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 § 101
35 U.S.C. 101 reads as follows:
Whoever invents or discovers any new and useful process, machine, manufacture, or composition of matter, or any new and useful improvement thereof, may obtain a patent therefor, subject to the conditions and requirements of this title.
Claims 1-20 are rejected under 35 U.S.C. 101 because the claimed invention is directed to as ineligible under subject eligibility test. In the Subject Matter Eligibility Test for Products and Processes (Federal Register, Vol. 79, No. 241, dated Tuesday, December 16, 2014, page 74621), The claim(s) does/do not include additional elements that are sufficient to amount to significantly more than the judicial exception because the additional device elements, which are recited at a high level of generality, provide conventional computer functions that do not add meaningful limits to practicing the abstract idea.
Claims 1, 8 & 15
Step 1
This step inquires “is the claim to a process, article of machine, manufacture or composition of matter?” Yes,
Claim 1 – “Method” is a process.
Claims 8 & 15 - “Systems” or “Non-Transitory CRM” are machines.
Step 2A - Prong 1
This step inquires “does the claim recite an abstract idea, law or natural phenomenon”. This claim appears to directed to an abstract idea.
The limitation of “recording video of a subject performing a walking task; analyzing the video to determine poses and relationships between a plurality of points of the subject corresponding to anatomical features of the subject while performing the walking task; analyzing, using a machine learning model, a gait of the subject based on the relationships between the plurality of points while performing the walking task; classifying, using the machine learning model, the subject as exhibiting or not exhibiting symptoms of a motor disorder based on the gait; and outputting, via a graphical user interface, a determination of the subject as exhibiting or not exhibiting the symptoms of the motor disorder.”, as drafted, is a process that, under its broadest reasonable interpretation, covers performance of the limitation in the mind (e.g. mathematical concepts, mental processes or certain methods of organizing human activity) but for the recitation of generic computer components. That is, other than reciting “a processor; and a memory” nothing in the claim element precludes the step from practically being performed in the mind. For example, but for the “a processor; and a memory” language, “recording, analyzing, outputting” in the context of this claim encompasses covers performance of the limitation in the mind (e.g. mathematical concepts, mental processes or certain methods of organizing human activity).
STEP 2A – PRONG 1 - CONCLUSION
If a claim limitation, under its broadest reasonable interpretation, covers performance of the limitation in the mind but for the recitation of generic computer components, then it falls within the “Mental Processes” grouping of abstract ideas. Accordingly, the claim recites an abstract idea.
Step 2A - Prong 2
This step inquires “does the claim recite additional elements that integrate the judicial exception into a practical application”. This judicial exception is not integrated into a practical application. In particular, the claim recites two additional element – using a “a processor; and a memory” to perform “recording, analyzing, outputting” steps. The “a processor; and a memory” are recited at a high-level of generality (i.e., as a generic processor) “recording video of a subject performing a walking task; analyzing the video to determine poses and relationships between a plurality of points of the subject corresponding to anatomical features of the subject while performing the walking task; analyzing, using a machine learning model, a gait of the subject based on the relationships between the plurality of points while performing the walking task; classifying, using the machine learning model, the subject as exhibiting or not exhibiting symptoms of a motor disorder based on the gait; and outputting, via a graphical user interface, a determination of the subject as exhibiting or not exhibiting the symptoms of the motor disorder.” such that it amounts no more than mere instructions to apply the exception using a generic computer component.
STEP 2A – PRONG 2 - CONCLUSION
Accordingly, this additional element does not integrate the abstract idea into a practical application because it does not impose any meaningful limits on practicing the abstract idea. The claim is directed to an abstract idea.
Step 2B
The critical inquiry here is does the claim recite additional elements that amount to “significantly more” than the judicial exception? The claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception. As discussed above with respect to integration of the abstract idea into a practical application, the additional element of using a “a processor; and a memory” to perform “recording, analyzing, outputting” steps amounts to no more than mere instructions to apply the exception using a generic computer component. Mere instructions to apply an exception using a generic computer component cannot provide an inventive concept. The claim is not patent eligible.
Dependent Claims
As to claims 2, 9 & 16, this claim is directed to mental process (“selecting/deriving numeric gait metrics from observed motion – analysis/calculation”) and insignificant extra-solution activity (“More detail of the analysis without adding a non-generic technical mechanism”). Thus, this claim does not integrate the abstract idea into a practical application or constitute significantly more than the abstract.
As to claims 3, 10 & 17, this claim is directed to generic computer components (“generic pose estimation software and processor”), mental process (“choosing which anatomical points to use is data selection/analysis”) and insignificant extra-solution activity (“narrowing a dataset/features used for the same abstract is “analyze/classify” ideas”). Thus, this claim does not integrate the abstract idea into a practical application or constitute significantly more than the abstract.
As to claims 4, 11 & 18, this claim is directed to generic computer components (“generic computing device and a fall alert system and generic comms”). Thus, this claim does not integrate the abstract idea into a practical application or constitute significantly more than the abstract.
As to claims 5, 12 & 19, this claim is directed to generic computer components (“camera/smartphone/tablet (generic video capture)”) and insignificant extra-solution activity (“data gathering/pre-solution activity not tied to a specific technical improvement”). Thus, this claim does not integrate the abstract idea into a practical application or constitute significantly more than the abstract.
As to claims 6, 13 & 20, this claim is directed to generic computer components (“generic processor/software performing/filtering/segmentation/masking”), mental process (“Filtering/segmentation classified as algorithmic/mathematical data processing steps”) and insignificant extra-solution activity (“routine pre-processing to improve the input data.”). Thus, this claim does not integrate the abstract idea into a practical application or constitute significantly more than the abstract.
As to claims 7 & 14, this claim is directed to generic computer components (“generic processor/software executing a kNN classifier”), mental process (“kNN is a mathematical classification operation”) and insignificant extra-solution activity (“Using a known mathematical model to classify i.e. analysis”). Thus, this claim does not integrate the abstract idea into a practical application or constitute significantly more than the abstract.
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 of this title, 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.
Claims 1-6, 8-13 & 15-20 are rejected under 35 U.S.C. 103 as being unpatentable over
Delp et al. (U.S. Publication 2021/0315486) in view of Mariottini et al. (U.S. Publication 2016/0147959)
As to claims 1, 8 & 15, Delp discloses a processor (405, Fig. 4 & [0043] discloses a processor); and a memory (420, Fig. 4 & [0043] discloses a memory), including instructions that when executed by the processor perform operations that include: recording video of a subject performing a walking task ([0004-0005, 0030] discloses identifying key point trajectories from frames of a video. Extracting clinically relevant variables from video of patients walking.); analyzing the video to determine poses and relationships between a plurality of points of the subject corresponding to anatomical features of the subject while performing the walking task ([0004-0007, 0031, 0078-0079] discloses identifies keypoint trajectories from frames; keypoints trajectories are trajectories of 2D coordinates of body parts (e.g. nose, hip, knees, ankles, toes). Keypoint trajectories extracted (e.g. OpenPose) as body landmark positions per frame.); analyzing, using a machine learning model, a gait of the subject based on the relationships between the plurality of points while performing the walking task ([0009, 0079-0080] discloses inputs trajectories to a CNN and computes the motion evaluation score from CNN outputs. Extract multivariate time series from keypoints and “train a model to predict a quantitative motion evaluation score.); classifying, using the machine learning model, the subject as exhibiting or not exhibiting symptoms of a motor disorder based on the gait ([0012-0013, 0084] discloses providing the output includes providing a diagnosis for disease; Parkinson’s Disease); and
Delp discloses output engines include displaying [0052]. Also, providing the output includes providing a diagnosis for a disease [0012], for example “Parkinson’s disease”, [0013].
Delp is silent to outputting, via a graphical user interface.
However, Mariottini outputting, via a graphical user interface. ([0006,0028-0029, 0058-0067] discloses Fig. 2 also shows a user interface device/module 231 further detail illustrated. Figs. 6A-6E and 9.)
It would have been obvious to one of ordinary skill in the art at the time of effective filing to modify Delp’s disclosure to include the above limitations in order to present the gait based diagnostic determination to the user through an explicit GUI module for an interactive experience.
As to claims 2, 9 & 16, Delp in view of Mariottini discloses everything as disclosed in claims 1, 8 & 15. In addition, Delp discloses wherein the relationships between the plurality of points include gait parameters including one or more of: a swing time; a stance time; a step time; a step length; a gait speed; and a step width. ([0010, 0035 discloses motion evaluation score is one of walking speed, stride length.])
As to claims 3, 10 & 17, Delp in view of Mariottini discloses everything as disclosed in claims 1, 8 & 15. In addition, Delp in view of Mariottini discloses wherein the plurality of points include: left eye, right eye, nose, neck, left shoulder, right shoulder, left elbow, right elbow, left wrist, right wrist, left hip, right hip, left knee, right knee, left ankle, right ankle, left heel, right heel, left hallux, right hallux, left small toes, and right small toes. (Delp’s [0031-0032, 0079] discloses keypoints trajectories are trajectories of 2D coordinates of body parts (e.g. nose, hip, ankles, toes))(Mariottini’s [0070] discloses neck, shoulders, elbows, wrists, hips, knees, ankles, feet/toes and head.)
As to claims 4, 11 & 18, Delp in view of Mariottini discloses everything as disclosed in claims 1, 8 & 15. In addition, Mariottini discloses supplying the subject with a fall alert system in response to determining that the subject exhibits the symptoms of the motor disorder. ([0022, 0044, 0049] discloses predicting a risk triggering an alert. The algorithm discloses triggering an alert to indicate that an abnormal gait event has occurred. Clinicians may instruct system to notify the patient such notification may inform the patient of an abnormal gait event.)
As to claims 5, 12 & 19, Delp in view of Mariottini discloses everything as disclosed in claims 1, 8 & 15. In addition, Mariottini discloses wherein the video is taken in a sagittal plane of the subject. (Examiner views sagittal plan of the subject as a side view. [0029] discloses cameras located on the left side of the patient and one on the right side when the patient walk along the path.)
As to claims 6, 13 & 20, Delp in view of Mariottini discloses everything as disclosed in claims 1, 8 & 15 but is silent to wherein analyzing the gait further comprises: noise smoothing and removal of background objects from the video.
However, Mariottini discloses wherein analyzing the gait further comprises: noise smoothing and removal of background objects from the video. [0033] discloses data processing “automatically suppress noise and artifacts in the captured data”)
It would have been obvious to one of ordinary skill in the art at the time of effective filing to modify Delp in view of Mariottini’s disclosure to include the above limitations in order to increase reliability and accuracy of ML gait analysis and resulting classification output.
Claims 7 & 14 are rejected under 35 U.S.C. 103 as being unpatentable over
Delp et al. (U.S. Publication 2021/0315486) in view of Mariottini et al. (U.S. Publication 2016/0147959) as applied in claims 1, 8 & 15 further in view of Lillo (U.S. Publication 2022/0284261)
As to claims 7 & 14, Delp in view of Mariottini discloses everything as disclosed in claims 1 & 8 but is silent to wherein the machine learning model is trained as a k Nearest Neighbors deep learning model.
However, Lillo’s discloses wherein the machine learning model is trained as a k Nearest Neighbors deep learning model.([0018, 0027] discloses list of ML models, expressly including “k-nearest neighbors models”. See wherein training/augmentation may be applied to any machine learning model including k-nearest neighbors.)
It would have been obvious to one of ordinary skill in the art at the time of effective filing to modify Delp in view of Mariottini’s disclosure to include the above limitations in order to implement a straightforward, instance-based model that can be trained with minimal model-fitting complexity (i.e. storing labeled examples) while still producing the desired predicted output for the task.
CONCLUSION
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Stephen P. Coleman
Primary Examiner
Art Unit 2675
/STEPHEN P COLEMAN/Primary Examiner, Art Unit 2675