Abstract: An underwriting estimator predictive machine learning model receives as inputs a limited number of details about an applicant, and outputs an immediate underwriting estimate of risk class. A preliminary pre-screening review redirects applicants with one or more screening impairments to a human-in-the-loop quick quote process. Model inputs include estimator inputs data that are pre-selected from the dataset of impairments data after excluding the screening impairments from the dataset of impairments. The underwriting estimator model may incorporate alternative pathways that output individualized underwriting estimates for some applicants and cohort-level marginal distributions for other applicants. Model outputs also include explanation files providing interpretability of underwriting estimates. The explanation files may include additive feature attribution data and rule based natural language explanations.

Abstract: System and method for automatically calling back a customer via a predictive model determines a plurality of call-back metrics for a plurality of advisor records. The predictive model is applied to call-back data to identify customers that are likely to require a series of call-backs, and automatically generates a preferred call-back to such customers to reduce this risk. The automated call-back may follow termination of an identified customer's inbound call, or at some time after completion of a previous call interaction of the identified customer with an advisor. In the predictive model, a first compilation of call-back metrics record is representative of an overall likelihood of call-backs associated with each advisor record, and a second compilation of the plurality of call-back metrics is representative of a likelihood of call-backs for each of the plurality of products of the enterprise associated with the advisor record.

Abstract: A method comprises receiving a request to generate a customized dataset comprising data stored onto a blockchain; retrieving user work history data associated with a user from one or more block instances of the blockchain; in response to presenting the user work history data on a display of the second computing device, receiving a selection of a subset of the user work history data from the second computing device; generating a blockchain address corresponding to one or more hash values of a subset of the one or more block instances associated with the selection of the subset of the user work history data; and transmitting the blockchain address to the second computing device.

Abstract: Disclosed herein are display techniques that will allow sensitive data displayed on a computer screen to only be viewed by authorized users and will render computer screen unreadable to unauthorized users. One or more display techniques are capable of automatically scrambling and unscrambling display screen of the computing device in which only an intended viewer is able to view data on the display screen using deciphering glasses.

Abstract: Systems and methods described herein can automatically route an inbound call from an identified customer to one of a plurality of agents, the agent being selected on the basis of likelihood of a favorable outcome. The method determines a predictive model appropriate for the identified customer, with model variables including call center data, and targeted marketing data based upon risk data for the customer. An analytical engine calculates outcome predictions by applying the predictive model to values of model variables over a recent time interval. In a time-series analysis, this calculation is repeated while dynamically adjusting the recent time interval, until identifying a call routing option that satisfies a favorable outcome criterion. This method may be used to select the agent to handle the incoming call, and optionally to select a product for that agent to discuss with the identified customer.

Abstract: Disclosed herein are embodiments of systems, methods, and products comprises an analytic server, which dynamically predicts future events for web users. The analytic server generates prediction models based on historical click-through analytics data received from the web server. The analytic server captures the current event (e.g., the current operation of the web user) on the web page, and determines the next event by predicting the web user behavior using the prediction models on an event-by-event basis. The analytic server also queries the web user data from a database to better understand the web user's intention, and improve the prediction accuracy. The analytic server modifies the HTML code to display the web page to include a graphical user interface comprising the predicted event. Based on the web users' reactions to the predicted event, the analytic server updates the prediction models.

Abstract: Disclosed herein is a product inspection apparatus that may include an electronic device having a visual inspection software application. The visual inspection software application may activate a camera of the electronic device to capture images of a product, such as a vehicle. A display screen of the electronic device may present product images. The product images may be digitally processed, and augmented by the addition of computer-generated images associated with a status of inspection of each element of the vehicle in the product images. Each computer-generated image may include a graphical indicator associated with the status of inspection of a particular element. Each computer-generated image may be projected on top of a real world image of the particular element presented on the display screen.

Abstract: This disclosure discloses systems, devices, and methods for parallelized data structure processing in context of machine learning and reverse proxy servers.

Abstract: Disclosed herein are embodiments of systems, methods, and products comprises an analytic server, which evaluates user data for premium financing status and dynamically renders graphical user interfaces. The server trains an artificial intelligence model based on historical user data. The artificial intelligence model comprises one or more data points with each data point representing one of a plurality of attributes and applies a logistic regression algorithm to identify a weight factor for each attribute. The server uses a dynamic algorithm to generate a score by combining the plurality of attributes based on the weight factors. The server receives responses regarding the scores that indicate the premium financing status of each case. The server retrains the artificial intelligence model to identify new weight factors based on negative responses data. The server automatically displays new scores calculated based on the new weight factors.

Abstract: The embodiments recite systems and methods that improve the traditional underwriting process within a financial institution. These embodiments produce an underwriting model that emulates the resolution patterns of top performing underwriters. The underwriting model once is built and tested is incorporated into decision tools that provide underwriters with insightful advices when underwriting a client. The embodiments use statistical learning techniques such as support vector machine and logistic regression. These techniques can assume a linear or nonlinear relationship between factors and risk classes. Furthermore, the underwriting model also uses artificial intelligence tools such as expert systems and fuzzy logic. A company's underwriting standards and best underwriting practices may be updated periodically so that underwriting model based on decision heuristic keep improving the quality of its output over time.

Abstract: Disclosed herein is a system for diagnosing faults in a vehicle using multiple audio sensors. The audio sensors are placed in predetermined locations within the vehicle. The audio sensors continually detect sound signals being originated from components of the vehicle. The audio sensors process detected sound signals to remove unwanted noise from the detected sound signals. The audio sensors compare processed sounds signals with reference sound signals to identify one or more faulty components. Each reference sound signal is associated with a particular fault. The audio sensors transmit information associated with the one or more faulty components to an analyst computer. An interactive graphical user interface of the analyst computer may present the information to an analyst.

Abstract: A routing system of a call center determines a plurality of advisor clusters to be assigned to each of a plurality of lead records stored in a lead model database. The predictive machine learning model inputs lead model data and advisor model data into a clustering analysis. Various modeling data are extracted from source lead data, sales data, and advisor data, in which the advisor data has been flattened for modeling. The predictive machine learning model applies a combination of a clustering analysis, a cluster model, and an aggregate conversion model to lead model data and user model data. The clustering analysis utilizes unsupervised clustering and supervised clustering, and outputs a plurality of advisor clusters and sales conversion scores. The clustering analysis clusters each of the advisors into one of the plurality of advisor clusters based on degree of similarity of a clustering vector.

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: Disclosed herein are systems and methods of artificial intelligence learning systems. In some embodiments the artificial intelligence system presents options to users based on their life stage and personality profile. Family or group structures may be created within an application. Options may be created and presented based on the family structure such as chores may be assigned to children, money may be transferred between family members, and scores may be assigned to different users.

Abstract: Disclosed herein a system having an artificial intelligence model, which is executed to generate and display valuation reports on an interactive graphical user interface. The valuation reports include valuation information of companies. The valuation reports include multiple variables associated with the valuation information of the companies whose values are dynamic, and the values may be updated in real-time. The swift turnaround time of the valuation reports on the interactive graphical user interface may allow the client user to trade swiftly and efficiently.

Abstract: System and method for providing personalized, time-varying layered bundles of computer records to users. The system includes personalized servers, a communications network, user interfaces, and client devices employed by users. The personalized server includes a needs analysis module, a bundle building module, and an bundle generating module. A method of providing personalized bundle of computer records includes receiving a request for a personalized bundle of computer records, and requesting user needs data associated with the client. The method further includes converting the user data into determined needs data, and building a bundle of computer records personalized to the user using the determined needs data, which may include a determined needs timeline. The personalized, time varying bundle of computer records includes a plurality of computer records and plurality of types of bundles of computer records represented in the determined needs data.

Abstract: Disclosed herein are embodiments of systems, methods, and products comprises an analytic server, which aggregates different accounts for a user. The analytic server queries data associated with the user. From the queried data, the analytic server determines the accounts and the account servers. From the account servers, the analytic server queries the transactions of the accounts. The analytic server generates an instance to aggregate the determined accounts and transactions. The analytic server further scans the user's email content to determine potentially unknown transactions. The analytic server compares the potentially unknown transaction from the email content with the transactions in the instance. If there is no match, the analytic server determines the account of the potentially unknown transaction from the email content is a new account that is not aggregated. The analytic server notifies the user regarding the new account and updates the instance to reflect the new account.

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.