Abstract: An example described herein may involve receiving a temperature measurement associated with an input component, wherein the temperature measurement is received from a temperature sensor, and wherein the temperature measurement indicates a temperature of an input element of the input component; determining that the temperature of the input element satisfies a threshold temperature; and causing an infrared element to emit infrared light in association with a position of the input component. While the infrared element is emitting the infrared light, capture information associated with a user interacting with the input component may be obfuscated.

Abstract: Aspects described herein may relate to methods, systems, and apparatuses that provide new capabilities for recommending purchases to a user based on the user's past purchasing history and the purchase history of others. A new descriptor referred to as “purchase embeddings” is disclosed, which are data records in a new multi-dimensional space for describing and tracking purchases of goods and services.

Abstract: Methods and systems for parallel processing of batch communications during data validation using a plurality of independent processing streams. For example, the system may receive a plurality of communications for batch processing during a predetermined time period. The system may process, with a batch configuration file, a first alphanumeric data string of a first communication of the plurality of communications. The system may process, with the batch configuration file, a second alphanumeric data string of a second communication of the plurality of communications. The system may direct the first communication to a first micro-batch for processing within the predetermined time period based on the first metadata tag, wherein the first micro-batch is processed using a first validation and enrichment protocol and a first micro-batch configuration file, wherein the first validation and enrichment protocol and the first micro-batch configuration file are specific to the first source.

Abstract: Aspects described herein may allow an automated generation of an interactive multimedia content with annotations showing vehicle damage. In one method, a server may receive vehicle-specific identifying information of a vehicle. Image sensors may capture multimedia content showing aspects associated with exterior regions of the vehicle, and may send the multimedia content to the server. For each of the exterior regions of the vehicle, the server may determine, using a trained classification model, instances of damage. Furthermore, the server may generate an interactive multimedia content that shows images with annotations indicating instances of damage. The interactive multimedia content may be displayed via a user interface.

Abstract: A monitoring platform may obtain information that identifies a product or service and may collect one or more reviews associated with the product or service from a plurality of sources, wherein each review includes respective review information. The monitoring platform may process the one or more reviews to determine respective additional review information associated with each review of the one or more reviews. The monitoring platform may select, using a machine learning model, a particular review, of the one or more reviews, based on the review information and the additional review information associated with the one or more reviews. The monitoring platform may cause display, on a display of a client device, of a prompt for a response to the particular review and may obtain the response from the client device. The monitoring platform may cause the response to be posted to a source associated with the particular review.

Abstract: The present disclosure provides systems and methods for synthetic data generation. A recurrent neural network can be trained for synthetic data generation by obtaining a sequence of elements and determining, using a classifier, that the sequence corresponds to a token. In response to the determination, a recurrent neural network configured to use a first vocabulary including the elements can be modified to use a second vocabulary, the second vocabulary including the token and the first vocabulary. The modified recurrent neural network can be trained using the token and the sequence of elements. The trained recurrent neural network can be used to generate synthetic data. A classifier can detect sequences of elements in the synthetic data corresponding to tokens. The tokens can replace the sequences of elements in the generated synthetic data and can be provided to the trained recurrent neural network to continue synthetic data generation.

Abstract: Methods and systems for connecting data with non-standardized schemas in connected graph data exchanges. For example, the system generates a custom data structure corresponding to a user identifier for a user profile that includes pointers between user profile attributes (e.g., individual fields/categories within the user profile) and existing assets in a connected graph (e.g., an existing application, software profile for an application, data set, connections, etc.). The system then connects the custom data structure corresponding to the user identifier to the existing assets in the connected graph.

Abstract: Methods and systems are described herein for a resource allocation system. The resource allocation system may obtain a corresponding quantity of resources (e.g., memory, processor, storage, etc.) needed to be allocated for each resource class (e.g., for a given performance class) for a particular time period (e.g., for one month). Furthermore, the resource allocation system may track allocation of each class of resources and may predict that some classes of resources will be oversubscribed. Based on the prediction, the resource allocation system may, using a machine learning model, identify supplemental classes for each resource class predicted to be oversubscribed and generate a warning when a resource of a supplemental class is predicted to be used.

Abstract: Methods and systems are described herein for improving anomaly detection in timeseries datasets. Different machine learning models may be trained to process specific types of timeseries data efficiently and accurately. Thus, selecting a proper machine learning model for identifying anomalies in a specific set of timeseries data may greatly improve accuracy and efficiency of anomaly detection. Another way to improve anomaly detection is to process a multitude of timeseries datasets for a time period (e.g., 90 days) to detect anomalies from those timeseries datasets and then correlate those detected anomalies by generating an anomaly timeseries dataset and identifying anomalies within the anomaly timeseries dataset. Yet another way to improve anomaly detection is to divide a dataset into multiple datasets based on a type of anomaly detection requested.

Abstract: A synthetic identity network for detecting synthetic identities may receive a first request for credit including one or more user attributes, compare the one or more user attributes to one or more stored user identities, create a new user identity, flag the new user identity as a potentially synthetic identity based on comparing the one or more user attributes to the one or more stored user identities, receive a second request for credit including or more second user attributes, compare the one or more second user attributes to the one or more user attributes associated with the potentially synthetic identity, prepare a notice including the potentially synthetic identity and a credit request identifier, and transmit the notice to one or more servers.

Abstract: A method may include: providing a browser extension to user devices that is configured to: monitor data associated with website pages visited by the user devices; and in response to detecting that data associated with a website page is indicative of a presence of a platform, transmit a notification to an entity system, the notification including an identification of the website page and an identification of the platform; and in response to receiving the notification: generating and/or updating a database indicative of operation of the platform on one or more website pages based on the identification of the website page and the identification of the platform; quantifying a number of website pages on which the platform is operated based on the database; and determining a level of traffic to the website page.

Abstract: Systems and methods related to integrating blockchain functions with external systems using a variable non-fungible token. A variable non-fungible token comprises a non-fungible token that is minted with an indication of a non-transferable ownership wallet (e.g., a digital wallet for which the non-fungible token is minted for), a non-transferable smart contract address that has permissions for updating metadata for the variable non-fungible token, and a metadata property threshold based on a specific user corresponding to the digital wallet.

Abstract: Systems and methods described herein may generate a temporary location-associated cryptographic identification code (which can be used to uniquely identify the user) based on detection of the user at the associated location. As an example, such systems and methods allow for the user to be identified (e.g., as part of an authentication process) when the user visits a location without having to share personally identifiable information with any other users or computing systems at the location. Additionally, or alternatively, such systems and methods enable the practical use of shorter cryptographic identification codes as a primary identifier (or sole identifier) at the location for retrieving data related to the user (e.g., by avoiding or mitigating collision or security risks typically associated with short identifiers).

Abstract: Systems and methods related to a variable non-fungible token that dynamically update metadata during blockchain functions. A variable non-fungible token comprises a non-fungible token that is minted with an indication of a non-transferable ownership wallet (e.g., a digital wallet for which the non-fungible token is minted for), a non-transferable smart contract address that has permissions for updating metadata for the variable non-fungible token, and a metadata property threshold based on a specific user corresponding to the digital wallet.

Abstract: Disclosed are examples of a method, a wearable device and a system enabling authentication of a user of a payment account or completion of a purchase transaction via signals generated by the wearable device. A process may include a wearable device generating a modulated signal using an encryption algorithm. The modulated signal may contain authentication information related to the wearable device including a cryptographic authentication message. The modulated signal is output to a biological medium interface of the wearable device that is coupled to a biological medium of a wearer of the wearable device. The biological medium is operable to conduct the modulated signal. A receiving device processes including decrypting and demodulating the modulated signal received from the biological medium. Using the demodulated signal, the authentication information transmitted from the wearable device is obtained. Based on the obtained authentication information, a transaction may be authorized, or a user authenticated.

Abstract: A computing system may use time series data and machine learning to attribute power consumption to various devices in a location. The computing system may obtain time series data indicating a total amount of electricity being used at a location over time. The computing system may use a machine learning model, which has been trained to recognize devices based on an amount of electricity being used, to identify the devices at the location and determine how much electricity each device is using. Further, the computing system may use the machine learning model to detect changes in electricity consumption that may enable determination of devices that need to be repaired, turned on, or reconnected to the power source.

Abstract: Systems and methods related to a variable non-fungible token. A variable non-fungible token comprises a non-fungible token that is minted with an indication of a non-transferable ownership wallet (e.g., a digital wallet for which the non-fungible token is minted for), a non-transferable smart contract address that has permissions for updating metadata for the variable non-fungible token, and a metadata property threshold based on a specific user corresponding to the digital wallet.

Abstract: Methods and systems are described herein for bypassing secondary tiers of authentication for particular security categories. An authentication system, when authenticating a user, may receive an authentication request with authentication data enabling authentication through a multi-tier authentication mechanism. When the request has been authenticated through a multi-tier authentication mechanism, the authentication system may identify a category associated with the request and generate a temporal unlock flag for that category of future requests, such that the temporal unlock flag indicates that multi-tier authentication is not required for a predetermined amount of time for requests of that category. The temporal unlock flag may be inserted into the user's record. When future requests of that same category are received, only a single-tier authentication mechanism may be required for authentication.

Abstract: Disclosed herein are system, method, and device embodiments for time-based trust token (TBTT) depreciation. In an example embodiment, a service provider system (e.g., a service provider and API service) may receive a connection request including a demographic attribute associated with a first client account from a partner device, match the demographic attribute to client information associated with the first client account, send the partner device a connection request identifier and a URL including a depreciating token, and authenticate a second client account via a login page associated with the URL.

Abstract: Methods and systems are described herein for providing multi-tier machine learning model processing for document authenticity. A document authentication system built based on the current disclosure may rely on a camera to capture an image of a document and use a multi-tiered machine learning infrastructure to identify security features associated with the image of the document and determine based on those features whether the document is authentic. Furthermore, using the disclosed methods and system enable the provider of the machine learning model to improve the document authentication system by training the multi-tier machine learning model based on millions of interactions collected as part of processing. In addition, the document authentication system enables tracking where/when particular instances of documents are scanned. Based on the tracking, the document authentication system may further identify instances of documents that are not authentic.