Abstract: Disclosed embodiments may include systems and methods for providing modified order recommendations. Line item data from a set of transactions associated with a vendor can be collected. The line item includes transactional data such as price, quantity, and frequency. The set of transactions are associated with a plurality of customers of the vendor. Correlations between line item data can be determined via one or more machine learning techniques to train a recommendation model. An order recommendation to a customer can be determine based on the correlations of the recommendation model. An order of the customer can be modified according to the order recommendation.

Abstract: Systems and methods of caller identification differentiation via machine learning techniques are disclosed. In one embodiment, an exemplary computer-implemented method may include: receiving a permission indicator identifying a permission by the user to detect calls being received by a computing device; receiving an indication of a current call being received; utilizing a trained call differentiation machine learning model to determine a likelihood that the current call is of a first call type or a second call type, where the first call type is associated with a first type of activity and the second call type is associated with a second type of activity.

Abstract: Systems and methods of caller identification differentiation via machine learning techniques are disclosed. In one embodiment, an exemplary computer-implemented method may include: receiving a permission indicator identifying a permission by the user to detect calls being received by a computing device; receiving an indication of a current call being received; utilizing a trained call differentiation machine learning model to determine a likelihood that the current call is of a first call type or a second call type, where the first call type is associated with a first type of activity and the second call type is associated with a second type of activity.

Abstract: Methods and systems are described herein for manipulating a communication acceptance screen, manipulating an interactive communication acceptance icon, and restricting access to accounts based on voice communication parameters. In particular, when a communication is detected from one device to another device, that communication may be risky. Thus, the risk is mitigated by giving a user information about the source of the communication to give a user a chance to reject the communication. In addition, in instances where the user accepts the communication, the system enables monitoring the communication and restricting any accounts that are disclosed within the communication.

Abstract: Systems and methods of anti-vishing OTP protection via machine learning techniques are disclosed. In one embodiment, an exemplary computer-implemented method may comprise: receiving a permission indicator identifying a permission by the user to detect OTPs and calls being received by a computing device; receiving an indication of an OTP data item being received; processing the OTP data item to determine a time duration during which a particular OTP included therein is valid; utilizing a trained OTP protection machine learning model to determine phone number(s) as presenting a security risk with respect to the OTP data item; and instructing the computing device to commence at least one security measure based at least in part on a contact list updated with an indication that the phone number(s) present a security risk with regard to the particular OTP during the time duration of the particular OTP.

Abstract: A processor may receive data and generate a quantified representation of the data by processing the data using at least one machine learning (ML) algorithm, the quantified representation of the data indicating a sentiment of content of the data. The processor may automatically revise the content of the communications data. The revising may include determining a reaction to the content of the communications data, generating a quantified representation of the reaction, determining a difference between the quantified representation of the reaction and the quantified representation of the communications data, identifying, based on the difference, a portion of the content having an unintended sentiment, and replacing the portion of the content with different content.

Abstract: Systems and methods of the present disclosure enable IoT-based social networking to detect, using IoT devices at a location, device signals associated with a first user device of a first user. A first user profile associated with the first user device is determined in response to the device signals. User attributes stored in the first user profile are accessed and extracted. A trained profile similarity model is used to determine similarity measures between the first user profile and other user profiles based on the user attributes. At least one similar user profile to the first user profile is identified based on the similarity measures. A meet-up point occupancy of each meet-up point at the location is determined based on a record of meet-up points to identify an open meet-up point. A meet-up recommendation is generated indicating the open meet-up point and the other user profiles.

Abstract: Example embodiments of systems and methods for card production are provided. A card may include processing circuitry including a chip and memory. The card may include one or more antennas in communication with the chip. The card may include a first layer of material aligned within a perimeter of the card via laminate encapsulation. The first layer of material may comprise a non-rectangular shape. The first layer of material may be offset with a shape of the card. The first layer of material may be in communication with the chip. The first layer of material may comprise at least one selected from the group of steel, tungsten, titanium, or any combination thereof. The card may be compliant with one or more form factors.

Abstract: A system may receive an indication that a user is accessing an ATM, receive, from the ATM, average session duration data over a predetermined period, generate, using a machine learning model, a busyness score for the ATM based on the average session duration data over the predetermined period, and determine whether the busyness score for the ATM exceeds a busyness score threshold. When the busyness score for the ATM does not exceed the busyness score threshold, the system may cause the ATM to present, via a first graphical user interface, a default ATM experience. When the busyness score for the ATM exceeds the busyness score threshold, the system may cause the ATM to present via, a second graphical user interface, a busy ATM experience.

Abstract: A method receiving an electronic data modification instruction of a virtual record modification to cause at least one data modification to at least one attribute of a virtual record data structure; determining at least one user record associated with the virtual record; determining at least one record modification preference associated with the at least one user record on the at least one modification condition; utilizing a record-specific modification machine learning model to predict at least one record-specific modification in response to the virtual record modification based on the one modification condition and the one record modification preference; generating an authorization instruction to authorize the electronic data modification instruction to cause the modification by instructing at least one database management system to modify the user record according to the record-specific modification; and instructing a computing device to display the record-specific modification to the at least one user reco

Abstract: In some embodiments, the present disclosure provides an exemplary method that may include steps of obtaining a permission from the user to monitor a plurality of activities executed within the computing device; continually monitoring the plurality of activities executed within the computing device for a predetermined period of time; identifying an indication of an incoming interaction session within the predetermined period of time; verifying at least one session interaction parameter associated with the incoming interaction session to identify the incoming interaction session as a repeat interaction session; dynamically retrieving at least one relation-specific notation from a plurality of relation-specific notations to display; instructing an input GUI element to display input data associated with the repeat interaction; automatically updating the plurality of relation-specific notations associated with the historical data relationship; and instructing at least one programmable output GUI to display a notif

Abstract: Systems and methods of anti-vishing OTP protection via machine learning techniques are disclosed. In one embodiment, an exemplary computer-implemented method may comprise: receiving a permission indicator identifying a permission by the user to detect OTPs and calls being received by a computing device; receiving an indication of an OTP data item being received; processing the OTP data item to determine a time duration during which a particular OTP included therein is valid; utilizing a trained OTP protection machine learning model to determine phone number(s) as presenting a security risk with respect to the OTP data item; and instructing the computing device to commence at least one security measure based at least in part on a contact list updated with an indication that the phone number(s) present a security risk with regard to the particular OTP during the time duration of the particular OTP.

Abstract: A system may transmit an MLM to one or more entities. The system may receive, from the one or more entities, data associated with a plurality of customers, the data generated by the MLM. The system may train a federated deep NN based on the data. The system may receive, from a user, a first request for a loan. The system may generate, using the federated deep NN, a risk score associated with the user, wherein the risk score comprises a likelihood the user will satisfy condition(s) of the loan. The system may transmit, to lender(s), the risk score and a second request for loan option(s) for backing the first request for the loan. The system may receive, from the first lender(s), the loan option(s) for backing the first request for the loan, and may transmit the loan option(s) to the user.

Abstract: A computing system retrieves historical transaction data associated with a plurality of users. The historical transaction data includes stock-keeping unit (SKU) level data. The computing system trains a first prediction model to identify transaction patterns across the plurality of users and relationships between items to each transaction based on the historical transaction data. The computing system accesses transaction data corresponding to a first user of the plurality of users. The computing system generates a second prediction model by fine-tuning the first prediction model based on the transaction data of the first user. The computing system receives inventory data corresponding to one or more merchants with which the first user has transacted. The computing system accesses a news feed to identify upcoming events or ongoing events. The second prediction model learns a baseline spending pattern of the first user based on the transaction data.

Abstract: The innovation disclosed and claimed herein, in one aspect thereof, comprises systems and methods of omni-channel dining experiences. The innovation receives a customer request from a customer to recreate at home a menu item from a restaurant. Restaurant data and a transaction history of the customer can be retrieved and analyzed to determine a recipe for the restaurant dish and a restaurant scene for recreating the dining experience of the restaurant. A set of ingredients based on the determined recipe is automatically ordered. The set of ingredients can be altered based on a number of people in the customer request. Multimedia instructions can be presented to the customer to follow the recipe to recreate the restaurant dish. A restaurant scene is generated and presented via a multimedia system. The restaurant scene can be a view or ambiance the recreates the experience of the restaurant within the customer's home.

Abstract: Systems, methods, and non-transitory computer readable medium disclosed herein relate to identity verification and authorization method. In one embodiment, the system can generate and send a message to a device associated with a user based on an initiated request from the user and a determination the user should be authenticated, wherein the message requests a content-based response from the user to authenticate the user. In another embodiment, the system can receive the content-based response from the user in reply to the message, wherein the content-based response comprises SMS (short message service) metadata, emoji, photo, video, audio, or a combination thereof. In another embodiment, the system can authenticate the user based on a determination of a confirmed match between the content-based response from the user and a response key preselected by the user.

Abstract: Systems and methods for secure prediction can include a memory and a processor that is configured to retrieve location information associated with a user account and calculate an average spend over one or more route options for a first period of time based on the location information. The processor can be configured to apply a predictive model to the average spend to determine a predicted utilization amount, identify one or more users that have a user utilization amount exceeding the predicted utilization amount, transmit one or more notifications to adjust the user utilization amount from a first value to a second value for a second period of time, and receive one or more responses that are responsive to the one or more notifications. The processor can be configured to adjust the user utilization amount to the second value and revert the user utilization amount back to the first value.

Abstract: Systems and methods are disclosed including determining that a first activity data of a completed activity record of a user comprises a predictive characteristic indicative of a potential rejection claim. The computing device produces a request for a second activity data of the completed activity, based at least in part on the first activity data. The computing device receives a plurality of completed activity records related to a plurality of other users and trains an entity-identifying machine learning model to identify a plurality of entity-identifying characteristics related to a plurality of known entities to obtain a trained entity-identifying machine learning model. The computing device applies, when the predictive characteristic is present, the trained entity-identifying machine learning model to identify a known-entity data related to the completed activity of the user. The computing device updates the completed activity record of the completed activity of the user.

Abstract: A system, processes, and a computer-readable storage medium are provided method includes receiving a search query including one or more of a textual phrase, a document, or data including metadata from a user. For example, a processor may execute a theme-generating machine learning algorithm to generate a theme of the search query. Profiles of other users are identified as similar to a profile of the user may be located. A search history of each of the located other users may be located in the collected search history. Content in the searchable content that corresponds to the generated theme of the search query and according to a result of the evaluation of the located search history of each of the other users may be obtained. The obtained content may be scored based on a scoring algorithm and output a set of scored search results to the input/output device.

Abstract: Disclosed herein are system, method, and computer program product embodiments for providing secure access to a locker of a locker system based on a token that includes token data and a message authentication code (MAC) to authenticate the token data. The token data includes at least the unique locker identifier for the locker. The MAC can be generated using a secret key. The secret key can be obtained based on a unique derivation key associated with a master key. The locker system can determine whether the token is valid based on the token data, the MAC, and the secret key. In response to a determination that the token is valid, the locker system can send a signal to unlock the locker.