Abstract: Disclosed herein are system, method, and computer program product embodiments for machine learning systems to process incoming call-center calls based on caller preferences. Text of historical interactive communications of a set of first callers are used to train one or more machine learning models to extract caller preferences. These trained models extract caller preferences for a second caller to generate a customer specific profile. An automated call center assistance system is configured to selectively route, based on the customer specific profile, a current call from the second caller to a call center agent and communicate one or more of the caller preferences for the second caller to the call center agent for consideration in an interactive communication during the current call.

Abstract: A batteryless device is disclosed. According to certain embodiments, the batteryless device may include a first communication system and a second communication system, the second communication system being a near-field-communication (NFC) system. The batteryless device may also include a power receiver coupled to the first communication system and configured to wirelessly receive power from an external device for powering the first communication system. The batteryless device may further include a controller configured to: when the first communication system is powered, establish, via the first communication system, a first wireless connection with a user device; receive, through the first wireless communication, a token from the user device; establish, via the second communication system, a second wireless connection with a terminal; and transmit, through the second wireless connection, the token to the terminal.

Abstract: In some embodiments, a system may obtain first feedback indicating one or more first signal-strength-related results corresponding with one or more first token interactions occurring in connection with presentation of one or more first components at one or more first locations of a user interface of a user device. Based on the first feedback, the system may obtain one or more second locations of the user interface and cause presentation of one or more second components at the one or more second locations of the user interface. The system may obtain second feedback indicating one or more second signal-strength-related results corresponding with one or more second token interactions. Using the first and second feedback, the system may update a user interface presentation location at which to present a component for a token interaction within a profile associated with a user of the user device.

Abstract: Computer-implemented methods and systems are provided for automatically performing a task on a remote computer. During a registration stage, the system receives personal information of a human user, obtains an IP address and a device configuration for a computing device, and stores the personal information, IP address, and the device configuration in record in a database. The system receives a request to interact with a remote website to perform a task, the request including the personal information of the human user. The system then retrieves the record from the database using the personal information. The system creates a virtual machine based on the device configuration for the computing device, selects one of a geographically distributed set of proxy servers having an IP geographically address resembling the IP address for the computing device, and executes instructions causing the virtual machine to interact with the remote website using the proxy server to perform the task.

Abstract: A computer-implemented method may include: receiving, from at least one camera, image data associated with a first user at a public access user computing device; detecting, based on the received image data, by employing a machine learning model trained using a dataset of actions collected from a plurality of previous users, that the first user has moved away from the public access user computing device; automatically encrypting, based upon the detection, a user session associated with the first user, wherein the encrypted user session is configured to be subsequently activated by the first user; and initiating a new generic user session on the public access user computing device for a second user.

Abstract: Systems, apparatuses, computer-readable media, and methods are provided for activating a contactless card. A server may transmit, to a web browser on a client device, a web page. The server may receive, from the web browser, a request to activate a contactless card, the request including a cryptogram. The server may decrypt the cryptogram based on a key associated with the contactless card. The server may transition, based on the decryption of the cryptogram, the contactless card from an inactive payment state to an active payment state. The server may transmit, to the web page and based on the transition of the contactless card from the inactivated payment state to the activated payment state, an indication to activate a payment applet of the contactless card.

Abstract: Disclosed herein are system, method, and computer program product embodiments for determining a probability of a device at risk. The device may be associated with a plurality of security parameters. For a security parameter, the device can be in multiple states. A probability value corresponding to a security parameter can indicate the security parameter being in a state among the multiple states. A probabilistic graphical model may be used to represent dependences of the plurality of security parameters. A device security risk prediction module may determine a probability of the device at risk based on the probabilistic graphical model and the probability assignments to the plurality of nodes of the probabilistic graphical model, and further determine a user action instruction to be provided to a user of the device based on the probability of the device at risk.

Abstract: Disclosed herein are system, method, and computer program product embodiments for protecting a device from skill squatting by verifying the legitimacy of a skill to a user before operating the skill based on user instructions. Upon receiving an audio signal from a user to operate a skill on a device, the skill can retrieve unique and non-confidential information related to the user or the device, and further present the unique and non-confidential information to the user such that the information is used to verify the legitimacy of the skill. Upon a verification of the legitimacy of the skill, the user can operate the skill by voice or audio instructions.

Abstract: Embodiments disclosed herein generally related to a method and system for customizing a customer experience. In one embodiment, a method is provided herein. A computing system receives from a computing device positioned in a facility one or more video streams. The one or more video streams capture a customer in the facility. The computing system identifying an identity of the customer by parsing the one or more video streams to identify one or more audio or visual cues of the customer. The computing system determines, based on the identity of the customer, that the customer has one or more previous transactions at the facility. The computing system predicts, based on the one or more previous transactions, a new transaction at the facility. The computing system notifies the computing device positioned in the facility in preparation of the new transaction.

Abstract: A computer-implemented method for processing items or services in a container may include: initiating execution of a web module or an application installed on a user device based on user input; identifying an alternative for an item or service in the container; providing a user interface including: first information related to the item or the service in a first portion, second information related to alternative in a second portion, and one or more user interface elements overlaying the first portion or the second portion; detecting input to the one or more user interface elements, the input including: a command to cycle through display of the alternative item or service, selection of the alternative item or service, and/or an indication of a preference for the alternative item or service; and performing one or more actions based on the input.

Abstract: Described are methods and systems are for generating dynamic conversational queries. For example, as opposed to being a simply reactive system, the methods and systems herein provide means for actively determining a user's intent and generating a dynamic query based on the determined user intent. Moreover, these methods and systems generate these queries in a conversational environment.

Abstract: In some aspects, a computing system may train a machine learning model to classify a plurality of samples of a training dataset. The computing system may generate a plurality of counterfactual samples. The computing system may determine a distance score between the training dataset and a first counterfactual sample of the plurality of counterfactual samples. Based on determining that the distance between the training dataset and the first counterfactual sample is smaller than other distances corresponding to other counterfactual samples of the plurality of counterfactual samples, the computing system may generate a recommendation to use the first counterfactual sample.

Abstract: Disclosed herein are system, method, and computer program product embodiments for categorizing customer complaints on social media using a model trained on customer voice calls or chats with agents. Additionally, users interested in monitoring regulatory compliance issues based on customer complaints can receive notifications regarding complaints that are linked to regulatory topic areas, without the need to manually scan vast numbers of social media postings.

Abstract: Systems and methods disclosed herein provide automatic expense categorization of transactions or expenditures based on a machine learning (ML) model trained using anonymized transactional data for expenditures that are stored in a public blockchain. The anonymized transactional data for the expenditure and the expense category, may be distributed throughout the blockchain network and recorded in the blockchain. In some implementations, an expenditure may be submitted to the blockchain network for expense categorization. The transactional data for the expenditure may be anonymized to remove confidential and personal identifying information (PII) before it is distributed throughout the blockchain network. Each participating node of the blockchain network may utilize the ML model to identify an expense category for the expenditure. The participating nodes may provide a consensus mechanism in order to arrive at a shared understanding of how to categorize the expenditure.

Abstract: The packet capture manager uses a multi-tiered storage for storing captured network traffic. Captured packets are stored on a primary storage with a time-to-live according to a retention policy. The packet capture manager receives instructions from one or more network monitoring devices identifying one or more captured packets as packets of interest. The packet capture manager flags the identified packets as packets of interest, moves the flagged packets to a secondary storage, and changes the TTL of the moved packets. A machine learning model analyzes historical data of the instructions received from the one or more network monitoring devices. The packet capture manager uses the machine learning model to identify packets of interest and move identified packets to the secondary storage without specific instructions from a network monitoring device.

Abstract: Methods and systems disclosed herein describe generating virtual backgrounds for video communications. A virtual background generator may monitor a user's calendar and/or inbox for meetings. The virtual background generator may analyze the context of calendar invites and/or scheduled meetings to generate one or more virtual backgrounds for a video conference. A first background may be displayed for the video conference. Additionally, the virtual background generator may update the virtual background based on an analysis of one or more topics being discussed during the video conference. Based on the analysis of the one or more topics being discussed, the virtual background generator may generate a second virtual background to replace the first virtual background.

Abstract: In some implementations, a system may obtain document lineage training data associated with a plurality of historical documents and corresponding lineage data of independent historical documents of the plurality of historical documents. The system may train, based on the document lineage training data, a lineage analysis model to determine a lineage of edited sections of a source document. The system may receive a plurality of document files that correspond to a plurality of versions of a document. The system may determine, using a similarity analysis model, that a first section from a first version of the plurality of versions corresponds to a second section from a second version of the plurality of versions. The system may determine, using the lineage analysis model, a lineage of a corresponding section of the document that is associated with the first section and the second section.

Abstract: Example embodiments of systems and methods for data transmission system between transmitting and receiving devices are provided. In an embodiment, each of the transmitting and receiving devices can contain a master key. The transmitting device can generate a diversified key using the master key, protect a counter value and encrypt data prior to transmitting to the receiving device, which can generate the diversified key based on the master key and can decrypt the data and validate the protected counter value using the diversified key.

Abstract: A computer-implemented method for determining alternative plans for a user may include: determining an event status of an event based on one or more parameters, wherein the event status comprises a target event indicator, wherein the one or more parameters comprise event population; obtaining event data of a target event; obtaining transactional data of the user, wherein the transactional data includes transaction time and transaction location; determining, via one or more processors, a likelihood of a potential transaction of the user associated with the target event by processing the transactional data and the event data using a trained machine learning model; and transmitting, to the user, a notification based on the determined likelihood of the potential transaction, wherein the notification includes a suggestion for alternative travel plans of the user.

Abstract: A system including: one or more processors; a memory storing instructions that, when executed by the one or more processors are configured to cause the system to receive primary and secondary user account data. The system generates one or more predictive model systems based on the primary and secondary user account data. The system receives a first input from the primary user corresponding to a first spending limitation for the secondary user. The system identifies a first transaction of the secondary user exceeding the spending limitation and determines using the one or more predictive model systems whether to authorize a spending limitation override. The system automatically authorizes the spending limitation override when the first transaction exceeds the spending limitation by less than a predetermined threshold. The system can also identify and automatically fund recurring transactions with an associated funding account using the one or more predictive model systems.