Capital One Services, LLC

Vikramaditya Repaka

Sanjiv Yajnik

Raman Bajaj

Gurmeet Singh

Nicholas Dolle

Ajmal Karuthakantakath

Chandra Dhandapani

Fredrick Allen Crable

Satyajit Sajanrao Nalavade

Eddie Provencio

Sunil Raste

John Patrick Eckman

Hala Salim El-Ali

Dinesh Sundaram

Rajaboopathy Vijayaraghavan

Jacob Creech

Trent Jones

Jacques Morel

Thomas Sickert

Alex Baird

Alan Ilango

Ruoyu Shao

Fenglin Yan

Fred Crable

Methods and systems disclosed herein enable accessing and updating content using deep linking. The system may generate a deep link for a user that links the user to an operator that requested generation of the deep link. The deep link may be encoded into a wirelessly detectable tag or a visually scannable code, which may be stored with user action data and an operator identifier. When a visually scannable code is scanned or a wirelessly detectable tag is accessed, the deep link is decoded and all data associated with the deep link is retrieve and generated for display, when needed. By generating deep links in combination with the visually scannable codes or wirelessly detectable tags, the system is able to provide quick and efficient data retrieval and update.

Accessing application content and features using deep linking with wireless protocols or scannable codes

Methods and systems disclosed herein enable tracking user actions by generating different visually scannable codes (e.g., QR codes) for combinations of user, operator, and action(s). Each visually scannable code may be stored with user action data and an operator identifier and linked to another visually scannable code corresponding to the user. When a visually scannable code is scanned, all data associated with the visually scannable code and data associated with other linked visually scannable codes is generated for display. By generating the visually scannable codes and storing the codes in a linked state, the system is able to provide quick and efficient data retrieval.

Systems and methods for tracking actions using visually scannable codes

Disclosed embodiments provide systems and techniques for mass execution of analytical models across multiple dimensions of client, collateral, deal structure, third party, and other data relevant to predicting optimal decisions in real-time. In some embodiments, disclosed systems and techniques increase decisioning speed through the reduction of computational loads on disclosed decisioning systems. Further disclosed systems and techniques may scale-out analytical modeling computations through, among other technological solutions, advanced execution environments that are asynchronous and non-blocking in nature so as to allow the execution of a plurality of analytical models in parallel and optimizing the results.

Automated systems for reducing computational loads in the mass execution of analytical models using scale-out computing

The system and methods described herein allow users to apply for a purchase of a good from multiple providers using provider specific methodologies for generating offers for the product. For example, the system described herein may include a multi-layer architecture that includes interactive micro-services that communicate together in a bi-directional manner to create a normalized process for the purchase of a good, such as commercial goods/products (e.g. a vehicle) or real property. The micro-services may assess prequalification for a loan or financing for a good, followed by determining eligibility of the good for financing, and further followed by calculating pricing details for loans (e.g. for financing purchase of the good) that would be offered for a consumer's particular financial credentials, for each of a plurality of lenders. Prequalification and pricing may be performed on a good by good basis, or for a plurality of goods near or substantially simultaneously.

Multi-lender platform that securely stores proprietary information for generating offers

Systems, methods, and computer readable media are disclosed for generating, modifying, and using machine learning models to predict and evaluate differences between groups. Methods disclosed herein may include identifying variables that characterize members of a first group, generating shift indicators using the identified variables, generating a machine learning model using the shift indicators and the first group, using the machine learning model and the group to predict shifts between the first group and a predicted second group, determining an aggregate population shift and an aggregate performance shift between the first group and an actual second group, and identifying an impact of one or more of the shift indicators on the aggregate population shift or performance shift. Systems and methods disclosed herein may be configured to receive requests to predict and evaluate differences between group, and to return such predictions and evaluations to one or more users.

Machine learning models for evaluating differences between groups and methods thereof

Growing community of inventors

Allen, TX, United States of America

Vikramaditya Repaka