Q Bio, Inc.

Affirm, Inc.

Jeffrey Howard Kaditz

Andrew Gettings Stevens

Aaron Ng Ligon

Manuel De Jesus Arias

Max Levchin

Christopher Beckmann

Athanasios Polymeridis

Roberto Novoa

Jorge Fernandez Villena

Bradley Neale Selby

Matteo Alessandro Francavilla

Guanhua Wang

David Grijalva

Jacob K White

Deepak Ramaswamy

Thomas Witzel

Robert A Novoa

Manuel De Jesu Aria

Jacob White

A login system allows users to access computer systems without using a password. The passwordless system and method can use other information to securely and reliably identify true authorized system users. The identity of a user can be associated with their mobile device. The login can be based upon a minimal amount of information such as a name and a phone number which can be stored as an identification record for each of the users in a database.

System and method for passwordless logins

A computer system that performs a sparsity technique is described. During operation, the computer system accesses or obtains information associated with non-invasive measurements performed on at least an individual, historical non-invasive measurements, and a dictionary of predetermined features or basis functions associated with the historical non-invasive measurements. Note that the non-invasive measurements and the historical non-invasive measurements may include or correspond to magnetic resonance (MR) measurements. For example, the MR measurements may include magnetic resonance imaging (MRI) scans. Then, the computer system updates the dictionary of predetermined features based at least in part on the non-invasive measurements and the historical non-invasive measurements, where the updating includes performing a minimization technique with a cost function having an L2-norm term and an L0-norm term. Next, the computer system determines weights associated with features in the updated dictionary of predetermined features based at least in part on the non-invasive measurements.

Sparse representation of measurements

A system for MR Elastography of a sample, including ultrasound gel to sheath the sample, a vessel to accept the flow of the sample sheathed in ultrasound gel, a sensor array adapted to capture an ultrasound measurement and an MR measurement, wherein the sensor array including an ultrasound transmitter and an ultrasound receiver, and the sensor array is coupled to the vessel, and the vessel is capable of mechanical conductance between the ultrasound transducers, and the ultrasound gel, and a pump to create a pressure based flow of ultrasound fluid through the vessel and move the sample in proximity to the sensor array for capture of MR and ultrasound measurements of the sample as the sheathed sample passes by the sensor array.

System and method for magnetic resonance elastography

A web based finance system allows users to refinance past eligible purchasing transactions. The system can include a system server(s) which is in communication through a network with client computing devices associated with customers. The server can receive customer information and determine a refinancing value range based upon the user has a bank account and/or credit risk rating information. The server can identify prior eligible credit card purchases for refinancing and the customer can select one or more purchases for refinancing and the refinancing loan terms.

Refinancing tools for purchasing transactions

An apparatus for use in a magnetic resonance (MR) system for capturing an MR Elastography measurement of a biological lifeform may include a platform; a gel pad on a surface of the platform; and a sensor array. In some embodiments, the sensor array includes at least one ultrasound transducer, and at least one radiofrequency (RF) transmitter and receiver coil. The sensor array is at least partially embedded within the gel pad, and the gel pad is configured to provide mechanical impedance matching between the at least one ultrasound transducer and the biological lifeform. In some embodiments, a system includes the apparatus and an MR system, the MR system including an ultrasonic wave generator, an interface circuit, and a computing device. In some such embodiments, the ultrasonic wave generator is configured to generate one or more shear waves in the biological lifeform.

A system may measure, using a measurement device, a response associated with a sample to an excitation. Then, the system may compute, using the measured response and the excitation as inputs to a predetermined predictive model, model parameters on a voxel-by-voxel basis in a forward model with multiple voxels that represent the sample. The forward model may simulate response physics occurring within the sample to a given excitation. For example, the forward model may be based on differential or phenomenological equations that approximates the response physics. Moreover, the system may determine an accuracy of the model parameters by comparing at least the measured response and a calculated predicted value of the response using the forward model, the model parameters and the excitation. When the accuracy exceeds a predefined value, the system may provide the model parameters as an output to: a user, another electronic device, a display, and/or a memory.

Model parameter determination using a predictive model

Financing tools can provide a flexible credit services to customer. A credit service provider can collect personal data from clients that can include a mobile telephone number and a legal name of the client as well as purchase information from a merchant. Based upon the collected data, the system can determine a client credit risk. The system can make a credit decision to offer a client credit to purchase goods or services based upon the credit risk.

Tools for purchasing transactions

A system performs one or more magnetic resonance (MR) measurements on at least a portion of a biological life form. Moreover, the system quantitatively simulates an MR response of at least the portion of the biological life form, and compares the one or more MR measurements and the quantitative simulation to obtain a first test result. Next, the system determines one or more additional medical tests to perform. In response, the system accesses the biological sample in storage, and performs the one or more additional medical tests on at least a second portion of the biological sample to obtain one or more additional test results. Furthermore, the system computes a second test result based at least in part on the first test result and the one or more additional test results, where the second test result has an improved accuracy relative to the first test result.

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Wilson, WY, United States of America

Jeffrey Howard Kaditz