Abstract: Disclosed is a method and a system for efficiently and accurately processing air particulate datasets when facing a high number of air particulate datasets from multiple locations to generate an artificial intelligence model having one or more computer-based rules that determines eligibility of a user to avail a health-related service based on air particulate records associated with current and past locations of the user.

Abstract: Systems and methods for assessing the needs of customers using predictive modeling techniques are disclosed. The method receives customer data from a first database and provided by the user. The method generates an instruction to a second database and receives additional customer information received from external databases to generate a basic profile for data based on the customer. The system further analyzes data provided by the user. The method generates a customer profile based on the basic customer data and additional data. The method determines missing data from the customer profile associated and a set of attributes of the user. The method identifies a profile with the customer similar set of attributes and estimates the missing data using predictive modeling techniques to generate estimated customer information. The system further pre-populates one or more missing fields of the full profile associated with the customer based on said estimated customer information. The system.

Abstract: Disclosed herein is a security training apparatus configured to operate an interactive cybersecurity training application, which provides customized and tailored cybersecurity training to each employee of an organization. The security training apparatus uses augmented reality to facilitate customized cybersecurity training for each user. The augmented reality is a computer application, which deals with the combination of real world images of personal workspace environment of each user where the cyber-crime may occur and computer generated data associated with cybersecurity risk objects that may aid the cyber-crime. The interactive cybersecurity training comprises the use of live video imagery of the personal workspace environment of each user, which is digitally processed and augmented by the addition of computer generated graphics associated with the cybersecurity risk objects. The cybersecurity risk objects are selected based on the items within the personal workspace environment for each user.

Abstract: A DI recovery management system generates a plurality of ranked claimant records and recovery scores. A predictive machine learning model inputs disability income claim data and disability income claimant data into an event history model utilizing discrete-time survival analysis in conjunction with a gradient boosting machine learning model. The claim termination event is one of a plurality of preselected recovery events that indicate that a claimant has achieved return-to-work capacity. Claimant data used in modeling includes diagnosis data representative of workplace disability duration guidelines. The predictive machine learning model is continually trained using updated disability income claims data. The training procedure transforms claimant records extracted from a DI claims database into a longitudinal format that includes multiple person-year records corresponding to each claimant record.

Abstract: A computerized system and method may include, in response to receiving a blockchain via a communications network that includes information associated with an event, parsing, by a blockchain parsing engine being executed by a blockchain node, the information to identify a status state of an item related to the event. The blockchain may be inclusive of the information along with the status state of the item may be stored in a storage unit. An event tracking engine may determine from the parsed information that the status state of the item transitioned from a first state to a second state. Responsive to the event tracking engine determining that a qualifying state is satisfied by the item being in the second state, automatically executing, by the blockchain node, a smart code inclusive of initiating communications between a first party and a second party.

Abstract: A system and a method for identifying and ranking agents are disclosed herein. The system includes an analytics engine which retrieves information from external and internal databases. The analytics engine uses the information retrieved from these databases, in addition to one or more success factors or key attributes, to identify and rank prospective agents. The analytics engine can also match one or more prospective agents with a general agent and provide ranking and performance assessment reports for evaluating and following up on the agent’s career development.

Abstract: Methods and systems described herein describe a central server that continuously monitors network connectivity of remote computers operated by remote employees. When a customer establishes an electronic communication session with the server (e.g., call or chat session), the server identifies one or more applications to be executed to satisfy the customer's requests. The server then calculates a network traffic value threshold corresponding to a minimum network connectivity attributes needed to execute the identified applications. The server then route the customer's electronic communication session to an agent whose remote computer satisfies the network traffic value threshold.

Abstract: A method comprises receiving a device identifier and location information from a receiving device, where the device identifier comprises a distinctive combination of at least one of numbers and characters uniquely identifying the receiving device and the location information is received from a plurality of beacons. The method comprises determining a current location associated with the receiving device based on the location information. The method comprises generating a first instruction configured to query a calendar event associated with the receiving device, transmitting the first instruction to a database, and receiving data associated with the calendar event. The method comprises, in response to the current location of the receiving device being associated with a location associated with the calendar event, generating a meeting report associated with the calendar event and transmitting the meeting report to the receiving device.

Abstract: A call management system of a call center identifies an inbound caller based upon computer analysis of customer identifiers, which may include at least two of customer name, street address, and zip code. Approximate string matching analysis matches n-grams generated from strings within customer identifiers, with n-grams generated from customer identification fields while searching one or more databases. Approximate string matching can incorporate a closeness metric based on Jaccard distance, and a Gaussian mixture model of best matches. In one embodiment, a polymr search engine analyzes customer identifiers of inbound callers to retrieve customer data, such as customer demographic data, matched to the customer identifiers. In another embodiment, the polymr search engine analyzes customer identifiers of inbound callers to identify repeat callers and retrieve previously collected customer data.

Abstract: Methods and systems disclosed herein allow data to be transferred from a data source to a target database with little to no offline period or data corruptions. The methods and systems describe a server that generates a temporary data repository having a similar configuration as the target data repository; transmits the set of new data records from the data source to the temporary data repository; identifies dependency relationship attributes among the data records stored within the target data repository; and when the server identifies that a predetermined number of data records and their respective dependent data records are stored within the temporary data records, the server merges the set of data records and the set of new data records. The server also stores a pre/post merger record of data such that the server can revert to a previous version of data or roll forward to another version.

Abstract: A call management system of a call center retrieves from a customer database enterprise customer data associated with an identified customer in a customer call, which may include customer event data, attributions data, and activity event data. The customer database tracks prospects, leads, new business, and purchasers of an enterprise. The system retrieves customer demographic data associated with the identified customer. A predictive model is selected from a plurality of predictive models based on retrieved enterprise customer data. The selected predictive model, including a logistic regression model, and tree-based model, determines a value prediction signal for the identified customer, then classifies the identified customer into a first value group or a second value group. The system routes a customer call classified in the first value group to a first call queue assignment, and routes a customer call classified in the second value group to a second call queue assignment.

Abstract: Disclosed herein are embodiments of systems, methods, and products comprises an analytic server for electronic requests routing and distribution. The server receives a plurality of requests from a plurality of electronic user devices. Aiming to routing the plurality of requests to appropriate agents, the server trains an artificial intelligence model for each agent based on historical data. For each request, the server executes the artificial intelligence model to determine a score indicating the probability of the agent converting the request to a successful sale. The server determines an entropy value for each request based on the scores and order the requests into a queue based on the entropy values. The server also calculates a capacity for each agent based on historical agent data. For each request in the queue, the server routes the request to an agent based on at least one of the score and capacity of the agent.

Abstract: Methods and systems disclosed herein describe automatically establishing two concurrent electronic communication sessions. Participants of a primary electronic communication session may request a private (secondary) electronic communication session in which only a subset of the participants of the primary electronic communication session can participate. Methods and systems described herein also describe automatically identifying participants of the second electronic communication session based on various factors including predetermined lists, commonality among different users or user identifiers, and geographic location of each participant of the primary and/or secondary electronic communication session.

Abstract: Systems and methods for validating outputs from a machine learning model is disclosed. The machine learning model and a statistical model are executed to generate electronic documents in response to customer requests. A random sample of electronic documents generated from the machine learning model and the statistical model are then selected. A comparison is performed between the random sample of electronic documents generated from the machine learning model and the statistical model. The performance of the machine learning model is validated based on results of the comparison.

Abstract: Disclosed herein are embodiments of systems, methods, and products comprises an analytic server, which determines user health attributes by tracking the user’s behaviors and activities within a predetermined space. The analytic server receives tracking data from a set of sensors installed in the predetermined space. The sensors track a beacon worn by the user. The analytic server determines micro-locations and user behaviors based on the tracking data. The analytic server determines the coordinates of the sensors based on the sensor identifiers and maps the coordinates to regions by referring to a floor plan map. The analytic server determines the user behaviors and activities by aggregating the micro-locations and regions the user visited at different time. The analytic server determines the user’s health score based on the micro-locations and user behaviors by executing an artificial intelligence model. The analytic server determines a recommendation of premium based on the health score.

Abstract: A suite of fluidless predictive machine learning models includes a fluidless mortality module, smoking propensity model, and prescription fills model. The fluidless machine learning models are trained against a corpus of historical underwriting applications of a sponsoring enterprise, including clinical data of historical applicants. Fluidless models are trained by application of a random forest ensemble including survival, regression, and classification models. The trained models produce high-resolution, individual mortality scores. A fluidless underwriting protocol runs these predictive models to assess mortality risk and other risk attributes of a fluidless application that excludes clinical data to determine whether to present an accelerated underwriting offer.

Abstract: Methods and systems disclosed herein utilize location signals received from beacons and other indoor positioning systems along with an application program on customer devices for better management of customer traffic in physical queues and virtual queues, specifically in environments such as airports, food courts, shopping malls, and amusement parks. These methods and systems also provide a customer with a token for his place in the queue on his mobile device, so he is free to continue with his activities until it is time for him to acquire a product or a service.

Abstract: Method and system disclosed herein facilitate retrieval of a blockchain key. The method comprises receiving a key store comprising a first encryption method, a second encryption method, and identification information of one or more network nodes storing a plurality of encrypted storage keys; displaying an authentication request and receiving and input form the user in response to the authentication request; upon the input received matching a record within a database, instructing the one or more network nodes to transmit the encrypted key segments; decrypting each encrypted key segment based on the first encryption method; and generating a blockchain key by appending the strings of the key segments based on the second encryption method.

Abstract: A suite of fluidless predictive machine learning models includes a fluidless mortality module, smoking propensity model, and prescription fills model. The fluidless machine learning models are trained against a corpus of historical underwriting applications of a sponsoring enterprise, including clinical data of historical applicants. A data appended procedure supplements historical applications data with public records and credit risks. Various features of this data are engineered for improved predictive characteristics. Fluidless models are trained by application of a random forest ensemble including survival, regression and classification models. The trained models produce high-resolution, individual mortality scores. A fluidless underwriting protocol runs these predictive models to assess mortality risk and other risk attributes of a fluidless application that excludes clinical data to determine whether to present an accelerated underwriting offer.

Abstract: Various systems and methods use a value in a data file for a data process, as the data process is scaled up in terms of dataset dimensionality, data volume, data types, data content, data source quantity, and data source speed, while remaining compliant with ACID principles. As such, these technologies provide for sourcing of data from various data sources, where the data includes the data file storing the value. The data is cleansed and fused, which enables a report to be generated. In response to the value in the data file being modified, the data, inclusive of the data file storing the value, is again cleansed and fused based on the value being modified. This processing in-turn enables the report to modified based on the value being modified.