Koninklijke Philips Corporation N.V.

Ebay, Incorporated

Srinivas Hariharan

Anuj Kaul

Jonathan Kulisz

Nishant Kumar Das Pattanaik

Piyush Pattanayak

Sudip Chakrabarty

Shutanshu

Bryan Conroy

Erina Ghosh

Emma Holdrich Schwager

Kailash Swaminathan

Claire Yunzhu Zhao

Mohammad Shahed Sorower

Daniel Craig McFarlane

David Paul Noren

Vinod Kumar

Vinutha Kempanna

Vikram Shivanna

Ting Feng

Siripurapu Mahesh Reddy

Kiran Kumar Yadalam

Chaitanya Kulkarni

Kristen Tgavalekos

Systems and methods dynamically generate content security policy (CSP) headers using CSP definitions having dynamic source values. When a request for a web application is received, a CSP definition corresponding to the web application and having a dynamic source value is retrieved from a repository of CSP definitions. A CSP header is generated based on the CSP definition. The CSP header includes a source value dynamically generated based on the dynamic source value and a domain associated with the requested web application. The CSP header is provided as a response header for the requested web application.

Dynamic source values for content security policies

Systems and methods provide for self-healing content security policies (CSPs). In accordance with some aspects, CSP violation information is received identifying a CSP violation for a CSP header and a violating source for the CSP violation. The violating source for the CSP violation is compared against a list of trusted sources. Based on the comparison, a first trusted source in the list of trusted sources is identified as matching the violating source for the CSP violation. Responsive to identifying the first trusted source as matching the violating source, a CSP definition associated with the CSP header is caused to be updated to include a source value based on the violating source or the first trusted source to provide an updated CSP definition.

Self-healing content security policies

The present disclosure is directed to systems and methods for developing an individual-specific patient baseline for a target patient. An exemplary method involves: determining one or more acuity scores for the target patient; identifying patient health data corresponding to one or more low acuity time periods; storing retrospective clinical data from a group of patients in a second database; comparing the patient health data corresponding to the one or more low acuity time periods with retrospective clinical data from a group of patients by identifying one or more patient subgroups; determining the individual-specific patient baseline using an adaptive baseline selection algorithm, wherein the adaptive baseline selection algorithm is used to determine whether to determine the individual-specific patient baseline using patient health data or using retrospective clinical data from one or more patient subgroups; and displaying, using a user interface, the individual-specific patient baseline.

Methods and systems for patient baseline estimation

The present disclosure pertains to a system for providing model-based treatment recommendation via individual-specific machine learning models. In some embodiments, the system (i) obtains an audio recording of an individual, (ii) determines, from the audio recording, one or more utterance-related features of the individual; (iii) performs one or more queries based on the one or more utterance-related features to obtain health information (e.g., utterance-related conditions and treatments provided for the utterance-related conditions) associated with similar individuals having similar utterance-related conditions as the subject; (iv) provides the health information associated with the similar individuals to a machine learning model to train the machine learning model; and (v) provides, subsequent to the training of the machine learning model, the one or more utterance-related features to the machine learning model to determine one or more treatments for the individual.

System and method for providing model-based treatment recommendation via individual-specific machine learning models

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Bengaluru, India

Srinivas Hariharan