Patent Application Titled “Patient And Consumer Data De-Identification” Published Online (USPTO 20230352150): Patent Application

2023 NOV 17 (NewsRx) -- By a News Reporter-Staff News Editor at Insurance Daily News -- According to news reporting originating from Washington, D.C., by NewsRx journalists, a patent application by the inventors Brown, Chad Clayton (Cary, NC, US); Cetin, Doruk (Zurich, CH); Cramer, Christopher E. (Durham, NC, US); Huber, Niko Benjamin (Zug, CH); Meyer, Eric P. (Zurich, CH), filed on April 27, 2023, was made available online on November 2, 2023.

No assignee for this patent application has been made.

Reporters obtained the following quote from the background information supplied by the inventors: “Conventional approaches to removing personally identifying patient information from confidential patient data can be less than ideal in at least some respects. Many countries require that medical records comply with standards to protect patient data, such as the Health Insurance Portability and Accountability Act (“HIPAA”) in the United States and the General Data Protection Regulations (“GDPR”) in the European Union. These standards can require that personally identifiable information be removed prior to making patient data, such as patient images, publicly available. Treatment professionals such as physicians may wish to share treatment information with other professionals in order to disseminate knowledge with respect to patient care and best practices. In order to present such patient data to third-parties, personally identifiable information is typically removed. However, conventional approaches to remove identifying features/artifacts can result in less than complete patient images and images that appear unnatural or aesthetically unappealing.”

In addition to obtaining background information on this patent application, NewsRx editors also obtained the inventors’ summary information for this patent application: “Various example implementations are summarized. These example implementations are merely for illustration and should not be construed as limiting.

“In a first implementation, a computer-implemented method comprises: receiving patient data, wherein the patient data comprises a patient image corresponding with a patient; receiving treatment data, wherein the treatment data is based on a treatment goal for the patient; automatically determining, using one or more semantic controls, one or more anonymization parameters for the patient image; and generating, based on the one or more anonymization parameters, an anonymized patient image.

“A second implementation may further extend the first implementation. In the second implementation, the anonymized patient image preserves a clinically relevant portion of the patient image based on the treatment data.

“A third implementation may further extend the first or second implementation. In the third implementation, the one or more anonymization parameters are based on the treatment data.

“A fourth implementation may further extend any of the first through third implementations. In the fourth implementation, the one or more anonymization parameters are based on the patient data.

“A fifth implementation may further extend any of the first through fourth implementations. In the fifth implementation, the one or more anonymization parameters for the patient image specify one or more anonymization regions in the patient image that are to be modified to obfuscate an identity of the patient.

“A sixth implementation may further extend any of the first through fifth implementations. In the sixth implementation, the one or more anonymization parameters for the patient image specify one or more clinically relevant regions in the patient image that are not to be modified.

“A seventh implementation may further extend any of the first through sixth implementations. In the seventh implementation, the one or more anonymization parameters specify one or more image characteristics to be applied to generate the anonymized patient image.

“An eighth implementation may further extend the seventh implementation. In the eighth implementation, the one or more image characteristics include color parameters, blur filter parameters, geometric parameters, or a combination thereof.

“A ninth implementation may further extend any of the first through eighth implementations. In the ninth implementation, the one or more semantic controls are based on a user input.

“In a tenth implementation, a system comprises: at least one memory; and at least one processor coupled to the at least one memory, the at least one processor configured to: receive patient data, wherein the patient data comprises a patient image corresponding with a patient; receive treatment data, wherein the treatment data is based on a treatment goal for the patient; automatically determine, using one or more semantic controls, one or more anonymization parameters for the patient image; and generate, based on the one or more anonymization parameters, an anonymized patient image.

“An 11^th implementation may further extend the tenth implementation. In the 11^th implementation, the anonymized patient image preserves a clinically relevant portion of the patient image based on the treatment data.

“A 12^th implementation may further extend the tenth or 11^th implementation. In the 12^th implementation, the one or more anonymization parameters are based on the treatment data.

“A 13^th implementation may further extend any of the tenth through 12^th implementations. In the 13^th implementation, the one or more anonymization parameters are based on the patient data.

“A 14^th implementation may further extend any of the tenth through 13^th implementations. In the 14^th implementation, the one or more anonymization parameters for the patient image specify one or more anonymization regions in the patient image that are to be modified to obfuscate an identity of the patient.

“A 15^th implementation may further extend any of the tenth through 14^th implementations. In the 15^th implementation, the one or more anonymization parameters for the patient image specify one or more clinically relevant regions in the patient image that are not to be modified.

“A 16^th implementation may further extend any of the tenth through 15^th implementations. In the 16^th implementation, the one or more anonymization parameters specify one or more image characteristics to be applied to generate the anonymized patient image.

“A 17^th implementation may further extend the 16^th implementation. In the 17^th implementation, the one or more image characteristics include color parameters, blur filter parameters, geometric parameters, or a combination thereof.

“An 18^th implementation may further extend any of the tenth through 17^th implementations. In the 18^th implementation, the one or more semantic controls are based on a user input.

“In a 19^th implementation, a non-transitory computer-readable storage medium comprising at least one instruction for causing a computer or processor to: receive patient data, wherein the patient data comprises a patient image corresponding with a patient; receive treatment data, wherein the treatment data is based on a treatment goal for the patient; automatically determine, using one or more semantic controls, one or more anonymization parameters for the patient image; and generate, based on the one or more anonymization parameters, an anonymized patient image.

“A 20^th implementation may further extend the 19^th implementation. In the 20^th implementation, the anonymized patient image preserves a clinically relevant portion of the patient image based on the treatment data.

“A 21^st implementation may further extend the 19^th or 20^th implementations. In the 21^st implementation, the one or more anonymization parameters are based on the treatment data.

“A 22^nd implementation may further extend any of the 19^th through 21^st implementations. In the 22^nd implementation, the one or more anonymization parameters are based on the patient data.

“A 23^rd implementation may further extend any of the 19^th through 22^nd implementations. In the 23^rd implementation, the one or more anonymization parameters for the patient image specify one or more anonymization regions in the patient image that are to be modified to obfuscate an identity of the patient.

“A 24^th implementation may further extend any of the 19^th through 23^rd implementations. In the 24^th implementation, the one or more anonymization parameters for the patient image specify one or more clinically relevant regions in the patient image that are not to be modified.

“A 25^th implementation may further extend any of the 19^th through 24^th implementations. In the 25^th implementation, the one or more anonymization parameters specify one or more image characteristics to be applied to generate the anonymized patient image.

“A 26^th implementation may further extend any of the 19^th through 25^th implementations. In the 26^th implementation, the one or more image characteristics include color parameters, blur filter parameters, geometric parameters, or a combination thereof.

“A 27^th implementation may further extend any of the 19^th through 26^th implementations. In the 27^th implementation, the one or more semantic controls are based on a user input.”

The claims supplied by the inventors are:

“1. A computer-implemented method comprising: receiving patient data, wherein the patient data comprises a patient image corresponding with a patient; receiving treatment data, wherein the treatment data is based on a treatment goal for the patient; automatically determining, using one or more semantic controls, one or more anonymization parameters for the patient image; and generating, based on the one or more anonymization parameters, an anonymized patient image.

“2. The computer-implemented method of claim 1, wherein the anonymized patient image preserves a clinically relevant portion of the patient image based on the treatment data.

“3. The computer-implemented method of claim 1, wherein the one or more anonymization parameters are based on the treatment data.

“4. The computer-implemented method of claim 1, wherein the one or more anonymization parameters are based on the patient data.

“5. The computer-implemented method of claim 1, wherein the one or more anonymization parameters for the patient image specify one or more anonymization regions in the patient image that are to be modified to obfuscate an identity of the patient.

“6. The computer-implemented method of claim 1, wherein the one or more anonymization parameters for the patient image specify one or more clinically relevant regions in the patient image that are not to be modified.

“7. The computer-implemented method of claim 1, wherein the one or more anonymization parameters specify one or more image characteristics to be applied to generate the anonymized patient image.

“8. The computer-implemented method of claim 7, wherein the one or more image characteristics include color parameters, blur filter parameters, geometric parameters, or a combination thereof.

“9. The computer-implemented method of claim 1, wherein the one or more semantic controls are based on a user input.

“10. A system, comprising: at least one memory; and at least one processor coupled to the at least one memory, the at least one processor configured to: receive patient data, wherein the patient data comprises a patient image corresponding with a patient; receive treatment data, wherein the treatment data is based on a treatment goal for the patient; automatically determine, using one or more semantic controls, one or more anonymization parameters for the patient image; and generate, based on the one or more anonymization parameters, an anonymized patient image.

“11. The system of claim 10, wherein the anonymized patient image preserves a clinically relevant portion of the patient image based on the treatment data.

“12. The system of claim 10, wherein the one or more anonymization parameters are based on the treatment data.

“13. The system of claim 10, wherein the one or more anonymization parameters are based on the patient data.

“14. The system of claim 10, the one or more anonymization parameters for the patient image specify one or more anonymization regions in the patient image that are to be modified to obfuscate an identity of the patient.

“15. The system of claim 10, wherein the one or more anonymization parameters for the patient image specify one or more clinically relevant regions in the patient image that are not to be modified.

“16. The system of claim 10, wherein the one or more anonymization parameters specify one or more image characteristics to be applied to generate the anonymized patient image.

“17. The system of claim 16, wherein the one or more image characteristics include color parameters, blur filter parameters, geometric parameters, or a combination thereof.

“18. The system of claim 10, wherein the one or more semantic controls are based on a user input.

“19. A non-transitory computer-readable storage medium comprising at least one instruction for causing a computer or processor to: receive patient data, wherein the patient data comprises a patient image corresponding with a patient; receive treatment data, wherein the treatment data is based on a treatment goal for the patient; automatically determine, using one or more semantic controls, one or more anonymization parameters for the patient image; and generate, based on the one or more anonymization parameters, an anonymized patient image.

“20. The non-transitory computer-readable storage medium of claim 19, wherein the anonymized patient image preserves a clinically relevant portion of the patient image based on the treatment data.

“21. The non-transitory computer-readable storage medium of claim 19, wherein the one or more anonymization parameters are based on the treatment data.

“22. The non-transitory computer-readable storage medium of claim 19, wherein the one or more anonymization parameters are based on the patient data.

“23. The non-transitory computer-readable storage medium of claim 19, the one or more anonymization parameters for the patient image specify one or more anonymization regions in the patient image that are to be modified to obfuscate an identity of the patient.

“24. The non-transitory computer-readable storage medium of claim 19, wherein the one or more anonymization parameters for the patient image specify one or more clinically relevant regions in the patient image that are not to be modified.

“25. The non-transitory computer-readable storage medium of claim 19, wherein the one or more anonymization parameters specify one or more image characteristics to be applied to generate the anonymized patient image.

“26. The non-transitory computer-readable storage medium of claim 25, wherein the one or more image characteristics include color parameters, blur filter parameters, geometric parameters, or a combination thereof.

“27. The non-transitory computer-readable storage medium of claim 19, wherein the one or more semantic controls are based on a user input.”

For more information, see this patent application: Brown, Chad Clayton; Cetin, Doruk; Cramer, Christopher E.; Huber, Niko Benjamin; Meyer, Eric P. Patient And Consumer Data De-Identification. U.S. Patent Application Number 20230352150, filed April 27, 2023 and posted November 2, 2023. Patent URL (for desktop use only): https://ppubs.uspto.gov/pubwebapp/external.html?q=(20230352150)&db=US-PGPUB&type=ids

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