmission as a function of time. For example, the methods and system provide a probability of how likely a communication will succeed (or fail) if it is sent at various times. Additionally, in some embodiments, the methods and systems may alert a sender prior to the transmission of a communication that the transmission is likely to succeed or fail.
H04L 29/02 - Communication control; Communication processing
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
H04L 43/08 - Monitoring or testing based on specific metrics, e.g. QoS, energy consumption or environmental parameters
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]
42 - Scientific, technological and industrial services, research and design
Goods & Services
(1) Providing online non-downloadable software for electronically trading securities; providing online non-downloadable software for securities lending; providing online non-downloadable software for analyzing financial data to predict securities lending rates and fee income on securities loans
36 - Financial, insurance and real estate services
42 - Scientific, technological and industrial services, research and design
Goods & Services
(1) Electronic financial trading services; securities trading and investing services for others via the internet; securities lending services; management of portfolios comprising securities; providing financial services with respect to securities and other financial instruments and products, namely, trading of and investments in securities and financial instruments and products for others; investment advisory services; financial administration of investment accounts; providing digitized financial and financial investment related information for use by others
(2) Providing online non-downloadable software using artificial intelligence for use in securities trading and investing services and securities lending services; providing a website featuring non-downloadable software using artificial intelligence for use in securities lending, securities trading, and investing services; providing online non-downloadable software for use in securities trading, investing services, and securities lending services
91.
Methods and systems for balancing loads in distributed computer networks for computer processing requests with variable rule sets and dynamic processing loads
Methods and systems are described for balancing loads in distributed computer networks for computer processing requests with variable rule sets and dynamic processing loads. The methods and systems may include determining an initial allocation of the plurality of processing requests to the plurality of available domains that has a lowest initial sum excess processing load. The methods and systems may then retrieve an updated estimated processing load for at least one of the plurality of processing requests and determine a secondary allocation of the plurality of processing requests to the plurality of available domains.
A method for real-time data processing is described. The method being implemented on a computer system having one or more physical processors programmed with computer program instructions which, when executed, perform the method. The method comprising allocating a real-time dataset associated with a real-time data interaction to a node in a chain of nodes, wherein each node is representative of a user in the real-time data interaction; setting a node status of the node for the real-time dataset to pending; and independently of (i) a node status of the one or more upstream nodes and (ii) a node status of the one or more downstream nodes: periodically determining, by the computer system, an availability status of the node; and in response to the availability status satisfying the criterion, setting the node status for the real-time dataset as settled.
A system for processing performance prediction decisions includes one or more processors configured to execute one or more program modules. The modules are configured to receive, via the one or more processors, a prediction for an account at a prediction timestamp. The modules are also configured to identify, via the one or more processors, a prediction rule using attributes from the prediction. Responsive to the prediction rule having a network trigger associated therewith, the modules are configured to retrieve, via the one or more processors, a network trigger time associated with the network trigger, compare, via the one or more processors, the prediction timestamp to the network trigger time, and apply, via the one or more processors, a prediction decision based on the comparison of the prediction timestamp and the network trigger time. Applying the prediction decision includes determining a confidence level that a communication associated with the prediction will occur by a given time.
In some embodiments, a request may be received from a customer via an interface, wherein the request indicates (i) a social media stream associated with the customer to be monitored for instances of investment triggering content (ITC). A customer account of the customer may be configured based on the request. The social media stream may be monitored for instances of ITC based on the customer account configuration. Responsive to the monitoring detecting an instance of ITC, a first amount may be withdrawn from the customer account, and a purchase of a quantity of shares of an investment fund may be executed on behalf of the customer using the withdrawn first amount.
In certain embodiments, transaction-subset-assignment of computer processing nodes may be facilitated to perform collateral allocation. In some embodiments, a query may be performed to obtain a set of transactions. Computer processing nodes may be selected from a set of available nodes for performing collateral allocations. Collateral allocation rules associated with a lender may be obtained, and each of the computer processing nodes may be caused to perform collateral allocations for one subset of the transaction set in accordance with the collateral allocation rules by assigning transactions of the transaction set respectively to the computer processing nodes such that the computer processing nodes collectively perform collateral allocations for the transaction set. In some embodiments, each of the computer processing nodes may be configured to transmit parameter updates to be provided to the other computer processing nodes and perform its respective collateral allocation based on the other computer processing nodes' parameter updates.
Methods and system are described for optimizing data processing in cloud-based, machine learning environments. For example, through the use of a machine learning model utilizing a self organizing map and/or the use of specific processing nodes in a computer system to perform specific tasks the methods and system may more efficiently distribute tasks through a cloud computing environment and increase overall processing speeds despite increasing amounts of data. The methods and system described herein are particularly related to collateral allocation computer systems that automate the management of numerous collateral assets. For example, as the amount of collateral assets and the complexity of given transactions grow, typical allocation systems face frequent processing delays related to collateral allocations (e.g., allocations of collateral associated with Tri-Party Repos).
Methods and systems are described for implementing automated controls assessment through an application programming interface (“API”) driven software development kit. For example, the system may receive a response from an API-based agent to an automated controls assessment audit. The system may process the response, using a library of reusable features for controls assessment audits for a plurality of computer domains, to generate a result of the automated controls assessment audit. The system may then generate an outcome of the first automated controls assessment audit.
H04L 9/00 - Arrangements for secret or secure communicationsNetwork security protocols
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
36 - Financial, insurance and real estate services
Goods & Services
Multiple currency and foreign exchange transaction services, namely, providing, processing, facilitating, monitoring, tracking and collecting individual and batched payments and payments to and from financial account in multiple currencies; electronic payment processing services; clearing and reconciling payments in multiple currencies; financial advisory and information services in the field of multiple currency and foreign exchange payment transactions; multiple currency and foreign exchange transaction services offered via an information delivery, transaction management, and reporting platform.
99.
Methods and systems for predicting successful data transmission during mass communications across computer networks featuring disparate entities and imbalanced data sets using machine learning models
Methods and systems for predicting successful data transmission during mass communications across computer networks featuring disparate entities and imbalanced data sets using machine learning models. For example, the methods and systems provide a prediction as to whether or not a communication will be successful prior to the transmission being sent. Moreover, in some embodiments, the methods and systems described herein provide probability of a successful transmission as a function of time. For example, the methods and system provide a probability of how likely a communication will succeed (or fail) if it is sent at various times. Additionally, in some embodiments, the methods and systems may alert a sender prior to the transmission of a communication that the transmission is likely to succeed or fail.
The system uses the non-repudiatory persistence of blockchain technology to store all task statuses and results across the distributed computer network in an immutable blockchain database. Coupled with the resiliency of the stored data, the system may determine a sequence of processing tasks for a given processing request and use the sequence to detect and/or predict failures. Accordingly, in the event of a detected system failure, the system may recover the results prior to the failure, minimizing disruptions to processing the request and improving hardware resiliency.
H04L 29/06 - Communication control; Communication processing characterised by a protocol
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
