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 Objections
Claim 21 is objected to because of the following informalities:
In claim 21, lines 5 and 8, it appears ‘accuracy’ should be ‘uncertainty’, as this is what is compared to a threshold.
Appropriate correction is required.
Claim Rejections - 35 USC § 102
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.
Claims 1-7, 9, 10, 13, 14, and 22-24 are rejected under 35 U.S.C. 102(a)(1) as being anticipated by Myers (US Pub 2022/0400989).
Re claim 1: Myers discloses a vehicle computing system of a vehicle [0052; the processor can be implemented in a vehicle], comprising:
one or more processors [0052; see the processor];
memory storing instructions that when executed cause the one or more processors [0052; see the storage medium with instructions] to:
obtain video data comprising a plurality of frames from one or more visual sensing devices within the vehicle [0092, fig 10; see the video data that contains a plurality of frames];
extract motion data from the video data [0093; see the motion-based analysis];
determine, from the motion data, a breathing wave and one or more respiratory characteristics of a subject within the vehicle [0095, 0097, 0098; see the respiratory waveforms 1025 and see the respiratory information 1035, 1045 and respiratory parameters]; and
output the breathing wave and one or more respiratory characteristics to one or more of an advanced driver assistance system (ADAS) and a health and wellbeing system integrated into a vehicle computing system (Abstract, 0047, fig 9; see the remote monitoring output to a wearable or mobile device or other processing system that is in a “vehicle”; Note: While the claimed ADAS and wellbeing systems may have other functions described in the specification (i.e. instant publ. 0021), these are not read into the claim; as such, these claimed systems are merely considered to be processing systems which is disclosed by Myer’s; even if additional weight is given to the terms, it is noted that Myers processing systems are considered as driver assistance systems and wellbeing systems because the systems may be used in a vehicle as mobile devices commonly are used and/or the systems provide health information as patient vital information).
Re claims 2, 3: To extract motion data from the video data, the one or more processors are configured to: identify, within a first frame of the plurality of frames, one or more regions of interest (ROIs); dynamically adjust the one or more ROIs based on the subject’s movements; and perform sparse motion analysis that includes an optical flow analysis to the one or more ROIs [0097, 0098; see the feature tracking which identifies a ROI, wherein the tracking is performed dynamically through a plurality of frames as the subject moves, wherein an sparse motion analysis (i.e. “optical flow”) is performed].
Re claim 4: To determine, from the motion data, the breathing wave and the one or more respiratory characteristics, the one or more processors are configured to deploy a respiratory characteristic estimation model trained to determine the breathing wave and the one or more respiratory characteristics from the motion data [0053, 0065; see the AI machine learning system that trains a model to determine the output parameters].
Re claim 5: The model is a convolutional neural network [0065; see the CNN as an example of the model].
Re claim 6: The model is trained based on a ground truth breathing wave and respiratory characteristics that are semi-automatically converted from the ground truth breathing wave based on a combination of automated scripts and human supervised annotations [0053; see the training of the model wherein the data used for training is considered ‘ground truth’ data in machine learning, wherein the training is performed via human input as well as being automatically performed, thereby being semi-automated].
Re claim 7: The memory stores further instructions that when executed cause the one or more processors to estimate uncertainty in the breathing wave and one or more respiratory characteristics [0063; see the errors that are determined which are considered as uncertainty estimates].
Re claim 9: Myers discloses a method for training a respiratory characteristic estimation model, comprising:
capturing video data in a vehicle environment [Abstract, 0047, 0092, figs 9 and 10; see the video data that is captured by a wearable or mobile device or other processing system that is in a “vehicle” or at a hospital/home which have vehicles included ambulances, thereby being in a vehicle environment];
determining, from the video data, ground truth motion data [0053, 0065, 0093; see the motion-based analysis that used as training ground truth data];
determining, based on the ground truth motion data, ground truth breathing waves [0095, 0097, 0098; see the respiratory waveforms 1025 determined from the motion data];
semi-automatically converting the ground truth breathing waves to one or more annotated respiratory characteristics [0095, 0097, 0098; see the respiratory information 1035, 1045 and respiratory parameters determined from the respiratory waveforms 1025 via automated computations and via human input (semi-automatic)];
training the respiratory characteristic estimation model on training trios of the ground truth motion data, the ground truth breathing waves, and the one or more annotated respiratory characteristics [0053, 0065; see the AI machine learning system that trains a model to determine the output parameters].
Re claim 10: The converting comprises applying one or more automated scripts to the ground truth breathing waves [0053; see the training is performed automatically].
Re claim 13: The model is configured for deployment to determine, from visual signals, a breathing wave and one or more corresponding respiratory characteristics [0095, 0097, 0098; see the respiratory waveforms 1025 and the respiratory information and respiratory parameters, obtained from visual motion signals].
Re claim 14: The annotated respiratory characteristics comprise one or more of breathing rate, breathing phase, and breathing rate variability [0095, 0097, 0098; the respiratory information and respiratory parameters correspond at least to a breathing rate of respiration over time].
Re claim 22: The method includes deploying the trained respiratory characteristic estimation model to a vehicle computing system of a vehicle; and configuring the vehicle computing system to output the one or more annotated respiratory characteristics to an ADAS of the vehicle [0053, 0065, Fig 9; see the vehicle systems as described in regard to claim 1 and see the AI machine learning system that trains a model to determine the output parameters].
Re claims 23, 24: The one or more visual sensing devices comprise one or more near-infrared (NIR) cameras configured to capture near-infrared images of the subject in the vehicle environment, and wherein dynamically adjusting the one or more ROIs based on the subject's movements comprises adjusting the one or more ROIs based on the subject twisting at a waist when turning a steering wheel of the vehicle [0090, 0096-0098; see the IR sensing and see the feature tracking which identifies a ROI, wherein the tracking is performed dynamically through a plurality of frames as the subject moves, wherein an sparse motion analysis (i.e. “optical flow”) is performed, wherein the data is acquired in the vehicle environment as described in relation to claim 9].
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.
Claims 8 and 21 are rejected under 35 U.S.C. 103 as being unpatentable over Myers, as applied to claim 7, in view of Carter et al (US Pub 2024/0293078).
Re claim 8: Myers discloses all features except to determine an interquartile range (IQR) of quantities of the one or more respiratory characteristics and determine, based on the IQR, an accuracy of the one or more respiratory characteristics. However, Carter teaches of a machine learning classification system determine an interquartile range (IQR) of quantities of the one or more respiratory characteristics and determine, based on the IQR, an accuracy of the one or more respiratory characteristics [abstract, 0212; see the “quartile” range determine for the machine learning classification]. It would have been obvious to the skilled artisan to modify Myers, to determine a quartile range as taught by Carter, in order to improve accuracy for the machine learning.
Re claim 21: Myers discloses the memory stores instructions that when executed cause the one or more processors to: output the breathing wave and the one or more respiratory characteristics to the one or more of the ADAS and the health and wellbeing
system integrated into the vehicle computing system in response to the accuracy being
above a threshold; and flag the one or more respiratory characteristics as unreliable and filter out the one or more respiratory characteristics in response to the accuracy being below the threshold [0063; see the errors that are determined which are considered as uncertainty estimates, which are compared to a standard value to be removed from the dataset unless determined to be accurate above a certain level].
Claims 11 and 12 are rejected under 35 U.S.C. 103 as being unpatentable over Myers, as applied to claim 10, in view of Aoki et al (US Pub 2010/0249631).
Re claims 11, 12: Myers discloses semi-automatically converting the ground truth breathing waves further comprises receiving human supervised annotations to one or more portions of the ground truth breathing waves, but does not disclose this is based on reliability scores, wherein applying the one or more automated scripts comprises identifying peaks and valleys within the ground truth breathing wave and the binary flat denoting signal reliability scores. However, Aoki teaches of respiratory waveform analyzer wherein applying the one or more automated scripts comprises identifying peaks and valleys within the ground truth breathing wave and the binary flat denoting signal reliability scores (Abstract; see the respiratory waveform and the reliability calculator that calculates a reliability of the respiratory waveform based on flatness of the concentration signal). It would have been obvious to the skilled artisan to modify Myers, to utilize reliability scores as taught by Aoki, in order to show a parameter that indicates how consistent the determined information will be.
Response to Arguments
Applicant's arguments filed 7/29/26 have been fully considered but they are not persuasive. Applicant argues that Myers does not specifically disclose a vehicle or ADAS and that the new limitations are not met. While Applicant is correct that an ADAS is not mentioned, the limitation is still met. First, Myers does disclose an “vehicle” [0047]. The remote monitoring output is communicated to a wearable or mobile device or other processing system that is in a “vehicle” and in a vehicle environment. While the claimed ADAS and wellbeing systems may have other functions described in the specification (i.e. instant publ. 0021), these are not read into the claim and, as such, these claimed systems are merely considered to be processing systems which is disclosed by Myers. Even if additional weight is given to the terms, it is noted that Myers processing systems are considered as driver assistance systems and wellbeing systems because the systems may be used in a vehicle as mobile devices commonly are used and/or the systems provide health information as patient vital information. Therefore, the limitations are met.
The prior claim objections are withdrawn due to amendments.
Conclusion
THIS ACTION IS MADE FINAL. Applicant is reminded of the extension of time policy as set forth in 37 CFR 1.136(a).
A shortened statutory period for reply to this final action is set to expire THREE MONTHS from the mailing date of this action. In the event a first reply is filed within TWO MONTHS of the mailing date of this final action and the advisory action is not mailed until after the end of the THREE-MONTH shortened statutory period, then the shortened statutory period will expire on the date the advisory action is mailed, and any nonprovisional extension fee (37 CFR 1.17(a)) pursuant to 37 CFR 1.136(a) will be calculated from the mailing date of the advisory action. In no event, however, will the statutory period for reply expire later than SIX MONTHS from the mailing date of this final action.
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/MICHAEL T ROZANSKI/Primary Examiner, Art Unit 3797
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