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 § 112
The following is a quotation of 35 U.S.C. 112(b):
(b) CONCLUSION.—The specification shall conclude with one or more claims particularly pointing out and distinctly claiming the subject matter which the inventor or a joint inventor regards as the invention.
The following is a quotation of 35 U.S.C. 112 (pre-AIA ), second paragraph:
The specification shall conclude with one or more claims particularly pointing out and distinctly claiming the subject matter which the applicant regards as his invention.
Claims 1-20 are rejected under 35 U.S.C. 112(b) or 35 U.S.C. 112 (pre-AIA ), second paragraph, as being indefinite for failing to particularly point out and distinctly claim the subject matter which the inventor or a joint inventor (or for applications subject to pre-AIA 35 U.S.C. 112, the applicant), regards as the invention.
Claims 1, 14, and 20 are directed to “a machine-learned movement health diagnostic model” where the output is the” movement health condition based on the biomarkers” with a “score descriptive of the performance of the clinical movement health diagnostic test”. It is not clear how the model is used to diagnose the health condition or specifically how the score provides indication of the specific type of health condition diagnosis. It is also not clear from the claim language as to type of conditions being diagnosed using the speech data.
As per the specification, “the data descriptive of a movement health condition can include a diagnostic score. The diagnostic score can be descriptive of a performance on a clinical movement health diagnostic test. In some implementations, the diagnostic score can be or can include a movement disorder severity score. For instance, in some implementations, the diagnostic score can include a numerical value (e.g., from 0 to 4) descriptive of a severity of a patient's movement health condition. The clinical movement health diagnostic test can be or can include any suitable test. For instance, in some implementations, the clinical movement health diagnostic test can be or can include at least one of a speech test, a facial expression test, a finger tapping test, a hand movement test, a hand pronation test, a hand supination test, a hand gesture test, a walking or gait analysis test, and/or a chair arising test” [0057].
“Furthermore, one example implementation of example aspects of the present disclosure can simulate hand movements of a 3.4-finger-tapping exercise that is outlined in the UPDRS manual for diagnosing Parkinson's Disease. For instance, in some implementations, a machine-learned hand perception model (e.g., a body landmark model configured to operate on a user's hand) can convert raw video and/or image data to a plurality of body landmark positions indicative of a skeletal model of a user's hand. For instance, the body landmark positions can include coordinates (e.g., three-dimensional coordinates) of hand positions (e.g., joints, bones, outlines, etc.). These coordinates over time can be analyzed and used in determining a numerical value indicative of severity of Parkinson's Disease (e.g., from zero to four, as in the UPDRS test)” [0068].
It is suggested claims 1, 14, and 20 provide further clarity with respect to the practical application of the model as to how the speech data is being used to diagnose the types of health conditions (as set forth in the specification with respect to Parkinson’s Disease).
The dependent claims do not provide additional clarity and therefore stand rejected under 112(b).
Claim Rejections - 35 USC § 103
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.
Claim(s) 1-20 is/are rejected under 35 U.S.C. 103 as being unpatentable over Howard (2020/0037942).
With respect to claims 1, 6, 14, and 19, Howard teaches of a computing system, computer-readable media storing instructions to be executed by a processor, and method to measure and integrate behavioral and cognitive features enabling early detection and progression tracking of degenerative disease [0007]. Howard teaches of one or more processors to perform operation comprising instructing patient to perform one or more test movements and capturing speech data during the one or more test movements or generating information indicating movement conditions, wherein analyzing the gathered cognitive function data comprises analyzing the gathered speech data for motor and non-motor correlations related to severity of neurodegenerative disease data [0008, 0010]. Howard teaches of generating by a biomarker model, data descriptive of one or more biomarkers comprising speech production based on speech data [0191]. Howard teaches of providing data descriptive of the one or more biomarkers to a machine-learned movement health diagnostic model [0206, 0274, 0275]. Howard also teaches of using genetic algorithms to analyze voice features to classify health subjects from Parkinson’s Disease subjects including a scoring of the speech data as an indicator of overall disease severity [0657, 0658]. Howard therefore teaches of receiving an output of the machine-learned movement health diagnostic model, data descriptive of a movement of a health condition based on the one or more biomarkers where the data comprises a diagnostic score or UPDRS score descriptive of performance of the clinical movement health diagnostic test, the one or more clinical movement health diagnostic test comprising at least a speech test where the scoring is used to examine relationship between speech and selected movement symptoms to uncover new biomarker patterns [0657, 0658, 0661] with mapping speech and movement symptoms that appear to progress at the same rate to see if these mutually reinforcing features can be used to develop new biomarkers [0687].
With respect to claims 2, 3, 15, and 16, Howard teaches of capturing the speech data using a mobile device [0265, 0548] that would obviously include a touchscreen for touchscreen interactions where there smartphones can measure behavior continuously with self-reporting and would therefore include touchscreen interactions [0265] while maintaining the mobile device on the person.
With respect to claims 4 and 17, Howard teaches of a microphone of the mobile device to capture audio data [0901].
With respect to claims 5 and 18, Howard teaches of real-time analysis of patient cognitive states based on analysis of speech [0242, 0246] and therefore under broadest reasonable interpretation, the speech data would have to be processed on the mobile device without offloading to a external processor in order to do real-time analysis of the speech data.
With respect to claims 6 and 19, Howard teaches of generating the biomarker model with the one or more biomarkers comprising a speech intelligibility score based on speech data or the UPDRS scores to test the predictability of speech tests as an overall indicator of overall disease severity [0657, 0658, 0687].
With respect to claims 7 and 8, Howard teaches of capturing video data depicting patient movements [0181, 0231, 0845, 0847, 0849]. Howard teaches of the plurality of body landmark positions to based on the video data such as upper-body movement aberrations [0181, 0847, 0849].
With respect to claims 9 and 10, Howard teaches of the plurality of body landmark positions to comprise a time series of three-dimensional coordinates of the body of the patient or local coordinate system from the landmarks and marker positions [0284, 0298, 0301, 0467] with the data descriptive of one or more biomarkers comprising motion tracking over time series of three-dimensional coordinates of the plurality of body landmark positions or the use of the motion tracking to measure the three-dimensional positions of the body landmark positions [0284, 0285].
With respect to claims 11 and 12, Howard teaches of determining a plurality of body landmark positions by a machine-learned body landmark model where the machine-learned body landmark model comprises a skeletal position model of bony landmarks [0284, 0286, 0351, 0353].
With respect to claim 13, Howard teaches of the biomarker model [0191, 0240] being configured to process the landmark positions to generate data descriptive of the biomarkers [0467] or identify biomarkers associated with different levels of cognitive functioning to develop models for predicting disease onset and progression [0240].
Howard do not teach of all the claimed elements in a single embodiment. It would have therefore been obvious to one of ordinary skill in the art combine the elements from the different embodiments to effectively integrate behavioral and cognitive feature enabling early detection and progression tracking of degenerative disease (see abstract).
Conclusion
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BR
/BAISAKHI ROY/ Primary Examiner, Art Unit 3797