+DETAILED ACTION
Notice of Pre-AIA or AIA Status
The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA .
Claim Rejections - 35 USC § 102
In the event the determination of the status of the application as subject to AIA 35 U.S.C. 102 and 103 (or as subject to pre-AIA 35 U.S.C. 102 and 103) is incorrect, any correction of the statutory basis (i.e., changing from AIA to pre-AIA ) for the rejection will not be considered a new ground of rejection if the prior art relied upon, and the rationale supporting the rejection, would be the same under either status.
The following is a quotation of the appropriate paragraphs of 35 U.S.C. 102 that form the basis for the rejections under this section made in this Office action:
A person shall be entitled to a patent unless –
(a)(1) the claimed invention was patented, described in a printed publication, or in public use, on sale, or otherwise available to the public before the effective filing date of the claimed invention.
Claim 15 is rejected under 35 U.S.C. 102(a)(1) as being obvious by Faisal et al (US 2022/0330879 A1).
With respect to claim 15, Faisal discloses a system for integrating SSL digital movement biomarker profiles with electronic health records, comprising: a biomarker profile generation component configured to process video data and generate digital movement biomarker profiles; an EHR communication component configured to interface with electronic health records systems using standardized healthcare data exchange protocols (see system # 100 having a motion data obtaining unit #110 to obtain data from video cameras as stated in paragraphs 0080, 0094 and 0122 of a subject); a data correlation component configured to associate digital movement biomarker profiles with corresponding patient identifiers in the electronic health records; a profile access component configured to enable clinician access to the correlated digital movement biomarker profiles within the electronic health records (see Figure 2 showing generating unit #111 and assessing unit to generate a value of the biomarker of the disease based on the obtained motion data and assess the value of the biomarker of the disease and, based on the assessment, to output information related to the state of the disease if a disease is present. E.g., the assessing unit 112 may use a function optimized in view of past detections of a disease state based on the biomarker, so as to assess the state of the disease on the basis of biomarker where the biomarkers are compared to predetermine values to detect different diseases according to paragraph 0059 and using learning or trained models according to paragraph 0157); and wherein the biomarker profile generation component utilizes a machine learning model trained on a dataset of known disorder-specific movement patterns (see paragraph 0161).
Claim Rejections - 35 USC § 103
In the event the determination of the status of the application as subject to AIA 35 U.S.C. 102 and 103 (or as subject to pre-AIA 35 U.S.C. 102 and 103) is incorrect, any correction of the statutory basis (i.e., changing from AIA to pre-AIA ) for the rejection will not be considered a new ground of rejection if the prior art relied upon, and the rationale supporting the rejection, would be the same under either status.
The following is a quotation of 35 U.S.C. 103 which forms the basis for all obviousness rejections set forth in this Office action:
A patent for a claimed invention may not be obtained, notwithstanding that the claimed invention is not identically disclosed as set forth in section 102, if the differences between the claimed invention and the prior art are such that the claimed invention as a whole would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to which the claimed invention pertains. Patentability shall not be negated by the manner in which the invention was made.
This application currently names joint inventors. In considering patentability of the claims the examiner presumes that the subject matter of the various claims was commonly owned as of the effective filing date of the claimed invention(s) absent any evidence to the contrary. Applicant is advised of the obligation under 37 CFR 1.56 to point out the inventor and effective filing dates of each claim that was not commonly owned as of the effective filing date of the later invention in order for the examiner to consider the applicability of 35 U.S.C. 102(b)(2)(C) for any potential 35 U.S.C. 102(a)(2) prior art against the later invention.
Claims 1-2, 5-8, and 10-14 are rejected under 35 U.S.C. 103 as being unpatentable over Faisal et al (US 2022/0330879 A1) in view of Edgar et al. (“A Parkinsonian Digital Biomarker Learned as an Anomaly Deep Generative Representation”).
With respect to claim 1, Faisal discloses a system for generating a self-supervised learning (SSL) digital movement biomarker, comprising (see Figure 2): a video input and process configuration component configured to receive video data of a subject (see system # 100 having a motion data obtaining unit #110 to obtain data from video cameras as stated in paragraphs 0080, 0094 and 0122 of a subject); a compliance engine configured to assess the video data against predefined compliance criteria; a pose detection component including an learning model trained to perform pose estimation and object detection on the video data (see Figure 2 showing generating unit #111 and assessing unit to generate a value of the biomarker of the disease based on the obtained motion data and assess the value of the biomarker of the disease and, based on the assessment, to output information related to the state of the disease if a disease is present. E.g., the assessing unit 112 may use a function optimized in view of past detections of a disease state based on the biomarker, so as to assess the state of the disease on the basis of biomarker where the biomarkers are compared to predetermine values to detect different diseases according to paragraph 0059 and using learning or trained models according to paragraph 0157); a kinematics component configured to derive kinematic features from the pose estimation (see paragraphs 0059 and 0107 disclosing joint velocity profile as a kinematic feature); a digital movement biomarker profile generator configured to create a digital movement biomarker profile based on the kinematic features (see paragraphs 0059 and 00135); and a data storage and access component configured to store the digital movement biomarker profile (see Figure 10 showing computer #300 having storage media #320 to store any information according to paragraphs 0137-0138). Furthermore, Faisal discloses the claimed invention as stated above except for specifying that the learning model is a self-supervised learning model. However, Edgar discloses the use of self-supervise learning model (Abstract). Therefore, it would have been obvious to one of ordinary skill in the art at the time the invention was filed to use a self-supervised learning model as taught by Edgar in combination with Faisal’s learning model for the purpose of providing an alternative to support diagnosis and to quantify kinematic patterns during locomotion and avoid subjectivity when an expert made a diagnosis (see Abstract and page 4189; left portion under II. Proposed approach).
With respect to claims 2 and 8, Faisal discloses the video input and process configuration component is further configured to convert video data from portrait to landscape orientation (see Figures 4A and 4B showing different orbitals considered as orientation change).
With respect to claims 5 and 11, Faisal discloses the kinematics component is configured to calculate kinematic features including at least one of: velocity, acceleration, angular velocity, and angular acceleration for each body part and joint (see paragraphs 0059 and 0107 disclosing joint velocity profile as a kinematic feature).
With respect to claim 6, Faisal discloses the digital movement biomarker profile generator is further configured to assign a significance factor to each movement characteristic, with a scale ranging from a lower score, indicating normal movement, to a higher score, indicating severely impacted movement (see paragraphs 0107 and 0174-0175 and Figure 8 showing score graphs).
With respect to claim 7, Faisal discloses a method for generating a self-supervised learning (SSL) digital movement biomarker, comprising: receiving video data of a subject (see system # 100 having a motion data obtaining unit #110 to obtain data from video cameras as stated in paragraphs 0080, 0094 and 0122 of a subject); assessing the video data for compliance with predefined criteria (see Figure 2 showing generating unit #111 and assessing unit to generate a value of the biomarker of the disease based on the obtained motion data and assess the value of the biomarker of the disease and, based on the assessment, to output information related to the state of the disease if a disease is present. E.g., the assessing unit 112 may use a function optimized in view of past detections of a disease state based on the biomarker, so as to assess the state of the disease on the basis of biomarker where the biomarkers are compared to predetermine values to detect different diseases according to paragraph 0059 and using learning or trained models according to paragraph 0157); applying a learning model to the video data to perform pose estimation and object detection (see paragraphs 0059 and 0107 disclosing joint velocity profile as a kinematic feature); extracting kinematic features from the pose estimation; creating a digital movement biomarker profile based on the kinematic features (see paragraphs 0059 and 00135); and storing the digital movement biomarker profile in a data repository (see Figure 10 showing computer #300 having storage media #320 to store any information according to paragraphs 0137-0138). Furthermore, Faisal discloses the claimed invention as stated above except for specifying that the learning model is a self-supervised learning model. However, Edgar discloses the use of self-supervise learning model (Abstract). Therefore, it would have been obvious to one of ordinary skill in the art at the time the invention was filed to use a self-supervised learning model as taught by Edgar in combination with Faisal’s learning model for the purpose of providing an alternative to support diagnosis and to quantify kinematic patterns during locomotion and avoid subjectivity when an expert made a diagnosis (see Abstract and page 4189; left portion under II. Proposed approach).
With respect to claim 10, Faisal discloses training the learning model to recognize and classify objects within the video data to provide context to a movement analysis (see paragraphs 0083, 0087 and 0160-0161).
With respect to claim 12, Faisal discloses assigning a significance factor to each identified movement characteristic based on a scale from zero to one hundred (see paragraphs 0107 and 0174-0175 and Figure 8 showing score graphs where the scores are considered as the claimed factor).
With respect to claim 13, Faisal discloses providing a user interface for clinicians to review and adjust the digital movement biomarker profiles prior to integration with electronic health records (see paragraphs 0075- 0078).
With respect to claim 14, Faisal discloses generating visual representations of the digital movement biomarker profiles to aid clinicians in an interpretation and diagnosis of disorders (see Figures 1A and 4B showing visual representations of the digital movement biomarker).
Claims 3-4 and 9 are rejected under 35 U.S.C. 103 as being unpatentable over Faisal et al (US 2022/0330879 A1) and Edgar et al. (“A Parkinsonian Digital Biomarker Learned as an Anomaly Deep Generative Representation”) in view of Yerushalmy et al. (US 2023/0419730 A1).
With respect to claims 3, 4 and 9, Faisal and Edgar disclose the claimed invention as stated above except for the compliance engine is further configured to detect compliance issues including at least one of: body parts out of frame, excessive camera movement, poor lighting conditions, and presence of multiple people in the video wherein the trained model is further trained to predict future body key point positions based on temporal context derived from the video data. However, Yerushalmy discloses the compliance engine is further configured to detect compliance issues including at least one of: body parts out of frame, wherein the trained model is further trained to predict future body key point positions based on temporal context derived from the video data (see paragraphs 0042-0044). Therefore, it would have been obvious to one of ordinary skill in the art at the time the invention was filed to have a compliance engine is further configured to detect compliance issues including at least one of: body parts out of frame, excessive camera movement, poor lighting conditions, and presence of multiple people in the video wherein the trained model is further trained to predict future body key point positions based on temporal context derived from the video data as taught by Yerushalmy in combination with Faisal and Edgar’s compliance issue for the purpose of determining body landmarks for those non-visible body joints based on the position of other body joints of the body of the user that are visible and inferred position for one or more body landmarks that are determined to not be visible, such as those that are within the field of view of the image but occluded, may be considered in determining the 3D model (see paragraph 0039).
Claims 16-19 are rejected under 35 U.S.C. 103 as being unpatentable over Faisal et al (US 2022/0330879 A1) in view of Noumeir (“Active Learning of the HL& Medical Standard”).
With respect to claim 16, Faisal discloses the claimed invention as stated above except for specifying that the EHR communication component employs Health Level Seven (HL7) or Fast Healthcare Interoperability Resources (FHIR) standards for data exchange. However, Noumeir discloses the EHR communication component employs Health Level Seven (HL7) (see Abstract). Therefore, it would have been obvious to one of ordinary skill in the art at the time the invention was filed to have the EHR communication component employs Health Level Seven (HL7) as taught by Noumeir in combination with Faisal’s digital health system for the purpose of achieving interoperability in healthcare using a widely used for communicating medical information between various information systems; and therefore, a cornerstone to implement the Electronic Health Record (EHR). EHR improves healthcare decisions by allowing access to the patient’s relevant clinical information at the decision-making point; and is a distributed system that results from the interactions and cooperation of various independent information systems to achieve a specific healthcare process (see page 354, Introduction section).
With respect to claim 17, Faisal discloses the data correlation component further includes a matching algorithm to ensure accurate association of biomarker profiles with patient records (see paragraphs 0157 and 0164).
With respect to claim 18, Faisal discloses the profile access component includes a user interface designed to display biomarker profiles in conjunction with clinical notes and lab results (see paragraph 0138).
With respect to claim 19, Faisal discloses the claimed invention as stated above except for specifying that the system further comprises a data security component configured to encrypt digital movement biomarker profiles during storage and transmission. However, Noumeir discloses the system further comprises a data security component configured to encrypt digital movement biomarker profiles during storage and transmission (see page 354, right side of the Introduction section). Therefore, it would have been obvious to one of ordinary skill in the art at the time the invention was filed to have a data security component configured to encrypt digital movement biomarker profiles during storage and transmission as taught by Noumeir in combination with Faisal’s programing for the purpose of securing the patients’ personal and medical information according to the HIPAA law that mandates administrative, physical, and technical safeguards to ensure the confidentiality and integrity of electronic protected health information.
Conclusion
Any inquiry concerning this communication or earlier communications from the examiner should be directed to DIXOMARA VARGAS whose telephone number is (571)272-2252. The examiner can normally be reached Monday-Friday 8am-5pm.
Examiner interviews are available via telephone, in-person, and video conferencing using a USPTO supplied web-based collaboration tool. To schedule an interview, applicant is encouraged to use the USPTO Automated Interview Request (AIR) at http://www.uspto.gov/interviewpractice.
If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Raymond Keith can be reached at 571-270-1790. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
Information regarding the status of published or unpublished applications may be obtained from Patent Center. Unpublished application information in Patent Center is available to registered users. To file and manage patent submissions in Patent Center, visit: https://patentcenter.uspto.gov. Visit https://www.uspto.gov/patents/apply/patent-center for more information about Patent Center and https://www.uspto.gov/patents/docx for information about filing in DOCX format. For additional questions, contact the Electronic Business Center (EBC) at 866-217-9197 (toll-free). If you would like assistance from a USPTO Customer Service Representative, call 800-786-9199 (IN USA OR CANADA) or 571-272-1000.
/DIXOMARA VARGAS/Primary Examiner, Art Unit 3798