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 Objections
Claims 5, 15 and 19 are objected to because of the following informalities:
Regarding claim 5, “copying the preset probability distribution template ... generating” – there should be “and” or the like before generating;
Regarding claim 9, “a 1-st gait event” should be “a first gait event”;
Regarding claims 15 and 19, “…determine an intersection over union (IoU)… and generating an IoU variation diagram…” -- “generating” should be changed to “generate” to maintain parallel structure. Appropriate correction is required.
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.
Claim 19 and associated dependent claims is/are rejected under 35 U.S.C. 101 because the claimed invention is directed to non-statutory subject matter. The claim(s) does/do not fall within at least one of the four categories of patent eligible subject matter because “computer readable storage medium” needs to be “non-transitory” in order to distinguish from ineligible transitory signals.
Claims 1-5,7-16 and 18-20 are rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more. Under the two-step 101 analysis, the claims fail to satisfy the criteria for subject matter eligibility.
Step 1: Claims 1-3, 11-16, 18-20 are within at least one of the four statutory categories.
Claim 1 and associated dependent claims disclose a method.
Claim 15 and associated dependent claims disclose an apparatus.
Claim 19 and associated dependent claim disclose a product of manufacture [if amended to non-transitory].
Step 2A, Prong One: The independent claims 1, 15, and 19 recite limitations directed to an abstract idea that is part of the Mathematical Concepts group identified in the 2019 Revised Patent Subject Matter Eligibility Guidance published in the Federal Register (84 FR 50) on January 7, 2019.
Mathematical Concepts: “A mathematical relationship is a relationship between variables or numbers. A mathematical relationship may be expressed in words ….” October 2019 Update: Subject Matter Eligibility, II. A. i. “[T]here are instances where a formula or equation is written in text format that should also be considered as falling within this grouping.” Id. at II. A. ii. “[A] claim does not have to recite the word “calculating” in order to be considered a mathematical calculation.” Id. at II. A. iii. See for example, SAP Am., Inc. v. InvestPic, LLC, 898 F.3d 1161, 1163-65 (Fed. Cir. 2018) (performing a resampled statistical analysis to generate a resampled distribution).
Claims 1, 15, and 19 recite the following abstract ideas:
"determining a plurality of probability distributions" and "determining an intersection over union (IoU) between the preset probability distribution template and each of the probability distribution segments" because these are data-processing and mathematical-comparison limitations. MPEP § 2106.04(a)(2) identifies mathematical concepts as abstract ideas, and the specification at [0063]-[0064] describes IoU as a numerical comparison between distributions.
final result-focused step, "determining the event time point of each of the gait events belonging to the specific step based on the IoU variation diagram corresponding to each of the probability distributions," is framed at a functional level without claiming a specific algorithm beyond segmentation, IoU calculation, and diagram generation. The specification at [0060]-[0065] confirms the technique is implemented by sliding a template and calculating IoU values, which supports that the core advance is an analytical method rather than a distinct machine or technical mechanism.
Regarding the dependent claims, they are directed to either 1) steps that are also abstract or 2) additional data output that is well-understood, routine, and previously known to the industry:
"the N motion data is derived from a motion sensor configured to be set or worn on a foot, and each of the motion data comprises 6 degrees of freedom values" still reads as data analysis using generic sensor input -- MPEP § 2106.05(f) explains that appending a field-of-use or physical environment does not necessarily provide significantly more than an abstract idea when the core steps remain analytical
"feeding the N motion data into an artificial intelligence model, wherein the artificial intelligence model generates the plurality of probability distributions in response to the N motion data" reinforces the abstract-data-processing character of the claim
“obtaining consecutive N training otion data," "generating a reference probability distribution," and "updating a plurality of model parameters ... based on a comparison result between the reference probability distribution and the prediction probability distribution" provide more detail about how the model is trained, but they still describe mathematical/data-processing operations
"finding out at least one specific data time point corresponding to the j-th gait event" and "copying the preset probability distribution template to each of the specific data time points" remains a data-manipulation method centered on templating probability distributions, which still presents an abstract idea
"the preset probability distribution template is intercepted from a normal distribution with a specific standard deviation and a specific mean value" reinforces that the claim is built around a mathematical distribution
"combining the IoU variation diagram," "finding out a plurality of sampling point groups," and "determining the gait event corresponding to each of the sampling point groups" is focused on mathematical comparison, thresholding, grouping, and classification of sampled data, all of which fit squarely within the abstract-idea realm
"in response to determining that consecutive K sampling point groups in the plurality of sampling point groups correspond to the 1-st gait event to the K-th gait event in sequence, determining that the consecutive K sampling point groups are the specific sampling point groups satisfying the preset condition" is a rule for classifying sampled data based on ordered correspondence
"the plurality of specific sampling point groups comprise a j-th specific sampling point group" and that the event time point is obtained by "finding out a specific sampling point from the j-th specific sampling point group" and "taking a time point corresponding to the specific sampling point ... as the event time point" are selection-and-assignment rule for choosing a representative point from a group of data, which is an abstract data-processing operation
an iterative sliding-window workflow, including obtaining the event time point of the K-th gait event and then "obtaining other consecutive N motion data from the event time point of the K-th gait event" to determine probability distributions for another step -- still an analytical method for repeatedly processing motion data and does not, on its face, provide a concrete technical improvement beyond reusing the detected time as a new starting point
a fallback window shift when the event time points are not found: "in response to determining that the event time point of each gait event corresponding to a specific step is not found based on the N motion data, obtaining other consecutive N motion data" and then determining other probability distributions -- high-level algorithmic workaround for handling unsuccessful detection
"determining at least one first gait index corresponding to the first foot," "obtaining at least one second gait index of a second foot," and "determining a gait asymmetry" based on those indices introduce additional analytic comparisons between two feet
"the plurality of gait events comprise an initial contact, a foot-flat, a heel-off and a toe-off" adds only a list of gait-event labels
a display device plus a downstream symmetry-reporting workflow: determining gait asymmetry and controlling "the display device to display the gait asymmetry" -- merely displaying information resulting from an abstract analysis does not necessarily integrate the exception into a practical application unless the claim improves the display device or another technology
first-foot/second-foot gait-asymmetry workflow plus a display-related omission in the provided text fragment -- still recites the comparative analytical steps of determining gait indices and gait asymmetry
Step 2A, Prong Two: The judicial exceptions (abstract ideas) are not integrated into a practical application because:
The abstract idea amounts to simply implementing the abstract idea on a computer. For example, the recitations regarding the generic computing components for obtaining motion data and displaying gait analysis results merely invoke a computer as a tool.
The data-gathering step (obtaining motion data data) and the data-output step (displaying gait analysis results) do not add a meaningful limitation to the method as they are insignificant extra-solution activity.
There is no improvement to a computer or other technology. “The McRO court indicated that it was the incorporation of the particular claimed rules in computer animation that "improved [the] existing technological process", unlike cases such as Alice where a computer was merely used as a tool to perform an existing process.” MPEP 2106.05(a) II.
The claims recite a computer/processor that is used as a tool for obtaining motion data and displaying gait analysis results.
The claims do not apply the abstract idea to affect a particular treatment or prophylaxis for a disease or medical condition. Rather, the abstract idea is utilized for obtaining motion data to provide the gait analysis results.
The claims do not apply the abstract idea to a particular machine. “Integral use of a machine to achieve performance of a method may provide significantly more, in contrast to where the machine is merely an object on which the method operates, which does not provide significantly more.” MPEP 2106.05(b). II. “Use of a machine that contributes only nominally or insignificantly to the execution of the claimed method (e.g., in a data gathering step or in a field-of-use limitation) would not provide significantly more.” MPEP 2106.05(b) III. The pending claims utilize a computer/processor for obtaining motion data and outputting gait analysis results.
The additional elements are identified as follows:
motion sensor
gait analysis device
storage circuit
processor
display device
computer-readable storage medium
Step 2B: Claims do not include additional elements that are sufficient to provide for an inventive concept nor amount to significantly more than the judicial exception.
Those in the relevant field of art would recognize the above-identified additional elements as being well-understood, routine, and conventional means for obtaining measurement data and outputting results as demonstrated by the specification. The applicant discloses nothing unique about the motion sensor (Specification [0031]), gait analysis device (Specification [0025]), processor (Specification [0028]), display device (Specification [0112]), or storage circuit/computer-readable storage medium (Specification [0027]), configured to perform the generic computer functions (e.g., obtaining measurement data and outputting results) that are well-understood, routine, and conventional activities previously known to the pertinent industry.
Thus, the claimed additional elements “are so well-known that they do not need to be described in detail in a patent application to satisfy 35 U.S.C. § 112(a).” Berkheimer Memorandum, III. A. 3. Furthermore, the court decisions discussed in MPEP § 2106.05(d)(lI) note the well-understood, routine and conventional nature of such additional generic computer components as those claimed. See option III. A. 2. in the Berkheimer memorandum.
When considered in combination, the additional elements (i.e., the generic computer functions and conventional equipment/steps) do not amount to significantly more than the abstract idea. Looking at the claim limitations as a whole adds nothing that is not already present when looking at the elements taken individually. There is no indication that the combination of elements improves the functioning of the motion sensor, gait analysis device/processor, display device, or storage circuit/computer-readable storage medium, or any other technology. Their collective functions merely provide conventional computer implementation.
Therefore, the claims are directed to patent ineligible subject matter.
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, 4, 11, 12, 13, 15, 18, 19, 20 and associated dependent claims 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.
Regarding claims 1, 15, 19, the claims recite: “…determining an intersection over union (IoU) between the preset probability distribution template and each of the probability distribution segments on the i-th probability distribution, and generating an IoU variation diagram corresponding to the i-th probability distribution accordingly; determining the event time point of each of the gait events belonging to the specific step based on the IoU variation diagram corresponding to each of the probability distributions.” The claim explicitly generates an IoU variation diagram only for the “i-th probability distribution.” However, the very next limitation requires determining event time points based on the IoU variation diagrams corresponding to each of the probability distributions.
Regarding claim 4, the claim does not clearly specify whether the recited training process is performed as part of the claimed method or merely characterizes the artificial intelligence model used for inference. If claim 4 requires actual training during the claimed method, the training steps are considered as part of the method limitations. If the claim merely characterizes the model as having been trained in a particular way, the training process may not meaningfully distinguish the deployed model in the claimed method.
Regarding claims 11 and 12, the claims use “has been found” and “is not found” as conditions for changing the window position, but do not define the objective criterion by which the processor determines that all event time points have or have not been found. Whether an event time point has been “found” depends on the unspecified event-selection procedure. Claim 1 does not state the criterion for successful detection. Thus, the same data may satisfy the claim under one implementation but not another.
Regarding claims 13, 18, 20, the claims recite determining or displaying “gait asymmetry” based on gait indices of two feet, but do not identify the asymmetry metric or the mathematical operation used to obtain it. The specification describes several qualitative comparisons but does not establish an objective boundary for the claimed result. If “gait asymmetry” is construed as a particular established metric in the art, the claim should identify that metric. If it is construed broadly to encompass any comparison, the boundary of the claim is indefinite. Clarification with support is requested.
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, 3-5, 7-12, 15, and 19 is/are rejected under 35 U.S.C. 103 as being unpatentable over “JAMSRANDORJ”, “Deep Learning Networks for View-independent Gait Events Detection”, in view of “YU”, “Learning Generalized Intersection Over Union for Dense Pixelwise Prediction”.
With regards to claim 1, JAMSRANDORJ discloses a gait analysis method, adaptable for a gait analysis device, and comprising:
obtaining consecutive N motion data, wherein N is a positive integer [Section 2.3.1: A total of 27 subsequent frames were used as the input; 2.2.1: total pressure data from smart sensor insoles];
determining a plurality of probability distributions based on the N motion data, wherein the plurality of probability distributions respectively correspond to a plurality of gait events [Section 2.2.2: produce probability distributions that fitted around the frames in which gait events occurred]; and
determining an event time point of each of the gait events belonging to a specific step according to the plurality of probability distributions [Section 2.2.1: The closest point respectively determined the FC [foot contact] and FO [foot off] times].
wherein determining the event time point comprises determining a preset probability distribution template [Section 2.2.2: The target was four one-dimensional Gaussian distribution curves that were associated with the four gait events].
However, JAMSRANDORJ does not explicitly disclose wherein the plurality of probability distributions comprise an i-th probability distribution, and determining the event time point comprises: determining a plurality of probability distribution segments on the i-th probability distribution according to the preset probability distribution template; determining an intersection over union (IoU) between the preset probability distribution template and each of the probability distribution segments on the i-th probability distribution, and generating an IoU variation diagram corresponding to the i-th probability distribution accordingly; determining the event time point of each of the gait events belonging to the specific step based on the IoU variation diagram.
YU, also directed towards machine learning-based evaluation of data and prediction localization, discloses determining an intersection over union (IoU) between a preset template [ground truth] and probability distribution segments [predictions] to evaluate overlap, and generating an IoU variation diagram across different locations to determine the exact center/event time point [Section 1: IoU is defined to calculate the overlap between two given set of elements; Fig. 1(b) showing IoU variation across “Different locations” to find the most centered prediction; Section 3.1: PixIoU, a generalized IoU for pixelwise prediction that is sensitive to the distance and the location].
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify the teachings of JAMSRANDORJ with the teachings of YU -- utilizing a preset ground-truth template and calculating the IoU across different segments to generate an IoU variation diagram, as taught by YU, would enhance the base AI evaluation model of JAMSRANDORJ by allowing the system to find the exact temporal segment with the maximum overlap between the predicted probability distribution and the preset Gaussian template, thereby accurately pinpointing the exact event time point of the gait event.
With regards to claim 3, JAMSRANDORJ discloses feeding the N motion data into an artificial intelligence model, wherein the artificial intelligence model generates the plurality of probability distributions in response to the N motion data [Section 2.3.1+: Attention-based network… output was the probability of each gait event for each frame].
With regards to claim 4, JAMSRANDORJ discloses a training process of the artificial intelligence model comprising: obtaining consecutive N training motion data, and obtaining a plurality of data time points corresponding to each of the gait events; generating a reference probability distribution corresponding to each of the gait events based on the plurality of data time points; training the artificial intelligence model… updating a plurality of model parameters based on a comparison result [Section 2.2.2: We smoothed our raw and frame-specific labels to produce probability distributions that fitted around the frames in which gait events occurred; Section 2.4.2: During all training experiments, the Mean Squared Error (MSE) and the Adam optimizer were used].
With regards to claim 5, JAMSRANDORJ discloses finding out at least one specific data time point corresponding to the j-th gait event, and copying the preset probability distribution template to each of the specific data time points corresponding to the j-th gait event, generating the reference probability distribution [Section 2.2.2: The target was four one-dimensional Gaussian distribution curves that were associated with the four gait events].
With regards to claim 7, JAMSRANDORJ discloses wherein the preset probability distribution template is intercepted from a normal distribution with a specific standard deviation and a specific mean value [Section 2.2.2 explicitly disclosing “one-dimensional Gaussian distribution curves”, which implicitly possess a specific standard deviation and mean value).
With regards to claims 8, 9, and 10, these claims depend from claim 1 and add additional limitations regarding combining IoU variation diagrams, finding sampling point groups higher than a preset threshold, and determining consecutive K sampling point groups to find the specific event time point. These limitations are disclosed by the combination of JAMSRANDORJ and YU. YU discloses evaluating IoU scores across different locations [sampling points] to find the maximum overlap [Fig. 1(b)]. JAMSRANDORJ discloses applying thresholds to identify specific events [Section 2.5: peak values were first picked at the threshold of 0.6 to precise the frame numbers]. It would have been obvious to an ordinary artisan to group these sampling points and apply a threshold to identify the specific step and event time point, as it is a well-known signal processing technique to filter noise and isolate the true peaks.
With regards to claims 11 and 12, JAMSRANDORJ discloses obtaining other consecutive N motion data to find event time points of another specific step [Section 2.3.1: A total of 27 subsequent frames were used as the input… The sequence of feature vectors injected positional information, implicitly requiring processing subsequent windows of data to continuously analyze consecutive steps].
With regards to claim 15, JAMSRANDORJ discloses a gait analysis device, comprising: a storage circuit, which stores a program code; a processor, which is coupled to the storage circuit to access the program code [Section 2.4.2: All experiments were carried out with the PyTorch framework, implicitly requiring a processor and storage circuit]. The processor is configured to perform the identical method steps recited in claim 1. JAMSRANDORJ and YU disclose the method steps for the same reasons set forth in the analysis of claim 1. The motivation to combine is identical to that set forth in claim 1.
With regards to claim 19, JAMSRANDORJ discloses a computer-readable storage medium, wherein the computer-readable storage medium records an executable computer program, and the executable computer program is loaded by a gait analysis device to perform the identical method steps recited in claim 1 [Section 2.4.2: implicitly requiring a computer-readable medium to store the PyTorch framework and model weights]. JAMSRANDORJ and YU disclose the method steps for the same reasons set forth in the analysis of claim 1. The motivation to combine is identical to that set forth in claim 1.
Claim(s) 2, 13, 14, 16, 18, and 20 is/are rejected under 35 U.S.C. 103 as being unpatentable over “JAMSRANDORJ”, in view of “YU”, and further in view of “NEWTON”, US Publication 20230329587.
With regards to claims 2 and 16, these claims depend from claims 1 and 15 respectively. JAMSRANDORJ discloses wherein the N motion data is derived from a motion sensor configured to be set or worn on a foot [Section 2.2.1: Moticon SCIENCE pressure sensor insoles]. However, JAMSRANDORJ does not explicitly disclose wherein each of the motion data comprises 6 degrees of freedom values.
NEWTON, also directed towards computerized gait analysis using wearable sensors and machine learning, discloses a wearable gait analysis sensor wherein the motion data comprises 6 degrees of freedom values [0048: accelerometers… gyroscopes; 0049: measure and record data in all three dimensions (x, y, and z axes) -- providing 3 axes of acceleration and 3 axes of angular velocity, totaling 6 DOF].
With regards to claims 13 and 20, these claims depend from claims 1 and 19 respectively. JAMSRANDORJ discloses tracking data for both a first foot and a second foot [Section 2.2.1: total pressure data from smart sensor insoles for both legs]. However, JAMSRANDORJ does not explicitly disclose determining a gait asymmetry between the first foot and the second foot.
NEWTON discloses determining at least one first gait index corresponding to the first foot, obtaining at least one second gait index of a second foot, and determining a gait asymmetry between the first foot and the second foot [0148: determine gait symmetry indices by comparing the gait parameters of the left and right limbs… A high degree of gait symmetry may indicate a healthy walking pattern, whereas a significant asymmetry could suggest an underlying health issue].
With regards to claim 14, JAMSRANDORJ discloses identifying gait events including heel-strike, midfoot-strike, toe-strike, and toe-off [Fig 4 note]. NEWTON explicitly discloses wherein the plurality of gait events comprise an initial contact, a foot-flat, a heel-off and a toe-off [0088: The events include initial contact 302, foot flat 304… heel off 308… toe off 312].
With regards to claim 18, NEWTON discloses further comprising a display device, and controlling the display device to display the gait asymmetry [0067: visualize and present the results on an electronic display… display the results on a user-friendly interface or dashboard; 0148: determine gait symmetry indices].
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify the teachings of JAMSRANDORJ and YU with the teachings of NEWTON -- incorporating 6-DOF sensors, explicit gait asymmetry calculations, and standardized clinical gait event terminology (foot-flat, initial contact) as taught by NEWTON, would enhance the base device of JAMSRANDORJ by providing a diagnostic tool capable of tracking bilateral gait health and asymmetry.
Response to Arguments
Applicant’s arguments filed 4/17/26 have been considered but are moot because the new ground of rejection does not rely on any reference applied in the prior rejection of record for any teaching or matter specifically challenged in the argument.
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure. “FILTJENS”, “Automated freezing of gait assessment with marker-based motion capture and multi-stage spatial-temporal graph convolutional neural networks” teaches using IoU in gait analysis.
Any inquiry concerning this communication or earlier communications from the examiner should be directed to Tse Chen whose telephone number is (571)272-3672. The examiner can normally be reached M-F 7-3 EST.
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/TSE CHEN/Supervisory Patent Examiner, Art Unit 3791
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