Abstract: Embodiments are directed to a method for determining an interview script in a claims submission. The method may comprising receiving data relating to a claim being submitted, which may include claims submission data input by a user, information relating to the user, and one or more features. Data associated with the one or more features may be determined from an artificial intelligence model. A first score based on the data associated with the one or more features and data associated with the information relating to the user may be determined and used to determine an interview script. In one embodiment, questions in the interview script may continue to be provided to the interviewer computer if a continually updated score remains above a predetermined threshold. In another embodiment, the user may be routed to a live interview with a human representative if a continually updated score drops below a predetermined threshold.

Abstract: A method includes receiving, by a server computer, data of a communication device; training, by the server computer, a neural network model based on the data of the communication device and communication device metadata from one or more additional communication devices, to generate a machine learning model configured to determine, based on a metadata associated with an application, a security value related to an indication of a security threat; and transmitting the machine learning model to the communication device. The communication device can use the machine learning model to determine the security value, by inputting the metadata associated with the application, as a vectorized data into the machine learning model. The communication device can determine whether to run or install the application based upon the security value.

Abstract: Disclosed herein are compositions and methods for assaying for low volumes of proteins and/or nucleic acids, optionally in parallel.

Abstract: Embodiments are directed to a method for determining an interview script in a claims submission. The method may comprising receiving data relating to a claim being submitted, which may include claims submission data input by a user, information relating to the user, and one or more features. Data associated with the one or more features may be determined from an artificial intelligence model. A first score based on the data associated with the one or more features and data associated with the information relating to the user may be determined and used to determine an interview script. In one embodiment, questions in the interview script may continue to be provided to the interviewer computer if a continually updated score remains above a predetermined threshold. In another embodiment, the user may be routed to a live interview with a human representative if a continually updated score drops below a predetermined threshold.

Abstract: A disclosed method an analysis computer determining a rolling window associated with interaction data for interactions that occur over time. The analysis computer can retrieve interaction data for interactions occurring in the rolling window. The analysis computer can then generate pseudo interaction data based upon historical interaction data. The analysis computer can optionally embed the interaction data for the interactions occurring within the rolling window and the pseudo interaction data to form interaction data matrices. The analysis computer can then form a neural network model using the interaction data matrices, which is derived from the interaction data in the rolling window and the pseudo interaction data.

Abstract: A computer system includes a memory configured to store instructions, and one or more processors configured to execute the instructions to cause the computer system to perform a method for event detection. The method includes obtaining a user profile and a persona category associated with the user profile corresponding to a user; receiving first data associated with the user and second data associated with one or more environmental or situational factors; detecting an event based on the first data or the second data; and querying a database in response to the detected event to determine one or more recommended actions for the user based on the user profile and the persona category of the user.

Abstract: A method for the use of electronic transactional tokens includes: generating a plurality of transactional tokens including a first token; associating each of the transactional tokens with a plurality of users, the first token associated with a first user; monitoring usage of the transactional tokens in a plurality of transactions in a local region or network; and in response to receiving transactional data for the first transaction, updating the first token from a first state to a second state (e.g., to provide a benefit when the token is applied in the local region or network). The tokens may be generated and monitored using a token processing system, which uses transaction data received by a transaction handler that is handling transaction processing for the transactions.

Abstract: A first node on the blockchain storage system node may include data from other blocks on the blockchain used for blockchain verification and an additional node which may include an analysis element. The analysis element may include computer executable code for receiving data added to the blockchain, determining a risk score for the data added to the blockchain based on past performance and in response to the risk score being over a threshold, alerting members of blockchain of the risk score.

Abstract: A disclosed method an analysis computer determining a rolling window associated with interaction data for interactions that occur over time. The analysis computer can retrieve interaction data for interactions occurring in the rolling window. The analysis computer can then generate pseudo interaction data based upon historical interaction data. The analysis computer can optionally embed the interaction data for the interactions occurring within the rolling window and the pseudo interaction data to form interaction data matrices. The analysis computer can then form a neural network model using the interaction data matrices, which is derived from the interaction data in the rolling window and the pseudo interaction data.

Abstract: A system receives transaction data from payment devices of a user. The system generates a transaction profile of the user based on the received transaction data. Based on the transactional parameters, the system determines a community of the user. The system further receives one or more predefined sub-groups of the community from a database. The one or more sub-groups define subsets of activities, geographic locations, and times. The system identifies one or more rules setting limits on the transactional parameters. Absent receiving additional user interactions, the system generates a suggested community for the user as a function of the one or more rules, the activities, geographic locations, and times of the one or more sub-groups of the community. The system generates a suggested activity to the user based on the suggested community.

Abstract: Client devices can send access request messages to resource management computers to request access to a resource. A data security hub can provide centralized routing between different client devices, resource management computers, and authentication data processing servers. The data security hub can reduce the risk of sensitive authentication information from leaking (e.g., due to a breach) by limiting the amount or types of authentication information distributed to the data processing servers. The data security hub can limited the authentication information being distributed based on its sensitivity, the trust level of the client device, and the security level of the requested resource. The data security hub can also evaluate the client devices and data processing servers to identify security breaches and can cancel or reroute access requests accordingly. Thus, the data security hub can maintain resource security while better preserving the privacy of the client device's authentication information.

Abstract: Disclosed is an injection stretch blow molded (ISBM) container containing a surface having a static coefficient of friction (COF) of 0.15 to 0.21, a dynamic COF of 0.06 to 0.1, wherein the surface retains a water contact angle of 76° or higher for up to three minutes after wetting of the surface with a water drop of 14 to 16 mm diameter and the container is made with a polymeric composition containing a high density polyethylene (HDPE) having a dispersity (Mw/Mn) of 9 or higher as measured by GPC; a MI2 of 1 g/10 min or higher as measured by ASTM D-1238; 190° C./2.16 kg, as measured by ASTM D-1238; and an environmental stress crack resistance (ESCR) at 100% Igepal of >150 hours as measured by ASTM D-1693, B.

Abstract: A first node on the blockchain storage system node may include data from other blocks on the blockchain used for blockchain verification and an additional node which may include an analysis element. The analysis element may include computer executable code for receiving data added to the blockchain, determining a risk score for the data added to the blockchain based on past performance and in response to the risk score being over a threshold, alerting members of blockchain of the risk score.

Abstract: A disclosed method an analysis computer determining a rolling window associated with interaction data for interactions that occur over time. The analysis computer can retrieve interaction data for interactions occurring in the rolling window. The analysis computer can then generate pseudo interaction data based upon historical interaction data. The analysis computer can optionally embed the interaction data for the interactions occurring within the rolling window and the pseudo interaction data to form interaction data matrices. The analysis computer can then form a neural network model using the interaction data matrices, which is derived from the interaction data in the rolling window and the pseudo interaction data.

Abstract: A method for the use of electronic transactional tokens includes: generating a plurality of transactional tokens including a first token; associating each of the transactional tokens with a plurality of users, the first token associated with a first user; monitoring usage of the transactional tokens in a plurality of transactions in a local region or network; and in response to receiving transactional data for the first transaction, updating the first token from a first state to a second state (e.g., to provide a benefit when the token is applied in the local region or network). The tokens may be generated and monitored using a token processing system, which uses transaction data received by a transaction handler that is handling transaction processing for the transactions.

Abstract: Systems and methods described herein are for transmitting a command to a remote system. A processing system determines the identity of the user based on the unique identifier and the biometric information. Thereafter, a sensor detects a gesture performed by the user. The sensor is configured to detect the gesture performed by the user when the user is located within the detectable range of the wireless antenna. The processing system determines an action associated with the detected gesture based on the identity of the user and sends a command to a remote computer system to cause it to perform the action associated with the detected gesture.

Abstract: Client devices can send access request messages to resource management computers to request access to a resource. A data security hub can provide centralized routing between different client devices, resource management computers, and authentication data processing servers. The data security hub can reduce the risk of sensitive authentication information from leaking (e.g., due to a breach) by limiting the amount or types of authentication information distributed to the data processing servers. The data security hub can limited the authentication information being distributed based on its sensitivity, the trust level of the client device, and the security level of the requested resource. The data security hub can also evaluate the client devices and data processing servers to identify security breaches and can cancel or reroute access requests accordingly Thus, the data security hub can maintain resource security while better preserving the privacy of the client device's authentication information.

Abstract: A system and a method for providing intelligent friction by receiving user information based on an interaction of a user with a user interface; and providing intelligent friction through the user interface using an intelligent system. This may comprise changing the user interface relative to a baseline based on the user information. Intelligent friction may provide changes to the user interface. The system may increase security and prevent unauthorized access by bad actors. The system may be less susceptible to hacking by altering how verification or authentication is performed. Systems using intelligent friction may be easily implemented because they may be less reliant on user devices. Rather than requiring multiple communication channels as may be the case in multi-factor authentication, intelligent friction may be advantageously carried out with the user using mobile devices of low complexity.

Abstract: A system and a method for providing a digital intervention relating to user interactions. A system may have at least one processor configured to perform operations comprising: receiving input data from at least one client device; accessing a data model configured to determine, based on historical data, risk levels associated with the user interactions; inserting the input data into the data model; receiving, from the data model, an indication that at least one determined risk level associated with the user interactions exceeds a preset threshold; and providing, in response to the at least one determined risk level exceeding the preset threshold, the digital intervention.

Abstract: Computer executable instructions including code sections are received and compared to previously analyzed computer executable instructions. The code sections are then analyzed and assigned a risk score. If the risk score is over a threshold, an alarm may be communicated or the system may substitute computer executable instructions that may have been created according to a standard or have been previously approved.