Capital One Services, LLC

Fardin Abdi Taghi Abad

Austin Grant Walters

Jeremy Edward Goodsitt

Vincent Pham

Anh Truong

Reza Farivar

Mark Watson

Kenneth Taylor

Kate Key

Mark Louis Watson

Noriaki Tatsumi

Ahn Truong

Michael Walters

Robert P Seaton

Anh Troung

An exemplary system, method, and computer-accessible medium can include, for example, establishing a unique rule-identifier in one-to-one correspondence with at least one set of unknown time-variable rules against which data is to be made compliant, obtaining at least one set of data marked compliant against the one or more set of rules, obtaining meta-data from the compliant data, obtaining at least one set of data marked non-compliant against the set of unknown time-variable rules, extracting meta-data from the non-compliant data, joining the set of compliant and non-compliant metadata to generate a set of estimated rules corresponding to the rule-identifier based at least one of (i) the meta-data of the joined set and (ii) machine learning algorithms.

System, method, and computer-accessible medium to verify data compliance by iterative learning

A computer-implemented method includes tracking, by a computing device, user browsing activity of a first page having known elements; mapping, by the computing device, the user browsing activity to the known elements; storing, by the computing device, mapping information that maps the user browsing activity to the known elements; tracking, by the computing device, user browsing activity of a second page having unknown elements; identifying, by the computing device, the unknown elements based on the mapping information and the user browsing activity of the second page; and executing, by the computing device, one or more computer-based instructions based on the determining the unknown elements that were identified.

Identifying web elements based on user browsing activity and machine learning

A cloud computing system can be configured to generate a synthetic data stream that tracks a reference data stream. A model optimizer of the cloud computing system can receive, from an interface of the cloud computing system, a synthetic data stream request indicating a reference data stream. A dataset generator of the cloud computing system can generate a synthetic data stream that tracks the reference data stream by repeatedly swapping data models of the reference data stream. One such repeat can include retrieving, by the dataset generator from a model storage, a current data model of the reference data stream and generating a new data model of the reference data stream. The model optimizer can store the new data model in the model storage. The dataset generator can generate a synthetic data stream using the current data model of the reference data stream.

Systems and methods for data stream simulation

Techniques for providing a secure mesh network based on a cryptocurrency blockchain are provided. An electronic device can receive data from a mesh network to establish the device as a node on the mesh network. The established node can generate a cryptocurrency wallet that can be loaded with an amount of cryptocurrency. The established node can receive a payment for facilitating transfers on the mesh network including routing transactional or communication data. Public and/or private keys associated with the cryptocurrency wallets can be used to encrypt communications, thereby providing a secure mesh network. Further, the blockchain, which can be managed by one or more authorized nodes of the mesh network, can provide a payment system within the mesh network. The distributed nature of the mesh network allows it to operate securely without a central authority. In turn, the mesh network is more adaptable and flexible.

System and method for creating a secure mesh network utilizing the blockchain

Techniques for providing a localized blockchain that uses a mesh network to facilitate transactions for a localized event are provided. An electronic device can receive data from a localized mesh network to establish the device as a node on the localized mesh network. The established node can generate a cryptocurrency wallet that can be loaded with an initial amount of localized cryptocurrency tokens from a total amount of localized cryptocurrency tokens. The total amount of localized cryptocurrency tokens can be previously mined by a central authority of the localized mesh network. The established node can conduct transactions within the localized mesh network utilizing the localized cryptocurrency tokens. The established node can receive a portion of a cryptocurrency token for transmitting transactional data to a next nearest node on the localized mesh network to help facilitate a transaction involving other nodes on the network.

Localized blockchain utilizing mesh networks for localized events

A system for returning synthetic database query results. The system may include a memory unit for storing instructions, and a processor configured to execute the instructions to perform operations comprising: receiving a query input by a user at a user interface; determining, based on natural language processing, a type of the query input; determining, based on the received query input and a database language interpreter, an output data format; returning, based on a generation model and the output data format, a result of the query input; providing, to a plurality of training models and based on the determined query type, the query input and the result; and training the training models, based on the query input and the result.

Systems and methods for synthetic database query generation

Systems and methods for censoring text-based data are provided. In some embodiments a censoring system may include at least one processor and at least one non-transitory memory storing application programming interface instructions. The censoring system may be configured to perform operations comprising storing a target pattern type and a computer-based model for identifying a target data pattern corresponding to a target pattern type within text based data. The censoring system may also be configured to receive text-based data by a server, and to retrieve the stored target pattern type to be censored in the text-based data. The censoring system may be configured to identify within the received text-based data, a target data pattern corresponding to the retrieved target pattern type. The censoring system may be configured to censor target characters within the identified target data pattern, and transmit the censored text-based data to a receiving party.

Systems and methods for censoring text inline

A computer-implemented method includes determining, by a processor based on a histogram of an environment, a most prevalent color of a plurality of prevalent colors associated with the environment by mapping the most prevalent color and the plurality of prevalent colors according to a red-green-blue (RGB) colorspace. The processor determines a related plurality of colors by determining at least one set of color coordinates for each one of the most prevalent color and the plurality of prevalent colors according to the another colorspace and determines at least one set of color coordinates for the related plurality of colors according to the another colorspace. A matrix barcode is generated using the related plurality of colors.

System and method using a histogram and colorspaces to create a matrix barcode having a plurality of colors

A system for obtaining user parameters of e-commerce users to auto complete checkout forms includes one or more processors configured to monitor user interactions of a user with a merchant website on a user device; determine an intent to purchase one or more items based of the monitoring; determine an unfilled template of a checkout form associated with the merchant website based on previously stored website information, wherein the unfilled template of the checkout form comprises a plurality of checkout form fields; determine one or more user parameters associated with the user device; apply a machine learning algorithm to predict a geolocation of the user device based on the one or more user parameters; and auto fill the at least one of the plurality of checkout form fields in the unfilled template of the checkout form based on the predicted geolocation.

System, method, and non-transitory computer-readable media for obtaining user parameters of e-commerce users to auto complete checkout forms

The disclosed methods may receive a first request for access to a first system memory from a new user and a first justification, the first request includes first role information and first current access information of the new user, determine whether the first request is within a request cluster based on first role history information and current access history information. When the first request is within the request cluster, generate a matrix from the first role information and the first current access information, and determine using a first neural network, whether to grant the first request based on the matrix. When the first request is granted by the first neural network, determine, using a second neural network, whether the first justification is similar to first justification history information. When the first justification is similar to the first justification history information, grant the new user access to the first system memory.

Systems and methods for processing requests for access

An exemplary system, method and computer-accessible medium can include receiving information regarding a purchase by a person, receiving financial account information regarding a financial account associated with the person that was used to fund the purchase, generating rewards information associated with the purchase, generating an authentication token(s) based on the financial account information, sending the rewards information and the authentication token(s) over a network, and receiving a confirmation, over the network, that a rewards account associated with the person has been updated.

System, method and computer-accessible medium for authentication of rewards information

Systems and methods for censoring text characters in text-based data are provided. In some embodiments, an artificial intelligence system may be configured to receive text-based data and store the text-based data in a database. The artificial intelligence system may be configured to receive a list of target pattern types identifying sensitive data and receive censorship rules for the target pattern types determining target pattern types requiring censorship. The artificial intelligence system may be configured to assemble a computer-based model related to a received target pattern type in the list of target pattern types. The artificial intelligence system may be configured to use a computer-based model to identify a target data pattern corresponding to the received target pattern type within the text-based data, identify target characters within the target data pattern, and to assign an identification token to the target characters.

Systems and methods for removing identifiable information

Exemplary embodiments may use word embeddings to enhance scanning of programming code scripts for sensitive subject matter, such as confidential subject matter. The scanning may be performed by a neural network in some exemplary embodiments. The neural network initially may be trained on a corpus of programming code scripts to identify keywords relating to sensitive subject matter, such as passwords, tokens or credentials. The neural network may not only identify instances of the keywords but also may identify related terms as well. The output of the scan may be a ranked list of terms in the programming code script that may relate to sensitive subject matter.

Use of word embeddings to locate sensitive text in computer programming scripts

Systems and methods for generating synthetic intercorrelated data are disclosed. For example, a system may include at least one memory storing instructions and one or more processors configured to execute the instructions to perform operations. The operations may include training a parent model by iteratively performing steps. The steps may include generating, using the parent model, first latent-space data and second latent-space data. The steps may include generating, using a first child model, first synthetic data based on the first latent-space data, and generating, using a second child model, second synthetic data based on the second latent-space data. The steps may include comparing the first synthetic data and second synthetic data to training data. The steps may include adjusting a parameter of the parent model based on the comparison or terminating training of the parent model based on the comparison.

Hierarchical system and method for generating intercorrelated datasets

Logic may adjust communications between customers. Logic may cluster customers into a first group associated with a first subset of synonyms and a second group associated with a second subset of the synonyms. Logic may associate a first tag with the first group and with each of the synonyms of the first subset. Logic may associate a second tag with the second group and with each of the synonyms of the second subset. Logic may associate one or more models with pairs of the groups. A first pair may comprise the first group and the second group. The first model associated with the first pair may adjust words in communications between the first group and the second group, based on the synonyms associated with the first pair, by replacement of words in a communication between customers of the first subset and customers of the second subset.

Growing community of inventors

Champaign, IL, United States of America

Fardin Abdi Taghi Abad