Abstract: A machine-learning system which utilizes neural networks and other machine learning concepts to automatically categorize a stream of data. Known financial data can be analyzed and trained as part of a machine learning system. Then, a new set of a financial data for a particular user can be analyzed and automatic determinations can be made whether the user is a small business or consumer, and what industry the user is in.

Abstract: At least some embodiments are directed to an entity matching detection system. The entity matching detection system includes a latent similarity identification machine learning model that receives one or more data records and generates a final similarity score indicative of a latent similarity between the one or more data records and a second data record. The entity matching detection system can identify lexical and semantic similarities between attribute values and can analyze and compute similarity scores for direct-linked attribute values and cross-linked attribute values extracted from different data records.

Abstract: The present disclosure presents systems and related methods for creating real-time predictions. One such method comprises receiving, by a computing device, a first set of data and a second set of data, wherein the first set of data comprises a plurality of items available from a first source for a first set of users and the second set of data comprises transaction purchase data for a second set of users that have reward accounts, utilizing a predictive data model that determines a propensity score for a user from only behavior data that is not attributed to the user; receiving a third set of data from a third source comprising social media channel data for a third set of users; and updating the predictive data model to determine the propensity score for the user based at least in part on the third set of data.

Abstract: Aspects of the subject disclosure may include, for example, collecting network data for a plurality of cells of a cellular communication network, identifying a particular cell of the plurality of cells is in an un-responsive state, wherein the identifying is based on the network data, selecting a plurality of user equipment (UE) devices as UE test devices, forcing cell reselection attempts, handover attempts, or both, by the UE test devices to communicate with the particular cell, confirming the particular cell is in the un-responsive state based on failure of the cell reselection attempts or failure of the handover attempts, and communicating a restart signal from the processing system to cause the particular cell to restart to a responsive state. Other embodiments are disclosed.

Abstract: Systems and methods are described related to using flexible circuitry in a payment device. In one embodiment, a payment device comprises a computing device and a memory. The computing device is configured to store a biometric identifier associated with a transaction account. The computing device is configured to at least receive, by the antenna, an interrogation signal from a point of sale (POS) device in order to activate the payment device for a payment transaction. The computing device is configured to determine a scanned fingerprint identifier of a user based at least in part on the interrogation signal activating the payment device and authenticate the scanned fingerprint identifier by matching the scanned fingerprint identifier to the biometric identifier. The transaction account is transmitted to the POS device for processing the payment transaction based at least in part on the authentication of the scanned fingerprint identifier.

Abstract: A BLUETOOTH low energy mobile payments system may comprise a BLE beacon that is deployed at a merchant location. The BLE beacon may be associated with a specific merchant terminal. The BLE beacon may be configured to broadcast a unique beacon ID that is detectable by a user device. The user device may be configured to communicate the beacon ID to a payment system In response to receiving the beacon ID, the user device may initiate a payment between a user and a merchant, via a user device and a merchant terminal.

Abstract: A system and method for generating personalized content for a user device. In one example, a system can cause a computing device to generate a merchant score for a plurality of merchants based at least in part on merchant statistics and display a user interface on a user device of a user. The user interface includes a subset of the merchants that have been identified for a user based at least in part on the merchant score. Clickstream data can be received from the user interface displayed on the user device. An updated merchant score can be generated based at least in part on the user preference. A sorting habit for the user is determined based at least in part on a user-specified sort received from the user interface. A modified subset of the merchants is displayed on the user device based on the sorting habit.

Abstract: The systems and methods disclosed herein comprise computer-based platforms configured for automated early-stage application security monitoring for allowing users (e.g., application developers) to make decisions at the early stage of the application development.

Abstract: An architecture to avoid handover ping-pong for aerial user equipment (UE) operational in terrestrial fourth generation (4G) and/or fifth generation (5G) long term evolution (LTE) networks. A method can comprise receiving a request to connect to the network equipment from aerial user equipment; based on the request to connect, retrieving subscription data; based on the subscription data, determining that the aerial user equipment is aerial equipment; determining that the aerial user equipment is engaged in a ping-pong handover event with terrestrial based network equipment situated within a defined geographic area; and transmitting instructions to the aerial user equipment to confirm the ping-pong handover event, wherein the instruction comprises a delay value representing a back off time value for the aerial user equipment to refrain from gathering measurement data within the defined geographic area.

Abstract: A hybrid identity service system is disclosed. The system may receive a transaction request. The system may generate a first passcode and a second passcode. The system may decrypt a first encrypted private key with the first passcode to recover a private key. The system may sign the transaction request with the private key and may encrypt the private key with the second passcode to generate a second encrypted private key.

Abstract: Systems and methods for automated data extraction and adaptation are disclosed. The system may receive a data input from an external source using various different input channels. The system may determine a data quality of the data input by comparing data fields of the data input to known metadata in the system. The system may reformat the data input based on the comparison to a format consumable by downstream applications and services. The system may apply various machine learning operations on the data input including a descriptive analytics analysis, a predictive learning analysis, and/or a prescriptive intelligence analysis.

Abstract: Aspects of this disclosure may include identifying a priority set of beams from a set of beams associated with an area, wherein a first count of beams included in the priority set of beams is less than a second count of beams included in the set of beams, transmitting, based on the identifying, at least one message that includes an identifier of each beam included in the priority set of beams, subsequent to the transmitting of the at least one message, transmitting a first paging message directed to a communication device in association with a first beam included in the priority set of beams, obtaining a first paging response from the communication device based on the transmitting of the first paging message, and transmitting first payload data associated with a communication service to the communication device based on the obtaining of the first paging response. Other embodiments are disclosed.

Abstract: Aspects of the subject disclosure may include, for example, obtaining data, wherein the data includes first data that indicates an amount of mobility of a user equipment, second data that indicates a function that is facilitated as part of a communication service involving the user equipment, and third data that indicates a requirement pertaining to a quality of service associated with the communication service, based on the obtaining of the data, analyzing the data to establish rules in relation to paging involving beams of network infrastructure and the user equipment, and transmitting at least one message to the user equipment so that the user equipment is configured to adhere to the rules. Other embodiments are disclosed.

Abstract: Aspects of the subject disclosure may include, for example, providing, over a mobile network, first instructions to a first serving base station to indicate to a first communication device communicatively coupled to the first serving base station to obtain a beam bitmap of a group of base stations in proximity to the first serving base station. Further embodiments can include receiving, over the mobile network, the beam bitmap from the first serving base station, and determining a beam density for each of the group of base stations. Additional embodiments can include adjusting a neighbor list to include the beam density for each of the group of base stations resulting in an adjusted neighbor list. Other embodiments are disclosed.

Abstract: Aspects of the subject disclosure may include, for example, receiving, over a communication network, a measurement report from each of group of communication devices associated with a mobile network resulting in a group of measurement reports, and determining a first communication device of the group of communication devices requests a handover from a serving base station to another base station. Further embodiments can include selecting a receiving base station from a group of base stations based on the group of measurement reports, and providing instructions to the serving base station to handover the first communication device to the receiving base station. Other embodiments are disclosed.

Abstract: Aspects of the subject disclosure may include, for example, identifying a first communication device in idle mode, determining a first location associated with the first communication device, and determining a first mobility type associated with the first communication device. Further embodiments can include obtaining a neighbor list associated with a group of base stations in proximity to the first communication device, the neighbor list include a group of beam identifiers associated with each of the group of base stations. Additional embodiments can include providing first instructions to a serving base station associated with the first communication device indicating the serving base station to generate a beam priority table based on the first location of the first communication device, first mobility type associated with the first communication device, and the group of beam identifiers associated with each of the group of base stations. Other embodiments are disclosed.

Abstract: A system and method for automated remote payments between a vehicle and a refueling station is disclosed. The system may enable the vehicle to initiate an automated remote payment to the refueling station without needing the user to manually input transaction account information, or to manually prepay for the transaction. The refueling station may detect when the vehicle is in proximity, and the vehicle and the refueling station may open communications to transmit data. The vehicle may transmit vehicle identifying data to the refueling station, and the refueling station may communicate the vehicle identifying data to a payment network to authorize the transaction. In response to authorizing the transaction, the vehicle may proceed with refueling at the refueling station.

Abstract: A funds-transfer system enables a buyer to transfer funds to a seller via a mobile telephone. The system receives a payment request transmitted from a mobile telephone of the buyer. The payment request is for a transfer of funds from a financial account held by the buyer to a financial account held by the seller, and the payment request is transmitted from a payment application stored in the buyer's mobile telephone. The payment request identifies a purchase amount to be transferred, and information identifying the financial account held by the buyer. The system charges the purchase amount to the financial account held by the buyer and transfers the purchase amount to a financial account held by the seller.

Abstract: Disclosed herein are method, system, and computer product embodiments for generating a graphical user interface (GUI) to visually represent a programmatic process with various process objects. This may also include interacting with interfaces of the GUI to update the programmatic process. Process objects may contain a hierarchy of functions. Through interaction with the GUI registration interface a process object may be modified such that the hierarchy of functions is changed. Additional process objects may be defined based on a hierarchy of functions. The GUI may also include an analysis interface which may display timing data associated with execution of the programmatic process.

Abstract: Disclosed are various approaches for managing the status of machine-learning models using distributed ledgers. A registration request for a machine-learning model can be received. The registration request can include a model name for the machine-learning model, a version identifier for the machine-learning model, a network address from which the machine-learning model can be retrieved, a source code hash for a source code version of the machine learning model, and a runtime hash for a binary executable version of the machine-learning model. A registration identifier can then be created based at least in part on the source code hash and the runtime hash. Subsequently, an entry in the distributed ledger can be created for the machine-learning model. The entry can include the registration identifier, the model name, the model version, the network address, the source code hash, and the runtime hash.