Abstract: A method for signal modulation includes: receiving network signals from a plurality of participants; analyzing the received network signals to detect latent signals; processing the network signals based on one or more external event data; processing the network signals to exclude sensitive data in the network signals and the latent signals; and encapsulating pan-network signals based on the processed network signals.

Abstract: A method for event detection includes: obtaining a subpopulation data from a graph structure for performing a graph analysis, wherein the subpopulation data is associated with personality and demographic characteristics of users; obtaining a user profile associated with a target user; inferring psychological traits of the user by performing the graph analysis based on the user profile and the subpopulation data; performing an outcome linkage analysis based on labeled event outcome profiles and the inferred psychological traits to generate personalized knowledge graph data associated with the target user; and profiling, monitoring, or performing an anomaly detection for event data streams based on the personalized knowledge graph data.

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.

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: A method is disclosed and includes receiving, by a first communication device, a first local authentication model, the first local authentication model being derived from a master authentication model at a remote server computer, and receiving a request to perform an interaction with a second communication device, the interaction being performed in an offline manner. The method may further include applying, by the first communication device, the first local authentication model to the interaction to determine a first authentication result and determining whether or not to allow the interaction to proceed based upon the first authentication result. The method may also include updating the first local authentication model using the master authentication model when the first communication device is online.

Abstract: Computer-implemented systems and methods are disclosed herein for use within secure multi-party computation. A system and method are used for storing an operation preference and a cryptographic preference. A data set is stored based on the operation preference and the cryptographic preference. A determination is made that processing the query involves performing an allowable operation on the data set based on the operation preference.

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: 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: Computer-implemented systems and methods are disclosed herein for use within secure multi-party computation. A system and method are used for storing an operation preference and a cryptographic preference. A data set is stored based on the operation preference and the cryptographic preference. A determination is made that processing the query involves performing an allowable operation on the data set based on the operation preference.

Abstract: A method including collecting, by a communication device comprising a machine learning model obtained at least in part from a server computer, metadata associated with an application. The communication device can then embed the metadata to form vectorized data. The communication device can input the vectorized data into the machine learning model to obtain a security value. The communication device can determine whether to run or install the application based upon the security value.

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: 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 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 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 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.